温庆可, 张增祥, 汪 潇, 乔竹萍, 徐进勇. 西北农牧交错区草地类型遥感划分方法[J]. 农业工程学报, 2010, 26(3): 171-177.
    引用本文: 温庆可, 张增祥, 汪 潇, 乔竹萍, 徐进勇. 西北农牧交错区草地类型遥感划分方法[J]. 农业工程学报, 2010, 26(3): 171-177.
    Classification method of grassland types using satellite images in northwest agro-pastoral zone of China[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(3): 171-177.
    Citation: Classification method of grassland types using satellite images in northwest agro-pastoral zone of China[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(3): 171-177.

    西北农牧交错区草地类型遥感划分方法

    Classification method of grassland types using satellite images in northwest agro-pastoral zone of China

    • 摘要: 草地是西北农牧交错区最大的生态屏障。为使各类型草地在生态环境建设中发挥最大的效应,以甘肃省为典型研究区,综合运用遥感技术,研究了西北农牧交错区草地类型的划分方法。利用草地生长期(4-9月)内11个时相的中分辨率成像光谱仪(MODIS)增强型植被指数(EVI)数据构建时间序列数据集,根据同期的野外采样点,提取各类型草地生长期内的典型时间谱特征曲线。在西北农牧交错区,要结合数字高程(DEM)信息和以黄河分界的东西两区域为主要自然因子,将研究区分成若干自然子区域。在各子区,分别根据各像素时间谱特征曲线与典型时间谱特征曲线的相似度,构建决策树完成分类。以1︰500 000草地资源类型图资料为真值样本,验证表明,分类结果精度总体一致性为71.41%,Kappa系数为0.66,各类型面积所占比例非常接近样本值。MODIS EVI时间序列影像弥补了其空间分辨率低的不足,草地类型生长季时间谱特征曲线增强了各草地类型之间的差异性,使得MODIS数据适合于草地二级类型的划分。加之MIODIS数据免费获取,且适合用于大区域遥感监测,使得低成本高精度且宏观的遥感草地类型划分成为可能。

       

      Abstract: Grasslands are the largest ecological barriers for the northwest agro-pastoral zone of China. Grassland classification is significant for ecological environmental conservation. This paper defines a grassland classification technique based on the remote sensing technology in Gansu Province, as the typical region for northwest agro-pastoral zone. High temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) is used to construct temporal profile of enhanced vegetation index (EVI) during the grass growth period (April to September). Typical temporal profile of EVI for each grassland type was constructed based on synchronous field samples. The study area is divided into a few of natural subregion based on elevation and the Yellow River division. Decision tree was established with similarities between each pixel and typical temporal profile, and classification was completed in each natural subregion. Based on the 1︰500 000 scale maps of China’s grassland resources, the validation process indicated that overall accuracy of classification results was 71.41%, and kappa coefficient was 0.66. The area of each grassland type was also close to that in the sample atlas, which proved that MODIS EVI was effective for grassland classification. As MODIS images are free and suitable for large area monitoring, it is possible to conducted low-cost, high precision and macro grassland classification.

       

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