森林砍伐监测的多时相遥感影像建模

D. Melati
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引用次数: 1

摘要

印度尼西亚的热带雨林面临着与森林砍伐有关的关键问题。将森林覆盖转化为非森林覆盖的人类活动一直是一个主要问题。为了保证森林资源的永续发展,有必要进行森林砍伐监测和森林覆盖预测。本研究使用了1996年、2000年和2005年的Landsat遥感数据。1996 - 2005年,研究区森林覆盖率下降了约6%。以森林覆盖建模为目的,对随机马尔可夫模型(Stochastic Markov model)、元胞自动机马尔可夫模型(cellular Automata Markov model, CA_Markov)和dan GEOMOD三种模型进行了测试。基于Kappa指数,GEOMOD表现较好,Kappa指数最高。因此,建议使用GEOMOD进行森林覆盖预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi Temporal Remotely Sensed Image Modelling For Deforestation Monitoring
Tropical rainforest in Indonesia faces critical issue related to deforestation. Human activities which convert forest cover into non-forest cover has been a major issue. In order to sustain the forest resources, monitoring on deforestation and forest cover prediction is necessary to be done. Remotely sensed data, Landsat images, with acquisition in 1996, 2000, and 2005 are used in this study. In this study area, forest cover decreased around 6 % in the period of 1996 - 2005. For the purpose of forest cover modelling, three model (i.e. Stochastic Markov Model, Cellullar Automata Markov (CA_Markov) Model, dan GEOMOD) were tested. Based upon the Kappa index, GEOMOD performed better with the highest Kappa index. Therefore, GEOMOD is recommended to forecast forest cover.
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