河南省冬小麦快速遥感制图
发布时间：2018-11-12 16:13
【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 km~2和47 296.11 km~2,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 km~2,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。
[Abstract]:On the provincial scale, winter wheat remote sensing recognition has many problems such as inconsistent phenology, complex surface environment, complex data processing, redundancy of remote sensing data, difficulty in selecting suitable classification samples and low classification accuracy. The remote sensing data cloud platform provides a good data base and data processing ability for solving these problems. Based on the (Google Earth Engine) cloud platform of Google Earth engine, the 2296 Landsat remote sensing images of winter wheat in Henan Province during the critical period of winter wheat recognition in 2015 and 2002 were used as the research area. The fast remote sensing mapping model of winter wheat in large area was established by using NDVI reconstruction increment algorithm, and the distribution mapping of winter wheat in Henan Province in 2015 and 2002 was realized. The results showed that the planting area of winter wheat in 2015 and 2002 was 56 055.79 km~2 and 47 296.11 km~2, respectively, and the precision was 97% compared with the statistical data. The planting distribution of winter wheat in Henan Province changed obviously from 2002 to 2015, and the total sowing area showed an increasing trend, and the increase of 8759.69 km~2, was 18.52% in 2015 than in 2002. Compared with the traditional computer mapping method of winter wheat, the efficiency of data processing and mapping based on Google Earth Engine cloud platform has been improved more than 1000 times.
【作者单位】： 北京林业大学;中国科学院遥感与数字地球研究所;中国科学院大学;
【基金】：国家自然科学基金项目(4130139、41371358) 河北省自然科学基金项目(D2015207008)
【分类号】：S127;S512.11
本文编号：2327558
[Abstract]:On the provincial scale, winter wheat remote sensing recognition has many problems such as inconsistent phenology, complex surface environment, complex data processing, redundancy of remote sensing data, difficulty in selecting suitable classification samples and low classification accuracy. The remote sensing data cloud platform provides a good data base and data processing ability for solving these problems. Based on the (Google Earth Engine) cloud platform of Google Earth engine, the 2296 Landsat remote sensing images of winter wheat in Henan Province during the critical period of winter wheat recognition in 2015 and 2002 were used as the research area. The fast remote sensing mapping model of winter wheat in large area was established by using NDVI reconstruction increment algorithm, and the distribution mapping of winter wheat in Henan Province in 2015 and 2002 was realized. The results showed that the planting area of winter wheat in 2015 and 2002 was 56 055.79 km~2 and 47 296.11 km~2, respectively, and the precision was 97% compared with the statistical data. The planting distribution of winter wheat in Henan Province changed obviously from 2002 to 2015, and the total sowing area showed an increasing trend, and the increase of 8759.69 km~2, was 18.52% in 2015 than in 2002. Compared with the traditional computer mapping method of winter wheat, the efficiency of data processing and mapping based on Google Earth Engine cloud platform has been improved more than 1000 times.
【作者单位】： 北京林业大学;中国科学院遥感与数字地球研究所;中国科学院大学;
【基金】：国家自然科学基金项目(4130139、41371358) 河北省自然科学基金项目(D2015207008)
【分类号】：S127;S512.11
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