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.

Assessments of ecosystem services (ES) and biodiversity (hereafter ecological parameters) provide a comprehensive view of the links between landscapes, ecosystem functioning and human well‐being. The investigation of consistent associations between ecological parameters, called bundles, and of their links to landscape composition and structure is essential to inform management and policy, yet it is still in its infancy.

This paper presents findings of a land-use and land-cover (LULC) change mapping exercise conducted in Freetown, Sierra Leone. Nine LULC classes were mapped from multi-temporal Landsat data of 1974, 1986 and 2000. Special attention was given to the growth or otherwise of agricultural land in relation to other LULC classes. Conversion of one land-use/-cover type to the other was identified, and its effects discussed. Major conversions occurred between agricultural lands, grasslands, evergreen forest, built-up areas and barren land.

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.

Integrating social demands into landscape management has been proven difficult because of a lack of suitable measures. In order to address this issue this article describes the development of the Index of Function Suitability (IFS). This offers an integrated conceptual tool for incorporating social demands into landscape management. The IFS links preferences to land cover spatial patterns as it uses quantitative indicators for gauging differences between the preferred landscape patterns by users, for a certain activity related to an amenity function (e.g.