青藏高原植被遥感精细识别方法研究  

A fine-scale vegetation identification method on the Qinghai-Xizang Plateau incorporating coarse spatial resolution vegetation and environmental features using remote sensing

作　　者：张慧 朱文泉 史培军[3,4] 唐海萍 何邦科 刘若杨 杨欣怡 赵涔良 ZHANG Hui;ZHU Wenquan;SHI Peijun;TANG Haiping;HE Bangke;LIU Ruoyang;YANG Xinyi;ZHAO Cenliang(State Key Laboratory of Remote Sensing Science,Beijing Normal University,Beijing 100875,China;Beijing Engineering Research Center for Global Land Remote Sensing Products,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China;Academy of Disaster Reduction and Emergency Management,Ministry of Emergency Management and Ministry of Education,Beijing Normal University,Beijing 100875,China;Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China)

机构地区：[1]北京师范大学遥感科学国家重点实验室,北京100875 [2]北京师范大学地理科学学部北京市陆表遥感数据产品工程技术研究中心,北京100875 [3]北京师范大学应急管理部—教育部减灾与应急管理研究院,北京100875 [4]北京师范大学地理科学学部,北京100875

出　　处：《地理学报》2025年第1期12-27,共16页Acta Geographica Sinica

基　　金：第二次青藏高原综合科学考察研究(2019QZKK0606);国家自然科学基金重大项目(42192580,42192581)。

摘　　要：青藏高原植被具有高海拔及垂直地带性分布特征,这给植被遥感精细分类带来了诸多挑战,主体表现为特定植被类型之间的遥感可分性较差,解决之道是有效融入其他非遥感特征。本文发展了一种逐步融入粗空间分辨率植被与环境特征的植被遥感精细分类新方法,以提高分类的准确性和精细度。新方法首先筛选出对植被分类改善贡献更大、特征差异更明显的植被与环境特征,并利用这些特征采用广义相加模型计算获得各类别的先验概率,同时利用遥感特征进行机器学习分类以获得各类别的后验概率,最后将粗空间分辨率的先验概率利用贝叶斯算法对高空间分辨率的后验概率进行调整,从而计算出最终的精细分类结果。基于10m空间分辨率的Sentinel-2遥感数据、90~10000 m空间分辨率的植被与环境数据以及地面调查数据,选择青藏高原的祁连山区、黄河源区和横断山区对新方法进行应用,获得了10 m空间分辨率的植被精细分类结果。相较于仅使用遥感特征的分类结果,新方法的分类精度提升8%~24%。新方法为提高植被分类的准确性和精细度提供了有效的技术支撑,对青藏高原及其他类似地区的植被精细分类具有重要参考价值。Vegetation on the Qinghai-Xizang Plateau exhibits high-altitude and vertical zonation distribution characteristics,which pose significant challenges for fine-scale vegetation classification based on remote sensing.A major issue is the limited separability of remote sensing features among certain vegetation types,necessitating the effective integration of additional non-remote sensing features to improve separability.To address this problem,the present study developed a novel method for fine-scale vegetation remote sensing classification by progressively incorporating coarse spatial resolution vegetation and environmental features.This approach aims to improve both the accuracy and precision of classification.The new method comprises three primary components.First,vegetation and environmental features that substantially enhance vegetation classification and exhibit distinct feature differences are selected.These features are then used to calculate the prior probabilities for each class through a generalized additive model.Concurrently,machine learning classification with remote sensing features is employed to obtain the posterior probabilities for each class.Finally,by applying the Bayesian algorithm,the prior probabilities derived from coarse spatial resolution data are employed to adjust the posterior probabilities obtained from high spatial resolution data,resulting in refined classification outcomes.The method was rigorously tested and applied to the Qilian Mountains,Yellow River Source Area,and Hengduan Mountains on the Qinghai-Xizang Plateau.Sentinel-2 remote sensing data with a spatial resolution of 10 m,vegetation and environmental data with spatial resolutions ranging from 90 m to 10000 m,and ground survey data were utilized.The fine-scale vegetation classification results with a spatial resolution of 10 m were achieved.Compared to using only remote sensing features,the new method improved classification accuracy by 8%to 24%.This new classification method provides effective technical support for improving

关 键 词：青藏高原 遥感 环境特征 植被特征 植被分类 

分 类 号：Q948[生物学—植物学] TP751[自动化与计算机技术—检测技术与自动化装置]