摘要
复杂地形地区农作物空间分布信息的精准监测对指导农业生产精细化管理、合理分配资源具有重要意义,而农作物分布零散、空间异质性高是精细分类的难点。本文旨在探索复杂地形条件下,多时相高分辨率卫星资料的多作物同步精细识别的方法,以期为重庆市域乃至西南低山丘陵复杂地区作物识别提供理论和现实依据。选取重庆市渝西地区为研究区,采用多尺度分割算法,将同种地物类型的田块进行同质单元构建,从而避免基于像素分类过于零散的分类结果,实现高精度分类。再利用多时相Sentinel-2/MSI遥感影像有效挖掘主要粮食农作物生育期内物候规律和特征参数,构建NDVI、RVI和NDWI等光谱指数,对比地面样本点不同作物类型不同生育期的遥感光谱信息差异,联合地形特征以确定农作物识别的最优组合,从而建立面向对象的决策树逻辑分类规则集提取主要粮食农作物种植区。结果表明:(1)采用多尺度分割方法使农作物识别在田块基础上进行,在30分割尺度下、紧凑度因子和形状因子均为0.5时,植被边缘分割最优;(2)结合农作物物候期发育特征,选用4月NDWI、6月RVI、NDVI和8月NDVI等光谱特征指数,联合海拔高度、坡度等地形特征建立目标地物判别阈值,构建面向对象决策树分类模型,总体精度达到90.8%,水稻、玉米、红薯的分类精度分别为85.7%、83.3%和80.7%,说明多种光谱指数联合地形特征的作物种植面积识别方法达到较高的识别精度,具有实践意义。
Accurate monitoring of crop spatial distribution in topographically complex areas is of great significance for guiding agricultural management production and reasonable allocation of resources.However,scattered crop distribution and high spatial heterogeneity pose challenges for precise classification.The objective of this study was to explore a method for simultaneous precise identification of multiple crops with multi-temporal high-resolution satellite data under complex terrain conditions,thus further providing a theoretical and practical basis for crop identification in Chongqing city area and the complex low hills area in southwest China.The western Chongqing was selected as the study area.First,a multi-scale segmentation algorithm was adopted to construct homogeneous units from fields of the same feature type.This approach helped to achieve high accuracy classification by avoiding overly fragmented classification results based on pixel classification.Secondly,the multi-temporal Sentinel-2/MSI remote sensing images were used to explore the weathering patterns and characteristic parameters during the fertility period of major food crops.The spectral indices,such as NDVI,RVI and NDWI,were constructed to compare the differences in remote sensing spectral information of different crop types at various fertility periods based on ground sample points.Such information was then combined with the topographic features to determine the optimal combination for crop identification.Finally,an object-oriented decision tree logical classification rule set was established to extract major grain crop growing areas.The results showed that,(1)the multi-scale segmentation method was an effective approach that can make crop identification on a field basis.In addition,the vegetation edge segmentation was optimal at a segmentation scale of 30 and a compactness and shape factor of 0.5.(2)The target feature discrimination threshold was established by combining the spectral feature indices of NDWI in April,RVI in June,NDVI in August a
作者
范莉
王妍
祝好
张继
FAN Li;WANG Yan;ZHU Hao;ZHANG Ji(Chongqing Institute of Meteorological Sciences,Chongqing 401147,China;Chongqing Engineering Research Center of Agrometeorology and Satellite Remote Sensing,Chongqing 401147;Chongqing Academy of Agricultural Sciences,Chongqing 401329)
出处
《中国农业气象》
CSCD
北大核心
2023年第9期845-856,共12页
Chinese Journal of Agrometeorology
基金
重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX0588)
重庆市气象部门业务技术攻关项目(YWJSGG-202315)
重庆市气象部门智慧气象技术创新团队项目(ZHCXTD-202022)
重庆市技术创新与应用示范专项社会民生类重点项目(cstc2019jscx-gksb X0138)。
