浏览全部资源
扫码关注微信
纸质出版日期： 2017-9 ，
录用日期： 2017-5-16
扫 描 看 全 文
蒙诗栎, 庞勇, 张钟军, 李增元, 王雪琼, 李世明. 2017. WorldView-2纹理的森林地上生物量反演. 遥感学报, 21(5): 812–824
Meng S L, Pang Y, Zhang Z J, Li Z Y, Wang X Q and Li S M. 2017. Estimation of aboveground biomass in a temperate forest using texture information from WorldView-2. Journal of Remote Sensing, 21(5): 812–824
使用高空间分辨率卫星WorldView-2的多光谱遥感影像，构建植被指数和纹理因子等遥感因子与森林地上生物量的关系方程，并计算模型估测精度和均方根误差，探索高分辨率数据的光谱与纹理信息在温带森林地上生物量估测应用中的潜力。以黑龙江省凉水自然保护区温带天然林及天然次生林为研究对象，通过灰度共生矩阵(GLCM)、灰度差分向量(GLDV)及和差直方图(SADH)对高分辨率遥感影像进行纹理信息提取，并利用外业调查的74个样地地上生物量与遥感因子建立参数估计模型。提取的遥感因子包括6种植被指数(比值植被指数RVI、差值植被指数DVI、规一化植被指数NDVI、增强植被指数EVI、土壤调节植被指数SAVI和修正的土壤调节植被指数MSAVI)以及3类纹理因子(GLCM、GLDV和SADH)。为避免特征变量个数较多对估测模型造成过拟合，利用随机森林算法对提取的遥感因子进行特征选择，将最优的特征变量输入模型参与建模估测。采用支持向量回归(SVR)进行生物量建模及验证，结果显示选入模型的和差直方图均值(sadh_mean)、灰度共生矩阵方差(glcm_var)和差值植被指数(DVI)等遥感因子对森林地上生物量有较好的解释效果；植被指数+纹理因子组合的模型获得较精确的AGB估算结果(
R
2
=0.85，RMSE=42.30 t/ha)，单独使用植被指数的模型精度则较低(
R
2
=0.69，RMSE=61.13 t /ha)。
The effect of forest biomass on carbon cycles has long been recognized.Therefore
an accurate assessment of forest biomassis required to understand ecosystem changes. This research uses vegetation indices and textural indices based on high spatial resolution Worldview-2 multispectral imagery to establish their relationship with forest AGB (Above Ground Biomass) and assess the accuracy of the estimation model. This research also explores the capability of spectral and textural information for AGB assessment at the Liang Shui National Nature Reserve
Northeast China. Remote sensing vegetation and texture indices were derived from high spatial resolution Worldview-2 multispectral data. We applied three different algorithms to extract the texture indices from the Worldview-2 data
including Gray Level Co-occurrence Matrix
Gray Level Difference Vector
and Sum and Difference Histograms. Six vegetation indices
namely
RVI
DVI
NDVI
EVI
SAVI
and MSAVI
were computed. The relationship among the above mentioned indices and 74 field measurements was established.However
the over fitting problems for the training regression model could occur due to the many input independent variables (i.e.
vegetation indices and texture indices)
which could decrease the robustness of the regression model. The random forest algorithm could avoid overfitting through the training process
so it was utilized to perform feature selection. Several optimal variables were selected to conduct the regression analysis.The support vector regression method was implemented to train and validate the AGB models. Results show that variables selection could better interpret forest AGB and obtain accurate predicted results. Comparisons between the two estimation models were made. The first model only applied vegetation indices
whereas the other model integrated vegetation and texture indices. The results also show that the accuracy of the vegetation indices model was lower than the vegetation+textural indices model (integrated vegetation indices with texture indices) at
R
2
=0.69
RMSE=61.13 t/ha and
R
2
=0.85
RMSE=42.30 t/ha
respectively. This research confirms that textural information could improve the accuracy of forest AGB estimation to a certain extent.
地上生物量纹理因子WorldView-2高分辨率遥感影像温带森林随机森林支持向量回归
aboveground biomasstexture indicesWorldView-2high spatial resolution remote sensing imagetemperate forestrandom forestSVR
Avitabile V, Baccini A, Friedl M A and Schmullius C. 2012. Capabilities and limitations of Landsat and land cover data for aboveground woody biomass estimation of Uganda. Remote Sensing of Environment, 117: 366–380
Bortolot Z J and Wynne R H. 2005. Estimating forest biomass using small footprint LiDAR data: an individual tree-based approach that incorporates training data. ISPRS Journal of Photogrammetry and Remote Sensing, 59(6): 342–360
Breiman L. 2001. Random forests. Machine Learning, 45(1): 5–32
Champion I, Dubois-Fernandez P, Guyon D and Cottrel M. 2008. Radar image texture as a function of forest stand age. International Journal of Remote Sensing, 29(6): 1795–1800
Chang C C and Lin C J. 2011. LIBSVM: a library for support vector machines[EB/OL]. http://www.csie.ntu.edu.tw/~cjlin/libsvmhttp://www.csie.ntu.edu.tw/~cjlin/libsvm
陈传国, 朱俊凤. 1989. 东北主要林木生物量手册. 北京: 中国林业出版社
Chen C G and Zhu J F. 1989. Woody Biomass Manual of Typical Species in the Northeast of China. Beijing: China Forestry Publishing House
陈尔学. 1999. 合成孔径雷达森林生物量估测研究进展. 世界林业研究, 12(6): 18–23
Chen E X. 1999. Development of forest biomass estimation using SAR data. World Forestry Research, 12(6): 18–23
Cohen W B and Spies T A. 1992. Estimating structural attributes of douglas-fir/western hemlock forest stands from landsat and SPOT imagery. Remote Sensing of Environment, 41(1): 1–17
Colombo R, Bellingeri D, Fasolini D and Marino C M. 2003.Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Sensing of Environment, 86(1): 120–131
Díaz-Uriarte R and de Andrés S A. 2006. Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7: 3 [DOI: 10.1186/1471-2105-7-3]
Dekker R J. 2003. Texture analysis and classification of ERS SAR images for map updating of urban areas in the Netherlands. IEEE Transactions on Geoscience and Remote Sensing, 41(9): 1950–1958
Dobson M C, Ulaby F T, LeToan T, Beaudoin A, Kasischke E S and Christensen N. 1992. Dependence of radar backscatter on coniferous forest biomass. IEEE Transactions on Geoscience and Remote Sensing, 30(2): 412–415
Foody G M, Cutler M E, Mcmorrow J, Pelz D, Tangki H, Boyd D S and Douglas I. 2001. Mapping the biomass of Bornean tropical rain forest from remotely sensed data. Global Ecology and Biogeography, 10(4): 379–387
Foody G M, Green R M, Lucas R M, Curran P J, Honzak M and Do Amaral I. 1997. Observations on the relationship between SIR-C radar backscatter and the biomass of regenerating tropical forests. International Journal of Remote Sensing, 18(3): 687–694
Franklin S E, Hall R J, Moskal L M, Maudie A J and Lavigne M B. 2000. Incorporating texture into classification of forest species composition from airborne multispectral images. International Journal of Remote Sensing, 21(1): 61–79
Franklin S E and McDermid G J. 1993. Empirical relations between digital SPOT HRV and CASI spectral response and lodgepole pine (Pinus contorta) forest stand parameters. International Journal of Remote Sensing, 14(12): 2331–2348
Fuchs H, Magdon P, Kleinn C and Flessa H. 2009. Estimating aboveground carbon in a catchment of the Siberian forest tundra: combining satellite imagery and field inventory. Remote Sensing of Environment, 113(3): 518–531
Haralick R M, Shanmugam K and Dinstein I H. 1973. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6): 610–621
Heenkenda M K, Joyce K E, Maier S W and Bartolo R. 2014. Mangrove species identification: comparingWorldView-2 with aerial photographs. Remote Sensing, 6(7): 6064–6088
Huete A R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3): 295–309
Hyde P, Dubayah R, Walker W, Blair J B, Hofton M and Hunsaker C. 2006. Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy. Remote Sensing of Environment, 102(1): 63–73
Hyyppä J, Hyyppä H, Inkinen M, Engdahl M, Linko S and Zhu Y H. 2000. Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128(1/2): 109–120
Labrecque S, Fournier R A, Luther J E and Piercey D. 2006. A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland. Forest Ecology and Management, 226(1/3): 129–144
Lefsky M A, Harding D, Cohen W B, Parker G and Shugart H H. 1999. Surface Lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA. Remote Sensing of Environment, 67(1): 83–98
李德仁, 王长委, 胡月明, 刘曙光. 2012. 遥感技术估算森林生物量的研究进展. 武汉大学学报(信息科学版), 37(6): 631–635
Li D R, Wang C W, Hu Y M and Liu S G. 2012. General review on remote sensing-based biomass estimation. Geomatics and Information Science of Wuhan University, 37(6): 631–635
李智峰, 朱谷昌, 董泰锋. 2011. 基于灰度共生矩阵的图像纹理特征地物分类应用. 地质与勘探, 47(3): 456–461
Li Z F, Zhu G C and Dong T F. 2011. Application of GLCM-based texture features to remote sensing image classification. Geology and Exploration, 47(3): 456–461
Liu H Q and Huete A. 1995. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 33(2): 457–465
刘吉春, 刘伯文, 张鹏. 1993. 凉水自然保护区的科研价值与优势. 野生动物, (1): 4–5
Liu J C, Liu B W and Zhang P. 1993. Scientific value and advantage of LiangShui nature conservation area. Journal of wildlife, (1): 4-5
柳钦火, 辛晓洲, 唐娉, 廖静娟, 吴炳方. 2010. 定量遥感模型、应用及不确定性研究. 北京: 科学出版社
Liu Q H, Xin X Z, Tang P, Liao J J and Wu B F. 2010. Research on Quantitative Remote Sensing Model, Application and Uncertainty. Beijing: Science Press
Lu D. 2005. Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. International Journal of Remote Sensing, 26(12): 2509–2525
Lu DS and Batistella M. 2005. Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon. Acta Amazonica, 35(2): 249–257
马建章, 刘传照, 张鹏. 1993. 凉水自然保护区研究. 哈尔滨: 东北林业大学出版社
Ma J Z, Liu C Z and Zhang P. 1993. Research of Liang Shui Nature Conservation. Harbin: Northeast Forestry University Press
马利群, 李爱农. 2011. 激光雷达在森林垂直结构参数估算中的应用. 世界林业研究, 24(1): 41–45
Ma L Q and Li A N. 2011. Review of application of LiDAR to estimation of forest vertical structure parameters. World Forestry Research, 24(1): 41–45
Marceau D J, Howarth P J, Dubois J M and Gratton D J. 1990. Evaluation of the grey-level co-occurrence matrix method for land-cover classification using SPOT imagery. IEEE Transactions on Geoscience and Remote Sensing, 28(4): 513–519
Meng S L, Pang Y, Zhang Z J, Jia W and Li Z Y. 2016. Mapping aboveground biomass using texture indices from aerial photos in a temperate forest of northeastern China. Remote Sensing, 8(3): 230
Mitchard E T A, Saatchi S S, Woodhouse I H, Nangendo G, Ribeiro N S, Williams M, Ryan C M, Lewis S L, Feldpausch T R and Meir P. 2009. Using satellite radar backscatter to predict above-ground woody biomass: a consistent relationship across four different African landscapes. Geophysical Research Letters, 36(23): L23401
Mutanga O, Adam E and Cho MA. 2012. High density biomass estimation for wetland vegetation usingworldview-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observation and Geoinformation, 18: 399–406
Muukkonen P and Heiskanen J. 2007. Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: a possibility to verify carbon inventories. Remote Sensing of Environment, 107(4): 617–624
Nelson R, Krabill W and Tonelli J. 1988. Estimating forest biomass and volume using airborne laser data. Remote Sensing of Environment, 24(2): 247–267
Nelson R F, Kimes D S, Salas W A and Routhier M. 2000. Secondary forest age and tropical forest biomass estimation using thematic mapper imagery: single-year tropical forest age classes, a surrogate for standing biomass, cannot be reliably identified using single-date tm imagery. Bioscience, 50(5): 419–431
庞勇, 李增元. 2012. 基于机载激光雷达的小兴安岭温带森林组分生物量反演. 植物生态学报, 36(10): 1095–1105
Pang Y and Li Z Y. 2012. Inversion of biomass components of the temperate forest using airborne Lidar technology in Xiaoxing’an Mountains, northeastern of China. Chinese Journal of Plant Ecology, 36(10): 1095–1105
庞勇, 李增元, 陈尔学, 孙国清. 2005. 激光雷达技术及其在林业上的应用. 林业科学, 41(3): 129–136
Pang Y, Li Z Y, Chen E X and Sun G Q. 2005. Lidar remote sensing technology and its application in forestry. Scientia Silvae Sinicae, 41(3): 129–136
Patenaude G, Hill R A, Milne R, Gaveau D L A, Briggs B B J and Dawson T P. 2004. Quantifying forest above ground carbon content using LiDAR remote sensing. Remote Sensing of Environment, 93(3): 368–380
Pearson R L and Miller L D. 1972. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie//Proceedings of the Eighth International Symposium on Remote Sensing of Environment.Ann Arbor, Michigan, USA: [s.n.]: 1355
Podest E and Saatchi S. 2002. Application of multiscale texture in classifying JERS-1 radar data over tropical vegetation. International Journal of Remote Sensing, 23(7): 1487–1506
Qi J, Chehbouni A, Huete A R, Kerr Y H and Sorooshian S. 1994. A modified soil adjusted vegetation index. Remote Sensing ofEnvironment, 48(2): 119–126
R Development Core Team. 2009. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing
Rapinel S, Clément B, Magnanon S, Sellin V and Hubert-Moy L. 2014. Identificationand mapping of natural vegetation on a coastal site using aWorldview-2 satellite image. Journal of Environmental Management, 144: 236–246
Richardson A J amd Wiegand C L. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12): 1541–1552
Rouse Jr J W, Haas R H, Schell J A and Deering D W. 1974. Monitoring Vegetation Systems in the Great Plains with Erts. [s.l.]: NASA Special Publication, 351: 309
Roy P S and Ravan S A. 1996. Biomass estimation using satellite remote sensing data-an investigation on possible approaches for natural forest. Journal of Biosciences, 21(4): 535–561
Sarker M L R. 2011. Estimation of Forest Biomass Using Remote Sensing.Hong Kong: Hong Kong Polytechnic University
Stumpf A and Kerle N. 2011. Object-oriented mapping of landslides using random forests. Remote Sensing of Environment, 115(10): 2564–2577
Unser M. 1986. Sum and difference histograms for texture classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(1): 118–125
Wang C K. 2006. Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests. Forest Ecology and Management, 222(1/3): 9–16
Zheng D, Rademacher J, Chen J, Crow T, Bresee M and Le Moine J. 2004. Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment, 93(3): 402–411
相关作者
相关机构
京公网安备11010802024621