نتایج جستجو برای: landuse landcover lulc
تعداد نتایج: 2260 فیلتر نتایج به سال:
Land use and land cover (LULC) maps are remote sensing products that are used to classify areas into different landscapes. The newest techniques have been applied to improve the final LULC classification and most of them are based on SVM classifiers. In this paper, a new method based on a multiple classifiers ensemble to improve LULC map accuracy is shown. The method builds a statistical raster...
Land use land cover (LULC) changes frequently in ecotones due to the large climate and soil gradients, and complex landscape composition and configuration. Accurate mapping of LULC changes in ecotones is of great importance for assessment of ecosystem functions/services and policy-decision support. Decadal or sub-decadal mapping of LULC provides scenarios for modeling biogeochemical processes a...
It is now common to use data from two or more sensors for land cover change detection. Since the spatial and spectral resolutions of different sensors vary significantly, the ability to discriminate the land cover also varies greatly. In this paper the applications of landuse change detection including area statistics, temporal trajectories and spatial pattern are discussed. The area statistics...
This study analyzed the spatiotemporal pattern of settlement expansion in Abuja, Nigeria, one of West Africa’s fastest developing cities, using geoinformation and ancillary datasets. Three epochs of Land-use Land-cover (LULC) maps for 1986, 2001 and 2014 were derived from Landsat images using support vector machines (SVM). Accuracy assessment (AA) of the LULC maps based on the pixel count resul...
India has experienced significant Land-Use and Land-Cover Change (LULCC) over the past few decades. In this context, careful observation and mapping of LULCC using satellite data of high to medium spatial resolution is crucial for understanding the long-term usage patterns of natural resources and facilitating sustainable management to plan, monitor and evaluate development. The present study u...
This paper discusses the development and implementation of a geospatial data processing method and multi-decadal Landsat time series for computing general coastal U.S. land-use and land-cover (LULC) classifications and change products consisting of seven classes (water, barren, upland herbaceous, nonwoody wetland, woody upland, woody wetland, and urban). Use of this approach extends the observa...
Classifying remote sensing imageries to obtain reliable and accurate land use and land cover (LULC) information still remains a challenge that depends on many factors such as complexity of landscape, the remote sensing data selected, image processing and classification methods, etc. The aim of this paper is to extract reliable LULC information from Landsat imageries of the Lower Hunter region o...
Understanding how local land use and land cover (LULC) shapes intra-urban concentrations of atmospheric pollutants-and thus human health-is a key component in designing healthier cities. Here, NO₂ is modeled based on spatially dense summer and winter NO₂ observations in Portland-Hillsboro-Vancouver (USA), and the spatial variation of NO₂ with LULC investigated using random forest, an ensemble d...
Remote sensing based on imagery has traditionally been the main tool used to extract land uses and land cover (LULC) maps. However, more powerful tools are needed in order to fulfill organizations requirements. Thus, this work explores the joint use of orthophotography and LIDAR with the application of intelligent techniques for rapid and efficient LULC map generation. In particular, five types...
Monitoring Urban Tree Cover Using Object-Based Image Analysis and Public Domain Remotely Sensed Data
Urban forest ecosystems provide a range of social and ecological services, but due to the heterogeneity of these canopies their spatial extent is difficult to quantify and monitor. Traditional per-pixel classification methods have been used to map urban canopies, however, such techniques are not generally appropriate for assessing these highly variable landscapes. Landsat imagery has historical...
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