热带原始森林类型分类和蓄积量遥感反演研究

Remote Sensing inversion of Classification and Stocking Volume of Tropical Virgin Forest Types Based on Multivariate Data

森林生态系统蓄积量的空间分布及反演研究对碳储量估测、生物多样性保护以及全球气候变化研究起着至关重要的作用,然而,由于森林植被类型的多样性,尤其是对人力所不能及的热带原始林区,森林调查数据缺失,森林蓄积量的估测和反演存在巨大挑战。以巴布亚新几内亚西塞皮克省18.80万hm^2的热带原始雨林区为研究区,利用高分遥感影像RapidEye,QuickBird与Landsat TM,结合野外地面调查数据,对研究区土地覆盖类型进行分类。基于遥感影像得到森林植被参数信息,提取各波段地表反射率、各种植被指数和其他光谱变换形式等遥感因子,利用多元线性逐步回归构建森林蓄积量遥感反演模型,估算研究区森林蓄积量,并结合GIS技术分析其小班尺度上的空间分布特征。结果显示:1)研究区土地覆盖类型可以分为低海拔平原森林、低海拔高地森林、低山森林、稀疏森林、沼泽森林和其它类型共6种,分类精度达79.2%;2)蓄积量遥感反演模型的多元回归模型R^2为0.694,对森林蓄积量有较好的反演精度;3)研究区森林蓄积量的分布特点表现为中部高于周边、北部和中东部山区明显高于西北和东南地区,其与研究区的土地覆盖类型分布相对应。构建的森林蓄积量反演模型对全球热带原始林区的森林资源蓄积量估测具有重要的参考价值。

Study on the spatial distribution and inversion of forest ecosystem stocks play a crucial role in carbon stock estimation,biodiversity and global climate change research.However,due to the diversity of forest vegetation types,especially in tropical primary forest areas that are beyond the reach of human,forest survey data is missing,the estimates and inversions of forest stocks still present significant challenges.This study takes the tropical primitive rain forest area of 18.80 million ha in the West Syepik Province of Papua New Guinea as the study area,and uses the high-resolution remote sensing images of RapidEye,QuickBird and Landsat TM combining the field survey data to classify the land cover types in the study area.Based on the forest vegetation parameter information obtained by remote sensing image,the remote sensing inversion model of forest stock quantity is established in cooperation with the ground sample plot.The optimal inversion model is selected to estimate the forest stock volume,and combined with GIS technology to analyze the spatial distribution characteristics of the small class scale.The results show that the land cover types in the study area can be divided into low-altitude plain forests,low-altitude highland forests,low-mountain forests,sparse forests,swamp forests and other types,with a classification accuracy of 79.2%.The multivariate regression model R^2 of the stock volume remote sensing inversion model is 0.694,which has a good inversion accuracy for the forest stock volume.The distribution of forest stocks in the study area is characterized by a higher central area than the surrounding,northern and central eastern regions,which is significantly higher than the northwest and southeast regions,which corresponds to the distribution of land cover types in the study area.The forest stock inversion model used has important reference value for the estimation of forest resource stocks in tropical forest areas.