树种水平的大兴安岭地上生物量变化特征及其与气候和干扰的关系
|
摘要
树种水平地上生物量(每个树种的地上生物量)是衡量森林生态系统结构功能的重要指标。为揭示树种水平森林地上生物量变化机制及其与气候变化和干扰的关系,运用KNN (k-nearest neighbor distance)方法将森林调查数据和MODIS数据相结合,估算了黑龙江大兴安岭2000、2010和2015年树种水平的森林地上生物量,在此基础上运用典型对应分析和随机森林方法,分析了研究区树种水平地上生物量变化特征及其与气候和干扰因素的关系。研究结果表明:2000—2015年期间,研究区总的森林地上生物量增加了8.9%(0.41×10~8 t),其中2010—2015年期间地上生物量的增加速度要明显高于2000—2010年;地上生物量增加最多的树种为白桦(Betula platyphylla Suk.),与2000年相比生物量增加了0.40×10~8 t,其次为樟子松(Pinus sylvestris var.mongolica Litv.)、山杨(Populus davidiana Dode)和蒙古栎(Quercus mongolica Fisch. ex Ledeb.),落叶松(Larix gmelinii(Rupr.) Kuzen)地上生物量下降了0.08×10~8 t,柳树(Chosenia arbutifolia(Pall.) A. Skv.)和云杉(Picea koraiensis Nakai)基本上没有变化;林火、采伐和造林等森林干扰均对树种水平地上生物量影响显著,林火对树种水平地上生物量的影响要高于造林和采伐;气候要素显示出了比干扰要素更为重要的作用,多年平均温度和降水解释了最多的树种水平地上生物量变异。年均温度与阔叶树种的生物量以及林火干扰有显著的正相关性,与总的森林地上生物量呈现出显著的负相关,与落叶松和白桦表现出微弱的负相关,预示着气候变暖将影响该区域的树种组成并降低该区域的森林生产力。
Species-level biomass and its change are important indicators in the measurement of the reliability of forest ecosystem structure and function. To understand the changes in species-level biomass and its linkage to climate and forest disturbances, maps of changes in species-level biomass in the Great Xing′an Mountains between 2000 and 2015 were generated by integrating Moderate Resolution Imaging Spectroradiometer(MODIS) images with forest inventory data using k-nearest neighbor distance(kNN) methods. The distribution information regarding fires, logging, and afforestation in the Great Xing′an Mountains between 2000 and 2015 were also extracted from MODIS production. The relationships between species-level biomass changes, climatic factors, and forest disturbances were explored using canonical correspondence analysis(CCA) and the random forests method. The results showed that the total aboveground biomass in the study area increased by 8.9%(0.41×10~8 t) from 2000 to 2015. For the species-level biomass, white birch(Betula platyphylla Suk.) increased most significantly(0.40×10~8 t), followed by pine(Pinus sylvestris var. mongolica Litv.), aspen(Populus davidiana Dode), and Mongolian oak(Quercus mongolica Fisch. ex Ledeb.). The biomass of larch(Larix gmelinii(Rupr.) Kuzen) declined the most, decreasing by 0.08×10~8 t from 2000 to 2015. There were no obvious changes for the species-level biomass of willow(Chosenia arbutifolia(Pall.) A. Skv.) and spruce(Picea koraiensis Nakai) during these period. The areas of fire disturbance derived from MODIS products were consistent with the records in the yearbook for the Great Xing′an Mountains(R~2=0.97). Fire disturbance, logging, and afforestation all significantly influenced the changes in the species-level biomass(P<0.01). Among the three disturbance factors, fire disturbance exhibited the greatest explanatory ability for the variation in species-level biomass change based on the random forests method. However, climatic factors exhibited a relatively greater importance value than did forest disturbances. Annual temperature and precipitation explained the greatest variation in species-level biomass change among all the climatic factors and forest disturbances, and exhibited a strong positive correlation with broad-leaf species biomass and fire disturbance, but a negative correlation with total aboveground biomass(AGB), logging, and afforestation. A weak negative correlation between annual temperature and the biomass of white birch and larch was observed. Annual precipitation was positively correlated with the biomass of white birch and total AGB but negatively correlated with the biomass of larch and annual temperature. Our results indicated that climatic warming would increase the proportion of deciduous broad-leaved tree species and reduce forest ecosystem productivity in the Great Xing′an Mountains in northeast China.
Species-level biomass and its change are important indicators in the measurement of the reliability of forest ecosystem structure and function. To understand the changes in species-level biomass and its linkage to climate and forest disturbances, maps of changes in species-level biomass in the Great Xing′an Mountains between 2000 and 2015 were generated by integrating Moderate Resolution Imaging Spectroradiometer(MODIS) images with forest inventory data using k-nearest neighbor distance(kNN) methods. The distribution information regarding fires, logging, and afforestation in the Great Xing′an Mountains between 2000 and 2015 were also extracted from MODIS production. The relationships between species-level biomass changes, climatic factors, and forest disturbances were explored using canonical correspondence analysis(CCA) and the random forests method. The results showed that the total aboveground biomass in the study area increased by 8.9%(0.41×10~8 t) from 2000 to 2015. For the species-level biomass, white birch(Betula platyphylla Suk.) increased most significantly(0.40×10~8 t), followed by pine(Pinus sylvestris var. mongolica Litv.), aspen(Populus davidiana Dode), and Mongolian oak(Quercus mongolica Fisch. ex Ledeb.). The biomass of larch(Larix gmelinii(Rupr.) Kuzen) declined the most, decreasing by 0.08×10~8 t from 2000 to 2015. There were no obvious changes for the species-level biomass of willow(Chosenia arbutifolia(Pall.) A. Skv.) and spruce(Picea koraiensis Nakai) during these period. The areas of fire disturbance derived from MODIS products were consistent with the records in the yearbook for the Great Xing′an Mountains(R~2=0.97). Fire disturbance, logging, and afforestation all significantly influenced the changes in the species-level biomass(P<0.01). Among the three disturbance factors, fire disturbance exhibited the greatest explanatory ability for the variation in species-level biomass change based on the random forests method. However, climatic factors exhibited a relatively greater importance value than did forest disturbances. Annual temperature and precipitation explained the greatest variation in species-level biomass change among all the climatic factors and forest disturbances, and exhibited a strong positive correlation with broad-leaf species biomass and fire disturbance, but a negative correlation with total aboveground biomass(AGB), logging, and afforestation. A weak negative correlation between annual temperature and the biomass of white birch and larch was observed. Annual precipitation was positively correlated with the biomass of white birch and total AGB but negatively correlated with the biomass of larch and annual temperature. Our results indicated that climatic warming would increase the proportion of deciduous broad-leaved tree species and reduce forest ecosystem productivity in the Great Xing′an Mountains in northeast China.
引文
[1] Bonan G B.Forests and climate change:forcings,feedbacks,and the climate benefits of forests.Science,2008,320(5882):1444- 1449.
[2] Houghton R A,Hall F,Goetz S J.Importance of biomass in the global carbon cycle.Journal of Geophysical Research,2009,114(G2):G00E03.
[3] Führer E.Forest functions,ecosystem stability and management.Forest Ecology and Management,2000,132(1):29- 38.
[4] Dale V H,Joyce L A,McNulty S,Neilson R P,Ayres M P,Flannigan M D,Hanson P J,Irland L C,Lugo A E,Peterson C J,Simberloff D,Swanson F J,Stocks B J,Wotton B M.Climate Change and Forest Disturbances:climate change can affect forests by altering the frequency,intensity,duration,and timing of fire,drought,introduced species,insect and pathogen outbreaks,hurricanes,windstorms,ice storms,or landslides.BioScience,2001,51(9):723- 734.
[5] Gustafson E J,Shvidenko A Z,Sturtevant B R,Scheller R M.Predicting global change effects on forest biomass and composition in south-central Siberia.Ecological Applications,2010,20(3):700- 715.
[6] Du H B,Liu J,Li M H,Buntgen U,Yang Y,Wang L,Wu Z F,He H S.Warming-induced upward migration of the alpine treeline in the Changbai Mountains,northeast China.Global Change Biology,2018,24(3):1256- 1266.
[7] Liang Y,Duveneck M J,Gustafson E J,Serra-Diaz J M,Thompson J R.How disturbance,competition,and dispersal interact to prevent tree range boundaries from keeping pace with climate change.Global Change Biology,2018,24(1):e335-e351.
[8] Li X N,He H S,Wu Z W,Liang Y,Schneiderman J E.Comparing effects of climate warming,fire,and timber harvesting on a boreal forest landscape in northeastern China.PLoS One,2013,8(4):e59747.
[9] Zhang Y Z,Liang S L.Changes in forest biomass and linkage to climate and forest disturbances over Northeastern China.Global Change Biology,2014,20(8):2596- 2606.
[10] Savage S L,Lawrence R L,Squires J R.Predicting relative species composition within mixed conifer forest pixels using zero-inflated models and Landsat imagery.Remote Sensing of Environment,2015,171:326- 336.
[11] Beaudoin A,Bernier P Y,Guindon L,Villemaire P,Guo X J,Stinson G,Bergeron T,Magnussen S,Hall R J.Mapping attributes of Canada′s forests at moderate resolution through kNN and MODIS imagery.Canadian Journal of Forest Research,2014,44(5):521- 532.
[12] Wilson B T,Lister A J,Riemann R I.A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data.Forest Ecology and Management,2012,271:182- 198.
[13] Zhang Q L,He H S,Liang Y,Hawbaker T J,Henne P D,Liu J X,Huang S L,Wu Z W,Huang C.Integrating forest inventory data and MODIS data to map species-level biomass in Chinese boreal forests.Canadian Journal of Forest Research,2018,48(5):461- 479.
[14] 罗旭,王聿丽,张金荃.气候变化和林火干扰对大兴安岭林区地上生物量影响的动态模拟.应用生态学报,2018,29(3):713- 724.
[15] Fang L,Yang J,Zu J X,Li G C,Zhang J S.Quantifying influences and relative importance of fire weather,topography,and vegetation on fire size and fire severity in a Chinese boreal forest landscape.Forest Ecology and Management,2015,356:2- 12.
[16] 徐化成.中国大兴安岭森林.北京:科学出版社,1998.
[17] Zald H S J,Ohmann J L,Roberts H M,Gregory M J,Henderson E B,McGaughey R J,Braaten J.Influence of lidar,Landsat imagery,disturbance history,plot location accuracy,and plot size on accuracy of imputation maps of forest composition and structure.Remote Sensing of Environment,2014,143:26- 38.
[18] Mao D H,Wang Z M,Luo L,Ren C Y.Integrating AVHRR and MODIS data to monitor NDVI changes and their relationships with climatic parameters in Northeast China.International Journal of Applied Earth Observation and Geoinformation,2012,18:528- 536.
[19] Pouliot D,Latifovic R,Fernandes R,Olthof I.Evaluation of annual forest disturbance monitoring using a static decision tree approach and 250 m MODIS data.Remote Sensing of Environment,2009,113(8):1749- 1759.
[20] Sulla-Menashe D,Kennedy R E,Yang Z Q,Braaten J,Krankina O N,Friedl M A.Detecting forest disturbance in the Pacific Northwest from MODIS time series using temporal segmentation.Remote Sensing of Environment,2014,151:114- 123.
[21] 国务院.森林采伐更新管理办法.北京:林业部,1987.
[22] 王纪军,裴铁璠.气候变化对森林演替的影响.应用生态学报,2004,15(10):1722- 1730.
[23] 刘国华,傅伯杰.全球气候变化对森林生态系统的影响.自然资源学报,2001,16(1):71- 78.
[24] 肖辉林.森林衰退与全球气候变化.生态学报,1994,14(4):430- 436.
[25] 方精云,刘国华,徐嵩龄.我国森林植被的生物量和净生产量.生态学报,1996,16(5):497- 508.
[26] 程肖侠,延晓冬.气候变化对中国东北主要森林类型的影响.生态学报,2008,28(2):534- 543.
[27] Tautenhahn S,Lichstein J W,Jung M,Kattge J,Bohlman S A,Heilmeier H,Prokushkin A,Kahl A,Wirth C.Dispersal limitation drives successional pathways in Central Siberian forests under current and intensified fire regimes.Global Change Biology,2016,22(6):2178- 2197.
[28] 李晓娜,贺红士,吴志伟,梁宇.大兴安岭北部森林景观对气候变化的响应.应用生态学报,2012,23(12):3227- 3235.
[29] 杨光,舒立福,邸雪颖.气候变化影响下大兴安岭地区21世纪森林火险等级变化预测.应用生态学报,2012,23(12):3236- 3242.
[30] 程肖侠,延晓冬.气候变化对中国大兴安岭森林演替动态的影响.生态学杂志,2007,26(8):1277- 1284.
[31] 罗旭,贺红士,梁宇,吴志伟,黄超,张庆龙.林火干扰对大兴安岭主要林分类型地上生物量预测的影响模拟研究.生态学报,2016,36(4):1104- 1114.
[32] 冷文芳,贺红士,布仁仓,胡远满.气候变化条件下东北森林主要建群种的空间分布.生态学报,2006,26(12):4257- 4266.
[33] 李月辉,胡远满,常禹,徐崇刚,李秀珍,布仁仓,贺红士.大兴安岭呼中林业局森林景观格局变化及其驱动力.生态学报,2006,26(10):3347- 3357.
[34] 罗旭.林火和采伐对大兴安岭森林地上生物量预测的影响[D].北京:中国科学院大学,2014.
[35] 徐文茹,贺红士,罗旭,黄超,唐志强,刘凯,丛毓,谷晓楠,宗盛伟,杜海波.停止商业性采伐对大兴安岭森林结构与地上生物量的影响.生态学报,2017,38(4):1203- 1215.
[36] 杨志香.气候变化对我国兴安落叶松天然林地理分布及生产力的影响[D].北京:中国科学院大学,2014.
[37] 蔡文华.气候变化下大兴安岭火干扰对森林演替及NPP的影响[D].北京:中国科学院大学,2014.
[38] 大兴安岭地区行署统计局.大兴安岭统计年鉴2012.大兴安岭:大兴安岭地区行署统计局,2012.
[39] 魏书精,胡海清,孙龙,周汝良.气候变化背景下我国森林防火工作的形势及对策.森林防火,2011,(2):1- 4.
[40] 胡海清,魏书精,孙龙.大兴安岭2001- 2010年森林火灾碳排放的计量估算.生态学报,2012,32(17):5373- 5386.
[41] 胡海清,孙龙,国庆喜,吕新双.大兴安岭1980- 1999年乔木燃烧释放碳量研究.林业科学,2007,43(11):82- 88.
[42] 侯振宏,张小全,肖文发.中国森林管理活动碳汇及其潜力.林业科学,2012,48(8):11- 15.
[43] 张佩昌.试论天然林保护工程.林业科学,1999,35(2):124- 131.
[44] 屈红军,朱颖,张忠林,孟庆彬.我国天然林保护工程一期实施效果概述.林业科技开发,2012,26(6):5- 8.
[45] 魏亚伟,周旺明,于大炮,周莉,方向民,赵伟,包也,孟莹莹,代力民.我国东北天然林保护工程区森林植被的碳储量.生态学报,2014,34(20):5696- 5705.
[46] 刘志华,常禹,贺红士,胡远满,王文娟.模拟不同森林可燃物处理对大兴安岭潜在林火状况的影响.生态学杂志,2009,28(8):1462- 1469.
[47] 郭笑怡.中国东北森林干扰遥感研究[D].长春:东北师范大学,2015.
[48] 陈宏伟,胡远满,常禹,布仁仓,贺红士,刘淼,刘志华,韩文权.落叶松毛虫对大兴安岭呼中林区森林的景观长期影响模拟.应用生态学报,2010,21(5):1090- 1096.
[49] 于跃.大兴安岭林区落叶松毛虫发生的影响因子分析[D].沈阳:沈阳师范大学,2016.
[50] Hawbaker T J,Vanderhoof M K,Beal Y J,Takacs J D,Schmidt G L,Falgout J T,Williams B,Fairaux N M,Caldwell M K,Picotte J J,Howard S M,Stitt S,Dwyer J L.Mapping burned areas using dense time-series of Landsat data.Remote Sensing of Environment,2017,198:504- 522.
[51] White J C,Wulder M A,Hermosilla T,Coops N C,Hobart G W.A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series.Remote Sensing of Environment,2017,194:303- 321.
[52] Guindon L,Bernier P Y,Beaudoin A,Pouliot D,Villemaire P,Hall R J,Latifovic R,St-Amant R.Annual mapping of large forest disturbances across Canada′s forests using 250 m MODIS imagery from 2000 to 2011.Canadian Journal of Forest Research,2014,44(12):1545- 1554.
[53] Zhu Z,Woodcock C E.Continuous change detection and classification of land cover using all available Landsat data.Remote Sensing of Environment,2014,144:152- 171.
[54] Zhao F,Huang C Q,Zhu Z L.Use of vegetation change tracker and support vector machine to map disturbance types in greater yellowstone ecosystems in a 1984- 2010 landsat time series.IEEE Geoscience and Remote Sensing Letters,2015,12(8):1650- 1654.
[55] Beaudoin A,Bernier P Y,Villemaire P,Guindon L,Guo X J.Tracking forest attributes across Canada between 2001 and 2011 using a k nearest neighbors mapping approach applied to MODIS imagery.Canadian Journal of Forest Research,2018,48(1):85- 93.
[2] Houghton R A,Hall F,Goetz S J.Importance of biomass in the global carbon cycle.Journal of Geophysical Research,2009,114(G2):G00E03.
[3] Führer E.Forest functions,ecosystem stability and management.Forest Ecology and Management,2000,132(1):29- 38.
[4] Dale V H,Joyce L A,McNulty S,Neilson R P,Ayres M P,Flannigan M D,Hanson P J,Irland L C,Lugo A E,Peterson C J,Simberloff D,Swanson F J,Stocks B J,Wotton B M.Climate Change and Forest Disturbances:climate change can affect forests by altering the frequency,intensity,duration,and timing of fire,drought,introduced species,insect and pathogen outbreaks,hurricanes,windstorms,ice storms,or landslides.BioScience,2001,51(9):723- 734.
[5] Gustafson E J,Shvidenko A Z,Sturtevant B R,Scheller R M.Predicting global change effects on forest biomass and composition in south-central Siberia.Ecological Applications,2010,20(3):700- 715.
[6] Du H B,Liu J,Li M H,Buntgen U,Yang Y,Wang L,Wu Z F,He H S.Warming-induced upward migration of the alpine treeline in the Changbai Mountains,northeast China.Global Change Biology,2018,24(3):1256- 1266.
[7] Liang Y,Duveneck M J,Gustafson E J,Serra-Diaz J M,Thompson J R.How disturbance,competition,and dispersal interact to prevent tree range boundaries from keeping pace with climate change.Global Change Biology,2018,24(1):e335-e351.
[8] Li X N,He H S,Wu Z W,Liang Y,Schneiderman J E.Comparing effects of climate warming,fire,and timber harvesting on a boreal forest landscape in northeastern China.PLoS One,2013,8(4):e59747.
[9] Zhang Y Z,Liang S L.Changes in forest biomass and linkage to climate and forest disturbances over Northeastern China.Global Change Biology,2014,20(8):2596- 2606.
[10] Savage S L,Lawrence R L,Squires J R.Predicting relative species composition within mixed conifer forest pixels using zero-inflated models and Landsat imagery.Remote Sensing of Environment,2015,171:326- 336.
[11] Beaudoin A,Bernier P Y,Guindon L,Villemaire P,Guo X J,Stinson G,Bergeron T,Magnussen S,Hall R J.Mapping attributes of Canada′s forests at moderate resolution through kNN and MODIS imagery.Canadian Journal of Forest Research,2014,44(5):521- 532.
[12] Wilson B T,Lister A J,Riemann R I.A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data.Forest Ecology and Management,2012,271:182- 198.
[13] Zhang Q L,He H S,Liang Y,Hawbaker T J,Henne P D,Liu J X,Huang S L,Wu Z W,Huang C.Integrating forest inventory data and MODIS data to map species-level biomass in Chinese boreal forests.Canadian Journal of Forest Research,2018,48(5):461- 479.
[14] 罗旭,王聿丽,张金荃.气候变化和林火干扰对大兴安岭林区地上生物量影响的动态模拟.应用生态学报,2018,29(3):713- 724.
[15] Fang L,Yang J,Zu J X,Li G C,Zhang J S.Quantifying influences and relative importance of fire weather,topography,and vegetation on fire size and fire severity in a Chinese boreal forest landscape.Forest Ecology and Management,2015,356:2- 12.
[16] 徐化成.中国大兴安岭森林.北京:科学出版社,1998.
[17] Zald H S J,Ohmann J L,Roberts H M,Gregory M J,Henderson E B,McGaughey R J,Braaten J.Influence of lidar,Landsat imagery,disturbance history,plot location accuracy,and plot size on accuracy of imputation maps of forest composition and structure.Remote Sensing of Environment,2014,143:26- 38.
[18] Mao D H,Wang Z M,Luo L,Ren C Y.Integrating AVHRR and MODIS data to monitor NDVI changes and their relationships with climatic parameters in Northeast China.International Journal of Applied Earth Observation and Geoinformation,2012,18:528- 536.
[19] Pouliot D,Latifovic R,Fernandes R,Olthof I.Evaluation of annual forest disturbance monitoring using a static decision tree approach and 250 m MODIS data.Remote Sensing of Environment,2009,113(8):1749- 1759.
[20] Sulla-Menashe D,Kennedy R E,Yang Z Q,Braaten J,Krankina O N,Friedl M A.Detecting forest disturbance in the Pacific Northwest from MODIS time series using temporal segmentation.Remote Sensing of Environment,2014,151:114- 123.
[21] 国务院.森林采伐更新管理办法.北京:林业部,1987.
[22] 王纪军,裴铁璠.气候变化对森林演替的影响.应用生态学报,2004,15(10):1722- 1730.
[23] 刘国华,傅伯杰.全球气候变化对森林生态系统的影响.自然资源学报,2001,16(1):71- 78.
[24] 肖辉林.森林衰退与全球气候变化.生态学报,1994,14(4):430- 436.
[25] 方精云,刘国华,徐嵩龄.我国森林植被的生物量和净生产量.生态学报,1996,16(5):497- 508.
[26] 程肖侠,延晓冬.气候变化对中国东北主要森林类型的影响.生态学报,2008,28(2):534- 543.
[27] Tautenhahn S,Lichstein J W,Jung M,Kattge J,Bohlman S A,Heilmeier H,Prokushkin A,Kahl A,Wirth C.Dispersal limitation drives successional pathways in Central Siberian forests under current and intensified fire regimes.Global Change Biology,2016,22(6):2178- 2197.
[28] 李晓娜,贺红士,吴志伟,梁宇.大兴安岭北部森林景观对气候变化的响应.应用生态学报,2012,23(12):3227- 3235.
[29] 杨光,舒立福,邸雪颖.气候变化影响下大兴安岭地区21世纪森林火险等级变化预测.应用生态学报,2012,23(12):3236- 3242.
[30] 程肖侠,延晓冬.气候变化对中国大兴安岭森林演替动态的影响.生态学杂志,2007,26(8):1277- 1284.
[31] 罗旭,贺红士,梁宇,吴志伟,黄超,张庆龙.林火干扰对大兴安岭主要林分类型地上生物量预测的影响模拟研究.生态学报,2016,36(4):1104- 1114.
[32] 冷文芳,贺红士,布仁仓,胡远满.气候变化条件下东北森林主要建群种的空间分布.生态学报,2006,26(12):4257- 4266.
[33] 李月辉,胡远满,常禹,徐崇刚,李秀珍,布仁仓,贺红士.大兴安岭呼中林业局森林景观格局变化及其驱动力.生态学报,2006,26(10):3347- 3357.
[34] 罗旭.林火和采伐对大兴安岭森林地上生物量预测的影响[D].北京:中国科学院大学,2014.
[35] 徐文茹,贺红士,罗旭,黄超,唐志强,刘凯,丛毓,谷晓楠,宗盛伟,杜海波.停止商业性采伐对大兴安岭森林结构与地上生物量的影响.生态学报,2017,38(4):1203- 1215.
[36] 杨志香.气候变化对我国兴安落叶松天然林地理分布及生产力的影响[D].北京:中国科学院大学,2014.
[37] 蔡文华.气候变化下大兴安岭火干扰对森林演替及NPP的影响[D].北京:中国科学院大学,2014.
[38] 大兴安岭地区行署统计局.大兴安岭统计年鉴2012.大兴安岭:大兴安岭地区行署统计局,2012.
[39] 魏书精,胡海清,孙龙,周汝良.气候变化背景下我国森林防火工作的形势及对策.森林防火,2011,(2):1- 4.
[40] 胡海清,魏书精,孙龙.大兴安岭2001- 2010年森林火灾碳排放的计量估算.生态学报,2012,32(17):5373- 5386.
[41] 胡海清,孙龙,国庆喜,吕新双.大兴安岭1980- 1999年乔木燃烧释放碳量研究.林业科学,2007,43(11):82- 88.
[42] 侯振宏,张小全,肖文发.中国森林管理活动碳汇及其潜力.林业科学,2012,48(8):11- 15.
[43] 张佩昌.试论天然林保护工程.林业科学,1999,35(2):124- 131.
[44] 屈红军,朱颖,张忠林,孟庆彬.我国天然林保护工程一期实施效果概述.林业科技开发,2012,26(6):5- 8.
[45] 魏亚伟,周旺明,于大炮,周莉,方向民,赵伟,包也,孟莹莹,代力民.我国东北天然林保护工程区森林植被的碳储量.生态学报,2014,34(20):5696- 5705.
[46] 刘志华,常禹,贺红士,胡远满,王文娟.模拟不同森林可燃物处理对大兴安岭潜在林火状况的影响.生态学杂志,2009,28(8):1462- 1469.
[47] 郭笑怡.中国东北森林干扰遥感研究[D].长春:东北师范大学,2015.
[48] 陈宏伟,胡远满,常禹,布仁仓,贺红士,刘淼,刘志华,韩文权.落叶松毛虫对大兴安岭呼中林区森林的景观长期影响模拟.应用生态学报,2010,21(5):1090- 1096.
[49] 于跃.大兴安岭林区落叶松毛虫发生的影响因子分析[D].沈阳:沈阳师范大学,2016.
[50] Hawbaker T J,Vanderhoof M K,Beal Y J,Takacs J D,Schmidt G L,Falgout J T,Williams B,Fairaux N M,Caldwell M K,Picotte J J,Howard S M,Stitt S,Dwyer J L.Mapping burned areas using dense time-series of Landsat data.Remote Sensing of Environment,2017,198:504- 522.
[51] White J C,Wulder M A,Hermosilla T,Coops N C,Hobart G W.A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series.Remote Sensing of Environment,2017,194:303- 321.
[52] Guindon L,Bernier P Y,Beaudoin A,Pouliot D,Villemaire P,Hall R J,Latifovic R,St-Amant R.Annual mapping of large forest disturbances across Canada′s forests using 250 m MODIS imagery from 2000 to 2011.Canadian Journal of Forest Research,2014,44(12):1545- 1554.
[53] Zhu Z,Woodcock C E.Continuous change detection and classification of land cover using all available Landsat data.Remote Sensing of Environment,2014,144:152- 171.
[54] Zhao F,Huang C Q,Zhu Z L.Use of vegetation change tracker and support vector machine to map disturbance types in greater yellowstone ecosystems in a 1984- 2010 landsat time series.IEEE Geoscience and Remote Sensing Letters,2015,12(8):1650- 1654.
[55] Beaudoin A,Bernier P Y,Villemaire P,Guindon L,Guo X J.Tracking forest attributes across Canada between 2001 and 2011 using a k nearest neighbors mapping approach applied to MODIS imagery.Canadian Journal of Forest Research,2018,48(1):85- 93.