MODIS植被指数时间序列产品能够连续反映植被的覆盖情况,是农作物遥感测量的重要数据源。本文选取江苏省为研究区,利用2008年23个时相的MODIS NDVI数据,采用S-G滤波法进行时间序列的重构,提高NDVI时间序列信息的真实性。另结合农作物物候历、种植结构、地面调查样本等辅助资料,将水稻植被指数时间序列曲线参量化为水稻物候生长期的关键值——生长周期的起始时间、生长幅度、生长长度以及生长过程的NDVI最大值。最后,利用这些关键值确定分类规则,采取决策树分类器,建立区域水稻种植面积提取模型,总体提取精度为87.5%,其表明MODIS植被指数时序数据及本文研究方法在农作物信息提取中的有效性。
苗翠翠, 江南, 彭世揆, 吕恒, 李扬, 张瑜, 王妮, 李军
. 基于NDVI时序数据的水稻种植面积遥感监测分析——以江苏省为例[J]. 地球信息科学学报, 2011
, 13(2)
: 273
-280
.
DOI: 10.3724/SP.J.1047.2011.00273
As a kind of crucial data which was able to reflect the continuous coverage of vegetation on the earth surface, the long time-series MODIS NDVI products have already become a reliable data source used in remote sensing for crop measurement. In this paper, a long time-series and high-resolution (250m) MODIS data in 2008 was used to extract the area of the paddy land in Jiangsu Province, China. In the beginning, an annual time-series NDVI dataset of the study area was built up by pre-processing,and a reconstruction of time-series based on Savitzky-Golay filter provided this study excellent images with the better visual effect and NDVI temporal profile. Considering the paddy phenological calendar, the identifiers of paddy were parameterized as starting time, range, extent, and maximum of NDVI during the growth period. In the end, through comparing the phenological key values, a reasonable classification rule was generated based on the thresholds of phenological key values, and a decision tree classifier was constructed to extract Paddy land area, whose precision was 87.5%. It turned out the feasibilities of long time-series MODIS NDVI data and our classification strategy adapted to the extraction of crop land area.
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