The paper investigates the performance and potential of object based image analysis technique (OBIA) for land cover information extraction from high resolution satellite datasets. Efficiency of the technique has been assessed in different urban land cover situations using merged CARTOSAT-1 and IRS-P6 LISS-IV image subsets of Dehradun, India. Multi-scale iterative Nearest Neighbour classification has been applied. Classification results have been quantitatively evaluated.

Encroachment of nitrogen-fixing trees and shrubs into grasslands and savannas is a well-documented land cover change that occurs worldwide. In the Rio Grande Plains region of southern Texas, previous studies have shown woody encroachment by leguminous Prosopis glandulosa (mesquite) trees increases soil C and N, decreases microbial biomass N relative to soil N, and accelerates N mineralization and nitrification.

The main objective of the present work is to delineate the groundwater potential zones in Bilari watershed of district Shivpuri, Madhya Pradesh. Remote Sensing data and GIS were used to delineate the groundwater potential zones of the area. IRS-1D (LISS III) data have been utilized to extract information on various themes such as geomorphology, structure, drainage and land use/land cover. Available lithology and soil maps have also been used.

High N₂O emissions have been observed in semi-arid grasslands, especially during freeze/thaw periods, when denitrification might be the main process of N₂O production. However, there have been few denitrification studies in semi-arid grassland. This study was designed to determine the denitrification potential of four representative land cover types (typical steppe, meadow steppe, marshland, arid steppe) in two grasslands in Inner Mongolia, China.

Classifying multi-temporal image data to produce thematic maps and quantify land cover changes is one of the most common applications of remote sensing. Mapping land cover changes at the regional level is essential for a wide range of applications including land use planning, decision making, land cover database generation, and as a source of information for sustainable management of natural resources.

Support vector machines (SVM) are nowadays receiving increasing attention in remote sensing applications although this technique is very sensitive to the parameters setting and training set definition. Self-training is an effective semisupervised method, which can reduce the effort needed to prepare the training set by training the model with a small number of labeled examples and an additional set of unlabeled examples. In this study, a novel semisupervised SVM model that uses self-training approach is proposed to address the problem of remote sensing land cover classification.

Anthropogenic forces that alter the physical landscape are known to cause significant soil erosion, which has negative impact on surface water bodies, such as rivers, lakes/reservoirs, and coastal zones, and thus sediment control has become one of the central aspects of catchment management planning. The revised universal soil loss equation empirical model, erosion pins, and isotopic sediment core analyses were used to evaluate watershed erosion, stream bank erosion, and reservoir sediment accumulation rates for Ni Reservoir, in central Virginia.

Labeling of connected components in an image or a raster of non-imagery data is a fundamental operation in fields of pattern recognition and machine intelligence. The bulk of effort devoted to designing efficient connected components labeling (CCL) algorithms concentrated on the domain of binary images where labeling is required for a computer to recognize objects.

Countryside Survey is a unique large scale long-term monitoring programme investigating stock and change of habitats, landscape features, vegetation, soil and freshwaters of Great Britain. Repeat field surveys combine policy and scientific objectives to provide evidence on how multiple aspects of the environment are changing over time, a key goal of international science in the face of profound human impacts on ecosystems.

SUMMARYThis study analyses spatio-temporal patterns of wildfires in Greece using a multidimensional statistical framework based on non-parametric correlations, principal component analysis, clustering and stepwise discriminant analysis. Specifically, we assess the frequency, seasonal profile, severity and land-use type of 135 178 wildfires which occurred between 2000–2012 in Greece, one of the countries most affected by fire in Europe.

The most obvious, although not exclusive, explanation for the increase of species richness with increasing sample area (the species-area relationship) is that species richness is ultimately linked to area-based increases in habitat heterogeneity. The aim of this paper is to examine the relative importance of area and habitat heterogeneity in determining species richness in nature reserves. Specifically, the work tests the hypothesis that species-area relationships are not positive if habitat heterogeneity does not increase with area.

The German Remote Sensing Data Center operates the Biosphere Energy Transfer Hydrology Model, a process model that estimates the net primary productivity of agricultural areas. The model is driven by remote sensing data and meteorological data. Remotely sensed datasets including a time series of the leaf area index, which describes vegetation condition, and a land cover classification, which provides information about land use, are needed. Currently leaf area indices and land cover data derived from the sensor vegetation are used.