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1.
In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.  相似文献   

2.
Mountainous rangelands play a pivotal role in providing forage resources for livestock, particularly in summer, and maintaining ecological balance. This study aimed to identify environmental variables affecting range plant species distribution, ecological analysis of the relationship between these variables and the distribution of plants, and to model and map the plant habitats suitability by the Random Forest Method(RFM) in rangelands of the Taftan Mountain, Sistan and Baluchestan Province, southeastern Iran. In order to determine the environmental variables and estimate the potential distribution of plant species, the presence points of plants were recorded by using systematic random sampling method(90 points of presence) and soils were sampled in 5 habitats by random method in 0–30 and 30–60 cm depths. The layers of environmental variables were prepared using the Kriging interpolation method and Geographic Information System facilities. The distribution of the plant habitats was finally modelled and mapped by the RFM. Continuous maps of the habitat suitability were converted to binary maps using Youden Index(?) in order to evaluate the accuracy of the RFM in estimation of the distribution of species potentialhabitat. Based on the values of the area under curve(AUC) statistics, accuracy of predictive models of all habitats was in good level. Investigating the agreement between the predicted map, generated by each model, and actual maps, generated from fieldmeasured data, of the plant habitats, was at a high level for all habitats, except for Amygdalus scoparia habitat. This study concluded that the RFM is a robust model to analyze the relationships between the distribution of plant species and environmental variables as well as to prepare potential distribution maps of plant habitats that are of higher priority for conservation on the local scale in arid mountainous rangelands.  相似文献   

3.
Forest fire is one of the major causes of forest loss and therefore one of the main constraints for sustainable forest management worldwide. Identifying the driving factors and understanding the contribution of each factor are essential for the management of forest fire occurrence. The objective of this study is to identify variables that are spatially related to the occurrence and incidence of the forest fire in the State of Durango, Mexico. For this purpose, data from forest fire records for a five-year period were analyzed. The spatial correlations between forest fire occurrence and intensity of land use, susceptibility of vegetation, temperature, precipitation and slope were tested by Geographically Weighted Regression (GWR) method, under an Ordinary Least Square estimator. Results show that the spatial pattern of the forest fire in the study area is closely correlated with the intensity of land use, and land use change is one of the main explanatory variables. In addition, vegetation type and precipitation are also the main driving factors. The fitting model indicates obvious link between the variables. Forest fire was found to be the consequence of a particular combination of the environmental factors, and when these factors coexist with human activities, there is high probability of forest fire occurrence. Mandatory regulation of human activities is a key strategy for forest fire prevention.  相似文献   

4.
Burned area mapping is an essential step in the forest fire research to investigate the relationship between forest fire and climate change and the effect of forest fire on carbon budgets. This study proposed an algorithm to map forest fire burned area using the Moderate-Resolution Imaging Spectroradiameter (MODIS) time series data in Heilongjiang Province, China. The algorithm is divided into two steps: Firstly, the ‘core’ pixels were extracted to represent the most possible burned pixels based on the comparison of the temporal change of Global Environmental Monitoring Index (GEMI), Burned Area Index (BAI) and MODIS active fire products between pre- and post-fires. Secondly, a 15-km distance was set to extract the entire burned areas near the ‘core’ pixels as more relaxed conditions were used to identify the fire pixels for reducing the omission error as much as possible. The algorithm comprehensively considered the thermal characteristics and the spectral change between pre- and post-fires, which are represented by the MODIS fire products and the spectral index, respectively. Tahe, Mohe and Huma counties of Heilongjiang Province, China were chosen as the study area for burned area mapping and a time series of burned maps were produced from 2000 to 2011. The results show that the algorithm can extract burned areas more accurately with the highest accuracy of 96.61%.  相似文献   

5.
Suitable habitat is vital for the survival and restoration of a species.Understanding the suitable habitat range for lycophytes and ferns is prerequisite for effective species resource conservation and recovery efforts.In this study, we took Athyrium brevifrons as an example, predicted its suitable habitat using a Maxent model with 67 occurrence data and nine environmental variables in Northeast China.The area under the curve(AUC) value of independent test data, as well as the comparison with specimen county areal distribution of A.brevifrons exhibited excellent predictive performance.The type of environmental variables showed that precipitation contributed the most to the distribution prediction, followed by temperature and topography.Percentage contribution and permutation importance both indicated that precipitation of driest quarter(Bio17) was the key factor in determining the natural distribution of A.brevifrons, the reason could be proved by the fern gametophyte biology.The analysis of high habitat suitability areas also showed the habitat preference of A.brevifrons: comparatively more precipitation and less fluctuation in the driest quarter.Changbai Mountains, covering almost all the high and medium habitat suitability areas, provide the best ecological conditions for the survival of A.brevifrons, and should be considered as priority areas for protection and restoration of the wild resource.The potential habitat suitability distribution map could provide a reference for the sustainable development and utilisation of A.brevifrons resource, and Maxent modelling could be valuable for conservation management planning for lycophytes and ferns in Northeast China.  相似文献   

6.
The Qinghai-Tibet Plateau is the world’s highest and largest plateau. Due to increasing demands for environment exploration and tourism, a large transitional area is required for altitude adaptation. Hehuang valley, which locates in the transition zone between the Loess Plateau and the Qinghai-Tibet Plateau, has convenient transportation and relatively low elevation. Our question is whether the geographic conditions here are appropriate for adapted stay before going into the Qinghai-Tibet Plateau. Therefore, in this study, we examined the potential use of ecological niche modeling (ENM) for mapping current and potential distribution patterns of human settlements. We chose the Maximum Entropy Method (Maxent), an ENM which integrates climate, remote sensing and geographical data, to model distributions and assess land suitability for transition areas. After preprocessing and selection, the correlation between variables and spatial autocorrelation input data were removed and 106 occurrence points and 9 environmental layers were determined as the model inputs. The threshold-independent model performance was reasonable according to 10 times model running, with the area under the curve (AUC) values being 0.917 ± 0.01, and 0.923 ± 0.002 for test data. Cohen’s kappa coefficient of model performance was 0.848. Results showed that 82.22% of the study extent was not suitable for human settlement. Of the remaining areas, highly suitable areas accounted for 1.19%, moderately for 5.3% and marginally for 11.28%. These suitable areas totaled 418.79 km2, and 86.25% of the sample data was identified in the different gradient of suitable area. The decisive environmental factors were slope and two climate variables: mean diurnal temperature range and temperature seasonality. Our model showed a good performance in mapping and assessing human settlements. This study provides the first predicted potential habitat distribution map for human settlement in Ledu County, which could also help in land use management.  相似文献   

7.
Landslide susceptibility mapping is the first step in regional hazard management as it helps to understand the spatial distribution of the probability of slope failure in an area.An attempt is made to map the landslide susceptibility in Tevankarai Ar subwatershed,Kodaikkanal,India using binary logistic regression analysis.Geographic Information System is used to prepare the database of the predictor variables and landslide inventory map,which is used to build the spatial model of landslide susceptibility.The model describes the relationship between the dependent variable(presence and absence of landslide) and the independent variables selected for study(predictor variables) by the best fitting function.A forward stepwise logistic regression model using maximum likelihood estimation is used in the regression analysis.An inventory of 84 landslides and cells within a buffer distance of 10m around the landslide is used as the dependent variable.Relief,slope,aspect,plan curvature,profile curvature,land use,soil,topographic wetness index,proximity to roads and proximity to lineaments are taken as independent variables.The constant and the coefficient of the predictor variable retained by the regression model are used to calculate the probability of slope failure and analyze the effect of each predictor variable on landslide occurrence in thestudy area.The model shows that the most significant parameter contributing to landslides is slope.The other significant parameters are profile curvature,soil,road,wetness index and relief.The predictive logistic regression model is validated using temporal validation data-set of known landslide locations and shows an accuracy of 85.29 %.  相似文献   

8.
基于投影寻踪学习网络算法的植物群落高分遥感分类研究   总被引：2，自引：0，他引：2  
传统的植物群落调查方法主要是野外样地调查和抽样统计,其对于地形复杂的区域难以做到对数据的全面调查;将遥感技术应用于植物群落调查,可实现数据的全面获取,以及对植物群落的快速分类。在深圳市植物群落野外样地调查的基础上,本文应用高分辨率Pléiades影像,结合光谱、地形及纹理信息,采用投影寻踪学习网络的方法,实现了深圳市东部地区植物分类。在实验中,选取人工林和次生林中典型群落样本,将投影寻踪与学习网络算法结合应用于植被分类,通过分类结果与经典监督分类方法比较表明,该算法应用于植物群落分类是可行的;并且该算法分类精度高,更新速度快,能满足深圳市重点项目基本生态控制线专项调查的要求。  相似文献   

9.
Daily meteorological data are the critical inputs for distributed hydrological and ecological models. This study modified mountain microclimate simulation model （MTCLIM） with the data from 19 weather stations, and compared and validated two methods （the MTCLIM and the modified MTCLIM） in the Qilian Mountains of Northwest China to estimate daily temperature （i.e., maximum temperature, minimum temperature） and precipitation at six weather stations from i January 2000 to 31December 2009. The algorithm of temperature in modified MTCLIM was improved by constructing the daily linear regression relationship between temperature and elevation, aspect and location information. There are two steps to modify the MTCLIM to predict daily precipitation： firstly, the linear regression relationship was built between annual average precipitation and elevation, location, and vegetation index; secondly, the distance weight for measuring the contribution of each weather station on target point was improved by average wind direction during the rainy season. Several regression analysis and goodness-of-fit indices （i.e., Pearson＇s correlation coefficient, coefficient of determination, mean absolute error, root-mean-square error and modelingefficiency） were used to validate these estimated values. The result showed that the modified MTCLIM had a better performance than the MTCLIM. Therefore, the modified MTCLIM was used to map daily meteorological data in the study area from 2000 to 2009. These results were validated using weather stations with short time data and the predicted accuracy was acceptable. The meteorological data mapped could become inputs for distributed hydrological and ecological models applied in the Qilian Mountains.  相似文献   

10.
Newmark位移模型是研究地震滑坡易发性的经典模型,机器学习方法支持向量机模型也越来越多的应用到滑坡易发性评估研究。本文将Newmark位移模型与支持向量机模型相结合,建立基于物理机理的地震滑坡易发性评估模型并应用于2008年汶川地震重灾区汶川县。从震后遥感影像目视解译出汶川县1900处地震诱发滑坡,并将其随机划分为70%的训练数据集和30%的验证数据集。选择地形起伏度、坡度、地形曲率、与构造断裂带距离、与水系距离、与道路距离6个因子与Newmark位移值共同作为地震滑坡易发性影响因素。利用ROC曲线和模型不确定性等指标对模型结果进行评估,并与二元统计模型频率比和多元统计模型Logistic回归的结果进行对比。结果表明：与频率比和Logistic回归模型相比,支持向量机模型的正确率最高,训练集和验证集ROC曲线下的面积分别为0.876和0.851。将模型应用于绘制汶川县地震滑坡易发性图,结果显示滑坡易发性图与实际的滑坡点位分布一致性较高,有80.4%的滑坡位于极高和高易发区。这说明支持向量机与Newmark位移方法结合建立的地震滑坡易发性评估模型有较高的预测价值,可以为滑坡风险评估和管理提供依据。  相似文献   

11.
神农架林区是我国物种多样性最为丰富的地区之一,地形地貌复杂,对植被分布影响巨大。本文利用该地区2007年数字高程数据、2007年植被分布图以及2017年野外实地调查数据,基于最大熵模型和空间分析理论,从植被类型和种群两个角度研究该地区不同尺度植被空间分布的地形特征,分别量化植被类型和种群空间分布的地形范围,得到植被类型与地形因子关系模型、植被种群与地形因子关系模型。结果表明:①神农架林区影响植被空间分布的地形因子不同,其中影响针叶林分布的最重要的地形因子是高程和高程变异系数,影响阔叶林分布的是高程和坡向,影响灌丛分布的是坡向变率和坡向,影响草丛分布的较为分散;②典型植被种群分布的地形范围和植被类型的基本一致,其中90%针叶林分布在高程1600~2600 m间,典型种群巴山冷杉和华山松主要分布在高程1700~3200 m和1700~2200 m;85%的阔叶林分布在高程1000~2000 m间,典型种群青冈类和鹅耳枥主要分布在高程1200~2200 m间;95%的灌丛分布在坡向变率0~40°间,典型种群杜鹃和蔷薇主要分布在坡向变率小于40°的范围,但相应的关系模型存在差异,植被类型与地形因子为高斯模型,典型种群与地形因子关系模型相对复杂,不同种群的分布模式不同;③虽然坡度常作为数字地形的重要因子,但本文研究发现该地区坡度对植被类型和种群分布的影响不明显。研究结果可为神农架林区植被保护和恢复,以及植被规划和管理提供基础参考。  相似文献   

12.
We investigated the spatio-temporal and environmental factors that affect the distribution and abundance of wintering anchovy and quantifi es the infl uences of these factors. Generalized additive models(GAMs) were developed to examine the variation in species distribution and abundance with a set of spatiotemporal and oceanographic factors, using data collected by bottom trawl surveys and remote sensing in the central and southern Yellow Sea during 2000–2011. The fi nal model accounted for 28.21% and 41.03% of the variance in anchovy distribution and abundance, respectively. The results of a two-step GAM showed that hour, longitude, latitude, temperature gradient(TGR), and chlorophyll a(Chl- a) concentration best explained the anchovy distribution(presence/absence) and that a model including year, longitude, latitude, depth, sea surface temperature(SST), and TGR best described anchovy abundance(given presence). Longitude and latitude were the most important factors affecting both distribution and abundance, but the area of high abundance tended to be east and south of the area where anchovy were most likely to be present. Hour had a signifi cant effect on distribution, but year was more important for anchovy abundance, indicating that the anchovy catch ratio varied across the day but abundance had an apparent interannual variation. With respect to environmental factors, TGR and Chl- a concentration had effects on distribution, while depth, SST, and TGR affected abundance. Changes in SST between two successive years or between any year and the 2000–2011 mean were not associated with changes in anchovy distribution or abundance. This fi nding indicated that short- and long-term water temperature changes during 2000–2011 were not of suffi cient magnitude to give rise to variation in wintering anchovy distribution or abundance in the study area. The results of this study have important implications for fi sheries management.  相似文献   

13.
In karst regions,the spatial heterogeneity of soil mineral oxides and environmental variables is still not clear.We investigated the spatial heterogeneity of SiO2,Al2O3,Fe2O3,CaO,MgO,P2O5,K2O,and MnO contents in the soils of slope land,plantation forest,secondary forest,and primary forest,as well as their relationships with environmental variables in a karst region of Southwest China.Geostatistics,principal component analysis(PCA),and canonical correlation analysis(CCA)were applied to analyze the field data.The results show that SiO2was the predominant mineral in the soils(45.02%–67.33%),followed by Al2O3and Fe2O3.Most soil mineral oxide components had a strong spatial dependence,except for CaO,MgO,and P2O5in the plantation forest,MgO and P2O5in the secondary forest,and CaO in the slope land.Dimensionality reduction in PCA was not appropriate due to the strong spatial heterogeneity in the ecosystems.Soil mineral oxide components,the main factors in all ecosystems,had greater influences on vegetation than those of conventional soil properties.There were close relationships between soil mineral oxide components and vegetation,topography,and conventional soil properties.Mineral oxide components affected species diversity,organic matter and nitrogen levels.  相似文献   

14.
Soil organic matter(SOM) is an important parameter related to soil nutrient and miscellaneous ecosystem services. This paper attempts to improve the performance of traditional partial least square regression(PLSR) model by considering the spatial autocorrelation and soil forming factors. Surface soil samples(n = 180) were collected from Honghu City located in the middle of Jianghan Plain, China. The visible and near infrared(VNIR) spectra and six environmental factors(elevation, land use types, roughness, relief amplitude, enhanced vegetation index, and land surface water index) were used as the auxiliary variables to construct the multiple linear regression(MLR), PLSR and geographically weighted regression(GWR) models. Results showed that: 1) the VNIR spectra can increase about 39.62% prediction accuracy than the environmental factors in predicting SOM; 2) the comprehensive variables of VNIR spectra and the environmental factors can improve about 5.78% and 44.90% relative to soil spectral models and soil environmental models, respectively; 3) the spatial model(GWR) can improve about 3.28% accuracy than MLR and PLSR. Our results suggest that the combination of spectral reflectance and the environmental variables can be used as the suitable auxiliary variables in predicting SOM, and GWR is a promising model for predicting soil properties.  相似文献   

15.
利用遥感图像进行岩性分类,是遥感地质应用的重要方面之一.本文运用ASTER DEM提取地形因子,并与原始的光谱图像相结合用于遥感图像的岩性单元分类.文章分析了不同尺度的地形因子对岩性单元分类的作用,并进一步分析和比较各种地形因子对岩性单元分类的作用.结果表明,在岩性单元分类过程中加入不同的地形因子可不同程度地提高岩性单...  相似文献   

16.
高精度曲面建模的中国气候降尺度模型   总被引：2，自引：0，他引：2  
 与站点统计降尺度插值和动力降尺度相比,高精度曲面建模(HASM)降尺度,具有不需大尺度预报因子,直接从GCM结果构建区域上高空间分辨率的未来气候模拟曲面的优势。HASM降尺度将未来气候,分为历史观测拟合的气候基准值和GCM未来气候变化值进行模拟,精度明显高于传统方法,但常系数全局拟合的气候基准值忽略了降水分布的空间非平稳性,导致降水模拟受到较大影响。为增强降水降尺度的气候背景值的描述能力,通过分析全国尺度降水的非线性非平稳性特点,提出耦合空间变系数气候基准值的HASM空间变系数降尺度模型(HASM-SVDM)以改进HASM对非平稳要素的降尺度能力,并以1961-2010年全国气温降水观测数据结合地形特征信息,利用HASM降尺度方法对HadCM3的A1Fi、A2a和B2a 3种情景的1961-1990、2010-2039、2040-2069和2070-2099时段的全国未来气温与降水进行降尺度模拟。分析表明,耦合全局线性模型的HASM常系数降尺度模型适合全国气温的降尺度模拟,而耦合空间变系数拟合的HASM-SVDM增强了空间非平稳背景值的描述能力,模拟的空间分布更能体现降水总体的非均匀分布趋势,适合全国降水的降尺度模拟。  相似文献   

17.
The sub-pixel impervious surface percentage(SPIS) is the fraction of impervious surface area in one pixel,and it is an important indicator of urbanization.Using remote sensing data,the spatial distribution of SPIS values over large areas can be extracted,and these data are significant for studies of urban climate,environment and hydrology.To develop a stabilized,multi-temporal SPIS estimation method suitable for typical temperate semi-arid climate zones with distinct seasons,an optimal model for estimating SPIS values within Beijing Municipality was built that is based on the classification and regression tree(CART) algorithm.First,models with different input variables for SPIS estimation were built by integrating multi-source remote sensing data with other auxiliary data.The optimal model was selected through the analysis and comparison of the assessed accuracy of these models.Subsequently,multi-temporal SPIS mapping was carried out based on the optimal model.The results are as follows:1) multi-seasonal images and nighttime light(NTL) data are the optimal input variables for SPIS estimation within Beijing Municipality,where the intra-annual variability in vegetation is distinct.The different spectral characteristics in the cultivated land caused by the different farming characteristics and vegetation phenology can be detected by the multi-seasonal images effectively.NLT data can effectively reduce the misestimation caused by the spectral similarity between bare land and impervious surfaces.After testing,the SPIS modeling correlation coefficient(r) is approximately 0.86,the average error(AE) is approximately 12.8%,and the relative error(RE) is approximately 0.39.2) The SPIS results have been divided into areas with high-density impervious cover(70%–100%),medium-density impervious cover(40%–70%),low-density impervious cover(10%–40%) and natural cover(0%–10%).The SPIS model performed better in estimating values for high-density urban areas than other categories.3) Multi-temporal SPIS mapping(1991–2016) was conducted based on the optimized SPIS results for 2005.After testing,AE ranges from 12.7% to 15.2%,RE ranges from 0.39 to 0.46,and r ranges from 0.81 to 0.86.It is demonstrated that the proposed approach for estimating sub-pixel level impervious surface by integrating the CART algorithm and multi-source remote sensing data is feasible and suitable for multi-temporal SPIS mapping of areas with distinct intra-annual variability in vegetation.  相似文献   

18.
Relationships between topography,soil properties and the distribution of plant communities on two different rocky hillsides are examined in two subtropical karst forests in the Maolan National Natural Reserve,southwestern China.Surveys of two 1-ha permanent plots at each forest,and measurements of four topographic and thirteen edaphic factors on the slopes were performed.Twoway Indicator Species Analysis(TWINSPAN) and Detrended Canonical Correspondence Analysis(DCCA) were used for the classification of plant communities and for vegetation ordination with environmental variables.One hundred 10m×10m quadrats in each plot were classified into four plant community types.A clear altitudinal gradient suggested that elevation was important in community differentiation.The topography and soil explained 51.06% and 54.69% of the variability of the distribution of plant species in the two forest plots,respectively,indicating both topographic factors(eg.elevation,slope and rock-bareness rate) and edaphic factors(e.g.total P,K and exchangeable Ca) were the important drivers of the distribution of woody plant species in subtropical karst forest.However,our results suggested that topographical factors were more important than edaphic ones in affecting local plant distribution on steep slopes with extensive rock outcrops,while edaphic factors were more influential on gentle slope and relatively thick soil over rock in subtropical karst forest.Understanding relationships between vegetation and environmental factors in karst forest ecosystems would enable us to apply these findings in vegetation management strategies and restoration of forest communities.  相似文献   

19.
【Title】
There are knowledge gaps in our understanding of vegetation responses to multi-scale climate-related variables in tropical/subtropical mountainous islands in the Asia-Pacific region. Therefore, this study investigated inter-annual vegetation dynamics and regular/irregular climate patterns in Taiwan. We applied principal component analysis （PCA） on 11 years （2001~2011） of high-dimensional monthly photosynthetically active vegetation cover （PV） derived from the Moderate Resolution Imaging Spectroradiometer （MODIS） and investigated the relationships between spatiotemporal patterns of the eigenvectors and loadings of each component through time and multi-scale climate-related variations. Results showed that the first five components contributed to 96.4% of the total variance. The first component （PC1, explaining 94.5% of variance） loadings, as expected, were significantly correlated with the temporal dynamics of the PV （r = 0.94）, which was mainly governed by regional climate. The temporal loadings of PC2 and PC3 （0.8% and 0.6% of variance, respectively） were significantly correlated with the temporal dynamics of the PV of forests （r = 0.72） and the farmlands （r = 0.80）, respectively. The low-order components （PC4 and PC5, 0.3% and 0.2% of variance, respectively） were closely related to the occurrence of drought （r = 0.49） and to irregular ENSO associated climate anomalies （r = -0.54）, respectively. Pronounced correlations were also observed between PC5 and the Southern Oscillation Index （SOI） with one to three months of time lags （r = -0.35 ~ -0.43, respectively）, revealing biophysical memory effects on the time-series pattern of the vegetation through ENSO-related rainfall patterns. Our findings reveal that the sensitivity of the ecosystems in this tropical/subtropical mountainous island may not only be regulated by regional climate and human activities but also be susceptible to large-scale climate anomalies which are crucial and comparable to previous large scale analyses. This study demonstrates that PCA can be an effective tool for analyzing seasonal and inter-annual variability of vegetation dynamics across this tropical/subtropical mountainous islandin the Pacific Ocean, which provides an opportunity to forecast the responses and feedbacks of terrestrial environments to future climate scenarios.  相似文献   

20.
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