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首页> 外文会议>ISPRS Congress >EVALUATION OF ANNUAL MODIS PTC DATA FOR DEFORESTATION AND FOREST DEGRADATION ANALYSIS
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EVALUATION OF ANNUAL MODIS PTC DATA FOR DEFORESTATION AND FOREST DEGRADATION ANALYSIS

机译：森林砍伐和森林退化分析的年度Modis PTC数据评估

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摘要

Anthropogenic land-cover change, e.g. deforestation and forest degradation cause carbon emissions. To estimate deforestation and forest degradation, it is important to have reliable data on forest cover. In this analysis, we evaluated annual MODIS Percent Tree Cover (PTC) data for the detection of forest change including deforestation, forest degradation, reforestation and revegetation. The annual MODIS PTC data (2000 - 2010) were pre-processed by applying quality layer. Based on the PTC values of the annual MODIS data, forest change maps were produced and assessed by comparing with the data from visual interpretation of SPOT-5 images. The assessment was applied to two case-studies: Ayuquila Basin and Monarch Reserve. Results show that the detected deforestation patches by visual interpretation are roughly 4 times in quantity more than those by MODIS PTC data, which can be partially due to the much higher spatial resolution of SPOT-5, being able to pick up small deforestation patches. This analysis found poor spatial overlapping for both case-studies. Possible reasons for the discrepancy in quantity and spatial coincidence were provided. It is necessary to refine the methodology for forest change detection by PTC images; also to refine the validation data in terms of data periods and forest change categories to ensure a better assessment.
机译：人为陆地覆盖变化，例如。森林砍伐和森林退化导致碳排放。为了估算森林砍伐和森林退化，重要的是在森林覆盖上具有可靠的数据。在该分析中，我们评估了检测森林变革的年度Modis百分比树覆盖（PTC）数据，包括森林砍伐，森林退化，重新造林和再植被。通过应用质量层预处理年度Modis PTC数据（2000 - 2010）。基于年度MODIS数据的PTC值，通过与来自SPOT-5图像的视觉解释数据进行比较来生成和评估森林变更图。评估适用于两种案例研究：Ayuquila盆地和君主储备。结果表明，检测到的毁林通过视觉解释的遮挡补丁的数量大约超过MODIS PTC数据的数量超过了，这可能部分是由于SPOT-5的空间分辨率更高，能够拾取小型遮挡斑块。这种分析对两种情况研究发现了差的空间重叠。提供了数量和空间巧合的差异的可能原因。有必要通过PTC图像改进森林变化检测方法;还要在数据期间和森林变更类别中优化验证数据，以确保更好的评估。

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