额济纳荒漠绿洲植被生态需水量研究

荒漠绿洲的生态需水量主要指维持荒漠绿洲植被正常生长所需要消耗的水量.采用3S技术与野外生产力测定相结合的方法, 通过建立植被归一化指数(NDVI)、生产力、蒸腾系数之间的关系方程, 计算了额济纳荒漠绿洲的植被生态需水量.结果表明, 维持额济纳绿洲现状的需水量为1.53×10⁸ m³, 若使现有的植被恢复到目前最高生产力水平的生态需水量为3.49×10⁸ m³. 考虑到城镇居民生活用水、河道输水损失、绿洲植被耗水、绿洲内农田用水和降水补充等, 额济纳旗绿洲维持现状需要黑河下泄水量(狼心山)为1.93~2.23×10⁸ m³之间, 若使现有的植被恢复到目前最高生产力水平, 需要黑河下泄水量(狼心山)为4.28~5.17×10⁸ m³之间.     

A comparative analysis of the NDVIg and NDVI3g in monitoring vegetation phenology changes in the Northern Hemisphere

Phenology is a sensitive and critical feature of vegetation and is a good indicator for climate change studies. The global inventory modelling and mapping studies (GIMMS) normalized difference vegetation index (NDVI) has been the most widely used data source for monitoring of the vegetation dynamics over large geographical areas in the past two decades. With the release of the third version of the NDVI (GIMMS NDVI3g) recently, it is important to compare the NDVI3g data with those of the previous version (NDVIg) to link existing studies with future applications of the NDVI3g in monitoring vegetation phenology. In this study, the three most popular satellite start of vegetation growing season (SOS) extraction methods were used, and the differences between SOSg and SOS3g arising from the methods were explored. The amplitude and the peak values of the NDVI3g are higher than those of the NDVIg curve, which indicated that the SOS derived from the NDVIg (SOSg) was significantly later than that derived from the NDVI3g (SOS3g) based on all the methods, for the whole northern hemisphere. In addition, SOSg and SOS3g both showed an advancing trend during 1982–2006, but that trend was more significant with SOSg than with SOS3g in the results from all three methods. In summary, the difference between SOSg and SOS3g (in the multi-year mean SOS, SOS change slope and the turning point in the time series) varied among the methods and was partly related to latitude. For the multi-year mean SOS, the difference increased with latitude intervals in the low latitudes (0–30°N) and decreased in the mid- and high-latitude intervals. The GIMMS NDVI3g data-sets seemed more sensitive than the GIMMS NDVIg in detecting information about the ground, and the SOS3g data were better correlated both with the in situ observations and the SOS derived from the Moderate Resolution Imaging Spectroradiometer NDVI. For the northern hemisphere, previous satellite measures (SOS derived from GIMMS NDVIg) may have overestimated the advancing trend of the SOS by an average of 0.032 d yr^–1.     

Winter cover crops in Dutch maize fields: Variability in quality and its drivers assessed from multi-temporal Sentinel-2 imagery

Planting a cover crop between the main cropping seasons is an agricultural management measure with multiple potential benefits for sustainable food production. In the maize production system of the Netherlands, an effective establishment of a winter cover crop is important for reducing nitrogen leaching to groundwater. Cover crop establishment after maize cultivation is obliged by law for sandy soils and consequently implemented on nearly all maize fields, but the winter-time vegetative ground cover varies significantly between fields. The objectives of this study are to assess the variability in winter vegetative cover and evaluate to what extent this variability can be explained by the timing of cover crop establishment and weather conditions in two growing seasons (2017–2018). We used Sentinel-2 satellite imagery to construct NDVI time series for fields known to be cultivated with maize within the province of Overijssel. We fitted piecewise logistic functions to the time series in order to estimate cover crop sowing date and retrieve the fitted NDVI value for 1 December (NDVI_Dec). We used NDVI_Dec to represent the quality of cover crop establishment at the start of the winter season. The Sentinel-2 estimated sowing dates compared reasonably with ground reference data for eight fields (RMSE = 6.6 days). The two analysed years differed considerably, with 2018 being much drier and warmer during summer. This drought resulted in an earlier estimated cover crop sowing date (on average 19 days) and an NDVI_Dec value that was 0.2 higher than in 2017. Combining both years and all fields, we found that Sentinel-2 retrieved sowing dates could explain 55% of the NDVI_Dec variability. This corresponded to a positive relationship (R² = 0.50) between NDVI_Dec and the cumulative growing degree days (GDD) between sowing date and 1 December until reaching 400 GDD. Based on cumulative GDD derived from two weather stations within Overijssel, we found that on average for the past three decades a sowing date of 19 September (± 7 days) allowed to attain these 400 GDD; this provides support for the current legislation that states that from 2019 onwards a cover crop should be sown before 1 October. To meet this deadline, while simultaneously ascertaining a harvest-ready main crop, in practice implies that undersowing of the cover crop during spring will gain importance. Our results show that Sentinel-2 NDVI time series can assess the effectiveness and timing of cover crop growth for small agricultural fields, and as such has potential to inform regulatory frameworks as well as farmers with actionable information that may help to reduce nitrogen leaching.     

陕北榆林地区土地覆被变化分析

基于1981~2001 年NOVAA/AVHRR 和1998~2004 年SPOT VEGETATION 归一化植被 指数(NDVI) 数据, 对榆林地区植被动态变化进了定量研究, 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: 榆林地区植被有了明显改善, 其改善状况集中于8、9、10 月份; 植被 覆被变化具有明显的区域差异, 北部植被覆盖改善的趋势明显, 且变化比较平稳; 南部增加趋势 不明显, 有的地方还呈下降趋势, 变化幅度大。     

Spatial and temporal variations of vegetation in Qinghai Province based on satellite data

This paper used five years (2001-2006) time series of MODIS NDVI images with a 1-km spatial resolution to produce a land cover map of Qinghai Province in China. A classi-fication approach for different land cover types with special emphasis on vegetation, espe-cially on sparse vegetation, was developed which synthesized Decision Tree Classification, Supervised Classification and Unsupervised Classification. The spatial distribution and dy-namic change of vegetation cover in Qinghai from 2001 to 2006 were analyzed based on the land cover classification map and five grade elevation belts derived from Qinghai DEM. The result shows that vegetation cover in Qinghai in recent five years has been some improved and the area of vegetation was increased from 370,047 km2 in 2001 to 374,576 km2 in 2006. Meanwhile, vegetation cover ratio was increased by 0.63%. Vegetation cover ratio in high mountain belt is the largest (67.92%) among the five grade elevation belts in Qinghai Prov-ince. The second largest vegetation cover ratio is in middle mountain belt (61.80%). Next, in the order of the decreasing vegetation cover ratio, the remaining grades are extreme high mountain belt (38.98%), low mountain belt (25.55%) and flat region belt (15.46%). The area of middle density grassland in high mountain belt is the biggest (94,003 km2), and vegetation cover ratio of dense grassland in middle mountain belt is the highest (32.62%), and the in-creased area of dense grassland in high mountain belt is the greatest (1280 km2). In recent five years the conversion from sparse grass to middle density grass in high mountain belt has been the largest vegetation cover variation and the converted area is 15931 km2.     

秦岭地区NDVI与地形因子的相关性分析

秦岭山区地形因子是影响植被分布的重要因素。选取2001、2009和2017年MODIS陆地产品MOD13Q1数据和DEM数据,从DEM中提取地形因子,高程、坡度和坡向,与MODIS的NDVI数据结合,分析了地形对秦岭地区植被空间分布影响。研究结果表明:(1)NDVI随着高程的增大而逐渐增大,在高程1800 m左右时达到最大值,随后又随着高程的增大而减小;(2)NDVI在坡度0°～5°间逐渐增大,在5°～40°呈稳定趋势,从40°开始缓慢减小,60°达到乔木能够生长的坡面倾角临界值,当坡面倾角大于60°时植被指数开始快速减小;(3)受太阳辐射的影响,坡向在NW 270°～360°,SE 240°～270°之间的植被长势较好,其余坡向上长势一般。     

关中平原城市群植被覆盖的时空特征与影响因素

植被是全球及区域生态系统环境变化的重要指标,也是对人类社会活动有重要贡献的资源之一。为了研究关中平原城市群不同区域植被覆盖变化对自然和人文因子的响应,以划分为三个区域的植被作为研究对象,选取2000—2017年MODIS-NDVI遥感数据,运用趋势分析、探索性空间数据分析与地理探测器等方法,从时序演进与空间分布方面研究了18 a内植被覆盖的演化及分布特征,定量分析影响植被覆盖的主导因子。遥感数据要通过投影转换、拼接、最大值合成等方法进行处理,再运用Python程序进行影像批量裁剪,将遥感数据和气象数据进行分区统计,最后对该处理数据进行讨论研究。结论表明:(1)研究期内关中平原城市群植被覆盖呈显著上升趋势,NDVI平均值增速为0.077·(10 a)-1,阶段性变化特征明显,其中2005—2007阶段和2011—2013阶段极显著增加,最大上升速率达到了0.05·a-1。(2)空间上总体呈现"南高北低"的分布特征,研究区总体得到改善;高值区主要分布在南部秦岭北坡,受气候因子的影响更大,植被覆盖增加速度缓慢,达到轻度改善水平;低值区聚集在黄土高原边缘地区,植被增加趋势明显;中部关中平原极少...     

西辽河流域归一化植被指数与气象因子相关性分析

为了解西辽河流域归一化植被指数(NDVI)的分布规律、变化趋势及对气候变化的响应,利用2000—2018年西辽河流域11个气象站逐日气象资料和MODIS归一化植被指数数据集,通过线性回归和相关分析,探讨了生长季各月NDVI与气象因子的时滞性,以及气象站周围10 km缓冲区内不同植被类型NDVI与气象因子的相关性.结果表...     

基于MODIS数据的吐鲁番盆地2001—2017年植被变化及水热组合影响分析

中国西北干旱地区的气候变化及其对植被的影响一直备受关注。以地形特殊的吐鲁番盆地为研究对象,利用实测气象站点数据、再分析气象格点数据以及MODIS卫星遥感植被指数,采用趋势检验、线性回归、偏相关分析等方法,探究了该地区2001—2017年间的植被变化及其与水热组合特征之间的关系。结果表明:(1)吐鲁番盆地降水量整体没有显著变化,但北部山区降水增长较多,气温总体呈显著上升趋势,尤其是盆地底部中心区域增温较大。(2)全区域植被指数(NDVI)总体呈极显著上升趋势,山区及中心区域NDVI增长率较大。(3)受水汽来源和日照时长的影响,吐鲁番盆地周边山区高程3 000 m左右NDVI值最高,山区植被最好的坡向是西北坡。(4)吐鲁番盆地水热组合复杂,水分条件是大部分地区植被生长的主要限制因素,降水与NDVI有较好的正相关,在山区和荒漠区热量增加不利于植被生长,但中心区域受地下水和人类活动影响,水分的供给相对稳定,热量增加利于植被生长。     

内蒙古西鄂尔多斯国家自然保护区植被的遥感分布特征及变化探测

西鄂尔多斯自然保护区位于荒漠、半荒漠地带的中国北方生态环境脆弱带上,保护区内的植物从第三纪演化至今仍带有荒漠特有种群标记,对研究环境演化、生物多样性、全球变化等都具有重大的学术价值和诊断意义,受到国内外生物学、生态学、地理学等学科研究者的极大的关注。利用传统的研究方法和样方统计方法不仅费时、费工,而且进展缓慢,另外,很难实现对保护区植物分布规律与生境的全面了解与监测。我们利用Landsat-5、7卫星提供的1987年9月的TM和1999年8月的ETM数据,两景不同时相的数据间隔12年,分辨率为30m;1∶25万的DEM数据;地面样方统计数据等。用遥感技术对西鄂尔多斯珍稀濒危植物群落进行分布规律和变化探测的研究,可以快速、大范围、准确、实时获取资源环境状况及其变化数据,建立三维立体景观虚拟现实图,再结合野外实地考察样方统计数据的综合分析,揭示了间隔12年植被分布规律和植被覆盖动态变化情况。为合理利用这些植被资源,对受损荒漠生态系统的恢复提供了依据,同时,对于保护和改善我国荒漠地区的生活环境和探索荒漠地区可持续发展也具有重要的意义。