This article introduces an innovative approach using marker-controlled watershed segmentation (WS) in the Vegetation–Impervious Surface–Soil (V–I–S) feature space for urban land-use and land-cover (LULC) classification. The complement (e.g., the inverse) of the V–I–S feature space image shows depressions, which can be treated as topographic watersheds and they correspond to LULC classes. WS partitions the complement of V–I–S feature space image into LULC regions based on user-specified initial markers.

The RUSLE-3D (Revised Universal Soil Loss Equation-3D) model was implemented in geographic information system (GIS) for predicting the soil loss and the spatial patterns of soil erosion risk required for soil conservation planning. High resolution remote sensing data (IKONOS and IRS LISS-IV) were used to prepare land use/land cover and soil maps to derive the vegetation cover and the soil erodibility factor whereas Digital Elevation Model (DEM) was used to generate spatial topographic factor. Soil erodibility (K) factor in the sub-watershed ranged from 0.30 to 0.48.

To evolve a proper management scenario for groundwater utilization, identification of groundwater potential zones is an important step. In the present study, an attempt has been made to identify possible groundwater potential zones both in terms of quantity and quality. A methodology is proposed for identification of groundwater potential index (GWPI) and a new water quality index (WQI) based on analytic hierarchy process. The proposed methodology has been applied to the shallow alluvial aquifer of central Ganga basin, Kanpur (India).

Animal dispersal is highly non‐random and has important implications for the dynamics of populations in fragmented habitat. We identified interpatch dispersal events from genetic tagging, parentage analyses and assignment tests and modelled the factors associated with apparent emigration and post‐dispersal settlement choices by individual male agile antechinus (Antechinus agilis, a marsupial carnivore of south‐east Australian forests). Emigration decisions were best modelled with on data patch isolation and inbreeding risk.

Spatially organized distribution patterns of species and communities are shaped by both autogenic processes (neutral mechanism theory) and exogenous processes (niche theory). In the latter, environmental variables that are themselves spatially organized induce spatial structure in the response variables. The relative importance of these processes has not yet been investigated in urban habitats.

In support to the Remote Sensing Survey of the global Forest Resource Assessment 2010, the TREES-3 project has processed more than 12,000 Landsat TM and ETM+ data subsets systematically distributed over the tropics. The project aims at deriving area estimates of tropical forest cover change for the periods 1990–2000–2005.

Relationships between seasonal climate, land cover and surface energy exchange in tropical montane cloud forest environments are poorly understood. The goal of this study was to investigate the seasonality of flux partitioning in lower montane cloud forest (LMCF), shaded coffee (CO) and sugarcane (SU) in central Veracruz, Mexico, as well as to evaluate the changes in surface energy exchange associated with the conversion of LMCF to CO or SU.

Little is known about the potentially disrupting effects of wind farms on the habitat connectivity of flying vertebrates at the landscape scale. We developed a regional-scale model to assess the wind farm impact on bat migration and commuting routes. The model was implemented for the bat Nyctalus leisleri in a region of central Italy currently undergoing considerable wind farm development. A Species Distribution Model (SDM) for N.

An interesting alternative to wall-to-wall mapping approaches for the estimation of landscape metrics is to use sampling. Sample-based approaches are cost-efficient, and measurement errors can be reduced considerably. The previous efforts of sample-based estimation of landscape metrics have mainly been focused on data collection methods, but in this study, we consider two estimation procedures. First, landscape metrics of interest are calculated separately for each sampled image and then the image values are averaged to obtain an estimate of the entire landscape (separated procedure, SP).

For EU-Mediterranean olive groves (Olea europaea), mapped in CORINE, classes of olive farming intensities were derived from the outcome of a multi-temporal remote sensing vegetation dynamics analysis. The management intensity classes were developed in view of a differentiated accounting of olive groves when delineating High Nature Value Farmland areas (HNV) at pan-European level. The remote sensing input data used was the Green Vegetation Fraction (GVF), derived in 10-day intervals from a long-term time series of NOAA AVHRR data.

Land cover identification and monitoring agricultural resources using remote sensing imagery are of great significance for agricultural management and subsidies. Particularly, permanent crops are important in terms of economy (mainly rural development) and environmental protection.

The Okavango catchment in southern Africa is subject to environmental as well as socio‐economic transformation processes such as population growth and climate change. The degradation of soil and vegetation by deforestation and overgrazing is one of the downsides of this development, reducing the capacity of the land to provide ecosystem functions and services. In this study, climate simulations are brought together with secondary socioeconomic, pedologic and remote‐sensing data in a GIS‐based assessment of the factors commonly associated with land degradation risk.