基于Landsat长时间序列的森林扰动参数提取与树高估算
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摘要
【目的】研究基于遥感影像的森林扰动信息定量提取及其对树高估算的影响,为遥感反演森林参数(树高、生物量)提供参考和借鉴。【方法】选取黑龙江省凉水国家级自然保护区为研究区,以1984—2006年33期Landsat TM/ETM+多光谱遥感影像为数据源,对其进行缨帽变换提取缨帽角(TCA)和缨帽距离(TCD)2个扰动监测指数,采用时间轨迹分析方法(LandTrendr)对TCA与TCD指数进行时间序列重构,分别提取扰动发生的前一年(DBYEA)、扰动发生前的光谱值(DBVAL)、扰动持续时间(DDUR)、扰动量级(DMAG)、扰动后开始修复的时间(RBYEAR)、扰动后开始修复的光谱值(RBVAL)、修复量级(RMAG)和修复持续时间(RDUR)8个时间序列扰动参数。基于单时相Landsat影像光谱信息与单时相Landsat影像光谱信息+森林扰动参数2组变量分别采用随机森林(RF)算法估算树高。【结果】采用单时相Landsat影像光谱信息结合基于TCA和TCD提取的16个时间序列扰动参数建立的树高反演模型预估精度比采用单时相Landsat影像光谱信息建立的树高反演模型预估精度提高6.34%,均方根误差(RMSE)降低0.50 m。树高反演模型中基于TCA提取的时间序列扰动参数变量重要性高于基于TCD提取的时间序列扰动参数变量重要性。【结论】基于LandTrendr提取的森林时间序列扰动参数能够增强反射率与树高之间的相关性,提高遥感树高模型的反演精度,基于TCA提取的森林时间序列扰动参数对树高的解释能力高于基于TCD提取的森林时间序列扰动参数。
【Objective】 Forest disturbance is the main factor that influences forest height. This study was aimed to extract forest disturbance parameter based on remote sensing image and to know its effect on forest height estimation.【Method】 Liangshui National Nature Reserve in Dailing District, Heilongjiang Province, China, was selected as study area, thirty-three periods of Landsat thematic mapper and enhanced thematic mapper plus(Landsat TM/ETM+) multispectral data from 1984 to 2006 in Liangshui National Nature Reserve were acquired as data sources. The tasseled cap transform was conducted to extracttwo disturbance monitoring indices of the tasseled cap angle(TCA)and tasseled cap distance(TCD). Time trajectory analysis method Landsat-based detection of trends in disturbance and recovery(LandTrendr) was applied to conduct time series reconstruction of the Landsat TCA(TCD) images and extract timeseries disturbance parameters of forest: previous year prior to disturbance onset(DBYEA), spectral value prior to disturbance onset(DBVAL), disturbance duration(DDUR), disturbance magnitude(DMAG), time to start recovery after disturbance(RBYEAR), spectral value for recovery start after disturbance(RBVAL), recovery magnitude(RMAG), recovery duration(RDUR).Two sets of spectral information variables of single-temporal Landsat image with or without time-series disturbance parameters were applied to estimate forest height by using random forest algorithm.【Result】 Compared withthe tree height inversion model based on the spectral information of single-temporal Landsat image, the prediction accuracy of the tree height inversion model according tocombination of the spectral information of single-temporal Landsat image with 16 time-series perturbation parameters based on TCA and TCD increased by 6.34%, and the RMSE decreased by 0.5 m. In the tree height inversion model, the time-series perturbation parameters based on TCA extraction were more important than those based on TCD extraction.【Conclusion】 Time-series disturbance information of forest extracted based on LandTrendr method could enhance the correlation between reflectance and tree height, and could also improve the prediction accuracy of the tree height model based on remote sensing. Time-series perturbation parameters based on TCA extraction is better than those based on TCD extraction to interpret forest height estimation. The method can be used as a reference for remote sensing inversion of forest parameters(e.g., tree height and biomass).
【Objective】 Forest disturbance is the main factor that influences forest height. This study was aimed to extract forest disturbance parameter based on remote sensing image and to know its effect on forest height estimation.【Method】 Liangshui National Nature Reserve in Dailing District, Heilongjiang Province, China, was selected as study area, thirty-three periods of Landsat thematic mapper and enhanced thematic mapper plus(Landsat TM/ETM+) multispectral data from 1984 to 2006 in Liangshui National Nature Reserve were acquired as data sources. The tasseled cap transform was conducted to extracttwo disturbance monitoring indices of the tasseled cap angle(TCA)and tasseled cap distance(TCD). Time trajectory analysis method Landsat-based detection of trends in disturbance and recovery(LandTrendr) was applied to conduct time series reconstruction of the Landsat TCA(TCD) images and extract timeseries disturbance parameters of forest: previous year prior to disturbance onset(DBYEA), spectral value prior to disturbance onset(DBVAL), disturbance duration(DDUR), disturbance magnitude(DMAG), time to start recovery after disturbance(RBYEAR), spectral value for recovery start after disturbance(RBVAL), recovery magnitude(RMAG), recovery duration(RDUR).Two sets of spectral information variables of single-temporal Landsat image with or without time-series disturbance parameters were applied to estimate forest height by using random forest algorithm.【Result】 Compared withthe tree height inversion model based on the spectral information of single-temporal Landsat image, the prediction accuracy of the tree height inversion model according tocombination of the spectral information of single-temporal Landsat image with 16 time-series perturbation parameters based on TCA and TCD increased by 6.34%, and the RMSE decreased by 0.5 m. In the tree height inversion model, the time-series perturbation parameters based on TCA extraction were more important than those based on TCD extraction.【Conclusion】 Time-series disturbance information of forest extracted based on LandTrendr method could enhance the correlation between reflectance and tree height, and could also improve the prediction accuracy of the tree height model based on remote sensing. Time-series perturbation parameters based on TCA extraction is better than those based on TCD extraction to interpret forest height estimation. The method can be used as a reference for remote sensing inversion of forest parameters(e.g., tree height and biomass).
引文
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Hostert P,Roder A,Hill J.2003.Coupling spectral unmixing and trend analysis for monitoring of long-term vegetation dynamics in Mediterranean rangelands.Remote Sensing of Environment,87(2/3):183-197.
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Kennedy R E,Yang Z Q,Cohen W B.2010b.Detecting trends in forest disturbance and recovery using yearly Landsat time series:1.LandTrendr-Temporal segmentation algorithms.Remote Sensing of Environment,114(12):2897-2910.
Li A N,Huang,C Q,Sun G Q,et al.2011.Modeling the height of young forests regenerating from recent disturbances in Mississippi using Landsat and ICESat data.Remote Sensing of Environment,115(8):1837-1849.
Liu L Y,Peng D L,Wang Z H,et al.2014.Improving artificial forest biomass estimates using afforestation age information from time series Landsat stacks.Environmental Monitoring and Assessment,186(11):7293-7306.
Main-Knorn M,Cohen W B,Kennedy R E,et al.2013.Monitoring coniferous forest biomass change using a Landsat trajectory-based approach.Remote Sensing of Environment,139:277-290.
Olsson H.2009.A method for using Landsat time series for monitoring young plantations in boreal forests.International Journal of Remote Sensing,30(19):5117-5131.
Powell S L,Cohen W B,Yang Z Q,et al.2008.Quantification of impervious surface in the Snohomish Water Resources Inventory Area of Western Washington from 1972—2006.Remote Sensing of Environment,112(4):1895-1908.
Pflugmacher D,Cohen W B,Kennedy R E.2012.Using Landsat-derived disturbance history (1972—2010) to predict current forest structure.Remote Sensing of Environment,122(7):146-165.
Pflugmacher D,Cohen W B,Kennedy R E,et al.2014.Using Landsat-derived disturbance and recovery history and lidar to map forest biomass dynamics.Remote Sensing of Environment,151(SI):124-137.
Schroeder T A,Wulder M A,Healey S P,et al.2011.Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data.Remote Sensing of Environment,115(6):1421-1433.
Vogelmann J E,Kost J R,Tolk B,et al.2011.Monitoring landscape change for LANDFIRE using multi-temporal satellite imagery and ancillary data.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,4(2):252-264.
Duane M V,Cohen W B,Campbell J L,et al.2010.Implications of alternative field-sampling designs on Landsat-based mapping of stand age and carbon stocks in oregon forests.Forest Science,56(4):405-416.
Goodwin N R,Coops N C,Wulder M A,et al.2008.Estimation of insect infestation dynamics using a temporal sequence of Landsat data.Remote Sensing of Environment,112(9):3680-3689.
Garcia-Haro F J,Gilabert M A,Melia J.2001.Monitoring fire-affected areas using thematic mapper data.International Journal of Remote Sensing 22(4):533-549.
Healey S P,Yang Z Q,Cohen W B,et al.2006.Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data.Remote Sensing of Environment,101(1):115-126.
Huang C Q,Goward S N,Masek J G,et al.2010.An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks.Remote Sensing of Environment,114(1):183-198.
Hostert P,Roder A,Hill J.2003.Coupling spectral unmixing and trend analysis for monitoring of long-term vegetation dynamics in Mediterranean rangelands.Remote Sensing of Environment,87(2/3):183-197.
Kennedy R E,Yang Z Q,Cohen W.B.2010a.Detecting trends in forest disturbance and recovery using yearly Landsat time series:2.TimeSync-Tools for calibration and validation.Remote Sensing of Environment,114(12):2911-2924.
Kennedy R E,Yang Z Q,Cohen W B.2010b.Detecting trends in forest disturbance and recovery using yearly Landsat time series:1.LandTrendr-Temporal segmentation algorithms.Remote Sensing of Environment,114(12):2897-2910.
Li A N,Huang,C Q,Sun G Q,et al.2011.Modeling the height of young forests regenerating from recent disturbances in Mississippi using Landsat and ICESat data.Remote Sensing of Environment,115(8):1837-1849.
Liu L Y,Peng D L,Wang Z H,et al.2014.Improving artificial forest biomass estimates using afforestation age information from time series Landsat stacks.Environmental Monitoring and Assessment,186(11):7293-7306.
Main-Knorn M,Cohen W B,Kennedy R E,et al.2013.Monitoring coniferous forest biomass change using a Landsat trajectory-based approach.Remote Sensing of Environment,139:277-290.
Olsson H.2009.A method for using Landsat time series for monitoring young plantations in boreal forests.International Journal of Remote Sensing,30(19):5117-5131.
Powell S L,Cohen W B,Yang Z Q,et al.2008.Quantification of impervious surface in the Snohomish Water Resources Inventory Area of Western Washington from 1972—2006.Remote Sensing of Environment,112(4):1895-1908.
Pflugmacher D,Cohen W B,Kennedy R E.2012.Using Landsat-derived disturbance history (1972—2010) to predict current forest structure.Remote Sensing of Environment,122(7):146-165.
Pflugmacher D,Cohen W B,Kennedy R E,et al.2014.Using Landsat-derived disturbance and recovery history and lidar to map forest biomass dynamics.Remote Sensing of Environment,151(SI):124-137.
Schroeder T A,Wulder M A,Healey S P,et al.2011.Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data.Remote Sensing of Environment,115(6):1421-1433.
Vogelmann J E,Kost J R,Tolk B,et al.2011.Monitoring landscape change for LANDFIRE using multi-temporal satellite imagery and ancillary data.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,4(2):252-264.