Incorporating ecological processes and animal behaviour into Species Distribution Models (SDMs) is difficult. In species with a central resting or breeding place, there can be conflict between the environmental requirements of the ‘central place’ and foraging habitat. We apply a multi-scale SDM to examine habitat trade-offs between the central place, roost sites, and foraging habitat in Myotis nattereri. We validate these derived associations using habitat selection from behavioural observations of radio-tracked bats.

Surface runoff and soil loss from 2007 to 2010 related to land use and rainfall regimes in karst hill slopes in Guizhou Province, southwest China, were analyzed. Using the hierarchical clustering method, sixty-one rainfall events under the subtropical monsoon climate condition were classified into 5 types of rainfall regimes according to the depth, maximum 30-min intensity, and duration of rainfall.

The aggravating urban thermal environment has considerable adverse effects on urban physical environment, energy consumption, and public health. Due to the complexity of factors contributing to the urban thermal environment, traditional statistical methods are insufficient for acquiring data and analyzing the impacts of human activities on the thermal environment, especially for identifying dominant factors.

A reconstruction of land use changes and the implications thereof for landslide occurrence on critical slopes of Mount Elgon in Eastern Uganda were undertaken. Aerial photographs taken in 1960 formed the benchmark for the analysis of respective land use changes between 1995 and 2006, using 30m Landsat TM and 20m SPOT MS images. Landslide sites were mapped using a MobileMapper, and terrain parameters were derived using a 15m Digital Elevation Model.

Spatial and temporal resolution is essential for understanding the spatial and temporal characteristics and dynamics of wetland ecosystems. However, single satellite imagery with both high spatial resolution and high temporal frequency is currently unavailable. Instead, the development of a bi-sensor monitoring technique utilizing spatial details of middle-to-high resolution data and temporal details of coarse spatial resolution data is highly desirable.

Forest fragmentation threatens the sustainability of forest communities in the eastern United States. Forest communities exhibiting either a low total area or low percentage of intact forest are subject to relatively higher risk of shifts in stand composition towards edge-adapted and invasive species. Such changes in stand composition could result in local extirpation of communities, homogenization of forest communities at broader spatial scales, and a consequential reduction of the biodiversity values of forestland.

It is necessary to estimate the area of afforestation and deforestation in New Zealand, since 1990, to meet reporting obligations under the Kyoto Protocol. We describe a method for national mapping of forest change that achieves high accuracy, but only requires moderate effort. A national coverage of satellite imagery is standardised, classified (automatically) for land cover, and then compared with an existing 1990 land-use map to identify polygons (>1ha) of possible forest change. Each one of these possible change polygons is checked by operators for actual or spurious change.

This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification.

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).

The random forest (RF) classifier is a relatively new machine learning algorithm that can handle data sets with large numbers and types of variables. Multi-scale object-based image analysis (MOBIA) can generate dozens, and sometimes hundreds, of variables used to classify earth observation (EO) imagery. In this study, a MOBIA approach is used to classify the land cover in an area undergoing intensive agricultural development. The information derived from the elevation data and imagery from two EO satellites are classified using the RF algorithm.

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 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.