摘要
精细时空尺度的植被监测日趋成为农、林、生态、环境等领域的重要技术手段。低空无人机植被定量遥感是精细尺度植被监测的最有效手段之一。相对于卫星遥感,无人机遥感凭借其特有的数据采集机制和数据属性,逐步形成了相对独立的技术方法体系。自2010年起,低空无人机植被定量遥感相关研究呈现爆发式增长态势,然而,现有研究对其知识体系的系统梳理仍显不足,在理论框架、技术体系、关键科学与技术问题等方面仍存在明显的碎片化特征。为此,本研究从低空无人机植被定量遥感的核心目标出发,系统梳理了其技术链条与知识体系,重点分析了其在主被动遥感数据获取、数据预处理、遥感建模、植被要素监测4个关键环节的研究现状、最新进展与现存问题,并着重从数据预处理与植被要素监测两个方面,探讨了未来研究的前沿挑战及潜在的解决路径。随着“低空经济”被纳入国家战略布局,低空无人机植被定量遥感技术凭借其独特优势,正在农、林、生态、环境、应急等领域展现出日益重要的应用价值与技术不可替代性。
Spatiotemporal fine-scale vegetation monitoring has been a key component in applications across agriculture,forestry,ecology,and environmental fields.Low-altitude Unmanned Aerial Vehicle(UAV)-based quantitative remote sensing of vegetation is one of the most effective techniques for fine-scale monitoring.The advantages such as ultra-high spatial resolution,outstanding flight flexibility,highfrequency data acquisition capability,capability of obtaining multi-modal data,and exceptional geographical accessibility,enable UAVbased remote sensing can be tailored for specific ecological,agricultural and other needs.Compared to satellite-based remote sensing.UAV remote sensing exhibits significant differences in data acquisition modes(such as radiometric and geometric imaging,laser scanning)and data characteristics(such as ground sampling distance,data richness,and data modality),gradually leading to a unique methodology and technical framework.Since 2010,the UAV remote sensing-related research has grown explosively.A significant increase in publications,technical innovations,and interdisciplinary applications has demonstrated its expanding impact across various scientific and industrial domains.However,a comprehensive and systematic exposition of its knowledge structure is still lacking.In particular,its theoretical scope,technical framework,current advance,key challenges and potentials remain fragmented.Despite numerous studies focusing on specific UAV applications,a holistic synthesis is required to integrate scattered knowledge and establish a standardized methodology for vegetation monitoring.To address this,the present paper introduced the basic objectives of UAV-based quantitative remote sensing of vegetation,presented its technical chain and knowledge hierarchy.The paper systematically summarized the current status,advances,and unresolved issues in four key areas:active and passive remote sensing data acquisition(including UAV platforms,typical sensors,flight mode and configuration),data pre-processing(including image radiometric correction,image geometric correction,and point cloud pre-processing),physically and empirically-based modeling(including ultra-high-resolution radiative transfer modelling and machine learning modelling),and vegetation properties monitoring(including retrieval of vegetation variables,object recognition of vegetation and geometric measurement of vegetation),where each of these areas plays a crucial role in improving the reliability and applicability of UAV-based vegetation monitoring.Furthermore,the study identified several key challenges and potential solutions,particularly in data preprocessing(such as radiometric correction in complex illumination conditions and real-time onboard geometric correction)and vegetation properties monitoring(such as centimetric-resolution retrieval of vegetation variables,fundamental models or highly-generalizable task-oriented deep learning models).Against the backdrop of the Chinese national strategy“low-altitude economy”,low-altitude UAV-based quantitative remote sensing of vegetation is poised to play an increasingly irreplaceable role in agriculture,forestry,ecology,environmental management,and emergency response fields.
作者
李林源
黄华国
穆西晗
阎广建
漆建波
严正兵
江佳乐
杨浩
肖青
LI Linyuan;HUANG Huaguo;MU Xihan;YAN Guangjian;QI Jianbo;YAN Zhengbing;JIANG Jiale;YANG Hao;XIAO Qing(State Key Laboratory to Efficient Production of Forest Resources,Beijing Forestry University,Beijing 100083,China;State Key Laboratory of Remote Sensing and Digital Earth,Beijing Normal University,Beijing 100875,China;Key Laboratory of Vegetation and Environmental Change,Institute of Botany,Chinese Academy of Science,Beijing 100093,China;School of Atmospheric Science,Sun Yat-Sen University,Zhuhai 519082,China;National Engineering Research Center for Information Technology in Agriculture,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China;Research Center for UAV Application and Control,Chinese Academy of Science,Beijing 100101,China)
出处
《遥感学报》
北大核心
2025年第6期2083-2113,共31页
NATIONAL REMOTE SENSING BULLETIN
基金
国家重点研发计划(编号:2024YFF1306201)
遥感与数字地球全国重点实验室开放课题(编号:OFSLRSS202423)
国家自然科学基金(编号:42101328)。
关键词
无人机定量遥感
辐射与几何预处理
统计与物理建模
超高分辨率植被参数反演
植被目标识别与量测
UAV-based remote sensing of vegetation
radiometric and geometric correction
physically and empirically-based modelling
ultra-high-resolution retrieval of vegetation properties
vegetation object identification and measuring
作者简介
第一作者:李林源,研究方向为低空无人机植被定量遥感与应用。E-mail:lilinyuan@bjfu.edu.cn;通信作者:黄华国,研究方向为林业定量遥感与应用。E-mail:huaguo_huang@bjfu.edu.cn。
