Forest productivity is strongly affected by seasonal weather patterns and by natural or anthropogenic disturbances. However weather effects on forest productivity are not currently represented in inventory-based models such as CBM-CFS3 used in national forest C accounting programs. To evaluate different approaches to modelling these effects, a model intercomparison was conducted among CBM-CFS3 and four process models (ecosys, CN-CLASS, Can-IBIS and 3PG) over a 2500ha landscape in the Oyster River (OR) area of British Columbia, Canada.

This research selected water soil erosion indicators (land cover, vegetation cover, slope) to assess the risk of soil erosion, ARCMAP GIS ver.9.0 environments and ERDAS ver.9.0 were used to manage and process satellite images and thematic tabular data. Landsat TM images in 2003 were used to produce land/cover maps of the study area based on visual interpreting method and derived vegetation cover maps, and the relief map at the scale of 1:50,000 to calculate the slope gradient maps.

Support vector machine (SVM) was applied for land-cover characterization using MODIS time-series data. Classification performance was examined with respect to training sample size, sample variability, and landscape homogeneity (purity). The results were compared to two conventional nonparametric image classification algorithms: multilayer perceptron neural networks (NN) and classification and regression trees (CART).

A novel approach to image radiometric normalization for change detection is presented. The approach referred to as stratified relative radiometric normalization (SRRN) uses a time-series of imagery to stratify the landscape for localized radiometric normalization. The goal is to improve the detection accuracy of abrupt land cover changes (human-induced, natural disaster, etc.) while decreasing false detection of natural vegetation changes that are not of interest. These vegetation changes may be associated with such phenomena as phenology, growth and stress (e.g.

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.

AIM: For many applications in biodiversity and ecology, existing remote sensing‐derived land‐cover products have limitations due to among‐product inconsistency and their typically non‐continuous nature. Here we aim to help address these shortcomings by generating a 1‐km resolution global product that provides scale‐integrated and accuracy‐weighted consensus land‐cover information on an approximately continuous scale. LOCATION: Global. METHODS: Using a generalized classification scheme and an accuracy‐based integration approach, we integrated four global land‐cover products.

The rapid population growth and ongoing development activities has resulted in natural resources demolition. However, the dynamics of the natural resources in relation to different biophysical and socio-economic factors are still remains poorly understood. The present study investigates the basic natural resources i.e. forest, rangeland and surface water bodies’ status using satellite data for the years 1990, 1998, and 2006, and their change detection in relation to biophysical and socio-economic factors.

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.