运用卫星遥感数据对杨树人工林生物量的估算
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摘要
以Landsat 8(地球资源观测卫星8号)和GF-2(高分2号卫星)影像数据为基础,利用ENVI5.3软件对Landsat8和GF-2遥感影像进行相关预处理,提取反映杨树生物量信息的波段信息和植被指数,结合研究区实测样地胸高断面加权平均高,采用多元线性回归和多远逐步回归构建杨树生物量估算模型。结果表明:依据两种影像数据构建的杨树生物量估算模型决定系数(R~2)均大于0.7;GF-2影像数据构建的模型精度高于Landsat8影像数据构建的模型;引入样地胸高断面加权平均高后,模型精度都有所提高;GF-2影像数据结合样地胸高断面加权平均高,构建的杨树人工林生物量多元逐步回归估算模型最优,R~2达到0.891。
With Landsat 8(Earth Resources Observation Satellite 8) and GF-2(High Score Satellite 2) image data, the remote sensing images of Landsat 8 and GF-2 were pre-processed by ENVI5.3 software, the band information and vegetation index reflecting poplar biomass information were extracted. Combining with the weighted average height of breast height cross-section in the study area, the multi-linear regression and multi-far stepwise regression were used to construct the Poplar biomass estimation model. The determination coefficients(R~2) of poplar biomass estimation models based on two kinds of image data were greater than 0.7; the accuracy of models based on GF-2 image data was higher than that based on Landsat 8 image data; the accuracy of models was improved after introducing the weighted average of thoracic height cross-section of sample plot; GF-2 image data combined with weighted average height of breast height section of sample plot, the model of multiple stepwise regression for estimating biomass of poplar plantation is the best with R~2 of 0.891.
With Landsat 8(Earth Resources Observation Satellite 8) and GF-2(High Score Satellite 2) image data, the remote sensing images of Landsat 8 and GF-2 were pre-processed by ENVI5.3 software, the band information and vegetation index reflecting poplar biomass information were extracted. Combining with the weighted average height of breast height cross-section in the study area, the multi-linear regression and multi-far stepwise regression were used to construct the Poplar biomass estimation model. The determination coefficients(R~2) of poplar biomass estimation models based on two kinds of image data were greater than 0.7; the accuracy of models based on GF-2 image data was higher than that based on Landsat 8 image data; the accuracy of models was improved after introducing the weighted average of thoracic height cross-section of sample plot; GF-2 image data combined with weighted average height of breast height section of sample plot, the model of multiple stepwise regression for estimating biomass of poplar plantation is the best with R~2 of 0.891.
引文
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[20] JORDAN C F.Derivation of leaf-area index from quality of light on the forest floor[J].Ecology,1969,50(4):663-666.
[21] HUETE A R.A soil-adjusted vegetation index (SAVI)[J].Remote Sensing of Environment,1988,25(3):295-309.
[22] QI J,HUETE A R.Interpretaion of vegetation indices derived from multi-temporal SPOT images[J].Remote Sensing of Environment,1993,44(1):89-101.
[23] 付勇,汪立今,柴凤梅,等.多元线性回归和逐步回归分析在白石泉Cu-Ni硫化物矿床研究中的应用[J].地学前缘,2009,16(1):373-380.
[2] 董立新,吴炳方,唐世浩.激光雷达GLAS与ETM联合反演森林地上生物量研究[J].北京大学学报(自然科学版),2011,47(4):703-710.
[3] 申鑫,曹林,佘光辉.高光谱与高空间分辨率遥感数据的亚热带森林生物量反演[J].遥感学报,2016,20(6):1446-1460.
[4] EBERMAYER E.Die gesammte lehre der waldstreu mit rücksicht auf die chemische statik des waldbaues.unter zugrundlegung der in den k?nigl.staatsforsten bayerns angestellten untersuchungen[M].Berlin:Springer Berlin Heidelberg,2013.
[5] HAME T,SALLI A,ANDERSSON K,et al.A new methodology for the estimation of biomass of coniferdominated boreal forest using NOAA AVHRR data[J].International Journal of Remote Sensing,1997,18(15):3211-3243.
[6] BACCINI A,LAPORTE N,GOETZ S J,et al.A first map of tropical Africa’s above-ground biomass derived from satellite imagery[J].Environmental Research Letters,2008,3(4):45011-45019.
[7] FUCHS H,MAGDON P,KLEINN C,et al.Estimating aboveground carbon in a catchment of the siberian forest tundra:combining satellite imagery and field inventory[J].Remote Sensing of Environment,2009,113(3):518-531.
[8] 金丽芳,徐希孺,张猛.内蒙古典型草原地带牧草产量估算的光谱模型[J].中国草原与牧草,1986,3(2):51-54.
[9] 郭志华,彭少麟,王伯荪.利用TM数据提取粤西地区的森林生物量[J].生态学报,2002,22(11):1832-1839.
[10] 李明诗,谭莹,彭世揆.基于ASTER遥感数据的杨树林分因子建模及制图研究[J].南京林业大学学报(自然科学版),2006,30(5):123-126.
[11] 徐人蜀,岳彩荣.基于印度遥感卫星IRS-P6的森林生物量估测模型研究[J].中南林业调查规划,2008,27(l):42-45.
[12] 温小荣,蒋丽秀,林国忠,等.基于森林资源连淸数据的泗阳县杨树生物量遥感估算模型[J].安徽农业科学,2013,41(36):13935-13937.
[13] 方精云,刘国华,徐嵩龄.我国森林植被的生物量和净生产量[J].生态学报,1996,16(5):497-508.
[14] 朱超洪,刘勇.基于影像认知和地学理解的面向对象分类研究[J].遥感技术与应用,2012,27(4):536-541.
[15] 张超,彭道黎,涂云燕,等.利用TM影像和偏最小二乘回归方法估测三峡库区森林蓄积量[J].北京林业大学学报,2013,35(3):11-17.
[16] 罗亚,徐建华,岳文泽.基于遥感影像的植被指数研究力法述评[J].生态科学,2005,24(1):75-79.
[17] 李喆,胡蝶,赵登忠,等.宽波段遥感植被指数研究进展综述[J].长江科学院院报,2015,32(1):125-130.
[18] ROUSE J R J.Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation[D].Texas:Texas A & M University Remote Sensing Center College Station,1974.
[19] PEARSON R L,MILLER L D.Remote mapping of standing crop biomass for estimation of the productivity of the short-grass prairie:proceedings of the eighth International symposium on remote sensing of environment[C].Colorado:Dept.of Watershed Sciences,College of Forestry and Natural Resources,Colorado State University,1972.
[20] JORDAN C F.Derivation of leaf-area index from quality of light on the forest floor[J].Ecology,1969,50(4):663-666.
[21] HUETE A R.A soil-adjusted vegetation index (SAVI)[J].Remote Sensing of Environment,1988,25(3):295-309.
[22] QI J,HUETE A R.Interpretaion of vegetation indices derived from multi-temporal SPOT images[J].Remote Sensing of Environment,1993,44(1):89-101.
[23] 付勇,汪立今,柴凤梅,等.多元线性回归和逐步回归分析在白石泉Cu-Ni硫化物矿床研究中的应用[J].地学前缘,2009,16(1):373-380.