Modeling the Environment with Remote Sensing and GIS: Applied Case Studies from Diverse Locations of the United Arab Emirates (UAE)

Maintaining a healthy and sustainable environment is of paramount importance for human well-being and economic activity. Hence, environment protection, requiring continuous and reliable monitoring, has become a major mandate at various levels of government. There is a direct link between the availability of information about environmentally endangered areas and sound decision-making for effective and sustainable management. While remote sensing allows acquiring relevant information on a regular and repetitive basis even in areas where accessibility is limited, geographic information systems (GIS) provide unique tools for storing, processing, and integrating this information with other sources enabling the development of spatial models that help identify and characterize environmentally endangered areas. In this chapter, we will discuss how GIS-based modeling is applied for solving diverse environmental problems using case studies from United Arab Emirates

MODIS Land Data Products: Generation, Quality Assurance and Validation

The Moderate Resolution Imaging Spectrometer (MODIS) on-board NASA's Earth Observing System (EOS) Terra and Aqua Satellites are key instruments for providing data on global land, atmosphere, and ocean dynamics. Derived MODIS land, atmosphere and ocean products are central to NASA's mission to monitor and understand the Earth system. NASA has developed and generated on a systematic basis a suite of MODIS products starting with the first Terra MODIS data sensed February 22, 2000 and continuing with the first MODIS-Aqua data sensed July 2, 2002. The MODIS Land products are divided into three product suites: radiation budget products, ecosystem products, and land cover characterization products. The production and distribution of the MODIS Land products are described, from initial software delivery by the MODIS Land Science Team, to operational product generation and quality assurance, delivery to EOS archival and distribution centers, and product accuracy assessment and validation. Progress and lessons learned since the first MODIS data were in early 2000 are described

Etude et realisation d'un traitement de signal pour cinemometre Doppler

INIST T 74712 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

Continuous Mapping and Monitoring Framework for Habitat Analysis in the United Arab Emirates

In 2015, the Environment Agency—Abu Dhabi developed the extensive Abu Dhabi Habitat, Land Use, Land Cover Map based on very high resolution satellite imagery acquired between 2011 and 2013. This was the first integrated effort at such a scale. This information has greatly helped in environmental conservation and preservation activities along with future infrastructure planning. This map has created an excellent baseline and allows efficient monitoring. In this work, we aim to establish a framework for short term updates to the maps to quickly enable efficient planning. We make use of spectral–spatial approaches based on object-based image analysis to adapt the classification scheme. Training examples from the baseline maps and field surveys are used to train classifiers, such as support vector machines (SVM), to build the updated maps. Eventually, the goal is to develop a consistent classification approach and then adapt automatic change detection approaches to extend the baseline maps temporally

Fusion of MODIS-MISR aerosol inversion for estimation of aerosol absorption

International audienc

Estimating vegetation structural effects on carbon uptake using satellite data fusion and inverse modeling

Regional analyses of biogeochemical processes can benefit significantly from observational information on land cover, vegetation structure (e.g., leaf area index), and biophysical properties such as fractional PAR absorption. Few remote sensing efforts have provided a suite of plant attributes needed to link vegetation structure to ecosystem function at high spatial resolution. In arid and semiarid ecosystems (e.g., savannas), high spatial heterogeneity of land cover results in significant functional interaction between dominant vegetation types, requiring new approaches to resolve their structural characteristics for regional-scale biogeochemical research. We developed and tested a satellite data fusion and radiative transfer inverse modeling approach to deliver estimates of vegetation structure in a savanna region of Texas. Spectral mixture analysis of Landsat data provided verifiable estimates of woody plant, herbaceous, bare soil, and shade fractions at 28.5 m resolution. Using these subpixel cover fractions, a geometric-optical model was inverted to estimate overstory stand density and crown dimensions with reasonable accuracy. The Landsat cover estimates were then used to spectrally unmix the contribution of woody plant and herbaceous canopies to AVHRR multiangle reflectance data. These angular reflectances were used with radiative transfer model inversions to estimate canopy leaf area index (LAI). The suite of estimated canopy and landscape variables indicated distinct patterns in land cover and structural attributes related to land use. These variables were used to calculate diurnal PAR absorption and carbon uptake by woody and herbaceous canopies in contrasting land cover and land use types. We found that both LAI and the spatial distribution of vegetation structural types exert strong control on carbon fluxes and that intercanopy shading is an important factor controlling functional processes in spatially heterogeneous environments