Environmental heterogeneity is regarded as one of the most important factors governing species richness gradients. An increase in available niche space, provision of refuges and opportunities for isolation and divergent adaptation are thought to enhance species coexistence, persistence and diversification. However, the extent and generality of positive heterogeneity–richness relationships are still debated. Apart from widespread evidence supporting positive relationships, negative and hump‐shaped relationships have also been reported.

Alteration in climatic pattern has resulted to a steady decline in quality of life and the environment, especially in and around urbanized areas. These areas are faced with increasing surface temperature arising mostly from human activities and other natural sources; hence land surface temperature has become an important variable in global climate change studies. In this paper, Landsat TM/ETM imagery acquired between 1997 and 2013 were used to extract ground brightness temperature and land use/land cover change in Kuala Lumpur metropolis.

Recognition of geomorphic features, such as landslide scarps, is the first key step for landslide risk assessment and mitigation. Geomorphic features can be identified from high-resolution digital elevation model (DEM). Light Detection and Ranging (LiDAR) is a useful tool to collect high-density point elevation data from ground surfaces. LiDAR ground points are used to generate high-resolution DEMs. However, LiDAR sample sizes and interpolation methods are critical parameters for DEM estimation under various land cover types.

We analysed spectral and textural attributes from the Advanced Land Imager (ALI)/EO-1 for land-cover mapping and inspected their correlation with biophysical parameters of primary and secondary forests from Eastern Amazon. An artificial neural network (ANN) technique selected the most relevant spectral/textural attributes, which were combined for classification of the ALI scene. From the ANN land-cover map, areas classified as primary forest (PF), initial (SS1), intermediate (SS2) and advanced (SS3) stages of secondary succession were studied.

The land surface phenology (LSP) start of season (SOS) metric signals the seasonal onset of vegetation activity, including canopy growth and associated increases in land-atmosphere water, energy and carbon (CO₂) exchanges influencing weather and climate variability. The vegetation optical depth (VOD) parameter determined from satellite passive microwave remote sensing provides for global LSP monitoring that is sensitive to changes in vegetation canopy water content and biomass, and insensitive to atmosphere and solar illumination constraints.

Animal dispersal is highly non‐random and has important implications for the dynamics of populations in fragmented habitat. We identified interpatch dispersal events from genetic tagging, parentage analyses and assignment tests and modelled the factors associated with apparent emigration and post‐dispersal settlement choices by individual male agile antechinus (Antechinus agilis, a marsupial carnivore of south‐east Australian forests). Emigration decisions were best modelled with on data patch isolation and inbreeding risk.

Little is known about the potentially disrupting effects of wind farms on the habitat connectivity of flying vertebrates at the landscape scale. We developed a regional-scale model to assess the wind farm impact on bat migration and commuting routes. The model was implemented for the bat Nyctalus leisleri in a region of central Italy currently undergoing considerable wind farm development. A Species Distribution Model (SDM) for N.

An interesting alternative to wall-to-wall mapping approaches for the estimation of landscape metrics is to use sampling. Sample-based approaches are cost-efficient, and measurement errors can be reduced considerably. The previous efforts of sample-based estimation of landscape metrics have mainly been focused on data collection methods, but in this study, we consider two estimation procedures. First, landscape metrics of interest are calculated separately for each sampled image and then the image values are averaged to obtain an estimate of the entire landscape (separated procedure, SP).

AIM: Deforestation and climate change are two of the most serious threats to tropical birds. Here, we combine fine‐scale climatic and dynamic land cover models to forecast species vulnerability in rain forest habitats. LOCATION: Sulawesi, Indonesia. METHODS: We sampled bird communities on four mountains across three seasons in Lore Lindu National Park, Sulawesi, Indonesia (a globally important hotspot of avian endemism), to characterize relationships between elevation and abundance. Deforestation from 2000 to 2010 was quantified, and predictors of deforestation were identified.

The objective of this study was to quantify forest cover changes in three watersheds (Gilgel Abbay (1,646� km²), Birr (980� km²), and Upper-Didesa (1,980� km²) of the Blue Nile Basin between 1957 and 2001. Four land cover maps were produced for each watershed for 1957/1958, 1975, 1986, and 2000/2001. Nine different types of land cover were identified, five of which were forest cover classes. Between 1957 and 2001, the total forest cover increased in Gilgel Abbay (from 10 to 22� % cover) and decreased in Birr (from 29 to 22� % cover) as well as in Upper-Didesa (from 89 to 45� % cover).

This paper presents an objective approach for quantifying the amount of mangrove loss caused by expansion of shrimp farms in three villages of Krabi Province, Thailand. Landsat images from three time periods of shrimp farm development (pre-shrimp farms – 1989, development – 2001, and post-development – 2007) were analyzed using unsupervised classification algorithm. A post-classification change detection comparison approach revealed only moderate mangrove exploitation and shrimp farms, which displaced a variety of land-cover types in addition to mangroves.

National Agriculture Imagery Program (NAIP) orthophotography is a potentially useful data source for land cover classification in the United States due to its nationwide and generally cloud-free coverage, low cost to the public, frequent update interval, and high spatial resolution. Nevertheless, there are challenges when working with NAIP imagery, especially regarding varying viewing geometry, radiometric normalization, and calibration.