城市森林生物量遥感估测中DN值分层抽样的应用
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摘要
为了提高城市森林生物量遥感估测的精度,以杭州市西湖区为研究区,利用标准误差和平均绝对误差作为评价指标,与简单随机抽样、一般分层抽样等方法进行模拟抽样比较,根据简单随机抽样、一般分层抽样和DN值分层抽样3种方法分别进行样地实测和构建回归模型,通过均方根误差和相对均方根误差对生物量估算模型进行精度评价。结果表明:DN值分层抽样相对于简单随机抽样和一般分层抽样在稳定性上分别提升了43.3%和42.3%,在精确性上分别提升了60.1%和51.2%。基于DN值分层抽样方法建立回归模型的估算精度相对于其他2种方法有了明显的提高。
Taking Xihu District,Hangzhou as an example,a stratified sampling method based on Digital Number of remotely sensed imagery was proposed to improve the precision of urban forest biomass estimation.Standard error and average absolute error areused to compare the accuracy and stability of the Digital Number based stratified sampling with that of the simple random sampling and general stratified sampling.Based on the methods of random sampling,general stratified sampling and Digital Number based stratified sampling,the plots were collected,the regression models ware constructed,and the precisions of model fitness were evaluated with root mean square error and relative root mean square error.The results show that compared with random sampling and general stratified sampling methods,the stability of the stratified sampling are increased by 43.3% and 42.3%,respectively,and the accuracy increased by 60.1% and 51.2%,respectively.The estimation accuracy of the regression model based on the stratified sampling method of Digital Number is obviously improved compared with the other 2 methods.
Taking Xihu District,Hangzhou as an example,a stratified sampling method based on Digital Number of remotely sensed imagery was proposed to improve the precision of urban forest biomass estimation.Standard error and average absolute error areused to compare the accuracy and stability of the Digital Number based stratified sampling with that of the simple random sampling and general stratified sampling.Based on the methods of random sampling,general stratified sampling and Digital Number based stratified sampling,the plots were collected,the regression models ware constructed,and the precisions of model fitness were evaluated with root mean square error and relative root mean square error.The results show that compared with random sampling and general stratified sampling methods,the stability of the stratified sampling are increased by 43.3% and 42.3%,respectively,and the accuracy increased by 60.1% and 51.2%,respectively.The estimation accuracy of the regression model based on the stratified sampling method of Digital Number is obviously improved compared with the other 2 methods.
引文
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[2]曹荞,岳彩荣,李圣娇,等.森林生物量估测进展[J].林业调查规划,2015,40(6):22-25.
[3]刘茜,杨乐,柳钦火,等.森林地上生物量遥感反演方法综述[J].遥感学报,2015,19(1):62-74.
[4]Dillabaugh K A,King D J.Riparian marshland composition and biomass mapping using Ikonos imagery[J].Canadian Journal of Remote Sensing,2008,34(2):143-158.
[5]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.
[6]Nelson R,Ranson K J,Sun G,et al.Estimating Siberian timber volume using MODIS and ICESat/GLAS[J].Remote Sensing of Environment,2009,113(3):691-701.
[7]贾宝全,王成,邱尔发,等.城市林木树冠覆盖研究进展[J].生态学报,2013,33(1):23-32.
[8]Nowak D J,Hoehn R E,Bodine A R,et al.Urban forest structure,ecosystem services and change in Syracuse,NY[J].Urban Ecosystems,2016,19(4):1455-1477.
[9]徐丽华,张结存,黄博,等.基于Quick Bird影像的城市森林碳储量遥感估测[J].应用生态学报,2014,25(10):2787-2793.
[10]王紫君,申广荣,朱赟,等.基于遥感和空间分析的上海城市森林生物量分布特征[J].植物生态学报,2016,40(4):385-394.
[11]Thompson S K.Sampling[M].2nd ed.New York:John Wiley,2002.
[12]李坤.常用抽样方法概述[J].市场研究,2012(11):38-39.
[13]赵娟娟,欧阳志云,郑华,等.城市植物分层随机抽样调查方案设计的方法探讨[J].生态学杂志,2009,28(7):1430-1436.
[14]邹琪,孙华,王广兴,等.基于Landsat8的深圳市森林碳储量遥感反演研究[J].西北林学院学报,2017,32(4):164-171.
[15]应天玉,李明泽,范文义.哈尔滨城市森林碳储量的估算[J].东北林业大学学报,2009,37(9):33-35.
[16]Sah B P,Hmlinen J M,Sah A K,et al.The use of satellite imagery to guide field plot sampling scheme for biomass estimation in ghanaian forest[J].ISPRS Annals of Photogrammetry,Remote Sensing and Spatial Information Sciences,2012,4:221-226.
[17]Andersen H E,Breidenbach J.Statistical properties of mean stand biomass estimators in a LIDAR-based double sampling forest survey design[C]//ISPRS.Proceedings of the ISPRS Workshop'Laser Scanning2007 and Silvi Laser 2007'.Espoo,Finland:ISPRS,2007:8-13.
[18]Andersen H E,Strunk J,Temesgen H.Using airborne light detection and ranging as a sampling tool for estimating forest biomass resources in the upper tanana valley of interior alaska[J].Western Journal of Applied Forestry,2011,26(4):157-164.
[19]蔡壬侯,何绍箕.杭州西湖山区的植被类型及其分布[J].杭州大学学报(自然科学版),1980(4):100-112.
[20]慎佳泓.西湖风景名胜区森林植被多样性及人为干扰的影响研究[D].杭州:浙江大学,2006.
[21]王向雨,胡东,贺金生.神农架地区米心水青冈林和锐齿槲栎林生物量的研究[J].首都师范大学学报(自然科学版),2007,28(2):62-67.
[22]吴泽民,孙启祥,陈美工.安徽长江滩地杨树人工林生物量和养分积累[J].应用生态学报,2001,12(6):806-810.
[23]赵坤,田大伦.会同杉木人工林成熟阶段生物量的研究[J].中南林学院学报,2000,20(1):7-13.
[24]张治军,王彦辉,袁玉欣,等.马尾松天然次生林生物量的结构与分布[J].河北农业大学学报,2006,9(5):37-43.
[25]袁位高,江波,葛永金,等.浙江省重点公益林生物量模型研究[J].浙江林业科技,2009,29(2):1-5.
[26]陈蜀蓉,张超,郑超超,等.公益林生物量估算方法研究:以浙江省缙云县公益林为例[J].浙江林业科技,2015,35(5):20-28.
[27]Hawbaker T J,Keuler N S,Lesak A A,et al.Improved estimates of forest vegetation structure and biomass with a Li DAR-optimized sampling design[J].Journal of Geophysical Research:Biogeosciences,2009,114(3):DOI:10.1029/2008jg000870.