Please wait a minute...
img

官方微信

遥感技术与应用  2016, Vol. 31 Issue (6): 1083-1090    DOI: 10.11873/j.issn.1004-0323.2016.6.1083
模型与反演     
基于MODIS时间序列森林扰动监测指数比较研究
李洛晞1,沈润平1,李鑫慧2,郭佳3
(南京信息工程大学地理与遥感学院,江苏 南京 210044)
Comparison of Forest Disturbance Indices based on MODIS Time-Series Data
Li Luoxi1,Shen Runping1,Li Xinhui2,Guo Jia3
(School ofGeography and Remote Sensing,Nanjing University of
Information Science and Technology,Nanjing 210044,China)
 全文: PDF(1360 KB)  
摘要:

森林扰动是影响陆地生态系统的重要因素之一,遥感可定期地获得大面积森林覆盖数据,成为定期和连续森林扰动监测的重要手段,基于时间序列数据的森林监测成为主要方式。研究利用2001~2013年MODIS时间序列遥感影像,以福建省为例,利用NDVI、NBRI、NDMI、IFZ和DI 5种森林扰动监测指数,结合植被变化追踪算法提取森林扰动区域,并从光谱响应特征和对不同扰动类型的响应能力等方面,分析了对我国南方森林扰动的监测能力。结果表明:DI对森林砍伐、森林病虫害和植树造林3种扰动类型的响应能力较强,NBR对森林火灾最为敏感,NDVI对4种扰动类型的响应能力相对较弱;5种指数中DI对森林扰动的响应能力较强,森林扰动提取精度最高,IFZ次之,NDMI和NBR监测精度相当,且优于NDVI。

关键词: 森林扰动MODIS时间序列NDVI    
Abstract:

Forest disturbance play an important impact on terrestrial ecosystems.Remote sensing technique has become the most important way to detect the forest disturbance at regular intervals and in a sequential manner because of the capacity of obtaining large area synchronous forest observation data at regular intervals.Forest disturbance monitoring based on time series data is becoming the main method.Fujian Province is taken as a case study.Five kinds of forest disturbance indices of DI,IFZ,NBR,NDMI and NDVI,and the different disturbance types spectral response capacity are studied,and the classification accuracy is evaluated by using MODIS time series data set from 2001~2013.The results show that extraction capacity of DI for forest cutting,plant diseases and insect pests,and afforestation is strong,and NBR is most sensitive to forest fire,in addition,spectral response capacity of NDVI for four disturbance types is relatively weak.The separability index(SI) of DI and IFZ are higher than 1 for different disturbance,which indicate that these two indices can be used to monitor multiple disturbance types.The accuracy assessment shows that DI among the indices,has the highest extraction capability.Its total accuracy to monitor the different disturbance is the highest of 92.97% and its kappa coefficient reaches to 0.92,followed by IFZ,which has the total accuracy of 89.66% and kappa coefficient of 0.88.The monitoring accuracy of NBR and NDMI nearly are the same,and are higher than NDVI.

Key words: Forest disturbance    MODIS    Time series data    NDVI
收稿日期: 2015-08-08 出版日期: 2016-12-30
:  TP 79  
基金资助:

国家自然科学重点基金支持项目(91437220)和国家重点基础研究发展计划(2010CB950700)资助。

通讯作者: 沈润平(1963-),男,江西湖口人,教授,博士生导师,主要从事遥感建模与分析研究。Email:rpshen@nuist.edu.cn。   
作者简介: 李洛晞(1989-),女,广西藤县人,硕士研究生,主要从事环境遥感研究。Email:liluoxinuist@163.com。
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  
李洛晞
沈润平
李鑫慧
郭佳

引用本文:

李洛晞,沈润平,李鑫慧,郭佳. 基于MODIS时间序列森林扰动监测指数比较研究[J]. 遥感技术与应用, 2016, 31(6): 1083-1090.

Li Luoxi,Shen Runping,Li Xinhui,Guo Jia. Comparison of Forest Disturbance Indices based on MODIS Time-Series Data. Remote Sensing Technology and Application, 2016, 31(6): 1083-1090.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2016.6.1083        http://www.rsta.ac.cn/CN/Y2016/V31/I6/1083

[1]Nakicenovic N.Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios[J].Department of the Environment,2000,114(D14):4856.

[2]Yang Chen,Shen Runping.Progress in the Study of Forest Disturbance by Remote Sensing[J].Remote Sensing for Land and Resources,2015,27(1):18.[杨辰,沈润平.森林扰动遥感监测研究进展[J].国土资源遥感,2015,27(1):18.]

[3]Yang Chen,Shen Runping,Yu Dawei,et al.Forest Disturbance Monitoring based on the Timeseries Trajectory of Remote Sensing Index[J].Journal of Remote Sensing,2013,17(5):12461263.[杨辰,沈润平,郁达威,等.利用遥感指数时间序列轨迹监测森林扰动[J].遥感学报,2013,17(5):12461263.]

[4]Jin S,Sader S A.Comparison of Time Series Tasseled Cap Wetness and the Normalized Difference Moisture Index in Detecting Forest Disturbances[J].Remote Sensing of Environment,2005,94(3):364372.

[5]Healey S P,Cohen W B,Yang Zhiqiang,et al.Comparison of Tasseled Capbased Landsat Data Structures for Use in Forest Disturbance Detection[J].Remote Sensing of Environment,2005,97(3):301310.

[6]Gómez C,White J C,Wulder M A.Characterizing the State and Processes of Change in a Dynamic Forest Environment Using Hierarchical Spatiotemporal Segmentation[J].Remote Sensing of Environment,2011,115(7):16651679.

[7]Huang C Q,Goward S N,Masek J G,et al.An Automated Approach for Reconstructing Recent Forest Disturbance History Using Dense Landsat Time Series Stacks[J].Remote Sensing of Environment,2010,114(1):183198.

[8]LópezGarcía M J ,Caselles V.Mapping Burns and Natural Reforestation Using Thematic Mapper Data[J].Geocarto International,1991,6(1):3137.

[9]Jin S,Sader S A.MODIS Timeseries Imagery for Forest Disturbance Detection and Quantification of Patch Size Effects[J].Remote Sensing of Environment,2005,99(4):462470.

[10]Hobbs R J,Wallace J F,Campbell N A.Classification of Vegetation in the Western Australian Wheatbelt Using Landsat MSS Data[J].Vegetatio,1989,80(2):91105.

[11]Xie Ninggao,Liu Dayou.The Protection of the World Cultural and Natural Hertage Revival of Wuyi Landscape Civilization[J].Famous Scenery,2008,(3):2831.[谢凝高,刘达友.保护世界自然文化遗产复兴武夷山水文明[J].风景名胜,2008,(3):2831.]

[12]Hansen M C,Potapov P V,Moore R,et al.HighResolution Global Maps of 21stCentury Forest Cover Change[J].Science,2013,342(6160):850853.

[13]Wang Hao,Lü Zhi,Gu Lei,et al.Observations of China's Forest Chage(2000~2013) based on Global Forest Watch Dataset[J].Biodiversity Science,2015,23(5):575582.[王昊,吕植,顾垒,等.基于Global Forest Watch观察2000~2013年间中国森林变化[J].生物多样性,2015,23(5):575582.] 

[14]Lobser S E,Cohen W B.MODIS Tasselled Cap:Land Cover Characteristics Expressed through Transformed MODIS Data[J].International Journal of Remote Sensing,2007,28(22):50795101.

[15]Huang Chunbo,Dian Yuanyong,Zhou Zhixiang,et al.Forest Change Detection based on Time Series Images with Statistical Properties[J].Journal of Remote Sensing,19(4):657668.[黄春波,佃袁勇,周志翔,等.基于时间序列统计特性的森林变化监测[J].遥感学报,2015,19(4):657668.]

[16]Hardisky M A,Smart R M,Klemas V.Growth Response and Spectral Characteristics of a Short Spartina Alterniflora Salt Marsh Irrigated with Freshwater and Sewage Effluent[J].Remote Sensing of Environment,1983,13(83):5767.

[17]Wilson E H,Sader S A.Detection of Forest Harvest Type Using Multiple Dates of Landsat TM Imagery[J].Remote Sensing of Environment,2002,80(1):385396.

[18]Zhu Z,Key C H,Ohlen D,et al.Evaluate Sensitivities of BurnSeverity Mapping Algorithms for Different Ecosystems and Fire Histories in the United States[R].Bios,Idaho,Final Report to the Joint Fire Science Program,2006.

[19]Rouse J W.Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation[R].Greenbelt,MD:NASA/GSFC Type Ⅲ Final Report,1974.

[20]Sellers P J,Berry J A,Collatz G J,et al.Canopy Reflectance,Photosynthesis,and Transpiration.III.A Reanalysis Using Improved Leaf Models and a New Canopy Integration Scheme[J].Remote Sensing of Environment,1992,42(3):187216.

[21]Huete A R.A Soiladjusted Vegetation Index(SAVI)[J].Remote Sensing of Environment,1988,25(3):295309.

[22]Asrar G,Fuchs M,Kanemasu E T,et al.Estimating Absorbed Photosynthetic Radiation and LeafArea Index from Spectral Reflectance in Wheat[J].Agronomy Journal,1984,76(2):300306.

[23]Turner D P,Cohen W B,Kennedy R E,et al.Relationships between Leaf Area Index and Landsat TM Spectral Vegetation Indices across Three Temperate Zone Sites[J].Remote Sensing of Environment,1999,70(99):5268.

[24]Healey J S,Baranchuk A,Crystal E,et al.Prevention of Atrial Fibrillation with AngiotensinConverting Enzyme Inhibitors and Angiotensin Receptor Blockers:A MetaAnalysis[J].Journal of the American College of Cardiology,2005,45(11):18321839.

[25]Huang C Q,Goward S N,Schleeweis K,et al.Dynamics of National Forests Assessed Using the Landsat Record:Case Studies in Eastern United States[J].Remote Sensing of Environment,2009,113(7):14301442.

[26]Kennedy R E,Cohen W B,Schroeder T A.Trajectorybased Change Detection for Automated Characterization of Forest Disturbance Dynamics[J].Remote Sensing of Environment,2007,110(3):370386.

[27]Kaufman Y J,Remer L A.Detection of Forests Using MidIR reflectance:An Application for Aerosol Studies[J].IEEE Transactions on Geoscience & Remote Sensing,1994,32(3):672683.

[28]Lasapona R.Estimating Spectral Separability of Satellite Derived Parameters for Burned Areas Mapping in the Calabria Region by Using SPOTVegetation Data[J].Ecological Modelling,2006,196(12):265270.

[29]Wu Liye,Shen Runping,Li Xinhui,et al.Evaluating Differet Remote Sensing Indexes for Forest Burn Scars Extraction[J].Remote Sensing Technology and Application,2014,29(4):567574.[吴立叶,沈润平,李鑫慧,等.不同遥感指数提取林火迹地研究[J].遥感技术与应用,2014,29(4):567574.]


 

[1] 金点点,宫兆宁. 基于Landsat 系列数据地表温度反演算法对比分析—以齐齐哈尔市辖区为例[J]. 遥感技术与应用, 2018, 33(5): 830-841.
[2] 冯姣姣,王维真,李净,刘雯雯. 基于BP神经网络的华东地区太阳辐射模拟及时空变化分析[J]. 遥感技术与应用, 2018, 33(5): 881-889.
[3] 张滔,唐宏. 基于Google Earth Engine的京津冀2001~2015年植被覆盖变化与城镇扩张研究[J]. 遥感技术与应用, 2018, 33(4): 593-599.
[4] 汪航,师茁. 基于MODIS时间序列数据的春尺蠖虫害遥感监测方法研究—以新疆巴楚胡杨为例[J]. 遥感技术与应用, 2018, 33(4): 686-695.
[5] 苗茜,王昭生,王荣,黄玫,孙佳丽. 基于NDVI数据评估O3污染对华北地区夏季植被生长的影响[J]. 遥感技术与应用, 2018, 33(4): 696-702.
[6] 周玉科,刘建文. 基于MODIS NDVI和多方法的青藏高原植被物候时空特征分析[J]. 遥感技术与应用, 2018, 33(3): 486-498.
[7] 拉巴卓玛,次珍. 2002~2015年西藏雅鲁藏布江流域积雪变化及影响因子分析研究[J]. 遥感技术与应用, 2018, 33(3): 508-519.
[8] 张帅,师春香,梁晓,贾炳浩,吴捷. 风云三号积雪覆盖产品评估[J]. 遥感技术与应用, 2018, 33(1): 35-46.
[9] 王佳鹏,施润和,张超,刘浦东,曾毓燕. 基于光谱分析的长江口湿地互花米草叶片叶绿素含量反演研究[J]. 遥感技术与应用, 2017, 32(6): 1056-1063.
[10] 周晓宇,陈富龙. 四川大熊猫栖息地PALSAR时序数据森林覆盖动态监测研究[J]. 遥感技术与应用, 2017, 32(6): 1100-1106.
[11] 杨涛,黄法融,李倩,白磊,李兰海. 新疆北部植被生长季NDVI时空变化及其与冬季降雪的关系[J]. 遥感技术与应用, 2017, 32(6): 1132-1140.
[12] 孙晓,吴孟泉,何福红,张安定,赵德恒,李勃 . 2015年黄海海域浒苔时空分布及台风“灿鸿”影响研究[J]. 遥感技术与应用, 2017, 32(5): 921-930.
[13] 周金霖,马明国,肖青,闻建光. 西南地区植被覆盖动态及其与气候因子的关系[J]. 遥感技术与应用, 2017, 32(5): 966-972.
[14] 黎微微,胡斯勒图,陈洪滨,尚华哲. 利用MODIS资料计算不同云天条件下的地表太阳辐射[J]. 遥感技术与应用, 2017, 32(4): 643-650.
[15] 方雨晨,王培燕,田庆久. 不同覆盖度下小麦农田土壤对NDVI影响模拟分析[J]. 遥感技术与应用, 2017, 32(4): 660-666.