The monitoring of rivers based on remote sensing data provides an opportunity for the observation of the natural dynamics of water and flood conditions. Taking advantage of the availability of radar data, we describe a methodology used to detect the river’s water surface flooding patterns and the size of floods using observations from Sentinel-1 imagery. With the proposed methodology, characteristics of the river’s normal average riverbed surface are determined, as well as the flood footprint. As a case study, it shows the annual and interannual water variability at the lower Coatzacoalcos Basin in the state of Veracruz, Mexico. This site was chosen after considering the evidence of seasonal flooding and the changes that happened during the 2015 to 2017 period. Our paper details the results on surface changes during this time period. The results on water surface cover have been validated through different sources of information to confirm the ability to differentiate flood areas and know the maximum flood extensions. An agreement above 92% is estimated to be reached with the use of the evaluated maps of binary water/no-water. Recurrent flood areas were detected where water remained during one or several weeks during the month of October, both in 2015 and 2017. Finally, a change in the extent of water maps in the framework of the Sustainable Development Goals is achieved with the integration of historical data over time and space of the natural behavior of the rivers in the case study.
Using satellite data to study small water bodies (SWB) and medium-sized water bodies (MSWB) is extremely useful for understanding their status, how to conserve them as water reservoirs, and their vulnerability to climate variability. The images studied in our work correspond to different-sized lagoons located in areas with high and low topography in a tropical region of Chiapas, Mexico. Our research project delineates SWB and MSWB. For this analysis, we considered water bodies to be uniform regions in a synthetic aperture radar image. The robustness of the method was determined based on an analysis of the morphologies of 23 lagoons. Several methods, including Hermite transform, were analyzed and compared with other image denoising methods used to improve speckle reduction. To obtain additional spatial information for image classification, we analyzed texture using the gray-level co-occurrence matrix. The results indicate that the Hermite filter is the best method for identifying water bodies. The advantage of this filter is the identification of local patterns such as edges and lines. It also preserves and improves aspects related to the homogeneity of water bodies, using the Hermite coefficient selection criteria for local pattern feature selection/extraction. The lake water extent products demonstrate that Sentinel-1 is useful for identifying SWB in this study area. The results show very high detection of water bodies, with adequate detection for water bodies larger than 2 ha, and an area accuracy of 80%.
Water body classification is a topic of great interest, especially for the effective management of floods. Synthetic aperture radar (SAR) imaging has demonstrated a great potential for water monitoring, given its capacity to register images independent of weather conditions. Several algorithms for water detection using SAR images are based on optimal thresholding techniques. However, these simple methodologies produce false classification results when small water bodies embedded in mountain ranges are presented in the image. We present an unsupervised and easy-to-implement methodology, based on local Moran index of spatial association in combination with morphological closing operations, for inland water body extraction. According to several experiments, we demonstrate that our method is capable of effectively extracting lakes and rivers located at different land surface reliefs without the requirement of a training step. In addition, comparisons with the state-of-the-art techniques demonstrate the effectiveness of our procedure, performing an overall accuracy of 96.37% and Kappa = 0.927.
Mexico’s largest freshwater body, Lake Chapala, is the main source of drinking water for over 4-million inhabitants. This study used satellite and field data to obtain annual estimates of Lake Chapala’s area and water volume from 1973 to 2009, as well as water intakes from 1934 to 2013. Twenty-one Landsat images (multispectral scanner system, 2, 5, and 7) for the period of 1973 to 2009 were processed. Gap filling correction was applied to Landsat 7 enhanced thematic mapper plus scan line corrector-off images. Binary water/no-water segmentation was obtained with a Markov random field model using the normalized difference water index. An area-based model was developed to calculate water volume, which used the lake’s area estimated based on the images as well as an exponential function obtained from area-elevation curves. Three of the resulting water/no-water layers were validated against reference points taken from aerial photography. Overall accuracies of 77% to 90% were obtained. A correlation coefficient of 0.9 resulted when comparing the results from using remote sensing to calculate water volume against field measurements. Given the observed reduction in volume, we concluded that it will be difficult to continue to use the lake as the main source of freshwater for the Guadalajara metropolitan area without substantial interventions.
The tropical coastal landscape of Tulum in Quintana Roo, Mexico has a high ecological, economical, social and cultural
value, it provides environmental and tourism services at global, national, regional and local levels. The landscape of the
area is heterogeneous and presents random fragmentation patterns. In recent years, tourist services of the region has been
increased promoting an accelerate expansion of hotels, transportation and recreation infrastructure altering the complex
landscape. It is important to understand the environmental dynamics through temporal changes on the spatial patterns
and to propose a better management of this ecological area to the authorities. This paper addresses a multi-temporal
analysis of land cover changes from 1993 to 2000 in Tulum using Thematic Mapper data acquired by Landsat-5. Two
independent methodologies were applied for the analysis of changes in the landscape and for the definition of
fragmentation patterns. First, an Iteratively Multivariate Alteration Detection (IR-MAD) algorithm was used to detect
and localize land cover change/no-change areas. Second, the post-classification change detection evaluated using the
Support Vector Machine (SVM) algorithm. Landscape metrics were calculated from the results of IR-MAD and SVM.
The analysis of the metrics indicated, among other things, a higher fragmentation pattern along roadways.
Southeastern Mexico, particularly Tabasco's flatlands which experienced a severe flood in 2007, was used as a case
study for testing a methodology for the estimation of direct damage looses on agricultural crops by flooding. We
proposed an accurate delineation of agricultural lands of multispectral images (SPOT-5) which consist on ensemble
classifiers trough a majority voting, that combine spatial and spectral information. Finally in order to evaluate the impact
of floodwater, a radar data (RADARSAT-1), were used for both, delineating the flood extent and estimating water depth.
These layers were overlaid on the agricultural crop classification layer, and crop yield damage was estimated using a
depth damage function. The results of this research quantified and evaluated the overall economic loss (tangible damage)
from the impact of floodwater on agricultural crops.
The coralline reefs in Banco Chinchorro, Mexico, are part of the great reef belt of the western Atlantic. This reef
complex is formed by an extensive coralline structure with great biological richness and diversity of species. These
colonies are considered highly valuable ecologically, economically, socially and culturally, and they also inherently
provide biological services. Fishing and scuba diving have been the main economic activities in this area for decades.
However, in recent years, there has been a bleaching process and a decrease of the coral colonies in Quintana Roo,
Mexico. This drop is caused mainly by the production activities performed in the oil platforms and the presence of
hurricanes among other climatic events. The deterioration of the reef system can be analyzed synoptically using remote
sensing. Thanks to this type of analysis, it is possible to have updated information of the reef conditions.
In this paper, satellite imagery in Landsat TM and SPOT 5 is applied in the coralline reefs classification in the 1980-
2006 time period. Thus, an integral analysis of the optical components of the water surrounding the coralline reefs, such
as on phytoplankton, sediments, yellow substance and even on the same water adjacent to the coral colonies, is
performed. The use of a texture algorithm (Markov Random Field) was a key tool for their identification. This algorithm,
does not limit itself to image segmentation, but also works on edge detection. In future work the multitemporal analysis
of the results will determine the deterioration degree of these habitats and the conservation status of the coralline areas.
In Mexico, and everywhere else, the ecosystems are constantly changing either by natural factors or anthropogenic
activity. Remote sensing has been a key tool to monitoring these changes throughout history and also to understanding
the ecological dynamics. Hence, sustainable development plans have been created in order to improve the decisionmaking
process; thus, this paper analyses deforestation impact in a very important natural resourcing area in Mexico,
considering land cover changes. The study area is located in the coast of Jalisco, Mexico, where deforestation and
fragmentation as well as high speed touristic development have been the causes of enormous biodiversity losses; the
Chamela-Cuixamala Biosphere Reserve is located within this area. It has great species richness and vast endemism. The
exploitation of this biome is widespread all over the country and it has already had an impact in the reserve. The change
detection multi-temporal study uses Landsat satellite imagery during the 1970-2003 time period. Thus, the objective of
change detection analysis is to detect and localize environmental changes through time. The change detection method
consists in producing an image of change likelihood (by post-classification, multivariate alteration detection) and
thresholding it in order to produce the change map. Experimental results confirmed that the patterns of land use and land
cover changes have increased significantly over the last decade. This study also analyzes the deforestation impact on
biodiversity. The analysis validation was carried out using field and statistic data. Spatial-temporal changing range
enables the analysis of the structural and dynamic effects on the ecosystem and it enhances better decision-making and
public environmental policies to decrease or eliminate deforestation, the creation of natural protected areas as a
biodiversity conservation method, and counteracting the global warming phenomena.
Roads are a necessary condition for the social and economical development of regions. We present a methodology for
rural road extraction from SPOT images. Our approach is centered in a fusion algorithm based on the Hermite transform
that allows increasing the spatial resolution to 2.5 m. The Hermite transform is an image representation model that
mimics some of the more important properties of human vision such as multiresolution and the Gaussian derivative
model of early vision. Analyzing the directional energy of the expansion coefficients allows classifying the image
according to the local pattern dimensionality; roads are associated to 1D patterns.
In Mexican academic and government circles, research on criminal spatial behavior has been neglected. Only recently
has there been an interest in criminal data geo-reference. However, more sophisticated spatial analyses models are
needed to disclose spatial patterns of crime and pinpoint their changes overtime. The main use of these models lies in
supporting policy making and strategic intelligence. In this paper we present a model for finding patterns associated with
crime. It is based on a fuzzy logic algorithm which finds the best fit within cluster numbers and shapes of groupings. We
describe the methodology for building the model and its validation. The model was applied to annual data for types of
felonies from 2005 to 2006 in the Mexican city of Hermosillo. The results are visualized as a standard deviational ellipse
computed for the points identified to be a "cluster". These areas indicate a high to low demand for public security, and
they were cross-related to urban structure analyzed by SPOT images and statistical data such as population, poverty
levels, urbanization, and available services. The fusion of the model results with other geospatial data allows detecting
obstacles and opportunities for crime commission in specific high risk zones and guide police activities and criminal
investigations.
Thermal and spectral remotely sensed data make the monitoring from flux energy variables in the land atmosphere
interface possible. Therefore, remotely sensed data can be used as an alternative to estimate actual evapotranspiration
(ET) by applying the energy balance equation. In order to test the applicability of this approach in Mexico, MODIS
(Moderate Resolution Imaging Spectroradiometer) estimations from land surface variables are used at 16-day intervals of
composite data. Ancillary information is collected from 2000 ground stations. The methodology includes the Simplified
Surface Energy Balance model (SSEB) and its intercomparison with a combined model from the Surface Energy Balance
Algorithm (SEBAL) and the Two Source Energy Balance (TSEB) procedures. Preliminary results applied to one 16-day
interval during winter, 2002, showed that ET is spatially structured at a landscape level. The most significant
discrepancies between estimations are found due to the general assumptions applied to each model. Secondly, the use of
interpolated ancilliary data from local observations, along with remote sensing data, provides a better representation of
spatial variations of ET with SEBAL-TSEB model for the study period. There is not enough evidence to asses
objectively the performance of both applied procedures. Further testing is required to evaluate at a local scale the
reliability from estimations.
The Lake Chapala is the largest natural lake in Mexico. It presents a hydrological imbalance problem caused by
diminishing intakes from the Lerma River, pollution from said volumes, native vegetation and solid waste. This article
presents a study that allows us to determine with high precision the extent of the affectation in both extension and
volume reduction of the Lake Chapala in the period going from 1990 to 2007. Through satellite images this above-mentioned
period was monitored. Image segmentation was achieved through a Markov Random Field model, extending
the application towards edge detection. This allows adequately defining the lake's limits as well as determining new
zones within the lake, both changes pertaining the Lake Chapala. Detected changes are related to a hydrological balance
study based on measuring variables such as storage volumes, evapotranspiration and water balance. Results show that the
changes in the Lake Chapala establish frail conditions which pose a future risk situation. Rehabilitation of the lake
requires a hydrologic balance in its banks and aquifers.
The steered Hermite Transform is presented as an efficient tool for multi-sensor image fusion. The fusion algorithm is
based on the Hermite transform, which is an image representation model based on Gaussian derivatives that mimic some
of the most important properties of human vision. Moreover, rotation of the Hermite coefficients allows efficient
detection and reconstruction of oriented image patterns in reconstruction applications such as fusion and noise reduction.
We show image fusion with different image sensors, namely synthetic aperture radar (SAR) and multispectral optical
images. This case is important mainly because SAR sensors can obtain information independently of weather conditions;
however, the characteristic noise (speckle) present in SAR images possesses serious limitations to the fusion process.
Therefore noise reduction is a key point in the problem of image fusion. In our case, we combine fusion with speckle
reduction in order to discriminate relevant information from noise in the SAR images. The local analysis properties of
the Hermite transform help fusion and noise reduction adapt to the local image orientation and content. This is especially
useful considering the multiplicative nature of speckle in SAR images.
Nowadays, it is very common to have readily available remotely-sensed spatial information, at different resolutions,
thanks to the different satellite sensors that are acquiring multispectral images at both low and high resolutions. Fusion
techniques have then arisen as an alternative to integrate this information, which result in new images that contain better
spectral and spatial information in terms of contents and resolution.
Several fusion techniques based on the Wavelet transformation have been developed, in which the "à trous" algorithm
stands out as one of the most important tool that is able to preserve spectral and spatial properties. As an alternative, we
have introduced an algorithm based on an undecimated Hermite transform (HT) that preserves these properties, with
better image quality. In this paper, fused images are analyzed in the framework of biophysical-variables such as leaf-area-
index and sparse-fractional-vegetation-cover, all of them derived from reflectance values in the visible-red and
near-infrared bands, from multi-temporal SPOT-5 images [2005-2007]. Multi-temporal analyses are conducted to test
the consistency of these variables for different illumination conditions, and vegetation amount, in order to determine
indicators of land-cover-change. Results were used to characterize a change vector analysis, by differentiating land
transformation from modifications based on the results with fused and original images. Results also showed how the HT
algorithm resulted in the smallest modification of the bi-dimensional space of the vegetation and soil isolines after
fusion. This method also preserves the information integrity necessitated to obtain similar biophysical variable values.
By improving spatial resolution, while preserving spectral characteristics of the resulting images, the HT-based
algorithm is able to better characterize land-cover-change.
The steered multiscale Hermite transform is introduced as a tool for image fusion. It is shown how this transform's
particular characteristics, closely related to important visual perception properties, efficiently reproduce relevant image
structures in the fused products. Two cases of remote sensing image fusion are presented, namely multispectral with
panchromatic fusion and SAR with multispectral fusion. In the latter, a noise reduction algorithm also based on the
Hermite transform is incorporated within the fusion scheme so that characteristic SAR image speckle is reduced and thus
limited from corrupting fused products.
In this paper, we present two novel methodologies for image-fusion. First we fuse multispectral images from the same satellite (Landsat ETM+) with different spatial resolutions. In this case we show how the proposed method can help improve spatial resolution. In the second case, we fuse multispectral Landsat ETM+ and SAR images combining with a speckle reduction method for the latter. Both algorithms are based on a Gaussian-derivative image transform. This is a multi-channel model for image representation based on the scale-space theory. Moreover, locally rotating the image transform allows better edge reconstruction and restoration from noise. We show that multispectral image fusion with the Hermite Transform preserves the biophysical variable interpretation of the original images.
Mapping and characterization of forest and vegetation are particularly challenging in urban areas. High resolution imagery is needed for mapping and characterization purposes, due to the areal extent of urban forests, parks and recreational areas. Fusion techniques of panchromatic (1m resolution) and multiband (4m resolution) IKONOS data were used for mapping and characterization of land covering characteristics of urban green areas, allowing the identification of parks, tree areas and fields with a minimal mapping unit of 160 m2. Techniques, that integrate the fine details of the input data into the fused image, are used. Experimental results for different image fusion methods (Laplacian, Gradient pyramids, Principal Component Analysis and Wavelet transform) are also demonstrated in order to improve spatial resolution. Classification of urban areas, mapped with fused data, results in higher accuracies than when using a multiband approach with 4 m data alone. Furthermore, high spatial resolution data permitted to obtain new areal extents of green areas of the city, giving a better estimate of international indicators for a suitable green areas policy. Vegetation indexes derived from red and near infrared data IKONOS are used to evaluate vegetation conditions, which, along with their distribution, location and urban context, resulted in better indicators of green areas.
The Hermite Transform is an image representation model that incorporates some important properties of visual perception such as the analysis through overlapping receptive fields and the Gaussian derivative model of early vision. It also allows the construction of pyramidal multiresolution analysis-synthesis schemes. We show how the Hermite Transform can be used to build image fusion schemes that take advantage of the fact that Gaussian derivatives are good operators for the detection of relevant image patterns at different spatial scales. These patterns are later combined in the transform coefficient domain. Applications of this fusion algorithm are shown with remote sensing images, namely LANDSAT, IKONOS, RADARSAT and SAR AeS-1 images.
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