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林农间作模式下和田绿洲特色林果结构遥感信息提取
引用本文:靳镜宇,白洁,包安明,杨涵,李均力,韩宏伟.林农间作模式下和田绿洲特色林果结构遥感信息提取[J].农业工程学报,2022,38(3):146-154.
作者姓名:靳镜宇  白洁  包安明  杨涵  李均力  韩宏伟
作者单位:1. 新疆师范大学地理科学与旅游学院,乌鲁木齐 830054; 2. 荒漠与绿洲生态国家重点实验室,中国科学院新疆生态与地理研究所,乌鲁木齐 830011; 5. 新疆维吾尔自治区测绘成果中心,乌鲁木齐 830002;;2. 荒漠与绿洲生态国家重点实验室,中国科学院新疆生态与地理研究所,乌鲁木齐 830011; 3. 新疆遥感与地理信息系统应用重点实验室,乌鲁木齐 830011;;4. 新疆林科院经济林研究所,乌鲁木齐 830000;
基金项目:中科院西部之光(2020-XBQNXZ-009);国家自然科学基金(U2003201);中国科学院创新交叉团队项目(JCTD-2019-20);新疆天山创新团队(2020D14016);2020年度青海省"昆仑英才高端创新创业人才-领军人才"项目联合资助
摘    要:实时准确地获取林农间作模式下的果树结构信息对推进新疆特色林果业的提质增效,增加农民收入,稳定脱贫攻坚成果,实现乡村振兴具有重要意义。该研究以新疆和田绿洲林农间作区为研究对象,提出一种综合高分遥感数据的纹理和光谱特征以及中分遥感数据的时序物候特征的果树提取方法。基于GF-2遥感数据利用面向对象方法提取高分辨率的林田地块空间信息;基于多时相Sentinel-2遥感数据构建植被归一化指数(Normalized Difference Vegetable Index,NDVI)时间序列产品,并依据果树物候特征建立决策树模型,提取间作核桃、纯核桃、枣树和葡萄4种类型;最后将多时相的分类结果与高分的林田地块叠加,获取和田绿洲特色林果作物分布信息。该方法对2020年和田绿洲核桃、枣树和葡萄提取结果的用户精度和总体分类精度均在90%以上,Kappa系数为0.95,能够满足县市级尺度的林果遥感监测精度需求。基于遥感提取的和田绿洲主要林果作物面积为4.28×105 hm2,以核桃(间作和纯核桃)为主,间作核桃面积占比63.80%,其次为枣树(19.38%)和葡萄(3.30%)。该方法可为林农间作立体种植模式下的果树类型和面积精确信息提取研究提供参考和借鉴。

关 键 词:多时相  面向对象  多尺度分割  林农间作  特色林果  种植结构  和田绿洲
收稿时间:2021/10/16 0:00:00
修稿时间:2022/1/16 0:00:00

Extraction of the fruit trees based on satellite imagery under the pattern of forest and crops interplanting in Hotan oasis
Jin Jingyu,Bai Jie,Bao Anming,Yang Han,Li Junli,Han Hongwei.Extraction of the fruit trees based on satellite imagery under the pattern of forest and crops interplanting in Hotan oasis[J].Transactions of the Chinese Society of Agricultural Engineering,2022,38(3):146-154.
Authors:Jin Jingyu  Bai Jie  Bao Anming  Yang Han  Li Junli  Han Hongwei
Institution:1. College of Geographic Sciences and Tourism, Xinjiang Normal University, Urumqi 830054, China; 2. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China; 5. Survey and Mapping Achievement Center of Xinjiang, Urumqi 830002, China;;2. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China; 3. Key Laboratory of RS & GIS Application Xinjiang, Urumqi 830011, China;; 4. Research Institute of Economic Forestry Xinjiang Academy of Forestry Sciences, Urumqi 830000, China;
Abstract:A planting area of fruit trees has accounted for up to 13% of the national planting area in Xinjiang, China. Local specialty fruits (e.g. walnut, jujube, apricot, fragrant pear, and apple) have been produced in four prefectures of Southern Xinjiang around the Tarim River Basin, accounting for more than 80% of the total fruit planting area in Xinjiang. It is very necessary to real-time and accurately acquire the fruit tree type and area information under the pattern of forest and crops interplanting, further to improve the quality and efficiency of the specialty fruit industry. Taking the continuous area of forest and crops interplanting in Hotan oasis of southern Xinjiang in China as an example, this study aims to extract the structure information of fruit trees from the satellite images. The high-resolution remote sensing image data with abundant texture and spectral characteristics was also integrated with the medium-resolution sensing image data with multi-temporal characteristics. Firstly, an object-oriented programming was used to extract the high-precision boundary of fruit trees parcel using GF-2 (PMS) image data. The GF-2 image data was divided into the winter (February) and summer-autumn (July-September). The Normalized Difference Vegetation Index (NDVI) was determined to identify the spectral and texture features between target objects and other ground objects. Four plots were obtained to gradually eliminate the non-target ground categories. Then, the NDVI time series products were constructed using multi-temporal Sentinel-2 image data. A decision tree model was established to extract the interplanting walnut, pure walnut, jujube, and grape orchard, according to the characteristics of phenology. There were many peaks and troughs in the NDVI time series curve of fruit trees in one year. The peak represented the flourishing period of fruit trees'' growth, and the trough reflected the orchard management (such as irrigation and pruning branches). There were some outstanding and different time windows for the NDVI timing series of pure walnut, interplanting walnut, jujubes, and grapes, particularly for the rarely staggered overlapping. These differences were greatly contributed to the classification of fruit trees. Finally, the multi-phase orchard classification was overlaid the high-resolution of fruit trees parcel, further to obtain the distribution of fruit crops in the study area. The research results show that the major area of fruit was 4.28×105 hm2 here, with 3.31×105 hm2 of the walnut orchard (including interplanting and pure walnut), 8.29×104 hm2 of jujube, and 1.40×104 hm2 of grapes. The area of interplanting walnuts accounted for 63.8% of the total fruit area, followed by jujube (19.38%), and grapes (3.3%). The user accuracy and overall classification accuracy were both exceed 90%, and the Kappa coefficient was 0.95, fully meeting the accuracy requirements of agroforestry classification at the county and city level. The relative accuracies of walnuts, jujube, and grapes were 62.1%, 97.8%, and 85.2%, respectively, using remote sensing extraction, compared with the forestry survey in 2019. The areas of jujube and grape using remote sensing extraction were close to the forestry survey data. The walnut planting areas in the Hotan area were mainly distributed in the upper reaches of Yurunkax and Karakax River with suitable soil and water conditions. The jujube trees were mostly distributed in the downstream oasis-desert ecotone, and the grapes were mostly distributed in the sandy desert of the lower reaches of the Karakax River. This finding can provide a strong reference for the fruit tree extraction under the pattern of forest and crop intercropping.
Keywords:multi-temporal  object-oriented  multi-scale segmentation  forest and crops intercropping  fruit tree  planting structure  Hotan oasis
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