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 ...

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...

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...

Zhe Jiang, [email protected] Abstract: My research explores novel computational techniques to map the physical cover (e.g., forests) of the earth’s surface from satellite images. Processing these images is labor-intensive and a significant burden on scientists. Existing methods ignore spatial information and assume that pixels are statistically independent. Consequently, they produce erroneous map...

Since the launch of the first land observation satellite Landsat-1 in 1972, many machine learning algorithms have been used to classify pixels in Thematic Mapper (TM) imagery. Classification methods range from parametric supervised classification algorithms such as maximum likelihood, unsupervised algorithms such as ISODAT and k-means clustering to machine learning algorithms such as artificial...

Time sequential Landsat MSS and TM images were used to map land use/cover of the Atlanta metropolitan area for the past 25 years as a component of the NASA-funded Project ATLANTA (ATlanta Land-use ANalysis: Temperature and Air-quality), which has the objective to model the impact of land use/cover change on temperature and air quality in Atlanta. This paper describes a suite of techniques that ...

New aerial cameras and new advanced geoprocessing tools improve the generation of urban land cover maps. Elevations can be derived from stereo pairs with high density, positional accuracy, and efficiency. The combination of multispectral high-resolution imagery and high-density elevations enable a unique method for the automatic generation of urban land cover maps. In the present paper, imagery...