Impact assessment of watershed development activity assumes greater importance in present day agriculture. Considering the ability of remote sensing technology in watershed monitoring and impact assessment, a study was carried out to investigate the Impact Assessment of Karnataka Watershed Development Project (DANIDA) in Koralahallihalla Sub watershed in Sindagi taluk of Bijapur district in Northern Karnataka using satellite data of two periods i.e., IRS 1 C, LISS-III data of 30 December, 1997 (pre-treatment) and IRS P6, LISS-III data of 17 December, 2004 (post-treatment).

The provision of ecosystem services at the landscape level can be significantly influenced by land management practices. Within an agriculturally dominated case study area in Saxony, Germany, a more detailed land use classification, which includes differentiated information on agricultural management practices, was utilized within the raster-based planning support tool GISCAME. “Management” refers to typical, regional crop rotations and soil tillage practices.

Although developments in remote sensing have greatly improved land cover mapping, the mixed pixel problem has not yet been fully addressed. Soft classification techniques have been introduced to address the problem, but they do not show the spatial location of the class proportions in a pixel. Subpixel mapping has been introduced to address the drawbacks of soft classifications. In this work, the feedforward backpropagating neural network (FFBPNN) was used for subpixel mapping.

We conducted extensive mist netting and radio tracking of common frugivorous, seed-dispersing, and tropical forest-dwelling blue-crowned manakins (Lepidothrix coronata; BCMA) and white-ruffed manakins (Corapipo altera; WRMA) to study their habitat use, movements, breeding success, and seed dispersal potential in the fragmented landscape of southern Costa Rica. We obtained 1354 GPS locations from 20 BCMAs and 4040 GPS locations from 54 WRMAs we tracked. These birds were dependent on forest remnants and rarely moved through open habitats.

Understanding species distributions and population responses to environmental parameters is important for addressing landscape-level species conservation. We assessed American black bear (Ursus americanus) resource selection based on spatial distribution of a recolonizing population in Mississippi, USA. Given the philopatric behavior of female bears and the risk-disturbance hypothesis, we predicted that bears recolonizing Mississippi would occupy areas close to their source population but avoid areas near roads and with greater human population density.

In this article, we evaluate and compare results from three integrated assessment models (GCAM, IMAGE, and ReMIND/MAgPIE) regarding the drivers and impacts of bioenergy production on the global land system. The considered model frameworks employ linked energy, economy, climate and land use modules. By the help of these linkages the direct competition of bioenergy with other energy technology options for greenhouse gas (GHG) mitigation, based on economic costs and GHG emissions from bioenergy production, has been taken into account.

In the last decades, due to climate changes, soil deterioration and land use/land cover (LULC) changes, land degradation (LD) has become one of the most important issues at the global, regional and local scale. In concrete terms, LD determines a reduction in the productivity of a territory and in its capacity of providing ecosystem goods and services. “Syndromes” of LD can be assessed in the past, and scenarios, conversely, can be developed for the future, as information baselines for sustainable land management strategies and interventions.

An optimized artificial immune network-based classification model, namely OPTINC, was developed for remote sensing-based land use/land cover (LULC) classification.

The economic value of the Tonle Sap Lake Floodplain to Cambodia is arguably among the highest provided to a nation by a single ecosystem around the world. Nonetheless, the Mekong River Basin is changing rapidly due to accelerating water infrastructure development (hydropower, irrigation, flood control, and water supply) and climate change, bringing considerable modifications to the flood pulse of the Tonle Sap Lake in the foreseeable future.

The frequency and size of wildfires within the Mojave Desert are increasing, possibly due to climate and land cover changes and associated increases in non-native invasive plant biomass, as measured by normalized difference vegetation index (NDVI). These patterns are of particular concern to resource managers in regions where native plant communities are not well adapted to fire.

Boreal forests are a critical component of the global carbon cycle, and timely monitoring allows for assessing forest cover change and its impacts on carbon dynamics. Earth observation data sets are an important source of information that allow for systematic monitoring of the entire biome. Landsat imagery, provided free of charge by the USGS Center for Earth Resources Observation and Science (EROS) enable consistent and timely forest cover updates.

The Zagros region of western Iran has been affected by the recent changes both in amount and in structure of forest cover. We evaluated the influence of several driving forces on forest cover and structure, including socioeconomic (urban and rural population and rural income) and climatic (mean annual rainfall and mean annual temperature) variables. We acquired all time series Landsat images of a study site from 1972 to 2009. The images were classified to produce a land cover map of each year.