Land cover maps derived from satellite images play a key role in regional and national land cover assessments. In order to compare maximum likelihood and minimum distance to mean classifiers, LISS-III images from IRS-P6 satellite were acquired in August 2008 from the western part of Isfahan. First, the LISS-III image was georeferenced. The Root Mean Square error of less than one pixel was the result of registration.

Elephant-induced thicket degradation in the Addo Elephant National Park (AENP), Eastern Cape, South Africa, was assessed during 1973 and 2010 using multitemporal satellite imagery. Changes in the thicket condition, in relation to the AENP expansion, were analyzed using the Normalized Difference Vegetation Index, postclassification, and landscape metrics. The change detection of land-cover classes was analyzed by postclassification. Landscape-spatial metrics were used in order to gain an understanding of vegetation-fragmentation trends.

Mato Grosso has emerged as the Brazilian state with the highest deforestation rate, and with the most dynamic changes in vegetation and land cover. In this article, we focus on the following two main objectives: (i) to quantitatively assess the extent of vegetation change over the past two decades for more accurate eco-climatic impact analysis; and (ii) to clarify the causes to the changes in this state, with special focus to agro-pastoral activities and roads networks.

Adult coho salmon Oncorhynchus kisutch return each autumn to freshwater spawning habitats throughout western North America. The migration coincides with increasing seasonal rainfall, which in turn increases storm water run‐off, particularly in urban watersheds with extensive impervious land cover. Previous field assessments in urban stream networks have shown that adult coho are dying prematurely at high rates (>50%).

Super-resolution mapping (SRM) is a process that provides land-cover spatial distribution with a spatial resolution finer than the size of a remotely sensed image pixel. Usually, the fraction images generated from soft classifiers are used as constraints in SRM, making the accurate estimation of fraction images an important task in SRM. Supervised fuzzy c-means (sFCM), which belongs to the fuzzy-set technology commonly applied for unmixing mixed pixels, is capable of providing accurate estimates of fraction images used for SRM.

Large-scale land conversion for agriculture in Brazilian Amazonia is occurring at persistently high rates. Basin-wide net land use and land cover changes imply substantially different situations between distinct regions and states due to different agricultural policies. This research used eight landscape metrics to quantify and investigate the spatial patterns of cattle pasture and cropland throughout the states of Pará, Mato Grosso, Rondônia, and Amazonas.

Intensification of human activities has caused drastic losses in semi-natural habitats, resulting as well in declining connectivity between remaining fragments. Successful future restoration should therefore increase both habitat area and connectivity. The first steps in a framework for doing so are addressed here, which involve the mapping of past habitat change.

We apply a linear regression mixed effects model to explore the influence of landscape factors on nitrate-N concentrations in a coastal watershed of Portugal. Landscape composition and configuration metrics, together with variables assessing the physical characteristics of the study area, were used. The analysis was performed using seasonal data from the years 2001 and 2006. The seasonal influence was included as a random effect to account for temporal correlations. Together, the fixed and the random factors explain 78% of the variance, whereas the fixed factors alone explain 10%.

This research investigated the effects of climate and land cover change on variation in sagebrush ecosystems. We combined information of multi-year sagebrush distribution derived from multitemporal remote sensing imagery and climate data to study the variation patterns of sagebrush ecosystems under different potential disturbances. We found that less than 40% of sagebrush ecosystem changes involved abrupt changes directly caused by landscape transformations and over 60% of the variations involved gradual changes directly related to climatic perturbations.

Cropland fires are an important source of black carbon (BC) emissions. Previous research has suggested that springtime cropland burning in Eastern Europe, more specifically Russia, is a main contributor of BC in the Arctic atmosphere, acting as a short-lived climate forcer strongly influencing snow-ice albedo and radiation transmission.

Recent land cover change estimates show overall decline of tropical forests at the regional and global scales caused by multiple social, cultural and economic factors. There is an overall concern on the prevailing land use practices, such as shifting cultivation and extraction of forest materials as agents of forests losses, but also new, emerging land uses are threatening tropical forests. Understanding of the long-term development and driving forces of forest changes are needed, especially at local levels where many decisions on forest policies and land uses are made.

It is difficult to monitor forests in tropical regions with frequent cloud cover using optical remote-sensing data. Adequate multi-temporal, high-resolution imagery is often not available. Microwave imagery is able to penetrate cloud cover, enabling imagery of the land surface to be recorded more frequently. This study seeks to improve tropical forest mapping by combining optical and microwave imagery, with one of the main objectives being the discrimination of planted and natural forests.