基于遥感的植被覆盖变化景观分析——以北京海淀区为例  

Landscape Spatial-Temporal Pattern Analysis on Change in the Fraction of Green Vegetation Based on Remotely Sensed Data:A Case Study in Haidian District, Beijing

作　　者：陈云浩[1] 李晓兵[1] 史培军[1] 

机构地区：[1]北京师范大学资源科学研究所,北京师范大学环境演变与自然灾害教育部重点实验室,北京100875

出　　处：《生态学报》2002年第10期1581-1586,T001,共7页Acta Ecologica Sinica

基　　金：国家自然科学基金项目 ( 30 0 0 0 0 2 7);北京师范大学"高等学校骨干教师资助计划"资助项目

摘　　要：提出了基于土地利用分类的植被覆盖度计算的亚象元模型 ,针对不同的植被覆盖类型 ,综合利用“等密度模型”和“变密度模型”计算植被覆盖度 ,使其能有效地从遥感数据中提取植被覆盖信息。在 GIS的支持下 ,应用该模型对北京海淀区 1 975年、1 991年和 1 999年 5月份植被覆盖度进行计算 ,并着重对其景观变化过程进行了分析。研究结果表明 :1 975至 1 999年来 ,海淀区植被覆盖整体表现为增加趋势 ,但空间格局分布不均衡 ,全植被覆盖区和高植被覆盖区过分集中在西北部狭长地带 ,东南部则多为低植被覆盖区 。Modern land surface parameterizations(LSP)in numerical weather prediction and general circulation models require specification of two major vegetation characteristics——vegetation type and vegetation amount. Vegetation amount is parameterized through the fraction of green vegetation and the leaf area index, the number of leaf layer of the vegetated part. The fraction of green vegetation, the radio of vegetation occupying a unit area, is a very important parameter in development of climatic and ecological models. However, on-ground fieldwork surveys of the fraction of green vegetation are time consuming and expensive and produce low-precision results. Estimation of the fraction of green vegetation using remotely sensed data may be a more efficient approach. The main goal of the current work was to explore the potential of deriving the fraction of green vegetation from normalized difference vegetation index(NDVI)using remotely sensed data, such as TM and MSS data. As each pixel of a satellite image represents a mosaic of structures on the ground surface, the sub-pixel models,including a “dense vegetation model” and a “nondense vegetation model”,for the fraction of green vegetation estimation have been developed. The choice between the “dense vegetation model” and the “nondense vegetation model” would depend upon the structure of vegetation, which is geographic region and vegetation type-specific. In this paper, a simplified approach, depending on the value of leaf area index, was used concerning the spatial resolution of satellite sensor data. The utility of different sub-pixel models for the fraction of green vegetation estimation based on land cover classification was also described. ;The accuracy of the model was checked, and the regression analysis was carried out to find the linear trend and bias of the curve from the 1:1 line. As a result, estimated fraction of green vegetation values are highly correlated with those observed at the ground (r 2=0.79, p<0.005, n=31). Comparison of the

关 键 词：植被覆盖变化 景观分析 北京海淀区 亚象元模型 遥感数据 

分 类 号：Q149[生物学—生态学]