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61.
To evaluate and provide an appropriate theoretical direction for research into climate-vegetation interactions using meteorological station data at different time scales, we examined differences between the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) and their responses to climate factors. We looked for correlations between data extracted from MOD13Q1 remote sensing images and meteorological station data for the two indexes. The results showed that even though NDVI and EVI are derived from the same remote sensing image, their response to climate factors was significantly different. In the same meteorological station, the correlation coefficients for NDVI, EVI and climate factors were different; correlation coefficients between NDVI, EVI and climate factors varied with meteorological station. In addition, there was a lag effect for responses of NDVI to average minimum temperature, average temperature, average vapor pressure, minimum relative humidity, extreme wind speed, maximum wind speed, average wind speed and average station air-pressure. EVI had a lag only for average minimum temperature, average vapor pressure, extreme wind speed, maximum wind speed and average station air-pressure. The lag period was variable, but most were in the -3 period. Different vegetation types had different sensitivities to climate. The correlation between meteorological stations and vegetation requires more attention in future research. 相似文献
62.
Bingwen Qiu Zhuangzhuang Wang Zhenghong Tang Zhe Liu Difei Lu Chongcheng Chen 《地理信息系统科学与遥感》2016,53(5):596-613
Given the complexity of vegetation dynamic patterns under global climate change, multi-scale spatiotemporal explicit models are necessary in order to account for environmental heterogeneity. However, there is no efficient time-series tool to extract, reconstruct and analyze the multi-scale vegetation dynamic patterns under global climate change. To fill this gap, a Multi-Scale Spatio-Temporal Modeling (MSSTM) framework which can incorporate the pixel, scale, and time-specific heterogeneity was proposed. The MSSTM method was defined on proper time-series models for multi-temporal components through wavelet transforms. The proposed MSSTM approach was applied to a subtropical mountainous and hilly agro-forestry ecosystem in southeast China using the moderate resolution imaging spectroradiometer enhanced vegetation index (EVI) time-series data sets from 2001 to 2011. The MSSTM approach was proved to be efficient in characterizing and forecasting the complex vegetation dynamic patterns. It provided good estimates of the peaks and valleys of the observed EVI and its average percentages of relative absolute errors of reconstruction was low (6.65). The complexity of the relationship between vegetation dynamics and meteorological parameters was also revealed through the MSSTM method: (1) at seasonal level, vegetation dynamic patterns are strongly associated with climatic variables, primarily the temperature and then precipitation, with correlations slight decreasing (EVI–temperature)/increasing (EVI–precipitation) with altitudinal gradients. (2) At inter-annual scale, obvious positive correlations were primarily observed between EVI and temperature. (3) Despite very low-correlation coefficients observed at intra-seasonal scales, considerable proportions of EVI anomalies are associated with climatic variables, principally the precipitation and sunshine durations. 相似文献
63.
植被遥感监测中长时间序列数据择优的重建方法,已成为当今一个研究热点。本文以东北地区5种主要植被覆盖类型为例,在定性分析TIMESAT提供的3种常用重建方法对EVI（Enhanced Vegetation Index）时序曲线重建效果的基础上,定量对比研究了各方法,对原始高质量EVI点真实值的保真性,及对原始曲线整体特征的保持度。结果表明：S-G（Savitzky-Golay）滤波对原始曲线生长季的峰值及宽度重建效果较好,但容易因过度拟合保留过多噪声,特别是草地和灌丛类型;非对称性高斯函数（AG）和双Logistic曲线（DL）方法相似,对草地、灌丛和耕地的重建结果更接近真实值,但AG拟合对波峰处异常值的处理结果较差,重建后波峰表现低平。3种算法对原始EVI时序数据的保真性和对原始时序数据曲线特征的保持度,都表现出与植被类型分布相关的空间分布格局。分析结果表明,在东北地区,AG算法对草原和灌丛的重建效果最好,DL算法对耕地重建效果最优,S-G算法最适合对落叶阔叶林和落叶针叶林进行重建处理。 相似文献
64.
Global warming has led to significant vegetation changes in recent years. It is necessary to investigate the effects of climatic variations（temperature and precipitation） on vegetation changes for a better understanding of acclimation to climatic change. In this paper, we focused on the integration and application of multi-methods and spatial analysis techniques in GIS to study the spatio-temporal variation of vegetation dynamics and to explore the vegetation change mechanism. The correlations between EVI and climate factors at different time scales were calculated for each pixel including monthly, seasonal and annual scales respectively in Qinghai Lake Basin from the year of 2001 to 2012. The primary objectives of this study are to reveal when, where and why the vegetation change so as to support better understanding of terrestrial response to global change as well as the useful information and techniques for wise regional ecosystem management practices. The main conclusions are as follows：（1） Overall vegetation EVI in the region increased 6% during recent 12 years. The EVI value in growing seasons（i.e. spring and summer） exhibited very significant improving trend, accounted for 12.8% and 9.3% respectively. The spatial pattern of EVI showed obvious spatial heterogeneity which was consistent with hydrothermal condition. In general, the vegetation coverage improved in most parts of the area since nearly 78% pixel of the whole basin showed increasing trend, while degraded slightly in a small part of the area only.（2） The EVI change was positively correlated with average temperature and precipitation. Generally speaking, in Qinghai Lake Basin, precipitation was the dominant driving factor for vegetation growth; however, at different time scale its weight to vegetation has differences.（3） Based on geo-statistical analysis, the autumn precipitation has a strong correlation with the next spring EVI values in the whole region. This findings explore the autumn precipitation is an important indicator 相似文献
65.
LiPing Li ChunYan Zhang Eimear Nic Lughadha Tarciso CCLeão Kate Hardwick YaoMin Zheng HuaWei Wan Ming Ma Nurbay Abudusalih Ying Hai Zhen Pu JiangShan Lai ZhanFeng Shen Li Liu Tuo Wang YangMing Jiang HuiHui Zhao QingJie Liu 《寒旱区科学》2022,14(1):54-67
Patterns in species geographic range size are relatively well-known for vertebrates,but still poorly known for plants.Contrasts of these patterns between groups have rarely been investigated.With a detailed flora and fauna distribution database of Xinjiang,China,we used regression methods,redundancy analysis and random forests to explore the relationship of environment and body size with the geographic range size of plants,mammals and birds in Xinjiang and contrast these patterns between plants and animals.We found positive correlations between species range size and body size.The range size of plants was more influenced by water variables,while that of mammals and birds was largely influenced by temperature variables.The productivity variable,i.e.,Enhanced Vegetation Index(EVI)was far more correlated with range size than climatic variables for both plants and animals,suggesting that vegetation productivity inferred from remote sensing data may be a good predictor of species range size for both plants and animals. 相似文献
66.
文章利用2008至2011年中分辨率成像光谱辐射计(MODIS)250 m空间分辨率的增强型植被指数(EVI)作为检测植被覆盖程度的指标参数,分析了岩溶石漠化地区与非石漠化地区植被对于气候因子的响应。结果表明:(1)无论是石漠化地区还是非石漠化地区,EVI在干旱时期和非干旱时期,与气候因子——气温(T)的相关系数rEVI-T均大于0.5(rF=0.235,α=0.05),二者表现为显著相关;(2)在非石漠化地区,EVI在干旱时期和非干旱时期与气候因子——降水量(P)的相关系数rEVI-P为0.234、0.212,两者表现出不显著相关,但在石漠化地区则表现为相反的情况;(3)在干旱发生之后的一年时间,EVI在石漠化和非石漠化地区都与降水量有显著相关性,其相关系数rEVI-P分别为0.516和0.489。 相似文献
67.
The accurate and timely information of crop area is vital for crop production and food security. In this study, the Enhanced Vegetation Index (EVI) data from MODerate resolution Imaging Spectroradiometer (MODIS) integrated crop phenological information was used to estimate the maize cultivated area over a large scale in Northeast China. The fine spatial resolution China’s Environment Satellite (HJ-1 satellite) images and the support vector machine (SVM) algorithm were employed to discriminate distribution of maize in the reference area. The mean MODIS–EVI time series curve of maize was extracted in the reference area by using multiple periods MODIS–EVI data. By analysing the temporal shift of crop calendars from northern to southern parts in Northeast China, the lag value was derived from phenological data of twenty-one agro-meteorological stations; here integrating with the mean MODIS–EVI time series image of maize, a standard MODIS–EVI time series image of maize was obtained in the whole study area. By calculating mean absolute distances (MAD) map between standard MODIS–EVI image and mean MODIS–EVI time series images, and setting appropriate thresholds in three provinces, the maize cultivated area was extracted in Northeast China. The results showed that the overall classification accuracy of maize cultivated area was approximately 79%. At the county level, the MODIS-derived maize cultivated area and statistical data were well correlated (R2 = 0.82, RMSE = 283.98) over whole Northeast China. It demonstrated that MODIS–EVI time series data integrated with crop phenological information can be used to improve the extraction accuracy of crop cultivated area over a large scale. 相似文献
68.
利用遥感方法提取中国范围内的水稻关键生长发育期。首先, 对时间序列Terra MODIS-EVI(Enhanced Vegetation Index)进行傅里叶和小波低通滤波平滑处理, 然后, 根据水稻在移栽期、分蘖初期、抽穗期和成熟期的EVI变化特征, 实现对各个生长发育期的识别。通过将利用2005年MODIS数据识别的结果与当年气象台站的地面观测资料进行比较, 采用本研究中的识别方法得出的水稻各个生长发育期的绝对误差大部分小于16d, 经过F检验表明提取的结果与地面观测资料在0.05水平下具有显著一致性。研究中的信息提取方法可被用于其他年份的水稻生长发育期识别, 根据其他作物的生长发育特点, 也可能适合于提取其他作物的生长发育期。 相似文献