Landscape-scale characterization of cropland in China using Vegetation and landsat TM images
Landscape-scale characterization of cropland in China using Vegetation and landsat TM images
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DOI：
10.1080/01431160110106069
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发表时间：
2002-09-01
影响因子：
3.4
通讯作者：
Zhao, R
中科院分区：
文献类型：
--
作者：
Xiao, X;Boles, S;Zhao, R
在这项景观规模的研究中，我们结合Landsat TM图像和农业普查数据，探索了利用植被传感器的多时相10天复合数据来表征土地覆盖类型的可能性。研究区域位于江苏省东部，中国。计算了1999年3月11日至5月20日的7个10天综合植被指数(VGT-S10)的归一化差异植被指数(NDVI)和归一化差异水分指数(NDWI)。目测多时相NDVI和NDWI，并用于非监督分类。将得到的1公里分辨率的VGT分类图与1996年4月26日以30 m分辨率获取的Landsat 5 TM图像的非监督分类得到的TM分类图进行比较，以量化1公里VGT像素内农田的百分比；结果显示，归类为农田的像素的平均值为60%，归类为农田/自然植被镶嵌的像素的平均值为47%。利用行政区划图，将VGT数据和TM影像估算的耕地面积汇总到县级，并与1995年县级农业普查数据进行比较。这种景观尺度分析结合了影像分类(如高分辨率VGT数据、细分辨率TM数据)、统计普查数据(如县级农业普查数据)和地理信息系统(如行政县地图)，并展示了多时相VGT数据在从景观到区域的不同空间尺度上绘制农田地图的潜力。这一分析还说明了在1公里分辨率下对异质地貌按像素进行分类的一些局限性。
In this landscape-scale study we explored the potential for multitemporal 10-day composite data from the Vegetation sensor to characterize land cover types, in combination with Landsat TM image and agricultural census data. The study area ( 175 km by 165 km) is located in eastern Jiangsu Province, China. The Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) were calculated for seven 10-day composite n(VGT-S10) data from 11 March to 20 May 1999. Multi-temporal NDVI and NDWI were visually examined and used for unsupervised classification. The resultant VGT classification map at 1 km resolution was compared to the TM classification map derived from unsupervised classification of a Landsat 5 TM image acquired on 26 April 1996 at 30 m resolution to quantify percent fraction of cropland within a 1 km VGT pixel; resulting in a mean of 60% for pixels classified as cropland, and 47% for pixels classified as cropland/natural vegetation mosaic. The estimates of cropland area from VGT data and TM image were also aggregated to county-level, using an administrative county map, and then compared to the 1995 county-level agricultural census data. This landscape-scale analysis incorporated image classification (e. g. coarse-resolution VGT data, fine-resolution TM data), statistical census data (e. g. county-level agricultural census data) and a geographical information system (e. g. an administrative county map), and demonstrated the potential of multi-temporal VGT data for mapping of croplands across various spatial scales from landscape to region. This analysis also illustrated some of the limitations of per-pixel classification at the 1 km resolution for a heterogeneous landscape.