2019-12ml 目录

林业遥感经历了航片判读、卫片目视解译、蓄积量定量估测阶段后，已经进入参数定量反演阶段。在林业对遥感业务化监测和精度提升的强烈需求下，定量遥感逐步与林业遥感交叉融合，林业遥感定量化研究的人才队伍、理论模型、数据源和应用方法逐渐成熟。本文提出了林业定量遥感的概念和框架，指出了其中关键的科学问题:（1）如何使遥感解译、建模和反演适应复杂的森林状况；（2）如何提高参数反演的准确度；（3）如何丰富林业遥感数据源；（3）如何发展更为智能化的遥感数据自动化算法。在介绍适合林业的定量遥感模型和通用反演方法的基础上，阐述了高光谱、热红外、激光雷达和微波遥感数据源的林业应用方法。未来林业定量遥感将在全波段数据统一建模和信息融合机制、机理模型反演、大数据融合等方面进行突破。 

Progress and perspective of quantitative remote sensing of forestry

Forestry remote sensing has entered the stage of quantitative inversion of parameters after air-photo interpretation, satellite visual interpretation and quantitative estimation of forest volume. Under the background of strong demands of remote sensing from forestry on operational monitoring and accuracy improvement, quantitative remote sensing is gradually integrated with forestry remote sensing. It has gradually matured in talent teams, theoretical models, data sources and application methods for the quantitative studies in forestry remote sensing. This paper puts forward the concept and framework of quantitative remote sensing of forestry (QRSF), and points out the key scientific problems: (1) how to adapt remote sensing interpretation, modeling and inversion to complex forest conditions; (2) how to improve the accuracy of parameter inversion; (3) how to enrich forestry remote sensing data sources; (3) how to develop highly intelligent and automated information extraction algorithm on remote sensing data. On the basis of introducing quantitative remote sensing models and general inversion methods suitable for forestry, the application methods of hyperspectral, thermal infrared, lidar and microwave remote sensing data sources in forestry are expounded. In the future, QRSF will make breakthroughs in the unified modeling of full-band data, information fusion mechanism, physical model inversion and large-scale data fusion.