This paper addresses the problem of detecting land-cover transitions by analysing multitemporal remote-sensing images. In order to develop an effective system for the detection of land-cover transitions, an ensemble of non-parametric multitemporal classifiers is defined and integrated in the context of a multiple classifier system (MCS). Each multitemporal classifier is developed in the framewo...

North Hebei province lies in the ecotone from agriculture to animal husbandry and forest and is eco-fragile transitional region. Serous land degradation, especially grassland degradation occurs in this area, because of overgrazing and over-reclamation. There are also many disasters, such as drought, dust storm, forest fire, and so on. This area has characteristics of ecological degradation and ...

sabzkouh protected area, with the variety of natural ecosystems and landscapes, is very rich from the point of diversity in climate, topography, habitats and wildlife that is located in central zagros. evidences show that its ecosystems such as other iranian semiarid natural ecosystems, undergone changes in their vegetation structure over the past years. obtaining multi-spectral and multi-tempo...

Cognitive approaches to knowledge representation improve man-machine communication, as they are close to human reasoning. Conceptual spaces have been proposed as one such knowledge formalization method. Our research investigates the theory of conceptual spaces as a methodology for implementing semantic reference systems. Conceptual spaces are spanned by quality dimensions. Concepts are represen...

A refined land cover classification for the arid Phoenix (Arizona, USA) metropolitan area and some simple modifications to the surface energetics were introduced in the fifth-generation PSU/NCAR mesoscale meteorological model MM5. The single urban category in the existing 24-category United States Geological Survey (USGS) land cover classification used in MM5 was divided into three classes to a...

In this study, maximum likelihood supervised classification and post-classification change detection techniques were applied to Landsat images acquired in 1987 and 2001, respectively, to map land cover changes in the Northwestern coast of Egypt. A supervised classification was carried out on the six reflective bands for the two images individually with the aid of ground truth data. Ground truth...

The use of neural networks to classify land-cover from remote sensing imagery relies on the ability to determine a winner from the candidate land-cover types based on the imagery information available. In the case of a “winnertakes-all” scenario, this does not allow us a measure of how much the prediction of each pixel’s land-cover can be trusted. We present a three-stage method where only winn...

The objective of this research is to evaluate multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using a novel classification scheme. Six-date RADARSAT-2 Polarimetric SAR data in both ascending and descending orbits were acquired during June to September 2008 in the rural-urban fringe of the Greater Toronto Area. The major land-cover types are builtup areas, roa...

Estimating forest area at a national scale within the United Nations program of Reducing Emissions from Deforestation and Forest Degradation (REDD) is primarily based on land cover information using remote sensing technologies. Timely delivery for a country of a size like Mexico can only be achieved in a standardized and cost-effective OPEN ACCESS Remote Sens. 2014, 6 3924 manner by automatic i...

This paper explores the potential of extreme learning machine based supervised classification algorithm for land cover classification. In comparison to a backpropagation neural network, which requires setting of several user-defined parameters and may produce local minima, extreme learning machine require setting of one parameter and produce a unique solution. ETM+ multispectral data set (Engla...