Satellite imagery with fine spectral and spatial resolution and high temporal resolution provides a unique opportunity to map and monitor biodiversity. The aim of this article is (i) to review the state of the use of high-resolution remote sensing for ecological studies in the Indian Himalayan Region (IHR) and (ii) to suggest further potential avenues of research in the region using this technology.

Polarimetry is a technique for quantitatively estimating the change in polarization in a backscattered electromagnetic signal (in this case a radar signal) with reference to the polarization of the incident signal for estimating the geophysical properties (roughness and dielectric constant) of surface elements. In this study, quadrapole Radarsat-2 data have been utilized for mapping geological elements in Archaean terrain based on their geophysical parameters preserved in the four polarization channels of Radarsat-2 data.

Flood regulation is a widely valued and studied service provided by watersheds. Flood regulation benefits people directly by decreasing the socio-economic costs of flooding and indirectly by its positive impacts on cultural (e.g., fishing) and provisioning (e.g., water supply) ecosystem services. Like other regulating ecosystem services (e.g., pollination, water purification), flood regulation is often enhanced or replaced by technology, but the relative efficacy of natural versus technological features in controlling floods has scarcely been examined.

Critical to effective urban climate adaptation is a clearer understanding of the sensitivities of resource demand to changing climatic conditions and land cover situations. We used Bayesian Maximum Entropy (BME) stochastic procedures to estimate temperature and precipitation at the very small scale of urban Census Block Groups (CBGs) in Phoenix, Arizona and Portland, Oregon, and then compared average household water use patterns by climate conditions and land cover characteristics between and within the two cities.

Land-use land-cover (LULC) changes are one of the major threats to biodiversity worldwide, since their principal consequences are the loss, fragmentation or degradation of the habitat available for most species. Therefore, in order to provide guidelines for environmental management at the regional scale and thus reverse the trend in degradation, transformations of natural remnants into anthropogenic land uses must be identified and quantified.

The national network of weather surveillance radars (WSR-88D) detects flying birds and is a useful remote-sensing tool for ornithological study. We used data collected during fall 2008 and 2009 by 16 WSR-88D radars in the northeastern U.S. to quantify the spatial distribution of landbirds during migratory stopover. We geo-referenced estimates based on radar reflectivity, of the density of migrants aloft at their abrupt evening exodus from daytime stopover sites, to the approximate locations from which they emerged.

This work presents a classification technique for hyperspectral image analysis when concurrent ground truth is either unavailable or available. The method adopts a principal component analysis (PCA)-based projection pursuit (PP) procedure with an entropy index for dimensionality reduction, followed by a Markov random field (MRF) model-based segmentation. An ordinal optimization approach to PP determines a set of ‘good enough projections’ with high probability, the best among which is chosen with the help of MRF model-based segmentation.

Land area planted to row crops has expanded globally with increased demand for food and biofuels. Agricultural expansion in the Dakota Prairie Pothole Region (DPPR), USA affects a variety of agricultural and non-agricultural land-use types, including grasslands and wetlands that provide critical wildlife habitat and other ecosystem services. The purpose of this study was to quantify recent changes in rural land cover/land use, analyze trends, and interpret results in relation to climate, agronomic practice, and ethanol production.

We investigated the seasonal variability of the relationships between land surface temperature (LST) and land use/land cover (LULC) variables, and how the spatial and thematic resolutions of LULC variables affect these relationships. We derived LST data from Landsat-7 Enhanced Thematic Mapper (ETM+) images acquired from four different seasons.

To provide a quantitative spatial analysis of the riverscape (open-water bodies and their surrounding areas) of the Western Amazonian lowlands using a consistent surface of remotely sensed imagery. Taking into account the essential significance of fluvial environments for the Amazonian biota, we propose that an enhanced understanding of the Amazonian riverscape will provide new insight for biogeographical studies in the region and contribute to the understanding of these megadiverse tropical lowlands.

The objective of this paper is to exploit the potential application of an evidential belief function model to landslide susceptibility mapping at Kuala Lumpur city and surrounding areas using geographic information system (GIS). At first, a landslide inventory map was prepared using aerial photographs, high resolution satellite images and field survey. A total 220 landslides were mapped and an inventory map was prepared. Then the landslide inventory was randomly split into a testing dataset 70% (153 landslides) and remaining 30% (67 landslides) data was used for validation purpose.

Land cover classification using remotely sensed data requires robust classification methods for the accurate mapping of complex land cover area of different categories. In this regard, support vector machines (SVMs) have recently received increasing attention. However, small number of training samples remains a bottleneck to design suitable supervised classifiers. On the other hand, adequate number of unlabeled data is available in remote sensing images which can be employed as additional source of information about margins.