基于综合滤波的遥感LAI时序数据重建系统设计与实现

Design and Implementati on of LAI Time Series Data Reconstruction System Based on Integrated Filtering Method

叶面积指数(LAI)是生态研究中的一项重要参数,因大气等条件影响,遥感LAI时序数据存在着质量低、连续性差等问题。针对这种情况,提出了基于ArcGIS Engine和ENVI/IDL的LAI时序数据重建系统构建方案,采用C#/IDL混合编程技术,引入项目组提出的综合滤波算法,实现了遥感和GIS数据联合处理与分析,可识别和修复MODIS LAI时序产品的低质量数据和异常点,重建出整体稳定性较好的高质量数据。采用空间数据库引擎技术构建的LAI空间数据库,可实现对LAI遥感影像、土地覆盖数据、质量控制文件等异构数据的统一管理。LAI数据重建的系统化简化了操作步骤,并在可视化处理的同时,高效地为全球或区域环境变化、植被动态监测、土地覆盖变化和物候信息提取等生态环境研究提供更加完善的基础数据。

Leaf area index(LAI)is a crucial parameter in ecological research.Remotely sensed LAI time series data suffers from poor atmospheric conditions,which leads to problems such as low quality and poor continuity.In response to this situation,a data reconstruction system based on ArcGIS Engine and ENVI/IDL was proposed.The system realizes the joint processing and analysis of remote sensing and GIS data with C#/IDL mixed programming and the integrated filtering method proposed by the project team.And it can identify and repair the low-quality parts and outliers of MODIS LAI time series products and reconstruct high-quality data with better overall stability.The LAI spatial database constructed by the spatial database engine can achieve the unified management of various heterogeneous data such as LAI remote sensing images,land cover data,quality control files,etc.It can also efficiently provide more complete basic data for eco-environment researches such as global or regional environmental changes,vegetation dynamic monitoring,land cover change and phenological information extraction during visualizing processing.