Assessment of vegetation cover changes and the contributing factors in the Al-Ahsa Oasis using Normalized Difference Vegetation Index(NDVI)

The abandonment of date palm grove of the former Al-Ahsa Oasis in the eastern region of Saudi Arabia has resulted in the conversion of delicate agricultural area into urban area.The current state of the oasis is influenced by both expansion and degradation factors.Therefore,it is important to study the spatiotemporal variation of vegetation cover for the sustainable management of oasis resources.This study used Landsat satellite images in 1987,2002,and 2021 to monitor the spatiotemporal variation of vegetation cover in the Al-Ahsa Oasis,applied multi-temporal Normalized Difference Vegetation Index(NDVI)data spanning from 1987 to 2021 to assess environmental and spatiotemporal variations that have occurred in the Al-Ahsa Oasis,and investigated the factors influencing these variation.This study reveals that there is a significant improvement in the ecological environment of the oasis during 1987–2021,with increase of NDVI values being higher than 0.10.In 2021,the highest NDVI value is generally above 0.70,while the lowest value remains largely unchanged.However,there is a remarkable increase in NDVI values between 0.20 and 0.30.The area of low NDVI values(0.00–0.20)has remained almost stable,but the region with high NDVI values(above 0.70)expands during 1987–2021.Furthermore,this study finds that in 1987–2002,the increase of vegetation cover is most notable in the northern region of the study area,whereas from 2002 to 2021,the increase of vegetation cover is mainly concentrated in the northern and southern regions of the study area.From 1987 to 2021,NDVI values exhibit the most pronounced variation,with a significant increase in the“green”zone(characterized by NDVI values exceeding 0.40),indicating a substantial enhancement in the ecological environment of the oasis.The NDVI classification is validated through 50 ground validation points in the study area,demonstrating a mean accuracy of 92.00%in the detection of vegetation cover.In general,both the user’s and producer’s accuracies of NDVI classification are extremely high in 1987,2002,and 2021.Finally,this study suggests that environmental authorities should strengthen their overall forestry project arrangements to combat sand encroachment and enhance the ecological environment of the Al-Ahsa Oasis.

Retrospective analysis of two northern California wild-land fires via Landsat five satellite imagery and Normalized Difference Vegetation Index (NDVI)

Wild-land fires are a dynamic and destructive force in natural ecosystems. In recent decades, fire disturbances have increased concerns and awareness over significant economic loss and landscape change. The focus of this research was to study two northern California wild-land fires: Butte Humboldt Complex and Butte Lightning Complex of 2008 and assessment of vegetation recovery after the fires via ground based measurements and utilization of Landsat 5 imagery and analysis software to assess landscape change. Multi-temporal and burn severity dynamics and assessment through satellite imagery were used to visually ascertain levels of landscape change, under two temporal scales. Visual interpretation indicated noticeable levels of landscape change and relevant insight into the magnitude and impact of both wild-land fires. Normalized Burn Ratio (NBR) and delta NBR (DNBR) data allowed for quantitative analysis of burn severity levels. DNBR results indicate low severity and low re-growth for Butte Humboldt Complex “burned center” subplots. In contrast, DNBR values for Butte Lightning Complex “burned center” subplots indicated low-moderate burn severity levels.

Interannual Variability of the Normalized Difference Vegetation Index on the Tibetan Plateau and Its Relationship with Climate Change

The Qinghai-Xizang Plateau, or Tibetan Plateau, is a sensitive region for climate change, where the manifestation of global warming is particularly noticeable. The wide climate variability in this region significantly affects the local land ecosystem and could consequently lead to notable vegetation changes. In this paper, the interannual variations of the plateau vegetation are investigated using a 21-year normalized difference vegetation index （NDVI） dataset to quantify the consequences of climate warming for the regional ecosystem and its interactions. The results show that vegetation coverage is best in the eastern and southern plateau regions and deteriorates toward the west and north. On the whole, vegetation activity demonstrates a gradual enhancement in an oscillatory manner during 1982-2002. The temporal variation also exhibits striking regional differences： an increasing trend is most apparent in the west, south, north and southeast, whereas a decreasing trend is present along the southern plateau boundary and in the central-east region. Covariance analysis between the NDVI and surface temperature/precipitation suggests that vegetation change is closely related to climate change. However, the controlling physical processes vary geographically. In the west and east, vegetation variability is found to be driven predominantly by temperature, with the impact of precipitation being of secondary importance. In the central plateau, however, temperature and precipitation factors are equally important in modulating the interannual vegetation variability.

Drought trend analysis in a semi-arid area of Iraq based on Normalized Difference Vegetation Index, Normalized Difference Water Index and Standardized Precipitation Index

Drought was a severe recurring phenomenon in Iraq over the past two decades due to climate change despite the fact that Iraq has been one of the most water-rich countries in the Middle East in the past.The Iraqi Kurdistan Region(IKR)is located in the north of Iraq,which has also suffered from extreme drought.In this study,the drought severity status in Sulaimaniyah Province,one of four provinces of the IKR,was investigated for the years from 1998 to 2017.Thus,Landsat time series dataset,including 40 images,were downloaded and used in this study.The Normalized Difference Vegetation Index(NDVI)and the Normalized Difference Water Index(NDWI)were utilized as spectral-based drought indices and the Standardized Precipitation Index(SPI)was employed as a meteorological-based drought index,to assess the drought severity and analyse the changes of vegetative cover and water bodies.The study area experienced precipitation deficiency and severe drought in 1999,2000,2008,2009,and 2012.Study findings also revealed a drop in the vegetative cover by 33.3%in the year 2000.Furthermore,the most significant shrinkage in water bodies was observed in the Lake Darbandikhan(LDK),which lost 40.5%of its total surface area in 2009.The statistical analyses revealed that precipitation was significantly positively correlated with the SPI and the surface area of the LDK(correlation coefficients of 0.92 and 0.72,respectively).The relationship between SPI and NDVI-based vegetation cover was positive but not significant.Low precipitation did not always correspond to vegetative drought;the delay of the effect of precipitation on NDVI was one year.

Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor

A spectral reflectance sensor(SRS)fixed on the near-surface ground was developed to support the continuous monitoring of vegetation indices such as the normalized difference vegetation index(NDVI)and photochemical reflectance index(PRI).NDVI is useful for indicating crop growth/phenology,whereas PRI was developed for observing physiological conditions.Thus,the seasonal change patterns of NDVI and PRI are two valuable pieces of information in a crop-monitoring system.However,capturing the seasonal patterns is considered challenging because the vegetation index values estimated by the reflection from vegetation are often governed by meteorological conditions,such as solar irradiance and precipitation.Further,unlike growth/phenology,the physiological condition has diurnal changes as well as seasonal characteristics.This study proposed a novel filtering method for extracting the seasonal signals of SRS-based NDVI and PRI in paddy rice,barley,and garlic.First,the measurement accuracy of SRSs was compared with handheld spectrometers,and the R^(2)values between the two devices were 0.96 and 0.81 for NDVI and PRI,respectively.Second,the experimental study of threshold criteria with respect to meteorological variables(i.e.,insolation,cloudiness,sunshine duration,and precipitation)was conducted,and sunshine duration was the most useful one for excluding distorted values of the vegetation indices.After data processing based on sunshine duration,the R^(2)values between the measured vegetation indices and the extracted seasonal signals of vegetation indices increased by approximately 0.002–0.004(NDVI)and 0.065–0.298(PRI)on the three crops,and the seasonal signals of vegetation indices became noticeably improved.This method will contribute to an agricultural monitoring system by identifying the seasonal changes in crop growth and physiological conditions.

基于无人机多光谱NDVI值估测玉米产量

【目的】研究基于UAS-8无人机采集数据,运用归一化植被指数(Normalized Difference Vegetation Index)模型估测玉米产量,为大田无人机多光谱预测玉米产量提供理论依据。【方法】以新疆18份春播玉米为研究对象,获取开花期多光谱图像,经过辐射校正、大气校正、建立掩膜、提取NDVI图,计算植被覆盖率,得到区光谱反射率和归一化植被指数实际数值,将NDVI值与田间实测产量值进行模型拟合。【结果】幂函数Y=23411.46-10997.99/X(R^(2)=0.4886),二次函数为Y=39003.00-117963.03X+103130.25X 2(R^(2)=0.562),正反比函数(Inverse Proportional Function)为Y 2=2840.5 X/(1-X)(R^(2)=0.495),利用偏最小二乘回归(Partial Least Squares Regression),其线性函数Y=24458.22X-9620.55(R^(2)=0.521)。【结论】在数值0.5~0.8区间,NDVI与玉米产量具有较高的相关性,线性函数方程NDVI值可预测玉米的产量。

高寒气候区生长季NDVI与昼夜不对称增温的Copula分析

利用1982—2016年的青海地区归一化植被指数和气象数据,基于马尔科夫链蒙特卡罗的Copula函数方法,深入探索昼夜增温不对称性与植被活动之间的复杂关系,揭示了昼夜增温和NDVI之间的联合概率分布及其季节性差异。研究结果表明,昼夜增温与NDVI之间的关系在不同季节呈现显著差异。尤其在秋季,昼夜增温对NDVI的影响最为显著,其次是夏季和春季。通过Copula函数模型,发现昼夜增温与NDVI在特定温度区间内呈现正相关,表明适宜的温度条件下昼夜增温对植被生长具有促进作用。然而,当昼夜增温超过某一阈值时,其对NDVI的促进作用转变为抑制作用,从而限制了植被的生长。同时,还揭示了重现期与昼夜增温及NDVI之间的关系。在较低的重现期下,昼夜增温与NDVI的联合概率较高,表明在这些条件下,植被生长良好的情况出现的频率较高。反之,较高的重现期对应于昼夜增温与NDVI较低的联合概率,表明植被生长受到抑制。本研究通过Copula函数提供了一个全新的视角来理解昼夜增温与植被动态之间的相互作用,强调了气温变化对植被生长影响的复杂性。

2000—2021年渭河流域NDVI变化及其影响因素

渭河流域是黄河中游重要的生态涵养地,同时也是黄土高原水土流失的典型区域,监测该地区植被生长变化趋势,并分析其与气候变化和人类活动的关系,对科学评估区域生态建设成效、黄土高原植被恢复和生态修复具有重要意义。基于2000—2021年归一化植被指数(NDVI)、气温、降水量、人口密度、土地利用数据,分析了渭河流域NDVI的时空变化特征,探究了气候变化和人类活动对NDVI变化趋势的影响。结果表明,2000—2021年,渭河流域植被生长季NDVI呈增加趋势,全区年平均增速为0.004。年际尺度上,NDVI与年平均降水量呈正相关关系,与年平均气温的相关性不显著;月尺度上,NDVI与4月和8月的气温、降水量均呈正相关关系,与7月气温呈弱的负相关关系。人口密度变化与NDVI变化趋势呈负相关,流域人口密度的减小有利于植被的恢复和改善。土地利用类型内部变化是植被NDVI变化的主要原因。NDVI显著减少区NDVI的减少趋势主要由关中平原耕地NDVI的减少引起,NDVI显著增加区NDVI的增加趋势主要由草地、林地以及黄土丘陵区、黄土残塬区耕地NDVI的增加引起。

基于时空数据融合的塔吉克斯坦中高时空分辨率NDVI数据集(2010-2020)

归一化植被指数(Normalized Difference Vegetation Index,NDVI)是研究植被最常用的遥感指数之一。NDVI长时间序列数据对于植被变化研究有着重要的意义。然而由于传感器的限制,遥感数据的时间分辨率与空间分辨率不能兼顾,因此在目前广泛使用的NDVI数据产品中,高时空分辨率的数据还较为缺乏。本产品基于Cubist模型对MODIS数据与Landsat及哨兵等遥感数据进行时空数据融合,得到了塔吉克斯坦2010–2020年中高时空分辨率Landsat-MODIS融合数据,以及2020年中高时空分辨率Sentinel-MODIS融合数据。为保证数据的准确性和可靠性,本数据集从数据源的质控,模型训练优化,以及模型独立验证三个方面对数据产品进行质量控制,且取得了较好的验证效果。本数据集可反映塔吉克斯坦2010–2020年NDVI时空变化情况,可为该地区植被变化分析、生态环境监测等提供长时间序列数据支撑。

中亚地区2001-2020年250 m及2020年30 m分辨率植被生长季NDVI数据集

中亚地区是北半球最大的干旱和半干旱区,其生态环境十分脆弱,对全球气候变化的响应较为敏感。由于该区域的特殊地理位置,维护该区域生态系统的稳定对全球经济社会发展至关重要。植被具有重要的生态环境指示作用,其时空分布格局和变化趋势是评估区域生态状况的重要指标。归一化植被指数(Normalized Difference Vegetation Index,NDVI)作为研究植被最常用的遥感指数之一,能够表征植被的时空变化特征。本数据集利用MODIS13Q1产品生成了中亚地区2001–2020年长时间序列空间分辨率为250 m的生长季均值NDVI数据,并使用基于规则的分段回归Cubist算法,结合Landsat数据,融合得到了能够更好表征地物细节的30 m空间分辨率的2020年生长季均值NDVI数据。同时,本数据集从数据源的质控,模型训练优化,以及模型独立验证三个方面对数据产品进行质量控制,以确保数据的精度和可靠性。本数据集的生成为中亚地区植被动态变化和空间格局的分析提供了有力的数据支持。

基于Sentinel-2A NDVI时间序列数据和随机森林方法的高山冷凉蔬菜识别

该研究基于Sentinel-2A卫星的归一化差值植被指数(NDVI)时间序列数据,结合随机森林(RF)分类方法,对高山冷凉蔬菜种植区域进行精准识别与分类。以西吉县为研究区,利用2023年覆盖高山冷凉蔬菜全生育期的Sentinel-2A遥感数据,构建10 m高空间分辨率的NDVI时间序列数据,结合田间实测数据,使用RF分类方法对高山冷凉蔬菜进行识别分类。结果表明文章提出的方法在高山冷凉蔬菜种植区域识别中表现出了较高的精度和稳定性,总体精度达93.52%,Kappa系数为0.89。

基于MODIS-NDVI数据的延河流域植被覆盖时空变化及其对极端降水的响应

基于2010-2020年MODIS-NDVI数据,分析延河流域归一化植被指数(normalizeddifference vegetation index,NDVI)和极端降水指数,辅以利用Sen趋势分析、M-K趋势检验法、Pearson相关分析等方法,研究植被覆盖时空变化及其对极端降水的响应。结果表明:1)2010-2020年,延河流域NDVI呈显著增加趋势,植被覆盖状况逐年改善,其增加速率为0.073/(10 a)(P<0.001);2)空间上,延河流域NDVI呈现从东南到西北减小的布局;3)趋势上,NDVI呈上升和减小趋势的面积占比分别为98.02%和1.98%,整体呈上升趋势,植被覆盖显著改善;4)近11年极端降水指数总体趋势变化平缓,R20(number of very heavy precipitation days)、RX_(5day)(max5-day precipitation amount)、CDD(consecutive dry Days)和CWD(consecutive wet days)呈上升趋势,R_(95P)(very wet day precipitation)和SDII(simple daily intensity index)呈下降趋势;5)NDVI与极端降水指数的相关性整体上偏低,除SDII外,其余指数都与NDVI正相关,且NDVI与RX_(5day)相关性最强,与R20相关性最弱。

基于绿视率和NDVI的城市街道景观分析与优化研究

街道景观空间对市民健康和城市风貌具有重要影响。既往研究中常以归一化植被指数(NDVI)和绿视率(GVI)来分别代表二维和三维的绿色指标,但对二者的指标相关性研究甚少。采用基于深度学习的图像语义分割方法分析百度街景计算代表性街道的GVI,利用GF-1卫星数据计算NDVI,比较分析城市街道的GVI和NDVI指标特征及相关性。结果表明,1)中山市中心城区各代表街道GVI指标参差不齐,从8.06%到36.00%,其中石岐街道兴中道GVI最高;2)各街道观测点的NDVI均值随着缓冲区尺度的增加也随之呈现出不同变化,NDVI均值具有强烈的尺度敏感性;3)50 m GVI和DNVI均值的皮尔逊相关系数最高,达到0.832。在此基础上分析街道景观存在的不足并给出优化建议,为城市街景评估、空间优化、景观提升提供参考。

1961-2020年黄河流域干燥度时空变化及其对植被NDVI的影响

基于黄河流域224个气象站点1961—2020年气象数据和2000—2020年MODIS的归一化植被指数(NDVI)数据,分析干燥度时空分布规律及其对NDVI的影响。结果表明:1961—2020年黄河流域年平均干燥度气候倾向率为-0.03·(10 a)^(-1),其多年平均值为2.56;从各站变化的区域分布看,黄河流域北部站点变化以下降为主(52.2%),中部以南站点以上升为主(41.5%)。在空间上,黄河流域干燥度总体呈西北高、东南低,表现为上游(3.74)>中游(1.99)>下游(1.74),上游地区包含干旱亚区、半干旱亚区和半湿润亚区,中游和下游地区大多为半湿润亚区。逐步回归分析显示,干燥度主要受降水量的影响,大部分地区年均干燥度减少是由太阳总辐射减少、降水量增加和气温降低造成的。2000—2020年黄河流域NDVI平均值为0.30,在空间上整体呈东南高、西北低,上游较小、下游最高;与干燥度呈极显著负相关(r=-0.52,P<0.01,n=224),特别是在上中游地区。

基于决策树与NDVI时序变化检测的撂荒耕地的地形特征研究——以重庆市巫山县为例

[目的]揭示山区撂荒耕地的地形特征,为区域土地资源管理和农业可持续发展提供科学参考,[方法]以重庆市巫山县为研究区域,利用谷歌地球引擎(Google Earth Engine,GEE)云平台上的Landsat TM/ETM+/OLI和Sentinel-2数据,采用决策树与时间序列NDVI变化检测法,对2017—2021年研究区的撂荒地信息进行提取和分析。[结果](1)从时间序列特征上看,研究区内撂荒地面积整体呈上升趋势,2017—2021年的增加量为2123.50 hm^(2),增长率为19.61%,区间内曲线形态上表现为“W”字形特征。空间上撂荒地呈全局分散,局部集中特征,主要沿着水系走向分布,显著集中于河流两侧,周边被坡耕地围绕。(2)研究区内撂荒地在不同的高程带和坡度带分布不同。撂荒地主要集中于高程1000 m以下和坡度5°~20°范围内。高程1500 m以下的区域,撂荒地面积和撂荒率表现“先增后减”的规律,2019年达到最高点;高程1500 m以上的区域,撂荒地面积和撂荒率随着时间的变化呈现“先减后增”的规律,在2020年达到了最低值。(3)撂荒地在不同地形位等级下的分布指数表现为持续减少型,地形梯度1级、2级的分布指数大于1,为撂荒地的优势区。[结论]决策树与NDVI时序变化检测法结合能够精准识别撂荒地,识别精度为83.59%。

Impact of climate and human activity on NDVI of various vegetation types in the Three-River Source Region, China

The Three-River Source Region(TRSR)in China holds a vital position and exhibits an irreplaceable strategic importance in ecological preservation at the national level.On the basis of an in-depth study of the vegetation evolution in the TRSR from 2000 to 2022,we conducted a detailed analysis of the feedback mechanism of vegetation growth to climate change and human activity for different vegetation types.During the growing season,the spatiotemporal variations of normalized difference vegetation index(NDVI)for different vegetation types in the TRSR were analyzed using the Moderate Resolution Imaging Spectroradiometer(MODIS)-NDVI data and meteorological data from 2000 to 2022.In addition,the response characteristics of vegetation to temperature,precipitation,and human activity were assessed using trend analysis,partial correlation analysis,and residual analysis.Results indicated that,after in-depth research,from 2000 to 2022,the TRSR's average NDVI during the growing season was 0.3482.The preliminary ranking of the average NDVI for different vegetation types was as follows:shrubland(0.5762)>forest(0.5443)>meadow(0.4219)>highland vegetation(0.2223)>steppe(0.2159).The NDVI during the growing season exhibited a fluctuating growth trend,with an average growth rate of 0.0018/10a(P<0.01).Notably,forests displayed a significant development trend throughout the growing season,possessing the fastest rate of change in NDVI(0.0028/10a).Moreover,the upward trends in NDVI for forests and steppes exhibited extensive spatial distributions,with significant increases accounting for 95.23%and 93.80%,respectively.The sensitivity to precipitation was significantly enhanced in other vegetation types other than highland vegetation.By contrast,steppes,meadows,and highland vegetation demonstrated relatively high vulnerability to temperature fluctuations.A further detailed analysis revealed that climate change had a significant positive impact on the TRSR from 2000 to 2022,particularly in its northwestern areas,accounting for 85.05%of the total area.Meanwhile,human activity played a notable positive role in the southwestern and southeastern areas of the TRSR,covering 62.65%of the total area.Therefore,climate change had a significantly higher impact on NDVI during the growing season in the TRSR than human activity.

黑龙江省植被生长季NDVI时空变化及气候响应分析

为了解黑龙江省植被动态变化及探究植被与气候因子之间的关系,本文基于2000—2020年植被生长季(4—10月)的MOD13Q1-NDVI数据,分析其植被空间分布、时空动态变化及变异系数CV;同时结合气候因子,分析植被归一化植被指数(NDVI)变化与气温、降水和相对湿度的响应特征。结果表明,2000—2020年该地区植被生长季NDVI多年平均值为0.57,总体呈北部和东南部高,东北部和西南部低;21年来植被生长季NDVI整体呈显著改善趋势,年变化率为0.0035(P<0.01)。植被改善区域远大于其退化区域,且以明显改善为主,仅3.35%的区域呈退化趋势;NDVI的变异系数以低波动和相对低波动为主,分布占比为38.77%和51.89%;植被NDVI与气温、降水和相对湿度均呈正相关性。气温、降水和相对湿度对植被NDVI变化的相对贡献率分别为28.00%、26.17%和45.83%;相对湿度是影响植被NDVI变化的主要气候因子。

气候变化和人类活动对华北地区植被NDVI的影响研究

归一化植被指数(Normalized Differential Vegetation Index,NDVI)是反映植被生长状态的重要指标,是反映陆地生态环境状况的“指示器”。华北地区地处我国的政治和文化中心,土地覆盖类型复杂多样,是我国重要的粮食生产地,同时受到气候暖干化及加剧的人类活动影响,华北地区的植被生态变得十分脆弱。本研究基于卫星资料NOAACDRAVHRR NDVI和气象数据资料,采用趋势分析、偏相关分析和残差趋势分析等方法,探究了1982-2019年华北地区NDVI的时空变异特征及其对气候变化和人类活动的响应。研究结果表明:(1)1982-2019年华北地区春季、夏季、秋季和生长季的植被NDVI呈显著上升趋势,空间异质性强,其中夏季和生长季的增长速率最快为0.024(10a)^(-1),显著增加的区域面积占比分别为57.35%和58.10%。(2)华北地区春季、夏季和生长季NDVI与降水呈显著正相关关系,秋季NDVI主要受气温的影响,夏季NDVI同时受到气温、降水和相对湿度的积极影响。(3)气候变化和人类活动对华北地区植被的生长影响具有区域差异性,在植被改善区,气候变化的相对作用为45.64%,人类活动的相对作用为54.36%;在植被退化区,气候变化的相对作用为32.66%,人类活动的相对作用为67.34%。(4)不同土地利用类型中,华北地区森林和农田的植被生长较快,其植被改善主要受人类活动的影响,人类活动的相对作用分别为66.07%和60.82%,草地植被的退化也主要受人类活动的影响,相对作用为69.48%,人类活动对华北地区植被的重要影响主要源于我国近几十年来三北防护林等人类重大生态工程的建设以及城市扩张、人口激增的影响,该研究成果也对华北地区生态屏障的建设以及生态环境保护提供了重要理论支撑。

内蒙古不同草地NDVI变化及其驱动要素

在全球气候变化和人类活动的双重冲击下,干旱半干旱地区的草地正经历着不可逆转的退化。为遏制草地退化,增强对草地生态系统的保护,有必要厘清干旱半干旱区草地生长状况的主要驱动要素。基于卫星遥感数据、气象资料和统计年鉴资料,研究1990—2015年内蒙古草地生长的变化趋势,借助地理探测器来比较了不同驱动要素对内蒙古草地的影响程度,结合结构方程模型探讨内蒙古不同草地归一化植被指数(Normalized Difference Vegetation Index,NDVI)变化的驱动机制。研究结果显示:(1)内蒙古地区草地退化和改善同步发生,在时间上,草甸草原NDVI以-4.88×10^(-4)/a显著退化,荒漠地区NDVI以1.98×10^(-4)/a速度增长;在空间上,有23%的草甸草原区的草地严重退化,有32%的荒漠草原区的草地显著改善;(2)自然驱动要素特别是降水等气候驱动要素,仍然是草甸草原、典型草原和荒漠草原的主要驱动力,但相对脆弱的草原荒漠区和荒漠区,人类活动也存在显著影响;(3)坡度和畜牧密度对不同类型的草地的影响均逐渐减弱,而土壤水分和温度对草的影响则均呈现增加趋势,其中温度对草地的影响的增幅较快;降水对草地NDVI的影响普遍表现出周期性变化,其他驱动要素则在不同草地类型中存在较大的差异性。(4)温度和降水直接影响草地NDVI,而地形因素和人类活动因素普遍通过中介效应对草地NDVI施加影响。期冀为内蒙古的草地保护提供有针对性的指导,促进草畜平衡和草地生态系统的可持续发展。

1988—2018年粤北植被NDVI时空分布特征分析

植被是陆地生态系统的核心组成部分,而归一化植被指数(Normalized Difference Vegetation Index,NDVI)是反映地表植被覆盖和生长状况的重要参数。以粤北为研究区,结合1988—2018年长时间序列NOAA/AVHRR产品数据和高程数据,采用变化率分析法和空间分析法,分析31年间粤北NDVI变化特征及其与地形因子的关联性。结果表明,1988—2018年,粤北NDVI年均值整体呈现出北高南低、西高东低的分布特征,NDVI年均值介于0.20~0.87。其中,连山壮族瑶族自治县NDVI年均值最高,达到0.82,而清远市清城区NDVI年均值最低,仅为0.68。2018年,NDVI年均值达到最高,为0.84;1988年,NDVI年均值达到最低,为0.74。随着高程的增加,粤北NDVI年均值呈现出上升趋势,NDVI分布特征受到人类活动、地形和气候的综合影响。研究结果可为粤北山地城市生态屏障区建设提供重要理论支持。