Dynamic Drought Monitoring in Guangxi Using Revised Temperature Vegetation Dryness Index

Moderate resolution imaging spectroradiometer （MODIS） data are very suitable for vast extent, long term and dynamic drought monitoring for its high temporal resolution, high spectral resolution and moderate spatial resolution. The composite Enhanced Vegetation Index （EVI） and composite land surface temperature （Ts） obtained from MODIS data MOD11A2 and MOD13A2 were used to construct the EVI-Ts space. And Temperature Vegetation Dryness Index （TVDI） was calculated to evaluate the agriculture drought in Guangxi province, China in October of 2006. The results showed that the drought area in Guangxi was evidently increasing and continuously deteriorating from the middle of September to the middle of November. The TVDI, coming from the EVI-Ts space, could effectively indicate the spatial distribution and temporal evolution of drought, so that it could provide a strong technical support for the forecasting agricultural drought in south China.

Applying the Global Disturbance Index for Detecting Vegetation Changes in Lao Tropical Forests

Land cover change is a major challenge for many developing countries. Spatiotemporal information on this change is essential for monitoring global terrestrial ecosystem carbon, climate and biosphere exchange, and land use management. A combination of LST and the EVI indices in the global disturbance index (DI) has been proven to be useful for detecting and monitoring of changes in land covers at continental scales. However, this model has not been adequately applied or assessed in tropical regions. We aimed to demonstrate and evaluate the DI algorithm used to detect spatial change in land covers in Lao tropical forests. We used the land surface temperature and enhanced vegetation index of the Moderate Resolution Imaging Spectroradiometer time-series products from 2006-2012. We used two dates Google EarthTM images in 2006 and 2012 as ground truth data for accuracy assessment of the model. This research demonstrated that the DI was capable of detecting vegetation changes during seven-year periods with high overall accuracy;however, it showed low accuracy in detecting vegetation decrease.

基于NDVI和EVI不同植被指数表征的粤港澳大湾区植被空间格局驱动因子影响力比较分析

为研究归一化植被指数(normalized difference vegetation index,NDVI)和增强植被指数(enhanced vegetation index,EVI)的表征差异是否会造成有关植被研究的结果差异,分别在采用2005—2020年MODISNDVI、MODIS-EVI 2种不同遥感植被指数表征粤港澳大湾区植被空间特征的基础上,以同时期17个自然因子和人为因子作为驱动因子,通过地理探测器模型方法,计算各驱动因子对基于NDVI和EVI的植被空间特征的影响力。结果显示,虽然粤港澳大湾区南亚热带-热带植被在NDVI和EVI的表征下,其结果存在差异,但在不同植被指数下,通过地理探测器模型方法计算各驱动因子影响力量化及排序结果基本一致,未受不同植被指数表征差异的影响。在驱动因子中,土地利用类型、高程均是最主要驱动因子,对植被空间分布影响力均超过50%。因子之间均表现出双因子增强作用。土地利用类型协同人口分布因子对NDVI表征下的植被空间分布影响力最强;高程协同人口分布因子对EVI表征下的植被空间分布影响力最强。研究结果表明,虽然NDVI、EVI在表征植被覆盖特征方面存在差异,但是基于不同植被指数计算的驱动因子定量分析结果趋于一致。

Climate Change and Ecological Projects Jointly Promote Vegetation Restoration in Three-River Source Region of China

As the source of the Yellow River,Yangtze River,and Lancang River,the Three-River Source Region(TRSR)in China is very important to China’s ecological security.In recent decades,TRSR’s ecosystem has degraded because of climate change and human disturbances.Therefore,a range of ecological projects were initiated by Chinese government around 2000 to curb further degradation.Current research shows that the vegetation of the TRSR has been initially restored over the past two decades,but the respective contribution of ecological projects and climate change in vegetation restoration has not been clarified.Here,we used the Moderate Resolution Imaging Spectroradiometer(MODIS)Enhanced Vegetation Index(EVI)to assess the spatial-temporal variations in vegetation and explore the impact of climate and human actions on vegetation in TRSR during 2001–2018.The results showed that about 26.02%of the TRSR had a significant increase in EVI over the 18 yr,with an increasing rate of 0.010/10 yr(P<0.05),and EVI significantly decreased in only 3.23%of the TRSR.Residual trend analysis indicated vegetation restoration was jointly promoted by climate and human actions,and the promotion of human actions was greater compared with that of climate,with relative contributions of 59.07%and40.93%,respectively.However,the degradation of vegetation was mainly caused by human actions,with a relative contribution of71.19%.Partial correlation analysis showed that vegetation was greatly affected by temperature(r=0.62,P<0.05)due to the relatively sufficient moisture but lower temperature in TRSR.Furthermore,the establishment of nature reserves and the implementation of the Ecological Protection and Restoration Program(EPRP)improved vegetation,and the first stage EPRP had a better effect on vegetation restoration than the second stage.Our findings identify the driving factors of vegetation change and lay the foundation for subsequent effective management.

Effect of Topography and Accessibility on Vegetation Dynamic Pattern in Mountain-hill Region

Knowledge of both vegetation distribution pattern and phenology changes is very important.Their complicated relationship with elevation and accessibility were explored through a geographically weighted regression(GWR) framework in Fujian province,China.The 16-day time series of 250 m Moderate Resolution Imaging Spectroradiometer(MODIS) Enhanced Vegetation Index(EVI) dataset from 2000 to 2010 was applied.Wavelet transform method was adopted to decompose the original time series and construct the annual maximum EVI and amplitude of the annual phenological cycle(EVI).Candidate explaining factors included topographic conditions,accessibility variables and proportions of primary vegetation types.Results revealed very strong positive influence from parameters of elevation and accessibility to big rivers and negative effect from accessibility to resident on both maximum EVI and phenological magnitude through ordinary linear least square(OLS) regression analysis.GWR analysis revealed that spatially,the parameters of topography and accessibility had a very complex relationship with both maximum EVI and phenology magnitude,as a result of the various combinations of environmental factors,vegetation composition and also intensive anthropogenic impact.Apart from the continuously increasing trend of phenology magnitude with increasing altitude,the influence of topography and accessibility on maximum EVI and phenological magnitude generally decreased,even from strongly positive to negative,with increasing altitude or distance.Specially,the most rapid change of correlation coefficient between them was observed within a low elevation or close distance;less variation was discovered within a certain range of medium altitude or distance and their relationship might change above this range.Non-stationary approaches are needed to better characterize the complex vegetation dynamic pattern in Mountain-hill Region.

Analysis of Climate Variability and Relation to Vegetation in Garamba National Park from 1990-2020

Climatic factors impact vegetation. Our study was to examine and analyze the climate variability and relationship to vegetation in Garamba National Park of the Democratic Republic of the Congo over the past 30 years (1990 to 2020), then to relate the climatic variables. Mann Kendall’s non parametric test, ANOVA, and p-value tests are used to analyze existing trends and relationships between vegetation cover, climatic factors, land surface temperature (LST) and normalized difference in temperature Vegetation index (NDVI), Enhanced vegetation index (EVI) in Garamba national park which is of particular importance for the network of protected areas of the Democratic republic of Congo because its position at the northern limit of the savanna-forest mosaics gives it a unique biodiversity. The southern part of the park is dominated by grassy shrub savannas. The results showed that: 1) In Garamba, the monthly correlation coefficient of Kendall and Pearsan between temperature and precipitation are negative respectively 0.763 and −0.876 (p-value < 0.00001). 2) Annually during the three decades in Garamba, the correlation between precipitation and NDVI is significant 0.416 (Kendall) and 0.496 (Pearsan);the same between precipitation and EVI 0.291 (Kendall) and 0.496 (Pearsan) while LST and precipitation are negatively correlated (p-value < 0.00001).

Change of Vegetation Cover and Its Relationships with Climatic Factors in Large-area Eucalyptus Introduced Region of Yunnan

Taking Lancang County as a study area with a large area of eucalyptus introduction in Yunnan, spatiotemporal change characteristics of vegetation cover, as well as the relationships between Enhanced Vegetation Index（EVl） and climatic factors （temperature and precipitation） were analyzed by using the data of MODIS-EVI from 2005 to 2010. The results indicated that： （1） The vegetation cover was overall good, and the annual average values of EVl were greater than 0.395 and showed a slow increasing trend from 2005 to 2010 in study area; the monthly average values of EVl ranged from 0.296 to 0.538, and seasonal variability was obvious. Monthly average values of EVl usually fell to the lowest level in February and March, and reached the peak in July and August. From the perspective of space, average EVl over the years significantly varied in different towns of Lancang County. During 2005 -2010, in 92.534% area of total, vegetation coverage change were not obvious; in 7.25% area of total, vegeta- tion becoming better; only in 0.02% area of total, vegetation cover were getting worse. （2） Monthly average values of EVl were significantly correlated with monthly average rainfall in Lancang County. The maxima of monthly average EVI and rainfall appeared in August on summer, while the minima of monthly average EVl and rainfall appeared in February and January on winter respectively. （3） Monthly average EVl was somewhat relative with monthly average temperature. The maxima of monthly average EVl and temperature appeared in June and August respectively, while the minima appeared in January and February respectively.

Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake

The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems,including vegetation destruction.However,the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague,especially in the severely damaged areas(including Wenchuan,Beichuan,Mianzhu,Shifang,Qingchuan,Maoxian,Anzhou,Dujiangyan,Pingwu and Pengzhou).Here,we detected vegetation recovery in the severely damaged areas by using Ensemble Empirical Mode Decomposition(EEMD)to analyze the time series characteristics of the Enhanced Vegetation Index(EVI),and explored the driving effects of climate,land use types,nighttime light,water system,slope,and clay content on vegetation recovery based on Geographically and Temporally Weighted Regression(GTWR)model.The results indicated that the post-seismic vegetation recovery rate increased rapidly(acceleration>0)but slowed down after 2013.And the areas of best vegetation recovery(EVI increments>0.1)were distributed in the north of the study area,the Minjiang River Basin,and front fault and central fault of the Longmenshan Fault Zone.While the areas with the worst vegetation recovery(EVI increments<-0.1)were concentrated in the southern high-altitude areas and the Chengdu Plain.Additionally,a process attribution of the driving forces of vegetation recovery indicated that accumulated precipitation and maximum temperature promoted vegetation recovery(regression coefficients>0),but the impacts weakened after the earthquake,possibly due to the increase of secondary disasters induced by precipitation and the rise in maximum temperature.The impact of cultivated land on vegetation recovery was mostly positive(regression coefficients>0),which may be related to the implementation of the Grain for Green Project.The nighttime light inhibited vegetation recovery(regression coefficients<0),which could be closely associated with urbanization.The results indicated that more attention should be paid to the nonlinear variations of post-earthquake vegetation recovery trends,and the effects of climatic and anthropogenic factors on vegetation recovery also should not be underestimated.

Increased Browning of Woody Vegetation due to Continuous Seasonal Droughts in Yunnan Province, China

In this paper, based on the analysis of satellite measurements, the authors conclude that the continuous seasonal droughts intensify the browning of woody vegetation and that evergreen needleleaf forest(ENF) shows a larger browning percentage than other woody vegetation types over Yunnan Province. Based on the Tropical Rainfall Measuring Mission(TRMM) precipitation standardized anomaly, in the dry season, which is from October to March, the 2010 drought affected an area of Yunnan Province 1.77 times larger than the 2012 drought, but in the post-drought months(April to June), the browning area of all woody vegetation in 2012 was 1.11 times larger than that in 2010 on the basis of the enhanced vegetation index(EVI) standardized anomaly. The reduction of vegetation greenness over large areas of Yunnan Province represents a photosynthetic capacity loss which will have an impact on carbon fluxes to the atmosphere.

四川盆地植被EVI动态变化及其驱动机制

以四川盆地为研究区,基于MOD13A3EVI时间序列、气象数据、土地利用类型数据和夜间灯光数据,利用Theil-Sen Median趋势分析、相关性分析、多重共线性检验(VIF)、残差分析和相对作用分析等方法,探究2000~2020年四川盆地植被EVI时空变化及其变化的驱动机制.结果表明:2000~2020年四川盆地83.81%的植被EVI呈上升趋势,集中分布于四川盆地中部和东部;呈下降趋势的面积仅占16.19%,集中分布于成都城市圈和重庆城市圈,其他地区零星分布.城市尺度上,除成都市植被EVI呈下降趋势外,四川盆地各地级市植被EVI均呈上升趋势.四川盆地植被EVI变化受不同气候因子的调控,且表现出明显的地域差异.整体上,四川盆地植被EVI与最低气温和降水呈正相关,与气压呈负相关,且植被EVI与降水的相关性最高.最低气温和降水对四川盆地植被生长呈促进作用的面积大于呈抑制作用的面积,而气压对四川盆地植被生长呈抑制作用的面积大于呈促进作用的面积.四川盆地植被EVI变化受最低气温、降水和气压弱驱动的影响大于其他气候驱动类型.人类活动对植被EVI变化的影响具有双重作用,且积极作用大于消极作用.城市扩张所引起的建设用地的大量转入和现代化农业快速发展引起的林地向耕地的大量转入是导致四川盆地植被EVI下降的主要原因,而四川盆地生态工程推进所引起的林地的大量转入和农林业综合发展所引起的草地向耕地的大量转入是导致植被EVI上升的主要原因.由相对作用分析结果可知,气候变化和人类活动的共同驱动是四川盆地植被EVI上升的主要原因.气候变化对植被EVI上升的影响要大于人类活动,而人类活动对植被EVI下降的影响大于气候变化.研究结果可为四川盆地植被动态监测和生态环境质量评估等提供理论参考.

Multilevel Assessment of Spatiotemporal Variability of Vegetation in Subtropical Mountain-hill Region

The complex spatiotemporal vegetation variability in the subtropical mountain-hill region was investigated through a multi-level modeling framework. Three levels- parcel, landscape, and river basin levels- were selected to discover the complex spatiotemporal vegetation variability induced by climatic, geomorphic and anthropogenic processes at different levels. The wavelet transform method was adopted to construct the annual maximum Enhanced Vegetation Index and the amplitude of the annual phenological cycle based on the 16-day time series of250m Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index datasets during 2001-2010. Results revealed that land use strongly influenced the overall vegetation greenness and magnitude of phenological cycles.Topographic variables also contributed considerably to the models, reflecting the positive influence from altitude and slope. Additionally, climate factors played an important role: precipitation had a considerable positive association with the vegetation greenness, whereas the temperature difference had strong positive influence on the magnitude of vegetation phenology. The multilevel approach leads to a better understanding of the complex interaction of the hierarchical ecosystem, human activities and climate change.

基于EOS/MODIS数据的NDVI与EVI比较研究

作为NOAA/AVHRR归一化植被指数(NDVI)的延续和发展,EOS/MODIS归一化植被指数(NDVI)和增强植被指数(EVI)在许多领域得到广泛应用。应用数理统计和地统计学方法对二者进行的对比研究表明:NDVI在植被生长旺盛期容易达到饱和,而EVI则能克服这一现象,比较真实地反映植被的生长变化过程;相同空间分辨率下,EVI取值范围、标准差与变异系数均高于NDVI,NDVI数据比较均一,其空间相关性高于EVI,EVI更能反映研究区域内植被空间差异。

基于气候信息的喀斯特地区植被EVI模拟

该研究以喀斯特地区植被为研究对象,分析各种气候因子与植被指数的相关性及作用机制,在此基础上建立基于气候因子的植被EVI拟合模型,为定量分析气候条件对植被的综合影响奠定基础。结果表明:气候因子对喀斯特地区植被EVI影响显著,植被EVI与水汽压、平均气温、露点温度、最低气温、最高气温的相关性均大于0.8且空间一致性好。除日照时数和风速外,该地区植被EVI对其他气候因子的响应均存在显著滞后性,滞后期约16 d。对植被EVI起直接作用的主要是温度类气候因子,水分类气候因子对植被EVI的直接作用不明显,但通过其他气象因子起了较强的间接作用。根据该地区植被与气候因子的关系建立了2个EVI拟合模型,其中基于同期气候因子的同期模型中入选的气候因子为水汽压(0期)、日照时数(0期)、露点温度(0期),基于同期、前期气候因子的混合模型入选气候因子为水汽压(-1期)、最高气温(-1期)、降水量(-1期)、露点温度(-1期)、日照时数(0期)。分别利用2001-2010年建模数据和2011年非建模数据对2个模型进行了单站点和片区两种尺度的精度验证。验证结果表明,2个模型对整个片区植被EVI的拟合精度高于单站点,且混合模型的拟合精度高于同期模型。2001-2010年同期模型和混合模型的片区拟合R2分别为0.843、0.892,站点拟合R2分别为0.765±0.033、0.801±0.021。2011年2个模型的片区拟合R2分别为0.797、0.873,站点拟合R2分别为0.716±0.073、0.746±0.064。对大多数站点而言,混合模型的拟合精度较高,但是由于2个模型的建模气候因子不同及各个站点植被的EVI与气候因子的综合响应也存在较大差异,同期模型对部分站点植被EVI拟合精度高于混合模型。

植被指数研究进展:从AVHRR-NDVI到MODIS-EVI

目前应用广泛的植被指数 AVHRR- NDVI仍有一些缺陷 ,主要表现在 :(1 )在植被高覆盖区容易饱和 ,这除了红光通道就容易饱和外 ,主要是基于 NIR/Red比值的 NDVI算式本身存在容易饱和的缺陷 ;(2 )没有考虑树冠背景对植被指数的影响 ;(3 ) NDVI的比值算式和最大值合成算法 (MVC)确实消除了某些内部和外部噪音 ,但最终的合成产品仍然有较多噪音 ;(4) MVC不能确保选择最小视角内的最佳像元。所有这些 AVHRR- NDVI的局限性 ,在基于“中分辨率成像光谱仪 (MODIS)”的“增强型植被指数 (EVI)”产品中 ,都有不同程度改善。MODIS- EVI改善表现在 :(1 )大气校正包括大气分子、气溶胶、薄云、水汽和臭氧 ,而 AVHRR- NDVI仅对瑞利散射和臭氧吸收做了校正 ;这样 MODIS- EVI可以不采用基于比值的方法 ,因为比值算式是以植被指数饱和为代价来减少大气影响 ;(2 )根据蓝光和红光对气溶胶散射存在差异的原理 ,采用“抗大气植被指数 (ARVI)对残留气溶胶做进一步的处理 ;(3 )采用“土壤调节植被指数(SAVI)”减弱了树冠背景土壤变化对植被指数的影响 ;(4)综合 ARVI和 SAVI的理论基础 ,形成“增强型植被指数 (EVI)”,它可以同时减少来自大气和土壤噪音的影响 ;(5 )采用“限定视角内最大值合成法 (CV-MVC)”。

基于MODIS产品LST／NDVI／EVI的陕西旱情监测

以陕西省为研究区域,利用2005年4月的MODIS月合成产品数据MODIS11C3和MO-DIS13C3获取的归一化植被指数NDVI、增强型植被指数EVI和陆地表面温度LST,分别构建TS-NDVI和TS-EVI特征空间,从而得到了条件温度植被干旱指数TVDI和旱情等级的空间分布图,以监测评价陕西的旱情,同时将两者进行比较,最后结合94个气象站的气温和降水距平进行了相关性分析。结果表明:利用条件温度植被干旱指数进行陕西省旱情监测,能够较好反映当地旱情。根据地表温度以及增强植被指数之间的关系建立的旱情监测模型与降水距平的线性相关显著,相关系数为0.537,通过了0.05水平的检验。

MODIS NDVI与MODIS EVI的比较分析

MODIS NDVI与MODIS EVI是目前应用比较广泛的植被指数,MODIS EVI是对NDVI的发展和延续,从植被指数计算公式和合成方法两方面做了改进。具体表现在:避免了MODIS NDVI在植被高覆盖区易饱和的问题,考虑了土壤背景对植被指数的影响,对气溶胶等残留做了进一步校正,采用BRDF/CV-MVC合成方法保证了合成采用最佳像元。EVI时间序列相较于NDVI时间序列季节性更明显,能够更好地反映高植被覆盖区的季节性变化特征,并且很少有突降现象,时间序列曲线较平滑。EVI的这些优势为高覆盖植被物候特征的季节性变化监测提供了新的思路。

基于NDVI与EVI的作物长势监测研究

基于2015年大气校正后的时间序列Landsat8影像,研究了归一化植被指数NDVI与增强型植被指数EVI随植被覆盖度增加的变化规律,定量分析了二者监测低、中、高植被覆盖的差异,比较分析了NDVI和EVI分布频率曲线差异及时间序列曲线差异。结果表明:地表刚出现植被时,NDVI和EVI的增加速度最快,随着地表植被覆盖度的增加,NDVI与EVI的增加速度减缓。低植被覆盖下NDVI的增加速度大于EVI,中等植被覆盖下NDVI和EVI的增加速度接近,高植被覆盖下NDVI的增加速度小于EVI,不同植被覆盖下的NDVI值始终大于EVI值。NDVI和EVI分布频率曲线能描述不同植被覆盖度像元数量和随时间的变化。NDVI和EVI时间序列曲线能清晰反映一种作物的长势变化规律及不同作物在同一时期的长势差异。在作物生长初期或低植被覆盖下,NDVI、EVI都偏高估计植被覆盖度,NDVI估计值略高于EVI的估计值。在作物生育中期或中等植被覆盖下,二者对植被描述能力相似。在作物生育高峰期或高植被覆盖下,监测作物长势变化EVI比NDVI更敏感。综上所述,监测作物时可根据作物生育期植被覆盖度变化特点合理选取NDVI和EVI植被指数,也可同时选用NDVI和EVI两种植被指数互为补充。

海南岛典型植被区EVI特征及其对气象因子的响应

利用2001—2011年的MODIS EVI时间序列影像,分析海南岛热带雨林、橡胶林及农田3种典型植被覆盖区的EVI时空变化特征及对气象因子的响应。结果表明:(1)空间上,海南岛平均EVI指数为0.42,呈现为中间高四周低,南高北低的分布特征;(2)时间上,热带雨林EVI年内变化幅度较小,无明显季节性变化,橡胶林年内季节性变化明显,冬末最低,春季快速增长,随后保持较高水平,农田变化幅度与周期性介于两者之间;3种典型植被年际EVI最大值相对稳定,最小值波动较大,橡胶林最大值呈略增大趋势;(3)限制3种植被生长的气象因子不相同,农田受限于冬季日照;影响橡胶林最大的气象因子为年降水量与冬季气温,雨林喜好夏季高温而受制于冬季降水量。

基于EVI的大兴安岭火烧迹地植被恢复特征研究

[目的]选取Landsat数据源,基于dNBR和EVI指数开展火烧迹地识别和植被恢复特征研究。[方法]以1987年大兴安岭北部林区根河林业局金林林场森林火灾为研究背景,在地类划分的基础上,以dNBR为基础数据,采用K-means方法识别并提取火烧迹地,并进行轻、中、重火烈度等级划分;基于火烧迹地的EVI(增强型植被指数)值,采用一元线性回归分析、Mann-Kendall和Theil-Sen median趋势分析等方法分析火烧迹地1987—2019年的植被恢复特征,探究大兴安岭火烧迹地植被恢复进程。[结果]基于dNBR得到研究区过火面积为1291.68 hm^(2),轻、中、重度火烧迹地面积占比分别为45.70%、32.16%和22.14%,重度过火区分布于火烧迹地中心,从中心向外,过火强度逐渐降低;林火对迹地EVI影响明显,轻、中和重度火烧迹地EVI值分别下降约30.0%、48.3%和68.8%;林火后,随着植被的恢复,迹地EVI值逐渐增加,与对照区的差异逐渐缩小。不同烈度林地火烧迹地EVI恢复速率表现为重度>中度>轻度,灌草地火烧迹地的EVI值在林火发生后2 a即与对照持平;火烧迹地植被恢复过程中,林地EVI突变点较灌草地少,林地轻度火烧迹地EVI突变点较重、中度迹地少,对照区的突变时间点均滞后于火烧迹地。[结论]dNBR可用于研究区火烧迹地和火烈度提取研究。林火使迹地EVI值明显下降,下降程度随火烈度升高而增大。植被恢复过程中,迹地EVI值逐渐增加,林地轻度和中度火烧迹地在火后6~8 a,重度火烧迹地在火后14 a左右恢复为正常植被状态;而灌草地火烧迹地在林火发生后2 a即可恢复正常。火烈度和自然环境是影响大兴安岭火烧迹地植被恢复的主要因素。

2001—2019年台风干扰下我国东南沿海地区植被抵抗力和恢复力评估

台风是影响我国陆地生态系统结构和功能的主要自然灾害之一,尤其对东南沿海地区植被具有明显的干扰效应。为深入了解台风对我国沿海地区植被的影响及其响应,基于MODIS增强型植被指数(Enhanced Vegetation Index,EVI)和叶面积指数(Leaf Area Index,LAI),分析了2001—2019年间不同等级的台风对植被EVI和LAI的影响,评估台风登陆时植被的抵抗力及台风过后植被的恢复力。结果表明:(1)2001—2019年间,共有70个台风登陆我国东南沿海地区,其中,台风(TY)、强台风(STY)与超强台风(SuperTY)的数量分别为35、26和9个。我国台湾省的台风登陆次数最多,为26个,其次为广东省(20个);(2)在整个研究区范围内,台风(TY)、强台风(STY)与超强台风(SuperTY)登陆后的EVI净变化率分别为4.88%、-7.48%、-1.85%,LAI净变化率分别为22.28%、-65.70%、-17.60%;(3)在经历台风干扰后,沿海地区大部分植被可在3个月之内得到恢复,其中浙江省与广东省植被的恢复时间较长,海南省与台湾省植被的恢复时间较短。研究结果对我国沿海地区森林经营管理和生态环境保护具有重要意义。