基于GF-2 PMS影像和随机森林的甘肃临夏花椒树种植监测  

Monitoring of Zanthoxylum bungeanum Maxim planting using GF-2 PMS images and the random forest algorithm:A case study of Linxia,Gansu Province

作　　者：柳明星 刘建红[1,2] 马敏飞 蒋娅 曾靖超 LIU Mingxing;LIU Jianhong;MA Minfei;JIANG Ya;ZENG Jingchao(College of Urban and Environmental Science,Northwest University,Xi’an 710127,China;Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity,Northwest University,Xi’an 710127,China)

机构地区：[1]西北大学城市与环境学院,西安710127 [2]西北大学陕西地表系统与环境承载力重点实验室,西安710127

出　　处：《自然资源遥感》2022年第1期218-229,共12页Remote Sensing for Natural Resources

基　　金：国家自然科学基金项目“农作物物候遥感反演方法的适用性研究”(编号:41401494);陕西省教育厅自然科学基金项目“基于遥感时间序列数据的复种指数自动提取方法改进及其应用”(编号:14JK1475)共同资助。

摘　　要：在目前植被遥感监测中,大类作物往往得到更多的关注,而对于某些具有重要生态功能和经济效益的小类树种却明显关注不足。花椒树是我国重要但小众的生态树种,其果实花椒是常见的油料和药用原料。对花椒树种植信息进行及时、准确的监测,对当地生态、经济和社会的协调发展至关重要。该文基于GF-2 PMS影像和随机森林算法探讨了花椒树遥感监测的可行性。结合光谱波段、归一化植被指数、纹理特征以及数字高程模型共4种分类特征,分别设计了3种分类方案。通过分析各分类方案的分类精度,进一步探讨了不同分类特征在花椒种植识别中的作用。研究结果表明,仅使用光谱波段时,总体精度最低,为65.90%;增加归一化植被指数和数字高程模型特征时,总体精度小幅提升,为67.67%;进一步增加纹理特征,总体精度大幅提高为74.43%,说明纹理特征的重要性。基于最优分类方案的结果显示,2018年研究区内花椒树主要种植在黄河沿岸和刘家峡库区周边,其总种植面积为231.59 km^(2),占研究区总面积的22.56%。其中,仅种植花椒树地块的面积为189.06 km^(2),混合种植花椒树的地块面积为42.53 km^(2)。90%以上的花椒树分布在[1683,2300)m海拔范围内,且随着海拔升高呈现出“先减少-再增加-再减少”的变化趋势;58%的花椒树分布在[8,25)°坡度范围内。总的来说,GF-2 PMS影像在花椒树种植监测中具有较大的潜力。开发对花椒树的遥感识别方法,不仅有助于当地生态产业调控和后续生态工程布局,并且对其他地区的生态树种或小类植被物种开展相关遥感监测工作也具有较强的借鉴意义。Major crops tend to receive far more attention in current remote sensing(RS)monitoring of vegetation than minor tree species with ecological and economic benefits.Zanthoxylum bungeanum Maxim(ZBM)is an important but niche ecological tree,and its fruits are common oil and medicinal materials.It is vital for the sustainable development of local economy,ecology,and society to obtain accurate information of planting area and spatial distribution ZBM in time.Using the GF-2 PMS images and the random forest algorithm,this study discussed the feasibility of RS monitoring of ZBM planting.Three classification schemes were designed using four classification features,namely spectral bands,normalized difference vegetation index(NDVI),textural features,and digital elevation model(DEM).Furthermore,this study explored the role of different classification features in identifying ZBM by analyzing the classification accuracy of the schemes.Results show that it is difficult to obtain satisfactory classification accuracy when only spectral band characteristics were used(overall accuracy:65.90%).Combining NDVI and DEM with the spectral band characteristics can slightly improve the classification effect(overall accuracy:67.67%).After textural features were further combined,the overall accuracy was greatly increased(74.43%).This indicates that textural features play an important role in monitoring ZBM planting.As revealed by the results of the optimal classification scheme,ZBM in Linxia,Gansu Province is mainly distributed along the Yellow River and around the Liujiaxia Reservoir,with a total area of 231.59 km^(2),which accounts for 22.56%of the total area of the study area.The area of ZBM planted in the patterns of single cropping and mixed cropping is 189.06 km^(2) and 42.53 km^(2),respectively.More than 90%of ZBM grows at an elevation of[1683,2300)m and its number tends to decrease,increase,and decrease successively with an increase in the elevation.Moreover,58%of ZBM are planted in regions with a slope of[8,25)°.Overall,GF-2 PMS ima

关 键 词：GF-2 PMS影像 花椒树 随机森林 生态工程 种植监测 

分 类 号：S573.9[农业科学—作物学] S127