The impact of climate change on hydrological processes and nutrient input is one of the major uncertainties in projecting future global warming. The Soil and Water Assessment Tool (SWAT) model is applied to simulate the effects of temperature on the hydrology and sediment and nutrient load in the lower Pearl River Basin, South China. Calibration and validation results for SWAT showed that the Yamen estuary is appropriate for simulating the impacts of temperature change on both hydrological processes and nutrient input.

Earth’s land cover has been extensively transformed over time due to both human activities and natural causes. Previous global studies have focused on developing spatial and temporal patterns of dominant human land-use activities (e.g., cropland, pastureland, urban land, wood harvest). Process-based modeling studies adopt different strategies to estimate the changes in land cover by using these land-use data sets in combination with a potential vegetation map, and subsequently use this information for impact assessments.

The ungauged wet semi-arid watershed cluster, Seethagondi, lies in the Adilabad district of Telangana in India and is prone to severe erosion and water scarcity. The runoff and soil loss data at watershed, catchment, and field level are necessary for planning soil and water conservation interventions. In this study, an attempt was made to develop a spatial soil loss estimation model for Seethagondi cluster using RUSLE coupled with ARCGIS and was used to estimate the soil loss spatially and temporally.

Rice (Oryza sativa L.) agriculture is estimated to cover 161millionha of land on Earth, with 10% grown in temperate regions. Currently there are strong concerns about surface water nutrient pollution, and the purpose of this study was to determine the impacts of temperate rice cultivation on nutrient dynamics at the small watershed scale. Over the course of the 2008 growing season (May through September), bi-weekly grab samples were collected from outlets of 11 agricultural subwatersheds in California.

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.

AIM: Anthropogenic environmental change is having a major impact on biodiversity. By identifying traits that correlate with changes in species range, comparative studies can shed light on the mechanisms driving this change; but such studies will be more useful for conservation if they have true predictive power, i.e. if their trait‐based models can be transferred to new regions. We aim to examine the ability of trait‐based models to predict changes in plant distribution across seven geographic regions that varied in terms of land cover and species composition.

This research examined the spatial and temporal patterns of error in time-series classified maps as a first step to creating a model to propagate error in post-classification change analysis. Two Landsat images were acquired for Pittsfield Township, MI, USA, classified, and overlaid to produce a map of change. Error variables were created for the classified maps. Hypotheses were proposed describing the spatial and temporal structures of error in the classified maps, and evaluated using geostatistics and point pattern analysis.

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.