基于遥感与地统计的森林生物量时空变异分析

大兴安岭林区是我国最大的天然林区,估算该区域的森林生物量,并研究该区域的森林生物量的空间格局特征具有重要意义。以20世纪末至21世纪10年代期间(1994—1997、2006—2007、2010—2011)的Landsat5系列TM遥感影像为基础数据,建立遥感信息模型,估算黑龙江省大兴安岭地区3个时期的森林生物量。采用0°、45°、90°、135°方向以及全局Morans I系数和半变异函数块金值、基台值、变程、块金值与基台值比值、拱高与基台值比值5个参数,对20世纪末至21世纪10年代研究区域3个时期的森林生物量进行异质性和空间自相关性分析。结果表明:研究区3个时期的森林生物量的整体性良好,均成连片化分布,没有出现破碎情况。森林生物量的半变异函数模型均为线性模型,3个时期的森林生物量的块金值与基台值比值都接近1。通过遥感反演方法获得森林生物量且估算精度均达到75%以上,为地统计分析提供了可靠的数据源。采用地统计分析方法对森林生物量进行空间异质性和自相关性分析,是对单独使用GIS工具对森林生物量进行空间分析的有益补充。因此,基于遥感与地统计学相结合的方法能够更好地实现森林生物量的时空变异分析。 

Spatial and temporal variation of forest biomass based on remote sensing and geostatistics

The Daxing’an Mountain, located in Heilongjiang Province of northeastern China, is the largest natural forest area of China. It is of great significance to estimate its forest biomass and further study the characteristics of spatial pattern of forest biomass regionally. Based on the data of TM remote sensing images from Landsat5 series during late 20th century to 2010s, we constructed a remote sensing information model in order to estimate the forest biomass of the Daxing’an Mountain. Morans I coefficient from the direction of 0°, 45°, 90°, 135° and overall direction, and five parameters of semivariagram (namely, nugget value, sill value, variation value, ratio of nugget to sill value, ratio of arch rise to sill) were selected to analyze the heterogeneity and spatial self-correlation of forest biomass separately during three periods of 1994--1997, 2006--2007 and 2010--2011. Results showed that: the integrity of forest biomass in the three periods was good and all continuously distributed without any broken observation. The semivariagram models of biomass of different periods were linear with C/(C0 + C) approaching to 1. Using remote sensing inversion method to estimate forest biomass could provide reliable data source for geostatistics with an estimation precision higher than 75%. Meanwhile, heterogeneity and self-spatial correlation analysis of forest biomass according to geostatistics is a useful supplement to spatial relations of forest biomass by simply using GIS tool. Therefore, combination of remote sensing and geostatistics is confirmed to perform better in spatial-temporal variation analysis of forest biomass.