基于Sentinel-2A影像的复合轮作种植结构提取
Extraction of compound rotation planting structure based on Sentinel-2A image
王钰 1董亚坤 1何紫玲 1王鹏 1赵昊 2曾维军1
作者信息
- 1. 云南农业大学水利学院,云南昆明 650201;自然资源部云南山间盆地土地利用野外科学观测研究站,云南昆明 650201;云南省智慧农业与水安全国际联合研发中心,云南昆明 650201
- 2. 自然资源部云南山间盆地土地利用野外科学观测研究站,云南昆明 650201;云南省智慧农业与水安全国际联合研发中心,云南昆明 650201;云南农业大学资源与环境学院,云南昆明 650201
- 折叠
摘要
[目的]利用遥感技术探索云南大理洱海流域复合轮作种植结构提取方法,为流域作物种植结构的调整和管理提供理论参考.[方法]对洱海流域的Sentinel-2A影像进行多尺度分割,在光谱和纹理等常用特征的基础上加入位置特征构建分类特征集,选用C5.0决策树算法挖掘分类规则,使用混淆矩阵进行分类精度评价.[结果]作物分类中,光谱特征Mean_Green为分类最重要的参数,位置特征Y_Center主要区分紫叶莴笋和玉米,X_Center为提取蚕豆的重要特征之一.依据C5.0决策树分类规则绘制大春和小春时期的作物精细地块图,大春作物分为水稻、玉米、紫叶莴笋及其他作物,小春作物分为蚕豆、油菜、紫叶莴笋及其他作物,得到复合轮作种植结构共16种,其中,水稻—蚕豆和玉米—蚕豆轮作占流域内耕地总面积比例最大,分别为29.54%和23.66%;水稻—油菜和玉米—油菜轮作所占面积较小,分别占耕地总面积的2.05%和1.58%;大春和小春时期均种植紫叶莴笋的区域占耕地总面积的4.26%;其余11种轮作方式面积之和为7726.77 ha,占耕地总面积的38.91%.大春作物分类总体精度为80.00%,Kappa系数为0.7142,小春作物分类总体精度为81.75%,Kappa系数为0.6983.[建议]提取复合轮作种植结构时,建议考虑C5.0决策树算法并加入位置特征提取作物信息,针对复合轮作种植结构存在分布不均等问题,建议当地农业部门加强宣传引导,遵循因地制宜的原则,优化调整洱海流域农作物种植结构,提高流域农业种植生态、经济与社会效益.
Abstract
[Objective]Remote sensing technology was used to explore the extraction method of compound rotation planting structure in Erhai Lake basin in Dali,Yunnan,which aimed to provide theoretical reference for the adjustment and management of crop planting structure in the basin.[Method]Multi-scale segmentation of Sentinel-2A image of Erhai Lake basin was conducted,and position features were added in addition to common features such as spectrum and texture to build a classification feature set.Then C5.0 decision tree algorithm was used to study classification rules,and confusion matrix was used to evaluate classification accuracy.[Result]In crop classification,the spectral feature Mean_Green was the most important parameter,the position feature Y_Center was mainly used to distinguish between purple leaf lettuce and corn,and X_Center was one of the important features for broad bean extraction.According to C5.0 decision tree clas-sification rules,fine plot maps of crops in the late spring and early spring were drawn.Late spring crops were divided into rice,corn,purple leaf lettuce and other crops,while early spring crops were divided into broad bean,oilseed rape,pur-ple leaf lettuce and other crops.Then,a total of 16 kinds of compound rotation planting structure were generated,among which rice-broad bean(29.54%)and corn-broad bean rotation(23.66%)accounted for the largest proportion of the total cultivated land area in the basin,while rice-oilseed rape and corn-oilseed rape rotation occupied only 2.05%and 1.58%of the total cultivated land area,which were smaller.The area planted with purple leaf lettuce in both late spring and early spring accounted for 4.26%of the total cultivated land area,and the total area using the remaining 11 rotation methods was 7726.77 ha,accounting for 38.91%of the total cultivated land area.The overall classification accuracy of late spring crops was 80.00%,and the Kappa coefficient was 0.7142.The overall classification accuracy of early spring crops was 81.75%,and the Kappa coefficient was 0.6983.[Suggestion]When extracting the compound rotation planting structure,it is suggested to consider the C5.0 decision tree algorithm and add position features to extract crop information.In view of the maldistribution of the compound rotation planting structure,it is recommended that local agricultural departments should strengthen publicity and guidance,optimize and adjust the crop planting structure in Erhai Lake basin by following the principle of adaptation to local conditions,so as to improve the ecological,economic and social benefits of agriculture planting in the basin.
关键词
洱海流域/面向对象/决策树/种植结构/复合轮作Key words
Erhai Lake basin/object orientation/decision tree/planting structure/compound rotation引用本文复制引用
基金项目
国家自然科学基金(41961040)
云南省农业联合专项面上项目(202101BD070001-101)
教育部产学合作协同育人项目(220504899113903)
云南农业大学一流本科课程建设项目(2021YLKC122)
出版年
2023
国家科技期刊平台
NSTL
维普
万方数据