浙江省植被NDVI动态及其对气候的响应

利用GIMMS和MODIS两种归一化植被指数(NDVI)资料反演了1982—2010年浙江植被覆盖状况,结合同期研究区63个气象站点的气温、降水和湿润指数等气候指标,分析了该地区植被年际变化、月际变化及其对气候要素的响应特征。结果表明:(1)研究期间,浙江气候总体呈暖干化趋势,植被覆盖缓慢下降,主要是由于森林植被遭破坏,农业生产活动受抑制影响所致,其中NDVI显著减少的地区约占全省陆域面积的29.1%,主要发生在6—11月;(2)降水量及干湿程度对浙江植被NDVI年变化起着决定性作用。植被与气候要素年变化相关分析发现,NDVI与湿润指数关系较降水、气温更为密切,两者相关及偏相关系数均通过0.05水平的置信度检验,这表明在年际尺度上,湿度的增加增大了植被的生长势,有利于植被生长;(3)植被与气候要素月变化分析表明冬季的热量供给是影响浙江植被生长的重要因子,而植被变化对夏季降水和干湿程度的最大响应为滞后两个月;(4)农业生产水平的提高使得农作物种植区NDVI有所增加,人类活动对浙江植被覆盖的影响不可忽视。     

植被指数的地形效应研究进展

植被指数是定性、定量评估绿色植被的关键指标,已经广泛应用于地表植被的监测.森林多分布在地形复杂山区,利用植被指数进行森林植被信息反演时地形对其影响较大.本文从几何光学模型原理分析了冠层反射率的地形效应,分析比较完全比值型植被指数(简单比值植被指数SR、归一化植被指数NDVI和湿度调整植被指数MAVI)、非完全比值型植被指数(增强型植被指数EVI和土壤调整植被指数SAVI)、非比值型植被指数(减化比值植被指数RSR、修正归一化植被指数MNDVI和绿度植被指数GVI),以及地形调节植被指数TAVI对地形的响应,试图为复杂地形山区选取植被指数提供参考.最后分析了植被指数地形效应研究的不足并对未来发展进行展望.     

宁夏灵武白芨滩自然保护区植被覆盖变化地形效应

基于MODIS—NDVI时序数据,运用ArcGIS软件,采取植被指数法和趋势分析法,估算了宁夏灵武白芨滩国家级自然保护区2000—2019年植被覆盖度,分析研究了植被覆盖变化趋势及其与地形因子的关系.结果表明:该保护区植被覆盖度由波动变化不明显逐渐变为波动上升趋势.植被覆盖类型以低覆盖度和中覆盖度类型为主.随着高程的增...     

基于MODIS数据的神农架大九湖泥炭藓沼泽植被指数变化研究

泥炭藓是陆地生态系统中最重要的固碳植物之一,固碳量约占全球土壤碳的15%。近几十年来,由全球气候变暖导致的泥炭藓沼泽水热状况变化对泥炭藓的固碳量和速率产生影响。选取我国最重要的亚高山泥炭沼泽——神农架大九湖泥炭藓沼泽为试验区,以分析中纬度地区泥炭藓沼泽植被生长状况受气候变化的影响。研究以2000—2017年MODIS植被指数NDVI和EVI为数据源,通过对比Logistic模型订正后的NDVI和EVI时间序列在泥炭藓沼泽植被生长状况监测中的优劣,选出最佳指标以获得18年来泥炭藓沼泽植被生长状况的变化趋势。研究结果表明:1)Logistic模型能够很好的消除泥炭藓沼泽植被指数时间序列的噪声;2)在季节和年际两个时间尺度上,EVI对泥炭藓沼泽植被生长状况的监测效果均优于NDVI。在季节周期上,虽然EVI和NDVI均得到泥炭藓沼泽植被生长周期规律,但EVI更灵敏。在年际分析中,EVI有更大的值域响应空间,以准确反映泥炭藓沼泽植被的年际变化规律;3)由EVI获得18年来泥炭藓沼泽植被变化趋势指出,泥炭藓沼泽植被呈显著微弱增长,年均EVI增长率为3.8‰(R~2=0.45,P0.01)。相比于EVI年均值,EVI年内最大值(R~2=0.47,P0.01)更敏锐的反映泥炭藓沼泽植被生长状况的动态变化。     

和田地区植被覆盖变化及气候因子驱动分析

基于MODIS NDVI(2000—2016年)数据集,结合气温、降水数据,利用最大值合成法、斜率分析法及相关分析等方法,分析和田地区近17年的植被覆盖时空变化特征及其与气候因子的相关性。结果表明:(1)近17年和田地区植被覆盖的月际变化表现为先增加再减少,年际变化表现为显著上升趋势,增速为0.452/10a;(2)近17年和田地区植被覆盖增加和减少的区域分别占总面积的4.48%和0.21%,绿洲及昆仑山北部部分区域变化剧烈,高海拔区域基本不变;(3)近17年温度和降水小幅增加,增速分别为0.144/10a和0.156/10a;月尺度上,植被覆盖与温度为正相关,植被生长滞后于温度两个月;与降水以负相关为主,滞后效应不明显。(4)NDVI对气候因子响应的空间分布中,NDVI与平均温度以负相关为主,与降水以正相关为主,与降水的相关性较温度的相关性高;昆仑山北部植被对降水变化更敏感,和墨洛绿洲、策勒-于田绿洲和和田地区西南部山区对温度变化更敏感。     

基于MODIS数据的草地生物量估算模型比较

准确估算草地生物量对合理规划区域畜牧业、评估草地植被的生态效益有重要意义.目前,在常用的遥感估算模型中,采用的植被指数和模型函数形式多样.本文根据野外生物量调查结果和MODIS数据,分别采用归一化植被指数(NDVI)、增强植被指数(EVI)和修正的土壤调节植被指数(MSAVI)建立了内蒙古科尔沁左翼后旗草地地上生物量和地上地下总生物量估测的3种(线性、乘幂和指数)模型,并进行了比较.结果表明:3种模型能够对草地生物量进行较好的模拟,其中指数模型效果最佳;3个植被指数(NDVI,EVI和MSAVI)与草地生物量均有较高的相关性,可用于该草地产量估测,其中MSAVI对地上生物量拟合效果最好(R2=0.900);NDVI和EVI的线性模型对总生物量的模拟明显好于对地上生物量的模拟.     

2000-2019年长江流域植被覆盖时空演化及其驱动因素

长江流域作为中国重要的生态屏障,科学认识长江流域植被覆盖时空变化及其驱动因素,对有效开展长江流域生态工程建设具有重要的指导意义与应用价值。基于2000—2019年间MODIS-NDVI与相关气象等数据,采用Theil-Sen median趋势分析、Mann-Kendall检验、变异系数、偏相关分析、残差分析等方法,研究了近20年来长江流域植被覆盖的时空分布与变化特征,并探究了研究区植被覆盖的驱动因素。结果表明：(1)时间变化上,长江流域生长季NDVI呈现波动增长趋势,显著改善面积大于退化面积;(2)空间分布上,流域植被覆盖空间分布格局大致呈现为“中部高,东西低”,生长季NDVI多年均值为0.6164,呈较高植被覆盖状态;(3)变化趋势上,植被增长区域大于减少区域,具体表现为“中部强于东部、东部强于西部”;(4)变化稳定性上,流域植被变异系数介于0.0104—1.3199之间,呈现出“中间低,东西高,东西部局部区域高低波动并存,地域性差异明显”的空间变化稳定性特征;(5)影响因素上,流域植被覆盖变化受气温和降水的共同影响,大部分区域生长季NDVI变化以气候驱动为主,局部区域表现自然因素叠...     

近20年辽宁省植被动态特征及其对气候变化的响应

区域归一化植被指数(NDVI)变化特征对环境容量和生态发展方向有重要指示作用。基于SPOT/VEGETATION NDVI数据和ESA CCI-LC植被分类数据，利用Theil-Sen+Mann-Kendall、变异系数、Hurst指数和相关性分析方法，对辽宁省2000—2019年不同植被类型归一化植被指数时空变化特征和气候因子之间的响应关系进行分析。结果表明：(1)NDVI均值呈现从乔木到草原逐渐降低的趋势，不同植被类型在生长季具有不同的生长习性；(2)各植被类型都呈增加趋势，结合Hurst指数和Sen趋势，辽宁省36.26%的植被将趋于改善，约有61.51%的植被将趋于退化；(3)变异系数结果表明：所有植被类型中以乔木植被最为稳定，草原型植被最不稳定。(4)辽宁省各植被类型NDVI与降水显著正相关，与气温相关性相对较低。结果可为辽宁省生态评价和碳循环研究提供植被覆盖动态参考。     

2000-2019年京津冀地区植被覆盖状况变化及驱动因素

近20年来中国和印度通过土地利用活动改变地表覆盖使植被变得更绿,京津冀地区是植被变得更绿的典型地区。收集京津冀地区2000-2019年MODIS增强型植被指数（EVI）、植被覆盖百分比数据,以及年平均温度和降水量等数据,分析该区近20年来自然植被和农田植被EVI变化过程和趋势,揭示其变化的驱动因素,结果显示2000-2019年京津冀地区自然植被和农田植被EVI显著增加,自然植被EVI增加速率是农田植被的1.8倍。99.51%的自然植被和96.95%的农田植被生长状况改善。2000-2019年京津冀地区自然植被EVI与年平均温度和年降水量相关性不显著。农田植被EVI与年平均温度显著相关,与年降水量相关性不显著。农田灌溉和年平均温度变化是农田植被EVI显著增加的主要驱动因素。近20年京津冀地区通过实施以造林为主的生态建设工程,自然植被生长覆盖状况呈现极显著变好。同时森林植被比非森林植被覆盖百分比增加趋势更明显。以造林为主的生态建设工程是京津冀地区自然植被生长覆盖状况显著变好的主要驱动因素。     

黄土高原植被覆盖时空变化及其对气候因子的响应

为研究黄土高原地区退耕还林(草)后,植被覆盖变化及其对水热条件的响应,利用1999—2013年SPOT VGT NDVI 1km/10d分辨率数据,采用最大合成法、一元线性回归法和偏相关分析法,系统分析了黄土高原地区NDVI(归一化植被指数)的时空分布及变化趋势,及其与气候因子的关系。结果表明:黄土高原1999—2013年年最大NDVI的平均值为0.31,NDVI较高的区域位于黄土高原南部,而西北部植被覆盖度较低;自1999年开始,黄土高原地区NDVI呈极显著(P0.01)增加趋势,年最大NDVI的变化斜率为0.0099;不同季节(春、夏、秋、冬)和生长季的植被状况均呈现良性发展趋势;1998—2013年间,黄土高原地区气候呈现不显著的"冷湿化"特征;NDVI年际(及生长季和季节)变化与降雨和温度的相关性不显著,而在月时间尺度上,呈显著的相关性,并且月NDVI与当月降雨量的相关性要强于与当月温度的相关性;植被生长对温度的响应存在一个月的滞后期,而对降雨的响应无滞后效应。     

岷江上游植被冠层降水截留的空间模拟

 通过对岷江上游实地踏查和定位观测研究,结合MODIS遥感数据,利用“3S”技术对岷江上游植被冠层降水截留进行了空间模拟。研究结果表明：岷江上游植被叶面积指数（LAI）与增强性植被指数（EVI）以二项式关系拟合效果较好。由于归一化植被指数（NDVI）存在的饱和问题,研究采用EVI反演LAI,统计结果表明：岷江上游LAI值在0～2之间的占28.57%,在2～4.5之间的占63.06%,大于4.5的占8.37%,其中LAI最大值为7.394;从冠层最大降水截留模拟结果来看: 植被较好的地区,如卧龙、米亚罗的植被冠层最大降水截留量较大,而干旱河谷、上游高山草甸等地的植被冠层最大降水截留量相对较低;附加冠层降水截留与降雨量呈线性相关,模型验证时以此为基础,模型模拟的结果较为理想。     

Moist deciduous forest identification using temporal MODIS data — A comparative study using fuzzy based classifiers

The two soft fuzzy based classifiers, Possibilistic c-Means (PCM) approach and Noise Clustering (NC) were compared for the Moist Deciduous Forest (MDF) identification from MODIS temporal data. Seven date temporal MODIS data were used to identify MDF and temporal Advanced Wide Field Sensor (AWiFS) data was used as reference data for testing. Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index 2 (EVI2) were used to generate the temporal spectral index datasets for both the MODIS and AWiFS. The parameter weighting exponent m for PCM and resolution parameter δ for NC were optimized. Results show that the optimized value of m for MDF is 2.1, while δ value is 3.6 × 10⁴ for temporal MODIS data. For assessment of the accuracy AWiFS datasets were also optimized using entropy approach. The optimized dataset of AWiFS was then used for accuracy assessment of the soft classified outputs from MODIS using Fuzzy ERror Matrix (FERM). It was found from this study that, for PCM classifier the highest fuzzy overall accuracy of 97.44% was obtained using the SAVI for the temporal dataset ‘Five’ consisting to one scene of ‘Full greenness’, three scenes in ‘Intermediate frequency stage of Onset of Greenness (OG) and End of Senescence (ES) activity’ and the last image pertaining corresponds to the ‘Maximum frequency stage of OG activity’ as per phenology of MDF. Similarly, for NC classifier the highest fuzzy overall accuracy of 95.19% was obtained for the EVI2 with temporal dataset ‘Five’ consisting with two scene of ‘Full greenness’, two scenes in ‘Intermediate frequency stage of OG and ES activity’ and the last one corresponds to the ‘Maximum frequency stage of OG activity’as per phenology of MDF.     

The responses of Moso bamboo (Phyllostachys heterocycla var. pubescens) forest aboveground biomass to Landsat TM spectral reflectance and NDVI

Bamboo is a special forest type in subtropical regions of china. Its huge biomass and carbon storage and its ecological function play an important role in global carbon sink. In this paper, Moso bamboo (Phyllostachys heterocycla var. pubescens) forest in Anji county, Zhejiang province, China was selected to examine the relationships between Landsat Thematic Mapper (TM) data, and aboveground biomass (AGB, Kg), This research indicates that (1) near infrared (TM4) and mid-infrared bands (TM5,TM7) are more important in explaining Moso bamboo AGB than the visible bands. In particular, TM band5 has higher path coefficient with AGB than any other TM bands, implying its important role in explaining Moso bamboo AGB; (2) the Normalized Difference Vegetation Index (NDVI) has weak correlation with Moso bamboo AGB, however, other vegetable indices such as Perpendicular Vegetation Index (PVI), Enhanced Vegetation Index(EVI), and Soil Adjust Vegetation Index (SAVI) which are related to soil adjustment parameters are significantly correlated with Moso bamboo AGB; (3) the new vegetation index developed in this paper is significantly correlated with Moso bamboo AGB (correlation coefficient is 0.48), and has higher correlation coefficient (R) than any other selected vegetation indices, implying that this new vegetation index can better explain Moso bamboo AGB than any other vegetation indices.     

The responses of Moso bamboo (Phyllostachys heterocycla var. pubescens) forest aboveground biomass to Landsat TM spectral reflectance and NDVI

Bamboo is a special forest type in subtropical regions of china. Its huge biomass and carbon storage and its ecological function play an important role in global carbon sink. In this paper, Moso bamboo (Phyllostachys heterocycla var. pubescens) forest in Anji county, Zhejiang province, China was selected to examine the relationships between Landsat Thematic Mapper (TM) data, and aboveground biomass (AGB, Kg), This research indicates that (1) near infrared (TM4) and mid-infrared bands (TM5,TM7) are more important in explaining Moso bamboo AGB than the visible bands. In particular, TM band5 has higher path coefficient with AGB than any other TM bands, implying its important role in explaining Moso bamboo AGB; (2) the Normalized Difference Vegetation Index (NDVI) has weak correlation with Moso bamboo AGB, however, other vegetable indices such as Perpendicular Vegetation Index (PVI), Enhanced Vegetation Index(EVI), and Soil Adjust Vegetation Index (SAVI) which are related to soil adjustment parameters are significantly correlated with Moso bamboo AGB; (3) the new vegetation index developed in this paper is significantly correlated with Moso bamboo AGB (correlation coefficient is 0.48), and has higher correlation coefficient (R) than any other selected vegetation indices, implying that this new vegetation index can better explain Moso bamboo AGB than any other vegetation indices.     

湖南省MODIS遥感植被指数的时空变化

采用最大值合成法,以MODIS 250 m分辨率图像为基础,提取湖南省2005年逐月植被指数值.通过月植被指数对比分析,将湖南省分为6个区描述其空间分布特征.利用5个分布均匀的气象站观测的月降水量和月平均气温数据,分析了湖南省植被指数的时相变化特征.结果表明：湖南省MODIS植被指数空间分布与植被覆盖率呈正相关,且具有一定的地域性;MODIS植被指数随季节变化,其月平均植被指数曲线形似开口向下的二次抛物线,最大值出现在7月份;MODIS月平均EVI值小于MODIS月平均NDVI值;植被指数的季节变化受温度影响较大,并且随着纬度的降低,温度对植被指数的影响力下降;MODIS EVI的变化规律比MODIS NDVI更加明显,其二次曲线更为光滑,月平均值由低逐渐上升到最大值,再逐渐降低,而后者的曲线在最大值两侧有细微波动现象.     

Estimating root zone soil moisture at distant sites using MODIS NDVI and EVI in a semi-arid region of southwestern USA

This study investigates the potential of using Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) to estimate root zone soil moisture at native in-situ measured sites, and at distant sites under the same climatic setting. We obtained in-situ data from Soil Climate Analysis Network (SCAN) sites near the Texas-New Mexico border area, and NDVI and EVI products from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the Terra satellite. Results show that soil moisture values of the same depth are highly correlated (r = 0.53 to 0.85) among sites as far as 150 km apart, and that NDVI and EVI are highly correlated at the same site (r = 0.87 to 0.91). Correlation based on raw time series of NDVI and soil moisture is in general higher than that based on deseasonalized time series at every depth. The correlation reaches maximum value when vegetation index (VI) lags soil moisture by 5 to 10 days. NDVI shows a slightly higher correlation with soil moisture than EVI does by using the deseasonalized time series of NDVI and soil moisture. It is found that deseasonalized time series of NDVI and soil moisture are correlated at native sites (r = 0.33 to 0.77), but not at sites where soil moisture is very low. Regression analysis was conducted using deseasonalized time series soil moisture and deseasonalized time series NDVI with a 5-day time lag. Regression models developed at one site and applied to a similar distant site can estimate soil moistures, accounting for 50–88% of the variation in observed soil moistures.     

Investigating phenological changes using MODIS vegetation indices in deciduous broadleaf forest over continental U.S. during 2000–2008

Vegetation phenology describing the seasonal cycle of plants is currently one of the main concerns in the study of climate change and carbon balance estimation in ecosystems. Remote sensing techniques which can capture canopy reflectance allow vegetation photosynthetic capacity to be assessed. In this study, the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) measurements were used to identify onset date of greenness in deciduous broadleaf forest (DBF) over the continental United States from year 2000 to 2008. Onset dates determined by these three indices were compared with North American First Leaf Lilac Phenology Data over the same time period. NDVI has a best agreement with the field data among the three vegetation indices. Spatial analysis was performed on the onset dates predicted with NDVI. Four major conclusions were drawn from this study are: 1) onset dates are not only dependent on latitude but also associated with ecoregions and altitudes; 2) onset of greenness moves northward gradually and the average change of onset dates along latitude is about 3 days per degree. Interannual variability of onset dates is greater at higher latitudes (> 43 °N) than at lower latitudes (≤ 43 °N); 3) at the same latitude, DBF in mountain area tends to green up latter and coastal forest tends to green up earlier than other ecoregions; and 4) the impact of altitude is more obvious when the range of elevation achieves more than 1000 m. These conclusions provide insight for assessing vegetation indices in determining onset date of greenness at regional scale, and can be exploited to analyze the impacts of climate change on terrestrial ecosystem.     

盐池县2000-2012年植被变化及其驱动力

荒漠草原区的植被对防治荒漠化、维护生态屏障具有决定性作用,宁夏盐池县作为其典型代表,近13年的植被变化深受气候变化和人类活动的综合影响。基于MODIS NDVI等数据,运用趋势分析、经验模态分解和空间叠置分析等方法,对盐池县2000—2012年的植被动态变化进行研究,结果表明:(1)2000—2012年盐池县NDVI在0.2—0.4之间呈波动上升趋势,上升幅度为0.078/10 a,上升趋势显著;总体来说,植被稳定性低,年际间波动或转换频繁、幅度大;(2)NDVI的波动分量与残余分量方差贡献率各占50%,且NDVI波动呈减弱趋势。促使NDVI波动的主控因子是年降水量,但其影响在减弱;(3)推动NDVI趋势性上升的主要因素是土地利用方式改善和类型变化,但土地利用方式改善对NDVI的贡献远远大于土地利用类型变化对NDVI的贡献。因此,荒漠草原区的生态改善应以保护为主,辅之以必要的生态重建,走以适度开发带动整体保护的道路。     

2000—2018年黄河流域生长季植被指数变化及其对气候因子的响应

黄河流域位于我国干旱、半干旱地区,生态环境脆弱,探究其植被指数变化和对气候因子的响应对该地区生态建设具有重要意义。基于黄河流域2000—2018年MODIS归一化植被指数、增加型植被指数和气象数据,利用最大值合成法、趋势分析和相关分析等方法,分析了两种植被指数的时空变化特征及受气候因子的影响机制,探讨了NDVI与EVI在反映植被变化和对气候因子响应的差异。结果表明：2000—2018年,黄河流域地区植被NDVI、EVI分别以0.059/10a、0.038/10a的变化率增加,空间上以显著改善为主,面积占比分别为77.13%和75.27%,大多分布在1000—1500 m海拔处,中游地区改善较为良好,林地改善率最高。显著退化区域较小,主要分布在巴颜喀拉山西北部、西宁市、银川市、包头市、呼和浩特市、太原市、西安市及关中盆地和洛阳市周边,建设用地退化率最高。在生长季期间,植被指数变化与气温和降水以正相关为主,气温滞后时间为1个月,降水滞后时间为3个月,都为草地最为相关;与辐射之间为负相关,滞后时间为3个月,其中林地最为相关。在0.05显著性检验水平下,驱动黄河流域生长季植被变化的主要气候因子...     

Rangeland vegetation dynamics and moisture availability in Tunisia: an investigation using satellite and meteorological data

Improved knowledge of the interactions between regional climatic patterns and vegetation dynamics are necessary for predicting the future impacts of climate change on vegetation and biogeochemical processes. This paper describes how Normalized Difference Vegetation Index (NDVI) images generated from Advanced Very High Resolution Radiometer (AVHRR) satellite data were used to investigate the dynamics of rangeland vegetation in Tunisia. The NDVI images provided information about intra- and inter-annual variations in vegetation over nine growing seasons (1983–1992). Comparison of the NDVI data with field-collected ecological parameters for nine individual field sites indicated a strong relationship between the NDVI and percentage vegetation cover. The relationship between biomass measurements and NDVI was, however, less strong. Rainfall and NDVI data for each field site were compared, and significant relationships were found between the two. These indicated that there was a delay in the vegetation response to rainfall. In addition, the NDVI data showed that the vegetation at some of the field sites remained active throughout the summer although there was no rainfall during this period. TuMERT (Tunisian Model to Estimate Rangeland Transpiration), a simple water-balance model, was developed to estimate the amount of rainfall available for use by the vegetation during transpiration. The estimates of actual transpiration derived from TuMERT were found to be more strongly correlated with the AVHRR-NDVI measurements than the rainfall data.