应用衍生纹理指数对杉木林分蓄积量的估测
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摘要
以福建省三明市将乐国有林场的杉木纯林为研究对象,利用高分2号遥感影像数据,将光谱因子、地形因子分别与全色波段纹理特征、融合数据纹理特征、衍生纹理指数组合为自变量,样地蓄积量为因变量,采用多元逐步回归算法构建不同窗口下的蓄积量估测模型。结果表明:全色波段纹理特征模型和融合数据纹理特征模型最佳窗口为3×3窗口,衍生纹理指数模型最佳窗口为7×7窗口;以最优窗口建立的蓄积量回归模型,拟合效果最优的是衍生纹理指数模型(R~2=0.705,R_(MSE)=37.183 m3/hm2),其估测精度为80.67%,而融合数据纹理特征模型和全色波段纹理特征模型的估测精度分别为72.89%、60.35%,说明衍生纹理指数能够有效地提升高分2号影像数据对杉木林分蓄积量估测的精度。
With Cunninghamia lanceolate forest in the Jiangle Forest Farm of Fujian Province,by multiple linear regression analysis,stand volume estimation models were established under different window based on GF-2 remote sensing image data by combination of the spectral reflectance,topographic factors respectively with panchromatic band texture,combined texture indices,derivative texture indices.The 3×3 window was the optimal texture generation window for both panchromatic band texture model and combined texture indices model,and 7×7 window was the optimal texture generation window for derivative texture indices model. The introduction of derivative texture indices yield highest estimate,as 70.5% of the variability in the field data was accounted for by the model( R~2= 0.705 and RMSE= 37.183 m~3/hm~2),and the estimated accuracy of the model reached 80.67%. The estimation accuracy of combined texture indices model and panchromatic band texture feature model is 72.89% and 60.35%,respectively. It's suggested that the obvious improvement for stand volume of the C.lanceolate estimation can be obtained using derivative texture indices based on the GF-2 satellite images.
With Cunninghamia lanceolate forest in the Jiangle Forest Farm of Fujian Province,by multiple linear regression analysis,stand volume estimation models were established under different window based on GF-2 remote sensing image data by combination of the spectral reflectance,topographic factors respectively with panchromatic band texture,combined texture indices,derivative texture indices.The 3×3 window was the optimal texture generation window for both panchromatic band texture model and combined texture indices model,and 7×7 window was the optimal texture generation window for derivative texture indices model. The introduction of derivative texture indices yield highest estimate,as 70.5% of the variability in the field data was accounted for by the model( R~2= 0.705 and RMSE= 37.183 m~3/hm~2),and the estimated accuracy of the model reached 80.67%. The estimation accuracy of combined texture indices model and panchromatic band texture feature model is 72.89% and 60.35%,respectively. It's suggested that the obvious improvement for stand volume of the C.lanceolate estimation can be obtained using derivative texture indices based on the GF-2 satellite images.
引文
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[2]CHRYSAFIS I,MALLINIS G,SIACHALOU S,et al.Assessing the relationships between growing Stock volume and Sentinel-2imagery in a Mediterranean forest ecosystem[J].Remote Sensing Letters,2017,8(6):508-517.
[3]MURA M,BOTTALICO F,GIANNETTI F,et al.Exploiting the capabilities of the Sentinel-2 multi spectral instrument for predicting growing Stock volume in forest ecosystems[J].International Journal of Applied Earth Observation and Geoinformation,2018,66:126-134.
[4]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.
[5]杨柳,冯仲科,岳德鹏,等.结合纹理因子和地形因子的森林蓄积量多光谱估测模型[J].光谱学与光谱分析,2017,37(7):2140-2145.
[6]王佳,宋珊芸,刘霞,等.结合影像光谱与地形因子的森林蓄积量估测模型[J].农业机械学报,2014,45(5):216-220.
[7]庞勇,蒙诗栎,李增元.机载高分辨率遥感影像的傅氏纹理因子估测温带森林地上生物量[J].林业科学,2017,53(3):94-104.
[8]GMEZ C,WULDER M A,MONTES F,et al.Modeling forest structural parameters in the mediterranean pines of central Spain using QuickBird-2 imagery and classification and regression tree analysis(CART)[J].Remote Sensing,2012,4(1):135-159.
[9]房秀凤,谭炳香,刘清旺,等.基于纹理信息的森林蓄积量估计[J].东北林业大学学报,2017,45(11):21-25.
[10]WOOD E M,PIDGEON A M,RADELOFF V C,et al.Image texture as a remotely sensed measure of vegetation structure[J].Remote Sensing of Environment,2012,121:516-526.
[11]李明诗,谭莹,潘洁,等.结合光谱、纹理及地形特征的森林生物量建模研究[J].遥感信息,2006,21(6):6-9,66.
[12]KAYITAKIRE F,HAMEL C,DEFOURNY P.Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery[J].Remote Sensing of Environment,2006,102(3/4):390-401.
[13]SHAMSODDINI A,TRINDER J C,TURNER R.Pine plantation structure mapping using WorldView-2 multispectral image[J].International Journal of Remote Sensing,2013,34(11):3986-4007.
[14]谢士琴,赵天忠,王威,等.结合影像纹理、光谱与地形特征的森林结构参数反演[J].农业机械学报,2017,48(4):125-134.
[15]SARKER L R,NICHOL J E.Improved forest biomass estimates using ALOS AVNIR-2 texture indices[J].Remote Sensing of Environment,2011,115(4):968-977.
[16]蒙诗栎,庞勇,张钟军,等.WorldView-2纹理的森林地上生物量反演[J].遥感学报,2017,21(5):812-824.
[17]NICHOLJE,SARKERM L R.Improved biomass estimation using the texture parameters of two high-resolution optical sensors[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(3):930-948.
[18]SARKERLR,NICHOL J E.Improved forest biomass estimates using ALOS AVNIR-2 texture indices[J].Remote Sensing of Environment,2011,115(4):968-977.
[19]刘俊,毕华兴,朱沛林,等.基于ALOS遥感数据纹理及纹理指数的柞树蓄积量估测[J].农业机械学报,2014,45(7):245-254.
[20]国家国防科技工业局.我国首批亚米级高分辨率卫星影像图发布[J].科技创新导报,2014,11(30):1-1.
[21]孙玉军,王新杰,马炜,等.林改区域典型树种森林碳储量监测技术研究[M].北京:中国林业出版社,2014:72-76.
[22]HARALICK R M,SHANMUGAM K,DINSTEIN I.Textural features for image classification[J].IEEETransactionson Systems,Man,and Cybernetics,1973,SMC-3(6):610-621.DOI:10.1109/TSMC.1973.4309314.
[23]王正兴,刘闯,HUETE A.植被指数研究进展:从AVHRR-NDVI到MODIS-EVI[J].生态学报,2003,23(5):979-987.
[24]郝泷.基于Landsat OLI的西藏林芝县森林类型提取方法和蓄积量反演模型研究[D].北京:中国林业科学研究院,2017.
[25]尤静妮.基于高分遥感纹理信息的森林蓄积量估测研究[D].西安:西安科技大学,2017.
[26]潘洁,李明诗.基于信息量的高分辨率影像纹理提取的研究[J].南京林业大学学报(自然科学版),2010,34(4):129-134.
[27]ZHOU J J,ZHAO Z,ZHAO Q X,et al.Quantification of aboveground forest biomass using Quickbird imagery,topographic variables and field data[J].Journal of Applied Remote Sensing,2013,7(1).doi:10.1117/1.JRS.7.073484.
[28]LU D.Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon[J].International Journal of Remote Sensing,2005,26(12):2509-2525.
[29]PLOTON P,BARBIER N,COUTERON P,et al.Toward a general tropical forest biomass prediction model from very high resolution optical satellite images[J].Remote Sensing of Environment,2017,200:140-153.