地理信息系统支持下Spot/vegetation NDVI影像的大尺度神经网络分类

以内蒙古地区Spot/vegetation归一化植被指数(NDVI)影像为基本信息源,综合应用地理信息系统(G IS)技术进行了大尺度神经网络分类实验研究.建立多年份高分辨影像数据库,通过G IS软件集成与遥感影像目视解译方法,在全区范围选取了“纯净”样本数据,并辅助应用DTM数据和影像化多年气像观测数据,完成土地覆盖类型的BP人工神经网络分类.结果表明,G IS技术支持下,大面积区域尺度上spot/vegetation NDVI影像的BP神经网络分类可达到较高的分类精度.

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.

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.

Response of vegetation variation to climate change and human activities in the Shiyang River Basin of China during 2001-2022

Understanding the response of vegetation variation to climate change and human activities is critical for addressing future conflicts between humans and the environment,and maintaining ecosystem stability.Here,we aimed to identify the determining factors of vegetation variation and explore the sensitivity of vegetation to temperature(SVT)and the sensitivity of vegetation to precipitation(SVP)in the Shiyang River Basin(SYRB)of China during 2001-2022.The climate data from climatic research unit(CRU),vegetation index data from Moderate Resolution Imaging Spectroradiometer(MODIS),and land use data from Landsat images were used to analyze the spatial-temporal changes in vegetation indices,climate,and land use in the SYRB and its sub-basins(i.e.,upstream,midstream,and downstream basins)during 2001-2022.Linear regression analysis and correlation analysis were used to explore the SVT and SVP,revealing the driving factors of vegetation variation.Significant increasing trends(P<0.05)were detected for the enhanced vegetation index(EVI)and normalized difference vegetation index(NDVI)in the SYRB during 2001-2022,with most regions(84%)experiencing significant variation in vegetation,and land use change was determined as the dominant factor of vegetation variation.Non-significant decreasing trends were detected in the SVT and SVP of the SYRB during 2001-2022.There were spatial differences in vegetation variation,SVT,and SVP.Although NDVI and EVI exhibited increasing trends in the upstream,midstream,and downstream basins,the change slope in the downstream basin was lower than those in the upstream and midstream basins,the SVT in the upstream basin was higher than those in the midstream and downstream basins,and the SVP in the downstream basin was lower than those in the upstream and midstream basins.Temperature and precipitation changes controlled vegetation variation in the upstream and midstream basins while human activities(land use change)dominated vegetation variation in the downstream basin.We concluded that there is a spatial heterogeneity in the response of vegetation variation to climate change and human activities across different sub-basins of the SYRB.These findings can enhance our understanding of the relationship among vegetation variation,climate change,and human activities,and provide a reference for addressing future conflicts between humans and the environment in the arid inland river basins.

Terrain or climate factor dominates vegetation resilience?Evidence from three national parks across different climatic zones in China

Vegetation resilience(VR),providing an objective measure of ecosystem health,has received considerable attention,however,there is still limited understanding of whether the dominant factors differ across different climate zones.We took the three national parks(Hainan Tropical Rainforest National Park,HTR;Wuyishan National Park,WYS;and Northeast Tiger and Leopard National Park,NTL)of China with less human interference as cases,which are distributed in different climatic zones,including tropical,subtropical and temperate monsoon climates,respectively.Then,we employed the probabilistic decay method to explore the spatio-temporal changes in the VR and their natural driving patterns using Geographically Weighted Regression(GWR)model as well.The results revealed that:(1)from 2000 to 2020,the Normalized Difference Vegetation Index(NDVI)of the three national parks fluctuated between 0.800 and 0.960,exhibiting an overall upward trend,with the mean NDVI of NTL(0.923)>HTR(0.899)>WYS(0.823);(2)the positive trend decay time of vegetation exceeded that of negative trend,indicating vegetation gradual recovery of the three national parks since 2012;(3)the VR of HTR was primarily influenced by elevation,aspect,average annual temperature change(AATC),and average annual precipitation change(AAPC);the WYS'VR was mainly affected by elevation,average annual precipitation(AAP),and AAPC;while the terrain factors(elevation and slope)were the main driving factors of VR in NTL;(4)among the main factors influencing the VR changes,the AAPC had the highest proportion in HTR(66.7%),and the AAP occupied the largest area proportion in WYS(80.4%).While in NTL,elevation served as the main driving factor for the VR,encompassing 64.2%of its area.Consequently,our findings indicated that precipitation factors were the main driving force for the VR changes in HTR and WYS national parks,while elevation was the main factors that drove the VR in NTL.Our research has promoted a deeper understanding of the driving mechanism behind the VR.

Drivers,Trends,and Patterns of Changing Vegetation-greenness in Nansha Islands,China from 2016 to 2022

Changes in vegetation status generally also represents changes in the ecological health of islands and reefs(IRs).However,studies are limited of drivers and trends of vegetation change of Nansha Islands,China and how they relate to climate change and human activities.To resolve this limitation,we studied changes to the Normalized Difference Vegetation Index(NDVI)vegetation-greenness index for 22 IRs of Nansha Islands during normal and extreme conditions.Trends of vegetation greenness were analyzed using Sen's slope and Mann-Kendall test at two spatial scales(pixel and island),and driving factor analyses were performed by time-lagged partial correlation analyses.These were related to impacts from human activities and climatic factors under normal(temperature,precipitation,radiation,and Normalized Difference Built-up Index(NDBI))and extreme conditions(wind speed and latitude of IRs)from 2016 to 2022.Results showed:1)among the 22 IRs,NDVI increased/decreased significantly in 15/4 IRs,respectively.Huayang Reef had the highest NDVI change-rate(0.48%/mon),and Zhongye Island had the lowest(–0.29%/mon).Local spatial patterns were in one of two forms:dotted-form,and degradation in banded-form.2)Under normal conditions,human activities(characterized by NDBI)had higher impacts on vegetation-greenness than other factors.3)Under extreme conditions,wind speed(R^(2)=0.2337,P<0.05)and latitude(R^(2)=0.2769,P<0.05)provided limited explanation for changes from typhoon events.Our results provide scientific support for the sustainable development of Nansha Islands and the United Nations‘Ocean Decade’initiative.

NDVI-Derived Vegetation Trends and Driving Factors in West African Sudanian Savanna

The Sudanian savanna is a key vegetation biome in West Africa providing food and vital ecosystem services. Recently, it has been reported alarming vegetation loss in this biome, calling for more investigation, relevant to tackle land degradation and ensure food security. However, vegetation dynamics in this area remains a matter of debate, and one of the main challenges is to document consistently the underlying driving factors. This study aimed at assessing vegetation trends and driving factors from 2000 to 2022. NDVI trend, detected using the Mann-Kendall’s monotonic trend test, was used as proxy to express vegetation dynamics. In addition to the non-parametric Spearman correlation analysis, variables importance scores, derived from Random Forest (RF) classifications, were used to determine key driving factors among climatic, topographic, edaphic, accessibility and demographic factors. During 2000-2022, no significant trends largely characterised the vegetation cover of the study area. However, patterns of strong (weak) browning and strong (weak) greening affected 7.1% (10.6%) and 12.8% (19.1%) of the study area respectively. According to the driving factors analysis, the observed vegetation trends were mainly driven by rainfall dynamics (trend and mean annual), population growth and anthropogenic activities. The results of this study can support the development of efficient strategies for safeguarding vegetation cover in the Sudanian savanna of Burkina Faso.

基于SPOT VEGETATION数据的榆林地区土地覆盖变化研究

榆林地区位于我国农牧交错带,能源富集区,生态环境脆弱。建国以来的土地整治工作取得显著成效。本文利用1998-2004年SPOT VEGETATION NDVI分析了榆林地区植被变化情况。并且利用多年气象数据分析了降水和温度变化情况。结果表明榆林地区植被状况有明显改善,改善集中于8、9、10月份。而且植被覆盖变化具有明显的区域差异,北部植被覆盖改善趋势明显,且变化平稳;南部增加趋势不明显,有的地方还呈下降趋势,变化幅度大。

The Vegetation Classification of the Return Farmland to Pasture or Forest Region in Shaanxi-Gansu-Ningxia Based on SPOT/VEGETATION Data

In order to assess the climatical and ecological effect which returned the farmland to pasture or forest, the vegetation and crop in Northwest China with suitable threshold value were classified in this experiment by using multi-temporal SPOT/VEGETATION dada and combing supervised classification with unsupervised classification. Compared with the data from Statistical Department and actual investigation, the precision of the classified result was above 85%.

基于SPOT-VEGETATION数据的张掖盆地植被覆盖变化动态分析

以1998-2006年9a的315景逐旬的SPOT-VEGETATION数据为主要数据源,利用趋势、差值等方法,分析了张掖盆地植被覆盖动态变化的时空特征。结果表明张掖盆地植被年内变化趋势中,峰值由1998-2001年的6、7月份推移到2002-2006年的7、8月份,这与黑河分水、张掖地区调整种植结构、夏禾作物种植面积减少、秋禾增加有关;1998-2006年间,年最大化归一化植被指数(Normalized Difference Vegetation Index,NDVI)整体呈变好的趋势,但是月最大化NDVI的年际变化趋势在不同月份存在很大差异;年最大化NDVI与月最大化NDVI在每相邻两年间的变化均存在很大差异,植被退化与改善波动出现;年际间植被变化幅度大的区域均分布在人工植被发育的走廊平原,而变化幅度小的区域则分布在无植被或少植被的戈壁沙漠。

基于SPOT/VEGETATION数据的陕甘宁退耕还林区植被分类

以评估退耕还林/还草的气候、生态效应为目的,利用多时相SPOT/VEGETATION数据,将监督分类与非监督分类相结合,选定合适的阈值对西北地区植被及作物进行分类,分类结果与统计面积、实际调查结果相比,精度在85%以上。

基于GIMMS与SPOT vegetation的中亚物候变化趋势及对比

植被物候是反映生态系统受全球气候变化影响的重要证据。作为生态与水资源系统最为脆弱的地区之一,中亚干旱区植被物候对气候变化的响应情况是当前的全球环境变化研究热点。文章以GIMMS和SPOT vegetation数据为基础,在TIMESAT物候信息提取软件的支持下,以动态阈值法提取了1982─2006年和1999─2012年中亚地区植被物候空间信息。结合Mann-Kendall趋势分析方法,对中亚地区2个时期的植被开始期,停止期和生长季长度的3种典型物候参数的历史变化情况和空间分布进行识别;同时,通过二维散点图和最小二乘一维线性回归的统计分析方法,开展了对1999─2006年8年重叠期期间GIMMS和SPOT vegetation所提取的3种物候数据对比分析。结果表明:1中亚研究区在1982─2006年和1999─2012年2个分段时期没有发生显著的整体性植被物候变化,其未发生显著性变化面积分别占研究区总面积的90%和95%;2农作物种植区域是中亚地区植被物候发生显著变化的主要区域;3对GIMMS与SPOT vegetation数据提取3种物候参数进行空间相关性分析结果表明,GIMMS和SPOT vegetation在提取的物候数据存在差异,开始期,停止期和生长季长度的相关性分别为[0.36,0.56],[0.32,0.49]和[0.28,0.45],且植被覆盖度高的区域要比覆盖度低的区域差异小,这也说明了不同遥感数据源在中亚干旱区植被物候信息提取一致性较差,其原因可能尺度差异和土壤背景值的严重影响。

基于SPOT VEGETATION数据的海南岛年际植被变化研究

基于1998-2007年逐旬的SPOT-VEGETATION NDVI数据,采用最大值合成MVC(Maximum ValueComposites)技术生成了每年的最大化NDVI(MNDVI)影像,结合均值法、一元线性回归分析、影像差异分析等方法研究了海南岛植被变化情况,得出岛内近10 a植被变化总体趋势及时空分布.结果显示,近10 a来海南岛植被生长状况呈下降趋势,且从东部到西部呈现明显的衰退趋势.

2000-2020年陕西省植被NDVI时空变化及气候因子探测

[目的]揭示陕西省不同生态系统植被时空变化,厘清不同气候因子及相互作用对植被变化的影响机制,为区域生态环境保护提供理论依据。[方法]基于MODIS NDVI及年均高温、年均低温、年均温、年总降水数据,采用Theil-Sen Median趋势、偏相关、地理探测器等方法,分析了2000—2020年各地貌分区植被NDVI时空变化特征,结合变量分离探究了植被NDVI变化与降水、气温的内部关联和响应机制。[结果]2000—2020年陕西植被NDVI波动增加,速率为5.9%/10 a,速率大小为陕北>关中>陕南;全省多年植被NDVI值为0.71,南高北低;植被NDVI显著改善区域占比67%,分区占比为陕北>陕南>关中。2000—2020年陕西气候因子随时间波动变化,速率大小为陕南>关中>陕北,空间上呈现年均高温降低、年均低温上升、年均温降低、降水增加。2000—2020年陕西及各分区植被NDVI与年均高温整体呈负相关,与年均低温、年均温、年总降水量呈正相关;全省及陕北年总降水贡献最大,关中和陕南年均高温贡献最大;年均高温与年总降水交互主导全省、陕北及陕南植被NDVI变化,年均温与年总降水的交互主导关中植被NDVI变化。[结论]研究期陕西及各分区植被整体变好,各分区植被对气候的响应关系、各因子的贡献及其相互作用不同,降水和年均高温、年均温的交互显著影响植被NDVI变化。

基于SPOT VEGETATION数据的中国西北植被覆盖变化分析

基于遥感和地理信息系统的技术,利用SPOT-VEGETATION NDVI(Normalized Difference VegetationIndex)数据对我国西部地区植被覆盖的情况进行了动态监测。采用MVC(Maximum Value Composites)、一元线性回归趋势分析和变化幅度百分比等方法分析西部地区植被变化特征,并结合西北五省土地利用类型图,分析不同植被类型的年最大化NDVI(MNDVI)变化趋势及特点。其结果是:近7 a来植被覆盖存在普遍退化的趋势,且2000-2001与2001-2002年度的变化幅度较大。在局部区域植被有改善的趋势,但总的改善幅度小于退化幅度。分析结果表明,植被改善的区域主要分布在陕西和宁夏的大部分地区以及新疆西北部和西南部地区。大部分地区植被退化。而且不同植被的MNDVI在相同的年份表现出相似的变化特点和趋势。

SPOT VEGETATION S10影像云和雪盖的检测与处理

以整个中国区域为例,采用3种检测方法对Spot vegetation S10影像上存在的云和雪盖的检测结果进行了对比分析。结果表明,BISE检测器得到的云层和实际最为接近,用Spot vegetation状态地图得到的云层过少,而用Spot vegetation检测器V2.0得到的云和雪盖像素有部分重叠。最后利用云、雪盖像素在时间序列上的相邻像素进行平滑处理方法达到去云和雪盖的目的,研究结果对于Spot vegetation S10影像的噪音消除和应用精度的提高具有借鉴意义。

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。