Please wait a minute...
 
国土资源遥感  2014, Vol. 26 Issue (2): 5-10    DOI: 10.6046/gtzyyg.2014.02.02
  综述 本期目录 | 过刊浏览 | 高级检索 |
Landsat卫星图像用于大面积森林扰动监测的研究进展
祝善友, 张莹, 张海龙, 曹云, 张桂欣
南京信息工程大学遥感学院, 南京 210044
Progress of researches on monitoring large-area forest disturbance by Landsat satellite images
ZHU Shanyou, ZHANG Ying, ZHANG Hailong, CAO Yun, ZHANG Guixin
School of Remote Sensing, Nanjing University of Information Science and Technology, Nanjing 210044, China
全文: PDF(719 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 自然因素和人为原因使不同程度的森林扰动现象频繁发生,对森林资源管理、气候变化等产生了重要影响。在全球变暖的大背景下,大面积森林扰动监测及其影响已成为目前国内外研究的热点与前沿问题之一,Landsat系列卫星是最为常用的一类数据。在深入分析国内外相关方法的基础上,综述了Landsat卫星图像用于大面积森林扰动遥感监测的研究进展,主要方法包括全地面覆盖制图、抽样方法和与较低空间分辨率图像复合方法3大类,并对比分析了这些方法的优缺点,最后对未来可能的研究方向做出了展望。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
许飞
张雪红
李栋
冯晓钰
关键词 遥感城市热岛效应“白屋顶计划”节能效率    
Abstract:The frequent forest disturbance caused by natural factors and human activities has very important effects on forest resources management, climate change and some other fields. Under the background of global warming, researches on forest disturbance monitoring and its corresponding influence have become one of the hot topics both in China and abroad. Based on a detailed analysis of the previous studies, this paper has reviewed the progress of monitoring methods for the large-area forest disturbance by using Landsat satellite imagery, which mainly include wall-to-wall mapping, sampling mapping and data fusion with the image of low spatial resolution. The advantages and disadvantages of these methods as well as the possible research prospects in the future are also discussed.
Key wordsremote sensing    city heat island effect    “white roof plan”    energy efficiency
收稿日期: 2013-04-22      出版日期: 2014-03-28
:  S771.8  
基金资助:国家重点基础研究发展计划(编号:2010CB950701);民用航天“十二五”预先研究项目(编号:D040103);国家自然科学基金项目(编号:41001289);江苏高校优势学科建设工程资助项目。
作者简介: 祝善友(1977- ),男,博士,副教授,主要从事热红外遥感与资源环境遥感研究。Email:zsyzgx@163.com。
引用本文:   
祝善友, 张莹, 张海龙, 曹云, 张桂欣. Landsat卫星图像用于大面积森林扰动监测的研究进展[J]. 国土资源遥感, 2014, 26(2): 5-10.
ZHU Shanyou, ZHANG Ying, ZHANG Hailong, CAO Yun, ZHANG Guixin. Progress of researches on monitoring large-area forest disturbance by Landsat satellite images. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(2): 5-10.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2014.02.02      或      https://www.gtzyyg.com/CN/Y2014/V26/I2/5
[1] 吴雪琼,覃先林,周汝良,等.森林覆盖变化遥感监测方法研究进展[J].林业资源管理,2010(4):82-87. Wu X Q,Qin X L,Zhou R L,et al.Progress of study on forest cover change detection by using remote sensing technique[J].Forest Resources Management,2010(4):82-87.
[2] Dale V H,Joyce L A,Mcnulty S,et al.Climate change and forest disturbances[J].Bioscience,2001,51(9):723-734.
[3] Houghton R A.The annual net flux of carbon to the atmosphere from changes in land use 1850—1990[J].Tellus Series B-Chemical and Physical Meteorology,1999,51(2):298-313.
[4] Avissar R,Werth D.Global hydroclimatological teleconnections resulting from tropical deforestation[J].Journal of Hydrometeorology,2005,6(2):134-145.
[5] Broich M,Stehman S V,Hansen M C,et al.A comparison of sampling designs for estimating deforestation from Landsat imagery:A case study of the Brazilian Legal Amazon[J].Remote Sensing of Environment,2009,113(11):2448-2454.
[6] Curran L M,Trigg S N.Sustainability science from space:Quantifying forest disturbance and land-use dynamics in the Amazon[J].Proceedings of the National Academy of Sciences of the United States of America,2006,103(34):12663-12664.
[7] FAO.State of the world's forests[R].Rome,2005.
[8] 李世明,王志慧,韩学文,等.森林资源变化遥感监测技术研究进展[J].北京林业大学学报,2011,33(3):132-138. Li S M,Wang Z H,Han X W,et al.Overview of forest resources change detection methods using remote sensing techniques[J].Journal of Beijing Forestry University,2011,33(3):132-138.
[9] 赵宪文,李崇贵,斯林.基于信息技术的森林资源调查新体系[J].北京林业大学学报,2002,24(5):147-155. Zhao X W,Li C G,Si L.Building a new system of forest resources inventory by information technology[J].Journal of Beijing Forestry University,2002,24(5):147-155.
[10] Hansen M C,DeFries R S,Townshend J R G,et al.Towards an operational MODIS continuous field of percent tree cover algorithm:Examples using AVHRR and MODIS data[J].Remote Sensing of Environment,2002,83(1/2),303-319.
[11] Hansen M C,DeFries R S,Townshend J R G,et al.Global percent tree cover at a spatial resolution of 500 meters:First results of the MODIS vegetation continuous fields algorithm[J].Earth Interactions,2003,7(10):1-15.
[12] Jin S,Sader S A.MODIS time-series imagery for forest disturbance detection and quantification of patch size effects[J].Remote Sensing of Environment,2005,99(4):462-470.
[13] Hansen M C,Stehman S V,Potapov P V,et al.Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data[J].Proceedings of the National Academy of Sciences,2008,105(27):9439-9444.
[14] Morton D C,DeFries R S,Shimabukuro Y E,et al.Rapid assessment of annual deforestation in the Brazilian Amazon using MODIS data[J].Earth Interactions,2005,9(8):1-22.
[15] Czaplewski R L.Can a sample of Landsat sensor scenes reliably estimate the global extent of tropical deforestation?[J].International Journal of Remote Sensing,2003,24(6):1409-1412.
[16] Duveiller G,Defourny P,Desclee B,et al.Deforestation in central Africa:Estimates at regional,national and landscape levels by advanced processing of systematically-distributed Landsat extracts[J].Remote Sensing of Environment,2008,112(5):1969-1981.
[17] Huang C Q,Kim S,Song K,et al.Assessment of Paraguay's forest cover change using Landsat observations[J].Global and Planetary Change,2009,67:1-12.
[18] Skole D,Tucker C.Tropical deforestation and habitat fragmentation in the Amazon:Satellite data from 1978 to 1988[J].Science,1993,260,1905-1910.
[19] INPE.Monitoramento da Floresta Amazônica Brasileira por Satélite. Projeto PRODES[EB/OL].http://www.obt.inpe.br/prodes/,2001.
[20] Guild L,Cohen W,Kauffman J.Detection of deforestation and land conversion in Rondonia,Brazil using change detection techniques[J].International Journal of Remote Sensing,2004,25(4):731-750.
[21] Townshend J R G,Bell V,Desch A C,et al.The NASA Landsat pathnder humid tropical deforestation project,land satellite information in the next decade[C]//Tyson's Corner,Virginia:American Society Of Photogrammetry and Remote Sensing,1995,IV76-IV87.
[22] Sánchez-Azofeifa G A,Harriss R C,Skole D L.Deforestation in Costa Rica:A quantitative analysis using remote sensing imagery[J].Biotropica,2001,33(3):378-384.
[23] Forest Survey of India.State of forest report 2003[R].Dehra Dun,India:Ministry of Environment and Forest,2004.
[24] Hansen M C,Roy D,Lindquist E,et al.A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin[J].Remote Sensing of Environment,2008,112(5):2495-2513.
[25] Shimabukuro Y E,Batista G T,Mello E M K,et al.Using shade fraction image segmentation to evaluate deforestation in Landsat Thematic Mapper images of the Amazon Region[J].International Journal of Remote Sensing,1998,19(3):535-541.
[26] FAO.Tropical resources assessment 1990[R].FAO Forestry Paper 112,Food and Agricultural Organization of the United Nations,Rome,1993.
[27] FAO.Forest resources assessment 1990:Survey of tropical forest cover and study of change processes[R].FAO Forestry Paper 130,Food and Agricultural Organization of the United Nations,Rome,1996.
[28] Tejaswi G.Manual on deforestation,degradation,and fragmentation using remote sensing and GIS[R].MAR-SFM working paper,Rome,2007.
[29] Tucker C J,Townshend J R G.Strategies for monitoring tropical deforestation using satellite data[J].International Journal of Remote Sensing,2000,21(6-7):1461-1471.
[30] Hansen M C,Shimabukuro Y E,Potapov P,et al.Comparing annual MODIS and PRODES forest cover change data for advancing monitoring of Brazilian forest cover[J].Remote Sensing of Environment,2008,112:3784-3793.
[31] Goward S N,Masek J G,Cohen W B,et al.Forest disturbance and North American carbon flux[J].Earth Observing System Transactions,2008,89(11):105-116.
[32] Skole D,Chomentowski W H,Salas W A,et al.Physical and human dimensions of deforestation in Amazonia[J].Bioscience,1994,44(5):314-321.
[33] Stone T A,Schlesinger P,Houghton R A,et al.A map of the vegetation of South America based upon satellite imagery[J].Photogrammetric Engineering and Remote Sensing,1994,60(5):541-551.
[34] FAO.Global forest resources assessment 2000:Main report[R].Forestry Paper No140,Food and Agriculture Organization of the United Nations,Rome,2001.
[35] Achard F,Eva H D,Stibig H J,et al.Determination of deforestation rates of the world's humid tropical forests[J].Science,2002,297(5583):999-1002.
[36] McRoberts R E,Wendt D G,Nelson M D,et al.Using a land cover classification based on satellite imagery to improve the precision of forest inventory area estimates[J].Remote Sensing of Environment,2002,81:36-44.
[37] McRoberts R E,Holden G R,Nelson M D,et al.Using satellite imagery as ancillary data for increasing the precision of estimates for the forest inventory and analysis program of the USDA forest service[J].Canadian Journal of Forest Research,2005,35(12):2968-2980.
[38] Stehman S V,Sohl T L,Loveland T R.An evaluation of sampling strategies to improve precision of estimates of gross change in land use and land cover[J].International Journal of Remote Sensing,2005,26:4941-4957.
[39] Richards T,Gallego J,Achard F.Sampling for forest cover change assessment at the pan-tropical scale[J].International Journal of Remote Sensing,2000,21(6/7):1473-1490.
[40] Baumann M,Ozdogan M,Kuemmerle T,et al.Using the Landsat record to detect forest-cover changes during and after the collapse of the Soviet Union in the temperate zone of European Russia[J].Remote Sensing of Environment,2012,124:174-184.
[41] Sanchez-Azofeifa G,Skole D L,Chomentowski W.Sampling global deforestation databases:The role of persistence[J].Mitigation and Adaptation Strategies for Global Change,1997,2(2/3):177-189.
[42] Gallego F J.Stratified sampling of satellite images with a systematic grid of points[J].ISPRS Journal of Photogrammetry and Remote Sensing,2005,59(6):369-376.
[43] Mayaux P,Holmgren P,Achard F,et al.Tropical forest cover change in the 1990s and options for future monitoring[J].Philosophical Transactions of the Royal Society B,2005,360(1454):373-384.
[44] Tomppo E,Czaplewski R L,Mkisara K.The role of remote sensing in global forest assessment [R].Forest Resources Assessment Working Paper 61 Rome:Food and Agriculture Organization of the United Nations.ftp://ftp.fao.org/docrep/fao/006/ad650e/ad650e00.pdf,2002.
[45] Dunn R,Harrison A R.Two dimensional systematic sampling of land use[J].Applied Statistics,1993,42(4):585-601.
[46] Ridder R M.Global forest resource assessment 2010[R].Options and Recommendations for a Global Remote Sensing Survey of Forests.Forest Resources Assessment Programme.Working Paper 141.Rome,2007.
[47] Gao F,Masek J,Schwaller M,Hall F,et al.On the blending of the Landsat and MODIS surface reflectance:Predicting daily Landsat surface reflectance[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(8):2207-2218.
[48] Ranson K J,Kovacs K,Sun G,et al.Disturbance recognition in the boreal forest using radar and Landsat-7[J].Canadian Journal of Remote Sensing,2003,29(2):271-285.
[49] Leckie D.Advances in remote sensing technologies for forest survey and management[J].Canadian Journal of Forest Research,1990,20(4):464-483.
[50] Arai E,Shimabukuro Y E,Pereira G,et al.A multi-resolution multi-temporal technique for detecting and mapping deforestation in the Brazilian Amazon Rainforest[J].Remote Sensing,2011,3(12):1943-1956.
[51] Zhu Z,Evans D L.US forest types and predicted percent forest cover from AVHRR data[J].Photogrammetric Engineering and Remote Sensing,1994,60(5):525-531.
[52] Fazakas Z,Nilsson M.Volume and forest cover estimation over southern Sweden using AVHRR data calibrated with TM data[J].International Journal of Remote Sensing,1996,17(9):1701-1709.
[53] Hayes D J,Cohen W B.Spatial,spectral and temporal patterns of tropical forest cover change as observed with multiple scales of optical satellite data[J].Remote Sensing of Environment,2007,106(1):1-16.
[1] 刘文, 王猛, 宋班, 余天彬, 黄细超, 江煜, 孙渝江. 基于光学遥感技术的冰崩隐患遥感调查及链式结构研究——以西藏自治区藏东南地区为例[J]. 自然资源遥感, 2022, 34(1): 265-276.
[2] 王茜, 任广利. 高光谱遥感异常信息在阿尔金索拉克地区铜金矿找矿工作中的应用[J]. 自然资源遥感, 2022, 34(1): 277-285.
[3] 吕品, 熊丽媛, 徐争强, 周学铖. 基于FME的矿山遥感监测矢量数据图属一致性检查方法[J]. 自然资源遥感, 2022, 34(1): 293-298.
[4] 张大明, 张学勇, 李璐, 刘华勇. 一种超像素上Parzen窗密度估计的遥感图像分割方法[J]. 自然资源遥感, 2022, 34(1): 53-60.
[5] 薛白, 王懿哲, 刘书含, 岳明宇, 王艺颖, 赵世湖. 基于孪生注意力网络的高分辨率遥感影像变化检测[J]. 自然资源遥感, 2022, 34(1): 61-66.
[6] 宋仁波, 朱瑜馨, 郭仁杰, 赵鹏飞, 赵珂馨, 朱洁, 陈颖. 基于多源数据集成的城市建筑物三维建模方法[J]. 自然资源遥感, 2022, 34(1): 93-105.
[7] 李伟光, 侯美亭. 植被遥感时间序列数据重建方法简述及示例分析[J]. 自然资源遥感, 2022, 34(1): 1-9.
[8] 丁波, 李伟, 胡克. 基于同期光学与微波遥感的茅尾海及其入海口水体悬浮物反演[J]. 自然资源遥感, 2022, 34(1): 10-17.
[9] 高琪, 王玉珍, 冯春晖, 马自强, 柳维扬, 彭杰, 季彦桢. 基于改进型光谱指数的荒漠土壤水分遥感反演[J]. 自然资源遥感, 2022, 34(1): 142-150.
[10] 张秦瑞, 赵良军, 林国军, 万虹麟. 改进遥感生态指数的宜宾市三江汇合区生态环境评价[J]. 自然资源遥感, 2022, 34(1): 230-237.
[11] 贺鹏, 童立强, 郭兆成, 涂杰楠, 王根厚. 基于地形起伏度的冰湖溃决隐患研究——以希夏邦马峰东部为例[J]. 自然资源遥感, 2022, 34(1): 257-264.
[12] 艾璐, 孙淑怡, 李书光, 马红章. 光学与SAR遥感协同反演土壤水分研究进展[J]. 自然资源遥感, 2021, 33(4): 10-18.
[13] 李特雅, 宋妍, 于新莉, 周圆锈. 卫星热红外温度反演钢铁企业炼钢月产量估算模型[J]. 自然资源遥感, 2021, 33(4): 121-129.
[14] 刘白露, 管磊. 南海珊瑚礁白化遥感热应力检测改进方法研究[J]. 自然资源遥感, 2021, 33(4): 136-142.
[15] 吴芳, 金鼎坚, 张宗贵, 冀欣阳, 李天祺, 高宇. 基于CZMIL测深技术的海陆一体地形测量初探[J]. 自然资源遥感, 2021, 33(4): 173-180.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
版权所有 © 2015 《自然资源遥感》编辑部
地址:北京学院路31号中国国土资源航空物探遥感中心 邮编:100083
电话:010-62060291/62060292 E-mail:zrzyyg@163.com
本系统由北京玛格泰克科技发展有限公司设计开发