Large remote sensing datasets, that either cover large areas or have high spatial resolution, are often a burden of information mining for scientific studies. Here, we present an approach that conducts clustering after gray-level vector reduction. In this manner, the speed of clustering can be considerably improved. The approach features applying eigenspace transformation to the dataset followed by compressing the data in the eigenspace and storing them in coded matrices and vectors.

As part of the development of the 2011 National Land Cover Database (NLCD) tree canopy cover layer, a pilot project was launched to test the use of high-resolution photography coupled with extensive ancillary data to map the distribution of tree canopy cover over four study regions in the conterminous US. Two stochastic modeling techniques, random forests (RF) and stochastic gradient boosting (SGB), are compared.

In this study, a multi-scale approach was used for classifying land cover in a high resolution image of an urban area. Pixels and image segments were assigned the spectral, texture, size, and shape information of their super-objects (i.e. the segments that they are located within) from coarser segmentations of the same scene, and this set of super-object information was used as additional input data for image classification.

Within the investigated river basins the deforestation ranged from 0-45.5%, dependent on the amount of windstorm damage. Our water temperature readings revealed that the canopy elimination above the streams in the areas damaged by the windstorm caused increase in daily and annual water temperature and also wider daily water temperature range, than those in the undisturbed reference stream, which caused the decline of cold stenotherm species abundance. The stream basins deforestation was collinear with FPOM and UFPOM concentrations, water temperature gradient and nitrate concentrations.

Shallow landslides induced by heavy rainfall events represent one of the most disastrous hazards in mountainous regions because of their high frequency and rapid mobility. Recent advancements in the availability and accessibility of remote sensing data, including topography, land cover and precipitation products, allow landslide hazard assessment to be considered at larger spatial scales. A theoretical framework for a landslide forecasting system was prototyped in this study using several remotely sensed and surface parameters.

The response of fragmented vegetation to human impact has been analysed in degraded and degrading areas in Tanzania (Lake Manyara).

Many terrestrial species of birds have been observed to depend on large contiguous areas of forest; in the tropics this appears to be especially true for terrestrial insectivores. Far less is understood about how more-common species, that are less dependent on interior forests, use both forested and non-forested habitat in fragmented landscapes.

There have been no landscape-scale assessments which quantify the relative importance of the organic and mineral properties of BS (bed sediment) and associated catchment characteristics (geology, land cover and mean topsoil phosphorus (P) content) for BSP concentration.

This paper presents the application of remote sensing techniques, digital image analysis and Geographic Information System tools to delineate the degree of landslide hazard and risk areas in the Balik Pulau area in Penang Island, Malaysia. Its causes were analysed through various thematic attribute data layers for the study area. Firstly, landslide locations were identified in the study area from the interpretation of aerial photographs, satellite imageries, field surveys, reports and previous landslide inventories.

This paper explores the possibility of using non-geometric cadastral maps from the 17th and 18th century together with aerial photographs from 1945 and 1981 to analyse land-cover change in south-east Sweden. Habitats rich in plant species in the European rural landscape seem to be correlated with a long continuity of management. Accurate spatial data from historical data sources are fundamental to understand patterns of vegetation and biodiversity in the present-day landscape.

Anthropogenic land‐cover change is driving biodiversity loss worldwide. At the epicenter of this crisis lies Southeast Asia, where biodiversity‐rich forests are being converted to oil‐palm monocultures. As demand for palm oil increases, there is an urgent need to find strategies that maintain biodiversity in plantations. Previous studies found that retaining forest patches within plantations benefited some terrestrial taxa but not others. However, no study has focused on aquatic taxa such as fishes, despite their importance to human well‐being.

Land use/land cover (LULC) change occurs when humans alter the landscape, and this leads to increasing loss, fragmentation and spatial simplification of habitat. Many fields of study require monitoring of LULC change at a variety of scales. LULC change assessment is dependent upon high-quality input data, most often from remote sensing-derived products such as thematic maps. This research compares pixel- and object-based classifications of Landsat Thematic Mapper (TM) data for mapping and analysis of LULC change in the mixed land use region of eastern Ontario for the period 1995–2005.