This article emphasizes landslide susceptibility mapping along Ghat road of Kolli hills, Tamil Nadu, India, using frequency ratio, relative effect and fuzzy gamma operator models with the help of remote sensing data and GIS technique. The purpose of the study is to generate, compare and validate landslide susceptibility zones. Landslide inventory was done with data collected from the State Highways department.

Habitat loss and climate change pose a double jeopardy for many threatened taxa, making the identification of optimal habitat for the future a conservation priority. Using a case study of the endangered Bornean orang‐utan, we identify environmental refuges by integrating bioclimatic models with projected deforestation and oil‐palm agriculture suitability from the 1950s to 2080s. We coupled a maximum entropy algorithm with information on habitat needs to predict suitable habitat for the present day and 1950s.

The green vegetation fraction (GVF) and surface albedo are important land surface parameters often used for validation of climate and land surface models that are influenced largely by environmental gradients and human activities. In this study, fine resolution GVF and albedo values derived from Landsat Thematic Mapper/Enhanced Thematic Mapper Plus images from 1990 to 2000 were used to examine the relationship of both GVF and albedo values to the spatial gradients of parameters related to dramatic urbanization in the Greater Guangzhou metropolitan area, Guangdong Province, in South China.

Nest survival is a critical factor affecting avian demographics, and can be influenced by nesting chronology, fine scale nest-site selection and broad-scale landscape characteristics. We modeled the relative influences of nest age, temporal variation in nest success and habitat-related covariates at two spatial scales (nest-site and patch scale) on daily nest survival during incubation for eastern wild turkeys Meleagris gallopavo silvestris in a mixed-use landscape. Daily survival rate of turkey nests during incubation increased as percent understory cover (vegetation

Extracting information about saline soils from remote sensing data is useful, particularly given the environmental significance and changing nature of these areas in arid environments. One interesting case study to consider is the delta oasis of the Weigan and Kuqa rivers, China, which was studied using a Landsat Enhanced Thematic Mapper Plus (ETM+) image collected in August 2001. In recent years, decision tree classifiers have been successfully used for land cover classification from remote sensing data.

Arid regions in Asia are commonly characterized by rapidly growing populations with limited land resources and varying rainfall frequencies under climatic change. Despite being one of the most important environmental challenges in Asia, the changing aridity in this region, particularly due to large-scale land cover change, has not been well documented. In this study, we used rainfall data and a new land heterogeneity index to identify recent trend in land cover changes in the Asian arid regions.

Many production landscapes are complex human-environment systems operating at various spatio-temporal scales and provide a variety of ecosystem goods and services (EGS) vital to human well-being. EGS change over space and time as a result of changing patterns of land use or changes in the composition and structure of different vegetation types. Spatio-temporal assessment of EGS can provide valuable information on the consequences of changing land use and land cover for EGS and helps to deal with this complexity.

Urbanization is a global phenomenon with still unknown consequences for vegetation dynamics of urban ecosystems, especially in subtropical areas of developing countries. In this paper we analyze the vegetation productivity trend associated to urban densification and urban expansion during the last decade, in twelve cities of northern Argentina. We used time series analysis of MODIS-NDVI images to reconstruct the phenological patterns to retrieve a productivity trend under three spatial classes of urban dynamics: (1) urban, (2) expansion and (3) periphery.

The geospatial information is significant for many socio-technical activities like urban planning, the prediction of natural hazards, the monitoring of land use, weather forecasting, cadastral surveys etc. It is possible to acquire geospatial information from a distance using remote sensing technologies, but remotely sensed images don’t have semantics without a previous recognition. The classification of geospatial information is expensive and time consuming process. The paper describes the automatic land cover recognition method, which is based on min-cut/max-flow segmentation.

During recent decades, forests have expanded into new areas throughout the whole of Norway. The processes explained as causing the forest expansion have focused mainly on climate or land use changes. To enable a spatially explicit separation of the effects following these two main drivers behind forest expansion, the authors set out to model the potential for natural forest regeneration following land use abandonment, given the present climatic conditions.

Effectively identifying soil properties in relation to non-point source (NPS) phosphorus pollution is important for NPS pollution management. Previous studies have focused on particulate P loads in relation to agricultural non-point source pollution. In areas undergoing rapid urbanization, dissolved P loads may be important with respect to conditions of surface infiltration and rainfall runoff. The present study developed an integrated model for the analysis of both dissolved P and particulate P loads, applied to the Meiliang Bay watershed, Taihu Lake, China.

Analysis of land-cover change in the seasonal tropical forests of the Southern Yucatán, Mexico presents a number of significant challenges for the fine-scale land-cover information required of land-change science. Subtle variation in mature forest types across the regional ecocline is compounded by vegetation transitions following agricultural land uses. Such complex mapping environments require innovation in multispectral classification methodologies. This research presents an application of a step-wise maximum likelihood/In-Process Classification Assessment (IPCA) procedure.