河南省冬小麦快速遥感制图

doi:10.3724/SP.J.1047.2017.00846

摘要/Abstract

摘要：

在省域尺度上,冬小麦遥感识别中存在冬小麦物候不一致、地表环境复杂、数据处理复杂、遥感数据冗余、选择适当的分类样本困难、分类精度低等问题,而遥感数据云平台为解决这些问题提供了良好的数据基础和数据处理能力。以河南省为研究区,以谷歌地球引擎(Google Earth Engine)云平台为支撑,基于2015年和2002年前后年份河南省冬小麦识别关键期内的2296景Landsat遥感影像,采用NDVI重构增幅算法建立冬小麦大区域遥感快速制图模型,实现了2015年和2002年的河南省冬小麦分布制图。结果表明：2015年和2002年冬小麦种植面积分别为56 055.79 km²和47 296.11 km²,与统计数据比,精度达到97%;2002-2015年,河南省冬小麦种植分布存在明显变化,总体播种面积呈增加趋势,2015年比2002年增加8759.69 km²,增幅为18.52%。与传统计算机冬小麦制图方法相比,基于Google Earth Engine云平台的数据处理和制图效率均获得千倍以上的提升。

关键词: Google Earth Engine, Landsat, 河南省, 冬小麦, 遥感

Abstract:

At present, there are several problems in mapping winter wheat using remote sensing technology at regional scale. These problems can be the differences in phenology of winter wheat, complex ground environment and data-processing, redundant remotely sensed data, difficulty of choosing appropriate samples and low accuracy. In order to solve these problems, a novel method was proposed and tested in Henan province. 2296 scenes of Landsat images in 2002 and 2015 were processed using Google Earth Engine. Google Earth Engine is the most advanced cloud-based geospatial processing platform in the world. It combines Google-scale storage and processing power in order to make substantial progress on global challenges involving large geospatial datasets. A novel method called Normalized Difference Vegetation Index (NDVI)-remodel-amplification was proposed to construct a universal model for mapping winter wheat at regional scale. The steps of the method is as follows: Landsat images from September 15 to November 15 were chosen to compute NDVI. Then, we selected the minimum NDVI as the first sequence of NDVI (recorded as NDVI₁) at the pixel scale. In the same way, Landsat images from December 1st to March 31st were chosen to compute NDVI. Then, we selected the maximum NDVI as the second sequence NDVI (recorded as NDVI₂) at the pixel scale. Then, amplification between NDVI₁ and NDVI₂ was computed and recorded as NDVI_increase. A pixel would be regarded as winter wheat if its NDVI_increase value is more than 1.3 and its NDVI₂ value is more than 0.34. The results showed that winter wheat is mainly located in the middle-eastern plains and in Nanyang basin of Henan province with the characteristics of concentrated and contiguous distribution. The planting area of winter wheat in 2015 and 2002 was 56 055.79 km² and 47 296.11 km², respectively, with an accuracy of 97% based on statistical data. From 2002 to 2015, there was a significant change in the distribution of winter wheat in Henan Province The trend of overall sown area was increasing. Compared with that in 2002, the area of winter wheat in 2015 increased by 8759.69 km² or 18.52%. Comparing with conditional winter wheat mapping method, this proposed method is based on Google Earth Engine showing a great improvement in both of data-processing and mapping efficiency.

Key words: Google Earth Engine, Landsat, Henan province, winter wheat, remote sensing

王九中, 田海峰, 邬明权, 王力, 王长耀. 河南省冬小麦快速遥感制图[J]. 地球信息科学学报, 2017, 19(6): 846-853.

WANG Jiuzhong,TIAN Haifeng,WU Mingquan,WANG Li,Wang Changyao. Rapid Mapping of Winter Wheat in Henan Province[J]. Journal of Geo-information Science, 2017, 19(6): 846-853.

图/表 5

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