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Displaying 446 - 450 of 661Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image
This article presents a sufficient comparison of two types of advanced non-parametric classifiers implemented in remote sensing for land cover classification. A SPOT-5 HRG image of Yanqing County, Beijing, China, was used, in which agriculture and forest dominate land use. Artificial neural networks (ANNs), including the adaptive backpropagation (ABP) algorithm, Levenberg–Marquardt (LM) algorithm, Quasi-Newton (QN) algorithm and radial basis function (RBF) were carefully tested.
global land-cover validation data set, part I: fundamental design principles
A number of land-cover products, both global and regional, have been produced and more are forthcoming. Assessing their accuracy would be greatly facilitated by a global validation database of reference sites that allows for comparative assessments of uncertainty for multiple land-cover data sets. We propose a stratified random sampling design for collecting reference data.
Urban sprawl impact assessment on the agricultural land in Egypt using remote sensing and GIS: a case study, Qalubiya Governorate
Urban sprawl is one of the main problems that threaten the limited highly fertile land in the Nile Delta of Egypt. In this research, satellite images of Landsat Thematic Mapper (TM) 1992, Landsat Enhanced Thematic Mapper (ETM+) 2001 and Egypt Sat-1 2009 have been used to study the urban sprawl and its impact on agricultural land in Qalubiya Governorate. Maximum likelihood supervised classification and post-classification change detection techniques were applied for monitoring the urban sprawl in this study area.
Estimating urban vegetation cover fraction using Google Earth® images
We exploited publicly available satellite- and aircraft-based imagery to estimate urban vegetation cover fraction and land use by class for a semiarid urban area that includes Phoenix, AZ, USA, using low-cost and technologically modest tools. This technique is also used to evaluate two satellite-derived tree cover datasets as well as to compare estimates from the present study with land cover data generated from another study performed using the same study domain.
Detection and analysis of land-use and land-cover changes in the Midwest escarpment of the Ethiopian Rift Valley
This study detects patterns of land-use and land-cover changes in the last three decades (1972–2004) and analyses its causative factors in the Upper Dijo River catchment, Midwest escarpment of Ethiopian Rift Valley. Data captured through the synergy of an aerial photo, satellite image and ground-based socio-economic survey were analysed by GIS and SPSS. The results showed a decline in shrub-grassland and riverine trees at 21.5 and 16.3 ha per year, respectively, and increase in plantation trees, annual crops and bare/open grasslands at 2.8, 12.5 and 24.8 ha per year, respectively.