Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods
نویسندگان
چکیده
منابع مشابه
Comparing the Capability of Sentinel 2 and Landsat 8 Satellite Imagery in Land Use and Land Cover Mapping Using Pixel-based and Object-based Classification Methods
Introduction: Having accurate and up-to-date information on the status of land use and land cover change is a key point to protecting natural resources, sustainable agriculture management and urban development. Preparing the land cover and land use maps with traditional methods is usually time and cost consuming. Nowadays satellite imagery provides the possibility to prepare these maps in less ...
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The 2009 Victorian bushfires, also called the Black Saturday bushfires, ignited across the Australian state of Victoria on Saturday 7 February 2009, resulting in Australia’s highest ever loss of life from a bushfire. According to the Victorian Police, the bushfires caused at least 173 known deaths of people and 414 people injured. The use of multispectral Landsat Thematic Mapper (TM) data was a...
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Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize...
متن کاملLand Cover Change Detection Using Texture Analysis
Problem statement: It is an important task to detect land cover changes from remotely sensed data for environmental monitoring. Although there are some applications of visual textures to the land use, they are limited to a few land cover categories with the application of one texture measure. Since land cover types are complex and often the integration of various objects, applying one texture m...
متن کاملAn Automated Artificial Neural Network System for Land Use/Land Cover Classification from Landsat TM Imagery
This paper focuses on an automated ANN classification system consisting of two modules: an unsupervised Kohonen’s Self-Organizing Mapping (SOM) neural network module, and a supervised Multilayer Perceptron (MLP) neural network module using the Backpropagation (BP) training algorithm. Two training algorithms were provided for the SOM network module: the standard SOM, and a refined SOM learning a...
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ژورنال
عنوان ژورنال: Remote Sensing of Environment
سال: 2009
ISSN: 0034-4257
DOI: 10.1016/j.rse.2009.02.004