Review of the use of remote sensing for biomass estimation to support renewable energy generation

Lalit Kumar; Priyakant Sinha; Subhashni Taylor; Abdullah F. Alqurashi

doi:10.1117/1.JRS.9.097696

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16 June 2015 Review of the use of remote sensing for biomass estimation to support renewable energy generation

Lalit Kumar, Priyakant Sinha, Subhashni Taylor, Abdullah F. Alqurashi

Author Affiliations +

J. of Applied Remote Sensing, 9(1), 097696 (2015). https://doi.org/10.1117/1.JRS.9.097696

Abstract

The quantification, mapping and monitoring of biomass are now central issues due to the importance of biomass as a renewable energy source in many countries of the world. The estimation of biomass is a challenging task, especially in areas with complex stands and varying environmental conditions, and requires accurate and consistent measurement methods. To efficiently and effectively use biomass as a renewable energy source, it is important to have detailed knowledge of its distribution, abundance, and quality. Remote sensing offers the technology to enable rapid assessment of biomass over large areas relatively quickly and at a low cost. This paper provides a comprehensive review of biomass assessment techniques using remote sensing in different environments and using different sensing techniques. It covers forests, savannah, and grasslands/rangelands, and for each of these environments, reviews key work that has been undertaken and compares the techniques that have been the most successful.

1. Introduction

Lignocellulosic biomass or plant dry matter (biomass) is a highly abundant renewable energy resource that can be used to generate a continuous supply of heat and electricity as well as solid, liquid, and gaseous fuels.¹ Therefore, plant biomass plays an important role in the global quest for sustainable energy solutions since it is a renewable energy source that is easily available to humans. Although it is considered that all fossil fuels such as coal and oil originated from buried living material, they are usually excluded from the definition of biomass. Biomass has stored energy through the process of photosynthesis. It exists in one form as plants and may be transferred through the food chain to animal bodies and their wastes, all of which can be converted to energy through processes such as combustion. Biomass has been converted by partial pyrolysis to charcoal for thousands of years. Charcoal, in turn, has been used for forging metals and for light industry for hundreds of years. Both wood and charcoal formed part of the backbone of the early industrial revolution prior to the discovery of coal for energy. Wood is still used extensively for energy in both household situations and in industry, particularly in the timber, paper, pulp, and other forestry-related industries. The easiest and most efficient way to use biomass as energy is through burning. When it is burned, a part of the internal chemical energy converts to heat. Biomass can also be burned in special plants called waste-to-energy plants which use the heat energy to create steam, which is then used to either heat buildings or create electricity.

The main benefit of biomass is that it is a renewable fuel. Not only does this give us a renewable source of energy to heat our homes, power our vehicles, and produce electricity, but it also helps us to utilize discarded waste that is filling up large dump sites. Many Asian countries are looking to biomass power plants to increase domestic energy outputs and reduce reliance on foreign energy supplies. Asia is expected to construct about 1000 MW of biomass energy capacity annually by 2020—twice as much as is expected in Europe.² Thailand, Indonesia, Malaysia, and the Philippines all have introduced feed-in tariffs to encourage biomass energy production.³ Also, due to Asian climates, many countries can produce sufficient amounts of biomass.

Many countries of the world are now expanding resources toward quantifying, mapping and monitoring biomass due to its importance as a renewable energy source. However, biomass resources are distributed over wide geographical areas and their biochemical properties are highly variable over space. Furthermore, its suitability as a renewable resource is also site-specific. This makes biomass estimation a challenging task, especially in areas with complex forest stand structures and environmental conditions,³ and requires accurate and consistent measurement methods.⁴ Traditionally, two methods are available for the determination of biomass.⁵ The first method is destructive sampling, which involves the complete harvesting of plots and subsequent extrapolation to a unit area of hectare.⁶ The second method is based on allometry where allometric equations are used to extrapolate both in situ and remotely sampled data to a larger area to derive biomass and canopy volume from an easily measured attribute such as diameter at breast height (DBH), tree height, etc. Allometric relationships are used for estimating tree allometry which establishes quantitative relations between some key tree characteristic such as dimensions of trees (easy to measure) and other properties (which are difficult to assess). Both these traditional methods are accurate but are extremely time-consuming, costly, and generally limited to small areas and small tree sample sizes.⁷^–⁹ Moreover, extending this method to map forest biomass across a large area is extremely challenging when factors such as ecological differences, variations in inventory systems, and scattered sources of biomass data are considered. In addition, since the allometric coefficients are site and species specific and are based on a certain range of tree diameters, the use of standard allometric equations can lead to significant errors in vegetation biomass estimations if used outside the area where they were originally produced.¹⁰ There have been efforts in developing generalized regional and national tree biomass equations that could be applied to a larger geographic footprint than most existing allometric equations.¹¹^,¹²

Another vegetation type of great interest is the tropical savanna, not only for the large regions it covers but also for the high interannual biomass dynamics. Grasslands and rangelands also have considerable biomass and thus energy generation capacities, especially since they cover around 40% of the earth’s land surface. Remote sensing can be used to ascertain the potential availability of biomass over large regions and also to estimate biomass energy potential for different land-cover classes.¹³ However, the actual recovery of this biomass will depend on the availability of technology to collect and utilize this material in an economical fashion.¹⁴ Remote sensing techniques can be used in combination with geographical information systems (GIS) to evaluate the feasibility of such initiatives. These techniques can be used to evaluate the feasibility of and optimization of the locations of new biomass power plants¹³ to evaluate the cost effectiveness of energy production from biomass¹ and to devise a framework for estimating residual biomass using satellite imagery and forest inventory data.¹⁵

Additionally, remote sensing is the best approach to estimate biomass at a regional level where field data are scarce or difficult to collect. Almost two decades have passed since pioneers such as Refs. 16 and 17 related biomass to reflectance recorded at the sensor. Since then, many studies in different regions have found strong correlations between biomass and reflectance at different wavelengths. In this paper, we review various techniques and platforms for biomass estimation. We look at forests, savanna, and grasslands/rangelands separately as each has its own characteristics and problems when it comes to biomass estimation. There have been several review papers on biomass estimation in the past few years; however, most of them have described remote sensing based estimation for forest biomass.³^,¹⁸^–²⁰ This current review incorporates remote sensing-based biomass estimation for three major vegetation ecosystems: forest, grassland and rangelands, and tropical savanna, that cover $\sim 80 %$ of earth’s vegetative cover.²¹^,²² These vegetative surfaces on earth are more “natural” ecosystems without much human disturbance, unlike agricultural lands which are heavily dependent on cropping management, and thus provide an opportunity to the reader to assess the challenges and differences in remote sensing-based biomass estimations for these natural ecosystems.

2. Remote Sensing

One of the recent advances in biomass estimation approaches is the incorporation of inferences derived from remote sensing. Remotely sensed data have the provision of a synoptic view of the surface area of interest, thereby capturing the spatial variability in attributes of interest like tree height, crown closure, etc. The spatial coverage of large area biomass estimates that are constrained by the limited spatial extent of forest inventories may be expanded through the use of remotely sensed data. Biomass and carbon stock estimates derived from forest inventory data usually have some spatial, attributional, and temporal gaps. Remotely sensed data can be used to fill these gaps, thereby leading to estimates closer to the actual value. Remote sensing data are available at different scales, from local to global, from various sources including optical or microwave, and hence are expected to provide information which can be related directly, and in different ways, to biomass information.²³^,²⁴ Although remote sensing technology cannot effectively be used for underground biomass, it has the ability to provide important information for aboveground biomass (AGB).³^,²⁵ A large range of studies has been conducted for biomass estimation from remote sensing data.²⁴^,²⁶^–³¹ The advantages of remote sensing include the ability to obtain measurements from every location in the forest, the speed with which remotely sensed data can be collected and processed, the relatively low cost of many remote sensing data types, and the ability to collect data easily in areas which are difficult to access on the ground.³² There are many sensors available with different characteristics of spectral, spatial, and temporal resolutions used for biomass estimation based on availability, efficiency and cost. Optical remote sensing, radar and light detection and ranging (LiDAR) sensors provide the three main sources of remotely sensed data for biomass estimation.

2.1.

Optical Remote Sensing

Due to its coverage, repetitiveness and cost-effectiveness, optical remote sensing provides a potential alternative to tedious hand sampling as a means of estimating biomass over large areas.³³^,³⁴ Optical remote sensing data can be acquired at a variety of spatial and temporal resolutions. High-spatial resolution data from sensors such as Quickbird, WorldView, GeoEye, IKONOS, and DigitalGlobe as well as aerial photographs come in spatial resolutions ranging from submeters to $< 5 m$ in both multispectral and panchromatic images. Images at high resolution offer a fundamental shift in vegetation assessment capability where a multispectral pixel can image a single tree crown, unlike sensors with medium resolution such as Landsat or Systeme Probatoire D’Observation De La Terre (SPOT) where a single pixel can encompass many tree crowns or significant noncrown features.³⁵^,³⁶ Satellite data covering 10 to 100 m of ground in 1 pixel are termed as medium-spatial resolution data and Landsat time series and SPOT sensors have been the two primary sources of medium-resolution data. Coarse-resolution data ( $> 100 m$ ) [e.g., MODIS, national oceanic and atmospheric administration (NOAA), advanced very high resolution radiometry (AVHRR), SPOT vegetation] can be useful for biomass estimation at regional to continental scales since their high temporal frequency increases the probability of acquiring cloud-free data for generating consistent datasets over large areas. AVHRR data have been the most widely used datasets for studies of vegetation dynamics on a continental scale. However, the MODIS sensor has improved spectral and spatial resolutions compared to the widely used AVHRR and provides a suite of biophysical products that are useful in biomass estimation, including vegetation indices, leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), gross primary production, net photosynthesis, and net primary productivity (NPP).³⁷^,³⁸ The mid-infrared (MIR) reflectance from optical remote sensing data is closely related to biomass and thus was found to be more useful in assessing alterations in vegetation characteristics compared to reflectance in visible (VIS) and near-infrared (NIR) bands.³⁹ Hyperspectral remote sensing is an another important source of optical satellite data for biomass estimation. Unlike multispectral satellite sensors, hyperspectral remote sensing allows the acquisition of many, very narrow, contiguous spectral bands throughout the VIS, NIR, MIR, and thermal infrared portions of the electromagnetic spectrum.⁴⁰ This ability to collect reflectance in many narrow bands makes hyperspectral remote sensing particularly useful for extracting vegetation parameters, such as LAI, chlorophyll content, and leaf nutrient concentration.⁴¹ Optical sensors collect data from only the aboveground vegetation and have been used mainly for aboveground biomass assessment.

A range of techniques are used with optical remote sensing data to estimate biomass.⁴² A commonly used technique involves the use of vegetation indices such as ratio vegetation index (RVI), normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI).⁴³ Alternatively, remote sensing data can be used to obtain indirect estimates of absorbed photosynthetically active radiation (APAR) from the red and infrared reflectance characteristics of the vegetation.⁴⁴ The APAR gives an indication of how efficiently absorbed energy is converted into dry biomass by a vegetation type.⁴⁵ Another technique involves the use of process-based models which estimate biomass production from remote sensing data by combining canopy functioning process-based models with physical radiative transfer models.⁴⁶^,⁴⁷

2.2.

Radar

Over recent years, there has been increasing interest in synthetic aperture radar (SAR) data for aboveground biomass analyses, particularly in the areas of frequent cloud conditions where obtaining high quality optical data is difficult. The capability of radar systems to collect data in all weather and light conditions overcomes this issue. Furthermore, the SAR sensor can penetrate vegetation to different degrees and provides information on the amount and three-dimensional (3-D) distribution of structures within the vegetation.⁴⁸ Airborne SAR has been operating for many years, but since the 2000s, space-borne SAR sensors such as TerraSAR-X, Advanced Land Observing Satellite (ALOS) and Phased Array L-band SAR (PALSAR) have become available.⁴⁹ Many studies based on SAR have focused on the development of algorithms for classification and biomass estimation in closed-canopy forests.⁴⁸^,⁵⁰ A commonly used approach to biomass retrieval from SAR has been to establish empirical relationships between field-based estimates and single channel data.⁴⁸

The SAR sensor can detect the horizontal (H) or the vertical (V) components of the backscattered radiation. Hence, there are four possible polarization configurations for an SAR system: horizontal transmit and horizontal receive (HH), vertical transmit and vertical receive (VV), horizontal transmit and vertical receive (HV), and vertical transmit and horizontal receive, depending on the polarization states of the transmitted and received radar signals. The SAR on the ERS satellite is VV polarized while the RADARSAT satellite is HH polarized. Radar backscatters (P and L bands) have been found to be positively correlated with major forest parameters, such as tree age, tree height, DBH, basal area, and total aboveground dry biomass.²⁸^,⁵¹^–⁵⁴ A detailed review on the use of radar data for biomass estimation can be found in the literature.⁵⁵^,⁵⁶ Various studies have utilized radar data in biomass analyses of a range of biomes.⁵³^,⁵⁴^,⁵⁷^,⁵⁸

There are a number of advantages to radar remote sensing compared to optical remote sensing in terms of its utility in biomass estimation in savannas. The ability of radar to penetrate cloud and haze makes it especially useful in the tropics. Furthermore, radar based sensors are active and have a controlled power outlet, which ensures consistent transmit and return rates. Thus, radar sensors can function independently of solar radiation variations, unlike optical sensors where spectral reflectance measurements are affected by variations in solar radiation.⁵⁹ On the other hand, radar use has limited applications in regional studies due to the small swath width, high costs of airborne acquisitions, lower sampling density of the large footprint waveform, and the limited extent of coverage.⁴⁸

2.3.

LiDAR

The two-dimensional (2-D) nature of optical remote sensing data limits its use in direct quantification of some vegetation characteristics like tree height, canopy height, volume, etc. LiDAR is a relatively new and sophisticated technology that helps to overcome this limitation due to its ability to extend the spatial analysis to a third dimension. LiDAR instruments have the ability to sample the vertical distribution of canopy and ground surfaces,⁶⁰^,⁶¹ and several studies have established a strong correlation between LiDAR metrics and aboveground biomass, thus allowing estimation of biomass in forested environments.⁶²^–⁶⁴ LiDAR technology has seen considerable advancement with the advent of full waveform digitizing sensors,⁶⁵ which has allowed this tool to be increasingly used in the study of forest structures in a variety of forest environments.⁶⁶^–⁶⁸ It has become the most efficient technology for structural assessment since it captures landscape structural data that are suitable for volume and biomass estimation.⁶⁹ Biomass can be estimated at the individual tree level with allometric equations using LiDAR data of sufficient post spacing (e.g., $> 1 return / m^{2}$ ).⁴⁸ A detailed review of LiDAR data application in forestry can be found in Lim et al.⁷⁰

The 3-D LiDAR points represent latitude, longitude, and ellipsoidal height based on the WGS84 reference ellipsoid. Ellipsoidal heights are converted to elevations. There are currently two types of LiDAR in operation: (1) discrete return LiDAR (small footprint) and (2) full waveform LiDAR (large footprint).⁷¹ Both are generally calibrated to operate in the 900- to 1064-nm wavelengths where vegetation reflectance is highest.⁶⁸ A combination of either small or large footprint LiDAR systems along with GPS and accurate time referencing allow the extraction of position in 3-D of the reflecting surface.⁶⁸ Discrete return airborne LiDAR systems are more suitable for fine-scale biomass mapping, while waveform space-borne LiDAR, e.g., The Geoscience Laser Altimeter System (GLAS) on board Ice, Cloud, and Land Elevation Satellite (ICESat) has the potential for broad-scale biomass mapping.⁷²^,⁷³

Although LiDAR data have some advantages over optical data, there are a few issues that restrict its use for field applications. For example, LiDAR data analyses are not simple and require more image processing knowledge and skill and specific software. The LiDAR data acquisition process is expensive and covers smaller areas, hence study areas are still limited to specific areas and have not been applied extensively to larger areas for biomass estimation.

3. Biomass Estimation in Forests

The remote sensing methods, data types, and some examples for forest biomass estimation are shown in Table 1.

Table 1

Summary of the remote sensing methods, data types, and some examples for forest biomass estimation.

Category	Methods	Data used	Characteristics	Examples
Remote sensing-based methods	Methods based on fine spatial resolution data ( $< 5 m$ ) (parametric classifiers, MLC, MDM, etc.; nonparametric classifier, ISODAT, k-means)	Aerial photographs, IKONOS, Quick Bird, GeoEye, WorldView	Per-pixel level	Refs. 4, 36, and 74 75.76.–77
	Methods based on medium-spatial resolution data (10–100 m) (linear, exponential and multiple regression analysis, neural network, k-nearest neighbor method, productivity model)	Landsat 4 5 7 $TM / En h a n c e d TM +$ , Systeme Probatoire D’Observation De La Terre (SPOT)	Per-pixel level	Refs. 78 79.80.81.82.–83
	Methods based on coarse-spatial resolution data ( $> 100 m$ ) (regression models, multiple regression and artificial neural network (ANN), k-nearest neighbor, statistical models)	IRS-1C WiFS, AVHRR, MODIS, SPOT vegetation	Per-pixel level	Refs. 81 and 84 85.86.87.88.–89
	Methods based on radar data (regression models, canopy height model, multiplicative models)	SIR-C, SAR-L JERS-1 SAR-L, AeS-1 SAR-P, InSAR, airborne laser, large and small footprint LiDAR	Per-pixel level	Refs. 54, 57, 72, and 90 91.92.93.94.95.96.97.98.99.–100
	Method based on image fusion techniques (intensity hue and saturation (HIS), Brovey, PCA	Multispectral and PAN		Refs. 101 102.103.–104
	Vegetation index-based method (NDVI, ratio)			Refs. 105 106.107.–108
	Object based (segmentation and classification, ANNs, k-nearest neighbor, statistical models; random forest)		Object-level	Refs. 109 110.111.112.–113
	Advanced classifier spectral mixture analysis (SVM), random forest, support vector machine (SVM)	Multispectral	Per-pixel level	Refs. 113 114.115.–116

3.1.

Use of Optical Remote Sensing

Optical remote sensing data, with a variety of spatial and temporal resolutions, have been widely used for forest biomass estimation using different types of image processing techniques.⁴^,⁷^,²⁴^,²⁹^,³⁰^,⁸⁴^,⁸⁷^,¹¹⁷^–¹²¹ For biomass estimation from optical data, the commonly used approaches are multiple regression analysis, $k$ -nearest neighbor, and neural network.²⁴^,²⁹^,³⁰^,¹²²^,¹²³ Optical data can be used to carry out spatial stratification of vegetation from which estimates of biomass distribution can be generated. For indirect biomass estimation, remote sensing data are used to determine tree canopy parameters, such as crown diameter using multiple regression analysis or canopy reflectance models.¹²⁴^,¹²⁵ Different types of vegetation indices and band ratios derived from optical data are also used to extract biomass by correlating vegetation index values or band ratio values with field estimations.⁸⁷

The ready availability of high-resolution data from a range of sensors has permitted the modeling of tree parameters or forest canopy structures. For example, Song et al.³⁶ estimated tree crown size from IKONOS and Quickbird images and concluded that this approach could provide estimates of average tree crown size for hardwood stands. Greenberg et al.⁷⁷ have effectively used IKONOS data (spatial resolution 4 m) in estimating crown projected area, DBH and stem density. There are numerous methods applied for the extraction of biophysical parameters using high-spatial resolution data.¹²⁶ Large scale photographs and photomensuration methods have been used to measure various forest characteristics, such as tree height, crown diameter, crown closure, and stand area.⁷⁵^,¹²⁷ De Jong et al.⁷⁶ used digital airborne data to estimate biomass in southern France using linear regression analysis. In another study, Thenkabail et al.⁴ used IKONOS data to estimate biomass of oil palm plantations in Africa. Although high-spatial resolution and associated multispectral characteristics may become an important data source for forest biomass estimation and have attained great success, the shadows and intracrown spectral variance and the low spectral separability between tree crowns and other vegetated surfaces in the understory¹²⁸^–¹³⁰ create difficulty in developing biomass estimation models. High-resolution data need large data storage and processing time and are much more expensive to cover a given area. These factors influence the application of high-spatial resolution images for biomass estimation over broad areas. The absence of shortwave-infrared images, an important parameter for biomass estimation, also limits its application in biomass assessment. The problem is greater when traditional pixel-based spectral classifiers are used for vegetation classification. However, the incorporation of contextual information and object-based methods into the classification process has overcome this problem to an extent.¹⁰⁹^,¹¹¹ Object-based methods consider both spectral and context information during the classification process by segmenting the image into meaningful objects.¹¹⁰^,¹¹² The size of the image objects is determined by a scale parameter.¹³¹ The selections of segmentation parameters are subjective and determined through a combination of trial and error steps. Statistics on spectral bands (mean, standard deviation, etc.) along with other contextual information, such as geometric features (area, length, compactness, shape, etc.), and texture features-gray-level co-occurrence matrix (GLCM) (homogeneity, contrast, entropy, dissimilarity, correlation, etc.), and gray-level difference vector (entropy, contrast, etc.) of spectral bands are used to statistically derive features for each object that best separate the vegetation classes. Numerous studies have extracted GLCM textures from remote sensing images.¹¹¹^,¹¹³^,¹³² In Rondônia State, Brazil, Lu and Batistella¹¹³ used the GLCM texture (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation) with different moving window sizes and Landsat thematic mapper (TM) spectral bands 2 to 5 and 7 to examine the relationships between biomass and textural images for secondary and mature forest. They found a stronger relationship between textural images and biomass for mature forest with complex stand structure than original spectral bands. However, for secondary forest with a simple stand structure, biomass was closely related to spectral bands.

Medium-spatial resolution data have also been widely used in forest biomass estimation. For example, Lefsky et al.⁸⁰ estimated stand tree structure attributes such as basal area, biomass and DBH using remote sensing data. Linear or nonlinear regression models, $k$ -nearest neighbor, neural network, and vegetation canopy models are the main methods applied in this case. In a Bornean tropical rain forest, Foody et al.⁸² used neural networks for biomass estimation using Landsat TM. Ghasemi et al.¹³³ used SPOT 5 data to estimate aboveground forest biomass from canopy reflectance model inversion in the mountainous terrain of Kananaskis, Alberta. Landsat TM data were used to estimate tree volume and biomass using the $k$ -nearest neighbor estimation method.⁷⁸^,⁷⁹^,⁸¹ The task of estimating biomass from optical data for humid tropical forests is challenging because of its complex multilayered closed canopy structure combined with high levels of biomass.³^,²⁴^,²⁹^,⁸²^,¹¹⁸^,¹²³^,¹³⁴ In such cases, spectral reflectance and vegetation indices were found not to be reliable indicators of biomass²⁴ and were not sensitive to biomass change.²⁹ However, with the inclusion of some other factors, a few studies have shown positive results in estimating tropical forest biomass. For example, Nelson et al.¹²³ included the age of the forest into Landsat TM image analysis to estimate tropical forest biomass, while with the use of texture information into the image analysis process, Lu¹¹⁸ and Sarker and Nichol¹³⁵ improved biomass estimation results in tropical forests. Lu¹¹⁸ concluded image texture features to be more important than spectral reflectance for biomass estimation for forests with more complex stand structure. However, it is critical to identify suitable image textures that are strongly correlated with biomass but are weakly correlated with each other and this requires a great deal of effort.¹³⁶ In addition, image textures vary with the landscape and images used, therefore, not all texture measures can effectively extract biomass information and guidelines on how to select an appropriate texture needs more research. Several vegetation indices have been developed, mostly from VIS and infrared bands and applied to biomass estimation and biophysical parameter studies.¹⁰⁵^,¹⁰⁶ Vegetation indices have been found useful in minimizing spectral variability caused by canopy geometry, soil background, sun view angles, and atmospheric conditions when measuring biophysical properties.¹⁰⁷^,¹⁰⁸ Although not all vegetation indices were found to be directly correlated with biomass,²⁴ by minimizing the impact of environmental conditions and shadow on spectral reflectance, there was improved correlation between biomass and vegetation indices, especially in complex vegetation stand structures.¹⁰⁵ Therefore, a combination of image textures and spectral responses can be considered useful in determining forest stand parameters and to establish more accurate biomass estimation models.¹¹⁸ In addition to pixel-based spectral responses and textural images, subpixel-based variables such as green vegetation, shade, and soil can also be used as input variables for biomass estimation.²⁰^,¹³⁷ Spectral mixture analysis (SMA) has been found useful in developing these fractional images from multispectral images such as Landsat TM.¹¹³^,¹¹⁵ Lu and Batistella¹¹³ used SMA to extract fractional images from a Landsat TM image to examine the relationship between biomass and the subpixel variables for secondary and mature forests in Rondônia State. They found fractional images to be more useful for biomass estimation as compared to individual spectral bands. A detailed description of the SMA approach and its applications can be found in the literature.¹¹⁴^–¹¹⁶

Coarse-spatial resolution AVHRR NDVI data have been used to estimate biomass in Africa⁸⁶ and boreal and temperate forest woody biomass in Canada, Finland, Norway, Russia, Sweden, and the USA.⁸⁷ The advantages of a large number of spectral bands of MODIS data and their availability have improved biomass estimation accuracy at the continental or global scale. Recent studies have achieved promising results using tree-based models and metrics derived from MODIS data, in combination with radar data and ancillary information (climate, topography, and vegetation maps), to map the biomass distribution for the Amazon basin,⁸⁹ the United States,¹³⁸ and tropical Africa.⁸⁵ Baccini et al.⁸⁴ used MODIS data in combination with precipitation, temperature, and elevation for mapping biomass in national forest lands in California, USA. Overall, the application of forest biomass estimation using coarse-spatial resolution data is limited due to the occurrence of mixed pixels, saturation of spectral data at high biomass density and by the mismatch between the size of field plots and pixel size. A few studies have used coarse-resolution data along with medium-resolution data in combination with different modeling approaches to get more accurate biomass estimates for large areas. For example, Hame et al.⁸⁸ used Landsat TM and AVHRR data to estimate coniferous forest biomass. In another study, Tomppo et al.⁸¹ used TM as an intermediate step between field data and IRS-1C wide field sensors data to estimate tree stem volume and biomass in Finland and Sweden.

Overall, optical sensor data are found suitable for extracting horizontal vegetation structures such as vegetation types and canopy cover; however, the 2-D data have limitations in estimating vertical vegetation structures such as canopy height, which is one of the critical parameters for biomass estimation. Recently, optical data such as ALOS, panchromatic remote-sensing instrument for stereo mapping (PRISM), IKONOS stereo satellite images, and SPOT provide a stereo viewing capability that can be used to develop vegetation canopy height, thus can improve biomass estimation performance.¹³⁹^,¹⁴⁰ For example, St‐Onge et al.¹³⁹ assessed the accuracy of the forest height and biomass estimates derived from an IKONOS stereo pair and an LiDAR digital terrain model. Reinartz et al.¹⁴¹ used SPOT 5 HRS for forest height estimations in Bavaria and Spain, while Wallerman et al.¹⁴² investigated 3-D information derived from SPOT 5 stereo imagery to map forest variables such as tree height, stem diameter and volume. These studies show that high-resolution stereo data can be used as a valuable alternative to derive vegetation height information; however, more studies are needed to support this.

3.2.

Use of Radar

Studies that utilized radar data in forest biomass estimations found SAR L-band data to be more useful⁵³ than SAR C-band data.⁹⁰ Beaudoin et al.¹⁴³ found that VV and HV radar backscatter at high frequencies (C-bands and X-bands) were linked to crown biomass while radar backscatter HH at lower frequencies (P-bands and L-bands) were related to both trunk and crown biomass. Harrell et al.¹⁴⁴ used SIR C- and L-band multipolarization radar data for pine forest biomass estimation in the southeastern USA and found L-band HH data to be critical in biomass estimation. They noted that the inclusion of C-band HV or HH significantly improved biomass estimation performance. For biomass estimation of regenerating forests, Kuplich et al.⁹¹ found JERS-1/SAR data to be useful when forests are regenerating after block logging and not after selective logging. For mountainous area forest biomass estimation, multipolarization L-band SAR data were found to be useful.⁵³ Santos et al.⁹² found that JERS-1/SAR double bounce scattering and forest structural-physiognomic characteristics are the two important factors for biomass estimation of forest and savanna. For biomass estimation, most of the previous studies used the radar system from JERS-1, ERS-1/2 of single polarization, single incident angle, and low resolution SAR sensor. However, with the establishment of PALSAR and RADARSAT-2 (C-band), data are now available in different polarizations, different resolutions, and varying incident angles, which offer more opportunities to the scientific community to re-examine the potential of SAR data in forest biomass estimation. PALSAR data results have shown its ability to map forest in the Amazon and Siberia; however, the retrieval of forest biomass is still typically limited to values less than $50 t {ha}^{- 1}$ , which excludes most temperate and tropical forests.¹⁴⁵ Sarker et al.⁵⁷ investigated the capability of RADARSAT-2 fine-beam dual-polarization (C-HV and C-HH) data for forest biomass estimation in complex subtropical forest and found encouraging results. Radar data saturation problem is greater in complex forest stand structure when backscattering values are used for biomass estimation.¹⁴⁶^,¹⁴⁷ Interferometry SAR (InSAR) has been found useful in reducing this problem by increasing the saturation range to a certain degree by coherently collecting data over a short time increment with two identical instruments.⁹³^,⁹⁴^,¹³³ This improves the height-based biomass and volume estimation when the L-band saturation point increases to $200 t {ha}^{- 1}$ .⁷³ Balzter⁹³ reviewed InSAR for forest mapping and monitoring covering tree volume and biomass, forest types and land cover, fire scars, forest thermal state, and forest canopy height. The high correlation between vegetation canopy height and biomass of InSAR makes it a promising tool for broad-scale biomass estimation, especially for tropical and subtropical regions where frequent cloud cover is a problem.⁹⁴^,⁹⁵ However, other weather conditions, such as wind speed, moisture, and temperature, affect the InSAR estimation accuracy.¹⁴⁸ Recently, the polarimetric SAR interferometry (Pol-InSAR), a combined polarization and interferometry, has been found useful in estimating forest height using coherence information¹⁴⁹ and then correlating it to biomass.¹⁵⁰

3.3.

Use of LiDAR

The structural forest measurements from LiDAR data permit the accurate estimation of height, crown size, basal area, stem volume, LAI, NPP, and aboveground biomass, even in high biomass forests, a difficult task with passive sensors.⁶⁶ Biomass mapping from airborne discrete return LiDAR is based on two approaches: (1) area-based and (2) individual tree-based methods.⁷² Area-based methods develop statistical models to relate biomass with metrics derived from a LiDAR point cloud at the plot or stand level and apply the models over the whole study area.¹⁵¹^–⁹⁷ The development of statistical models requires field data for calibration and validation. The most widely used area-based LiDAR metrics for biomass prediction are various height metrics⁷⁰^,¹⁵²^,¹⁵³ calculated based on first, last, or all returns. Height metrics can also be calculated from grids of the canopy height model.⁹⁶^,¹³⁹^,¹⁵² Individual tree-based methods identify individual tree crowns and extract individual tree information from LiDAR point cloud, such as tree height and crown size, which can be related to biomass and other canopy structure variables through allometric equations. ¹⁵⁴^–¹⁵⁶ In this case, the amount of fieldwork required is much smaller than that for area-based methods because field data are needed only for a sample tree and not for sample plots or stands. Discrete return systems have been used to estimate biomass at the individual tree level up to the stand level.¹⁵⁴^,¹⁵⁵^,¹⁵⁷^,¹⁵⁸ The DEMs generated from airborne LiDAR data are very accurate and widely used in forest mapping and tree parameter estimations. It captures elevation information from the forest canopy as well as the ground beneath and can be used to assess the complex 3-D patterns of canopy and forest stand structure such as tree density, stand height, basal area, LAI, and forest biomass and volume.⁶⁸^,¹⁵⁹ In densely vegetated areas when passive sensors saturate at high biomass levels (higher than $100 mg {ha}^{- 1}$ ),¹⁶⁰ LiDAR has been found to accurately estimate LAI and biomass in such high biomass ecosystems.⁶⁸ In British Colombia, Canada, Loos et al.¹⁶¹ identified understory canopies between the dominant canopies of Douglas-Fir and Western Hemlock tree species by creating bare earth DEM and DSMs (digital surface models). The estimation of biomass is generally based on regression equations relating vegetation biomass to LiDAR derived variables. Studies are being conducted using LiDAR to determine the most appropriate laser-based predictors in regression models for estimation of forest structural variables. For example, García et al.¹⁶² have explored several biomass estimation models based on LiDAR height or intensity, separately, or height-intensity combined. They found height-related variables provided accurate estimation of biomass; however, normalized intensity-related variables were found to be more useful in explaining variance and also estimated biomass more accurately. The combined use of height and intensity data has been shown to be a robust method to estimate biomass. For broad-scale applications, space-borne LiDAR (ICESat GLAS) was found useful for biomass estimation.⁹⁸^–¹⁰⁰ The waveform extent of GLAS is the most important metric for biomass estimation¹⁰⁰ as it is directly related to vegetation height in flat terrain; however, for sloped areas, waveform exacerbates estimation and needs terrain steepness index into a regression model.¹⁰⁰

In summary, remote sensing data (optical, SAR, LiDAR) have been found to be a major source of data for forest biomass estimation and also in the selection of suitable variables important for developing biomass estimation models. However, the performance of remote sensing data and methods in biomass estimation have been found to be highly dependent on image data type, forest cover type and state, geographical and environmental conditions and methods used. Optical data are found suitable for extracting horizontal vegetation structures such as vegetation types and canopy cover and also in extracting variables for biomass estimation models. They have been used for biomass estimation of almost all forest types, either alone or in combination with other remote sensing data with varying degrees of success. However, optical data have an issue of clear weather condition at the time of data acquisition and also of saturation problems in forest sites with high biomass density. Spectral-based variables have been found to be influenced by external factors such as soil moisture, vegetation phenology and growth vigor, and also the 2-D nature of optical data limits its use in estimation of vertical vegetation structures such as canopy height, a critical parameter for biomass estimation. Recently, data from ALOS/PRISM and other stereo images have provided an opportunity to develop vegetation canopy height and can improve biomass estimation performance. Radar data can overcome many of the optical data problems for forest biomass estimation because of its ability to penetrate forest canopy to a certain depth, its sensitivity to water content in vegetation and its weather independency. The regression of radar backscattering (amplitudes) and interferometry (amplitudes and phases) are commonly used methods in biomass estimation. Radar data have been used extensively in forest cover and type mapping, estimation of forest stand parameters and in estimating biomass in tropical, temperate and boreal forests. However, radar data suffer from saturation problems in complex mature forest stands and also have difficulty in distinguishing vegetation types. L-band SAR images have been found suitable in discriminating forest biomass up to a certain threshold of regenerating forests in tropical regions. PALSAR data have shown its ability to estimate forest biomass in the Amazon and Siberia up to $50 t {ha}^{- 1}$ , which excludes most temperate and tropical forests. The stereo viewing capability of InSAR data has been found to improve biomass estimation in more complex forest stands and has been found useful in reducing saturation problems by increasing the saturation range to a certain degree. The high correlation between vegetation canopy height and biomass of InSAR makes it a promising tool for broad-scale biomass estimation for tropical and subtropical regions of frequent cloud cover. However, InSAR biomass estimation accuracy has been found to be sensitive to weather conditions. Improved systems, such as Pol-InSAR, have been found useful in estimating forest height and biomass estimation. LiDAR sensor can directly measure 3-D components of vegetation canopy structure and is widely used in estimation of forest biophysical parameters. Discrete return small footprint laser data are used for biomass estimation for different forest environments: tropical forest biomass, temperate mixed deciduous forest biomass; and also in measurements of biophysical parameters such as tree height and stand volume, tree and crown diameter, and canopy structure. For regional to global scale applications, spaceborne LiDAR (ICESat GLAS) has been found useful for biomass estimation.

4. Biomass Estimation in Grasslands and Rangelands

Grassland and rangeland ecosystems cover large areas of the earth’s surface and provide many ecosystem services including carbon storage, biodiversity preservation and the production of livestock forage.¹⁶³ Being dominant over approximately 52.5 million square kilometers (near 40%) of the Earth’s land surface,¹⁶⁴^,¹⁶⁵ grasslands and rangelands are important sources for developing renewable energy. They can provide an alternative source for energy supply which reduces the dependence on fossil fuels and minimizes greenhouse gas and other environmental impacts.¹⁶⁶ In addition to biofuel production, grassland ecosystems play an important role in providing food, goods, and services for humans, and are central to livestock grazing.¹⁶⁷^,¹⁶⁸

4.1.

Use of Optical Remote Sensing

Optical remote sensing has been extensively used for estimating grassland and rangeland biomass. Coarse-, medium-, and high-spatial resolution images have been used and examined in order to better map the distribution of grassland and rangeland biomass. For example, Li et al.¹⁶⁹ used multitemporal MODIS data to estimate the grassland aboveground biomass in the West Songnen Plain, China. Their results indicated that multitemporal remotely sensed data along with statistical models and artificial neural network (ANN) techniques have advantages for estimating grassland aboveground biomass. Mundava et al.¹⁷⁰ used Landsat $ETM +$ to test the relationship between AGB in rangelands and remotely sensed indices by measuring dry and green biomass fractions and found that single vegetation indices were moderately more accurate for green biomass than dry biomass. For high-spatial resolution images, Dusseux et al.¹⁷¹ estimated grassland biomass in agricultural areas by applying NDVI and two biophysical variables including LAI and fraction of vegetation cover on five SPOT images. Zandler et al.¹⁷² found that both a high-spatial resolution sensor (RapidEye) with its additional red edge band and a coarse-spatial resolution sensor (Landsat-8) showed very similar performances for modeling the total dwarf shrub biomass in the desert landscape. The red edge reflectance curve performs better than traditional vegetation indices for estimating the distribution of grassland over a desert environment.¹⁷³^,¹⁷⁴

Hyperspectral remote sensing data were also used to estimate grassland and rangeland biomass. Among others, Rahman and Gamon¹⁷⁵ examined the utility of hyperspectral remote sensing to detect fresh and dry biomass, water content and plant area index of burned and unburned grassland in Southern California. Xiaoping et al.¹⁷⁶ concluded that grassland and rangeland biomass could be estimated at the canopy level using hyperspectral reflectance. Clevers et al.¹⁷⁷ found that one band in the NIR region from 859 to 1006 nm and one band in the red edge region from 668 to 776 nm that were used in the weighted difference vegetation index had the best predictive power of grassland biomass variation.

4.2.

Use of Radar and LiDAR

Despite the popularity of radar and LiDAR data in forest biomass analyses, very few studies have utilized such data in the estimation of grassland biomass. For instance, Dusseux et al.¹⁷⁸ compared the performance of variables extracted from four optical and five SAR satellite images to monitor grassland biomass. They concluded that the classification accuracy of SAR variables was higher than those using optical data. Buckley and Smith⁵⁸ used radar, LiDAR, and hyperspectral data to monitor grassland biomass and they argued that radar and LiDAR data were not affected by weather conditions as optical remote sensing data is.

Vegetation indices, including SAVI,¹⁷⁹ the modified soil adjusted vegetation index (MSAVI),¹⁸⁰ NDVI,¹⁸¹^,¹⁸² and normalized difference water index,¹⁸³ have been widely used in grassland and rangeland biomass estimation. Image classification, such as support vector machine classifier,¹⁷⁷^,¹⁸⁴ object-based classification,¹⁸⁵ and ANN,¹⁸² were other techniques frequently used for deriving grassland and rangeland biomass. In addition, multiple regression analysis models were the most commonly used statistical approaches.¹⁸⁶ However, the performance of these techniques varied and depended on the structure of the study area and the nature of the remotely sensed data used to estimate grassland and rangeland biomass.

5. Biomass Estimation in Tropical Savanna

Savanna ecosystems are generally comprised of herbaceous plants dominated by grasses, with variable tree cover.¹⁸⁷^,¹⁸⁸ These ecosystems cover approximately 18% of the Earth’s surface and account for approximately 30% of the primary production of all terrestrial vegetation, thus forming an integral part of global vegetation.¹⁸⁹^,¹⁹⁰ The largest areas of savanna can be found in Africa where it occupies approximately 50% of the territory.¹⁹¹ Considerable areas of savanna can also be found in South and Central America, Australia, India, Southeast Asia, and the Pacific Islands.¹⁹²^–¹⁹⁷ Furthermore, savanna ecosystems are characterized by a pattern of strong seasonality in available soil moisture, determined by a wet-dry climate.¹⁹⁵^,¹⁹⁸ This seasonality in water availability impacts plant productivity and consequently biomass production in savanna ecosystems.¹⁹⁵ Tropical savanna ecosystems can be highly productive with a global average NPP ranging from $720 g C m^{- 2}$ year-1143 to $782 g C m^{- 2}$ year-1199. The arid and semiarid savannas of Africa, Australia, and South America show lower NPP compared to the margins of the Amazon and Congo River basin.¹⁹⁹ Fire is also a dominant feature and a major determinant of the ecology and distribution of savannas worldwide.¹⁹⁰^,²⁰⁰^,²⁰¹ Thus, fires have an impact on the proportions of dead and live biomass in savannas.²⁰²

The rate of biomass production is an important attribute of most ecosystems. In the savanna ecosystem, as in all ecosystems, the rate of biomass production determines the amount of energy available for higher trophic levels.²⁰³ Thus, biomass estimation will provide crucial information on the health of the ecosystem and the biodiversity it supports. Additionally, there is a growing recognition of the value of natural carbon stores in savanna biomass and the significance of savannas in the global carbon cycle.¹⁹⁰ These ecosystems also face increasing pressure from human interventions in the form of agricultural expansion,¹⁸⁷^,²⁰⁴ logging and burning.¹⁸⁷^,²⁰⁵ Given the important role of savanna ecosystems in the global carbon cycle and the threats they face, it is vital to undertake a detailed census of biomass in these ecosystems. Techniques that will reliably measure, map and monitor biomass in savanna ecosystems are required that will support conservation and management actions, as well as determine optimum use for renewable energy. Field measurements to estimate biomass are labor intensive and time-consuming. Remote sensing and LiDAR sensors provide many opportunities in this respect.²⁰⁶

Remote sensing and LiDAR systems have quite commonly been used in biomass assessment of closed forests; however, their use in savannas has become more popular only in recent times. The main reasons for this are that the distribution of vegetation biomass in savannas is uneven in 3-D space with biomass allocated to above and below ground components.⁴⁸ Furthermore, the structure of savanna vegetation is variable with the occurrence of an herbaceous layer with variable tree cover and open spaces.¹⁸⁸ These two factors make the retrieval of savanna vegetation characteristics from remote sensing data difficult.

5.1.

Use of Optical Remote Sensing

Vegetation indices have been used extensively by researchers in the context of savanna ecosystems.²⁰⁷^–²¹¹ For example, Sannier et al.²¹² found high correlations of biomass with NDVI from NOAA–AVHRR images for both herbaceous and woody vegetation in the savanna region of Etosha National Park in Namibia. Other studies have also shown the sensitivity of NDVI to the herbaceous biomass of savannas in the Sahel zone of Senegal using NOAA–AVHRR imagery.¹⁶^,²¹³ On the other hand, Mutanga and Skidmore¹⁰⁶ found that the NDVI performed poorly in estimating pasture biomass of Cenchrus ciliaris grass in the low-lying savannas of Kruger National Park in South Africa. They suggest that some indices, such as the NDVI, had limited value in biomass estimation since they saturate in dense vegetation, a finding that agrees with Gill et al.,²¹⁴ who found that the NDVI had limited application in monitoring changes in vegetation in Australia due to saturation. Indices such as simple ratio or RVI and the red edge position may perform better, particularly when estimating pasture biomass with high canopy density.¹⁰⁶ Verbesselt et al.²¹⁵ used RVI from SPOT vegetation time series to monitor the vegetation biomass in the savanna ecosystem of Kruger National Park in South Africa. On the other hand, van Leeuwen et al.²¹⁶ argued that soil background influences altered the responses of most vegetation indices and thus utilized SAVI in their estimation of herbaceous biomass using reflectance data in a shrub savanna landscape in Niger.

Monteith’s efficiency model using indirect estimates of APAR obtained from remotely sensed data has been applied in the African Sahel to assess the productivity of savanna ecosystems.²¹⁷^,²¹⁸ The findings support the idea that savannas play an important role in global carbon cycle, particularly given the large areas that they cover. Other global savanna biomass assessments have been made possible through NASA’s Terra satellite platform with MODIS on board. Fensholt et al.³⁷ have utilized LAI, FAPAR, and NPP produced by MODIS in estimating biomass production in the savannas of the semiarid Sahel zone in Senegal. An assessment of the MODIS LAI product for Australian ecosystems revealed that the savanna and shrub-land group LAIs show strong seasonal patterns, mainly associated with summer rainfall seasons.²¹⁹

Process-based models are becoming increasingly popular in studies involving productivity assessments of terrestrial ecosystems.²²⁰^–²²⁵ These studies combined satellite “greenness” data from the AVHRR sensor into the NASA–Carnegie Ames Stanford Approach (CASA) model to estimate spatial variability in global biomass accumulation in terrestrial ecosystems. Potter et al.²²⁰ applied a similar methodology but used MODIS EVI data, which represent the optimized vegetation index from the MODIS satellite, to estimate aboveground biomass (AGB) in savanna ecosystems worldwide and found it to be second only to tropical evergreen forests. However, the MODIS data also showed that the productivity of savanna ecosystems worldwide is highly dependent on seasonal climate anomalies such as El Niño Southern Oscillation.²²⁵ For example, research conducted on the Brazilian Amazon Cerrado (savanna) established that the productivity of the Cerrado was highly impacted by variability in precipitation rates caused by the 2002–2003 El Niño phase.²²⁰^,²²⁶ The general pattern observed was an increase in seasonal FPAR cover in savannas during increased precipitation and decrease in FPAR cover during reduced precipitation (FPAR is an indicator of biomass production). This pattern suggests that the productivity of savanna ecosystems is very dependent on future rainfall patterns, particularly in parts of the world that are likely to be affected by climate change.¹⁹⁹

5.2.

Use of Radar and LiDAR

McGlinchy et al.⁶⁵ have used LiDAR for biomass estimation in savanna ecosystems with some success in a South African savanna landscape. Others have utilized new approaches involving the fusion of high-fidelity VIS/NIR imaging spectrometer data with scanning, waveform light detection and ranging (wLiDAR) data to assess biomass in African savannas.²⁰⁶^,²²⁷^,²²⁸ The findings established the potential of fused hyperspectral and wLiDAR data for herbaceous biomass modeling in savannas.

Collins et al.⁵⁹ examined the relationship between the backscatter intensity of polarimetric SAR data and the aboveground biomass of a north Australian savanna to estimate above and below ground biomass and carbon storage of this ecosystem. They found no significant difference between their predicted and observed aboveground biomass, thus demonstrating the potential of SAR for predicting and mapping aboveground biomass in the tropical savannahs of northern Australia. However, the open canopy of savannas and the spatial resolution of the sensor lead to complications for the use of SAR data in savannas.⁴⁸ For example, Viergever et al.²²⁹ evaluated SAR data for aboveground biomass estimation in tropical savanna woodland in Belize, Central America. Their findings showed a relatively low correlation between SAR backscatter and aboveground biomass, although retrieved canopy heights gave a better representation of the aboveground biomass. Nevertheless, it could not be used to estimate biomass directly due to the heterogeneity of the canopy.

Savannas are extremely productive systems and they have a lot of potential for renewable energy through biomass, making it very important to develop accurate and precise methods for estimating biomass. These ecosystems also face many threats, both human and climate change induced. Remote sensing can provide cost-effective and timely biomass estimates over large areas as opposed to direct field measurements of biomass which are labor intensive, costly and sometimes destructive.

6. Image Processing for Biomass Estimation

6.1.

Spatial Data Processing

Although a range of remote sensing data (optical, radar, LiDAR) at different spectral, spatial, and temporal resolutions have been used for biomass estimation with varying degrees of success, it has been found that improvement in biomass estimation depends not only on the data type but also on efficient image processing techniques.²³⁰ There are a number of environmental and topographic factors that can affect the accuracy of biomass estimation from remote sensing data. A thorough understanding of previous efforts in biomass estimation can be used in designing an optimal image analysis procedure suitable for the specific study area. Radiometric and atmospheric corrections are important in improving image quality, and a range of methods have been developed for these corrections under different conditions.²³¹ Topographic factors (slope, aspect) that affect vegetation reflectance and biomass are also important for mountainous regions. More details on these corrections can be found in Hale and Rock.²³² The problem associated with remote sensing data for biomass estimation is that the images become saturated at fairly low biomass levels. Use of narrow-wavelength images can reduce this data saturation problem.¹⁰⁶ The large number of spectral bands in the hyperspectral image may improve the biomass estimation performance. However, because of data volume and processing time, there is often a trade-off between spatial, spectral, and radiometric resolutions.

Image classification is the simplest way of extracting information from remote sensing data, and a range of classification algorithms are available for different data types and conditions. The conventional pixel-based classification method, relying only on spectral information, works well with medium- to coarse-resolution images but is often found insufficient when applied to very high-resolution imagery²³³ and LiDAR. Object-based classification methods based on both spectral and contextual information have been shown to improve performances for many applications, including biomass estimation.²³⁴ However, the implementation of contextual information in classification is a complex process.¹³⁶ Use of advanced classifiers, such as SMA, can also improve classification results.¹¹³

6.2.

Image Fusion

Most previous studies involving biomass estimation from remote sensing data have used a single sensor or single date image, which may not be sufficient for complex applications such as biomass estimation in certain areas.³¹^,¹⁰¹ Since remote sensing data are available from a range of sensors, each with its own characteristics and time series, it would be more useful if they were combined or fused to produce a better understanding of the observed site.¹⁰² For example, the fusion of optical and radar data may reduce mixed pixels and data saturation problems and has the potential to improve biomass estimation. Multisensor or multiresolution data fusion takes advantage of the strengths of distinct image data for improvement of visual interpretation and quantitative analysis³ and numerous methods have been developed to integrate spectral and spatial information from different sensors.¹⁰³^,¹⁰⁴^,²³⁵^,²³⁶ Studies in the past have shown that the fusion of optical (multi and PAN) and also SAR data resulted in an improved performance for biomass estimation.²³⁷^–²⁴¹ However, more research is needed to explore the improvement of biomass estimation through multisensor data fusion. Several studies have also tried to combine high-resolution multispectral imagery and LiDAR data to produce more effective forest classification.²⁴²^–²⁴⁵ Tonolli et al.²⁴⁶ studied the prediction of forest stem volume using LiDAR and IRS 1C, LISS III data. Popescu et al.¹²⁵ explored the feasibility of small footprint LiDAR and multispectral imagery to estimate volume and biomass in deciduous and pine stands in Virginia, USA. The results showed that, though LiDAR accurately estimated the biophysical parameters of forest stand at the individual tree level alone, it was more effective when used in conjunction with optical data. Vaglio-Laurin et al.²⁴⁷ estimated aboveground biomass in an African tropical forest with LiDAR and hyperspectral data. Their findings showed that the integration of hyperspectral bands with LiDAR improved the model based on LiDAR or hyperspectral bands alone.

7. Remote Sensing Techniques and Accuracies Among Forest, Grassland/Rangeland, and Tropical Savanna Ecosystems

The environmental structure for forests, grasslands/rangelands, and tropical savanna biomes is different based on the nature, distribution, characteristic, density, and energy produced from each ecosystem. These elements interact with incoming radiation to impact remote sensing data and affect the information provided. In the past, a wide range of remote sensing techniques has been used to extract information related to biomass estimation from forests, grasslands/rangelands and savanna ecosystems. Most of the techniques used were vegetation indices, image transform algorithms [e.g., principal component analysis (PCA), minimum noise fraction transform (MNF), and tasselled cap transform (TCT)], texture images, radar, and LiDAR. However, these techniques have shown different accuracies in various ecosystems.

Based on vegetation extraction using remote sensing data, the most frequently used techniques for forest, grassland/rangeland and savanna ecosystems are vegetation indices. The common vegetation indices have included NDVI, EVI, SAVI, and NDBI and have been used to estimate biophysical variables including LAI, FAPAR and biomass. In biomass estimation, however, vegetation indices can be a more suitable technique for grassland, savanna and forest sites with a simple stand structure rather than those of a complex stand structure since the relationships of NIR wavelength with biomass are weak.²⁰^,¹¹⁵ Lu et al.¹¹⁵ found that the relationships of shortwave-infrared wavelength with biomass are stronger than the NIR wavelength in a complex stand structure. Roy and Ravan¹²² emphasized the strength of shortwave infrared in the relationships between spectral response and biomass, but these relationships have a seasonal dependency in varying phonological conditions. This is because the shortwave-infrared bands are less affected by atmospheric changes. For grasslands/rangelands and savanna biomass estimation, the performance of vegetation indices has shown differing accuracies. For example, Ullah et al.²⁴⁸ concluded that band depth analysis consistently showed a higher accuracy than vegetation indices using MERIS data in grassland ecosystems, while Paruelo et al.²⁴⁹ found a positive relationship between NDVI and aboveground net primary production (ANPP) with mean annual precipitation between 280 and 1150 mm, and mean annual temperature between 4 deg and 20 deg using AVHRR/NOAA. However, Mutanga and Skidmore¹⁰⁶ emphasized that NDVI provides a poor performance in estimating pasture biomass. Thus, the accuracy obtained by applying vegetation indices in grasslands/rangelands and savanna ecosystems depends on a number of variables including type of data used, study area characteristics and environmental and atmospheric conditions. Additionally, the problem of saturation under high vegetation density limits the performance of vegetation indices.

Classification and linear or nonlinear regression have also shown different results and accuracies among different ecosystems. While, for example, $k$ -nearest neighbor analysis provided a consistent accuracy when applied for forest biomass estimation,²⁵⁰^,²⁵¹ it may not be a reliable technique for grassland/rangeland and savanna biomass estimation. The $k$ -nearest neighbor analysis failed in some cases²⁵⁰ to provide a higher accuracy when applied to large area vegetation detection. Applying hyperspectral remote sensing may overcome some of the problems, however, hyperspectral data are mainly airborne and are captured over small areas.²⁰ ANN has been applied to estimate biomass from both forest and grassland ecosystems. For example, Xie et al.¹⁸² compared ANN and multiple linear regression to estimate grassland aboveground dry biomass in Mongolia and Wang and Xing²⁵² applied ANN to estimate natural forest biomass in Jilin Province, China. Both studies provided improved accuracies using ANN for both grassland and forest biomass estimation. Other techniques, such as image transformation (PCA, MNF, and TCT), texture analysis, and SMA, have shown differences between the obtained results of biomass estimations of forest, grassland/rangeland and savanna ecosystems. However, most previous studies have applied these techniques only for forest biomass estimation rather than for other environments.

Although LiDAR has improved the accuracy of biomass estimation in forest biomes,⁶⁷^–²⁵⁵ the availability of LiDAR data, particularly for large areas, has limited the usefulness of this technology. Similarly, while radar data have been widely applied in forest biomass estimation (as discussed in Sec 3.2), very few studies have used radar data for biomass estimation in grasslands/rangelands and savanna ecosystems.

8. Conclusions

To efficiently and effectively use biomass as a renewable energy source, it is important to have a detailed knowledge of its distribution, abundance, and quality. Remote sensing offers the technology to enable rapid assessment of biomass over large areas relatively quickly and at a low cost. It is a technology that can be used to ensure that biomass as a renewable energy source is used in a sustainable manner. Remote sensing techniques have many potential benefits in biomass estimation over traditional field measurement methods at different scales ranging from local to regional, including cost, labor, and time. However, the selection of suitable remote sensing data based on information on the scale of the study area, the data analysis procedure and costs is an important factor to be considered for the most appropriate aboveground biomass estimation procedure. High-spatial resolution data from both airborne and satellite platforms can provide accurate biomass estimates at local scales; however, for regional scales, a large volume of data is required, which is not only expensive but also difficult to process; this limits its application for larger areas. Landsat TM (medium-spatial resolution) data have been found more effective for biomass estimation at a regional scale; however, mixed pixels and data saturation problems have been reported with these data in biomass estimation for complex environments. At the national and global scales, coarse-spatial resolution data, such as AVHRR or MODIS, have been found useful in biomass estimation; however, the data have not been used much because of the difficulty in linking coarse-spatial resolution data and field measurements. Most of the previous studies based on radar systems in biomass estimation used single polarization, single incident angle, and low resolution SAR sensor, and hence have attained limited success. However, data from PALSAR and RADARSAT-2 with different polarizations, resolutions, and incident angles can offer greater opportunity to re-examine the potential of SAR data in biomass estimation. With the advent of LiDAR systems, the analysis can be extended to the third dimension in quantifying some vegetation characteristics directly, such as tree height, canopy height, and volume and can assist in improved biomass estimation. Overall remote sensing data, ranging from optical to microwave and also to LiDAR, have shown great potential in biomass estimation at all scales.

Biomass estimation from remote sensing data is a complex analysis process which involves many factors such as mixed pixels, data saturation, and complex biophysical environments. The selection of suitable algorithms for information extraction is also difficult and needs higher analytical skills. The most commonly used methods for biomass estimation are linear or nonlinear regression models, neural network, and $k$ -nearest neighbor, and also biomass is estimated indirectly from remotely sensed canopy parameters. Use of contextual information along with the spectral information has proven useful in improving biomass estimation. Advanced classifiers, such as SMA, can also improve classification results. The fusion of multisensor and multiresolution data may reduce mixed pixels and data saturation problems and has the potential to improve biomass estimation.

Anthropogenic actions have diminished the size of this pool of renewable energy over the years. Additionally, issues of biodiversity conservation and soil and water protection will restrict the amount of biomass that can ultimately be retrieved from forests and other land cover types.²⁵⁶ Also, in order to be truly renewable, the removal of forest biomass must be undertaken sustainably so that impacts on local ecosystems and their biodiversity are limited.¹⁴ Remote sensing can play an effective role in determining the areas from which plant biomass can be sustainably harvested and used in energy generation.

References

1.

K. Calvert and W. Mabee, “Spatial analysis of biomass resources within a socio-ecologically heterogeneous region: identifying opportunities for a mixed feedstock stream,” ISPRS Int. J. Geo-Inf., 3 (1), 209 –232 (2014). http://dx.doi.org/10.3390/ijgi3010209 2220-9964 Google Scholar

2.

“Asia to Take Lead in Biomass Power Production,” 2013). Google Scholar

3.

D. Lu, “The potential and challenge of remote sensing‐based biomass estimation,” Int. J. Remote Sens., 27 (7), 1297 –1328 (2006). http://dx.doi.org/10.1080/01431160500486732 IJSEDK 0143-1161 Google Scholar

4.

P. S. Thenkabail et al., “Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data,” Int. J. Remote Sens., 25 (23), 5447 –5472 (2004). http://dx.doi.org/10.1080/01431160412331291279 IJSEDK 0143-1161 Google Scholar

5.

K. S. Murali, D. M. Bhat and N. H. Ravindranath, “Biomass estimation equations for tropical deciduous and evergreen forests,” Int. J. Agric. Resour. Governance Ecol., 4 (1), 81 –92 (2005). http://dx.doi.org/10.1504/IJARGE.2005.006440 1462-4605 Google Scholar

6.

H. Klinge and R. Herrera, “Phytomass structure of natural plant communities on spodosols in southern Venezuela: the tall Amazon Caatinga forest,” Vegetation, 53 (2), 65 –84 (1983). http://dx.doi.org/VGTOA410.1007/BF00043025 VHAREV Google Scholar

7.

P. Hyde et al., “Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR,

ETM +

, Quickbird) synergy,” Remote Sens. Environ., 102 (12), 63 –73 (2006). http://dx.doi.org/10.1016/j.rse.2006.01.021 RSEEA7 0034-4257 Google Scholar

8.

Q. M. Ketterings et al., “Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests,” For. Ecol. Manage., 146 (1–3), 199 –209 (2001). http://dx.doi.org/10.1016/S0378-1127(00)00460-6 FECMDW 0378-1127 Google Scholar

9.

P. Hyde et al., “Exploring LiDAR–RaDAR synergy—predicting aboveground biomass in a southwestern ponderosa pine forest using LiDAR, SAR and InSAR,” Remote Sens. Environ., 106 (1), 28 –38 (2007). http://dx.doi.org/10.1016/j.rse.2006.07.017 RSEEA7 0034-4257 Google Scholar

10.

J. Chave et al., “Tree allometry and improved estimation of carbon stocks and balance in tropical forests,” Oecologia, 145 (1), 87 –99 (2005). http://dx.doi.org/10.1007/s00442-005-0100-x OECOBX 0029-8549 Google Scholar

11.

M. C. Lambert, C. H. Ung and F. Raulier, “Canadian national tree aboveground biomass equations,” Can. J. For. Res., 35 (8), 1996 –2018 (2005). http://dx.doi.org/10.1139/x05-112 CJFRAR 0045-5067 Google Scholar

12.

B. S. Case and R. J. Hall, “Assessing prediction errors of generalized tree biomass and volume equations for the boreal forest region of west-central Canada,” Can. J. For. Res., 38 (4), 878 –889 (2008). http://dx.doi.org/10.1139/x07-212 CJFRAR 0045-5067 Google Scholar

13.

X. Shi et al., “Using spatial information technologies to select sites for biomass power plants: a case study in Guangdong Province, China,” Biomass Bioenergy, 32 (1), 35 –43 (2008). http://dx.doi.org/10.1016/j.biombioe.2007.06.008 BMSBEO 0961-9534 Google Scholar

14.

W. E. Mabee, P. N. McFarlane and J. N. Saddler, “Biomass availability for lignocellulosic ethanol production,” Biomass Bioenergy, 35 (11), 4519 –4529 (2011). http://dx.doi.org/10.1016/j.biombioe.2011.06.026 BMSBEO 0961-9534 Google Scholar

15.

A. García-Martín et al., Using Remote Sensing to Estimate a Renewable Resource: Forest Residual Biomass, INTECH Open Access Publisher, Rijeka, Croatia (2012). Google Scholar

16.

C. J. Tucker et al., “Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel: 1980–1984,” Remote Sens. Environ., 17 (3), 233 –249 (1985). http://dx.doi.org/10.1016/0034-4257(85)90097-5 RSEEA7 0034-4257 Google Scholar

17.

S. A. Sader et al., “Tropical forest biomass and successional age class relationships to a vegetation index derived from Landsat TM data,” Remote Sens. Environ., 28 143 –198 (1989). http://dx.doi.org/10.1016/0034-4257(89)90112-0 RSEEA7 0034-4257 Google Scholar

18.

C. Song, “Optical remote sensing of forest leaf area index and biomass,” Prog. Phys. Geogr., 37 (1), 98 –113 (2013). http://dx.doi.org/10.1177/0309133312471367 PPGEEC 0309-1333 Google Scholar

19.

S. Goetz et al., “Mapping and monitoring carbon stocks with satellite observations: a comparison of methods,” Carbon Balance Manage., 4 (1), 2 (2009). http://dx.doi.org/10.1186/1750-0680-4-2 CBMACP 1750-0680 Google Scholar

20.

D. Lu et al., “A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems,” Int. J. Digital Earth, 1 –43 (2014). http://dx.doi.org/10.1080/17538947.2014.990526 Google Scholar

21.

Grasslands of the World Plant Production and Protection Series, Food and Agriculture Organization of the United Nations, Rome, Italy (2005). Google Scholar

22.

State of the World’s Forests, 10th ed.Food and Agriculture Organization of the United Nations, Rome, Italy (2012). Google Scholar

23.

Å. Rosenqvist et al., “A review of remote sensing technology in support of the Kyoto protocol,” Environ. Sci. Policy, 6 (5), 441 –455 (2003). http://dx.doi.org/10.1016/S1462-9011(03)00070-4 ESCPFS 1462-9011 Google Scholar

24.

G. M. Foody, D. S. Boyd and M. E. J. Cutler, “Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions,” Remote Sens. Environ., 85 (4), 463 –474 (2003). http://dx.doi.org/10.1016/S0034-4257(03)00039-7 RSEEA7 0034-4257 Google Scholar

25.

F. Rosillo-Calle, The Biomass Assessment Handbook: Bioenergy for a Sustainable Environment, Earthscan, Oxford, UK (2007). Google Scholar

26.

R. Nelson, W. Krabill and J. Tonelli, “Estimating forest biomass and volume using airborne laser data,” Remote Sens. Environ., 24 (2), 247 –267 (1988). http://dx.doi.org/10.1016/0034-4257(88)90028-4 RSEEA7 0034-4257 Google Scholar

27.

J. Franklin and P. H. Y. Hiernaux, “Estimating foliage and woody biomass in Sahelian and Sudanian woodlands using a remote sensing model,” Int. J. Remote Sens., 12 (6), 1387 –1404 (1991). http://dx.doi.org/10.1080/01431169108929732 IJSEDK 0143-1161 Google Scholar

28.

K. J. Ranson and G. Sun, “Mapping biomass of a northern forest using multifrequency SAR data,” IEEE Trans. Geosci. Electron., 32 (2), 388 –396 (1994). http://dx.doi.org/IEGEAO0018-9413 IEGEAO 0018-9413 Google Scholar

29.

M. K. Steininger, “Satellite estimation of tropical secondary forest above-ground biomass: data from Brazil and Bolivia,” Int. J. Remote Sens., 21 (67), 1139 –1157 (2000). http://dx.doi.org/10.1080/014311600210119 IJSEDK 0143-1161 Google Scholar

30.

D. Zheng et al., “Estimating aboveground biomass using Landsat 7

ETM +

data across a managed landscape in northern Wisconsin, USA,” Remote Sens. Environ., 93 (3), 402 –411 (2004). http://dx.doi.org/10.1016/j.rse.2004.08.008 RSEEA7 0034-4257 Google Scholar

31.

G. Sun and K. J. Ranson, “Forest biomass retrieval from lidar and radar,” in 2009 IEEE Int. on Geoscience and Remote Sensing Symp. (IGARSS 2009), V–300 –V–303 (2009). Google Scholar

32.

J. B. Zachary and H. W. Randolph, “Estimating forest biomass using small footprint LiDAR data: an individual tree-based approach that incorporates training data,” ISPRS J. Photogramm. Remote Sens., 59 (6), 342 –360 (2005). http://dx.doi.org/10.1016/j.isprsjprs.2005.07.001 IRSEE9 0924-2716 Google Scholar

33.

R. L. Weiser et al., “Assessing grassland biophysical characteristics from spectral measurements,” Remote Sens. Environ., 20 (2), 141 –152 (1986). http://dx.doi.org/10.1016/0034-4257(86)90019-2 RSEEA7 0034-4257 Google Scholar

34.

R. Darvishzadeh, “Hyperspectral remote sensing of vegetation parameters using statistical and physical models,” (2008). Google Scholar

35.

M. Palace et al., “Necromass in undisturbed and logged forests in the Brazilian Amazon,” For. Ecol. Manage., 238 (1–3), 309 –318 (2007). http://dx.doi.org/10.1016/j.foreco.2006.10.026 FECMDW 0378-1127 Google Scholar

36.

C. Song et al., “Estimating average tree crown size using spatial information from IKONOS and QuickBird images: across-sensor and across-site comparisons,” Remote Sens. Environ., 114 (5), 1099 –1107 (2010). http://dx.doi.org/10.1016/j.rse.2009.12.022 RSEEA7 0034-4257 Google Scholar

37.

R. Fensholt et al., “Evaluation of satellite based primary production modelling in the semi-arid Sahel,” Remote Sens. Environ., 105 (3), 173 –188 (2006). http://dx.doi.org/10.1016/j.rse.2006.06.011 RSEEA7 0034-4257 Google Scholar

38.

G. P. Asner, S. R. Levick and I. P. J. Smit, “Remote sensing of fractional cover and biochemistry in savannas,” Ecosystem Function in Savannas Measurement and Modeling at Landscape to Global Scales, 195 –217 CRC Press, Boca Raton, Florida (2010). Google Scholar

39.

D. S. Boyd, “The relationship between the biomass of Cameroonian tropical forests and radiation reflected in middle infrared wavelengths (3.0–5.0 μ m),” Int. J. Remote Sens., 20 (5), 1017 –1023 (1999). http://dx.doi.org/10.1080/014311699213055 IJSEDK 0143-1161 Google Scholar

40.

M. Govender, K. Chetty and H. Bulcock, “A review of hyperspectral remote sensing and its application in vegetation and water resource studies,” Water SA, 33 (2), 145 –151 (2007). WASADV 0378-4738 Google Scholar

41.

J. Im and J. R. Jensen, “Hyperspectral remote sensing of vegetation,” Geogr. Compass, 2 (6), 1943 –1961 (2008). http://dx.doi.org/10.1111/geco.2008.2.issue-6 1749-8198 Google Scholar

42.

S. Moreau et al., “Assessing the biomass dynamics of Andean bofedal and totora high-protein wetland grasses from NOAA/AVHRR,” Remote Sens. Environ., 85 (4), 516 –529 (2003). http://dx.doi.org/10.1016/S0034-4257(03)00053-1 RSEEA7 0034-4257 Google Scholar

43.

M. J. Hill and N. P. Hanan, “Current approaches to measurement, remote sensing, and modeling in savannas,” Ecosystem Function in Savannas Measurement and Modeling at Landscape to Global Scales, 515 –545 CRC Press, Boca Raton, Florida (2010). Google Scholar

44.

A. Ruimy, B. Saugier and G. Dedieu, “Methodology for the estimation of terrestrial net primary production from remotely sensed data,” J. Geophys. Res.: Atmos., 99 (D3), 5263 –5283 (1994). http://dx.doi.org/10.1029/93JD03221 0747-7309 Google Scholar

45.

J. L. Monteith, “Solar radiation and productivity in tropical ecosystems,” J. Appl. Ecol., 9 (3), 747 –766 (1972). http://dx.doi.org/10.2307/2401901 JAPEAI 1365-2664 Google Scholar

46.

M. R. Raupach et al., “Model-data synthesis in terrestrial carbon observation: methods, data requirements and data uncertainty specifications,” Global Change Biol., 11 (3), 378 –397 (2005). http://dx.doi.org/10.1111/gcb.2005.11.issue-3 1354-1013 Google Scholar

47.

C. A. Williams, “Integration of remote sensing and modeling to understand carbon fluxes and climate interactions in Africa,” Ecosystem Function in Savannas Measurement and Modeling at Landscape to Global Scales, 327 –345 CRC Press, Boca Raton, Florida (2010). Google Scholar

48.

R. M. Lucas et al., “Quantifying carbon in savannas,” Ecosystem Function in Savannas Measurement and Modeling at Landscape to Global Scales, 155 –174 CRC Press, Boca Raton, Florida (2010). Google Scholar

49.

X. Zhou, N.-B. Chang and S. Li, “Applications of SAR interferometry in earth and environmental science research,” Sensors, 9 (3), 1876 –1912 (2009). http://dx.doi.org/10.3390/s90301876 SNSRES 0746-9462 Google Scholar

50.

R. M. Lucas et al., “Empirical relationships between AIRSAR backscatter and LiDAR-derived forest biomass, Queensland, Australia,” Remote Sens. Environ., 100 (3), 407 –425 (2006). http://dx.doi.org/10.1016/j.rse.2005.10.019 RSEEA7 0034-4257 Google Scholar

51.

M. L. Imhoff et al., “BioSAR (TM): an inexpensive airborne VHF multiband SAR system for vegetation biomass measurement,” IEEE Trans. Geosci. Remote Sens., 38 (3), 1458 –1462 (2000). http://dx.doi.org/10.1109/36.843042 IGRSD2 0196-2892 Google Scholar

52.

T. Castel et al., “Retrieval biomass of a large Venezuelan pine plantation using JERS-1 SAR data. Analysis of forest structure impact on radar signature,” Remote Sens. Environ., 79 (1), 30 –41 (2002). http://dx.doi.org/10.1016/S0034-4257(01)00236-X RSEEA7 0034-4257 Google Scholar

53.

G. Sun, K. J. Ranson and V. I. Kharuk, “Radiometric slope correction for forest biomass estimation from SAR data in the Western Sayani Mountains, Siberia,” Remote Sens. Environ., 79 (23), 279 –287 (2002). http://dx.doi.org/10.1016/S0034-4257(01)00279-6 RSEEA7 0034-4257 Google Scholar

54.

J. R. Santos et al., “Airborne P-band SAR applied to the aboveground biomass studies in the Brazilian tropical rainforest,” Remote Sens. Environ., 87 (4), 482 –493 (2003). http://dx.doi.org/10.1016/j.rse.2002.12.001 RSEEA7 0034-4257 Google Scholar

55.

E. S. Kasischke, J. M. Melack and M. C. Dobson, “The use of imaging radars for ecological applications—a review,” Remote Sens. Environ., 59 (2), 141 –156 (1997). http://dx.doi.org/10.1016/S0034-4257(96)00148-4 RSEEA7 0034-4257 Google Scholar

56.

E. S. Kasischke et al., “Temperate and boreal forests,” Remote Sensing for Natural Resource Management and Environmental Monitoring, 147 –238 John Wiley & Sons, Hoboken, New Jersey (2004). Google Scholar

57.

M. L. R. Sarker et al., “Forest biomass estimation using texture measurements of high-resolution dual-polarization C-band SAR data,” IEEE Trans. Geosci. Remote Sens., 51 (6), 3371 –3384 (2013). http://dx.doi.org/10.1109/TGRS.2012.2219872 IGRSD2 0196-2892 Google Scholar

58.

J. R. Buckley and A. M. Smith, “Monitoring grasslands with RADARSAT 2 quad-pol imagery,” in 2010 IEEE Int. on Geoscience and Remote Sensing Symp. (IGARSS), 3090 –3093 (2010). Google Scholar

59.

J. N. Collins et al., “Estimating landscape‐scale vegetation carbon stocks using airborne multi‐frequency polarimetric synthetic aperture radar (SAR) in the savannahs of North Australia,” Int. J. Remote Sens., 30 (5), 1141 –1159 (2009). http://dx.doi.org/10.1080/01431160802448935 IJSEDK 0143-1161 Google Scholar

60.

R. O. Dubayah and J. B. Drake, “Lidar remote sensing for forestry,” J. For., 98 (6), 44 –46 (2000). Google Scholar

61.

D. J. Harding et al., “Laser altimeter canopy height profiles: methods and validation for closed-canopy, broadleaf forests,” Remote Sens. Environ., 76 (3), 283 –297 (2001). http://dx.doi.org/10.1016/S0034-4257(00)00210-8 RSEEA7 0034-4257 Google Scholar

62.

M. A. Lefsky et al., “Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests,” Remote Sens. Environ., 70 (3), 339 –361 (1999). http://dx.doi.org/10.1016/S0034-4257(99)00052-8 RSEEA7 0034-4257 Google Scholar

63.

J. E. Means et al., “Use of large-footprint scanning airborne Lidar to estimate forest stand characteristics in the Western Cascades of Oregon,” Remote Sens. Environ., 67 (3), 298 –308 (1999). http://dx.doi.org/10.1016/S0034-4257(98)00091-1 RSEEA7 0034-4257 Google Scholar

64.

J. B. Drake et al., “Estimation of tropical forest structural characteristics using large-footprint Lidar,” Remote Sens. Environ., 79 (23), 305 –319 (2002). http://dx.doi.org/10.1016/S0034-4257(01)00281-4 RSEEA7 0034-4257 Google Scholar

65.

J. McGlinchy et al., “Extracting structural vegetation components from small-footprint waveform lidar for biomass estimation in savanna ecosystems,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7 (2), 480 –490 (2014). Google Scholar

66.

M. A. Lefsky et al., “Lidar remote sensing for ecosystem studies: lidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists,” BioScience, 52 (1), 19 –30 (2002). http://dx.doi.org/10.1641/0006-3568(2002)052[0019:LRSFES]2.0.CO;2 BISNAS 0006-3568 Google Scholar

67.

M. A. Lefsky et al., “Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA,” Remote Sens. Environ., 67 (1), 83 –98 (1999). http://dx.doi.org/10.1016/S0034-4257(98)00071-6 RSEEA7 0034-4257 Google Scholar

68.

M. A. Lefsky et al., “Lidar remote sensing of above-ground biomass in three biomes,” Global Ecol. Biogeogr., 11 (5), 393 –399 (2002). http://dx.doi.org/10.1046/j.1466-822x.2002.00303.x GEBIFS 1466-8238 Google Scholar

69.

K. Zhao, S. Popescu and R. Nelson, “Lidar remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers,” Remote Sens. Environ., 113 (1), 182 –196 (2009). http://dx.doi.org/10.1016/j.rse.2008.09.009 RSEEA7 0034-4257 Google Scholar

70.

K. Lim et al., “LiDAR remote sensing of forest structure,” Prog. Phys. Geogr., 27 (1), 88 –106 (2003). http://dx.doi.org/10.1191/0309133303pp360ra PPGEEC 0309-1333 Google Scholar

71.

K. W. Todd, F. Csillag and P. M. Atkinson, “Three-dimensional mapping of light transmittance and foliage distribution using lidar,” Can. J. Remote Sens., 29 (5), 544 –555 (2003). http://dx.doi.org/10.5589/m03-021 CJRSDP 0703-8992 Google Scholar

72.

Q. Chen, “LiDAR remote sensing of vegetation biomass,” Remote Sensing of Natural Resources, 399 –420 CRC Press, Boca Raton, Florida (2013). Google Scholar

73.

S. Saatchi et al., “Impact of spatial variability of tropical forest structure on radar estimation of aboveground biomass,” Remote Sens. Environ., 115 (11), 2836 –2849 (2011). http://dx.doi.org/10.1016/j.rse.2010.07.015 RSEEA7 0034-4257 Google Scholar

74.

G. P. Asner et al., “Estimating canopy structure in an Amazon forest from laser range finder and IKONOS satellite observations,” Biotropica, 34 (4), 483 –492 (2002). http://dx.doi.org/BTROAZ10.1111/j.1744-7429.2002.tb00568.x BTROAZ Google Scholar

75.

D. R. Bertolette and D. B. Spotskey, “Fuel model and forest type mapping for FARSITE input,” The Joint Fire Science Conf. and Workshop, University of Idaho and International Association of Wildland Fire, Boise, Idaho (1999). Google Scholar

76.

S. M. De Jong, E. J. Pebesma and B. Lacaze, “Above‐ground biomass assessment of Mediterranean forests using airborne imaging spectrometry: the DAIS Peyne experiment,” Int. J. Remote Sens., 24 (7), 1505 –1520 (2003). http://dx.doi.org/10.1080/01431160210145560 IJSEDK 0143-1161 Google Scholar

77.

J. A. Greenberg, S. Z. Dobrowski and S. L. Ustin, “Shadow allometry: estimating tree structural parameters using hyperspatial image analysis,” Remote Sens. Environ., 97 (1), 15 –25 (2005). http://dx.doi.org/10.1016/j.rse.2005.02.015 RSEEA7 0034-4257 Google Scholar

78.

H. Franco-Lopez, A. R. Ek and M. E. Bauer, “Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method,” Remote Sens. Environ., 77 (3), 251 –274 (2001). http://dx.doi.org/10.1016/S0034-4257(01)00209-7 RSEEA7 0034-4257 Google Scholar

79.

M. Halme and E. Tomppo, “Improving the accuracy of multisource forest inventory estimates to reducing plot location error—a multicriteria approach,” Remote Sens. Environ., 78 (3), 321 –327 (2001). http://dx.doi.org/10.1016/S0034-4257(01)00227-9 RSEEA7 0034-4257 Google Scholar

80.

M. A. Lefsky, W. B. Cohen and T. A. Spies, “An evaluation of alternate remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forests in western Oregon,” Can. J. For. Res., 31 (1), 78 –87 (2001). http://dx.doi.org/10.1139/x00-142 CJFRAR 0045-5067 Google Scholar

81.

E. Tomppo et al., “Simultaneous use of Landsat-TM and IRS-1C WiFS data in estimating large area tree stem volume and aboveground biomass,” Remote Sens. Environ., 82 (1), 156 –171 (2002). http://dx.doi.org/10.1016/S0034-4257(02)00031-7 RSEEA7 0034-4257 Google Scholar

82.

G. M. Foody et al., “Mapping the biomass of Bornean tropical rain forest from remotely sensed data,” Global Ecol. Biogeogr., 10 (4), 379 –387 (2001). http://dx.doi.org/10.1046/j.1466-822X.2001.00248.x GEBIFS 1466-8238 Google Scholar

83.

S. A. Soenen et al., “Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain,” Remote Sens. Environ., 114 (7), 1325 –1337 (2010). http://dx.doi.org/10.1016/j.rse.2009.12.012 RSEEA7 0034-4257 Google Scholar

84.

A. Baccini et al., “Forest biomass estimation over regional scales using multisource data,” Geophys. Res. Lett., 31 (10), L10501 (2004). http://dx.doi.org/10.1029/2004GL019782 GPRLAJ 0094-8276 Google Scholar

85.

A. Baccini et al., “A first map of tropical Africa’s above-ground biomass derived from satellite imagery,” Environ. Res. Lett., 3 (4), 045011 (2008). http://dx.doi.org/10.1088/1748-9326/3/4/045011 ERLNAL 1748-9326 Google Scholar

86.

P. M. Barbosa et al., “An assessment of vegetation fire in Africa (1981–1991): burned areas, burned biomass, and atmospheric emissions,” Global Biogeochem. Cycles, 13 (4), 933 –950 (1999). http://dx.doi.org/10.1029/1999GB900042 GBCYEP 0886-6236 Google Scholar

87.

J. Dong et al., “Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks,” Remote Sens. Environ., 84 (3), 393 –410 (2003). http://dx.doi.org/10.1016/S0034-4257(02)00130-X RSEEA7 0034-4257 Google Scholar

88.

T. Hame et al., “A new methodology for the estimation of biomass of conifer-dominated boreal forest using NOAA AVHRR data,” Int. J. Remote Sens., 18 (15), 3211 –3243 (1997). http://dx.doi.org/10.1080/014311697217053 IJSEDK 0143-1161 Google Scholar

89.

S. S. Saatchi et al., “Distribution of aboveground live biomass in the Amazon basin,” Global Change Biol., 13 (4), 816 –837 (2007). http://dx.doi.org/10.1111/j.1365-2486.2007.01323.x 1354-1013 Google Scholar

90.

T. Le Toan et al., “Relating forest biomass to SAR data,” IEEE Trans. Geosci. Remote Sens., 30 (2), 403 –411 (1992). http://dx.doi.org/10.1109/36.134089 IGRSD2 0196-2892 Google Scholar

91.

T. M. Kuplich, V. Salvatori and P. J. Curran, “JERS-1/SAR backscatter and its relationship with biomass of regenerating forests,” Int. J. Remote Sens., 21 (12), 2513 –2518 (2000). http://dx.doi.org/10.1080/01431160050030600 IJSEDK 0143-1161 Google Scholar

92.

J. R. Santos et al., “Savanna and tropical rainforest biomass estimation and spatialization using JERS-1 data,” Int. J. Remote Sens., 23 (7), 1217 –1229 (2002). http://dx.doi.org/10.1080/01431160110092867 IJSEDK 0143-1161 Google Scholar

93.

H. Balzter, “Forest mapping and monitoring with interferometric synthetic aperture radar (InSAR),” Progr. Phys. Geogr., 25 (2), 159 –177 (2001). http://dx.doi.org/10.1177/030913330102500201 PPGEEC 0309-1333 Google Scholar

94.

J. Kellndorfer et al., “Vegetation height estimation from shuttle radar topography mission and national elevation datasets,” Remote Sens. Environ., 93 (3), 339 –358 (2004). http://dx.doi.org/10.1016/j.rse.2004.07.017 RSEEA7 0034-4257 Google Scholar

95.

S. Solberg et al., “Forest biomass change estimated from height change in interferometric SAR height models,” Carbon Balance Manage., 9 (1), 5 (2014). http://dx.doi.org/10.1186/s13021-014-0005-2 CBMACP 1750-0680 Google Scholar

96.

G. Asner et al., “Environmental and biotic controls over aboveground biomass throughout a tropical rain forest,” Ecosystems, 12 (2), 261 –278 (2009). http://dx.doi.org/10.1007/s10021-008-9221-5 ECOSFJ 1432-9840 Google Scholar

97.

C. J. Gleason and J. Im, “Forest biomass estimation from airborne LiDAR data using machine learning approaches,” Remote Sens. Environ., 125 80 –91 (2012). http://dx.doi.org/10.1016/j.rse.2012.07.006 RSEEA7 0034-4257 Google Scholar

98.

M. A. Lefsky et al., “Estimates of forest canopy height and aboveground biomass using ICESat,” Geophys. Res. Lett., 32 (22), L22S02 (2005). http://dx.doi.org/10.1029/2005GL023971 GPRLAJ 0094-8276 Google Scholar

99.

S. C. Popescu et al., “Satellite lidar vs. small footprint airborne lidar: comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level,” Remote Sens. Environ., 115 (11), 2786 –2797 (2011). http://dx.doi.org/10.1016/j.rse.2011.01.026 RSEEA7 0034-4257 Google Scholar

100.

M. García et al., “Characterization of canopy fuels using ICESat/GLAS data,” Remote Sens. Environ., 123 81 –89 (2012). http://dx.doi.org/10.1016/j.rse.2012.03.018 RSEEA7 0034-4257 Google Scholar

101.

D. Amarsaikhan and T. Douglas, “Data fusion and multisource image classification,” Int. J. Remote Sens., 25 (17), 3529 –3539 (2004). http://dx.doi.org/10.1080/0143116031000115111 IJSEDK 0143-1161 Google Scholar

102.

W. Shi et al., “Wavelet-based image fusion and quality assessment,” Int. J. Appl. Earth Obs. Geoinf., 6 (3–4), 241 –251 (2005). http://dx.doi.org/10.1016/j.jag.2004.10.010 ITCJDP 0303-2434 Google Scholar

103.

D. Chen and D. Stow, “Strategies for integrating information from multiple spatial resolutions into land-use/land-cover classification routines,” Photogramm. Eng. Remote Sens., 69 (11), 1279 –1287 (2003). http://dx.doi.org/10.14358/PERS.69.11.1279 PGMEA9 0099-1112 Google Scholar

104.

M. Choi et al., “Fusion of multispectral and panchromatic satellite images using the curvelet transform,” IEEE Geosci. Remote Sens. Lett., 2 (2), 136 –140 (2005). http://dx.doi.org/10.1109/LGRS.2005.845313 IGRSBY 1545-598X Google Scholar

105.

D. Lu et al., “Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon basin,” For. Ecol. Manage., 198 (1–3), 149 –167 (2004). http://dx.doi.org/10.1016/j.foreco.2004.03.048 FECMDW 0378-1127 Google Scholar

106.

O. Mutanga and A. K. Skidmore, “Narrow band vegetation indices overcome the saturation problem in biomass estimation,” Int. J. Remote Sens., 25 (19), 3999 –4014 (2004). http://dx.doi.org/10.1080/01431160310001654923 IJSEDK 0143-1161 Google Scholar

107.

C. D. Elvidge and Z. Chen, “Comparison of broad-band and narrow-band red and near-infrared vegetation indices,” Remote Sens. Environ., 54 (1), 38 –48 (1995). http://dx.doi.org/10.1016/0034-4257(95)00132-K RSEEA7 0034-4257 Google Scholar

108.

G. A. Blackburn and C. M. Steele, “Towards the remote sensing of Matorral vegetation physiology: relationships between spectral reflectance, pigment, and biophysical characteristics of semiarid bushland canopies,” Remote Sens. Environ., 70 (3), 278 –292 (1999). http://dx.doi.org/10.1016/S0034-4257(99)00044-9 RSEEA7 0034-4257 Google Scholar

109.

T. Blaschke, “Object based image analysis for remote sensing,” ISPRS J. Photogramm. Remote Sens., 65 (1), 2 –16 (2010). http://dx.doi.org/10.1016/j.isprsjprs.2009.06.004 IRSEE9 0924-2716 Google Scholar

110.

T. Blaschke and J. Strobl, “What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS,” GeoBIT/GIS, 6 (1), 12 –17 (2001). Google Scholar

111.

F. Kayitakire, C. Hamel and P. Defourny, “Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery,” Remote Sens. Environ., 102 (3–4), 390 –401 (2006). http://dx.doi.org/10.1016/j.rse.2006.02.022 RSEEA7 0034-4257 Google Scholar

112.

M. F. Goodchild, M. Yuan and T. J. Cova, “Towards a general theory of geographic representation in GIS,” Int. J. Geogr. Inf. Sci., 21 (3), 239 –260 (2007). http://dx.doi.org/10.1080/13658810600965271 1365-8816 Google Scholar

113.

D. Lu and M. Batistella, “Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon,” Acta Amazonica, 35 249 –257 (2005). http://dx.doi.org/10.1590/S0044-59672005000200015 AAMZAZ 0044-5967 Google Scholar

114.

P. E. Dennison and D. A. Roberts, “Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE,” Remote Sens. Environ., 87 (23), 123 –135 (2003). http://dx.doi.org/10.1016/S0034-4257(03)00135-4 RSEEA7 0034-4257 Google Scholar

115.

D. Lu, E. Moran and M. Batistella, “Linear mixture model applied to Amazonian vegetation classification,” Remote Sens. Environ., 87 (4), 456 –469 (2003). http://dx.doi.org/10.1016/j.rse.2002.06.001 RSEEA7 0034-4257 Google Scholar

116.

M. A. Theseira et al., “Sensitivity of mixture modelling to end‐member selection,” Int. J. Remote Sens., 24 (7), 1559 –1575 (2003). http://dx.doi.org/10.1080/01431160210146631 IJSEDK 0143-1161 Google Scholar

117.

T. Calvão and J. M. Palmeirim, “Mapping Mediterranean scrub with satellite imagery: biomass estimation and spectral behaviour,” Int. J. Remote Sens., 25 (16), 3113 –3126 (2004). http://dx.doi.org/10.1080/01431160310001654978 IJSEDK 0143-1161 Google Scholar

118.

D. Lu, “Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon,” Int. J. Remote Sens., 26 (12), 2509 –2525 (2005). http://dx.doi.org/10.1080/01431160500142145 IJSEDK 0143-1161 Google Scholar

119.

M. M. Rahman, E. Csaplovics and B. Koch, “An efficient regression strategy for extracting forest biomass information from satellite sensor data,” Int. J. Remote Sens., 26 (7), 1511 –1519 (2005). http://dx.doi.org/10.1080/01431160500044705 IJSEDK 0143-1161 Google Scholar

120.

M. Li et al., “Modeling forest aboveground biomass by combining spectrum, textures and topographic features,” Front. For. China, 3 (1), 10 –15 (2008). http://dx.doi.org/10.1007/s11461-008-0013-z Google Scholar

121.

P. Muukkonen and J. Heiskanen, “Estimating biomass for boreal forests using ASTER satellite data combined with standwise forest inventory data,” Remote Sens. Environ., 99 (4), 434 –447 (2005). http://dx.doi.org/10.1016/j.rse.2005.09.011 RSEEA7 0034-4257 Google Scholar

122.

P. S. Roy and S. Ravan, “Biomass estimation using satellite remote sensing data—an investigation on possible approaches for natural forest,” J. Biosci., 21 (4), 535 –561 (1996). http://dx.doi.org/10.1007/BF02703218 JOBSDN 0250-5991 Google Scholar

123.

R. F. Nelson et al., “Secondary forest age and tropical forest biomass estimation using thematic mapper imagery: single-year tropical forest age classes, a surrogate for standing biomass, cannot be reliably identified using single-date tm imagery,” BioScience, 50 (5), 419 –431 (2000). http://dx.doi.org/10.1641/0006-3568(2000)050[0419:SFAATF]2.0.CO;2 BISNAS 0006-3568 Google Scholar

124.

M.-H. Phua and H. Saito, “Estimation of biomass of a mountainous tropical forest using Landsat TM data,” Can. J. Remote Sens., 29 (4), 429 –440 (2003). http://dx.doi.org/10.5589/m03-005 CJRSDP 0703-8992 Google Scholar

125.

S. C. Popescu, R. H. Wynne and R. F. Nelson, “Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass,” Can. J. Remote Sens., 29 (5), 564 –577 (2003). http://dx.doi.org/10.5589/m03-027 CJRSDP 0703-8992 Google Scholar

126.

D. S. Culvenor, “Extracting individual tree information: a survey of techniques for high spatial resolution imagery,” Remote Sensing of Forest Environments: Concepts and Case Studies, 255 –277 Kluwer Academic, Boston, Massachusetts (2003). Google Scholar

127.

M. L. Clark, D. A. Roberts and D. B. Clark, “Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales,” Remote Sens. Environ., 96 (3–4), 375 –398 (2005). http://dx.doi.org/10.1016/j.rse.2005.03.009 RSEEA7 0034-4257 Google Scholar

128.

F. A. Gougeon and D. G. Leckie, “The individual tree crown approach applied to IKONOS images of a coniferous plantation area,” Photogramm. Eng. Remote Sens., 72 (11), 1287 –1297 (2006). http://dx.doi.org/10.14358/PERS.72.11.1287 PGMEA9 0099-1112 Google Scholar

129.

M. Hirschmugl et al., “Single tree detection in very high resolution remote sensing data,” Remote Sens. Environ., 110 (4), 533 –544 (2007). http://dx.doi.org/10.1016/j.rse.2007.02.029 RSEEA7 0034-4257 Google Scholar

130.

D. A. Pouliot et al., “Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration,” Remote Sens. Environ., 82 (2–3), 322 –334 (2002). http://dx.doi.org/10.1016/S0034-4257(02)00050-0 RSEEA7 0034-4257 Google Scholar

131.

U. C. Benz et al., “Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information,” ISPRS J. Photogramm. Remote Sens., 58 (3–4), 239 –258 (2004). http://dx.doi.org/10.1016/j.isprsjprs.2003.10.002 IRSEE9 0924-2716 Google Scholar

132.

M. L. R. Sarker et al., “Potential of texture measurements of two-date dual polarization PALSAR data for the improvement of forest biomass estimation,” ISPRS J. Photogramm. Remote Sens., 69 146 –166 (2012). http://dx.doi.org/10.1016/j.isprsjprs.2012.03.002 IRSEE9 0924-2716 Google Scholar

133.

N. Ghasemi, M. R. Sahebi and A. Mohammadzadeh, “A review on biomass estimation methods using synthetic aperture radar data,” Int. J. Geomat. Geosci., 1 (4), 776 –788 (2011). 0976-4380 Google Scholar

134.

J. Goh et al., “Biomass estimation in humid tropical forest using a combination of ALOS PALSAR and SPOT 5 satellite imagery,” Asian J. Geoinf., 13 (4), (2013). 1513-6728 Google Scholar

135.

L. R. Sarker and J. E. Nichol, “Improved forest biomass estimates using ALOS AVNIR-2 texture indices,” Remote Sens. Environ., 115 (4), 968 –977 (2011). http://dx.doi.org/10.1016/j.rse.2010.11.010 RSEEA7 0034-4257 Google Scholar

136.

D. Chen, D. A. Stow and P. Gong, “Examining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case,” Int. J. Remote Sens., 25 (11), 2177 –2192 (2004). http://dx.doi.org/10.1080/01431160310001618464 IJSEDK 0143-1161 Google Scholar

137.

D. R. Peddle, F. G. Hall and E. F. LeDrew, “Spectral mixture analysis and geometric-optical reflectance modeling of boreal forest biophysical structure,” Remote Sens. Environ., 67 (3), 288 –297 (1999). http://dx.doi.org/10.1016/S0034-4257(98)00090-X RSEEA7 0034-4257 Google Scholar

138.

J. A. Blackard et al., “Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information,” Remote Sens. Environ., 112 (4), 1658 –1677 (2008). http://dx.doi.org/10.1016/j.rse.2007.08.021 RSEEA7 0034-4257 Google Scholar

139.

B. St-Onge, Y. Hu and C. Vega, “Mapping the height and above‐ground biomass of a mixed forest using lidar and stereo IKONOS images,” Int. J. Remote Sens., 29 (5), 1277 –1294 (2008). http://dx.doi.org/10.1080/01431160701736505 IJSEDK 0143-1161 Google Scholar

140.

W. Ni et al., “Features of point clouds synthesized from multi-view ALOS/PRISM data and comparisons with LiDAR data in forested areas,” Remote Sens. Environ., 149 47 –57 (2014). http://dx.doi.org/10.1016/j.rse.2014.04.001 RSEEA7 0034-4257 Google Scholar

141.

P. Reinartz et al., “Comparison and fusion of DEM derived from SPOT-5 HRS and SRTM data and estimation of forest heights,” in Proc. EARSeL Workshop on 3D-Remote Sensing, Porto, (2005). Google Scholar

142.

J. Wallerman et al., “Forest mapping using 3D data from SPOT-5 HRS and Z/I DMC,” in 2010 IEEE Int. on Geoscience and Remote Sensing Symp. (IGARSS 2009), 64 –67 (2010). Google Scholar

143.

A. Beaudoin et al., “Retrieval of forest biomass from SAR data,” Int. J. Remote Sens., 15 (14), 2777 –2796 (1994). http://dx.doi.org/10.1080/01431169408954284 IJSEDK 0143-1161 Google Scholar

144.

P. A. Harrell et al., “Evaluation of approaches to estimating aboveground biomass in Southern pine forests using SIR-C data,” Remote Sens. Environ., 59 (2), 223 –233 (1997). http://dx.doi.org/10.1016/S0034-4257(96)00155-1 RSEEA7 0034-4257 Google Scholar

145.

T. Le Toan et al., “The BIOMASS mission: mapping global forest biomass to better understand the terrestrial carbon cycle,” Remote Sens. Environ., 115 (11), 2850 –2860 (2011). http://dx.doi.org/10.1016/j.rse.2011.03.020 RSEEA7 0034-4257 Google Scholar

146.

R. M. Lucas et al., “The potential of L-band SAR for quantifying mangrove characteristics and change: case studies from the tropics,” Aquat. Conserv. Mar. Freshwater Ecosyst., 17 (3), 245 –264 (2007). http://dx.doi.org/10.1002/(ISSN)1099-0755 AQCOEY 1099-0755 Google Scholar

147.

S. Solberg et al., “Estimating spruce and pine biomass with interferometric X-band SAR,” Remote Sens. Environ., 114 (10), 2353 –2360 (2010). http://dx.doi.org/10.1016/j.rse.2010.05.011 RSEEA7 0034-4257 Google Scholar

148.

J. Pulliainen, M. Engdahl and M. Hallikainen, “Feasibility of multi-temporal interferometric SAR data for stand-level estimation of boreal forest stem volume,” Remote Sens. Environ., 85 (4), 397 –409 (2003). http://dx.doi.org/10.1016/S0034-4257(03)00016-6 RSEEA7 0034-4257 Google Scholar

149.

S. R. Cloude and K. P. Papathanassiou, “Three-stage inversion process for polarimetric SAR interferometry,” IEEE Proc. Radar Sonar Navig., 150 125 –134 (2003). http://dx.doi.org/10.1049/ip-rsn:20030449 Google Scholar

150.

F. Garestier and T. Le Toan, “Forest modeling for height inversion using single-baseline InSAR/Pol-InSAR data,” IEEE Trans. Geosci. Remote Sens., 48 (3), 1528 –1539 (2010). http://dx.doi.org/10.1109/TGRS.2009.2032538 IGRSD2 0196-2892 Google Scholar

151.

V. Thomas et al., “Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: an examination of scanning density,” Can. J. For. Res., 36 (1), 34 –47 (2006). http://dx.doi.org/10.1139/x05-230 CJFRAR 0045-5067 Google Scholar

152.

G. Patenaude et al., “Quantifying forest above ground carbon content using LiDAR remote sensing,” Remote Sens. Environ., 93 (3), 368 –380 (2004). http://dx.doi.org/10.1016/j.rse.2004.07.016 RSEEA7 0034-4257 Google Scholar

153.

S. A. Hall et al., “Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests,” For. Ecol. Manage., 208 (1–3), 189 –209 (2005). http://dx.doi.org/10.1016/j.foreco.2004.12.001 FECMDW 0378-1127 Google Scholar

154.

H. Saremi et al., “Sub-compartment variation in tree height, stem diameter and stocking in a Pinus radiata D. Don plantation examined using airborne LiDAR data,” Remote Sens., 6 (8), 7592 –7609 (2014). http://dx.doi.org/10.3390/rs6087592 RSEND3 2072-4292 Google Scholar

155.

H. Saremi et al., “Airborne LiDAR derived canopy height model reveals a significant difference in radiata pine (Pinus radiata D. Don) heights based on slope and aspect of sites,” Trees, 28 (3), 733 –744 (2014). http://dx.doi.org/10.1007/s00468-014-0985-2 TRESEY 1432-2285 Google Scholar

156.

Q. Chen et al., “Isolating individual trees in a savanna woodland using small footprint lidar data,” Photogramm. Eng. Remote Sens., 72 (8), 923 –932 (2006). http://dx.doi.org/10.14358/PERS.72.8.923 PGMEA9 0099-1112 Google Scholar

157.

K. S. Lim and P. M. Treitz, “Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators,” Scand. J. For. Res., 19 (6), 558 –570 (2004). http://dx.doi.org/10.1080/02827580410019490 SJFRE3 Google Scholar

158.

S. C. Popescu, “Estimating biomass of individual pine trees using airborne lidar,” Biomass Bioenergy, 31 (9), 646 –655 (2007). http://dx.doi.org/10.1016/j.biombioe.2007.06.022 BMSBEO 0961-9534 Google Scholar

159.

E. Næsset and T. Økland, “Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve,” Remote Sens. Environ., 79 (1), 105 –115 (2002). http://dx.doi.org/10.1016/S0034-4257(01)00243-7 RSEEA7 0034-4257 Google Scholar

160.

W. B. Cohen and T. A. Spies, “Estimating structural attributes of Douglas-fir/western hemlock forest stands from Landsat and SPOT imagery,” Remote Sens. Environ., 41 (1), 1 –17 (1992). http://dx.doi.org/10.1016/0034-4257(92)90056-P RSEEA7 0034-4257 Google Scholar

161.

R. Loos, O. Niemann and F. Visintini, “Identification of partial canopies using first and last return LiDAR data,” Our Common Borders—Safety, Security, and the Environment Through Remote Sensing, Ottawa, Ontario, Canada(2007). Google Scholar

162.

M. García et al., “Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data,” Remote Sens. Environ., 114 (4), 816 –830 (2010). http://dx.doi.org/10.1016/j.rse.2009.11.021 RSEEA7 0034-4257 Google Scholar

163.

M. Lee et al., “A global comparison of grassland biomass responses to

{CO}_{2}

and nitrogen enrichment,” Philos. Trans. R. Soc. B: Biol. Sci., 365 (1549), 2047 –2056 (2010). http://dx.doi.org/10.1098/rstb.2010.0028 PTRBAE 0962-8436 Google Scholar

164.

J. M. Suttie, S. G. Reynolds and C. Batello, Grasslands of the World, Food & Agriculture Organization of the United Nations, Rome, Italy (2005). Google Scholar

165.

J. M. Adams et al., “Increases in terrestrial carbon storage from the last glacial maximum to the present,” Nature, 348 (6303), 711 –714 (1990). http://dx.doi.org/10.1038/348711a0 NATUAS 0028-0836 Google Scholar

166.

M. E. Mangan et al., “Native perennial grassland species for bioenergy: establishment and biomass productivity,” Agron. J., 103 (2), 509 –519 (2011). http://dx.doi.org/10.2134/agronj2010.0360 AGJOAT 0002-1962 Google Scholar

167.

M. Boval and R. Dixon, “The importance of grasslands for animal production and other functions: a review on management and methodological progress in the tropics,” Animal, 6 (5), 748 –762 (2012). http://dx.doi.org/10.1017/S1751731112000304 ANIMF4 1751-7311 Google Scholar

168.

F. P. O’Mara, “The role of grasslands in food security and climate change,” Ann. Bot., 110 (6), 1263 –1270 (2012). http://dx.doi.org/10.1093/aob/mcs209 ANBOA4 0305-7364 Google Scholar

169.

F. Li et al., “Estimating grassland aboveground biomass using multitemporal MODIS data in the West Songnen Plain, China,” APPRES, 7 (1), 073546 (2013). http://dx.doi.org/10.1117/1.JRS.7.073546 Google Scholar

170.

C. Mundava et al., “Evaluation of vegetation indices for rangeland biomass estimation in the Kimberley area of Western Australia,” ISPRS J. Photogramm. Remote Sens., II-7 (7), 47 –53 (2014). http://dx.doi.org/10.5194/isprsannals-II-7-47-2014 IRSEE9 0924-2716 Google Scholar

171.

P. Dusseux et al., “Evaluation of SPOT imagery for the estimation of grassland biomass,” Int. J. Appl. Earth Obs. Geoinf., 38 72 –77 (2015). http://dx.doi.org/10.1016/j.jag.2014.12.003 ITCJDP 0303-2434 Google Scholar

172.

H. Zandler, A. Brenning and C. Samimi, “Quantifying dwarf shrub biomass in an arid environment: comparing empirical methods in a high dimensional setting,” Remote Sens. Environ., 158 140 –155 (2015). http://dx.doi.org/10.1016/j.rse.2014.11.007 RSEEA7 0034-4257 Google Scholar

173.

H. Ren, G. Zhou and X. Zhang, “Estimation of green aboveground biomass of desert steppe in Inner Mongolia based on red-edge reflectance curve area method,” Biosyst. Eng., 109 (4), 385 –395 (2011). http://dx.doi.org/10.1016/j.biosystemseng.2011.05.004 BEINBJ 1537-5110 Google Scholar

174.

L. Xiaosong et al., “Potential of high resolution RapidEye data for sparse vegetation fraction mapping in arid regions,” in 2012 IEEE Int. on Geoscience and Remote Sensing Symp. (IGARSS 2012), 420 –423 (2012). Google Scholar

175.

A. F. Rahman and J. A. Gamon, “Detecting biophysical properties of a semi-arid grassland and distinguishing burned from unburned areas with hyperspectral reflectance,” J. Arid Environ., 58 (4), 597 –610 (2004). http://dx.doi.org/10.1016/j.jaridenv.2003.12.005 JAENDR Google Scholar

176.

W. Xiaoping et al., “Hyperspectral remote sensing estimation models of aboveground biomass in Gannan rangelands,” Proc. Environ. Sci., 10 (Part A)), 697 –702 (2011). http://dx.doi.org/10.1016/j.proenv.2011.09.112 PESRBV 1878-0296 Google Scholar

177.

J. Clevers et al., “Estimating grassland biomass using SVM band shaving of hyperspectral data,” Photogramm. Eng. Remote Sens., 73 (10), 1141 –1148 (2007). http://dx.doi.org/10.14358/PERS.73.10.1141 PGMEA9 0099-1112 Google Scholar

178.

P. Dusseux et al., “Combined use of multi-temporal optical and radar satellite images for grassland monitoring,” Remote Sens., 6 (7), 6163 –6182 (2014). http://dx.doi.org/10.3390/rs6076163 RSEND3 2072-4292 Google Scholar

179.

Y. Jin et al., “Remote sensing-based biomass estimation and its spatio-temporal variations in temperate grassland, Northern China,” Remote Sens., 6 (2), 1496 –1513 (2014). http://dx.doi.org/10.3390/rs6021496 RSEND3 2072-4292 Google Scholar

180.

F. Yan, B. Wu and Y. Wang, “Estimating aboveground biomass in Mu Us Sandy Land using Landsat spectral derived vegetation indices over the past 30 years,” J. Arid Land, 5 (4), 521 –530 (2013). http://dx.doi.org/10.1007/s40333-013-0180-0 Google Scholar

181.

S. Todd, R. Hoffer and D. Milchunas, “Biomass estimation on grazed and ungrazed rangelands using spectral indices,” Int. J. Remote Sens., 19 (3), 427 –438 (1998). http://dx.doi.org/10.1080/014311698216071 IJSEDK 0143-1161 Google Scholar

182.

Y. Xie et al., “A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China,” Ecol. Model., 220 (15), 1810 –1818 (2009). http://dx.doi.org/10.1016/j.ecolmodel.2009.04.025 ECMODT 0304-3800 Google Scholar

183.

A. Psomas et al., “Hyperspectral remote sensing for estimating aboveground biomass and for exploring species richness patterns of grassland habitats,” Int. J. Remote Sens., 32 (24), 9007 –9031 (2011). http://dx.doi.org/10.1080/01431161.2010.532172 IJSEDK 0143-1161 Google Scholar

184.

M. Mirik et al., “Evaluating biomass of juniper trees (Juniperus pinchotii) from imagery-derived canopy area using the support vector machine classifier,” Adv. Remote Sens., 2 (2), 181 –192 (2013). http://dx.doi.org/10.4236/ars.2013.22021 Google Scholar

185.

M. Mirik and R. J. Ansley, “Utility of satellite and aerial images for quantification of canopy cover and infilling rates of the invasive woody species honey mesquite (Prosopis glandulosa) on rangeland,” Remote Sens., 4 (7), 1947 –1962 (2012). http://dx.doi.org/10.3390/rs4071947 RSEND3 2072-4292 Google Scholar

186.

M. Friedl et al., “Estimating grassland biomass and leaf area index using ground and satellite data,” Int. J. Remote Sens., 15 (7), 1401 –1420 (1994). http://dx.doi.org/10.1080/01431169408954174 IJSEDK 0143-1161 Google Scholar

187.

J. Beringer et al., “Savanna fires and their impact on net ecosystem productivity in North Australia,” Global Change Biol., 13 (5), 990 –1004 (2007). http://dx.doi.org/10.1111/gcb.2007.13.issue-5 1354-1013 Google Scholar

188.

J. Ratnam et al., “When is a ‘forest’ a savanna, and why does it matter?,” Global Ecol. Biogeogr., 20 (5), 653 –660 (2011). http://dx.doi.org/10.1111/geb.2011.20.issue-5 GEBIFS 1466-8238 Google Scholar

189.

D. Michelakis et al., “Local-scale mapping of biomass in tropical lowland pine savannas using ALOS PALSAR,” Forests, 5 (9), 2377 –2399 (2014). http://dx.doi.org/FOPEA410.3390/f5092377 FOPEA4 Google Scholar

190.

J. Grace et al., “Productivity and carbon fluxes of tropical savannas,” J. Biogeogr., 33 (3), 387 –400 (2006). http://dx.doi.org/10.1111/jbi.2006.33.issue-3 JBIODN 1365-2699 Google Scholar

191.

J. C. Menaut, “The vegetation of African Savannas. Tropical savannas,” Ecosystems of the World, 109 –149 Elsevier, Amsterdam(1983). Google Scholar

192.

T. T. Cochrane et al., “Land use and productive potential of American Savannas,” International Savanna Symposium, 114 –124 Australian Academy of Science, Canberra (1985). Google Scholar

193.

R. O. Whyte, Tropical Grazing Lands: Communities and Constituent Species, Springer, New York (1974). Google Scholar

194.

M. Gadgil and V. M. Meher-Homji, “Land use and productive potential of Indian savannas,” International Savanna Symposium, 107 –113 The Australian Academy of Science, Canberra (1985). Google Scholar

195.

P. A. Werner, B. H. Walker and P. A. Stott, “Introduction: savanna ecology and management,” J. Biogeogr., 17 343 –345 (1990). http://dx.doi.org/10.2307/2845362 JBIODN 1365-2699 Google Scholar

196.

G. M. McKeon et al., “Northern Australian savannas: management for pastoral production,” Australian Perspectives and Intercontinental Comparisons, 11 –28 Blackwell Scientific Publications, London (1991). Google Scholar

197.

R. J. Scholes and B. H. Walker, An African Savanna: Synthesis of the Nylsvley Study, Cambridge University Press, Cambridge, UK (1993). Google Scholar

198.

D. Eamus et al., “Seasonal changes in photosynthesis of eight savanna tree species,” Tree Physiol., 19 (10), 665 –671 (1999). http://dx.doi.org/10.1093/treephys/19.10.665 TRPHEM 0829-318X Google Scholar

199.

C. Potter, “Carbon cycles and vegetation dynamics of savannas based on global satellite products,” Ecosystem Function in Savannas Measurement and Modeling at Landscape to Global Scales, 425 –441 CRC Press, Boca Raton, Florida (2010). Google Scholar

200.

D. P. Roy, L. Boschetti and L. Giglio, “Remote sensing of global savanna fire occurrence, extent, and properties,” Ecosystem Function in Savannas Measurement and Modeling at Landscape to Global Scales, 239 –254 CRC Press, Boca Raton, Florida (2010). Google Scholar

201.

W. J. Bond, F. I. Woodward and G. F. Midgley, “The global distribution of ecosystems in a world without fire,” New Phytol., 165 (2), 525 –538 (2005). http://dx.doi.org/10.1111/nph.2005.165.issue-2 NEPHAV 0028-646X Google Scholar

202.

R. J. Fensham et al., “Effects of fire and drought in a tropical eucalypt savanna colonized by rain forest,” J. Biogeogr., 30 (9), 1405 –1414 (2003). http://dx.doi.org/10.1046/j.1365-2699.2003.00934.x JBIODN 1365-2699 Google Scholar

203.

J. M. Paruelo et al., “Estimation of primary production of subhumid rangelands from remote sensing data,” Appl. Veg. Sci., 3 (2), 189 –195 (2000). http://dx.doi.org/10.2307/1478997 1402-2001 Google Scholar

204.

J. M. C. Silva and J. M. Bates, “Biogeographic patterns and conservation in the South American Cerrado: a tropical savanna hotspot,” BioScience, 52 (3), 225 –234 (2002). http://dx.doi.org/10.1641/0006-3568(2002)052[0225:BPACIT]2.0.CO;2 BISNAS 0006-3568 Google Scholar

205.

N. P. Hanan et al., “Do fires in savannas consume woody biomass? A comment on approaches to modeling savanna dynamics,” Am. Nat., 171 (6), 851 –856 (2008). http://dx.doi.org/10.1086/523971 AMNTA4 0003-0147 Google Scholar

206.

M. J. D. Sarrazin et al., “Fusing small-footprint waveform LiDAR and hyperspectral data for canopy-level species classification and herbaceous biomass modeling in savanna ecosystems,” Can. J. Remote Sens., 37 (6), 653 –665 (2011). http://dx.doi.org/10.5589/m12-007 CJRSDP 0703-8992 Google Scholar

207.

O. Mutanga and D. Rugege, “Integrating remote sensing and spatial statistics to model herbaceous biomass distribution in a tropical savanna,” Int. J. Remote Sens., 27 (16), 3499 –3514 (2006). http://dx.doi.org/10.1080/01431160600639735 IJSEDK 0143-1161 Google Scholar

208.

K. J. Wessels et al., “Relationship between herbaceous biomass and

1 - {km}^{2}

advanced very high resolution radiometer (AVHRR) NDVI in Kruger National Park, South Africa,” Int. J. Remote Sens., 27 (5), 951 –973 (2006). http://dx.doi.org/10.1080/01431160500169098 IJSEDK 0143-1161 Google Scholar

209.

A. Skidmore and J. Ferwerda, “Resource distribution and dynamics: mapping herbivore resources,” Resource Ecology, 57 –77 Springer, Netherlands (2008). Google Scholar

210.

L. S. Galvão, Í. Vitorello and R. A. Filho, “Effects of band positioning and bandwidth on NDVI measurements of tropical savannas,” Remote Sens. Environ., 67 (2), 181 –193 (1999). http://dx.doi.org/10.1016/S0034-4257(98)00085-6 RSEEA7 0034-4257 Google Scholar

211.

S. D. Prince, “Satellite remote sensing of primary production: comparison of results for Sahelian grasslands 1981–1988,” Int. J. Remote Sens., 12 (6), 1301 –1311 (1991). http://dx.doi.org/10.1080/01431169108929727 IJSEDK 0143-1161 Google Scholar

212.

C. A. D. Sannier, J. C. Taylor and W. D. Plessis, “Real-time monitoring of vegetation biomass with NOAA–AVHRR in Etosha National Park, Namibia, for fire risk assessment,” Int. J. Remote Sens., 23 (1), 71 –89 (2002). http://dx.doi.org/10.1080/01431160010006863 IJSEDK 0143-1161 Google Scholar

213.

O. Diallo et al., “AVHRR monitoring of savanna primary production in Senegal, West Africa: 1987–1988,” Int. J. Remote Sens., 12 (6), 1259 –1279 (1991). http://dx.doi.org/10.1080/01431169108929725 IJSEDK 0143-1161 Google Scholar

214.

T. K. Gill et al., “Estimating tree‐cover change in Australia: challenges of using the MODIS vegetation index product,” Int. J. Remote Sens., 30 (6), 1547 –1565 (2009). http://dx.doi.org/10.1080/01431160802509066 IJSEDK 0143-1161 Google Scholar

215.

J. Verbesselt et al., “Monitoring herbaceous biomass and water content with SPOT Vegetation time-series to improve fire risk assessment in savanna ecosystems,” Remote Sens. Environ., 101 (3), 399 –414 (2006). http://dx.doi.org/10.1016/j.rse.2006.01.005 RSEEA7 0034-4257 Google Scholar

216.

W. J. D. van Leeuwen et al., “Radiative transfer in shrub savanna sites in Niger: preliminary results from HAPEX-Sahel. 3. Optical dynamics and vegetation index sensitivity to biomass and plant cover,” Agric. For. Meteorol., 69 (3–4), 267 –288 (1994). http://dx.doi.org/10.1016/0168-1923(94)90029-9 0168-1923 Google Scholar

217.

X. Le Roux et al., “Radiation absorption and use by humid savanna grassland: assessment using remote sensing and modelling,” Agric. For. Meteorol., 85 (1–2), 117 –132 (1997). http://dx.doi.org/10.1016/S0168-1923(97)00002-6 0168-1923 Google Scholar

218.

J. W. Seaquist, L. Olsson and J. Ardö, “A remote sensing-based primary production model for grassland biomes,” Ecol. Model., 169 (1), 131 –155 (2003). http://dx.doi.org/10.1016/S0304-3800(03)00267-9 ECMODT 0304-3800 Google Scholar

219.

M. J. Hill et al., “Assessment of the MODIS LAI product for Australian ecosystems,” Remote Sens. Environ., 101 (4), 495 –518 (2006). http://dx.doi.org/10.1016/j.rse.2006.01.010 RSEEA7 0034-4257 Google Scholar

220.

C. Potter et al., “Terrestrial carbon sinks in the Brazilian Amazon and Cerrado region predicted from MODIS satellite data and ecosystem modeling,” Biogeosciences, 6 947 –969 (2009). http://dx.doi.org/10.5194/bgd-6-947-2009 BIOGGR 1726-4170 Google Scholar

221.

Z. Li et al., “Modeling gross primary production of alpine ecosystems in the Tibetan Plateau using MODIS images and climate data,” Remote Sens. Environ., 107 (3), 510 –519 (2007). http://dx.doi.org/10.1016/j.rse.2006.10.003 RSEEA7 0034-4257 Google Scholar

222.

X. Xiao et al., “Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest,” Remote Sens. Environ., 94 (1), 105 –122 (2005). http://dx.doi.org/10.1016/j.rse.2004.08.015 RSEEA7 0034-4257 Google Scholar

223.

X. Xiao et al., “Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data,” Remote Sens. Environ., 91 (2), 256 –270 (2004). http://dx.doi.org/10.1016/j.rse.2004.03.010 RSEEA7 0034-4257 Google Scholar

224.

C. Potter et al., “Continental-scale comparisons of terrestrial carbon sinks estimated from satellite data and ecosystem modeling 1982–1998,” Global Planet. Change, 39 (34), 201 –213 (2003). http://dx.doi.org/10.1016/j.gloplacha.2003.07.001 GPCHE4 0921-8181 Google Scholar

225.

C. Potter et al., “Terrestrial vegetation dynamics and global climate controls,” Clim. Dyn., 31 (1), 67 –78 (2008). http://dx.doi.org/10.1007/s00382-007-0339-5 CLDYEM 0930-7575 Google Scholar

226.

V. Liesenberg, L. S. Galvão and F. J. Ponzoni, “Variations in reflectance with seasonality and viewing geometry: implications for classification of Brazilian savanna physiognomies with MISR/Terra data,” Remote Sens. Environ., 107 (1–2), 276 –286 (2007). http://dx.doi.org/10.1016/j.rse.2006.03.018 RSEEA7 0034-4257 Google Scholar

227.

D. Kelbe, Towards Scale-Invariant Aboveground Biomass Estimation in Savanna Ecosystems Using Small-Footprint Waveform Lidar, Rochester Institute of Technology, United States (2010). Google Scholar

228.

G. P. Asner et al., “Carnegie airborne observatory: in-flight fusion of hyperspectral imaging and waveform light detection and ranging for three-dimensional studies of ecosystems,” APPRES, 1 (1), 013536 (2007). http://dx.doi.org/10.1117/1.2794018 Google Scholar

229.

K. M. Viergever et al., “Backscatter and interferometry for estimating above-ground biomass of sparse woodland: a case study in Belize,” in 2009 IEEE Int. on Geoscience and Remote Sensing Symp. (IGARSS 2009), III–1047 –III–1050 (2009). Google Scholar

230.

M. L. Imhoff, “Radar backscatter and biomass saturation: ramifications for global biomass inventory,” IEEE Trans. Geosci. Remote Sens., 33 (2), 511 –518 (1995). http://dx.doi.org/10.1109/36.377953 IGRSD2 0196-2892 Google Scholar

231.

C. Song et al., “Classification and change detection using Landsat TM data: when and how to correct atmospheric effects?,” Remote Sens. Environ., 75 (2), 230 –244 (2001). http://dx.doi.org/10.1016/S0034-4257(00)00169-3 RSEEA7 0034-4257 Google Scholar

232.

S. R. Hale and B. N. Rock, “Impact of topographic normalization on land-cover classification accuracy,” Photogramm. Eng. Remote Sens., 69 (7), 785 –791 (2003). http://dx.doi.org/10.14358/PERS.69.7.785 PGMEA9 0099-1112 Google Scholar

233.

S. W. Myint et al., “Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery,” Remote Sens. Environ., 115 (5), 1145 –1161 (2011). http://dx.doi.org/10.1016/j.rse.2010.12.017 RSEEA7 0034-4257 Google Scholar

234.

T. M. Kuplich, P. J. Curran and P. M. Atkinson, “Relating SAR image texture to the biomass of regenerating tropical forests,” Int. J. Remote Sens., 26 (21), 4829 –4854 (2005). http://dx.doi.org/10.1080/01431160500239107 IJSEDK 0143-1161 Google Scholar

235.

L. Kumar, P. Sinha and S. Taylor, “Improving image classification in a complex wetland ecosystem through image fusion techniques,” APPRES, 8 (1), 083616 (2014). http://dx.doi.org/10.1117/1.JRS.8.083616 Google Scholar

236.

S. Taylor, L. Kumar and N. Reid, “Mapping Lantana camara,” Photogramm. Eng. Remote Sens., 76 (6), 691 –700 (2010). http://dx.doi.org/10.14358/PERS.76.6.691 PGMEA9 0099-1112 Google Scholar

237.

P. A. Townsend, “Estimating forest structure in wetlands using multitemporal SAR,” Remote Sens. Environ., 79 (2–3), 288 –304 (2002). http://dx.doi.org/10.1016/S0034-4257(01)00280-2 RSEEA7 0034-4257 Google Scholar

238.

B. N. Haack et al., “Radar and optical data comparison/integration for urban delineation: a case study,” Photogramm. Eng. Remote Sens., 68 (12), 1289 –1296 (2002). PGMEA9 0099-1112 Google Scholar

239.

Y. Ban, “Synergy of multitemporal ERS-1 SAR and Landsat TM data for classification of agricultural crops,” Can. J. Remote Sens., 29 (4), 518 –526 (2003). http://dx.doi.org/10.5589/m03-014 CJRSDP 0703-8992 Google Scholar

240.

C. Wang and J. Qi, “Biophysical estimation in tropical forests using JERS‐1 SAR and VNIR imagery. II. Aboveground woody biomass,” Int. J. Remote Sens., 29 (23), 6827 –6849 (2008). http://dx.doi.org/10.1080/01431160802270123 IJSEDK 0143-1161 Google Scholar

241.

P. T. Wolter and P. A. Townsend, “Multi-sensor data fusion for estimating forest species composition and abundance in northern Minnesota,” Remote Sens. Environ., 115 (2), 671 –691 (2011). http://dx.doi.org/10.1016/j.rse.2010.10.010 RSEEA7 0034-4257 Google Scholar

242.

R. A. Hill and A. G. Thomson, “Mapping woodland species composition and structure using airborne spectral and LiDAR data,” Int. J. Remote Sens., 26 (17), 3763 –3779 (2005). http://dx.doi.org/10.1080/01431160500114706 IJSEDK 0143-1161 Google Scholar

243.

D. G. Leckie et al., “Stand delineation and composition estimation using semi-automated individual tree crown analysis,” Remote Sens. Environ., 85 (3), 355 –369 (2003). http://dx.doi.org/10.1016/S0034-4257(03)00013-0 RSEEA7 0034-4257 Google Scholar

244.

J. Boudreau et al., “Regional aboveground forest biomass using airborne and spaceborne LiDAR in Québec,” Remote Sens. Environ., 112 (10), 3876 –3890 (2008). http://dx.doi.org/10.1016/j.rse.2008.06.003 RSEEA7 0034-4257 Google Scholar

245.

G. Chen and G. J. Hay, “A support vector regression approach to estimate forest biophysical parameters at the object level using airborne lidar transects and Quickbird data,” Photogramm. Eng. Remote Sens., 77 (7), 733 –741 (2011). http://dx.doi.org/10.14358/PERS.77.7.733 PGMEA9 0099-1112 Google Scholar

246.

S. Tonolli et al., “Mapping and modeling forest tree volume using forest inventory and airborne laser scanning,” Eur. J. For. Res., 130 (4), 569 –577 (2011). http://dx.doi.org/10.1007/s10342-010-0445-5 EJFRBG 1612-4669 Google Scholar

247.

G. Vaglio-Laurin et al., “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” ISPRS J. Photogramm. Remote Sens., 89 49 –58 (2014). http://dx.doi.org/10.1016/j.isprsjprs.2014.01.001 IRSEE9 0924-2716 Google Scholar

248.

S. Ullah et al., “Estimation of grassland biomass and nitrogen using MERIS data,” Int. J. Appl. Earth Obs. Geoinf., 19 196 –204 (2012). http://dx.doi.org/10.1016/j.jag.2012.05.008 ITCJDP 0303-2434 Google Scholar

249.

J. M. Paruelo et al., “ANPP estimates from NDVI for the central grassland region of the United States,” Ecology, 78 (3), 953 –958 (1997). http://dx.doi.org/10.1890/0012-9658(1997)078[0953:AEFNFT]2.0.CO;2 ECOLAR 0012-9658 Google Scholar

250.

H. Reese et al., “Applications using estimates of forest parameters derived from satellite and forest inventory data,” Comp. Electron. Agric., 37 (1–3), 37 –55 (2002). http://dx.doi.org/10.1016/S0168-1699(02)00118-7 CEAGE6 0168-1699 Google Scholar

251.

S. Labrecque et al., “A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland,” For. Ecol. Manage., 226 (13), 129 –144 (2006). http://dx.doi.org/10.1016/j.foreco.2006.01.030 FECMDW 0378-1127 Google Scholar

252.

L. H. Wang and Y. Q. Xing, “Remote sensing estimation of natural forest biomass based on an artificial neural network,” Chin. J. Appl. Ecol., 19 (2), 261 –266 (2008). 1001-9332 Google Scholar

253.

J. B. Drake et al., “Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest,” Remote Sens. Environ., 81 (2–3), 378 –392 (2002). http://dx.doi.org/10.1016/S0034-4257(02)00013-5 RSEEA7 0034-4257 Google Scholar

254.

G. P. Asner et al., “High-resolution forest carbon stocks and emissions in the Amazon,” Proc. Nat. Acad. Sci. U. S. A., 107 (38), 16738 –16742 (2010). http://dx.doi.org/10.1073/pnas.1004875107 1091-6490 Google Scholar

255.

G. Zhang et al., “Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data,” Remote Sens. Environ., 151 44 –56 (2014). http://dx.doi.org/10.1016/j.rse.2014.01.025 RSEEA7 0034-4257 Google Scholar

256.

K. E. Skog and H. N. Rosen, “United States wood biomass for energy and chemicals: possible changes in supply, end uses, and environmental impacts,” For. Prod. J., 47 (2), 63 –69 (1997). FPJOAB 0015-7473 Google Scholar

Biography

Lalit Kumar received his PhD in remote sensing from UNSW, Australia, in 1998 and his Master of Science from University of the South Pacific, Fiji, in 1992. He is an associate professor in ecosystem management at the University of New England, Australia. His expertise is in the fields of remote sensing and GIS related to natural resources management and agricultural systems. He has over 20 years’ experience and has published more than 100 journal papers.

Priyakant Sinha received his PhD in remote sensing from the University of New England in 2013 and his master’s degree in remote sensing from Birla Institute of Technology, India, 1999. He is a research fellow in the School of Environmental and Rural Science, University of New England, Australia. He has worked in remote sensing and GIS applications in natural resource management and environmental monitoring at various organizations for 15 years.

Subhashni Taylor is a postdoctoral fellow at the University of New England (UNE), Armidale, Australia. She is researching the impacts of climate change on the endemic biodiversity of the Pacific Island states. She completed her PhD at the UNE in 2012, and her thesis investigated remote sensing and modeling techniques that contribute to better mapping and projected modeling of lantana, a weed of global significance.

Abdullah F. Alqurashi received his master’s degree in GIS and remote sensing from the University of New England, Australia, 2011. He obtained his bachelor’s degree in geography from Umm Al-Qura University in Makkah, Saudi Arabia, in 2005. He is a PhD candidate in ecosystem management at the University of New England, Australia. His field of study is change detection and urban environment using remote sensing techniques.

© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Lalit Kumar, Priyakant Sinha, Subhashni Taylor, and Abdullah F. Alqurashi "Review of the use of remote sensing for biomass estimation to support renewable energy generation," Journal of Applied Remote Sensing 9(1), 097696 (16 June 2015). https://doi.org/10.1117/1.JRS.9.097696

Published: 16 June 2015

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