地理加权回归模型结合卫星遥感的松阳县森林地上碳储量估算

doi:10.13466/j.cnki.lyzygl.2023.04.016

摘要/Abstract

摘要：

森林地上碳储量(Aboveground Carbon,AGC)是反映森林生态系统基本特征的重要指标,也是评价森林功能结构和生产潜力的理论基础。松阳县作为浙江省九大林业重点县之一,生态地位十分重要,全县以中、低山丘陵地带为主,四面环山,如何解决复杂地形对AGC时空变异的影响,是实现山区森林AGC精准估算的关键。为此,基于Landsat TM卫星影像,并结合松阳县森林AGC调查数据,构建结合空间变异特征的地理加权回归模型(GWR)估算森林AGC,并与普通最小二乘法(OLS)的结果进行对比,最后选取最优模型预测松阳县森林AGC及其空间分布。研究表明:Landsat TM卫星影像的纹理信息对预测松阳县森林AGC有重要作用;GWR模型能够准确估算松阳县森林AGC及空间分布,并且比OLS模型精度提升了9%,R²达到0.71;松阳县森林AGC总量为3.901×10⁶ Mg,平均AGC为23.70 Mg/hm²,占丽水市森林植被AGC总量的10%左右,在服务区域生态功能上具有较为重要的地位。研究将为松阳县森林AGC精准估算提供先进的技术手段,同时也为松阳县森林碳汇功能评价提供科学的数据。

关键词: 森林地上碳储量, 遥感, 地理加权回归, 模型, 松阳县

Abstract:

Aboveground carbon(AGC)is an important indicator of the basic characteristics of forest ecosystems and a theoretical basis for evaluating the functional structure and productive potential of forests.As one of the nine key forestry counties in Zhejiang Province,Songyang County has a very important ecological status,so the accurate estimation of forest AGC in Songyang County is an important reference value for the stability evaluation of forest ecosystems and forest management in Songyang County.However,Songyang County is dominated by medium and low hilly areas surrounded by mountains,and how to consider the influence of complex terrain on the temporal and spatial variation of AGC is an urgent problem to be solved to achieve accurate estimation of AGC in mountainous forests.Therefore,based on Landsat TM satellite imagery and forest AGC survey data in Songyang County,a geographically weighted regression model(GWR)combined with spatial variation characteristics was constructed to estimate forest AGC,and compared with the results of ordinary least squares(OLS),finally,the optimal model was selected to predict forest AGC and its spatial distribution in Songyang County.The results were asfollows:Texture information from Landsat TM satellite imagerywas important for predicting forest AGC in Songyang County;The GWR model accurately estimated the AGC and spatial distribution of forests in Songyang County,and improved the accuracy by 9% over the OLS model,with an R² of 0.71.The total AGC of forests in Songyang County was 3.901×10⁶ Mg,with an average AGC of 23.70 Mg/hm²,accounting for about 10% of the total AGC of forest vegetation in Lishui City,which had a relatively important position in serving regional ecological functions.The study will provide advanced technical tools for accurate estimation of forest AGC in Songyang County,as well as scientific data for evaluating the function of forest carbon sink in Songyang County.

Key words: forest aboveground carbon storage, remote sensing, geographic weighted regression, model, Songyang County

中图分类号:

S771.8

邹为民, 陈超, 黄蕾, 宋美萱, 李雪建, 杜华强. 地理加权回归模型结合卫星遥感的松阳县森林地上碳储量估算[J]. 林业资源管理, 2023, 0(4): 132-140.

ZOU Weimin, CHEN Chao, HUANG Lei, SONG Meixuan, LI Xuejian, DU Huaqiang. Geographic Weighted Regression Model Combined with Remote Sensing for Estimating Forest Aboveground Carbon Storage of Songyang County[J]. FOREST RESOURCES WANAGEMENT, 2023, 0(4): 132-140.

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遥感变量类型	遥感变量因子	参考文献
原始波段	蓝光波段(B)、绿光波段(G)、红光波段(R)、近红外波段(NIR)、短波红外1波段(SWIR 1)、短波红外2波段(SWIR 2)
植被指数	归一化植被指数(Normalized Difference Vegetation Index,NDVI)、增强植被指数(Enhanced Vegetation Index,EVI)、比值植被指数(Ratio Vegetation Index,RVI)、差值植被指数(Difference Vegetable Index,DVI)、归一化水指数(Normalized Difference Water Index,NDWI)、归一化红外指数(Normalized Difference Infrared Index,NDII)	[20?-22]
纹理特征	对比度(Contrast,CON)、相关性(Correlation,COR)、相异性(Dissimilarity,DIS)、熵(Entropy,ENT)、方差(Variance,VAR)、均质性(Homogeneity,HOM)	[23]