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首页> 外文会议>International Symposium on Remote Sensing of Environment >SPECTRAL MIXTURE ANALYSIS (SMA) OF LANDSAT IMAGERY FOR LAND COVER STUDY OF HIGHLY DEGRADED PEATLAND IN INDONESIA
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SPECTRAL MIXTURE ANALYSIS (SMA) OF LANDSAT IMAGERY FOR LAND COVER STUDY OF HIGHLY DEGRADED PEATLAND IN INDONESIA

机译：印度尼西亚高度退化的泥炭地土地覆盖研究的LANDSAT影像光谱混合分析（SMA）

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Indonesian peatland, one of the world's largest tropical peatlands, is facing immense anthropogenic pressures such as illegal logging, degradation and also peat fires, especially in fertile peatlands. However, there still is a lack of appropriate tools to assess peatland land cover change. By taking Pelalawan district located in Sumatra Island, this study determines number of land cover endmembers that can be detected and mapped using new generation of Landsat 8 OLI in order to develop high-quality burned peat fraction images. Two different image transformations, i.e. Principle Component Analysis (PCA), Minimum Noise Fraction (MNF) and two different scatterplot analyses, i.e. global and local, were tested and their accuracy results were compared. Analysis of image dimensionality was reduced by using PCA. Pixel Purity Index (PPI), formed by using MNF, was used to identify pure pixel. Four endmembers consisting of two types of soil (peat soil and dry soil) and two types of vegetation (peat vegetation and dry vegetation) were identified according to the scatterplot and their associated interpretations were obtained from the Pelalawan Fraction model. The results showed that local scatterplot analysis without PPI masking can detect high accuracy burned peat endmember and reduces RMSE value of fraction image to improve classification accuracy.
机译：印度尼西亚泥炭地是世界上最大的热带泥炭地之一，正面临着巨大的人为压力，如非法伐木，降解和​​泥炭火灾，特别是在肥沃的泥炭泥中。但是，仍然缺乏适当的工具来评估泥炭地陆地覆盖变化。通过乘坐位于苏门答腊岛的Pelalawan区，本研究确定了可以使用新一代Landsat 8 Oli检测和映射的陆地覆盖终端中的数量，以便开发高质量的烧焦泥炭分数图像。两个不同的图像变换，即原理分量分析（PCA），最小噪声分数（MNF）和两个不同的散点图分析，即全球和局部，比较了它们的准确性结果。使用PCA减少了图像维度的分析。使用MNF形成的像素纯度索引（PPI）用于识别纯像素。根据散点图鉴定由两种类型的土壤（泥炭土壤和干燥土壤）和两种类型的植被（泥炭植被和干燥植被）组成的四种终点，并从Pelalawan级分模型获得了相关的解释。结果表明，没有PPI掩蔽的局部散点图分析可以检测高精度烧焦的泥炭末端，并降低分数图像的RMSE值以提高分类精度。

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