文章基本信息

标题：Forest Canopy Density Monitoring, Using Satellite Images
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作者：M.S. Jamalabad ; A.A. Abkar
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B7
页码：244-249
出版社：Copernicus Publications
摘要：The increasing use of satellite Remote Sensing for civilian use has proved to be the most cost effective means of mapping and monitoring environmental changes in terms of vegetation and non-renewable resources, especially in developing countries. Data can be obtained as frequently as required to provide information for determination of quantitative and qualitative changes in terrain. Forests as one part of the wild life of the human societies have a special place in economic development and stability of water and soil in the countries of the world. But because of various reasons such as development of population, increasingly changing forest to the other unsuitable applications such as: agriculture, providing energy and fuel, million of hectares from this natural resource are destroyed every year and the remainder of the surfaces change quantitatively and qualitatively. For better management of the forests, the change of forest area and rate of forest density should be investigated. It is possible that there isn't any change in the area of forest during the time but the density of forest canopy is changed. Therefore, in this research the method of Forest Canopy Density (FCD) monitoring that have been developed by other researcher is tested in an area, which is located in the north of Iran. This model calculates forest density using the four indexes of soil, shadow, thermal and vegetation. For this, the LANDSAT TM & ETM + images from different dates are used. At first, the forest density map was prepared by using Biophysical Spectral Response Modelling for two images. Overall accuracy 83% and kappa coefficient 0.78 for ETM+ 2002 image was achieved. Then, the changing of the area and forest density during these periods was distinguished
关键词：Remote Sensing; Forestry; Change Detection; Landsat; Multispectral; Thermal