基于深度学习的内蒙古河套灌区2000—2021年种植结构提取与动态  被引量:7

Extraction and dynamics of planting structure in Hetao Irrigation District of Inner Mongolia from 2000 to 2021 using deep learning

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作  者:张圣微[1,2,3] 杨林 叶德成 张凤霞 白燕英 李河[4] 陈婧 方科迪 ZHANG Shengwei;YANG Lin;YE Decheng;ZHANG Fengxia;BAI Yanying;LI He;CHEN Jing;FANG Kedi(College of Water Conservancy and Civil Engineering,Inner Mongolia Agricultural University,Hohhot 010018,China;Key Laboratory of Water Resources Protection and Utilization of Inner Mongolia Autonomous Region,Hohhot 010018,China;Autonomous Region Collaborative Innovation Cente for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River,Hohhot 010018,China;Inner Mongolia Autonomous Region Water Resources Development Center,Hohhot 010020,China)

机构地区:[1]内蒙古农业大学水利与土木建筑工程学院,呼和浩特010018 [2]内蒙古自治区水资源保护与利用重点实验室,呼和浩特010018 [3]黄河内蒙古段水资源与水环境综合治理自治区协同创新中心,呼和浩特010018 [4]内蒙古自治区水利事业发展中心,呼和浩特010020

出  处:《农业工程学报》2023年第18期142-150,共9页Transactions of the Chinese Society of Agricultural Engineering

基  金:国家重点研发计划项目(2021YFC3201201);国家自然科学基金项目(52279017);内蒙古自治区科技计划项目(2020CG0054,2022YFDZ0050);内蒙古自治区高校创新科研团队资助计划(NMGIRT2313)。

摘  要:河套灌区是中国重要的粮油生产基地,掌握其多年种植结构的时空演化,不但可以为探讨农田产量和灌溉用水量提供数据,还可以为未来农业结构调整提供科学依据。该研究基于Landsat-5和Landsat-8遥感影像,计算得到灌区年内时间序列归一化植被指数(normalized differnce vegetation index,NDVI)数据,对影像缺失区域则利用MODIS NDVI数据进行插补,在此基础上还计算得到了光谱特征、纹理特征等数据。构建了多层感知机(multilayer perceptron,MLP)神经网络的深度学习算法,并将模型进行年际迁移从而得到2000—2021年的种植结构,进行多年时空变化分析。结果表明,2021年总体分类精度达到89%,Kappa系数为0.87,其中玉米、小麦和蔬菜等主要作物的分类精度高于90%,与统计种植面积比较,相对误差在2%~8%之间。2000—2021年间,葵花、玉米的种植面积呈上升趋势,小麦种植面积大量缩减;研究时段内葵花种植面积发生演变较大,玉米次之,均主要由小麦转入。该模型具有较高的分类准确性及迁移效果,可为内蒙古河套灌区农业管理、资源合理利用提供参考。Hetao Irrigation District is one of the most important grain and oil production bases in China.The spatial and temporal evolution of the cropping structure over the years can provide the basic data for the yield of farmland and the amount of water for irrigation in the future adjustment of agricultural structures.In this study,the errors from the different sensors were calibrated to calculate the normalized difference vegetation index(NDVI)of the irrigation area for the whole year using Landsat-5 and Landsat-8 remote sensing images.The MODIS NDVI data was also used to interpolate the missing areas.The MODIS NDVI data was interpolated and S-G filtered for the missing areas of the image.The spectral and texture features were also calculated after that.The samples were then augmented to obtain 13004 samples with a uniform distribution.The deep learning of the multilayer perceptron(MLP)neural network was constructed to adjust the MLP model for the actual planting and climatic conditions.The model was then migrated each year to obtain the planting structure from 2000 to 2021.Finally,a systematic analysis was made of the multi-year spatial and temporal changes.The results showed that the overall classification accuracy reached 89%in 2021,and the Kappa coefficient was 0.87.Overall,the classification accuracy was ranked in descending order as follows:wheat,maize,alfalfa,vegetables,sunflower(melons),and rice.Specifically,the accuracies of the rice and melons were lower at 0.86 and 0.88,respectively,in terms of the user’s accuracy indexes.All accuracies were higher than 90%in the main crops of maize,wheat,and alfalfa.The relative error of the area in each year was less than 8%,compared with the statistical planting area.The smaller relative error was found in the sunflower,corn and wheat,whereas,the larger one was vegetables and melons(5%to 8%).A comparison of the MLP classification revealed that there was a relatively high consistency of the spatial distribution.Meanwhile,the contiguous planting area was distributed in

关 键 词:遥感 深度学习 物候差异 作物识别 河套灌区 

分 类 号:S127[农业科学—农业基础科学]

 

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