摘要
为减少利用遥感光谱特征解译农作物过程中样本选取的主观性误差,同时克服现场调研样本数据不充分的不足。该文基于玉田县物候特征,选择研究所需4个遥感时相,进而结合4时相中耕地地表覆盖状态(绿地和裸地)以及4时相叠置后绿地、裸地的地类组合特征,建立农作物解译标志,从而提取主要的农作物种植结构信息。根据该方法提取的研究区主要作物冬小麦、夏玉米和大白菜的面积与统计数据的相对误差分别为4.01%、3.25%和4.16%,且空间分布符合与实际情况;提取结果总体精度和Kappa系数分别为86.19%和0.83。
In order to reduce the subjective error of sample selection in the process of interpreting crops by remote sensing spectral characteristics,and overcome the insufficiency of field investigation sample data,a method was proposed.In this research,we proposed a four-phase remote sensing method based on phenological feature selection in the study area,and then combined the four-phase land cover with the state of green land and bare land,and the combination characteristics of green land and bare land after four-phase overlap,to establish the interpretation markers of crops,so as to extract the main crop planting structure.Judging by the difference of the materials,it is determined that the main crops are winter wheat,summer corn and Chinese cabbage.The relative errors between the area of winter wheat,summer maize and Chinese cabbage extracted by this method and the statistical data were 4.01%,3.25%and 4.16%,and the spatial distribution accorded with the theory and the actual situation;the overall accuracy and Kappa coefficient of the extracted results were 86.19%and 0.83 respectively,which accorded with the accuracy discrimination principle of remote sensing image.
作者
郭力娜
李帅
牛振国
曹应举
曲衍波
GUO Lina;LI Shuai;NIU Zhenguo;CAO Yingju;QU Yanbo(College of Mining Engineering,North China University of Science and Technology,Tangshan,Hebei 063009,China;State Key Laboratory of Sensing,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101,China;School of Public Administration and Policy,Shandong University of Finance and Economics,Jinan 250014,China)
出处
《测绘科学》
CSCD
北大核心
2019年第10期50-58,共9页
Science of Surveying and Mapping
基金
国家自然科学基金项目(41771560)
河北省教育厅优秀青年基金项目(YQ2014016)
河北省高等学校人文社会科学研究项目(SD181066)
关键词
农作物
物候差
遥感时相
地类组合
玉田
crops
phenological difference
remote sensing phase
land class combination
Yutian
