摘要
为高效提取高精度水稻种植分布及其面积,该研究基于谷歌地球引擎(Google Earth Engine,GEE)平台,以辽宁省盘锦市为研究区,利用2020年Sentinel-2影像提取水稻生命周期内4个水稻物候期相应的光谱指数,利用简单非迭代聚类(Simple Non-Iterative Clustering,SNIC)算法来分割影像,灰度共生矩阵(Gray Level Co-occurrence Matrix,GLCM)来计算纹理特征,结合支持向量机(Support Vector Machine,SVM)和随机森林(Random Forest,RF)算法构建6种不同的模型进行水稻种植分布提取,并基于目视解译及实地调查数据,对比6种模型提取水稻的验证精度和实测精度,确定最优模型。结果表明:在水稻种植分布提取中,面向对象方法有助于提高水稻种植分布提取精度,且RF算法优于SVM算法。其中SNIC图像分割结合RF模型具有最高提取精度,总体精度和Kappa系数分别为96.83%、0.934,经实测数据验证,水稻实测精度为95.43%,可满足区域水稻种植分布和面积监测需求。
The planting distribution of paddy rice has been widely extracted to identify the flooding characteristics of paddy rice during the transplanting period.Optical remote sensing images can be mostly used as the data source at present.However,the transplanting period cannot characterize full spectral characteristics of the paddy rice lifespanthe,spectral characteristics of paddy rice should be considered in multiple phenological periods.The pixel-based extraction of paddy rice planting distribution can also be susceptible to the data source noise.Fortunately,an object-oriented extraction can be selected to effectively reduce the impact of data source noise in the field.Since the remote sensing images are limited by the acquisition and processing costs,only a few studies focused on the application of multiple paddy rice phenological stages in the extraction of paddy rice planting distribution in large areas.Alternatively,the emergence of the Google Earth Engine(GEE)platform in earth science data and analysis can be accessible to large amounts of remote sensing data for free and with high efficiency.Taking Panjin City,Liaoning Province of China as the research area,this study aims to realize the object-oriented extraction of rice planting areas using phenological features from the GEE platform.Four phenological periods of paddy rice were selected,namely the sowing,transplanting,heading,and maturity period.Specifically,the sowing period with the Bare Soil Index(BSI)was selected from March 15 to April 30.The transplanting period was selected as the Green Chlorophyll Vegetation Index(GCVI)and the Modified normalized difference water index(MNDWI)from May 10 to June 20.The heading period was the Normalized Difference Red Edge Index(NDRE)and Normalized difference vegetation index(NDVI)from June 30 to September 10.The maturity period with the Plant Senescence Reflectance Index(PSRI)was selected from September 20 to October 20.The 2020 Sentinel-2 time series images were filtered to construct the datasets in the four paddy ric
作者
刘通
任鸿瑞
Liu Tong;Ren Hongrui(Department of Geomatics,Taiyuan University of Technology,Taiyuan 030024,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2022年第12期189-196,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
中国气象局沈阳大气环境研究所联合开放基金(2021SYIAEKFMS39)
山西省重点研发计划(国际科技合作)项目(201903D421089)。
