The expansion of built environments and agriculture land in the Nan watershed, Thailand, to support the rapid increase of the national population has resulted in deforestation, thus affecting the ecological balance. This deforestation, especially in high mountainous areas, has led to serious environmental degradation. Recent reports reveal an increasing soil-erosion problem in the watershed. This study analyses land-use and land-cover (LULC) changes and their potential impact on soil erosion during a study period between 1995 and 2005.

Land-use information is required for a number of purposes such as to address food security issues, to ensure the sustainable use of natural resources and to support decisions regarding food trade and crop insurance. Suitable land-use maps often either do not exist or are not readily available. This article presents a novel method to compile spatial and temporal land-use data sets using multi-temporal remote sensing in combination with existing data sources.

- Brook charr (Salvelinus fontinalis) is a sentinel fish species that requires clean, cold water habitats generally resulting from landscapes that allow for surface water flows devoid of sediment and contaminants and high groundwater discharge of cold water. As such, brook charr are impacted by land cover changes that alter stream temperature regimes. We evaluated brook charr populations across their eastern and midwestern range in the United States with reference to thermal habitat availability in relationship to land cover and per cent baseflow.

The exploration of the relationships between plant biotic dynamics and scale can reveal important information on ecosystem spatial organization by addressing preservation of information integrity in upscaling/downscaling procedures of land-surface parameterization for environmental modeling applications. Scale-dependent relations of vegetation dynamics are investigated in this study by using emergent biophysical characteristics obtained through a predictive multidimensional model of vegetation anomalies derived from remote-sensing observations.

A Boosted Genetic Fuzzy Classifier (BGFC) is proposed in this paper, for land cover classification from multispectral images. The model comprises a set of fuzzy classification rules, which resemble the reasoning employed by humans. Fuzzy rules are generated in an iterative fashion, incrementally covering subspaces of the feature space, as directed by a boosting algorithm. Each rule is able to select the required features, further improving the interpretability of the obtained model.

The angular effects of emissivity are ignored in current land surface temperature (LST) products. As a result, the directionality of these LST products limits their further application in many fields. Accurate correction of the angular problem of LST products requires explicit understanding of the angular effects of emissivity at the pixel scale. Currently, nearly ten years of global emissivity products of MODIS are available. However, the pixel-scale directionality of emissivity has never been analyzed.

The authors researched changes in specific habitat types in areas considered to be the most important in Croatia for protection of endangered bird fauna during the period 1990–2000. The analysis focused on four areas in the continental part of Croatia (Danube and Drava River alluvium, Pokupsko depression, Sava River basin, Upper Drava River basin) and four areas in the Mediterranean part (Neretva River estuary, NW part of North Dalmatia, Paško field, Lake Vrana). For the classified habitat types, changes in Corine land cover databases in 1990 and 2000 were analysed.

This paper addresses two important issues for large Mediterranean city regions: the differential impact of compact urban ‘growth’ and low-density ‘sprawl’ on land cover changes (LCCs), and their final effect on changing land cover relationships (LCRs). The urban expansion of Rome (Italy) during the last 50 years and the related LCCs were investigated as a paradigmatic example of compact versus dispersed urban development.

Tidal salt marshes in the San Francisco Estuary region display heterogeneous vegetation patterns that influence wetland function and provide adequate habitat for native or endangered wildlife. In addition to analyzing the extent of vegetation, monitoring the dynamics of vegetation pattern within restoring wetlands can offer valuable information about the restoration process.

Land use change has a major impact on goods and services that our environment supplies for society. While detailed ecological or biophysical field studies are needed to quantify the exact amount of ecosystem service supply at local scales, such a monitoring might be unfeasible at the regional scale. Since field scale monitoring schemes for ecosystem services or ecosystem functioning are missing, proxy based indicators can help to assess the historic development of ecosystem services or ecosystem functioning at the regional scale.

We tested the possibility of using digital, color aerial ortho-photographs and monoscopic, panchromatic satellite images of comparable spatial and radiometric resolution, to map recent landslides in Italy and to update existing measures of landslide mobilization. In a 90-km² area in Umbria, central Apennines, rainfall resulted in abundant landslides in the period from September 2004 to June 2005. Analysis of the rainfall record determined the approximate dates of landslide occurrence and revealed that the slope failures occurred in response to moderately wet rainfall periods.

Coastal wetland vegetation classification with remotely sensed data has attracted increased attention but remains a challenge. This paper explored a hybrid approach on a Landsat Thematic Mapper (TM) image for classifying coastal wetland vegetation classes. Linear spectral mixture analysis was used to unmix the TM image into four fraction images, which were used for classifying major land covers with a thresholding technique. The spectral signatures of each land cover were extracted separately and then classified into clusters with the unsupervised classification method.