This PDF file contains the front matter associated with SPIE Proceedings Volume 6742, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

The remote sensing and GIS communities are still separate worlds with their own tools and data formats. It is extremely difficult to easily share data among scientists representing these communities without performing some cumbersome conversions. This paper shows in a case study how these two worlds can benefit from each other by implementing online satellite derived soil moisture in a GIS based operational flood early warning system. We obtained near real time satellite data from the currently active satellite microwave sensor AQUA AMSR-E from the National Snow and Ice Data Center data pool and converted the data to soil moisture maps with the Land Parameter Retrieval Model. The soil moisture maps, with a spatial resolution of 0.1 degree and temporal resolution of approximately 1 day, were converted in a gridded format and directly added to an operational Flood Early Warning System. The developed opportunity to directly visualize soil moisture in such a system appears to be a powerful tool, because it creates the ability to study both the spatial and temporal evolution of soil moisture within the river basin. Furthermore, near real time qualitative information on soil moisture conditions prior to rainfall events, such as generated by our system, can even lead to more accurate estimations for flood hazard conditions. Finally, the current and future role and value of remote sensing products in flood forecasting systems are discussed.

Simulating the main terms of forest carbon budget (GPP, NPP, NEE) is important for both scientific and practical reasons. This operation was performed for a region of Central Italy (Tuscany) by the integrated processing of ground and satellite data. Several data layers (meteorology, forest type, volume, etc.) were first collected in order to characterize the eco-climatic and forest features of the region. FAPAR estimates with 1 km resolution were obtained by processing VGT NDVI data. Relying on these data sets, monthly estimates of forest GPP were produced by means of a simplified, NDVIbased parametric model, C-Fix. These GPP estimates were used to calibrate a well known bio-geochemical model, BIOME-BGC, in order to find its best configurations for simulating all main functions (photosynthesis, respirations, allocations, etc.) of the most widespread Tuscany forest types. The calibrated versions of BIOME-BGC were then applied to produce respiration estimates for all regional forest surfaces during the study period. The obtained GPP and respiration estimates, which were referred to equilibrium conditions, were converted into the values of actual forests by applying a simplified approach which relies on the ratio of actual over potential tree volume as an indicator of forest distance from climax. The C-Fix photosynthesis estimates of actual forests were finally integrated with relevant BIOMEBGC simulated respirations in order to assess net forest carbon fluxes.

Minnaert constants are calculated from the intensity of the light scattered from the surface of objects. Focused on remote sensing of vegetation on the earth's surface, the light reflected from leaves is measured. A remote sensing simulator is used in an experimental room with a halogen lamp as an optical source and a Wratten gelatin filter No. 25 as a filter. Bidirectional reflectance from two kinds of leaves with different degrees of roughness is measured and their Minnaert constants are obtained by recurrent analysis of the results. The Minnaert constant for a leaf with a smooth surface is larger than 1 (Lambertian surface) and that with a rough surface is smaller than 1.

Forest fires are one of the major environmental hazards in Mediterranean Europe. Biomass burning reduces carbon fixation in terrestrial vegetation, while soil erosion increases in burned areas. For these reasons, more sophisticated prevention tools are needed by local authorities to forecast fire danger, allowing a sound allocation of intervention resources. Various factors contribute to the quantification of fire hazard, and among them vegetation moisture is the one that dictates vegetation susceptibility to fire ignition and propagation. Many authors have demonstrated the role of remote sensing in the assessment of vegetation equivalent water thickness (EWT), which is defined as the weight of liquid water per unit of leaf surface. However, fire models rely on the fuel moisture content (FMC) as a measure of vegetation moisture. FMC is defined as the ratio of the weight of the liquid water in a leaf over the weight of dry matter, and its retrieval from remote sensing measurements might be problematic, since it is calculated from two biophysical properties that independently affect vegetation reflectance spectrum. The aim of this research is to evaluate the potential of the Moderate Resolution Imaging Spectrometer (MODIS) in retrieving both EWT and FMC from top of the canopy reflectance. The PROSPECT radiative transfer code was used to simulate leaf reflectance and transmittance as a function of leaf properties, and the SAILH model was adopted to simulate the top of the canopy reflectance. A number of moisture spectral indexes have been calculated, based on MODIS bands, and their performance in predicting EWT and FMC has been evaluated. Results showed that traditional moisture spectral indexes can accurately predict EWT but not FMC. However, it has been found that it is possible to take advantage of the multiple MODIS short-wave infrared (SWIR) channels to improve the retrieval accuracy of FMC (r² = 0.73). The effects of canopy structural properties on MODIS estimates of FMC have been evaluated, and it has been found that the limiting factor is leaf area index (LAI). The best results are recorded for LAI>2 (r² = 0.83), while acceptable results (r² = 0.58) can still be achieved for lower vegetation cover density.

During the past few years, the European Space Agency has launched several projects related to forest fires from global to local scales. The ATSR World Fire Atlas (WFA) project started in late 1995 and is still running today. It provides the longest time series ever produced on the global distribution of active fires. The WFA consists of 12 years of coherent and consistent data sets. The night time fire occurrences are derived from the 3.7 micron channel on-board the ATSR instrument series hosted by the ERS-2 and ENVISAT satellites. The ATSR WFA products were validated first in 1998 with the support of IGBP and more recently by extensive comparisons with existing data sets on forest fires events. A smooth transition from the ERS-2 ATSR-2 to the ENVISAT AATSR has been performed in January 2003 and the quality of the WFA products continuity verified. The ATSR WFA products are available in near real time since May 2006. The distribution of the ATSR WFA products will be thoroughly analyzed in this paper and a synthesis of the work performed by more than 900 registered users will be presented. The GlobCarbon project started in early 2003 with the objective to develop a service for the production of multi-year / multi-sensors global level 3 Land products to be used as input to carbon assimilation models. Understanding the spatial and temporal variation in carbon fluxes is essential to constrain models that predict climate change. However our current knowledge of these spatial and temporal patterns is uncertain, particularly over land. One of the bio-geophysical parameters that the GlobCarbon project aims to measure is the fully calibrated estimate of the burned areas quasi-independent of the original satellite sensor. These burned areas estimates are used in dynamic global vegetation models, a central component of the IGBP-IHDP-WCRP Global Carbon Cycle Joint Project. The service will feature global estimates of burned areas amongst other variables from 1998 to 2007, derived from Earth Observation sensors (ERS-2 ATSR-2, ENVISAT AATSR and SPOT VEGETATION). Finally the RISK-EOS project started in 2003 under the framework of the Global Monitoring for Environment and Security (GMES) initiative, with the objective to establish a network of European service providers for the provision of geoinformation services in support to the risk management of meteorological hazards (floods and fires). The Fire component of RISK-EOS feature two main services: the Burn Scar Mapping (BSM) service that provides some seasonal mapping of forests and semi-natural burned areas at high spatial resolution (minimum mapping unit of 3 to 5 ha); and the Regional Fire Monitoring (RFM) service that provides near real time observation of active fires, based on middle resolution satellite data (AQUA/TERRA MODIS and MSG SEVIRI). The RISK-EOS BSM service builds on the achievements of ITALSCAR, a demonstration project for the yearly mapping of burned areas in Italy, using the LANDSAT Thematic Mapper sensor. The paper will provide a synthesis of the RISK-EOS products validation and utility reports collected during the 2006 summer season.

The dynamics of vegetation covers in burned and unburned areas can be monitored by using satellite data, which provide a wide spatial coverage and internal consistency of data sets. Several indices based on satellite data can be used for this aim. In particular, NDVI (Normalized Difference Vegetation Index) is the most widely used index for vegetation monitoring based on remote sensing. This paper aims to perform a dynamical characterization of burned and unburned vegetation covers, using time series of remotely sensed data of some fire-affected and fire-unaffected sites. For this purpose, we used the Detrended Fluctuation Analysis (DFA), which permits the detection of persistent properties in nonstationary signals. Our results point out that the persistence of vegetation dynamics is significantly increased by the occurrence of fires.

A thermal sharpening algorithm (TsHARP) providing fine resolution land surface temperature (LST) to the Two-Source- Model (TSM) for mapping evapotranspiration (ET) was applied over two agricultural regions in the U.S. One site is a rainfed corn and soybean production region in central Iowa, while the other is an irrigated agricultural area in the Texas High Plains. Application of TsHARP to coarse (1 km) resolution thermal data over the rainfed agricultural area is found to produce reliable fine/within-field (60 m) resolution ET estimates, while in contrast, the TsHARP algorithm applied to the irrigated area does not perform as well, possibly due to significant sub-pixel moisture variations from irrigation. As a result, there may be little benefit in applying TsHARP for generating TSM-derived 60 m ET maps for the irrigated compared to the rainfed region. Consequently, reliable estimation of fine/within-field ET and crop stress still requires fine native resolution thermal imagery in areas with significant sub-pixel moisture variations.

Two common approaches for estimating crop evapotranspiration (ET) using satellite imagery are the reflectance-based crop coefficient method and the energy balance method. The reflectance-based crop coefficient method relates a reflectance-based vegetation index such as the soil adjusted vegetation index (SAVI) to ET basal crop coefficients such as those described by Wright (1982) [1] and the FAO 56 manual [2]. A time-series of remotely sensed inputs is then used to build the crop coefficient curve in each field being monitored. In order to obtain actual ET, a water balance must be maintained in the root zone of the crop in order to make the appropriate adjustments due to soil moisture deficits and wet soil surface from irrigation and/or rain. Ground meteorological data must be provided by a weather station located in the modeled area for the estimation of reference ET. In the energy balance approach, surface temperatures are used in the estimation of sensible heat fluxes and depending on the complexity of the model, different methods are used to either handle the aerodynamic temperature term or deal with sparse canopies (empirical approaches, two-source model, SEBAL model). Remotely sensed inputs are also used for the estimation of net radiation and soil heat flux, with latent heat flux (ET) obtained as a residual from the energy balance equation. The energy balance approach results in the actual ET being estimated directly. Instantaneous values of ET must be extrapolated to the entire day and over time in between satellite overpass inputs. This paper describes a hybrid approach that uses both methods in combination to monitor actual ET over a growing season for irrigated and non-irrigated crops. The model has been coded in an ArcGIS environment, using visual basic for the calculations. This paper describes the modeling environment and coded ET models within and presents some application results.

The determination of soil moisture content is often based on the measurements of the ratio of the vertically and horizontally polarized cross sections for large angles of incidence, where the cross sections could be significantly different. Using the high frequency, physical optics model of the earth's surface, this ratio depends primarily on the Fresnel reflection coefficients for the two polarizations, while the impact of surface roughness factors out of the cross section ratio. Thus for highly conducting moisture saturated soils, this ratio approaches one. Using the low frequency, small height-small slope perturbation model of the earth's surface, the vertically and horizontally polarized cross sections are critically dependent on polarization for large angles of incidence, even for the perfectly conducting rough surfaces. However using the standard perturbation model, the ratios of the cross sections are also independent of the surface roughness. Applying the small perturbation approach to highly conducting rough surfaces, the ratio of the horizontally to vertically polarized cross sections approaches zero for grazing angles of incidence, for which the two cross sections differ significantly. There is ample experimental evidence that neither the physical optics nor the small perturbation models are adequate. The standard hybrid two scale physical optics-perturbation approach depends critically upon the decomposition of the composite surface into smaller and larger scale surfaces. The smaller scale surface is restricted to small Rayleigh roughness parameters, proportional to the mean square height, and the larger scale surface is restricted by the large radii of curvature criteria. Using a two scale full wave approach, the cross section are expressed as a weighted sum of a physical optics cross section for the larger scale surface, reduced by a factor equal to the square of the small scale surface characteristic function, and a cross section for the smaller scale surface that is modulated by the slopes of the larger scale surface. A variation technique is used to decompose the surface height spectral density function in a continuous, smooth manner into spectral density functions for the larger and smaller scale surfaces. It is shown that the corresponding polarization dependent rough surface cross sections are stationary over a wide range of the variation parameters. The ratio of the cross sections are dependent of the surface roughness, since the horizontally polarized cross sections are significantly dependent on modulation by the slopes of the larger scale surfaces, for large angles of incidence.

Ground-penetrating radar (GPR) is a high resolution surveying method applied to civil engineering, surface geology, archaeology and other disciplines. Mainly it is used solving the direct problem and obtaining a model of the studied medium. Otherwise, the study of the inverse problem could provide other valuable information: the electromagnetic properties of the medium. These parameters are obtained from the changes of the velocity, attenuation and frequency of the recorded wave. The physical properties of the medium related to those wave parameters are, mainly, the water content and the porosity. Several lab experiences are performed in order to obtain these parameters from different soil samples. Porosity and water content are measured and controlled. Velocity is obtained by measuring the two-way travel time of the reflected wave and comparing wave reflected amplitudes on the surface of the samples. Attenuation coefficients are determined from the analysis of the amplitude of the wave traveling in different thickness samples. Frequencies velocities and wave attenuation are analyzed in the different cases in order to characterize those different media and to relate its water content and its porosity with these measured parameters. The experimental results were also compared with the complex refraction index model (CRIM).

For a comprehensive vegetation monitoring and/or management, a good understanding of the distribution of the solar radiation energy among components of this vegetation is needed. The energy received by the vegetation is measured by spectroradiometers either at satellite elevations or near the ground (in situ measurements). In this study, in situ, radiometric data and laser scanning techniques are combined, in order to evaluate the contribution of the vegetation structure to the variability of canopy reflectance. Advanced processing laser techniques are not only an efficient tool for the generation of physical models but also give information about the vertical structure of canopies (height, shape, density) and their horizontal extension. To conduct this study, airborne multispectral radiation data and, laser pulse returns are recorded from a low flying helicopter above the vegetation of a boreal forest. These measurements are used to derive canopy optical and structural variables. The impact of the canopy 2-dimensional structural variability on the distribution of the solar radiation reflected by plants of this area is discussed. The results obtained show that the laser technology can be used for the selection of the most appropriate configuration of radiation measurements, and optimization of canopy physical characteristics, in future airborne missions.

The main goal of this research was to investigate the structural-spectral interactions that exist in managed, homogeneous, even-aged Eucalyptus plantations through plot-level volume and basal area modelling in Kwazulu-Natal, South Africa. Eucalyptus plantations used in this study range between four and ten years old. Small-footprint light detection and ranging (lidar; ALTM 3033 two-return laser system; 0.2 mrad footprint, 33 kHz pulse rate) and IKONOS multispectral data were collected during the spring season of 2006. Structural characterisation of 15 m radius inventory plots were performed by derivation of independent model variables from plot-level distributions of a canopy height model, lidar point heights, multispectral data, and all data sets combined. The multispectral data and lidar data were used to characterise the structural differences across a gradient of plot volume and basal area values towards determination of structural variability contribution to spectral responses. These aspects relate to the implementation of accepted remote sensing data sources for forest structure assessment and how forest structure affects model outcomes. Results for plotlevel volume and basal area were encouraging using structural (lidar) data, with adjusted R² values of 0.94 and 0.82 for volume and basal area, respectively. Values for multispectral data were distinctly lower at 0.60 and 0.55 for the same dependent variables. Adjusted R² values for all data sets combined were only marginally better than lidar data with values of 0.95 and 0.88 for volume and basal area, respectively. Results show that lidar data are more amenable than a multispectral approach to forest structure assessment, although integration of the two data sources should be further investigated for scaling to larger areas.

Earth Observation (E.O.) technologies provide a valuable data base for the monitoring of crop and soil characteristics on a large scale, in a rapid, accurate and cost-effective way. The present work aims at evaluating different methods and models for the estimation of the Leaf Area Index (LAI) by means of hyperspectral data acquired by the optical airborne instrument CASI during the ESA AgriSAR 2006 campaign. Inversion of a physical model using an iterative optimization technique (SQP) and a fast look-up-table (LUT) approach is performed and results are compared with an empirical model based on the relationship between LAI and WDVI. Furthermore, the analyses carried out on the inversion of the physical models provide the opportunity to test the spectral bands proposed for the upcoming E.O. satellite Sentinel-2 developed by ESA in the framework of GMES (Global Monitoring for Environment and Security). The Sentinel-2 spectral sampling is compared with the one proposed by an independent study determining the wavebands best characterizing vegetation and crops. Accuracy of LAI estimation, evaluated with the AgriSAR 2006 field measurements, is discussed in the context of operational agricultural monitoring.

This research is intended to develop a model to monitor rice yields using the photosynthetic yield index, which integrates solar radiation and air temperature effects on photosynthesis and grain-filling from heading to ripening. Monitoring crop production using remotely sensed and daily meteorological data can provide an important early warning of poor crop production to Asian countries, with their still-growing populations, and also to Japan, which produces insufficient grain for its population. The author improved a photosynthesis-and-sterility-based crop production CPI index to crop yield index CYI, which estimates rice yields, in place of the crop situation index CSI. The CSI gives a percentage of rice yields compared to normal annual production. The model calculates photosynthesis rates including biomass effects, lowtemperature sterility, and high-temperature injury by incorporating: solar radiation, effective air temperature, normalized difference vegetation index NDVI, and the effect of temperature on photosynthesis by grain plant leaves. The method is based on routine observation data, enabling automated monitoring of crop production at arbitrary regions without special observations. The method aims to quantity grain production at an early stage to raise the alarm in Asian countries, which are facing climate fluctuation through this century of global warming.

We propose using geostatistical methods for the spatial analysis of data pertaining to the size of trees (in terms of canopy surface area) obtained by means of remote sensing methods. Geostatistical methods are suitable because the locations of the trees are at the nodes of an unstructured grid. More specifically, we present a semivariogram analysis to detect correlations in the tree size spatial distribution, and we apply a novel method of anisotropy analysis to search for possible anisotropy in the size distribution. We use a combination of aerial photographs and satellite images in four snapshots covering 37 years to investigate the temporal behavior in addition to the spatial distribution at a single time. The aerial photographs were taken in 1964, 1984, 1993, and the IKONOS satellite image in 2001. We follow a study area covering over 139 ha and over 2,000 tree individuals. Our plots are located in the Southern Kalahari savanna near the city of Kimberley, South Africa.

Spatial variations in Southern Italy vegetation production were analyzed between 1995 and 2005. The analysis was carried out using Normalized Difference Vegetation Index (NDVI) time series derived from NOAA AVHRR images, retrieved from DLR/EOWEB archives and corrected using an ad-hoc method. The correction method exploits more accurate MODIS NDVI maps which only became available on NASA archives in the last three years. The changes occurred is "biomass production" in the last decade were analyzed by only considering vegetation behaviour during the growing seasons. This approach improves the results by excluding from the analysis the winter months, during which vegetation exhibits a stationary behaviour, NDVI data are less significant and the biomass production is poor. The correlation between vegetation relative change patterns and terrain altitude, as derived from SRTM topographic data, was also investigated. This analysis shows the presence of different changing patterns such as: i) a vegetation production degradation in several areas with variations up to -1% ; ii) a systematic vegetation production increase over the Apennines up to 6% . These results, in agreement with temperature trends for the winter months in the last years highlight climate change processes occurring in the Mediterranean areas: temperature mitigation facilitates "robust" vegetation, like conifer stands, deciduous stands and in general Mediterranean maquis, i.e, the kind of vegetation which can be found in upland and mountain zones. On the contrary, particularly in the plains, plant foliation in the autumn-winter period is strongly affected by sudden low temperature peaks and by human activities.

Low resolution images from MODIS multispectral sensor are used for extracting indices correlated with major parameters of productivity, for two deciduous forests (Fagus sylvatica, Quercus sp.) and one shrubland dominated by the semi-deciduous Phlomis fruticosa. Ground ecophysiological measurements were conducted for three growing periods (2005-2007) and are used for indices evaluation as well as input parameters for an ecosystem productivity model. The results of the ground-based productivity model are compared to the 8day MODIS GPP product, showing that MODIS algorithm underestimates productivity and does not closely follow ecosystem dynamics. In an attempt for a more precise productivity product a new light-use efficiency model based on satellite and meteorological data is designed and presented. Moreover, hyperspectral images from CHRIS/PROBA are used for a more detailed study of the semi-decidual Phlomis fruticosa ecosystem. Ground ecophysiological measurements from two growing periods (2006-2007) are used for evaluation purposes. Images are geometrically corrected and atmospherically adjusted. The reflectance spectra obtained are used for extracting indices related to numerous plant physiological parameters. Fast responsive plant processes, such as the function of the photosynthetic apparatus, the photoprotective response to stress factors (low or high temperature, lack of precipitation) and the detailed pigment content of leaves (chlorophyll a, chlorophyll b, carotenoids) may well be followed by such indices issued from hyperspetral data, offering great advantage over multispectral images for ecosystem remote sensing.

This study is focused on the use of Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data along with high and very high resolution satellite images. The use of these data requires special attention and the elaboration of novel preprocessing methods, which is presented through two case studies. In the first one, the aim is to assess subpixel-scale changes associated to agricultural practice in an agricultural landscape. This requires the integration of temporal information from MODIS daily time series and spatial information from high resolution satellite images by means of subpixel unmixing. Our second case study is concentrated on the use of these images in direct radiometric rectification of high-resolution optical imagery. Our results show that the "standard" preprocessing is not sufficient for carrying out accurate subpixel analysis in a MODIS time series or for reliable radiometric rectification. The main reasons include raster-based processing not taking into account the changes in observation dimensions throughout the scene, and pixel mixing caused by the triangular point spread function (PSF). To resolve this, we propose a novel method based on the vector data model, in which each MODIS pixel is replaced by a polygon with its real size and orientation. Our results show this method yields a significant improvement in the radiometric fit of high-resolution and MODIS data.

Numerous satellite sensor systems have been launched during the last twenty years and satellite data are increasingly being used in regional or global vegetation monitoring. The observation of global vegetation from multiple satellites requires much effort to ensure continuity and compatibility due to differences in sensor characteristics and product generation algorithms. More recently the launch of hyperspectral sensor like Hyperion make the compatibility problem even more difficult as the very narrow hyperspectral bands need to be simulated to the broader multispectral bands before proceed to any further comparison. In this study we tried to compare multispectral (Landsat ETM⁺ and EO-1 Advanced Land Imager) data with hyperspectral (Hyperion) data for the vegetation cover mapping of Milos Island. All the data were collected the same day within one-minute time. As a result the atmospheric conditions were exactly the same and that make the data ideal for comparison. The performance of the EO-1 Hyperion imaging spectrometer with the Advanced Land Imager (ALI) and the Landsat 7 ETM+ sensor was compared using a method that aggregated portions of the Hyperion 10 nm bands to simulate the broader multispectral bands of ALI and ETM+. The general process was to calculate a weighted sum of the Hyperion bands that covered each Landsat band. The weights used in the sum were derived, by comparing the spectral response of the hyperspectral bands with the respective multispectral band. The Normalized Difference Vegetation Index was used for the comparison of the three data sets and the results are presented in this study.

This study presents the application of LIDAR data to the evaluation and quantification of fluvial habitat in river systems, coupling remote sensing techniques with hydrological modeling and ecohydraulics. Fish habitat studies depend on the quality and continuity of the input topographic data. Conventional fish habitat studies are limited by the feasibility of field survey in time and budget. This limitation results in differences between the level of river management and the level of models. In order to facilitate upscaling processes from modeling to management units, meso-scale methods were developed (Maddock & Bird, 1996; Parasiewicz, 2001). LIDAR data of regulated River Cinca (Ebro Basin, Spain) were acquired in the low flow season, maximizing the recorded instream area. DTM meshes obtained from LIDAR were used as the input for hydraulic simulation for a range of flows using GUAD2D software. Velocity and depth outputs were combined with gradient data to produce maps reflecting the availability of each mesohabitat unit type for each modeled flow. Fish habitat was then estimated and quantified according to the preferences of main target species as brown trout (Salmo trutta). LIDAR data combined with hydraulic modeling allowed the analysis of fluvial habitat in long fluvial segments which would be time-consuming with traditional survey. LIDAR habitat assessment at mesoscale level avoids the problems of time efficiency and upscaling and is a recommended approach for large river basin management.

Advanced technology in airborne detection of crop growth can help optimize the strategies of fertilization, and help maximize the grain output by adjusting field inputs. In this study, Push-broom Hyperspectral Image sensor (PHI) was used to investigate the influence of soil nitrogen supplied and variable-rate fertilization to the growth of winter wheat. The objective was to determine to what extent the reflectance obtained in the 80 visible and near-infrared (NIR) wavebands (from 410nm to 832nm) might be related to differences of variance of soil nitrogen and variable-rate fertilization. Management plots were arranged at Beijing Precision Farming Experimental Station. Three flights were made during the wheat growing season. Several field experiments, including the crop sampling, soil sampling and variable-rate fertilization were carried out in the field. Data were analyzed for each flight and each band separately. Some spectrum indices were derived from PHI images and statistical correlation analysis were carried out among the spectrum indices and soil nitrogen, variable-rate fertilization amount. In addition, the spectrum indices difference between elongation stage and grain filling stage are calculated and the correlation analysis was also carried out. The analysis results indicated that the reflectance of winter wheat is significantly influenced at certain wavelength by the soil nitrogen and the variable-rate fertilization. The soil nitrogen effect was detectable in all the three flights. Differences in response due to soil nitrogen variance were most evident at spectrum indices, such as ^dλ red, INFLEX, Green/Red, NIRness, DVI and RDVI. Furthermore, analysis results also indicated that the variable fertilization can reduce the growth difference of winter wheat caused by spatial distribution difference of soil nitrogen.

Land use/cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/cover. This paper presents different approaches to attain an optimal land use/cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/cover map was not sufficient for the delineation of HRUs, since the agricultural land use/cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Therefore we adopted a visual classification approach using optical data alone and also fused with ENVISAT ASAR data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modelling.

Surface albedo is one of the most important biophysical parameter responsible for energy balance control and the surface temperature and boundary-layer structure of the atmosphere. Forest land surface albedo is also highly variable temporally showing both diurnal as well as seasonal variations. In forest systems, albedo controls the microclimate conditions which affects ecosystem physical, physiological, and biogeochemical processes such as energy balance, evapotranspiration, photosynthesis. Due to anthropogenic and natural factors, land cover and land use changes result is the land surfaces albedo change. The main aim of this paper is to investigate the albedo patterns due to the impact of atmospheric pollution and climate variations of a forest ecosystem Branesti-Cernica, placed to the North-East of Bucharest city, Romania based on satellite Landsat ETM+, IKONOS and MODIS data and climate station observations. Our study focuses on 3 years of data (2003-2005), each of which had a different climatic regime. As the physical climate system is very sensitive to surface albedo, forest ecosystems could significantly feedback to the projected climate change modeling scenarios through albedo changes. The results of this research have a number of applications in weather forecasting, climate change, and forest ecosystem studies.

This paper combines a water balance model with satellite-based remote-sensing estimates of evapotranspiration (ET) to provide accurate irrigation scheduling guidelines for individual fields. The satellite-derived ET was used in the daily soil water balance model to improve accuracy of field-by-field ET demands and subsequent field-scale irrigation schedules. The combination of satellite-based ET with daily soil water balance incorporates the advantages of satellite remote-sensing and daily calculation time steps, namely, high spatial resolution and high temporal resolution. The procedure was applied to Genil - Cabra Irrigation Scheme in Spain, where irrigation water supply is often limited by regional drought. Compared with traditional applications of water balance models (i.e. without the satellite-based ET), the combined procedure provided significant improvements in irrigation schedules for both the average condition and when considering field-to-field variability. A 24% reduction in water use was estimated for cotton if the improved irrigation schedules were followed. Irrigation efficiency calculated using satellite-based ET and actual applied irrigation water helped to identify specific agricultural fields experiencing problems in water management, as well as to estimate general irrigation efficiencies of the scheme by irrigation and crop type. Estimation of field irrigation efficiency ranged from 0.72 for cotton to 0.90 for sugar beet.

Vegetation moisture dynamics plays a key role in wildland fire risk assessment. While dead fuel moisture can be considered only dependent on the dynamics of the meteorological variables, live fuel dynamics is also related to the phenological state of the considered species as well as the soil water content. Minimal variations in live moisture content can cause a great effect in wildfire risk condition, since the fine fuel load is mainly composed by live vegetation. This work presents a model able to predict the moisture content of live fine fuel starting from the phenological principles of leaf growth cycle. This approach assumes that the phenological state of a given group of vegetation at a given time instant (day of the year) can be modelled as a function of the local meteorological conditions, the soil parameters, and some vegetational parameters. The Vegetation Index products of MODIS sensor have been used to parameterize and calibrate the model. To this end, experimental parcels, fully representative of the Mediterranean vegetation cover of Liguria Region (Italy), were used as test areas. Such areas are equipped with a suite of meteorological sensors, and are periodically subject to sampling campaign aiming at characterizing the phenological state and the moisture contents of their different vegetation species. The data collected during the field campaigns were completed by the observations of MODIS-NDVI from 2001 to 2006. The paper provides a calibration procedure of phenological module carried on using the whole data sets (meteorological data, phenological and physiological data, NDVI imagery), and formalized through a mathematical programming approach. The phenological model was implemented with reference to five areas placed on the Italian territory.

Multi-spectral and multi-temporal satellite imagery provide the most reliable technique of monitoring of different urban structures regarding the net radiation and heat fluxes associated with urbanization at the regional scale. Investigation of radiation properties, energy balance and heat fluxes is based on satellite data from various satellite sensors and in-situ monitoring data, linked to numerical models and quantitative biophysical information extracted from spatially distributed NDVI-data and net radiation. Based on Landsat TM, Landsat ETM and IKONOS satellite images were classified for Bucharest, Romania, urban land use/cover and analyzed surface biophysical parameters like urban surface temperature and NDVI for 1984 - 2005 period. Spatio-temporal changes of surface biophysical parameters were examined in association with landuse changes to illustrate how these parameters respond to rapid urban expansion in Bucharest and surrounding region. This study attempts to provide environmental awareness to urban planners in future urban development. The land cover information, properly classified, can provide a spatially and temporally explicit view of societal and environmental attributes and can be an important complement to in-situ measurements.

The monitoring of vegetation in nearby urban regions is made difficult by the low spatial and temporal resolution image captures. Image fusion is one of the important techniques for spatial image resolution enhancing. In order to utilize respective information from different remote sensing images, we propose an image fusion method based on improved resolution method. Recent studies have successfully estimated NDVI using improved resolution method such as from the MODIS onboard EOS Terra satellite. Enhancement of MODIS NDVI image using LANDSAT TM image is performed on various images. The results demonstrate accurate spectral preservation on vegetated regions where MODIS image enhances the fusion product, which can be usefully applied for both visual analysis and classification purposes. Subjective visual effect and objective statistical results indicate that the performance of the improved resolution method is better than original MODIS images. It not only preserves spectral information of the original multi-spectral image well, but also enhances spatial detail information greatly. To provide a continuous monitoring capability for NDVI, in situ measurements of NDVI from paddy field was carried out in 2004 for comparison with remotely sensed MODIS data. We compare and discuss NDVI estimates from MODIS sensors and in-situ spectro-radiometer data over Ochang plain region. These results indicate that the MODIS NDVI is underestimated by approximately 50%.

A reliable mapping of fuel types is very important for computing fire hazard and risk and simulating fire growth and intensity across a landscape. Due to the complex nature of fuel characteristic a fuel map is considered one of the most difficult thematic layers to build up especially for large areas. The advent of satellite sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. In order to ascertain how well ASTER data can provide an exhaustive classification of fuel properties a sample area characterized by mixed vegetation covers was analysed. The selected sample areas has an extension at around 60 km2 and is located inside the Sila plateau in the Calabria Region (South of Italy). Fieldwork fuel type recognitions, performed before, after and during the acquisition of remote sensing ASTER data, were used as ground-truth dataset to assess the results obtained for the considered test area. Results from our analysis showed that the use ASTER data provided a valuable characterization and mapping of fuel types with a classification accuracy higher than 78%.

In this study, Hyperspectral data of two variety of rice (common rice and hybrid rice) in whole growing stage during 2002 and 2003 was measured using the ASD FieldSpec UV/VNIR Spectroradiometer with resolution of 3 nm, and the LAI and leaf chlorophyll content of rice agricultural parameter were obtained. Analyses of the correlation between rice agricultural parameter, and hyperspectal data, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and the red-edge position (REP) were studied. Results showed that a strong non-linear correlation was found between the rice LAI of two varieties and REP. The REP, EVI and NDVI were well related with LAI for the common rice, but the REP and EVI were more sensitive than the NDVI to rice LAI for the hybrid rice because of different body for two variety rice.

The sampling protocol adopted during a field campaign at an Alpine meadow site (Shandan site), during July 2002 is based on the so-called "Valeri" protocol (VALERI). The field campaign LAI measurements in Shandan are scaled up to 30×30 m² raster maps based on Landsat ETM+ imagery. Regression analysis is applied to construct empirical transfer functions for the determination of Leaf Area Index (LAI) raster imagery from ETM+ Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR) data. Subsequently, the scaling up of the LAI raster maps is performed by the aggregation of the 30x30 m² data into 1×1 km² pixels by calculating the average LAI values for the low resolution pixels. The up-scaled data are used to validate the MODIS LAI product at the Shandan site. A power regression model (LAI=2.3758*NDVI^3.5216, R²=0.66, P<0.01), established between field measured LAI and ETM+ NDVI, elicits a high statistical significance. A linear regression model (LAI=0.1798*SR-0.3574, R²=0.55, P<0.01) is established between field measured LAI and ETM+ SR. The MODIS LAI product correlates best with the ETM+ LAI transfer function obtained with NDVI data. Its R² reaches 0.46, its slope 0.97, but the intercept is 0.7, which suggests that MODIS LAI is systematically underestimated. The results illustrate that LAI measured with a LAI-2000 instrument at the VALERI Shandan site leads to an underestimation of the MODIS LAI product. A plausible cause for the systematic underestimation related with the LAI field measurements is discussed.

Advanced site-specific determination of grain protein content by remote sensing can provide opportunities to optimize the strategies for purchasing and pricing grain, and to maximize the grain output by adjusting field inputs. Field experiments were performed to study the relationship between grain quality indicators and foliar nitrogen concentration. Foliar nitrogen concentration at the anthesis stage is suggested to be significantly correlated with grain protein content, while spectral vegetation index is significantly correlated to foliar nitrogen concentration around the anthesis stage. Based on the relationships among nitrogen reflectance index (NRI), foliar nitrogen concentration, and grain protein content, a statistical evaluation model of grain protein content was developed. NRI proved to be able to evaluate foliar nitrogen concentration with a coefficient of determination of R²= 0.7302 in year 2002. The relationship between measured and remote sensing derived foliar nitrogen concentration had a coefficient of determination of R²=0.7279 in year 2003. The results mentioned above indicate that the inversion of foliar nitrogen concentration and the evaluation of grain protein content by NRI are surprisingly good.

The northwest China, typical arid and semi-arid regions, is the first or second-degree sensitivity zones for global change. Monitoring vegetation change is an important method to study the impacts of global climate change. Time-series satellite remote sensing data make it possible to monitor vegetation at different spatial and temporal resolutions globally. A long time series of Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data with 8x8 km² spatial resolution and during 1982 to 2003 were used to monitor the vegetation cover in the northwest China. The monitoring results indicate an obvious greening trend exists. The precipitation and relative humidity have high correlations with the SINDVI. So the water condition is the most important factors for the spatial distribution of the SINDVI levels. The precipitation and temperature are the primary driving factors for inter-annual vegetation changes.

For various reasons, aerial archaeologists use(d) film when studying their objects in the Near InfraRed (NIR). However, even the use of colour InfraRed (CIR) emulsions remained severely restricted till today due to some ignorance or a severe lack of knowledge about the subject and - not at least - the critical imaging process. This error-prone film-based workflow belongs now to the past, thanks to the advent of digital cameras. In this article, two new approaches will be outlined, both in an attempt to overcome the constraints on the common archaeological interpretation of varying visible colours in vegetation: the use of modified hand-held digital cameras to photograph the NIR spectrum on the one hand, as well as future plans to digitally capture both Red and NIR wavelengths simultaneously on the other. Besides additional technical background information on NIR photography, the paper treats the advantages (and disadvantages) of NIR to normal archaeological aerial imaging. In the end, an introduction of a new, remotely controlled system to support (aerial) archaeologists in their (NIR) photography is given together with several approaches to NIR image processing.

The main aim of this study is to evaluate the feasibility of utilizing satellite remote sensing and GIS tools to assist the technical flooding aspects of the catchment area of Agriakalamin River located in Kissonerga Village in Paphos-Cyprus. The remote sensing technique has been used to quantify the actual increase in urban area over the past forty three years. The Agriokalamin River area in Kissonerga Village in Paphos-Cyprus has been chosen as the study area. By analyzing the temporal satellite imageries and the observed meteorological and hydrologic data of the catchment, the local authorities can use the proposed methodology to investigate and assessing all the catchment areas versus the urbanization. The results of this study will encourage decision makers or the local authorities to consider land use planning as an effective non-structural measure for flood risk mitigation. Remotely sensed data such as aerial photos, Landsat-5 TM and Quickbird image data have been used to track the urbanization i.e. building activities near the catchment area (urbanization factor for the years 1963-2006: 0.9 % - 27.0 %). GPS measurements have been used to locate in-situ the boundaries of the catchment area. Indicative flood risk assessment study was applied.