白黎娜, 李增元, 陈尔学, 等. 2003. 干涉测量土地利用影像分类决策树法森林识别研究. 林业科学, 39(1):86-90. (Bai L N, Li Z Y, Chen E X, et al. 2003. A study on forest identification with the decision tree for interferometric land-use image. Scientia Silvae Sinicae, 39(1):86-90.[in Chinese]) 刘丽娟, 庞 勇, 范文义, 等. 2013. 机载LiDAR和高光谱融合实现温带天然林树种识别. 遥感学报,17(3):679-695. (Liu L J, Pang Y, Fan W Y, et al. 2013. Fused airborne LiDAR and hyperspectral data for tree species identification in a natural temperate forest. Journal of Remote Sensing, 17(3):679-695.[in Chinese]) 廖静娟, 郭华东, 邵 芸. 2000. 多波段多极化成像雷达图象识别森林类型效果分析. 中国图象图形学报, 5(1):30-33. (Liao J J, Guo H D, Shao Y. 2000. Effect of forest types discrimination using multifrequency and multipolarization imaging radar images. Journal of Image and Graphics, 5(1):30-33.[in Chinese]) 李明泽, 付 瑜, 于 颖,等. 2016. 基于多时相SAR数据和SPOT数据的盘古林场林分类型识别. 植物研究, 36(4):613-619. (Li M Z, Fu Y, Yu Y, et al. 2016. Forest type classification based on multi-temporal SAR and SPOT remote sensing data in Pangu forest farm. Bulletin of Botanical Research, 36(4):613-619.[in Chinese]) 庞 勇, 李增元, 陈尔学, 等. 2003. 干涉雷达技术用于林分高估测.遥感学报, 7(1):8-13. (Pang Y, Li Z Y, Chen E X, et al. 2003. InSAR technology and its application to estimate stand average height. Journal of Remote Sensing, 7(1):8-13.[in Chinese]) 任 冲, 鞠洪波, 张怀清, 等. 2016. 多源数据林地类型的精细分类方法. 林业科学, 52(6):54-65. (Ren C, Ju H B, Zhang H Q, et al. 2016. Multi-source data for forest land type precise classification. Scientia Silvae Sinicae, 52(6):54-65.[in Chinese]) 田 甜, 范文义, 卢 伟, 等. 2015. 面向对象的优势树种类型信息提取技术. 应用生态学报, 26(6):1665-1667. (Tian T, Fan W Y, Lu W, et al. 2015. An object-based information extraction technology for dominant tree species group types. Chinese Journal of Applied Ecology, 26(6):1665-1672.[in Chinese]) 王馨爽, 陈尔学, 李增元, 等. 2014. 多时相双极化SAR影像林地类型分类方法. 林业科学, 50(3):83-91. (Wang X S, Chen E X, Li Z Y, et al. 2014. Multi-temporal and dual-polarization SAR for forest land type classification. Scientia Silvae Sinicae, 50(3):83-91.[in Chinese]) 王宇航, 范文义, 刘超逸. 2016. 基于面向对象的QUICKBIRD数据和SAR数据融合的地物分类. 东北林业大学学报, 44(9):44-49. (Wang Y H, Fan W Y, Liu C Y. 2016. An object-based fusion of QuickBird data and Radarsat SAR data for classification analysis. Journal of Northeast Forestry University, 44(9):44-49.[in Chinese]) Baltsavias E P. 1999. Airborne laser scanning:basic relations and formulas. ISPRS Journal of Photogrammetry & Remote Sensing, 54:199-214. Benz U C, Hofmann P, Willhauck G, et al. 2004.Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. International Journal of Photogrammetry & Remote Sensing, 58(3/4):239-258. Chubey M S, Franklin S E, Wulder M A. 2006. Object-based analysis of IKONOS-2 imagery for extraction of forest inventory parameters. Photogrammetric Engineering and Remote Sensing, 72(4):383-394. Colstoun E C B D, Story M H, Thompson C, et al. 2003. National Park vegetation mapping using multi-temporal Landsat 7 data and a decision tree classifier. Remote Sensing of Environment, 85(3):316-327. Clark M L, Roberts D A, Clark D B. 2005. Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sensing of Environment, 96(3):375-398. Dalponte M, Bruzzone L, Gianelle D. 2012. Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sensing of Environment, 123(4):258-270. Ecognition B. 2010. User guide. Definiens Imaging GmbH. Sunnyvale:Trimble. Gitelson A A, Kaufman Y J, Merzlyak M N. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3):289-298. Goodenough D G, Dyk A, Niemann K O, et al. 2003. Processing hyperion and ALI for forest classification. IEEE Transactions on Geoscience & Remote Sensing, 41(6):1321-1331. Heikkinen V, Korpela I, Tokola T, et al. 2011. An SVM classification of tree species radiometric signatures based on the Leica ADS40 sensor. IEEE Transactions on Geoscience and Remote Sensing. 49(11), 4539-4551. Holmgren J, Persson A, Soderman U. 2008. Species identification of individual trees by combining high resolution LiDAR data with multi-spectral images. International Journal of Remote Sensing, 29(5):1537-1552. Hill R A, Thomson A G. 2005. Mapping woodland species composition and structure using airborne spectral and LiDAR data. International Journal of Remote Sensing, 26(17):3763-3779. Immitzer M, Atzberger C, Koukal T. 2012. Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote Sensing, 4(9):2661-2693. Im J, Jensen J R, Hodgson M E. 2008. Object-based land cover classification usinghigh-posting-density LiDAR data. GIScience and Remote Sensing, 45(2):209-229. Im J, Jensen J R, Tullis J A. 2007. Object-based change detection using correlationimage analysis and image segmentation. International Journal of Remote Sensing, 29(2):399-423. Johansen K, Arroyo L A, Phinn S, et al. 2010. Comparison of geo-object based and pixel-based change detection of riparian environments using high spatial resolution multi-spectral imagery. Photogrammetric Engineering and Remote Sensing, 76(2):123-136. Janssen L L F, Wei F J M. 1994. Accuracy assessment of satellite derived land-cover data:a review. Photogrammetric Engineering & Remote Sensing, 60(4):419-426. Ke Y H, Quackenbush L J, Im J. 2010. Synergistic use of QuickBird multispectral imagery and LiDAR data for object-based forest species classification. Remote Sensing of Environment, 114(6):1141-1154. Lawrence R L, Wood S D, Sheley R L. 2006. Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications(random Forest). Remote Sensing of Environment, 100(3):356-362. Liesenberg V, Gloaguen R. 2013. Evaluating SAR polarization modes at L-band for forest classification purposes in Eastern Amazon, Brazil. International Journal of Applied Earth Observation and Geoinformation, 21(1):122-135. Lucas R, Bunting P, Paterson M, et al. 2008. Classification of Australian forest communities using aerial photography, CASI and HyMap data. Remote Sensing of Environment, 112(5):2088-2103. Sun X Y, Du H Q, Han N, et al. 2014. Synergistic use of Landsat TM and SPOT5 imagery for object-based forest classification. Journal of Applied Remote Sensing,8(1):083550-1-083550-15. Vapnik V N. 2000. The nature of statistical learning theory. Berlin:Springer Verlag. Vieira I C G, Almeida A S D, Davidson E A, et al. 2003. Classifying successional forests using Landsat spectral properties and ecological characteristics in eastern Amazȏnia. Remote Sensing of Environment, 87(4):470-481. Wolter P T, Mladenoff D J, Host G E, et al. 1995. Improved forest classification in the northern Lake States using multi-temporal Landsat imagery. Photogrammetric Engineering & Remote Sensing, 61(9):1129-1143. Yu Q, Gong P, Clinton N, et al. 2006. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering and Remote Sensing, 72(7):799-811. |