基于遥感技术的植被分类研究现状与发展趋势

编号 lyqk002272

中文标题 基于遥感技术的植被分类研究现状与发展趋势

作者 郭航  张晓丽 

作者单位 北京林业大学省部共建森林培育与保护教育部重点实验室,北京,100083;北京林业大学省部共建森林培育与保护教育部重点实验室,北京,100083

期刊名称 世界林业研究 

年份 2007 

卷号 20

期号 3

栏目编号 1

栏目名称 专题论述 

中文摘要 综述了国内外基于遥感技术进行植被分类的研究现状,并提出植被分类的发展趋势:(1)从单时相、单源遥感分类向多时相、多源信息融合发展;(2)从单一分类方法向复合分类方法发展;(3)从“硬”分类向“软”分类方向发展;(4)从基于像元分类向混合像元分解分类和面向对象分类方向发展;(5)从传统分类向智能分类方向发展。

关键词 植被分类  遥感技术  面向对象  智能分类 

基金项目 北京市自然科学基金北京山区植被覆盖动态监测智能遥感模型研究(4052020)

英文标题 Current Status and Developing Trend in Vegetation Classification Based on RS Technology

作者英文名 Guo Hang and Zhang Xiaoli

单位英文名 The Key Laboratory for Silvicuture and Conservation of Ministry of Education,Beijing Forestry University,Beijing 100083,China;The Key Laboratory for Silvicuture and Conservation of Ministry of Education,Beijing Forestry University,Beijing 100083,China

英文摘要 This paper summarized the current status of vegetation classification based on RS technology at home and abroad,and pointed out the developing trend as follows:(1)from single-phase,single-source classification to fusion of multitemporal,multisource data;(2)from single classifier to hybird classifiers;(3)from hard classification to soft classification;(4)from pixel-based classification to pixel unmixing and object-oriented classification;(5)from traditional classification to intelligent classification.

英文关键词 vegetation classification;RS technology;object-oriented;intelligent classification

起始页码 14

截止页码 19

投稿时间 2006/5/13

分类号 Q948.15

参考文献 [1] 赵英时,等.遥感应用分析原理与方法[M]. 北京:科学出版社,2003.
[2] 黄建文,鞠洪波.利用航天遥感数据进行植被分类的研究[J]. 林业科技通讯,1998,(10):9-11.
[3] 刘峰,张贵.基于GIS和RS的广州市森林植被分类研究[J]. 湖南林业科技,2004,31(1):15-17.
[4] 盛永伟,陈维英,等.利用气象卫星植被指数进行我国植被的宏观分类[J]. 科技通报,1995,40(1):68-71.
[5] Tucker,et al.African land-cover classification using satellite data[J]. Science,1985,227:233-250.
[6] 李晓兵,史培军.基于NOAA/AVHRR数据的中国植被类型NDVI变化规律研究[J]. 植物学报,1999,41(3):314-324.
[7] 徐丽华,岳文泽,等.基于二维小波变换的遥感分类研究[J]. 遥感技术与应用,2003,18(5):317-321.
[8] Townshend,et al.Multispectral dimensionality of images of normalized difference vegetation index at continental scales[J]. IEEE Transaction on Geoscience and Remote Sensing,1985,23(6):888-895.
[9] Shefali Agrawal,et al.SPOT-VEGETATION multi-temporal data for classifying vegetation in south central Asia[J]. Current Science,2003,84:1440-1448.
[10] Townshend,et al.Remote sensing of forested wetlands:application of multitemporaI and multispectral satellite imagery to determine plant community composition and structure in southeastern USA[J]. Plant Ecology,2001,157:129-149.
[11] 黄建文,鞠洪波.山区林地多季相TM影像综合分类研究[J]. 福建林学院学报,2004,24(2):136-139.
[12] Lars Aurdal,et al.Use of hidden markov models and phenology for multitemporal satellite image classification:applications to mountain vegetation classification[C] //Proceedings of the Third International Workshop on the Analysis of Multi-Temporal Remote Sensing Images 2005,Beau Rivage Resort and Casino,Biloxi,Mississippi,USA,2005:220-224.
[13] Hideki Saito,et al.Classification of forest in Malaysia using JERS-1 SAR and Landsat TM data[C] //16th Asian Conference on Remote sensing,Thailand,1995:20-24.
[14] Junichi Susakil,Ryosuke Shibasaki.Fusion of AVHRR and TM data for vegetation classification based on unmixing technique[C] // Proceedings of IEEE International Geoscience and Remote Sensing Symposium,Hawaii,2000.
[15] Mayaux Philippe,et al.Central African forest cover revisited:a multisatellite analysis[J]. Remote Sensing of Environment,2000,71(2):183-196.
[16] 游晓斌,王蕾.应用辅助信息提高森林分类和森林区划能力的研究[J]. 北京林业大学学报,2003,25(特刊):41-42.
[17] 霍宏涛,王任华,等.三维及相关辅助信息在提高图像分类精度中的研究[J]. 北京林业大学学报,2001,23(2):28-31.
[18] 李道峰,田英,等.基于NDVI数据的黄河流域地表植被覆盖综合分类研究[J]. 水土保持研究,2003,10(4):88-91.
[19] 丁建丽,塔西甫拉提·特依拜,等.人工神经网络模型及其在遥感中的应用[J]. 新疆大学学报,2001,18(3):269-276.
[20] Suha Berveroglu,et al.Mapping and monitoring of coastal wetlands of ukurova Delta in the Eastern Mediterranean region[J]. Biodiversity and Conservation,2004,(13):615-633.
[21] 王任华,霍宏涛,等.人工神经网络在遥感图像森林植被分类中的应用[J]. 北京林业大学学报,2003,25(4):1-5.
[22] 刘旭升,张晓丽.基于BP神经网络的森林植被遥感分类研究[J]. 林业资源管理,2005,(1):51-54.
[23] 王立海,赵正勇.基于BP神经网络的针阔混交林TM遥感图像自动分类技术研究[J]. 林业科学,2005,41(6):94-101.
[24] 李仪,陈云浩,等.基于微种群遗传算法和自适应BP算法的遥感图像分类[J]. 光学技术,2005,31(1):17-20.
[25] 甘淑,袁希平,等.遥感专家分类系统在滇西北植被信息提取中的应用试验研究[J]. 云南大学学报,2003,25(6):553-557.
[26] 刘卫国,龚建华,等.地理信息系统支持下的知识获取及其在遥感图像植被分类中的应用研究[J]. 遥感学报,1998,2(3):234-240.
[27] 王旭红,肖平.基于知识库分类法在植被提取中的应用研究[J]. 测绘通报,2004,(7):48-50.
[28] D Wu,J Linders.Comparison of three methods to select feature for discriminating forest cover types using SAR imagery[J]. International Journal of Remote Sensing,2000,21(10):2089-2099.
[29] 周廷刚,苏迎春.基于人工神经网络的城市航空遥感图像植被分类研究[J]. 西南师范大学学报,2004,29(6):1037-1040.
[30] Filippi,et al.Fuzzy learning vector quantization for hyperspectral coastal vegetation classification[J]. Remote Sensing of Environment,2006,100(4):512-530.
[31] Dengsheng Lu,et al.Linear mixture model applied to Amazonian vegetation classification[J]. Remote Sensing of Envinment,2003,(87):456-469.
[32] D Lu,et al.Application of spectral mixture analysis to Amazonian land-use and land-cover classification[J]. International Journal of Remote Sensing,2004,25(23):5345-5358.
[33] 钱巧静,谢瑞,等.面向对象的土地覆盖信息提取方法研究[J]. 遥感技术与应用,2005,20(3):338-342.
[34] Jason S Walker,John M Briggs.An object-oriented classification of an arid urban forest with true-color aerial photography[C] //3rd International Symposium Remote Sensing and Data Fusion Over Urban Areas,2005.
[35] James Harken,Ramanathan Sugumaran.Classification of Iowa wetlands using an airborne hyperspectral image:a comparison of the spectral angle mapper classifier and an object-oriented approach[J]. Canadian Journal of Remote Sensing,2005,31(2):167-174.
[36] Renaud Mathieu,Jagannath Aryal.Object-oriented classification and Ikonos multispectral imagery for mapping vegetation communities in urban areas[C] // SIRC 2005,Dunedin,New Zealand,2005.

PDF全文 浏览全文