湟水流域2000—2019年植被变化趋势特征和延续性分析

植被的变化特征是流域生态监测的重要内容和环境保护最为关键的信息。利用MODIS EVI数据产品和Hurst指数,分析2000—2019年湟水流域植被的时空变化趋势及其趋势的延续性。结合气温、降水等气象观测数据,分析湟水流域9个县区植被变化的影响因素。研究结果表明：从2000年至2019年间,湟水流域植被EVI最大值年均增幅为0.0063,受气温、降水、土地利用等因素的不同影响,上、中、下游的不同县区表现出不同的变化特征。对于年EVI最大值,下游的增加趋势均最为显著,河道地区变化剧烈程度更加明显。Hurst指数分析表明这种变化趋势短期内具有一定的延续性。本研究通过监测植被时序变化,揭示了高原流域地区植被监测趋势的重要性,为流域管理和可持续性发展提供了一定的数据支撑和科学依据。     

基于Google Earth Engine的中国植被覆盖度时空变化特征分析

植被覆盖时空变化是全球及区域生态环境重要研究内容之一。基于Google Earth Engine云平台,利用2000~2017年250 m分辨率的MODIS-EVI长时间序列数据,采用像元二分模型并辅以趋势分析、去趋势标准差、Hurst指数方法定量估算中国自2000年来植被覆盖度时空变化,并从省域尺度分析中国植被覆盖度近18 a以及未来趋势变化的时空分异特征。研究结果表明:①2000年以来中国植被覆盖度的变化速率为0.09%/a(P<0.01),平均植被覆盖度为44.63%,空间分布格局上整体呈现“东南高、西北低”的特点,但存在空间异质性;②从省级尺度来看,海南省平均植被覆盖度最高(79%),新疆维吾尔自治区最低(13%),山西省改善趋势最显著(0.4%/a),天津市年际波动最大(DSD=0.039),位于中国最西部的3省:新疆、西藏、青海植被覆盖度年际波动最小;③全国尺度植被覆盖度Hurst指数为0.72,未来将继续保持改善的趋势。具有改善持续性的省份基本呈“T”型分布,位于东西两侧的省份应注重加强植被生态修复与防护工作,保障区域生态文明建设的持续性。     

基于GEE的石羊河流域植被覆盖变化特征及其影响因素分析

植被作为陆地生态系统的重要组成部分,常被用作评估气候变化和生态恢复成效的指标。以石羊河流域为研究对象,基于Google Earth Engine (GEE)平台采用Theil-Sen趋势分析和MannKendall检验（TS-MK）、Hurst指数揭示植被覆盖变化特征;采用偏相关分析、残差分析和地理探测器探究植被覆盖变化的影响因素。结果表明：2001～2020年间石羊河流域植被NDVI呈现波动增长趋势,增长率为0.023/10 a;呈显著增加趋势和显著减少趋势的面积占比分别为72.32%和2.40%。未来植被NDVI变化趋势保持一致（Hurst>0.5）的面积占比为63.84%,其中持续性显著增加的面积占比最大,为47.37%。偏相关分析结果表明降水对植被生长的影响较强,而温度、太阳辐射和饱和水汽压差的影响相对较弱。残差分析结果表明气候要素和人类活动影响下植被NDVI呈显著增加趋势的面积占比分别为21.59%和60.07%,石羊河流域的植被变化主要受人类活动的积极影响。此外,地理探测器的结果表明植被NDVI的空间分布主要受水热条件分布特征的影响。该研究结果有助于深化对植被覆盖变化...     

西南地区2001~2019年森林损失特征遥感监测与时空分析

森林在生态系统服务中起着重要作用,例如提供清洁空气、保护生物栖息地以及减少全球温室气体的排放等。全球森林变化数据集（Global Forest Change,GFC）每年以30 m的高空间分辨率绘制森林覆盖变化图,成为监测森林覆盖时空变化特征的有效工具。利用谷歌地球引擎（Google Earth Engine,GEE）,基于GFC产品,结合线性回归方法和空间自相关理论对西南地区2001~2019年森林变化情况进行研究,结果表明：近19 a来,西南地区森林损失面积为375.27万hm²,以2008年为拐点,2008年之前呈显著增加趋势（p<0.05）,在此之后波动下降,损失主要集中分布在广西、贵州东南和云南南部地区;森林损失与地形分析的关系表明损失主要分布在海拔2 000 m以下、坡度小于40°的区域,并逐渐向海拔更低坡度更缓的地方转移;森林损失面积具有一定的空间关联性,2001~2019年来Moran’s I 指数均为正,平均值为0.406,空间高值聚集在广西和贵州南部,低值聚集在重庆、四川和云南北部;政策因素在土地利用方式转变过程中发挥着重要作用,林业活动和农业扩张是影响损失现象发生的主要驱动因素,今后制定森林保护和管理战略时应充分考虑多方面因素造成的森林损失现象,研究结果可以为森林监测和保护提供更科学的指导。     

利用MODIS EVI时间序列数据分析福建省植被变化(2000~2017年)

以全国森林覆盖率最高的福建省为研究对象,利用2000~2017年夏季的MODIS EVI植被指数数据和气象与非气象因子进行协同分析,以揭示近17年福建植被的时空变化及其影响因子。结果表明:研究期内福建的EVI均值整体上升,从2000年的0.454上升至2017年的0.505,17 a间上升了11.2%,表明福建省的植被整体处于变好的状态,且在中部和西南部的变化最明显。相关分析表明,在研究期内,气象因子(气温和降水)对EVI变化的影响不显著,植被的变好主要为非气象因子的作用。EVI的提高主要得益于2003年福建省建设生态省后森林覆盖率的提高,并和2012年开始的水土流失治理有明显关系,这说明人类活动的积极作用对福建植被的变好起到了关键的作用。     

武汉地区1988~2002年植被覆盖度变化动态分析

植被是生态系统最重要的组成部分,而植被覆盖度是衡量地表植被状况的一个最重要的指标,是生态系统健康评价的前提和必要的基础。利用1988、1991、1996和2002年4个时相的TM和ETM+遥感影像为数据源,以归一化植被指数(NDVI)像元二分法为植被覆盖度估算模型,计算了武汉地区不同时期的植被覆盖度,生成了该地区不同时序的植被覆盖度图,以此分析武汉地区植被覆盖度的时空变化。结果表明,武汉地区在1988~2002年这14年,植被覆盖度变化明显,整个地区的平均植被覆盖度从58.41％下降到50.45％,下降值为7.96%,特别是江夏区和主城区变化幅度最为显著,下降值分别为9.98%和7.14%。植被覆盖度下降最快时间段是1996~2002年,恢复阶段是1991~1996年。从空间上来看,全地区都处于下降阶段,特别是郊区江夏区和市区,通过分析发现,这大部分原因都是城市发展的结果,导致了生态环境的恶性发展,值得各方面的注意。     

土地复垦整理植被覆盖度的遥感评价

随着土地复垦工作的广泛开展,土地复垦整理效果评价日益重要。以湖南益阳中洲土地复垦区为例,选取多源多时相遥感影像数据,利用ERDAS IMAGINE软件实现植被覆盖度的计算分析,结果表明通过土地复垦后该项目区的植被覆盖度比复垦前还要高,与实际情况相符,充分说明利用遥感技术对土地复垦后的植被覆盖度进行评价是可行的。     

2001~2010年松木希错流域植被动态变化遥感研究

遥感在区域植被变化研究中具有十分重要的作用,能为大面积监测植被状况的演化过程提供技术支持。NDVI在高植被覆盖地区存在过饱和现象,对稀疏地区的植被变化尤其敏感。以古里雅冰帽南部的松木希错流域植被相对稀疏区域为研究区,基于MODIS NDVI数据和逐月气象观测数据,以及RS和GIS平台,对该区域2001~2010年主要植被变化趋势进行了初步研究,并对植被变化与气候驱动因子的关系进行了分析和探讨。结果表明:① 2001~2010年间该区域的植被活动有加强趋势;② NDVI表明研究区植被生长季较短(5~9月),NDVI浮动区间为0.11～0.13,低于全国水平(0.3～0.35),也低于全球稀疏灌丛的平均水平(0.2～0.4);③NDVI与年均气温整体上呈正相关,而与年降水量相关性不强。表明近年来持续升温是影响该区域植被活动加强的最主要原因。     

基于遥感的海南本岛植被覆盖度时空变化及其地形效应研究北大核心CSCD

植被覆盖在维持生态系统结构稳定和防治水土流失等方面发挥着重要的作用,海南自1988年建省以来迅速发展,导致海南岛植被覆盖也产生了巨大的变化.为揭示海南本岛地形因子对植被覆盖度的影响以及为海南本岛进一步制定合理的生态环保策略提供依据,基于1988、1998、2008、2017和2020年Landsat-TM/OLI多光谱影像,以海南本岛为研究区域,采用归一化植被指数和像元二分模型进行植被覆盖度提取,通过线性趋势分析海南本岛近30a植被覆盖变化特征.并结合30 m_DEM获取的海、坡度和坡向数据,来进一步探讨海南本岛植被覆盖度在不同地形因子条件下的空间分布特征.结果表明:①1988～2020年海南岛平均植被覆盖度介于0.58～0.88之间,整体呈先下降后上升趋势;②海南岛高植被覆盖主要分布于海南岛中部、西南部和东南部地区,低植被覆盖主要出现在居住区、沿海地区等人为干扰因素较高的地区;③海南岛各等级植被覆盖均随海拔的增加而不断降低,在海拔小于100 m的区域分布面积最大;坡度为0～5°时植被覆盖面积达到最大值,随着坡度的增加,植被覆盖面积呈减少趋势;各等级植被覆盖在阴坡和阳坡的分布面积变化差异不大,主要以高植被覆盖类型为主.     

南方丘陵区植被覆盖度遥感估算与时空变化研究——以福建省永定县为例

植被覆盖度是生态环境监测的重要指标,而复杂地形因素影响对山地植被遥感信息准确提取。基于Landsat-8OLI遥感数据,分别采用像元二分模型和线性混合光谱分解法,在对比分析植被覆盖度的地形敏感性基础上,选择山地植被指数(NDMVI)估算了1992、2002和2014年永定县的植被覆盖度,并分析其变化。结果表明:1基于山地植被指数(NDMVI)的覆盖度估算模型的地形敏感性最弱,更适合于南方丘陵山地的植被覆盖度遥感反演;2永定县总体植被覆盖度较高,平均植被覆盖度达77.99%以上,高覆盖度区占59.73%以上,22年内植被覆盖度经历了先提高再下降的过程;3在空间上,高坎抚、金丰和西部片区的植被覆盖度较低,动态变化较明显。永定县金丰片区植被覆盖度明显提高;而近12年内高坎抚片区因矿业开采活动对生态环境的破坏,植被覆盖度降低幅度大,且变化面积较大。     

Characteristics and Sustainability Evaluation of Vegetation Change in Heihe River Basin during 2001 to 2017

Characteristics of vegetation variation play an important role in ecological monitoring and provide the basis for integrated river basin management decisions. In this study, the spatial-temporal trends in vegetation cover change and its sustainability in Heihe river basin during 2001~2017 were characterized, using MODIS-EVI time series data at a spatial resolution of 250 meters in Google Earth Engine(GEE) platform. Combined with temperature, precipitation and river runoff data, the factors affecting vegetation growth in Heihe River Basin were identified. The results show that: Over the last 17 years, the average annual increment of EVI in Heihe river basin was 0.003 9, and the annual expansion of vegetation area was 480.3 km². Vegetation in the upper, middle and lower reaches of Heihe river has changed in varying degrees affected by temperature, precipitation, reclamation of cultivated land, water resources management and related groundwater. Whether the annual maximum EVI value or vegetation area, the increase trend of vegetation in the middle reaches was the most significant, and the oasis area was more obvious than the non-oasis area. This trend is sustainable in the short term, but there is a greater risk for a long time scale. The study provides a demonstration for high-speed monitoring of vegetation changes, reflecting the equal importance of growth and type changes for monitoring vegetation in arid regions. The regional synergy of vegetation changes in river basin puts forward higher requirements for integrated river basin management, such as reasonable water separation and strengthening surface-groundwater collaborative management.     

Vegetation Cover Change and Urban Expansion in Beijing-Tianjin-Hebei during 2001~2015 based on Google Earth Engine

At present,the main mode of remote sensing image analysis is to download the data,preprocess and extract the thematic information by using the algorithm model.The model has disadvantages of huge amount of data and low efficiency in large scale area.Based on the massive remote sensing image data and powerful computing and storage capabilities of Google Earth Engine platform,we use a linear regression trend analysis method programming to process MOD13Q1-NDVI data,and then analyze the change of vegetation coverage from 2001 to 2015 in beijing\|tianjin\|hebei.We use threshold method of processing DMSP/OLS data to extract urban land,and analysis of 2001 and 2013 urban expansion and degradation by using change detection method.The results show that:(1)The trend of vegetation change was mainly improved,and the area proportion of improvement was 63%,which was far greater than the proportion of degradation 22%.The region of vegetation improvement is mainly in the northwestern part of the study area,and the region with obvious degraded vegetation is the mainly in the Middle East(Beijing,Tianjin and other megacities).(2)From 2001 to 2010,the area of Beijing,Tianjin and Hebei changed little,with a ratio of 60%.[JP2]In 2013,the area decreased by 13 thousand Km2 compared with 2010,with a decrease of 5.97%.(3)90.45% of the urban areas remained unchanged,and the proportion of urban degradation areas(7.2%) was significantly higher than that of the expansion areas(2.3%).This paper makes full use of GEE platform to realize data processing quickly and efficiently,and solve Geosciences problems,so as to provide reference for related research.[JP]     

Spatiotemporal Variations of Fractional Vegetation Coverage in China based on Google Earth Engine

The spatiotemporal variation of vegetation coverage is one of the main research fields in Global and Regional eco-environment. Based on the Google Earth Engine cloud platform, using the MODIS-EVI long-term series data of 250 m resolution from 2000 to 2017. The model of dimidiate pixel was applied in estimating the spatiotemporal variations of Fractional vegetation coverage in China since 2000. The spatiotemporal variation characteristics of China's vegetation coverage for nearly 18 years and future trends from the provincial scale also be analyzed. Trend analysis, Detrended Standard Deviation and Hurst index were employed. The results showed that: (1) The rate of variation of vegetation coverage in China since 2000 is 0.09%/a (P<0.01), the average vegetation coverage is 44.63%. The overall spatial distribution pattern shows the characteristics of “south-high and low-lying northwest”, but there is space Heterogeneity; (2) Hainan Province has the highest average vegetation coverage (79%), the lowest in Xinjiang Uygur Autonomous Region (13%), the most significant improvement in vegetation coverage in Shanxi Province (0.4%/a). Tianjin has the largest inter-annual volatility (DSD=0.039), Xinjiang, Tibet and Qinhai province which located in the westernmost of China have the least annual fluctuations in vegetation coverage; (3) The Hurst Index of Vegetation Coverage at National Scale is 0.72, China Future vegetation coverage will continue to improve. The provinces with improved sustainability are basically “T”-type distribution, and the provinces on both sides of the east and west should focus on strengthening the ecological restoration and protection of vegetation to guarantee the sustainability of regional ecological civilization construction.     

基于ASTER数据黑河中游植被含水量反演研究

植被含水量是影响植物生长的主要限制因子之一,也是衡量植被生理状态和形态结构的重要参数。应用遥感技术定量估测植被含水量,对于农业旱情监测、作物产量估计和科学研究具有重要意义。基于2012年黑河生态水文遥感试验期间获得的6景ASTER遥感数据和同步观测的研究区生物量观测数据集,选取NDVI、RVI、SAVI和MSAVI 4种植被指数分别与单位面积内植被含水量的关系进行比较分析,建立了不同植被指数的植被含水量反演模型,并对反演结果进行了验证。研究结果表明:4种植被指数均与实测的植被含水量有较高的相关性(R20.846),利用MSAVI反演的植被含水量精度略优于其他3种指数,其均方根误差(RMSE)在0.794kg/m2内。模型较为可靠,可以为大范围获取植被含水量信息提供有效方法。     

黑河流域下游天然植被生态需水预测

根据对黑河下游天然植被生态状况的分忻，对不同水平年提出了5个生态恢复方案，计算预测了各方案的生态需水量．提出了推荐生态恢复方案。     

Evaluation of MODIS Snow Products in Southwestern Xinjiang Using the Google Earth Engine

Google Earth Engine(GEE) is a cloud\|based geospatial processing platform that can analyze geospatial data to achieve parallel processing of massive remote sensing data on a global scale,providing support for remote sensing big data and large\|area research.MODIS snow cover mapping is a global snow cover product established using MODIS data and has been widely used in regional and global climate and environmental monitoring.In the GEE,millions of remote sensing images are stored,including MODIS daily snow products MOD10A1 V5 data and Landsat data.Taking the three research areas in southwestern Xinjiang as examples,the Landsat stored by the GEE were selected,and the NDSI was used to extract the snow cover as the true value of the land cover to evaluate the MOD10A1 accuracy.The results show that the average overall accuracy of MOD10A1 in the snow cover season in southwestern Xinjiang during the period from 2000 to 2016 is 82%,the average misjudgment rate is 2.9%,and the average missed rate is 58.8%.The overall accuracy of MOD10A1 can reach 98% under the clear sky conditions.The accuracy of MOD10A1 is effected by the terrain conditions and cloud cover in different regions.Therefore,the GEE can quickly and effectively filter high quality cloudless Landsat images,and evaluate the accuracy of the MOD10A1 in the snow area around the global regions,displaying intuitively the misjudgment and missed areas in the form of online maps.Meanwhile,GEE provides the Landsat simple cloud score function to calculate the regional cloud cover,which makes the influence of cloud cover on the MOD10A1 accuracy assessment more regionally representative.     

Land Change Patterns and Driving Mechanism in Beijing since 1990 based on GEE Platform

Land-cover and land-use dynamics is a key component for global change,and it is a significant form of the impact of human activities on physical environment.Basing Google Earth Engine platform and Classification And Regression Tree method,selected seven types of cultivated land,forest,grassland,wetland,water body,artificial surface and bare land as classification system,the paper used Landsat 5 TM and Landsat 8 OLI images to interpret the land\|cover and land\|use since 1990 of Beijing.Simultaneously,the paper analyzed and summarized the character of land\|use changing and driving force.The results show that：(1) GEE has outstanding advantages in remote sensing data analysis and processing at regional scales.(2) The CART method has high accuracy of remote sensing classification,and the overall accuracy of validation of 6 land cover products is above 93%.The spatial consistency of 2010 products and GlobeLand30\|2010 data showed that the spatial consistency ratios of woodland,water body and cultivated land were 84.28%,74.75%and 73.56% respectively.The spatial consistency of the distribution is 74.0%.(3) The main land types in Beijing were cultivated land,woodland and artificial surface,and the area accounted for about 90%.During the period from 1990 to 2016,the artificial surface and woodland area increased,and the cultivated land and water were shrinking.The artificial surface area increase of 1 371 km²,and cultivated land shrinkage 40%;On Beijing plain area,artificial surface by the circle of “spread pie” expansion trend to “blossom everywhere” expansion trend;The expansion of the artificial surface is mainly achieved through the encroachment of cultivated land.We constructed a multidimensional stepwise linear equation model to analyze the driving force of land type change,indicated that rapid population growth,rapid economic development,government\|related policies and other socio\|economic development factors jointly drive the Beijing land-cover/land-use evolution process.