基于RS和GIS技术的贵州省植被生态环境监测分析

为阐明贵州省植被生态环境变化的整体状况，基于RS和GIS技术，应用美国国家航空航天局最新的全球植被指数变化研究数据（GIMMS），通过计算月归一化植被指数（NDVI）变化率，并对研究区一元线性回归模拟，分析了贵州省1982年-2003年的地表植被覆盖。结果表明：22年来，研究区植被覆盖呈增加趋势，表明贵州省植被生态环境向好的方向发展；贵州省平均植被覆盖在春季和秋季呈上升趋势，夏季和冬季呈下降趋势，其中春季对植被覆盖总变化量的贡献最大；植被覆盖程度增减因区域不同而异，变化程度呈增加的区域主要位于贵,ki-I省的中部地区；变化程度呈减小的区域分布在贵州省的四周边缘。     

塔里木河下游植被指数的遥感监测

为了了解塔里木河下游9次应急输水的生态环境效益，利用MODIS—NDVI最大合成法、差值法，分析了塔里木河下游植被指数的年际变化情况，结果表明：（1）塔里木河下游地区植被覆盖度变化中增加区面积呈增加趋势。（2）2002年与输水初期相比，植被覆盖度增加区主要分布于上段和中段的老塔里木河及其文阔尔河河道两侧，下段河道覆盖度增加区仅零星分布于距河道0．3km左右的地势低洼区域。2003年以后，下段植被覆盖度增加区面积已达84．30km^2，是2002年的3．16倍，主要分布在距离河道0．5km左右的河道两侧。至2007年塔里木河下游河道两侧植被覆盖度增加区的变化是比较显著的，特别是下段植被覆盖增加区呈带状分布于距河道1．5km左右的范围内。植被指数遥感监测说明塔里木河下游地区的生态环境得到明显改善。     

基于遥感和像元二分模型的新疆植被覆盖度格局动态监测

利用MODIS归一化植被指数(NDVI)资料反演了2006—2015年新疆植被覆盖状况,获取不同时期的植被覆盖度图,并进一步分析了植被覆盖度变化的原因。结果表明:新疆植被覆盖度呈现北部高于南部、西部高于东部的分布特征,其中西部和西北部的山地森林、草地以及天山北坡绿洲农作物、草地区域植被覆盖情况最好。过去10年间,新疆植被覆盖度总体呈上升趋势,2015年植被覆盖度达到最高,为16.68%。生态恢复工程、降水和气温等是影响植被覆盖度变化的主要因素。     

基于神东中心区植被覆盖变化的多时相遥感监测

准确、快速地获取植被覆盖信息是矿区生态恢复和建设的关键与重点。以神东中心区为研究对象,利用2002、2005、2007、2010、2012年Landsat TM/ETM+和HJ1A-CCD1五景同期遥感数据,采用像元二分模型法,归一化植被指数(NDVI)值反演植被覆盖度,对研究区生态环境变化规律进行分析。结果表明,神东中心区平均植被覆盖度整体呈上升趋势,区内绝大部分地表覆盖程度得到改善,改善区面积达64.01%,退化区面积只有15.34%。该方法快速、定量地反映矿区植被覆盖及变化情况,为矿区生态环境动态监测和治理提供技术支持。     

基于TM影像的桂林市植被覆盖时空动态监测与分析

以西部地区中等规模城市——桂林市为例,利用1991、2006年两期TM卫星影像数据,在对原始数据预处理的基础上,提取归一化植被指数(NDVI)。依据混合像元二分模型,生成桂林市所辖5城区的两期植被覆盖度监测分类图像,对区域植被覆盖状况进行时空动态变化分析。结果表明,1991年~2006年间,城市植被覆盖区面积总体呈现下降趋势;在划分的3类植被覆盖类型中,低植被覆盖区面积减少幅度最为明显,中、高植被覆盖区面积有不同程度增长;低植被覆盖区主要分布在城市建成区与一些村镇外围区域,中、高植被覆盖区集中分布在城市远郊地带。此外,随着城市化的迅速发展,城乡各类建设用地面积不断增加,城市边缘地带的绿色植被正逐步被人工景观所取代。     

基于RS与GIS的福州新区热环境空间分布特征研究

采用2014—2021年福州新区的地表温度、归一化差值植被指数（NDVI）和土地利用类型等数据，从乡镇单元尺度分析热环境的空间分布情况，并探究植被和土地利用对热环境空间分布的影响。结果表明：2014—2021年福州新区地表温度在空间上有明显的空间自相关性，空间集聚特征显著，新区热岛比例指数（URI）呈现出下降趋势，表明在此期间热岛效应状况总体有所缓解；植被覆盖率（FVC）影响热环境的空间分布，可将福州新区的“热点”划分为FVC、NDVI均较低的乡镇和FVC较高、NDVI较低的乡镇两类；土地利用类型、土地利用程度及周边环境亦影响热环境的空间分布。     

“十三五”以来我国生态质量状况时空变化分析

采用多源卫星影像融合遥感监测技术，根据《生态环境状况评价技术规范》(HJ 192—2015),利用生物丰度指数、植被覆盖指数、水网密度指数、土地胁迫指数、污染负荷指数和环境限制指数建立综合评价模型，以面积加权平均实现尺度转换，采用一元线性回归模型的斜率(θ_slope)分析“十三五”以来我国生态质量现状及变化趋势。结果表明，当前我国生态质量状况优良、一般、较差的国土面积占比分别为46.6%、22.2%、31.2%。2016年以来，我国生态环境状况指数整体呈增加趋势，年平均增加幅度为0.24,其中北京、河北、辽宁、上海、宁夏等省份的增加趋势比较显著，对应的θ_slope分别为3.01、1.02、1.04、1.89、1.24。生态环境状况指数呈略微增加、明显增加、显著增加的县域数量分别为271、545、287个，面积占比分别为10.5%、19.8%、5.5%,主要分布在东北地区、黄土高原、三江源地区、秦岭山地、东部平原地区以及长江流域部分地区。其中：黄土高原、三江源等地区生态质量提升主要是因为上述地区实施生态保护修复工程后，整体绿化程度明显改善，林草...     

长江源区水生态系统健康研究进展

对截止至2021年6月报道的长江源区气候、水资源、水质、藻类、大型无脊椎动物和鱼类资源等水生态系统健康相关研究进行了综述,以期为进一步开展长江源区水生态系统健康研究与生态保护提供参考和依据。研究结果表明:①长江源区水生态相关研究主要关注于气候变化,其次为水资源变化和草地退化。②1948—2019年,长江源区全年平均气温呈上升趋势,增长速度为0.2~0.5℃/10 a;春季和冬季降水量呈增加趋势,增长速度分别为1.1~26.6 mm/10 a和0.2~9.1 mm/10 a;全年平均径流量呈增加趋势,增长速度为11.8~79.6 m³/(s·10 a);蒸发量呈增加趋势,增长速度为7.6~71.6 mm/10 a。③1969—2002年,冰川面积减少了68.1 km²,年均减少2.0 km²。1969—2015年,格拉丹东冰川面积减少了14.9~79.0 km²,减少速度为0.5~10.0 km²/a。1975—2015年,湖泊面积增加了2.7~831.6 km²,增速为0.3~96.2 km²/a。④1986—2015年,大部分河段水质为Ⅱ类及以上,且无明显年际变化。⑤针对水生生物的调查和研究非常匮乏。总体而言,长江源区水生态系统健康状况良好。近年来其气象因子以及水资源状况有所改变,未来气候变化可能会进一步影响长江源区水生态系统健康状况。今后亟须加强对长江源区的本底调查,完善基础数据,关注气候变化对水资源和水质的影响,并探索气候变化对水生生物的影响。     

20年间艾比湖流域植被覆盖度景观格局变化

选取艾比湖流域1990年、2001年、2011年同期（9月）3期I．and．satTM遥感影像，基于归一化植被指数NDVI，提取植被覆盖等级图，利用ArcGIS9．3和Fragstas3．3对该流域植被景观的变化进行了分析研究。结果表明：1990—2011年，该流域植被覆盖度变化明显，低植被覆盖区和较低植被覆盖区都有所减少，分别由1990年的34．05％和32．94％减少到2011年的32．8％和24．06％；较高植被覆盖区和高植被覆盖区有所增加，分别由8．49％和5．20％增长到15．13％和9．83％，但水域面积退化明显，由1990年的525．9765km2缩小至494．9876km2，减少了30．9889km2，退缩幅度达O．4％；最大斑块指数（LPI）由17．04上升到21．10，香农多样性指数（SHDI）和香农均势度指数（SHEI）分别由1．5387和0．8588增长到1．6395和0．9150。表明艾比湖流域景观格局混杂程度愈来愈高，空间异质性在逐年加强，总体空间格局向破碎化趋势发展。     

2013—2022年全国环境空气质量变化特征

基于2013—2022年全国339个地级及以上城市环境空气质量监测数据，分析了10年来环境空气质量变化特征。结果表明，2013—2022年全国环境空气质量持续改善，74个重点城市ρ（PM_2.5）从2013年的68μg/m³降至2022年的29μg/m³，降幅为57.4%；一次排放污染物ρ（SO₂）和ρ（NO₂）10年降幅分别为71.9%和27.6%，ρ（CO）自2015年以来下降42.1%；全国ρ（O₃）波动变化，受高温干旱影响，2019和2022年ρ（O₃）为148和145μg/m³，其他年份ρ（O₃）在137~139μg/m³波动。全国74个重点城市优良天数比例从2013年的65.7%上升至2022年的83.0%，相当于10年间每个城市优良天数增加65d；重污染天数比例从2013年的7.9%下降至2022年的0.6%，重污染天数减少90%。2020—2022年，90%以上的非沙尘重污染天出现在秋冬季（11—12月和1—2月），夏秋季O₃超标问题对优良天数比例影响显著，建议进一步强化重点区域污染联防联控与重点城市大气污染防治，加强多污染物协同治理，推进空气质量持续改善。     

Evaluating the difference between the normalized difference vegetation index and net primary productivity as the indicators of vegetation vigor assessment at landscape scale

Both the net primary productivity (NPP) and the normalized difference vegetation index (NDVI) are commonly used as indicators to characterize vegetation vigor, and NDVI has been used as a surrogate estimator of NPP in some cases. To evaluate the reliability of such surrogation, here we examined the quantitative difference between NPP and NDVI in their outcomes of vegetation vigor assessment at a landscape scale. Using Landsat ETM+ data and a process model, the Boreal Ecosystem Productivity Simulator, NPP distribution was mapped at a resolution of 90 m, and total NDVI during the growing season was calculated in Heihe River Basin, Northwest China in 2002. The results from a comparison between the NPP and NDVI classification maps show that there existed a substantial difference in terms of both area and spatial distribution between the assessment outcomes of these two indicators, despite that they are strongly correlated. The degree of difference can be influenced by assessment schemes, as well as the type of vegetation and ecozone. Overall, NDVI is not a good surrogate of NPP as the indicators of vegetation vigor assessment in the study area. Nonetheless, NDVI could serve as a fairish surrogate indicator under the condition that the target region has low vegetation cover and the assessment has relatively coarse classification schemes (i.e., the class number is small). It is suggested that the use of NPP and NDVI should be carefully selected in landscape assessment. Their differences need to be further evaluated across geographic areas and biomes.     

Pattern of NDVI-based vegetation greening along an altitudinal gradient in the eastern Himalayas and its response to global warming

The eastern Himalayas, especially the Yarlung Zangbo Grand Canyon Nature Reserve (YNR), is a global hotspot of biodiversity because of a wide variety of climatic conditions and elevations ranging from 500 to > 7000 m above sea level (a.s.l.). The mountain ecosystems at different elevations are vulnerable to climate change; however, there has been little research into the patterns of vegetation greening and their response to global warming. The objective of this paper is to examine the pattern of vegetation greening in different altitudinal zones in the YNR and its relationship with vegetation types and climatic factors. Specifically, the inter-annual change of the normalized difference vegetation index (NDVI) and its variation along altitudinal gradient between 1999 and 2013 was investigated using SPOT-VGT NDVI data and ASTER global digital elevation model (GDEM) data. We found that annual NDVI increased by 17.58 % in the YNR from 1999 to 2013, especially in regions dominated by broad-leaved and coniferous forests at lower elevations. The vegetation greening rate decreased significantly as elevation increased, with a threshold elevation of approximately 3000 m. Rising temperature played a dominant role in driving the increase in NDVI, while precipitation has no statistical relationship with changes in NDVI in this region. This study provides useful information to develop an integrated management and conservation plan for climate change adaptation and promote biodiversity conservation in the YNR.     

Vegetation NDVI Linked to Temperature and Precipitation in the Upper Catchments of Yellow River

Vegetation in the upper catchment of Yellow River is critical for the ecological stability of the whole watershed. The dominant vegetation cover types in this region are grassland and forest, which can strongly influence the eco-environmental status of the whole watershed. The normalized difference vegetation index (NDVI) for grassland and forest has been calculated and its daily correlation models were deduced by Moderate Resolution Imaging Spectroradiometer products on 12 dates in 2000, 2003, and 2006. The responses of the NDVI values with the inter-annual grassland and forest to three climatic indices (i.e., yearly precipitation and highest and lowest temperature) were analyzed showing that, except for the lowest temperature, the yearly precipitation and highest temperature had close correlations with the NDVI values of the two vegetation communities. The value of correlation coefficients ranged from 0.815 to 0.951 (p?<?0.01). Furthermore, the interactions of NDVI values of vegetation with the climatic indicators at monthly interval were analyzed. The NDVI of vegetation and three climatic indices had strong positive correlations (larger than 0.733, p?<?0.01). The monthly correlations also provided the threshold values for the three climatic indictors, to be used for simulating vegetation growth grassland under different climate features, which is essential for the assessment of the vegetation growth and for regional environmental management.     

Evaluating the utility and seasonality of NDVI values for assessing post-disturbance recovery in a subalpine forest

Forest disturbances around the world have the potential to alter forest type and cover, with impacts on diversity, carbon storage, and landscape composition. These disturbances, especially fire, are common and often large, making ground investigation of forest recovery difficult. Remote sensing offers a means to monitor forest recovery in real time, over the entire landscape. Typically, recovery monitoring via remote sensing consists of measuring vegetation indices (e.g., NDVI) or index-derived metrics, with the assumption that recovery in NDVI (for example) is a meaningful measure of ecosystem recovery. This study tests that assumption using MODIS 16-day imagery from 2000 to 2010 in the area of the Colorado’s Routt National Forest Hinman burn (2002) and seedling density counts taken in the same area. Results indicate that NDVI is rarely correlated with forest recovery, and is dominated by annual and perennial forb cover, although topography complicates analysis. Utility of NDVI as a means to delineate areas of recovery or non-recovery are in doubt, as bootstrapped analysis indicates distinguishing power only slightly better than random. NDVI in revegetation analyses should carefully consider the ecology and seasonal patterns of the system in question.     

Geo-spatial analysis of the temporal trends of kharif crop phenology metrics over India and its relationships with rainfall parameters

The Global Inventory Modeling and Mapping Studies bimonthly Normalized Difference Vegetation Index (NDVI) data of 8?×?8 km spatial resolution for the period of 1982–2006 were analyzed to detect the trends of crop phenology metrics (start of the growing season (SGS), seasonal NDVI amplitude (AMP), seasonally integrated NDVI (SiNDVI)) during kharif season (June to October) and their relationships with the amount of rainfall and the number of rainy days over Indian subcontinent. Direction and magnitude of trends were analyzed at pixel level using the Mann–Kendall test and further assessed at meteorological subdivision level using field significance test (α?=?0.1). Significant pre-occurrence of the SGS was observed over northern (Punjab, Haryana) and central (Marathwada, Vidarbha and Madhya Maharashtra) parts, whereas delay was found over southern (Rayalaseema, Coastal Andhra Pradesh) and eastern (Bihar, Gangetic West Bengal and Sub-Himalayan West Bengal) parts of India. North, west, and central India showed significant increasing trends of SiNDVI, corroborating the kharif food grain production performance during the time frame. Significant temporal correlation (α?=?0.1) between the rainfall/number of rainy days and crop phenology metrics was observed over the rainfed region of India. About 35–40 % of the study area showed significant correlation between the SGS and the rainfall/number of rainy days during June to August. June month rainfall/number of rainy days was found to be the most sensitive to the SGS. The amount of rainfall and the number of rainy days during monsoon were found to have significant influence over the SiNDVI in 24–30 % of the study area. The crop phenology metrics had significant correlation with the number of rainy days over the larger areas than that of the rainfall amount.     

2001—2021年黑龙江省森林植被生态质量变化分析

通过统计分析及空间插值等方法,使用气象和遥感数据,分析2001—2021年黑龙江省林区主要植被生长季的气候条件及植被生态质量变化特征。结果表明：2001—2021年黑龙江省林区生长季降水量、平均气温和日照时数分别呈增加、增加和减少趋势。在水分、热量整体逐步增加的背景下,气候条件有利于改善植被生态质量。2021年生长季大部分林区植被生态质量为优和良等级;与多年平均比较,85%以上的林区呈偏好趋势,尤其是中部、东南部部分林区呈很好趋势。植被生态质量指数平均每10 a增大43,99%以上的林区呈提高趋势,特别是北部、东南部及中部部分林区改善明显。     

Impacts of ecological water conveyance on groundwater dynamics and vegetation recovery in the lower reaches of the Tarim River in northwest China

The ecological water conveyance project (EWCP) in the lower reaches of the Tarim River provided a valuable opportunity to study hydro-ecological processes of desert riparian vegetation. Ecological effects of the EWCP were assessed at large spatial and temporal scales based on 13 years of monitoring data. This study analyzed the trends in hydrological processes and the ecological effects of the EWCP. The EWCP resulted in increased groundwater storage—expressed as a general rise in the groundwater table—and improved soil moisture conditions. The change of water conditions also directly affected vegetative cover and the phenology of herbs, trees, and shrubs. Vegetative cover of herbs was most closely correlated to groundwater depth at the last year-end (R?=?0.81), and trees and shrubs were most closely correlated to annual average groundwater depth (R?=?0.79 and 0.66, respectively). The Normalized Difference Vegetation Index (NDVI) responded to groundwater depth on a 1-year time lag. Although the EWCP improved the NDVI, the study area is still sparsely vegetated. The main limitation of the EWCP is that it can only preserve the survival of existing vegetation, but it does not effectively promote the reproduction and regeneration of natural vegetation.     

NDVI indicated characteristics of vegetation cover change in China��s metropolises over the last three decades

How urban vegetation was influenced by three decades of intensive urbanization in China is of great interest but rarely studied. In this paper, we used satellite derived Normalized Difference Vegetation Index (NDVI) and socioeconomic data to evaluate effects of urbanization on vegetation cover in China??s 117 metropolises over the last three decades. Our results suggest that current urbanization has caused deterioration of urban vegetation across most cities in China, particularly in East China. At the national scale, average urban area NDVI (NDVI_u) significantly decreased during the last three decades (P?<?0.01), and two distinct periods with different trends can be identified, 1982?C1990 and 1990?C2006. NDVI_u did not show statistically significant trend before 1990 but decrease remarkably after 1990 (P?<?0.01). Different regions also showed difference in the timing of NDVI_u turning point. The year when NDVI_u started to decline significantly for Central China and East China was 1987 and 1990, respectively, while NDVI_u in West China remained relatively constant until 1998. NDVI_u changes in the Yangtze River Delta and the Pearl River Delta, two regions which has been undergoing the most rapid urbanization in China, also show different characteristics. The Pearl River Delta experienced a rapid decline in NDVI_u from the early 1980s to the mid-1990s; while in the Yangtze River Delta, NDVI_u did not decline significantly until the early 1990s. Such different patterns of NDVI_u changes are closely linked with policy-oriented difference in urbanization dynamics of these regions, which highlights the importance of implementing a sustainable urban development policy.     

两种应用场景下NDVI时间序列数据拟合方法研究

植被覆盖指数（NDVI）时间序列数据集包含地表植被的长势、生长周期、时空变化等信息,其拟合重建结果可应用于物候信息提取、生态质量评价、人类活动扰动识别、覆被变化动态监测等方面。基于TIMESAT软件,选取物候参数提取和扰动识别2个应用场景,结合地面站点数据和Jacknife法模拟数据,对比分析非对称高斯函数拟合法（AG法）、双Logistic函数拟合法（D-L法）和Savitzky-Golay滤波法（S-G法）3种方法的拟合效果。结果表明：（1）3种方法拟合重建后提取的生长开始时间（SOS）、生长结束时间（EOS）、生长周期（LOS）等物候参数接近站点数据,AG法和D-L法保持NDVI时序曲线整体变化特征的能力较强,提取的SOS和EOS更接近站点数据;（2）人类活动扰动识别应用场景中,S-G法在滤波时能够最大限度地保留时序曲线细节变化,恢复速率相关系数达到0.618,回归估计标准差低于AG法和D-L法,因此识别精度最优。