Typical reclamation vegetation classification based on phenological feature parameters for coalfields in Shanxi Province
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摘要: 基于2001-2013年获取的MOD13Q1 NDVI数据,采用低通平滑Savitzky-Golay(S-G)滤波方法、插值法及切比雪夫多项式(Chebyshev Polynomial)拟合对NDVI时序数据进行重构;通过提取植被生长季开始日期、生长季长度、生长季结束日期、生长季NDVI最大值及NDVI最大值出现日期等关键物候特征参数,对研究区典型复垦植被类型进行分类.结果表明:研究区不同植被的物候特征具有显著差异,从生长季开始日期及NDVI最大值出现日期来看,农作物较有规律;而林地的生长季NDVI累积总值则明显区别于农作物及草地;农作物、草地和林地基于植被物候特征参数分类取得了较好结果,总体分类精度达到89.67%,优于采用多时相非监督分类的结果;该研究为山西省煤炭矿区生态环境恢复评价提供了一定的数据基础.Abstract: In this paper,the authors reconstructed MOD13Q1 time-series NDVI data from 2001 to 2013 using Savitzky-Golay filter and Chebyshev Polynomial methods for classifying vegetation types in the six coalfields in Shanxi Province.The key phenological parameters were extracted from the reconstructed NDVI data,such as the beginning dates of the growing season,length of the growing season,the ending dates of the growing season,the maximum NDVI value and the responding dates.The results show that different vegetation types of the six major coalfields in Shanxi have different phenological features.Cropland has distinguishable differences from grass and forest.Similarly,forest is distinguished from grass and cropland by integration of total growth.It is shown that the classification of vegetation types can achieve better results by extracting and analyzing the phonological parameters compared with multi-temporal unsupervised classification.The overall classification accuracy reaches 89.67%.This study provides a robust method for assessing long-term ecological conditions and monitoring vegetation coverage changes of the six major coalfields in Shanxi Province.
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Key words:
- remote sensing data /
- MODIS /
- NDVI /
- phenological parameters /
- vegetation classification
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