We introduce the use of remote sensing analysis in providing new insight in characterizing green turtle nesting habitat. A maximum likelihood classification (MLC) and a multiple endmember spectral mixture analysis (MESMA) were conducted on a Landsat image that contained six nesting beaches in Turkey that represent varying degrees of importance. Both techniques highlighted similarities of the ve...

The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land-cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is di...

In response to the major changes taking place across the Arctic climatic, environmental, economic, social, industrial there is an increasing need for improved land cover/land use change information, especially remotely sensed data, by indigenous reindeer herders on the characterization of pasture quality and migratory routes, such as vegetation distribution, snow cover, infrastructure developme...

one of the influential tools concerning the rangeland and vegetation sciences isthe technology of remote sensing and satellite data. satellite data have played essentialroles in preparing the needed information for studying the vegetation. vegetation has beenwidely recognized as one of the best indicators for determining the land conditions. usingvegetation indices is one of the techniques of r...

Traditional methods use NDVI to investigate vegetation cover from remote sensing imagery. These methods provide per-pixel vegetation distribution, and cause a modifiable areal unit problem (MAUP), when a meaningful statistical result is issued. In this paper, a new method based on advanced segmentation techniques and classification is proposed for urban vegetation investigation extraction. This...

Estimation of evapotranspiration (ET) is important for monitoring crop water stress and for developing decision support systems for irrigation scheduling. Techniques to estimate ET have been available for many years, while more recently remote sensing data have extended ET into a spatially distributed context. However, remote sensing data cannot be easily used in decision systems if they are no...

Epidemiologists are adopting new remote sensing techniques to study a variety of vector-borne diseases. Associations between satellite-derived environmental variables such as temperature, humidity, and land cover type and vector density are used to identify and characterize vector habitats. The convergence of factors such as the availability of multi-temporal satellite data and georeferenced ep...

Undirected graphical models have been successfully used to jointly model the spatial and the spectral dependencies in earth observing hyperspectral images. They produce less noisy, smooth, and spatially coherent land cover maps and give top accuracies on many datasets. Moreover, they can easily be combined with other state-of-the-art approaches, such as deep learning. This has made them an esse...

Title of Thesis: MAPPING SNOW COVER IN SIBERIA USING GIS AND REMOTE SENSING Aditya Saini, Master of Science, 2003 Thesis directed by: Associate Professor Kaye L. Brubaker Department of Civil and Environmental Engineering The seasonal snowpack dynamics of the Siberian mountains and plains play a critical role in the freshwater fluxes of northern rivers into the Arctic Ocean. This study is part o...

Pixel classification among overlapping land cover regions in remote sensing imagery is a challenging task. Detection of uncertainty and vagueness are always key features for classifying mixed pixels. This chapter proposes an approach for pixel classification using hybrid approach of Fuzzy C-Means and Particle Swarm Optimization methods. This new unsupervised algorithm is able to identify cluste...