基于遥感时序物候特征的耕地非粮化多模式监测方法  

Multi-modal recognition method for non-grain cropland using remote sensing time series

作　　者：杨悦 杨贵军 龙慧灵[3] 张静 陈伟男[2] 高美玲 杨耘 YANG Yue;YANG Guijun;LONG Huiling;ZHANG Jing;CHEN Weinan;GAO Meiling;YANG Yun(School of Land Engineering,Chang’an University,Xi’an 710054,China;College of Geological Engineering and Geomatics,Chang'an University,Xi'an 710054,China;Key Laboratory of Quantitative Remote Sensing in Agriculture,Ministry of Agriculture and Rural Affairs,Information Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China)

机构地区：[1]长安大学土地工程学院,西安710054 [2]长安大学地质工程与测绘学院,西安710054 [3]农业农村部农业遥感机理与定量遥感重点实验室,北京市农林科学院信息技术研究中心,北京100097

出　　处：《农业工程学报》2024年第2期283-294,共12页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(42171303)。

摘　　要：耕地非粮化对粮食生产和农业可持续发展构成潜在威胁,精准监测不同的耕地非粮化类型对制定针对性的农业管理政策至关重要。该研究以河北省石家庄市藁城区为研究区,首先采用最大类间方差算法(OTSU)提取果园和耕地范围,然后利用Google Earth Engine(GEE)云计算平台构建了基于Sentinel-2遥感数据的特征集,包括光谱特征、物候特征和NDVI(normalized difference vegetation index)时序特征。结合面向对象分割和随机森林(radom forest, RF)、时间加权的动态时间规整(time-weighted dynamic time warping, TW-DTW)算法,构建了4种不同的分类模式用于提取粮食作物和露天蔬菜、大棚种植等非粮食作物。通过选择最优模式,提取了研究区2019-2022年间不同非粮化类型的空间分布信息,并探讨了不同模式的优点和局限性。结果表明:1)采用面向对象的机器学习模式进行耕地内作物分类的精度最佳,两个生长季内总体精度分别达到93.23%和90.10%,Kappa系数分别达到0.91和0.88;2)基于时间序列匹配的模式在区分粮食作物和其他地类方面表现出较高的准确性,冬小麦、玉米和大豆的用户精度分别高于95.60%、74.70%、82.70%,制图精度分别高于97.70%、86.40%、93.10%;3)利用面向对象的机器学习模式进行耕地非粮化信息提取,在两个作物生长季的总体精度为87.00%和81.00%。分析耕地非粮化结果发现,藁城区2019-2022年的年际性非粮化面积为2 753.09 hm^(2),其中果园占比最高;而季节性非粮化结果显示,秋粮非粮化面积(3 174.86 hm^(2))明显高于夏粮非粮化面积(1 060.27 hm^(2))。该研究利用Sentinel-2时序遥感数据,为一年两熟区耕地非粮化监测提供一种新的思路,可以为制定差异化农业管理政策提供依据。Large-scale non-grain cultivation has posed a serious risk to national grain security.Non-grain cultivated land can also deteriorate the ecological environment,such as soil quality,greenhouse gas emissions and agricultural pollution.Therefore,it is of great significance for the accurate monitoring of non-grain cultivated land using remote sensing data.However,the existing research has focused mainly on the distribution of specific crop types or cash crop expansion.It is still lacking in the non-grain croplands and their spatial distribution.In this study,the remote sensing time series were selected to identify the non-grain croplands in Gaocheng District,Shijiazhuang City,Hebei Province,China.The spatial distribution of non-grain cropland was also obtained from^(2)019 to 2022.The results demonstrated that:1)Both pixel and object-oriented machine learning showed a high classification accuracy to identify non-grain cultivated land.Spectral bands were integrated to extract the phenological features from the NDVI time series.The growth status and temporal patterns of different crops were captured to improve the classification accuracy.The low accuracy was also found in the time series matching on the open-field vegetables,greenhouses,and uncultivated croplands,due to the high within-class variability.A better performance was achieved to distinguish between grain crops and other land cover classes.2)The pixel approach was more sensitive to the land cover at the pixel level,thus capturing the differences among various types of land cover.The object approach effectively reduced the salt-and-pepper noise,and then significantly improved the confusion among land cover categories with the high within-class variability.3)The object machine learning exhibited the highest accuracy for the classification and identification of non-grain cropland conversion,with overall accuracies of 87.00%and 81.00%for two growing seasons,respectively.4)The annual non-grain cropland area was 2753.09 hectares,with the orchards accounting for up t

关 键 词：遥感 时间序列 耕地非粮化 机器学习 时间加权的动态时间规整 

分 类 号：P237[天文地球—摄影测量与遥感]