沂蒙山区植被NDVI的时空特征及其对水热条件的响应

植被是生态环境变化的综合指示器,研究其对水热条件的响应已成为当前气候变化研究中的主要内容之一。选取北方土石山区典型代表--沂蒙山区为研究对象,基于沂蒙山区1980~2010年的气温、降水和2001~2010年MODIS\|NDVI数据,结合相关分析和最小二乘法,定量分析该区植被指数的年际、年内的时空变化及其对水热条件的响应。结果表明:①近10 a沂蒙山区NDVI max的变化斜率为0.0026;②植被显著退化区和良好区分别占研究区总面积的10.52%和28.62%;③不同季节(主要是春、夏和秋季)植被状况均呈现良性发展趋势;④台站数据显示植被年际变化与年降水和年均气温的关系并不密切,而在月时间尺度上植被与气温的相关性要强于与降水的相关性。综上所述,沂蒙山区植被状况总体呈良性发展趋势,气温可能是影响该区植被生长的主导因子。     

新疆北部植被生长季NDVI时空变化及其与冬季降雪的关系

植被是陆地生态系统最重要的组成部分,在调节陆地碳循环过程和气候变化中起着关键作用。冬季降雪为植被生长提供良好的水分条件,加强冬季降雪与植被关系研究具有重要的生态意义。利用1982~2013年GIMMS NDVI数据,基于趋势分析研究了北疆4~10月植被覆盖的时空变化特征,并结合WRF模拟冬季降雪数据,采用基于栅格的相关性分析方法,分析了各月NDVI对冬季降雪的响应及不同生态系统之间大小的差异。结果表明:(1)北疆地区4~10月NDVI总体呈增加趋势,增加区域主要位于农田地区和高海拔草地,但准噶尔盆地中东部地区呈减少趋势;(2)区域内冬季降雪基本呈环状分布,中部少、四周高,冬季降雪呈增加趋势;(3)冬季降雪与5、6月NDVI显著正相关的面积最大,且显著正相关区域主要位于准噶尔盆地的荒漠生态系统;(4)冬季降雪对整个研究区以及不同生态系统类型NDVI的影响具有显著的滞后性,对4~10月NDVI的影响均呈现先增大后减小的趋势,且对6月NDVI的影响最大。     

北美永久湿地NDVI时序变化特征分析——以哈德逊湾西南湿地区域为例

基于2001~2009年MCD12Q1数据,提取哈德逊湾西南区连续9年都是湿地的区域作为研究区。利用1982~2006年GIMMS NDVI数据,分析此区域25 a来NDVI季节、年际变化,并与温度、降水数据进行相关分析。结果显示:研究区域NDVI季节变化呈现单峰曲线形状,夏季达到最大值。1982~2006年NDVI年际变化在春秋两季与全年平均NDVI年际变化都呈增加趋势,夏季与冬季趋势则相反。此区域NDVI季节变化与温度、降水呈极显著相关,冬季NDVI年际变化与温度以及夏季NDVI年际变化与降水的相关性不显著,而其他各时期NDVI年际变化与温度、降水也都呈极显著相关。然而,由于人类活动及其他因素的影响,此区域的NDVI年际变化驱动还存在一定的不确定性。     

基于SPOT4数据的黄土高原植被动态变化研究

以SPOT4/VEGETATION数据为基础,以NDVI变化率和年均NDVI值作为植被覆盖动态变化的指标,分析了1998~2005年黄土高原植被覆盖的时空动态变化特征。结果表明黄土高原地区植被动态变化显著增强,1998~2001年黄土高原的植被覆盖有所减少,幅度约为10.5%,2001年后,植被活动显著增强,植被覆盖面积呈增加趋势,2004年后稍有回落。植被生长季的延长和生长加速是该区域NDVI值增加的主要原因,黄土高原地区植被增加和减少的区域相互交错,这一特性是由农业生产活动、城市建设、政府决策及植被对气候变化的响应等综合因素作用的结果。     

基于SPOT-NDVI的贺兰山植被覆盖的时空变化研究

对贺兰山区进行分区的基础上,运用SPOT\|NDVI数据对1998~2010年四季不同区的植被演变进行研究。结果表明,贺兰山植被高海拔区域的平均覆盖最高,其次为中海拔区域,低海拔区域的东坡较高,其他区域覆盖较低。研究区植被的生长季为5~10月,其中植被覆盖最好为8月。近13 a来,贺兰山植被总体表现为增长趋势,其中绝大部分区域的秋季植被增长,尤其是中高海拔区域。考虑到研究区干旱化的趋势以及对植被生长的影响,贺兰山(特别是低山地区)植被的增长趋势在未来可能难以持续。     

青藏高原1982~2015年FPAR时空变化分析

植被吸收利用太阳光合有效辐射比率反映了植被固碳释氧能力,根据青藏高原GIMMS NDVI3g（1982~2015年）和MODIS NDVI（2001~2015年）数据,采用非线性半理论半经验模型进行FPAR反演及时空变化分析。结果表明：①2001~2015年GIMMS NDVI3g和MODIS NDVI反演FPAR在空间分布上具有较高的一致性,相关系数为0.82（P<0.01）,年际变化趋势一致至少6年的区域占80%;②青藏高原FPAR受坡度和海拔影响较大,其中15~35坡度FPAR变化最快,700~2 100 m海拔区间FPAR值最大;不同坡向对应的FPAR除南坡方向偏低外其他方向差异不大。③1982~2015年青藏高原四季FPAR时空变化研究中,冬季FPAR年际变化最明显,约78.5%的区域表现为增长趋势;秋季FPAR下降区域最多,但超过71.5%区域变化不显著;④基于MODIS NDVI和GIMMS NDVI两数据反演的所有植被类型的FPAR都在2012年间出现小幅度下降趋势,且不同植被类型FPAR的年际变化趋势各不相同。     

土地利用/覆被变化对地质灾害发育的影响研究

2018年8月印度喀拉拉邦遭受强降雨,引发大量地质灾害,造成巨大的经济损失和人员伤亡。为研究农业化进程中土地利用/覆被变化对地质灾害发育的影响,探求适宜的人地协调发展模式,以该地区受灾最严重的伊都基为研究区,基于已有的灾害点数据,利用Google Earth高分辨率遥感图像目视解译获取研究区灾害发生前8 a(2010年)和灾害发生时（2018年）的土地利用数据,基于Landsat TM/OLI数据提取的归一化植被指数计算研究区植被覆盖度,对比分析该地区地质灾害的发育与土地利用/覆被变化之间的关系。研究结果表明：(1)伊都基地区灾害点主要集中在中北部地区,分布在种植林、种植灌丛、建筑物、道路等人类活动影响较大的区域,该区域灾害点占总灾害数的80.46%;(2)伊都基地区灾害点的土地利用变化虽然较小,总体变化率为37%,但土地利用变化主要发生在种植灌丛、种植林等与人类活动密切相关的土地利用类型中;(3)伊都基地区植被覆盖度下降率为16.70%,在空间分布上,灾害点易发区域与植被覆盖度下降区域有较强的关联性。     

塔里木河中游植被指数空间数据库设计

受环境因素与人类活动的影响,塔里木河中游地区近年植被长势及分布变化较大,通过对植被的动态监测分析,可为塔里木河生态保育对策的制定及植被保护研究提供科学依据.本文采用面向对象的Geodatabase模型,使用地理信息系统（GIS）对空间、属性数据的管理分析功能,以塔里木河中游为研究区,选取通过遥感图像处理平台ENVI计算得到的2000、2006、2010、2015年四期归一化植被指数（NDVI）为研究数据,通过使用ArcGIS对NDVI进行空间分布特征研究,设计出一种塔里木河中游植被指数空间数据库,以实现空间、属性数据的存储管理一体化.该植被指数空间数据库能直观的反映出研究区植被变化及生长情况.     

内蒙古浑善达克沙地南部草地盖度探测及其变化分析

陆地生态系统植被覆盖程度是评价区域生态环境变化的重要因子。以内蒙古浑善达克沙地南部(锡林郭勒盟正蓝旗北部地区)为研究区,应用中国环境与灾害监测预报小卫星数据HJ-1A CCD及美国陆地卫星数据Landsat TM,分别基于像元二分模型和三波段梯度差模型、使用NDVI和RDVI等参数,对研究区草地植被覆盖度进行了探测,并对比了不同模型方法和参数所得研究区草地植被盖度成果的分类精度。研究结果表明,基于像元二分模型和RDVI参数探测植被盖度的方法表现最好;以此为基础,进一步分析了研究区2000~2009年区域植被覆盖度动态变化,发现本地区在2000年之后草地覆盖改善区面积超过草地盖度下降区面积,浑善达克沙地南缘植被恢复状况总体较好。     

用Landsat TM资料分析浑善达克沙地东部的植被覆盖变化及其可能原因探讨

中纬度干旱半干旱草原是我国的主要地表类型之一,正在面临着严重的荒漠化问题。选择浑善达克沙地东部为研究地区,利用Landsat TM和ETM+资料对该地区的植被覆盖情况进行分析。在所选的4个年度中,以1996年的NDVI值为最高(0.67),1998年略次之(0.65),1987年居中(0.47),而2001年为最低,仅0.33。水分是决定干旱半干旱地区植被生长状况的关键因子。无论是年降水还是7、8月降水,与9月的NDVI都基本上呈现了一种线性关系。气温对NDVI的影响不明显。反映地表与植物冠层表面温度的热红外亮温经常与区域内的NDVI分布基本呈反相关。少数地区,特别是一些本身植被状况较差、生态相对更为脆弱的地区,植被遭到破坏后,即使在保证降水增加的情况下也不能在短时间内恢复。比较1996和1998年,荒漠化面积增长呈现出明显的上升趋势。2001年是严重干旱年,区域平均NDVI极低,但却有7.5%的地区出现NDVI增加。这一逆向变化可能与中央和地方政府于2000年启动的一系列沙源治理项目和禁牧规定有直接关系。由此说明,除了降水这一关键自然因子之外,人类活动也对干旱半干旱草原生态的恶化或恢复起着非常重要的作用。     

Planetscope Nanosatellites Image Classification Using Machine Learning

To adopt sustainable crop practices in changing climate, understanding the climatic parameters and water requirements with vegetation is crucial on a spatiotemporal scale. The Planetscope (PS) constellation of more than 130 nanosatellites from Planet Labs revolutionize the high-resolution vegetation assessment. PS-derived Normalized Difference Vegetation Index (NDVI) maps are one of the highest resolution data that can transform agricultural practices and management on a large scale. High-resolution PS nanosatellite data was utilized in the current study to monitor agriculture’s spatiotemporal assessment for the Al-Qassim region, Kingdom of Saudi Arabia (KSA). The time series of NDVI was utilized to assess the vegetation pattern change in the study area. The current study area has sparse vegetation, and exposed soil exhibits brightness due to low soil moisture, constraining NDVI. Therefore, a machine learning (ML) based Random Forest (RF) classification model was used to compare the vegetation extent and computational cost of NDVI. The RF model has been compared with NDVI in the current investigation. It is one of the most precise classification methods because it can model the complexity of input variables, handle outliers, treat noise effectively, and avoid overfitting. Multinomial Logistic Regression (MLR) was implemented to compare the performance of both NDVI and RF-based classification. RF model provided good accuracy (98%) for all vegetation classes based on user accuracy, producer accuracy, and kappa coefficient.     

The effect of soil moisture on the 37GHz microwave polarization difference index (MPDI)

Previous studies have shown that the 37 GHz microwave polarization difference index (MPDI) has an inverse nonlinear relationship to the normalized difference vegetation index (NDVI) with the MPDI (NDVI) being more sensitive to vegetation density under sparse (moderate) vegetation conditions. It has also been noted that soil moisture can have a significant influence on the MPDI. This study quantifies the effect of soil moisture on the MPDI using the RADTRAN model and comparison with measurements from a few geographically restricted (eastern USA) study sites. Model results show the MPDI increases with soil moisture but its sensitivity approaches zero when soil moisture values or vegetation densities are large. Results based on special sensor microwave/imager (SSM/I) measured values of MPDI, using the NDVI as a surrogate for vegetation density and an antecedent precipitation index (API) as a surrogate for soil moisture, were consistent with those based on the model. Linear equations, one for each of three categories of vegetation density, expressing MPDI as a function of API were derived based on SSM/I measurements. These equations demonstrate that soil moisture information can be extracted from the MPDI when the NDVI is used to account for the effect of vegetation and that the effect of soil moisture on the MPDI should be taken into account if it is to be used as a vegetation index. The potential to normalize MPDI values for variations in soil moisture is discussed.     

Global analysis of empirical relations between annual climate and seasonality of NDVI

This paper describes the use of satellite data to calibrate a new climate vegetation greenness relation for global change studies. We examined statistical relations between annual climate indexes (temperature, precipitation, and surface radiation) and seasonal attributes of the AVHRR Normalized Difference Vegetation Index (NDVI) time series for the mid-1980s in order to refine our understanding of intra-annual patterns and global controls on natural vegetation dynamics. Multiple linear regression results using global 1 gridded data sets suggest that three climate indexes: degree days (growing/chilling), annual precipitation total, and an annual moisture index together can account to 70-80% of the geographical variation in the NDVI seasonal extremes (maximum and minimum values) for the calibration year 1984. Inclusion of the same annual climate index values from the previous year explains no substantial additional portion of the global scale variation in NDVI seasonal extremes. The monthly timing of NDVI extremes is closely associated with seasonal patterns in maximum and minimum temperature and rainfall, with lag times of 1 to 2 months. We separated well-drained areas from 1 grid cells mapped as greater than 25% inundated coverage for estimation of both the magnitude and timing of seasonal NDVI maximum values. Predicted monthly NDVI, derived from our climate-based regression equations and Fourier smoothing algorithms, shows good agreement with observed NDVI for several different years at a series of ecosystem test locations from around the globe. Regions in which NDVI seasonal extremes are not accurately predicted are mainly high latitude zones, mixed and disturbed vegetation types, and other remote locations where climate station data are sparse.     

The application of polynomial analyses to detect global vegetation dynamics during 1982–2012

Use of the normalized difference vegetation index (NDVI) to build long-term vegetation trends is one of the most effective techniques for identifying global environmental change. Trend identification can be achieved by ordinary least squares (OLS) analysis or the Theil–Sen (TS) procedure with a Mann–Kendall (MK) significance test, and these linear regression approaches have been widely used. However, vegetation changes are not linear, and thus the response of vegetation to global climate change may follow non-linear trends. In this article, a polynomial trend-fitting method, which uses stepwise regression and expands on previous research, is presented. With an improved fitting ability, this procedure may reveal trends that were concealed by linear fitting methods. Globally, the traditional TS-MK method reveals significant greening trends for 37.27% of vegetated land, and significant browning trends for 7.98%. Using the polynomial analysis, 34.62% of pixels were fitted by high-order trends. The significant greening trends covered up to 30% of cultivated land, thus indicating that cultivated vegetation may be increasing faster than natural vegetation. Significant vegetation browning mostly occurred in sparse vegetation areas, which suggests that vegetation growth may be more sensitive to climate change in arid regions. Our results show that use of polynomial analysis can help further elucidate global NDVI trends.     

Parallel adjustments in vegetation greenness and ecosystem CO2 exchange in response to drought in a Southern California chaparral ecosystem

Some form of the light use efficiency (LUE) model is used in most models of ecosystem carbon exchange based on remote sensing. The strong relationship between the normalized difference vegetation index (NDVI) and light absorbed by green vegetation make models based on LUE attractive in the remote sensing context. However, estimation of LUE has proven problematic since it varies with vegetation type and environmental conditions. Here we propose that LUE may in fact be correlated with vegetation greenness (measured either as NDVI at constant solar elevation angle, or a red edge chlorophyll index), making separate estimates of LUE unnecessary, at least for some vegetation types. To test this, we installed an automated tram system for measurement of spectral reflectance in the footprint of an eddy covariance flux system in the Southern California chaparral. This allowed us to match the spatial and temporal scales of the reflectance and flux measurements and thus to make direct comparisons over time scales ranging from minutes to years. The 3-year period of this study included both “normal” precipitation years and an extreme drought in 2002. In this sparse chaparral vegetation, diurnal and seasonal changes in solar angle resulted in large variation in NDVI independent of the actual quantity of green vegetation. In fact, one would come to entirely different conclusions about seasonal changes in vegetation greenness depending on whether NDVI at noon or NDVI at constant solar elevation angle were used. Although chaparral vegetation is generally considered “evergreen”, we found that the majority of the shrubs were actually semi-deciduous, leading to large seasonal changes in NDVI at constant solar elevation angle. LUE was correlated with both greenness indices at the seasonal timescale across all years. In contrast, the relationship between LUE and PRI was inconsistent. PRI was well correlated with LUE during the “normal” years but this relationship changed dramatically during the extreme drought. Contrary to expectations, none of the spectral reflectance indices showed consistent relationships with CO₂ flux or LUE over the diurnal time-course, possibly because of confounding effects of sun angle and stand structure on reflectance. These results suggest that greenness indices can be used to directly estimate CO₂ exchange at weekly timescales in this chaparral ecosystem, even in the face of changes in LUE. Greenness indices are unlikely to be as good predictors of CO₂ exchange in dense evergreen vegetation as they were in the sparse, semi-deciduous chaparral. However, since relatively few ecosystems are entirely evergreen at large spatial scales or over long time spans due to disturbance, these relationships need to be examined across a wider range of vegetation types.     

NDVI responses to the forest canopy and floor from spring to summer observed by airborne spectrometer in eastern Siberia

A popular method of satellite-based monitoring of the photosynthetic potential of vegetation is to calculate the normalised difference vegetation index (NDVI) from measurements of the red (RED) and near-infrared (NIR) bands. Enormous amounts of vegetation information have been obtained over continental to global areas based on NDVI derived from NOAA-AVHRR, Terra/Aqua-MODIS, and SPOT-VEGETATION satellite observations. In eastern Siberia, where sparse boreal forests are dominant, the lack of landscape-scale canopy-reflectance observations impedes interpretation of how NDVI seasonality is controlled by the forest canopy and floor status. We discuss the NDVI of the canopy and floor separately based on airborne spectral reflectance measurements and simultaneous airborne land surface images acquired around Yakutsk, Siberia, using a hedgehopping aircraft from spring to summer 2000. The aerial land surface images (4402 scenes) were visually classified into four types according to the forest condition: no-green canopy and snow floor (Type 1), green canopy and snow floor (Type 2), no-green canopy and no-snow floor (Type 3), and green canopy and no-snow floor (Type 4). The spectral reflectance from 350 to 1200 nm was then calculated for these four types. Type 1 had almost no difference in reflectance between the RED and NIR bands, and the resultant NDVI was slightly negative (− 0.03). Although Type 2 showed a significant difference between the two bands because of canopy greenness, the resultant NDVI was rather low (0.17) because of high reflection from the snow cover on the floor. In Type 3, the significant difference between the two bands was mainly caused by the greenness of the floor, and the NDVI was relatively high (0.45). The NDVI for Type 4 was the highest (0.75) among the four types. The contributions of reflectance from the forest canopy and floor to the total reflectance were tested with a forest radiative transfer model. The reflectance difference between NIR and RED bands (NIR − RED) of Type 4 (15.6%) was approximately double the differences of Type 2 (7.0%) and of Type 3 (7.9%), suggesting half-and-half contributions of forest canopy greenness and floor greenness to the total greenness. The result also suggested that the satellite-derived NDVI in the larch forest around Yakutsk reaches 85% of the maximum NDVI owing to the forest floor greenness, and only the other 15% of the increase in NDVI should be attributed to the canopy foliation. These results quantitatively reveal that the NDVI depends considerably on forest floor greenness and snow cover in addition to canopy greenness in the case of relatively sparse forest in Siberia.     

Remote sensing of industrial impact on Arctic vegetation around Noril'sk,northern Siberia: Preliminary results

This study applies remote sensing techniques for monitoring non-ferrous metal smelting impacts in the extreme environment of northern Siberia. Ground and at-satellite reflectance and normalized difference vegetation index (NDVI) values for different vegetation types have been compared and a hybrid supervised-unsupervised classification of Landsat TM data performed, based on field and ancillary data. This has allowed us to distinguish several degrees of vegetation damage in tundra and forests. However, it was difficult to differentiate between some significant classes, such as damaged grass tundra and sparse dead larch forests with a grass understorey. We suggest possible refinement of our results, including the combination of images taken at different phenological stages and from different sensors. However, it should be noted that the north-Siberian environment presents unusually severe limitations of optical-infrared satellite observation possibilities and problems in imagery interpretation. Standard indicators of vegetation vigour, such as NDVI, widely applied at lower latitudes, become less informative in highly variable tundra and pre-tundra ecosystems.     

Effect of climate change on vegetation phenology of different land-cover types on the Tibetan Plateau

Research in vegetation phenology change has been one heated topic of current ecological and climate change study. The Tibetan Plateau, as the highest plateau of the earth, is more vulnerable and sensitive to climate change than many other regions. In this region, shifts in vegetation phenology have been intensively studied during recent decades, primarily based on satellite-retrieved data. In this study, we explored the spatiotemporal changes of vegetation phenology for different land-cover types in the Tibetan Plateau and characterized their relationship with temperature and precipitation by using long-term time-series datasets of normalized difference vegetation index (NDVI) from 1982 to 2014. Diverse phenological changes were observed for different land-cover types, with an advancing start of growing season (SOS), delaying end of growing season (EOS) and increasing length of growing season (LOS) in the eastern Tibetan Plateau where meadow was the dominant vegetation type, but with the opposite changes in the steppe and sparse herbaceous or sparse shrub regions which are mostly located in the northwestern and western edges of the Tibetan Plateau. Correlation analysis indicated that sufficient preseason precipitation may delay the SOS of evergreen forests in the southeastern Plateau and advance the SOS of steppe and sparse herbaceous or sparse shrub in relatively arid areas, while the advance of SOS in meadow areas could be related to higher preseason temperature. For EOS, because it is less sensitive to climate change than SOS, the response of EOS for different land-cover types to precipitation and temperature were more complicated across the Tibetan Plateau.     

Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS

The long term Advanced Very High Resolution Radiometer (AVHRR)‐Normalized Difference Vegetation Index (NDVI) record provides a critical historical perspective on vegetation dynamics necessary for global change research. Despite the proliferation of new sources of global, moderate resolution vegetation datasets, the remote sensing community is still struggling to create datasets derived from multiple sensors that allow the simultaneous use of spectral vegetation for time series analysis. To overcome the non‐stationary aspect of NDVI, we use an artificial neural network (ANN) to map the NDVI indices from AVHRR to those from MODIS using atmospheric, surface type and sensor‐specific inputs to account for the differences between the sensors. The NDVI dynamics and range of MODIS NDVI data at 1° is matched and extended through the AVHRR record. Four years of overlap between the two sensors is used to train a neural network to remove atmospheric and sensor specific effects on the AVHRR NDVI. In this paper, we present the resulting continuous dataset, its relationship to MODIS data, and a validation of the product.