基于遥感的黑龙江流域火烧迹地及其植被恢复研究
|
摘要
黑龙江流域地处欧亚大陆温带草原东缘及北方森林南缘的过度地带，同时地跨中、蒙、俄三国，极高的植被覆盖度及其固有的气候条件，使其成为受森林火灾影响较为严重的一个区域。特别是俄罗斯境内地区，火烧迹地的植被恢复过程基本上均为自然恢复过程，从而使其成为森林火灾及植被恢复研究的典型区域。因此，本文选择黑龙江流域作为研究区，对其2000-2011年的火烧迹地进行了提取，并从定性及定量的角度对其植被恢复过程进行了分析。
本文以遥感数据为基础，首先提出了基于MODIS时序数据的火烧迹地提取算法，对黑龙江流域2000-2011年的火烧迹地信息进行了提取。以此为基础，并以遥感物候参量为分析指标，对火烧迹地的植被恢复进行了定性分析。随后，以植被指数以及净初级生产力为手段，采用与邻近区域相比较的方法，对火烧迹地的植被恢复过程进行了定量分析。最后，以TM/ETM数据为基础数据，对不同火烈度下的植被恢复过程进行了监测。主要得到以下结论：
一、MODIS时序数据能够实现区域尺度下的火烧迹地研究，并达到一定的精度。本文以黑龙江省为例对基于MODIS时序数据的火烧迹地提取算法进行了精度验证，结果表明，算法总体精度为71%，较之以往的区域尺度下的火烧迹地提取算法有了一定的提高。
二、黑龙江流域受火灾影响严重。研究区2000-2011年，年均产生火烧迹地面积达53.21万公顷。年际间波动较大，最为严重的年份2003年，火烧迹地面积大146.79万公顷，而受火灾影响最小的年份（2010年），过火面积仅为18.39万公顷。火灾主要分布于俄罗斯境内以及我国的黑龙江省境内，森林覆被率较高的区域。
三、火烧迹地地表植被动态变化过程缓慢，特别是在火烧强烈的地区，地表植被的物候参量表现出森林的特征需要很长的时间。选取EVI每年的最大值、最小值、平均值、振幅、生长始期、生长末期以及生长季长度7个物候特征参量作为描述火烧迹地植被恢复的指标。结果显示，火灾发声前后EVI表现出明显差异，火烧将造成地表植被的显著减少。在火烧较轻的地区，植被恢复过程较快，如6号迹地；而在火烧较为严重的区域，火烧后基本表现为草地的状态，并逐年恢复，恢复过程受各种环境因子的影响，恢复过程缓慢，甚至经过10年的恢复仍表现为草地。
四、火烧迹地的植被指数及植被净初级生产力的恢复表现出相似的特征，且恢复过程较为缓慢。火灾的发生对森林覆被地区的植被指数有极为显著的影响，造成植被指数的显著下降。火灾发生后的次年表现为最小值，之后开始恢复。初期恢复速度较快，之后开始变缓，并且需要7-8年的时间才能接近火烧前的水平。此外，对火烧迹地NPP的分析表明，NPP的恢复过程与植被指数表现出相似的特征，恢复过程可能出现波动的状况，但总体呈增长趋势，恢复过程需要7年左右的时间。
不同森林覆被类型下的植被恢复速度存在一定的差别，针叶林地区恢复过程较慢，相对而言，阔叶林与混交林地区速度较快。
五、不同火烈度下的植被恢复过程差异明显。以火烈度分布图为基础，对不同火烈度等级下的植被覆盖度恢复过程进行了分析。结果表明，在火灾发生后的11年里，地表植被呈现持续增长的趋势，植被恢复总体较好，当仍未恢复到火烧前的水平。在植被恢复的第一个5年时间段内（2001-2006年），植被增长速率较快，所有火烈度等级下，植被增加80%以上的区域均占据主要地位。而到第二个5年时间段内（2006-2011年），植被增长速率减缓，各火烈度等级下的植被恢复过程出现明显差异。从总体植被恢复特征来看，除却极重度火烧强度外，其他火烈度等级下的植被均得到了较好的恢复，植被恢复80%以上的区域所占比重最大。而在极重度火烧强度下，植被恢复80%以上的区域只占到17.33%，比重最高的为植被恢复60-80%的区域，其次为40-60%的区域。表明这一火烈度等级下，植被的恢复过程还在继续，仍需要较长的时间才能恢复到火烧前的水平。
Heilongjiang Basin, which is located in both ecotones of the east of temperatesteppe in Eruasia and the south of boreal forest, is across over China, Mongolia andRussia. High vegetation cover and inherent climate conditions make it become one ofthe most effected regions by forest fire. Especially in Russia, the regeneration ofvegetation in burned area is mainly a natural process and makes it a representativeregion for forest fire study. Therefore, we chose Heilongjiang basin as the study area,extracted the burned area information of it from2000to2011, and analyzed thevegetation regeneration process of the burned area by both qualitative and quantitativemethods.
Based on remote sensing data, we firstly proposed a burned area extractingalgorithm using MODIS time series data, and extracted the burned area information ofHeilongjiang basin from2000to2011. Based on it, we took remote sensingphonological parameters as the evaluation indexes for qualitative analysis of thevegetation regeneration. Then, we used vegetation index and NPP quantitativeanalyzed the vegetation regrowth process. Finally, based on TM/ETM remote sensingdata, we analyzed the vegetation regeneration process under different fire severity.The main conclusions were as follows:
1. Under the regional scale, MODIS data can be used to extract the burned areawith higher accuracy. We took Heilongjiang province as the example to validate thealgorithm based on MODIS time series data. The result showed that the total accuracywas71%which is higher than other burned area mapping algorithm under regionalscale.
2. Heilongjiang basin was seriously affected by fire. From2000to2011, theannual average value of burned area was0.53million ha. The most affected year was2003with the burned area of14.68million ha. And the least affected year was2010,the burned area just0.18million ha. The forest fire mainly distributed in Russia andHeilongjiang province of China, which has higher vegetation cover.
3. The vegetation dynamic change of burned area was slowly, especially in thehigh burn severity region, and it took a long time of the vegetation phenology featuresreflected a forest’ characters. We chose seven phonological parameters as theevaluation index including EVI maximum value, minimum value, mean value andamplitude, the start time of growing season, the end time of the growing season andthe growing season length. The result showed that there was a obviously decline ofEVI value between pre-and post-fire. The vegetation regrowth would more fast wherethe burn severity was lighter. But in high burn severity area, the regeneration processwas slow.
4. The recovery of vegetation index of burned area was similar with the NPPrecovery process, both were a slow process. Fire had a serious effect on vegetationindex in forest covered area, would cause it a significant decline. The first yearpost-fires, vegetation index was the minimum value, and after it the value started toincrease. In the initial several years, the increase rate was fast and gradually decrease.It took7-8years of the vegetation index value recovery to the level of pre-fire.Moreover, the analysis of NPP recovery in burned area showed that it has a similarprocess with vegetation. The process might be found a fluctuation state, but thegeneral trend was increase. The recovery would take about7years.
There a different of the regeneration speed under different forest types. Thespeed under coniferous forest was relatively slow and which was faster underbroadleaf forest and mixed-forest.
5. There is a obviously difference of the regeneration process among differentburn severity. Based on the burn severity map, we analyzed the vegetation regrowthprocess under five severity grades. The result showed that, in the eleven years afterfire happened, the vegetation was a continuous increase trend, but had not beenachieve the level of pre-fire. In the first five years stage (2001-2006) of vegetationregeneration, the vegetation increase speed was fast. Under every burn severity grade,the area of vegetation increase80%were the predominance one. In the second fiveyears (2006-2011), the increase speed was relatively slow, and there was a differencebetween different burn severity grades.
本文以遥感数据为基础，首先提出了基于MODIS时序数据的火烧迹地提取算法，对黑龙江流域2000-2011年的火烧迹地信息进行了提取。以此为基础，并以遥感物候参量为分析指标，对火烧迹地的植被恢复进行了定性分析。随后，以植被指数以及净初级生产力为手段，采用与邻近区域相比较的方法，对火烧迹地的植被恢复过程进行了定量分析。最后，以TM/ETM数据为基础数据，对不同火烈度下的植被恢复过程进行了监测。主要得到以下结论：
一、MODIS时序数据能够实现区域尺度下的火烧迹地研究，并达到一定的精度。本文以黑龙江省为例对基于MODIS时序数据的火烧迹地提取算法进行了精度验证，结果表明，算法总体精度为71%，较之以往的区域尺度下的火烧迹地提取算法有了一定的提高。
二、黑龙江流域受火灾影响严重。研究区2000-2011年，年均产生火烧迹地面积达53.21万公顷。年际间波动较大，最为严重的年份2003年，火烧迹地面积大146.79万公顷，而受火灾影响最小的年份（2010年），过火面积仅为18.39万公顷。火灾主要分布于俄罗斯境内以及我国的黑龙江省境内，森林覆被率较高的区域。
三、火烧迹地地表植被动态变化过程缓慢，特别是在火烧强烈的地区，地表植被的物候参量表现出森林的特征需要很长的时间。选取EVI每年的最大值、最小值、平均值、振幅、生长始期、生长末期以及生长季长度7个物候特征参量作为描述火烧迹地植被恢复的指标。结果显示，火灾发声前后EVI表现出明显差异，火烧将造成地表植被的显著减少。在火烧较轻的地区，植被恢复过程较快，如6号迹地；而在火烧较为严重的区域，火烧后基本表现为草地的状态，并逐年恢复，恢复过程受各种环境因子的影响，恢复过程缓慢，甚至经过10年的恢复仍表现为草地。
四、火烧迹地的植被指数及植被净初级生产力的恢复表现出相似的特征，且恢复过程较为缓慢。火灾的发生对森林覆被地区的植被指数有极为显著的影响，造成植被指数的显著下降。火灾发生后的次年表现为最小值，之后开始恢复。初期恢复速度较快，之后开始变缓，并且需要7-8年的时间才能接近火烧前的水平。此外，对火烧迹地NPP的分析表明，NPP的恢复过程与植被指数表现出相似的特征，恢复过程可能出现波动的状况，但总体呈增长趋势，恢复过程需要7年左右的时间。
不同森林覆被类型下的植被恢复速度存在一定的差别，针叶林地区恢复过程较慢，相对而言，阔叶林与混交林地区速度较快。
五、不同火烈度下的植被恢复过程差异明显。以火烈度分布图为基础，对不同火烈度等级下的植被覆盖度恢复过程进行了分析。结果表明，在火灾发生后的11年里，地表植被呈现持续增长的趋势，植被恢复总体较好，当仍未恢复到火烧前的水平。在植被恢复的第一个5年时间段内（2001-2006年），植被增长速率较快，所有火烈度等级下，植被增加80%以上的区域均占据主要地位。而到第二个5年时间段内（2006-2011年），植被增长速率减缓，各火烈度等级下的植被恢复过程出现明显差异。从总体植被恢复特征来看，除却极重度火烧强度外，其他火烈度等级下的植被均得到了较好的恢复，植被恢复80%以上的区域所占比重最大。而在极重度火烧强度下，植被恢复80%以上的区域只占到17.33%，比重最高的为植被恢复60-80%的区域，其次为40-60%的区域。表明这一火烈度等级下，植被的恢复过程还在继续，仍需要较长的时间才能恢复到火烧前的水平。
Heilongjiang Basin, which is located in both ecotones of the east of temperatesteppe in Eruasia and the south of boreal forest, is across over China, Mongolia andRussia. High vegetation cover and inherent climate conditions make it become one ofthe most effected regions by forest fire. Especially in Russia, the regeneration ofvegetation in burned area is mainly a natural process and makes it a representativeregion for forest fire study. Therefore, we chose Heilongjiang basin as the study area,extracted the burned area information of it from2000to2011, and analyzed thevegetation regeneration process of the burned area by both qualitative and quantitativemethods.
Based on remote sensing data, we firstly proposed a burned area extractingalgorithm using MODIS time series data, and extracted the burned area information ofHeilongjiang basin from2000to2011. Based on it, we took remote sensingphonological parameters as the evaluation indexes for qualitative analysis of thevegetation regeneration. Then, we used vegetation index and NPP quantitativeanalyzed the vegetation regrowth process. Finally, based on TM/ETM remote sensingdata, we analyzed the vegetation regeneration process under different fire severity.The main conclusions were as follows:
1. Under the regional scale, MODIS data can be used to extract the burned areawith higher accuracy. We took Heilongjiang province as the example to validate thealgorithm based on MODIS time series data. The result showed that the total accuracywas71%which is higher than other burned area mapping algorithm under regionalscale.
2. Heilongjiang basin was seriously affected by fire. From2000to2011, theannual average value of burned area was0.53million ha. The most affected year was2003with the burned area of14.68million ha. And the least affected year was2010,the burned area just0.18million ha. The forest fire mainly distributed in Russia andHeilongjiang province of China, which has higher vegetation cover.
3. The vegetation dynamic change of burned area was slowly, especially in thehigh burn severity region, and it took a long time of the vegetation phenology featuresreflected a forest’ characters. We chose seven phonological parameters as theevaluation index including EVI maximum value, minimum value, mean value andamplitude, the start time of growing season, the end time of the growing season andthe growing season length. The result showed that there was a obviously decline ofEVI value between pre-and post-fire. The vegetation regrowth would more fast wherethe burn severity was lighter. But in high burn severity area, the regeneration processwas slow.
4. The recovery of vegetation index of burned area was similar with the NPPrecovery process, both were a slow process. Fire had a serious effect on vegetationindex in forest covered area, would cause it a significant decline. The first yearpost-fires, vegetation index was the minimum value, and after it the value started toincrease. In the initial several years, the increase rate was fast and gradually decrease.It took7-8years of the vegetation index value recovery to the level of pre-fire.Moreover, the analysis of NPP recovery in burned area showed that it has a similarprocess with vegetation. The process might be found a fluctuation state, but thegeneral trend was increase. The recovery would take about7years.
There a different of the regeneration speed under different forest types. Thespeed under coniferous forest was relatively slow and which was faster underbroadleaf forest and mixed-forest.
5. There is a obviously difference of the regeneration process among differentburn severity. Based on the burn severity map, we analyzed the vegetation regrowthprocess under five severity grades. The result showed that, in the eleven years afterfire happened, the vegetation was a continuous increase trend, but had not beenachieve the level of pre-fire. In the first five years stage (2001-2006) of vegetationregeneration, the vegetation increase speed was fast. Under every burn severity grade,the area of vegetation increase80%were the predominance one. In the second fiveyears (2006-2011), the increase speed was relatively slow, and there was a differencebetween different burn severity grades.
引文
Achim R, Joachim H, Beatriz D, et al. Using long time series of Landsat data to monitor fireevents and post-fire dynamics and identify driving factors. A case study in the Ayora region(eastern Spain). Remote Sensing of Environment,2008,112:259-273.
Amiro B D, Chen M, Liu J. Net primary productivity following forest fire for Canadianecoregions. Canadian Journal of Forest Research,2000,30:939-947.
Amiro B D, Orhcansky A L, Barr A G. The effect of post-fire stand age on the boreal forestenergy balance. Agricultural and Forest Meteorology,2006,140:41-50.
Amiro B D, Stocks B J, Alexander M E, et al. Fire, climate change, carbon and fuel managementin the Canadian boreal forest. International Journal of Wildland Fire,2001,10:405-413.
Amiro B, MacPherson J, Desjardins R. BOREAS flight measurements of forest fire effects oncarbon dioxide and energy fluxes. Agricultural and Forest Meteorology,1999,96:199-208.
Arai K. Nonlinear mixture model of mixed pixels in remote sensing satellite images based onMonte Carlo simulation. Advances in Space Research,2008,41:1725-1743.
Askeyev O V, Sparks T H, Askeyev I V, et al. East versus West: contrasts in phonologicalpatterns. Global Ecology and Biogeography,2010,19:783-793.
Asner G and Lobell D. A biogeophysical approach for automated SWIR unmixing of soils andvegetation. Remote Sensing of Environment,2000,74:99-112.
Asner G. Biophysical and biochemical sources of variability in canopy reflectance. RemoteSensing of Environment,1998,64:234-253.
Atkinson P, Cutler M, Lewis H. Mapping sub-pixel proportional land-cover with AVHRRimagery. International Journal of Remote Sensing,1997,18:917-935.
Ayres M P and Lombardero M J. Assessing the consequences of global change for forestdisturbance from herbivores and pathogens. Science of Total Environment,2000,262:263-286.
Balzter h, Gerard F F, George C T et al. Impact of the Arctic Oscillation pattern on interannualforest fire variability in Central Siberia. Geophysical Research Letters,2005,32, L14709.1-L14709.4.
Balzter H, Gerard F, George C, et al. Coupling of vegetation growing season anomalies and fireactivity with hemispheric and regional-scale climate patterns in central and east Siberia. JClimate,2007,20:3713-3729.
Barbosa P M, Gregoire J M, Pereira J M C. An algorithm for extracting burned areas from timeseries of AVHRR GAC data applied at a continental scale. Remote Sensing of Environment,1999,69:253-263.
Baret F, Clevers J C P W, Steven M D. The robustness of canopy gap fraction estimates from redand near-infrared reflectances: a comparison of approaches. Remote of Environment,1995,54:141-151.
Beauty R and Taylor A. Spatial and temporal variation of fire regimes in a mixed conifer forestlandscape, southern Cascades, California, USA. Journal of Biogeography,2001,28:955-966.
Beck P S A, Atzberger C, Hogda K A, et al. Improved monitoring of vegetation dynamics at veryhigh latitudes: A new method using MODIS NDVI. Remote Sensing of Environment,2006,100:321-334.
Belda F and Melia J. Relationships between climatic parameters and forest vegetation:application to burned area in Alicante (Spain). Forest Ecology and Management,2000,135:195-204.
Bertolette D and Spotskey D. Remotely sensed burn severity mapping. In: H. David Harmon,Michigan, Crossing Boundaries in Park Management: Proceeding of the11thConference onresearch and Resource Management in Parks and on Public Lands,2001, The GeorgeWright Socieyt:44-51.
Betancourt J L, Schwartz M D, Breshears D D, et al. Implementing a U.S. National PhenologyNetwork.2011, http://www.usanpn.org/files/publications/Beatancourt_etal_2005.pdf, Feb.(EOS Trans. AGU86,539-541).
Bisson M, Fornaciai A, Coli A, et al. The vegetation resilience after fire (VRAF) index:development, implementation and an illustration from central Italy. International Journal ofApplied Earth Observation and Geoinformation,2008,10:312-329.
Bobbe T, Lachowski H, Maus P, et al. A primer on mapping vegetation using remote sensing.International Journal of Wildland Fire,2001,10:277-287.
Bond-Lamberty B, Wang C, Gower S T. Net primary production and net ecosystem production ofa boreal black spruce wildfire chronosequence. Global Change Biology,2004,473-487.
Buermann W, Anderson B, Tucker C J, et al. Interannual covariability in northern hemisphere airtemperatures and greenness associated with El Nino-Southern Osillation and the ArcticOscillation. Journal of Geophysical Research,2003,108:4396.
Cai H Y, Zhang S W, Bu K et al. Intergrating geographical data and phenological characteristicsderived from MODIS data for improving land over mapping. Journal of GeographicSciences,2011,21(4):705-718.
Calvo L, Tarrega R, Luis E. The dynamics of Mediterranean shrubs species over12yearsfollowing perturbations. Plant Ecology,2002,160:25-42.
Carl H K. Ecological and sampling constraints on defining landscape fire severity. Fire Ecology,2006,2(2):34-59.
Carlson T and Ripley T. On the relation between NDVI, fractional vegetation cover and leaf areaindex. Remote Sensing of Environment,1997,62:241-252.
Chen X Q, Hu B, Yu R. Spatial and temporal variation of phonological growing season andclimate change impacts in temperate eastern China. Global Change Biology,2005,11:1118-1130.
Chmielewske F M, Rostzer T. Response of tree phenology to climate change across Eruope.Agricultural and Forest Meteorology,2001,108:101-112.
Chuvieco E, Martin M P, Palacios A. Assessment of different spectral indices in thered-near-infrared spectral domain for burned land discrimination. International Journal ofRemote Sensing,2002,23:5103-5110.
Chuvieco E. Satellite observation of biomass burning: Implications in global change research. InE. Chuvieco(Ed.), Earth observation and global change. New York: Springer,2008
Cleland E E, Chuine I, Menzel A, et al. Shifting plant phenology in response to global change.Trends in Ecology and Evolution,2007,22(7):357-365.
Clement R, Navarro C R, Gitas I. Monitoring post-fire regeneration in Mediterranean ecosystemsby employing multitemporal satellite imagery. International Journal of Wildland Fire,2009,18:648-658.
Cormack K J, Landsbrg J D, Everett R L. Assessing the impacts of sever fire on forest ecosystemrecovery. Journal of Sustainable Forestry,2001,11:177-228.
Crimmins T M, Crimmins M A, Bertelsen C D. Complex responses to climate drivers in onset ofspring flowering across a semi-arid elevation gradient. Journal of Ecology,2010,98:1042-1051.
Cuevas-Gonzalez M, Gerard F, Baltzer H, et al. Analysing forest recovery after wildfiredisturbance in boreal Siberia using remotely sensed vegetation indices. Global ChangeBiology,2009,15:561-577.
Cuevas-Gonzalez M, Gerard F, Baltzer H, et al. Analysing forest recovery after wildfiredisturbance in boreal Siberia using remotely sensed vegetation indices. Global ChangeBiology,2009,15:561-577.
Curz A, Perez B, Moreno J M. Resprouting of the Mediterranean type shrub Erica australis withmodified lignotuber carbohydrate content. Journal of Ecology,2003,91:348-356.
Dalel T and Bruce M K. Fire history of a sequoia-mixed conifer forest. Ecology,1979,60(1):129-142.
Daskalakou E N and Thanos C A. Postfire regeneration of Aleppo pine–The temporal pattern ofseeding recruitment. Plant Ecology,2004,171:81-89.
De Luis M, Garcia-Cano M, Cortina J, et al. Climatic trends, disturbances and short-termvegetation dynamics in a Mediterranean shrubland. Forest Ecology and Management,2001,147:25-37.
Diaz-Delgado R, Lloret F, Pons X. Influence of fire severity on plant regeneration by means ofremote sensing. International Journal of Remote Sensing,2003,24:1751-1763.
Diaz-Delgado R, Lloret F, Pons X. Influence of fire severity on plant regeneration by means ofremote sensing imagery. International Journal of Remote Sensing,2003,24:1751-1763.
Dixon R K, Brown S, Houghton R A, et al. Carbon pools and flux of global forest ecosystems.Science,1994,263:185-190.
Dozier J. A method for satellite identification of surface temperature fields of subpixel resolution.Remote Sensing of Environment,1981,11:221-229.
Eklundh L, Jonsson P. Timesat3.0Software Manual, Lund University, Sweden,2009.
Emilio C, Peter E, Alexander P T et al. Generation of long time series of burn area maps of theboreal forest from NOAA-AVHRR composite data. Remote Sens ing of Environment,2008,112:2381-2396.
EPA. Inventory of U.S. Greenhouse Gas Emissions and Sinks:1990-1999. In: EnvironmentalProtection Agency, U.S.,2001.
Epting J and Verbyla D L. Landscape level interactions of pre-fire vegetation, burn severity, andpost-fire vegetation over a16-year period in interior Alaska. Canadian Journal of ForestResearch,2005,35:1367-1377.
Escuin S, Fernandez-Rebollo P, Navarro R M. Aplicacion de escenas Landsat a la asignacion degrados de afectacion producidos por incendios forestales. Revista de Teledeteccion,2002,1:25-36.
Eshel A, Henig-Sever N, Neeman G. Spatial variation of seedling distribution in an eastMediterranean pine woodland at the beginning of post-fire succession. Plant Ecology,2000,148:175-182.
Farina A. Principles and methods in landscape ecology. Chapman and Hall,1998.
Fischer A. A model for the seasonal variations of vegetation indices in coarse resolution data andits inversion to extract crop parameters. Remote Sensing of Environment,1994,48(2):220-230.
Flannigan M D, Logan K A, Amiro B D. Future area burned in Canada. Climatic Change,2005,72:1-16.
Fraser R H and Li Z. Estimation fire-related parameters in boreal forest using SOPTVEGETATION. Remote Sensing of Environment,2002,82:95-110.
Fraser R H, Li Z, Cihlar J. Hotspot and NDVI Differencing Synergy (HANDS): A new techniquefor burned area mapping over boreal forest. Remote Sensing of Environment,2000,74,362-276.
Frey K E and Smith L C. Recent temperature and precipitation increases in West Siberia andtheir association with the Arctic Oscillation. Polar Research,2003,22:287-300.
Friedl M A, Sulla-Menashe D, Tan B, et al. MODIS Collection5global land cover: Algorithmrefinements and characterization of new datasets. Remote Sensing of Environment,2010,114:168-182.
Gao B C. NDWI-a Normalized Difference Water Index for remote sensing of vegetation liquidwater from space. Remote Sensing of Environment,1996,58:257-226.
Garcia-Ruiz J, Lasanta T, Ruiz-Flano P, et al. Land-use changes and sustainable development inmountain areas: A case study in the Spanish Pyrenees. Landscape Ecology,1996,11:267-277.
George C, Rowland C, Gerard F, et al. Retrospective mapping of burnt areas in Central Siberiausing a modification of the Normalised Difference Water Index. Remote Sensing ofEnvironment,2006,104:346-359.
Gerard F, Plummer S, Wadsworth R, Ferreruela A, et al. Forest fire scar detection in the borealforest with multitemporal SPOT-VEGETATION data. IEEE Transactions on Geoscienceand Remote Sensing,2003,41:2575-2585.
Giglio L, Descloitres J, Justice C O et al. An enhanced contextual fire detection algorithm forMODIS. Remote Sensing of Environment,2003,87:273-282.
Giglio L, Van der Werf G R, Randerson J T et al. Global estimation of burned area using MODISactive fire observations. Atmospheric Chemistry and Physics,2006,6:957-974.
Gillet N P, Weaver A J, Zwiers F W et al. Detecting the effect of climate change on Canadianforest. Geophysical Research Letters,2004,31, L18211.doi:18210.11029/12004GL020876.
Gillet N P, Weaver A J, Zwiers F W, et al. Detecting the effect of climate change on Canadianforest fires. Geophysical Research Letters.,2004,31, L18211. doi:18210.11029/12004GL020876.
Gitas I, De Santis A, Mitri G. Remote sensing of burn severity. In: Chuvieco, E.,.(Eds), Earthobservation of wildland fires in Mediterranean ecosystems. Springer-Verlag, Berlin,2009:129-148.
Goetz S J, Fiske G J, Bunn A G. Using satellite time-series data sets to analyze fire disturbanceand forest recovery across Canada. Remote Sensing of Environment,2006,101:352-365.
Gouveia C, DaCamara C, Trigo R M. Post-fire vegetation recovery in Portugal based onspot/vegetation data. Natural Hazards and Earth System Sciences,2010,10:673-64.
Gruninger J A, Ratkowski J, Hoke M L. The sequential maximum angle convex cone(SMACC)endmember model. Proceedings SPIE Algorithms for Multispectral and Hype-spectral andUltraspectral Imagery,2004,(5425-1):255-269.
Hall F, Botkin D, Strebel D, et al. Large-scale patterns of forest succession as determined byremote sensing. Ecology,1991,72:628-640.
Hanes T L. Succession after fire in the chaparral of southern California. Ecological Monographs,1971,41:27-52.
Hardisky M A, Klemas V, Smart R M. The influences of soil salinity, growth form, and leafmoisture on the spectral reflectance of Spartina alterniflora canopies. PhotogrammetricEngineering and Remote Sensing,1983,49:77-83.
Henry M and Hope S. Monitoring post-burn recovery of chaparral vegetation in southernCalifornia using multi-temporal satellite data. International Journal of Remote Sensing,1998,19:3097-3107.
Heumann B W, Seaquist J W, Eklundh L, et al. AVHRR derived phenological change in the Saheland Soudan, Africa,1982-2005. Remote Sensing of Environment,2007,108:385-392.
Hicke J A, Asner G P, Kasischke E S et al. Postfire response of North American boreal forest netprimary productivity analyzed with satellite observations. Global Change Biology,2003,9,1145-1157.
Hickler T, Eklundh L, Seaquist J, et al. Precipitation controls Sahel greening trend. GeophysicalResearch Letters,2005,32, L21415.
Huete A R, Didan K, Miura T, et al. Overview of the radiometric and biophysical performance ofthe MODIS vegetation indices. Remote Sensing of Environment,2002,83:195-213.
IPCC. Climate Change2007: The Physical Science Basis. Summary for Policymakers. In I.P.O.CCHANGE(Ed.). Geneva IPCC Secretariat,2007.
Jacobson C. Use of linguistic estimates and vegetation indices to assess post-fire vegetationregrowth in woodland areas. International Journal of Wildland Fire,2010,19:94-103.
Jay D M and Stephen R Y. Mapping forest post-fire canopy consumption in several overstorytypes using multi-temporal Landsat TM and ETM data. Remote Sensing of Environment,2002,82:481-496.
Jay D M, Andrea E T. Quantifying burn severity in a heterogeneous landscape with a relativeversion of the delta Normalized Burn Ratio(dNBR). Remote Sensing of Environment,2007,109:66-80.
Jose A, Moreno R, David R et al. Burned area mapping time series in Canada (1984-1999) fromNOAA-AVHRR LTDR: A comparison with other remote sensing products and fireperimeters. Remote Sensing of Environment,2012,117:407-414.
Jose P S V and Paulo B. Post-fire vegetation regrowth detection in the Deiva Marinaregion(Liguria-Italy) using Landsat TM and ETM+data. Ecological Modelling,2010,221:75-84.
Jupp T E, Taylor C M, Balzter H, et al. A statistical model linking Siberian forest fire scars withearly summer rainfall anomalies. Geophys Res Lett,2006,33: L14701.
Kashian D M, Romme W H.Carbon storage on landscapes with stand-replacing fires. BioScience,2006,56:598-606.
Kas ischke E S and French N H F. Constraints on using AVHRR composite index imagery tostudy patterns of vegetation cover in boreal forests. International Journal of Remote Sensing,1997,18:2430-2426.
Kas ischke E S, Hewson J H, Stock B et al. The use of ASTR active fire counts for estimatingrelative patterns of biomass burning-A study from the boreal forest region. GeophysicalResearch Letters,2003,30(18):1969.
Kas ischke E S, Turetsky M R. Recent changes in the fire regime across the North Americanboreal region—Spatial and temporal patterns of burning across Canada and Alaska.Geophysical Research Letters,2006,33:1-5.
Kas ischke E S. Boreal ecosystems in the global carbon cycle. In: Fire, Climate Change andCarbon Cycling in the Boreal Forest. Ecological Studies Series (eds Kasischke E S, StocksB J), PP.19-30. Springer-Verlag, New York, NY, USA.
Kazanis D and Arianoutsou M. Long-term post-fire vegetation dynamics in Pinus halepensisforests of cectral Greece: a functional-group approach. Plant Ecology,2004,171:101-121.
Kobak K I, Turchinovich I Y, Kondrasheva N Y, et al. Vulnerability and adaptation of the larchforest in eastern Siberia to climate change. Water, Air and Soil Pollution,1996,92:119-127.
Kozlowski T. Physiological ecology of natural regeneration of harvested and disturbed foreststands: implications for forest management. Forest ecology and management,2002:195-221.
Kucera J, Yasuoka Y, Dye D G. Creating a forest fire database for the Far East Asia usingNOAA/AVHRR observation. International Journal of Remote Sensing,2005,26,2423-2439.
Lhermitte S, Verbesselt J, Verstraeten W, et al. A pixel based regeneration index using time seriessimilarity and spatial context. Photogrammetric Engineering and Remote Sensing,2010,76:673-682.
Lhermitte S, Verbesselt J, Verstraeten WW, et al. Assessing intra-annual vegetation regrowthafter fire using the pixel based regeneration index. ISPRS Journal of Photogrammetry andRemote Sensing,2011,66:17-27.
Liu J Y, Zhuang D F, Luo D, et al. Land cover calssification of China: integrated analys is ofAVHRR imagery and geophysical data. International Journal of Remote Sensing,2003,24(12):2485-2500.
Lloyd D. A phonological classification of terrestrial vegetation cover using shortwave vegetationindex imagery. International Journal of Remote Sensing,1990,11(12):2269-2279.
Loboda T, O’Neal K J, Csiszar I. Regionally adaptable dNBR-based algorithm for burned areamapping from MODIS data. Remote Sensing of Envrionment,2007,109:429-442.
Los S O, Collatz G J, Bounoua L, et al. Global interannual variations in sea surface temperatureand land surface vegetation, air temperature, and precipitation. J. Climate,2001,14:1535-1549.
Louis G, Tatiana L, David P et al. An active-fire based burned area mapping algorithm for theMODIS sensor. Remote Sensing of Environment,2009,113:408-420.
Loveland T R, Beed B C, Brown J F, et al. Development of a global land cover characteristicsdatabase and IGBP DISCover from1km AVHRR data. International Journal of RemoteSensing,2000,21(6&7):1303-1330.
Madsen K, Nielsen H B, Tigleff O. Methods for non-linear least squares problems. Informaticsand Mathematical Modeling (IMM), Technical University of Denmark,2004.
Marchetti M, Ricotta C, Volpe F. A qualitative approach to the mapping of post-fire regrowth inMediterranean vegetation with Landsat TM data. International Journal of Remote Sensing,1995,16:2487-2494.
Maria C G, France G, Heiko B, et al. Analysing forest recovery after wildfire disturbance inboreal Siberia using remotely sensed vegetation indices. Global Change Biology,2009,15:561-577.
Markon C J, Fleming M D, Binnian E F. Characteristics of vegetation phenology over theAlaskan landscape using AVHRR time-series data. Polar Recognition,1995,31(177):179-190.
Martin M P, Chuvieco E. Mapping and evaluation of burned land from multitemporal analysis ofAVHRR NDVI images. EARSeL Advances in Remote Sensing,1995,4(3):7-13.
Martinez B, Gilabert M A. Vegetation dynamics from NDVI time series analysis using thewavelet transform. Remote Sensing of Environment,2009,113:1823-1842.
Maxim D, Peter P, Anna L et al. Reconstructing long time series of burned areas in aridgrasslands of southern Russia by satellite remote sensing. Remote Sensing of Environment,2010,114:1638-1648.
McMichael C, Hope A, Roberts D, et al. Post-fire recovery of leaf area index in Californiachaparral: a remote sensing-chronosequence approach. International Journal of RemoteSensing,2004,25:4743-4760.
Melillo J M, McGuire A D, Kicklighter D W, et al. Global climate change and terrestrial netprimary production. Nature,1993,363:234-240.
Menzel A. Trends in phonological phases in Europe between1951and1966. InternationalJournal of Biometeorol,2000,44:76-81.
Mitchell M and Yuan F. Assessing forest fire and vegetation recovery in the Blacd Hills, SouthDakota. GIScience and Remote Sensing,2010,47:276-299.
Mitri G and Gitas I Z. Mapping postfire vegetation recovery using EO-1Hyperion imagery.IEEE Transactions on Geoscience and Remote Sensing,2010,48:1613-1618.
Mitri G and Gitas I Z. Mapping the sever ity of fire using object-based classification of IKONOSimagery. International Journal of Wildland Fire,2010,17:431-442.
Monaghati S, Samadzadegan F, Azizi A. An agent-based approach for regional forest firedetection using MODIS data. Journal of Applied Sciences,2009,9(20):3672-3681.
Moulin S, Kergoat L, Viovy N, et al. Global-scale assessment of vegetation phenology usingNOAA/AVHRR satellite measurements. Journal of Climaet,1997,10(6):1154-1170.
Ne’eman G, Fotheringham C J, Keelky j. Patch to landscape patterns in post fire recruitment of aserotinous conifer. Plant ecology,1999,12:235-242.
Nepstad D, Verssimo A, Alencar A, et al. Large-scale impoverishment of Amazonian forest bylogging and fire. Nature,1999,398:505-508.
Noormets A. Phenology of ecosystem processes: applications in global change research. NewYork, NY: Springer,2009.
Oliver C and Larson B. Forest Stand Dynamics. Wiley, New York, NY, USA,1996.
Palandjian D, Gitas I, Wright R. Burned area mapping and post-fire impact assessment in theKassandra peninsula (Greece) using Landsat TM and Quickbird data. Geocarto International,2009,24:193-205.
Pausas J and Verdu M. Plant persistence traits in the fire-prone ecosystems of the Mediterraneanbasin: a phylogenetic approach. Oikos,2005,109:196-202.
Pausas J, Carbo E, Caturla R et al. Post-fire regeneration patterns in the eastern Iberian Peninsula.Acta Oecologica,1999,20:499-508.
Pereira J M C. A comparative evaluation of NOAA/AVHRR vegetation indexes for burnedsurface detection and mapping. IEEE Transactions on Geoscience and Remote Sensing,1999,37:217-226.
Perez-Cabello F, Echeverria M, Ibarra P, et al. Effects of fire on vegetation, soil andhydrogeomorphological behavior in Mediterranean ecosystems. In: Earth Observation ofWildland fires in Mediterranean ecosystems. Springer,2009:111-128.
Peterson S and Stow D. Using multiple image endmember spectral mixture analysis to studychaparral regrowth in Southern California. International Journal of Remote Sensing,2003,24:4481-4504.
Pinol J, Terradas J, Lloret F. Climatic warming hazard, and wildfire occurrence in coastal easternSpain. Climate Change,1998,38:345-357.
Pinty B, Verstraete M M. GEMI: a non-linear index to monitor global vegetation from satellites.Vegetatio,1992,101:15-20.
Pu R L, Li Z Q, Gong P et al. Development and analysis of a12-year daily1-km forest firedataset across North America form NOAA/AVHRR data. Remote Sensing of Environment.,2007,108:198-208.
Ramsey E, Nelson G, Sapkota D, et al. Using multiple-polarization L-band radar to monitormarsh burn recovery. IEEE Transactions on Geoscience and Remote Sensing,1999,37:636-639.
Reed B C, Brown J F, et al. Measuring phenological variability from satellite imagery. Journal ofVegetation Science,1994,5(5):703-714.
Rego F. Land use changes and wildfires. In: A. Teller, P. Mathy, J. Jeffers (Eds.) Rwsponse offorest fires to environmental change, Elsevier, London,1992:367-373.
Riano D, Chuvieco E, Ustin S et al. Assessment of vegetation regeneration after fire throughmultitemporal analysis of AVIRIS images in the Santa Monica Mountains. Remote Sensingof Environment,2002,79:60-71.
Richardson A D, Black T A, Ciais P, et al. Influence of spring and autumn phonologicaltransitions on forest ecosystem productivity. Philosophical Transactions of the RoyalScociety B,2010,365:3227-3246.
Roberts D A, Batista G T, Pereira J L G, et al. Change identification using multitemporal spectralmixture analysis: applications in Eastern Amazonia. Remote Sensing Change Detection:Environment Monitoring Applications and Method. Ann Arbor Press, MI,2001.
Roberts D, Gardner M, Church R, et al. Mapping chaparral in the Santa Monica mountains usingmultiple endmember spectral mixture models. Remote Sensing of Environment,1998,65:267-279.
Roder A, Hill J, Duguy B, et al. Using long time series of Landsat data to monitor fire events andpost-fire dynamics and identify driving factors. A case study in the Ayora region (easternSpain). Remote Sensing of Environment,2008,112:259-273.
Roerind G J, Menenti M, Soepboer W, et al. Assessment of climate impact on vegetationdynamics by using remote sensing. Physics and Chemistry of the Earth,2003,28:103-109.
Roerind G J, Menenti M, Verhoef W. Reconstruction cloudfree NDVI composites using Fourieranalys is of time series. International Journal of Remote Sensing,2000,21(9):1911-1917.
Rogan J, Franklin J, Roberts D A. A comparison of methods for monitoring multi-temporalvegetation change using Thematic Mapper imagery. Remote Sensing of Environment,2002,80:143-156.
Roy D P, Boschetti L, Justice C O et al. The collection5MODIS burned area product—Globalevaluation by comparison with the MODIS active fire product. Remote Sensing ofEnvironment,2008,112:3690-3707.
Roy D P, Jin Y, Lewis P E et al. Prototyping a golbal alforithm for systematic fire-affected areamapping using MODIS time series data. Remote Sensing of Environment,2005,97:137-162.
Roy D P, Lewis P E, Justice C O. Burned area mapping using multi-temporal moderate spatialresolution data—A bi-directional reflectance model-based expectation approach. RemoteSensing of Environment,2002,83:263-286.
Sankey T, Moffet C, Weber K. Postfire recovery of sagebrush communities: assessment usingSPOT-5and very large-scale aerial imagery. Rangeland Ecology and Management,2008,61:598-604.
Savage M. Structural dynamics of a southwestern pine forest under Chronic human influence.Annals of the Association of American Geographers,1991,81(1):271-289.
Schwartz M D, Reed B C, White M A. Assessing satellite derived start-of-season measures in theconterminous USA. International Journal of Climatology,2002,22(14):1793-1805.
Schwartz M D. Green-wave phenology. Nature,1998,394:839-840.
Segah H, Tani H, Hirano T. Detection of fire impact and vegetation recovery over tropical peatswamp forest by satellite data and ground-based NDVI instrument. International Journal ofRemote Sensing,2010,31:5297-5314.
Sergio M, Fernando P C, Teodoro L. Assessment of radiometric correction techniques inanslyzing vegetation variability and change using time series of Landsat images. RemoteSensing of Environment,2008,112:3916-3934.
Sirikul N. Comparisons of MODIS vegetation index products with biophysical and flux towermeasurements. PhD Dissertation, the University of Arizona,2006.
Somers B, Verbesselt J, Ampe E, et al. Spectral mixture analysis to monitor defoliation inmixed-age Eucalyptus globulus Labill plantations in southern Australia using Landsat5-TMand EO-A Hyperion data. International Journal of Applied Earth Observation andGeoinformation,2010,12:270-277.
Steyaert L, Hall F, Loveland T. Land cover mapping, fire regeneration, and scaling studies in theCanadian boreal forest with1km AVHRR and Landsat TM data. International Journal ofGeophysical Research,1997,102:29581-29598.
Stueve K, Cerney D, Rochefort R, et al. Post-fire tree establishment patterns at the alpine treelineecotone: Mount Rainier National Park, Washington, USA. Journal of Vegetation Science,2009,20:107-120.
Sukhinin A I, French N H F, Kasischke E S, et al. AVHRR-based mapping of fires in Russia: newproducts for fire management and carbon cycle studies. Remote Sensing of Environment,2004,93:546-564.
Tarrega R, Luis-Calabuig E, Valbuena L. Eleven years of recovery dynamic after experimentalburning and cutting in two Cistus communities. Acta Oecologica,2001,22:277-283.
Thonicke K, Venevsky S, Sitch S, et al. The role of fire disturbance for global vegetationdynamics: coupling fire into a Dynamic Global Vegetation Model. Global Ecology andBiogeography,2001,10:661-677.
Tian H, Melillo J M, Kicklighter D W, et al. Parameters for global ecosystem models. Nature,1999a,399:536.
Tsitsoni T. Conditions determining natural regeneration after wildfires in the Pinus halepensis(Miller,1768) forests of Kassandra Peninsula (North Greece). Forest Ecology andManagement,1997,92:199-208.
Tucker C. Red and photographic infrared linear combinations for monitoring vegetation. RemoteSensing of Environment,1979,8:127-150.
Vafeidis N A, Drake N A, Wainwrighe J. A proposed methon for modelling the hydrologicresponse fo cathments to burning with the use of remote sensing and GIS. Catena,2007,70(3):396-409.
van der Werf G R, Randerson J T, Collatz G J, et al. Continental-scale partitioning of fireemissions during the1997to2001El Nino/La Nina period. Science,2004,303:73-76.
van Leeuwen Wx. Monitoring the effects of forest restoration treatments on post-fire vegetationrecovery with MODIS multitemporal data. Sensors,2004,8:2017-2042.
Van Leeuwen W, Casady G, Neary D, et al. Monitoring post-wildfire vegetation response withremotely sensed time series data in Spain, USA and Israel. International Journal of WildlandFire,2010,19:75-93.
Vander Werf G R, Randerson J T, Giglio L, et al. Interannual variability in global biomassburning emission from1997to2004. Atmospheric Chemistry and Physics,2006,6:3423-3441.
Veraverbeke S, Gitas I, Katagis T, et al. Assessing post-fire vegetation recovery using red-nearinfrared vegetation indices: accounting for background and vegetation variability. ISPRSJournal of Photogrammetry and Remote Sensing,2012b,28-39.
Veraverbeke S, Lhermitte S, Verstraeten W W, et al. The temporal dimension of differencedNormalized Burn Ratio (dNBR) fire/burn severity studies: the case of the large2007Peloponnese wildfires in Greece. Remote Sensing of Environment,2010b,114:2548-2563.
Veraverbeke S, Lhermitte S, Verstraeten WW, et al. A time integrated MODIS burn severityassessment using the multi-temporal differenced Normalized Burn Ratio (dNBRMT).International Journal of Applied Earth Observation and Geoinformation,2011a,13:52-58.
Veraverbeke S, Somers B, Gitas I, et al. Spectral mixture analysis to assess post-fire vegetationregeneration using Landsat Thematic mapper imagery: accounting for soil brightnessvariation. International Journal of Applied Earth Observation and Geoinformation,2012a,14:1-11.
Vila G and Barbosa P. Post-fire vegetation regrowth detection in the Deiva Marina region(Liguria-Italy) using Landsat TM and ETM+data. Ecological Modelling,2010,221:75-84.
Wang C Y, Zha X D Z, Chen T. Application of EOS/MODIS data on the forest fire in Tibet.Plateau and Mountain Meteorology Research,2010,30(3):65-69.(In Chinese)
Wardlow B D, Egbert S L. Large-area crop mapping using time-series MODIS250m NDVI data:an assessment for the U.S. Central Great Plains. Remote Sensing of Environment,2008,112:1096-1116.
White J, Ryan K, Key C, et al. Remote sensing of forest fire severity and data for regionalecological analysis. International Journal of Applied Earth Observation and Geoinformation,1996,4:161-173.
White L L, Zak D R, Barnes B V. Biomass accumulation and soil nitrogen availability in an87-year-old Populus grandidentata chronosequence. Forest Ecology and Management,2004,191:121-127.
Wicks T, Smith G, Curran P. Polygon-based aggragation of remotely sensed data for regionalecological analysis. International Journal of Applied Earth Observation and Geoinformation,2002,4:161-173.
Wright A and Bailey W. Fire Ecology. New York: John Wiley＆Sons,1982.
Wsieman P E and Seiler J R. Soil CO2efflux across four age classes of plantation loblolly pine(Pinus taeda L.) on the Virginia Piedmont. Forest ecology management,2004,192:297-311.
Xin J F, Yu Z R, Leeuwen L V, et al. Mapping crop key phenological stages in the North ChinaPlain using NOAA time series images. International Journal of Applied Earth Observationand Geoinformation,2002,4(2):109-117.
Zhang X Y, Friedl M A, Schaaf C B, et al. Monitoring vegetation phenology using MODIS.Remote Sensing of Environment,2003,84(3):471-475.
Zhang X Y, Shobha K, Brad q. Estimation of biomass burned areas usingmultiple-satellite-observed active fires. IEEE Transactions on Geoscience and RemoteSensing,2011,49(11):4469-4482.
陈效逑,王林海.遥感物候学研究进展.地理科学进展,2009,28(1):33-40.
单建平.火灾后兴安落叶上长短枝变化及其对生存的影响.应用生态学报,1996,7:6-10.
邓湘雯,孙刚,文定元.林火对森林演替动态的影响极其应用,中南林学院学报,2004,24(1):0051-0055.
顾顺林.定量遥感.北京:科学出版社,2009.
关克志,张大军.大兴安岭森林火灾对植被的影响分析.环境科学,1990,11(5):82-89.
郭晋平.森林景观生态研究.北京:北京大学出版社,2001,12-26.
孔繁华,李秀珍,王绪高等.林火迹地森林恢复研究进展.生态学杂志,2003,22(2):60-64.
李登科,范建忠,王娟.基于MOD17A3的陕西省植被NPP变化特征.生态学杂志,2011,30(12):2776-2782.
李红军,郑力,雷玉平等.基于EOS/MODIS数据的NDVI与EVI比较研究.地理科学进展,2007,26(1):26-32.
李为海,崔克城,刘萍.火烧迹地次生林天然更新株数模型的建立.内蒙古林业勘察设计,2000,2:28-30.
刘恩海,昝德萍,刘兴刚等.樟子松开花结实规律研究——樟子松球果变异与种子预测方法的关系.林业科技,1995,2(2):19-24.
龙慧灵,李晓兵,王宏等.内蒙古草原区植被净初级生产力及其与气候的关系.生态学报,2010,30(5):1367-1378.
卢振兰,刘常梅,韩国辉.火生态学研究方法综述.吉林林业科技,2001,30(6):4-7.
吕爱锋,田汉勤,刘永强.火干扰与生态系统碳循环.生态学报,2005,25(10):2734-2743.
罗菊春.大兴安岭森林火灾对森林生态系统的影响.北京林业大学学报,2002,24(5/6):101.
马克明,祖元刚.植被格局的分形特征.植物生态学报,2000,24(1):111-117.
朴世龙,方精云,郭庆华.利用CASA模型估算我国植被净第一性生产力.植物生态学报,2001,25(5):603-608.
邱扬.森林植被的自然火干扰.生态学杂志,1998,17(1):54-60.
邱扬,李湛东,徐化成.兴安落叶松种群的稳定性与火干扰关系的研究.植物研究,1997,17(4):441-446.
邵国凡,赵士洞,赵光.应用地理信息系统模拟森林景观动态的研究.应用生态学报,1991,2(2):103-107.
舒立福.大兴安岭林火生态及火烧迹地天然更新的研究.哈尔滨:东北林业大学博士论文,1993.
宋小宁,赵英时. MODIS图像的云检测及分析.中国图像图形学报,2003,8(9):1079-1083.
王建国,樊军,王全九等.黄土高原水蚀风蚀交错区植被地上生物量及其影响因素.应用生态学报,2011,22(3):556-564.
王明玉,任云卯,李涛等.火烧迹地更新与恢复研究进展.世界林业研究,2008,21(6):0049-0054.
王莺,夏文韬,梁天刚等.基于MODIS植被指数的甘南草地净初级生产力时空变化研究.草业科学,2010a,19(1):201-210.
王正兴,刘闯, Huete A.植被指数研究进展：从AVHRR-NDVI到MODIS-EVI.生态学报,2003.,5(23):579-586.
王正兴,刘闯,陈文波等. MODIS增强型植被指数EVI与NDVI初步比较.武汉大学学报,2006,5(31):407-410.
文定元.森林防火基础知识.北京:中国林业出版社,1995.
武永峰,李茂松,宋吉青.植物物候遥感监测研究进展.气象与环境学报,2008,24(3):0051-0058.
刑伟,葛之葳,李俊清.大兴安岭北部林区林火干扰强度对兴安落叶松群落影响研究.科学技术与工程,2006,6(14):2042-2046.
徐化成.景观生态学.第一版.北京：中国林业出版社,1996.
徐化成,李湛东,邱杨.大兴安岭北部地区原始林火干扰历史的研究.生态学报,1997,17(4):337-346.
杨春田,李宝印.大兴安岭北坡火烧迹地更新的策略与技术.林业科技,1989,6:23-24.
杨树春,刘新田,曹海波等.大兴安岭林区火烧迹地植被变化研究.东北林业大学学报,1998,26(1):19-23.
于信芳,庄大方.基于MODIS NDVI数据的东北森林物候期监测.资源科学,2006,28(4):111-117.
俞孔坚.景观：文化、生态与感知.北京:科学技术出版社,2000.
张继义,赵哈林,张铜会等.科尔沁沙地植被恢复系列上群落演替与物种多样性的动态恢复.植物生态学报,2004,28(1):2-11.
张杰,潘晓玲,高志强等.干旱生态系统净初级生产力估算及变化探测.地理学报,2006,61(1):15-25.
张宪洲.我国自然植被净第一性生产力的估算与分布.自然资源,1993,1:15-21.
张学霞,葛全胜,郑景云.遥感技术在植物物候研究中的应用综述.地球科学进展,2003,18(4):534-544.
赵伟,李召良.利用MODIS/EVI时间序列数据分析干旱对植被的影响.地理科学进展,2007,6(26):40-47.
郑焕能,邸雪颖等.中国森林火灾与对策.自然灾害学报,1994,3(3):37-40.
郑焕能,胡海清.森林燃烧环.东北林业大学学报,1987,15(5):1-5.
郑焕能,胡海清,姚树人.林火生态.哈尔滨:东北林业大学出版社,1992.
郑焕能,贾松青,胡海清.大兴安岭林区的林火与森林恢复.东北林业大学学报,1986,14(4):1-7.
中国科学院地理研究所.中国动植物物候观测年报（第1号）.北京：科学出版社，1965.
钟章成.我国植物种群生态研究的成就与展望.生态学杂志,1992,11(1):4-8.
周道玮.草地火生态学研究进展.长春：吉林科学技术出版社,1995.
周广胜,郑元润,陈四清等.自然植被净第一性生产力模型及其应用.林业科学,1998,34(5):2-11.
周晓峰,张远东,孙慧珍等.气候变化对大兴安岭北部蒙古栎种群动态的影响.生态学报,2002,22(7):980-985.
周以良,乌弘奇,陈涛等.按植物群落生态学特性,加速恢复大兴安岭火烧迹地的森林.东北林业大学学报,1989,17(2):1-10.
竺可桢,宛敏渭.物候学.长沙：湖南教育出版社,1999.
左丽君,张增祥,董婷婷等. MODIS/NDVI和MODIS/EVI在耕地信息提取中的应用及对比分析.农业工程学报,2008,24(3):167-172.
Amiro B D, Chen M, Liu J. Net primary productivity following forest fire for Canadianecoregions. Canadian Journal of Forest Research,2000,30:939-947.
Amiro B D, Orhcansky A L, Barr A G. The effect of post-fire stand age on the boreal forestenergy balance. Agricultural and Forest Meteorology,2006,140:41-50.
Amiro B D, Stocks B J, Alexander M E, et al. Fire, climate change, carbon and fuel managementin the Canadian boreal forest. International Journal of Wildland Fire,2001,10:405-413.
Amiro B, MacPherson J, Desjardins R. BOREAS flight measurements of forest fire effects oncarbon dioxide and energy fluxes. Agricultural and Forest Meteorology,1999,96:199-208.
Arai K. Nonlinear mixture model of mixed pixels in remote sensing satellite images based onMonte Carlo simulation. Advances in Space Research,2008,41:1725-1743.
Askeyev O V, Sparks T H, Askeyev I V, et al. East versus West: contrasts in phonologicalpatterns. Global Ecology and Biogeography,2010,19:783-793.
Asner G and Lobell D. A biogeophysical approach for automated SWIR unmixing of soils andvegetation. Remote Sensing of Environment,2000,74:99-112.
Asner G. Biophysical and biochemical sources of variability in canopy reflectance. RemoteSensing of Environment,1998,64:234-253.
Atkinson P, Cutler M, Lewis H. Mapping sub-pixel proportional land-cover with AVHRRimagery. International Journal of Remote Sensing,1997,18:917-935.
Ayres M P and Lombardero M J. Assessing the consequences of global change for forestdisturbance from herbivores and pathogens. Science of Total Environment,2000,262:263-286.
Balzter h, Gerard F F, George C T et al. Impact of the Arctic Oscillation pattern on interannualforest fire variability in Central Siberia. Geophysical Research Letters,2005,32, L14709.1-L14709.4.
Balzter H, Gerard F, George C, et al. Coupling of vegetation growing season anomalies and fireactivity with hemispheric and regional-scale climate patterns in central and east Siberia. JClimate,2007,20:3713-3729.
Barbosa P M, Gregoire J M, Pereira J M C. An algorithm for extracting burned areas from timeseries of AVHRR GAC data applied at a continental scale. Remote Sensing of Environment,1999,69:253-263.
Baret F, Clevers J C P W, Steven M D. The robustness of canopy gap fraction estimates from redand near-infrared reflectances: a comparison of approaches. Remote of Environment,1995,54:141-151.
Beauty R and Taylor A. Spatial and temporal variation of fire regimes in a mixed conifer forestlandscape, southern Cascades, California, USA. Journal of Biogeography,2001,28:955-966.
Beck P S A, Atzberger C, Hogda K A, et al. Improved monitoring of vegetation dynamics at veryhigh latitudes: A new method using MODIS NDVI. Remote Sensing of Environment,2006,100:321-334.
Belda F and Melia J. Relationships between climatic parameters and forest vegetation:application to burned area in Alicante (Spain). Forest Ecology and Management,2000,135:195-204.
Bertolette D and Spotskey D. Remotely sensed burn severity mapping. In: H. David Harmon,Michigan, Crossing Boundaries in Park Management: Proceeding of the11thConference onresearch and Resource Management in Parks and on Public Lands,2001, The GeorgeWright Socieyt:44-51.
Betancourt J L, Schwartz M D, Breshears D D, et al. Implementing a U.S. National PhenologyNetwork.2011, http://www.usanpn.org/files/publications/Beatancourt_etal_2005.pdf, Feb.(EOS Trans. AGU86,539-541).
Bisson M, Fornaciai A, Coli A, et al. The vegetation resilience after fire (VRAF) index:development, implementation and an illustration from central Italy. International Journal ofApplied Earth Observation and Geoinformation,2008,10:312-329.
Bobbe T, Lachowski H, Maus P, et al. A primer on mapping vegetation using remote sensing.International Journal of Wildland Fire,2001,10:277-287.
Bond-Lamberty B, Wang C, Gower S T. Net primary production and net ecosystem production ofa boreal black spruce wildfire chronosequence. Global Change Biology,2004,473-487.
Buermann W, Anderson B, Tucker C J, et al. Interannual covariability in northern hemisphere airtemperatures and greenness associated with El Nino-Southern Osillation and the ArcticOscillation. Journal of Geophysical Research,2003,108:4396.
Cai H Y, Zhang S W, Bu K et al. Intergrating geographical data and phenological characteristicsderived from MODIS data for improving land over mapping. Journal of GeographicSciences,2011,21(4):705-718.
Calvo L, Tarrega R, Luis E. The dynamics of Mediterranean shrubs species over12yearsfollowing perturbations. Plant Ecology,2002,160:25-42.
Carl H K. Ecological and sampling constraints on defining landscape fire severity. Fire Ecology,2006,2(2):34-59.
Carlson T and Ripley T. On the relation between NDVI, fractional vegetation cover and leaf areaindex. Remote Sensing of Environment,1997,62:241-252.
Chen X Q, Hu B, Yu R. Spatial and temporal variation of phonological growing season andclimate change impacts in temperate eastern China. Global Change Biology,2005,11:1118-1130.
Chmielewske F M, Rostzer T. Response of tree phenology to climate change across Eruope.Agricultural and Forest Meteorology,2001,108:101-112.
Chuvieco E, Martin M P, Palacios A. Assessment of different spectral indices in thered-near-infrared spectral domain for burned land discrimination. International Journal ofRemote Sensing,2002,23:5103-5110.
Chuvieco E. Satellite observation of biomass burning: Implications in global change research. InE. Chuvieco(Ed.), Earth observation and global change. New York: Springer,2008
Cleland E E, Chuine I, Menzel A, et al. Shifting plant phenology in response to global change.Trends in Ecology and Evolution,2007,22(7):357-365.
Clement R, Navarro C R, Gitas I. Monitoring post-fire regeneration in Mediterranean ecosystemsby employing multitemporal satellite imagery. International Journal of Wildland Fire,2009,18:648-658.
Cormack K J, Landsbrg J D, Everett R L. Assessing the impacts of sever fire on forest ecosystemrecovery. Journal of Sustainable Forestry,2001,11:177-228.
Crimmins T M, Crimmins M A, Bertelsen C D. Complex responses to climate drivers in onset ofspring flowering across a semi-arid elevation gradient. Journal of Ecology,2010,98:1042-1051.
Cuevas-Gonzalez M, Gerard F, Baltzer H, et al. Analysing forest recovery after wildfiredisturbance in boreal Siberia using remotely sensed vegetation indices. Global ChangeBiology,2009,15:561-577.
Cuevas-Gonzalez M, Gerard F, Baltzer H, et al. Analysing forest recovery after wildfiredisturbance in boreal Siberia using remotely sensed vegetation indices. Global ChangeBiology,2009,15:561-577.
Curz A, Perez B, Moreno J M. Resprouting of the Mediterranean type shrub Erica australis withmodified lignotuber carbohydrate content. Journal of Ecology,2003,91:348-356.
Dalel T and Bruce M K. Fire history of a sequoia-mixed conifer forest. Ecology,1979,60(1):129-142.
Daskalakou E N and Thanos C A. Postfire regeneration of Aleppo pine–The temporal pattern ofseeding recruitment. Plant Ecology,2004,171:81-89.
De Luis M, Garcia-Cano M, Cortina J, et al. Climatic trends, disturbances and short-termvegetation dynamics in a Mediterranean shrubland. Forest Ecology and Management,2001,147:25-37.
Diaz-Delgado R, Lloret F, Pons X. Influence of fire severity on plant regeneration by means ofremote sensing. International Journal of Remote Sensing,2003,24:1751-1763.
Diaz-Delgado R, Lloret F, Pons X. Influence of fire severity on plant regeneration by means ofremote sensing imagery. International Journal of Remote Sensing,2003,24:1751-1763.
Dixon R K, Brown S, Houghton R A, et al. Carbon pools and flux of global forest ecosystems.Science,1994,263:185-190.
Dozier J. A method for satellite identification of surface temperature fields of subpixel resolution.Remote Sensing of Environment,1981,11:221-229.
Eklundh L, Jonsson P. Timesat3.0Software Manual, Lund University, Sweden,2009.
Emilio C, Peter E, Alexander P T et al. Generation of long time series of burn area maps of theboreal forest from NOAA-AVHRR composite data. Remote Sens ing of Environment,2008,112:2381-2396.
EPA. Inventory of U.S. Greenhouse Gas Emissions and Sinks:1990-1999. In: EnvironmentalProtection Agency, U.S.,2001.
Epting J and Verbyla D L. Landscape level interactions of pre-fire vegetation, burn severity, andpost-fire vegetation over a16-year period in interior Alaska. Canadian Journal of ForestResearch,2005,35:1367-1377.
Escuin S, Fernandez-Rebollo P, Navarro R M. Aplicacion de escenas Landsat a la asignacion degrados de afectacion producidos por incendios forestales. Revista de Teledeteccion,2002,1:25-36.
Eshel A, Henig-Sever N, Neeman G. Spatial variation of seedling distribution in an eastMediterranean pine woodland at the beginning of post-fire succession. Plant Ecology,2000,148:175-182.
Farina A. Principles and methods in landscape ecology. Chapman and Hall,1998.
Fischer A. A model for the seasonal variations of vegetation indices in coarse resolution data andits inversion to extract crop parameters. Remote Sensing of Environment,1994,48(2):220-230.
Flannigan M D, Logan K A, Amiro B D. Future area burned in Canada. Climatic Change,2005,72:1-16.
Fraser R H and Li Z. Estimation fire-related parameters in boreal forest using SOPTVEGETATION. Remote Sensing of Environment,2002,82:95-110.
Fraser R H, Li Z, Cihlar J. Hotspot and NDVI Differencing Synergy (HANDS): A new techniquefor burned area mapping over boreal forest. Remote Sensing of Environment,2000,74,362-276.
Frey K E and Smith L C. Recent temperature and precipitation increases in West Siberia andtheir association with the Arctic Oscillation. Polar Research,2003,22:287-300.
Friedl M A, Sulla-Menashe D, Tan B, et al. MODIS Collection5global land cover: Algorithmrefinements and characterization of new datasets. Remote Sensing of Environment,2010,114:168-182.
Gao B C. NDWI-a Normalized Difference Water Index for remote sensing of vegetation liquidwater from space. Remote Sensing of Environment,1996,58:257-226.
Garcia-Ruiz J, Lasanta T, Ruiz-Flano P, et al. Land-use changes and sustainable development inmountain areas: A case study in the Spanish Pyrenees. Landscape Ecology,1996,11:267-277.
George C, Rowland C, Gerard F, et al. Retrospective mapping of burnt areas in Central Siberiausing a modification of the Normalised Difference Water Index. Remote Sensing ofEnvironment,2006,104:346-359.
Gerard F, Plummer S, Wadsworth R, Ferreruela A, et al. Forest fire scar detection in the borealforest with multitemporal SPOT-VEGETATION data. IEEE Transactions on Geoscienceand Remote Sensing,2003,41:2575-2585.
Giglio L, Descloitres J, Justice C O et al. An enhanced contextual fire detection algorithm forMODIS. Remote Sensing of Environment,2003,87:273-282.
Giglio L, Van der Werf G R, Randerson J T et al. Global estimation of burned area using MODISactive fire observations. Atmospheric Chemistry and Physics,2006,6:957-974.
Gillet N P, Weaver A J, Zwiers F W et al. Detecting the effect of climate change on Canadianforest. Geophysical Research Letters,2004,31, L18211.doi:18210.11029/12004GL020876.
Gillet N P, Weaver A J, Zwiers F W, et al. Detecting the effect of climate change on Canadianforest fires. Geophysical Research Letters.,2004,31, L18211. doi:18210.11029/12004GL020876.
Gitas I, De Santis A, Mitri G. Remote sensing of burn severity. In: Chuvieco, E.,.(Eds), Earthobservation of wildland fires in Mediterranean ecosystems. Springer-Verlag, Berlin,2009:129-148.
Goetz S J, Fiske G J, Bunn A G. Using satellite time-series data sets to analyze fire disturbanceand forest recovery across Canada. Remote Sensing of Environment,2006,101:352-365.
Gouveia C, DaCamara C, Trigo R M. Post-fire vegetation recovery in Portugal based onspot/vegetation data. Natural Hazards and Earth System Sciences,2010,10:673-64.
Gruninger J A, Ratkowski J, Hoke M L. The sequential maximum angle convex cone(SMACC)endmember model. Proceedings SPIE Algorithms for Multispectral and Hype-spectral andUltraspectral Imagery,2004,(5425-1):255-269.
Hall F, Botkin D, Strebel D, et al. Large-scale patterns of forest succession as determined byremote sensing. Ecology,1991,72:628-640.
Hanes T L. Succession after fire in the chaparral of southern California. Ecological Monographs,1971,41:27-52.
Hardisky M A, Klemas V, Smart R M. The influences of soil salinity, growth form, and leafmoisture on the spectral reflectance of Spartina alterniflora canopies. PhotogrammetricEngineering and Remote Sensing,1983,49:77-83.
Henry M and Hope S. Monitoring post-burn recovery of chaparral vegetation in southernCalifornia using multi-temporal satellite data. International Journal of Remote Sensing,1998,19:3097-3107.
Heumann B W, Seaquist J W, Eklundh L, et al. AVHRR derived phenological change in the Saheland Soudan, Africa,1982-2005. Remote Sensing of Environment,2007,108:385-392.
Hicke J A, Asner G P, Kasischke E S et al. Postfire response of North American boreal forest netprimary productivity analyzed with satellite observations. Global Change Biology,2003,9,1145-1157.
Hickler T, Eklundh L, Seaquist J, et al. Precipitation controls Sahel greening trend. GeophysicalResearch Letters,2005,32, L21415.
Huete A R, Didan K, Miura T, et al. Overview of the radiometric and biophysical performance ofthe MODIS vegetation indices. Remote Sensing of Environment,2002,83:195-213.
IPCC. Climate Change2007: The Physical Science Basis. Summary for Policymakers. In I.P.O.CCHANGE(Ed.). Geneva IPCC Secretariat,2007.
Jacobson C. Use of linguistic estimates and vegetation indices to assess post-fire vegetationregrowth in woodland areas. International Journal of Wildland Fire,2010,19:94-103.
Jay D M and Stephen R Y. Mapping forest post-fire canopy consumption in several overstorytypes using multi-temporal Landsat TM and ETM data. Remote Sensing of Environment,2002,82:481-496.
Jay D M, Andrea E T. Quantifying burn severity in a heterogeneous landscape with a relativeversion of the delta Normalized Burn Ratio(dNBR). Remote Sensing of Environment,2007,109:66-80.
Jose A, Moreno R, David R et al. Burned area mapping time series in Canada (1984-1999) fromNOAA-AVHRR LTDR: A comparison with other remote sensing products and fireperimeters. Remote Sensing of Environment,2012,117:407-414.
Jose P S V and Paulo B. Post-fire vegetation regrowth detection in the Deiva Marinaregion(Liguria-Italy) using Landsat TM and ETM+data. Ecological Modelling,2010,221:75-84.
Jupp T E, Taylor C M, Balzter H, et al. A statistical model linking Siberian forest fire scars withearly summer rainfall anomalies. Geophys Res Lett,2006,33: L14701.
Kashian D M, Romme W H.Carbon storage on landscapes with stand-replacing fires. BioScience,2006,56:598-606.
Kas ischke E S and French N H F. Constraints on using AVHRR composite index imagery tostudy patterns of vegetation cover in boreal forests. International Journal of Remote Sensing,1997,18:2430-2426.
Kas ischke E S, Hewson J H, Stock B et al. The use of ASTR active fire counts for estimatingrelative patterns of biomass burning-A study from the boreal forest region. GeophysicalResearch Letters,2003,30(18):1969.
Kas ischke E S, Turetsky M R. Recent changes in the fire regime across the North Americanboreal region—Spatial and temporal patterns of burning across Canada and Alaska.Geophysical Research Letters,2006,33:1-5.
Kas ischke E S. Boreal ecosystems in the global carbon cycle. In: Fire, Climate Change andCarbon Cycling in the Boreal Forest. Ecological Studies Series (eds Kasischke E S, StocksB J), PP.19-30. Springer-Verlag, New York, NY, USA.
Kazanis D and Arianoutsou M. Long-term post-fire vegetation dynamics in Pinus halepensisforests of cectral Greece: a functional-group approach. Plant Ecology,2004,171:101-121.
Kobak K I, Turchinovich I Y, Kondrasheva N Y, et al. Vulnerability and adaptation of the larchforest in eastern Siberia to climate change. Water, Air and Soil Pollution,1996,92:119-127.
Kozlowski T. Physiological ecology of natural regeneration of harvested and disturbed foreststands: implications for forest management. Forest ecology and management,2002:195-221.
Kucera J, Yasuoka Y, Dye D G. Creating a forest fire database for the Far East Asia usingNOAA/AVHRR observation. International Journal of Remote Sensing,2005,26,2423-2439.
Lhermitte S, Verbesselt J, Verstraeten W, et al. A pixel based regeneration index using time seriessimilarity and spatial context. Photogrammetric Engineering and Remote Sensing,2010,76:673-682.
Lhermitte S, Verbesselt J, Verstraeten WW, et al. Assessing intra-annual vegetation regrowthafter fire using the pixel based regeneration index. ISPRS Journal of Photogrammetry andRemote Sensing,2011,66:17-27.
Liu J Y, Zhuang D F, Luo D, et al. Land cover calssification of China: integrated analys is ofAVHRR imagery and geophysical data. International Journal of Remote Sensing,2003,24(12):2485-2500.
Lloyd D. A phonological classification of terrestrial vegetation cover using shortwave vegetationindex imagery. International Journal of Remote Sensing,1990,11(12):2269-2279.
Loboda T, O’Neal K J, Csiszar I. Regionally adaptable dNBR-based algorithm for burned areamapping from MODIS data. Remote Sensing of Envrionment,2007,109:429-442.
Los S O, Collatz G J, Bounoua L, et al. Global interannual variations in sea surface temperatureand land surface vegetation, air temperature, and precipitation. J. Climate,2001,14:1535-1549.
Louis G, Tatiana L, David P et al. An active-fire based burned area mapping algorithm for theMODIS sensor. Remote Sensing of Environment,2009,113:408-420.
Loveland T R, Beed B C, Brown J F, et al. Development of a global land cover characteristicsdatabase and IGBP DISCover from1km AVHRR data. International Journal of RemoteSensing,2000,21(6&7):1303-1330.
Madsen K, Nielsen H B, Tigleff O. Methods for non-linear least squares problems. Informaticsand Mathematical Modeling (IMM), Technical University of Denmark,2004.
Marchetti M, Ricotta C, Volpe F. A qualitative approach to the mapping of post-fire regrowth inMediterranean vegetation with Landsat TM data. International Journal of Remote Sensing,1995,16:2487-2494.
Maria C G, France G, Heiko B, et al. Analysing forest recovery after wildfire disturbance inboreal Siberia using remotely sensed vegetation indices. Global Change Biology,2009,15:561-577.
Markon C J, Fleming M D, Binnian E F. Characteristics of vegetation phenology over theAlaskan landscape using AVHRR time-series data. Polar Recognition,1995,31(177):179-190.
Martin M P, Chuvieco E. Mapping and evaluation of burned land from multitemporal analysis ofAVHRR NDVI images. EARSeL Advances in Remote Sensing,1995,4(3):7-13.
Martinez B, Gilabert M A. Vegetation dynamics from NDVI time series analysis using thewavelet transform. Remote Sensing of Environment,2009,113:1823-1842.
Maxim D, Peter P, Anna L et al. Reconstructing long time series of burned areas in aridgrasslands of southern Russia by satellite remote sensing. Remote Sensing of Environment,2010,114:1638-1648.
McMichael C, Hope A, Roberts D, et al. Post-fire recovery of leaf area index in Californiachaparral: a remote sensing-chronosequence approach. International Journal of RemoteSensing,2004,25:4743-4760.
Melillo J M, McGuire A D, Kicklighter D W, et al. Global climate change and terrestrial netprimary production. Nature,1993,363:234-240.
Menzel A. Trends in phonological phases in Europe between1951and1966. InternationalJournal of Biometeorol,2000,44:76-81.
Mitchell M and Yuan F. Assessing forest fire and vegetation recovery in the Blacd Hills, SouthDakota. GIScience and Remote Sensing,2010,47:276-299.
Mitri G and Gitas I Z. Mapping postfire vegetation recovery using EO-1Hyperion imagery.IEEE Transactions on Geoscience and Remote Sensing,2010,48:1613-1618.
Mitri G and Gitas I Z. Mapping the sever ity of fire using object-based classification of IKONOSimagery. International Journal of Wildland Fire,2010,17:431-442.
Monaghati S, Samadzadegan F, Azizi A. An agent-based approach for regional forest firedetection using MODIS data. Journal of Applied Sciences,2009,9(20):3672-3681.
Moulin S, Kergoat L, Viovy N, et al. Global-scale assessment of vegetation phenology usingNOAA/AVHRR satellite measurements. Journal of Climaet,1997,10(6):1154-1170.
Ne’eman G, Fotheringham C J, Keelky j. Patch to landscape patterns in post fire recruitment of aserotinous conifer. Plant ecology,1999,12:235-242.
Nepstad D, Verssimo A, Alencar A, et al. Large-scale impoverishment of Amazonian forest bylogging and fire. Nature,1999,398:505-508.
Noormets A. Phenology of ecosystem processes: applications in global change research. NewYork, NY: Springer,2009.
Oliver C and Larson B. Forest Stand Dynamics. Wiley, New York, NY, USA,1996.
Palandjian D, Gitas I, Wright R. Burned area mapping and post-fire impact assessment in theKassandra peninsula (Greece) using Landsat TM and Quickbird data. Geocarto International,2009,24:193-205.
Pausas J and Verdu M. Plant persistence traits in the fire-prone ecosystems of the Mediterraneanbasin: a phylogenetic approach. Oikos,2005,109:196-202.
Pausas J, Carbo E, Caturla R et al. Post-fire regeneration patterns in the eastern Iberian Peninsula.Acta Oecologica,1999,20:499-508.
Pereira J M C. A comparative evaluation of NOAA/AVHRR vegetation indexes for burnedsurface detection and mapping. IEEE Transactions on Geoscience and Remote Sensing,1999,37:217-226.
Perez-Cabello F, Echeverria M, Ibarra P, et al. Effects of fire on vegetation, soil andhydrogeomorphological behavior in Mediterranean ecosystems. In: Earth Observation ofWildland fires in Mediterranean ecosystems. Springer,2009:111-128.
Peterson S and Stow D. Using multiple image endmember spectral mixture analysis to studychaparral regrowth in Southern California. International Journal of Remote Sensing,2003,24:4481-4504.
Pinol J, Terradas J, Lloret F. Climatic warming hazard, and wildfire occurrence in coastal easternSpain. Climate Change,1998,38:345-357.
Pinty B, Verstraete M M. GEMI: a non-linear index to monitor global vegetation from satellites.Vegetatio,1992,101:15-20.
Pu R L, Li Z Q, Gong P et al. Development and analysis of a12-year daily1-km forest firedataset across North America form NOAA/AVHRR data. Remote Sensing of Environment.,2007,108:198-208.
Ramsey E, Nelson G, Sapkota D, et al. Using multiple-polarization L-band radar to monitormarsh burn recovery. IEEE Transactions on Geoscience and Remote Sensing,1999,37:636-639.
Reed B C, Brown J F, et al. Measuring phenological variability from satellite imagery. Journal ofVegetation Science,1994,5(5):703-714.
Rego F. Land use changes and wildfires. In: A. Teller, P. Mathy, J. Jeffers (Eds.) Rwsponse offorest fires to environmental change, Elsevier, London,1992:367-373.
Riano D, Chuvieco E, Ustin S et al. Assessment of vegetation regeneration after fire throughmultitemporal analysis of AVIRIS images in the Santa Monica Mountains. Remote Sensingof Environment,2002,79:60-71.
Richardson A D, Black T A, Ciais P, et al. Influence of spring and autumn phonologicaltransitions on forest ecosystem productivity. Philosophical Transactions of the RoyalScociety B,2010,365:3227-3246.
Roberts D A, Batista G T, Pereira J L G, et al. Change identification using multitemporal spectralmixture analysis: applications in Eastern Amazonia. Remote Sensing Change Detection:Environment Monitoring Applications and Method. Ann Arbor Press, MI,2001.
Roberts D, Gardner M, Church R, et al. Mapping chaparral in the Santa Monica mountains usingmultiple endmember spectral mixture models. Remote Sensing of Environment,1998,65:267-279.
Roder A, Hill J, Duguy B, et al. Using long time series of Landsat data to monitor fire events andpost-fire dynamics and identify driving factors. A case study in the Ayora region (easternSpain). Remote Sensing of Environment,2008,112:259-273.
Roerind G J, Menenti M, Soepboer W, et al. Assessment of climate impact on vegetationdynamics by using remote sensing. Physics and Chemistry of the Earth,2003,28:103-109.
Roerind G J, Menenti M, Verhoef W. Reconstruction cloudfree NDVI composites using Fourieranalys is of time series. International Journal of Remote Sensing,2000,21(9):1911-1917.
Rogan J, Franklin J, Roberts D A. A comparison of methods for monitoring multi-temporalvegetation change using Thematic Mapper imagery. Remote Sensing of Environment,2002,80:143-156.
Roy D P, Boschetti L, Justice C O et al. The collection5MODIS burned area product—Globalevaluation by comparison with the MODIS active fire product. Remote Sensing ofEnvironment,2008,112:3690-3707.
Roy D P, Jin Y, Lewis P E et al. Prototyping a golbal alforithm for systematic fire-affected areamapping using MODIS time series data. Remote Sensing of Environment,2005,97:137-162.
Roy D P, Lewis P E, Justice C O. Burned area mapping using multi-temporal moderate spatialresolution data—A bi-directional reflectance model-based expectation approach. RemoteSensing of Environment,2002,83:263-286.
Sankey T, Moffet C, Weber K. Postfire recovery of sagebrush communities: assessment usingSPOT-5and very large-scale aerial imagery. Rangeland Ecology and Management,2008,61:598-604.
Savage M. Structural dynamics of a southwestern pine forest under Chronic human influence.Annals of the Association of American Geographers,1991,81(1):271-289.
Schwartz M D, Reed B C, White M A. Assessing satellite derived start-of-season measures in theconterminous USA. International Journal of Climatology,2002,22(14):1793-1805.
Schwartz M D. Green-wave phenology. Nature,1998,394:839-840.
Segah H, Tani H, Hirano T. Detection of fire impact and vegetation recovery over tropical peatswamp forest by satellite data and ground-based NDVI instrument. International Journal ofRemote Sensing,2010,31:5297-5314.
Sergio M, Fernando P C, Teodoro L. Assessment of radiometric correction techniques inanslyzing vegetation variability and change using time series of Landsat images. RemoteSensing of Environment,2008,112:3916-3934.
Sirikul N. Comparisons of MODIS vegetation index products with biophysical and flux towermeasurements. PhD Dissertation, the University of Arizona,2006.
Somers B, Verbesselt J, Ampe E, et al. Spectral mixture analysis to monitor defoliation inmixed-age Eucalyptus globulus Labill plantations in southern Australia using Landsat5-TMand EO-A Hyperion data. International Journal of Applied Earth Observation andGeoinformation,2010,12:270-277.
Steyaert L, Hall F, Loveland T. Land cover mapping, fire regeneration, and scaling studies in theCanadian boreal forest with1km AVHRR and Landsat TM data. International Journal ofGeophysical Research,1997,102:29581-29598.
Stueve K, Cerney D, Rochefort R, et al. Post-fire tree establishment patterns at the alpine treelineecotone: Mount Rainier National Park, Washington, USA. Journal of Vegetation Science,2009,20:107-120.
Sukhinin A I, French N H F, Kasischke E S, et al. AVHRR-based mapping of fires in Russia: newproducts for fire management and carbon cycle studies. Remote Sensing of Environment,2004,93:546-564.
Tarrega R, Luis-Calabuig E, Valbuena L. Eleven years of recovery dynamic after experimentalburning and cutting in two Cistus communities. Acta Oecologica,2001,22:277-283.
Thonicke K, Venevsky S, Sitch S, et al. The role of fire disturbance for global vegetationdynamics: coupling fire into a Dynamic Global Vegetation Model. Global Ecology andBiogeography,2001,10:661-677.
Tian H, Melillo J M, Kicklighter D W, et al. Parameters for global ecosystem models. Nature,1999a,399:536.
Tsitsoni T. Conditions determining natural regeneration after wildfires in the Pinus halepensis(Miller,1768) forests of Kassandra Peninsula (North Greece). Forest Ecology andManagement,1997,92:199-208.
Tucker C. Red and photographic infrared linear combinations for monitoring vegetation. RemoteSensing of Environment,1979,8:127-150.
Vafeidis N A, Drake N A, Wainwrighe J. A proposed methon for modelling the hydrologicresponse fo cathments to burning with the use of remote sensing and GIS. Catena,2007,70(3):396-409.
van der Werf G R, Randerson J T, Collatz G J, et al. Continental-scale partitioning of fireemissions during the1997to2001El Nino/La Nina period. Science,2004,303:73-76.
van Leeuwen Wx. Monitoring the effects of forest restoration treatments on post-fire vegetationrecovery with MODIS multitemporal data. Sensors,2004,8:2017-2042.
Van Leeuwen W, Casady G, Neary D, et al. Monitoring post-wildfire vegetation response withremotely sensed time series data in Spain, USA and Israel. International Journal of WildlandFire,2010,19:75-93.
Vander Werf G R, Randerson J T, Giglio L, et al. Interannual variability in global biomassburning emission from1997to2004. Atmospheric Chemistry and Physics,2006,6:3423-3441.
Veraverbeke S, Gitas I, Katagis T, et al. Assessing post-fire vegetation recovery using red-nearinfrared vegetation indices: accounting for background and vegetation variability. ISPRSJournal of Photogrammetry and Remote Sensing,2012b,28-39.
Veraverbeke S, Lhermitte S, Verstraeten W W, et al. The temporal dimension of differencedNormalized Burn Ratio (dNBR) fire/burn severity studies: the case of the large2007Peloponnese wildfires in Greece. Remote Sensing of Environment,2010b,114:2548-2563.
Veraverbeke S, Lhermitte S, Verstraeten WW, et al. A time integrated MODIS burn severityassessment using the multi-temporal differenced Normalized Burn Ratio (dNBRMT).International Journal of Applied Earth Observation and Geoinformation,2011a,13:52-58.
Veraverbeke S, Somers B, Gitas I, et al. Spectral mixture analysis to assess post-fire vegetationregeneration using Landsat Thematic mapper imagery: accounting for soil brightnessvariation. International Journal of Applied Earth Observation and Geoinformation,2012a,14:1-11.
Vila G and Barbosa P. Post-fire vegetation regrowth detection in the Deiva Marina region(Liguria-Italy) using Landsat TM and ETM+data. Ecological Modelling,2010,221:75-84.
Wang C Y, Zha X D Z, Chen T. Application of EOS/MODIS data on the forest fire in Tibet.Plateau and Mountain Meteorology Research,2010,30(3):65-69.(In Chinese)
Wardlow B D, Egbert S L. Large-area crop mapping using time-series MODIS250m NDVI data:an assessment for the U.S. Central Great Plains. Remote Sensing of Environment,2008,112:1096-1116.
White J, Ryan K, Key C, et al. Remote sensing of forest fire severity and data for regionalecological analysis. International Journal of Applied Earth Observation and Geoinformation,1996,4:161-173.
White L L, Zak D R, Barnes B V. Biomass accumulation and soil nitrogen availability in an87-year-old Populus grandidentata chronosequence. Forest Ecology and Management,2004,191:121-127.
Wicks T, Smith G, Curran P. Polygon-based aggragation of remotely sensed data for regionalecological analysis. International Journal of Applied Earth Observation and Geoinformation,2002,4:161-173.
Wright A and Bailey W. Fire Ecology. New York: John Wiley＆Sons,1982.
Wsieman P E and Seiler J R. Soil CO2efflux across four age classes of plantation loblolly pine(Pinus taeda L.) on the Virginia Piedmont. Forest ecology management,2004,192:297-311.
Xin J F, Yu Z R, Leeuwen L V, et al. Mapping crop key phenological stages in the North ChinaPlain using NOAA time series images. International Journal of Applied Earth Observationand Geoinformation,2002,4(2):109-117.
Zhang X Y, Friedl M A, Schaaf C B, et al. Monitoring vegetation phenology using MODIS.Remote Sensing of Environment,2003,84(3):471-475.
Zhang X Y, Shobha K, Brad q. Estimation of biomass burned areas usingmultiple-satellite-observed active fires. IEEE Transactions on Geoscience and RemoteSensing,2011,49(11):4469-4482.
陈效逑,王林海.遥感物候学研究进展.地理科学进展,2009,28(1):33-40.
单建平.火灾后兴安落叶上长短枝变化及其对生存的影响.应用生态学报,1996,7:6-10.
邓湘雯,孙刚,文定元.林火对森林演替动态的影响极其应用,中南林学院学报,2004,24(1):0051-0055.
顾顺林.定量遥感.北京:科学出版社,2009.
关克志,张大军.大兴安岭森林火灾对植被的影响分析.环境科学,1990,11(5):82-89.
郭晋平.森林景观生态研究.北京:北京大学出版社,2001,12-26.
孔繁华,李秀珍,王绪高等.林火迹地森林恢复研究进展.生态学杂志,2003,22(2):60-64.
李登科,范建忠,王娟.基于MOD17A3的陕西省植被NPP变化特征.生态学杂志,2011,30(12):2776-2782.
李红军,郑力,雷玉平等.基于EOS/MODIS数据的NDVI与EVI比较研究.地理科学进展,2007,26(1):26-32.
李为海,崔克城,刘萍.火烧迹地次生林天然更新株数模型的建立.内蒙古林业勘察设计,2000,2:28-30.
刘恩海,昝德萍,刘兴刚等.樟子松开花结实规律研究——樟子松球果变异与种子预测方法的关系.林业科技,1995,2(2):19-24.
龙慧灵,李晓兵,王宏等.内蒙古草原区植被净初级生产力及其与气候的关系.生态学报,2010,30(5):1367-1378.
卢振兰,刘常梅,韩国辉.火生态学研究方法综述.吉林林业科技,2001,30(6):4-7.
吕爱锋,田汉勤,刘永强.火干扰与生态系统碳循环.生态学报,2005,25(10):2734-2743.
罗菊春.大兴安岭森林火灾对森林生态系统的影响.北京林业大学学报,2002,24(5/6):101.
马克明,祖元刚.植被格局的分形特征.植物生态学报,2000,24(1):111-117.
朴世龙,方精云,郭庆华.利用CASA模型估算我国植被净第一性生产力.植物生态学报,2001,25(5):603-608.
邱扬.森林植被的自然火干扰.生态学杂志,1998,17(1):54-60.
邱扬,李湛东,徐化成.兴安落叶松种群的稳定性与火干扰关系的研究.植物研究,1997,17(4):441-446.
邵国凡,赵士洞,赵光.应用地理信息系统模拟森林景观动态的研究.应用生态学报,1991,2(2):103-107.
舒立福.大兴安岭林火生态及火烧迹地天然更新的研究.哈尔滨:东北林业大学博士论文,1993.
宋小宁,赵英时. MODIS图像的云检测及分析.中国图像图形学报,2003,8(9):1079-1083.
王建国,樊军,王全九等.黄土高原水蚀风蚀交错区植被地上生物量及其影响因素.应用生态学报,2011,22(3):556-564.
王明玉,任云卯,李涛等.火烧迹地更新与恢复研究进展.世界林业研究,2008,21(6):0049-0054.
王莺,夏文韬,梁天刚等.基于MODIS植被指数的甘南草地净初级生产力时空变化研究.草业科学,2010a,19(1):201-210.
王正兴,刘闯, Huete A.植被指数研究进展：从AVHRR-NDVI到MODIS-EVI.生态学报,2003.,5(23):579-586.
王正兴,刘闯,陈文波等. MODIS增强型植被指数EVI与NDVI初步比较.武汉大学学报,2006,5(31):407-410.
文定元.森林防火基础知识.北京:中国林业出版社,1995.
武永峰,李茂松,宋吉青.植物物候遥感监测研究进展.气象与环境学报,2008,24(3):0051-0058.
刑伟,葛之葳,李俊清.大兴安岭北部林区林火干扰强度对兴安落叶松群落影响研究.科学技术与工程,2006,6(14):2042-2046.
徐化成.景观生态学.第一版.北京：中国林业出版社,1996.
徐化成,李湛东,邱杨.大兴安岭北部地区原始林火干扰历史的研究.生态学报,1997,17(4):337-346.
杨春田,李宝印.大兴安岭北坡火烧迹地更新的策略与技术.林业科技,1989,6:23-24.
杨树春,刘新田,曹海波等.大兴安岭林区火烧迹地植被变化研究.东北林业大学学报,1998,26(1):19-23.
于信芳,庄大方.基于MODIS NDVI数据的东北森林物候期监测.资源科学,2006,28(4):111-117.
俞孔坚.景观：文化、生态与感知.北京:科学技术出版社,2000.
张继义,赵哈林,张铜会等.科尔沁沙地植被恢复系列上群落演替与物种多样性的动态恢复.植物生态学报,2004,28(1):2-11.
张杰,潘晓玲,高志强等.干旱生态系统净初级生产力估算及变化探测.地理学报,2006,61(1):15-25.
张宪洲.我国自然植被净第一性生产力的估算与分布.自然资源,1993,1:15-21.
张学霞,葛全胜,郑景云.遥感技术在植物物候研究中的应用综述.地球科学进展,2003,18(4):534-544.
赵伟,李召良.利用MODIS/EVI时间序列数据分析干旱对植被的影响.地理科学进展,2007,6(26):40-47.
郑焕能,邸雪颖等.中国森林火灾与对策.自然灾害学报,1994,3(3):37-40.
郑焕能,胡海清.森林燃烧环.东北林业大学学报,1987,15(5):1-5.
郑焕能,胡海清,姚树人.林火生态.哈尔滨:东北林业大学出版社,1992.
郑焕能,贾松青,胡海清.大兴安岭林区的林火与森林恢复.东北林业大学学报,1986,14(4):1-7.
中国科学院地理研究所.中国动植物物候观测年报（第1号）.北京：科学出版社，1965.
钟章成.我国植物种群生态研究的成就与展望.生态学杂志,1992,11(1):4-8.
周道玮.草地火生态学研究进展.长春：吉林科学技术出版社,1995.
周广胜,郑元润,陈四清等.自然植被净第一性生产力模型及其应用.林业科学,1998,34(5):2-11.
周晓峰,张远东,孙慧珍等.气候变化对大兴安岭北部蒙古栎种群动态的影响.生态学报,2002,22(7):980-985.
周以良,乌弘奇,陈涛等.按植物群落生态学特性,加速恢复大兴安岭火烧迹地的森林.东北林业大学学报,1989,17(2):1-10.
竺可桢,宛敏渭.物候学.长沙：湖南教育出版社,1999.
左丽君,张增祥,董婷婷等. MODIS/NDVI和MODIS/EVI在耕地信息提取中的应用及对比分析.农业工程学报,2008,24(3):167-172.