遥感技术在植物物候研究中的应用综述

通过遥感技术研究植物物候现象的机理分析,认为植被指数可反映植被各物候期的特征。国内外在探测植物生长季始末日期、花期变化、净第一性生产力变化、全球碳收支等方面的研究促进了植物物候的发展;同样物候研究也可提高遥感影像植物分类和作物估产的精度,同时可促进高光谱遥感的发展。通过我国物候研究从传统的农林业应用转向注重遥感探测、生态学应用的现状分析,展望了我国物候发展方向：关注植物生长季始末时间的时空分布规律;遥感监测植物季相变化;遥感监测植物花期;注重探讨植物生理和生态特征;植物高光谱遥感物候研究;重视物候科普普及工作。     

基于决策树分类的大屯矿区地物信息提取及矿区污染分析

本文在对国内外遥感图像分类方法充分研究分析的基础上,选择决策树分类法对大屯矿区的Landsat 8遥感图像进行分类研究。选取样本提取并分析研究区典型地类光谱特征曲线,依据光谱曲线特征和归一化植被指数建立了土地利用分类决策树模型,通过反复试验和修正,筛选出适宜大屯矿区地物分类的决策树最优阈值,对研究区进行分类和精度评价,最后通过分类结果对研究区的水体污染状况进行简要分析。     

辽宁省耕地植被指数变化特征分析

利用NOAA/AVHRR资料通道1(0.58~0.68μm)、通道2(0.725~1.1μm)数据计算归一化植被指数(NDVI),对辽宁省5个气候区内1999~2004年208个旱田监测点和84个水田监测点的作物生长状况进行连续监测,分析NDVI年际、旬际变化特征。结果表明:辽宁耕地(旱田、水田)植被指数年际间差异明显,造成旱田年际变化以及地区差异的主要原因是降水时空分布不均匀的结果,因此旱田2004年NDVI达到近年最大,2001年较低;而造成水田年际差异以及地区差异的原因是受水田用水状况以及光温条件的影响,水田2003年NDVI最大,2001年较低。在整个生长季内(5~9月份),辽宁耕地植被指数呈单峰型变化,从5月上旬开始到6月中旬是缓慢增长阶段,6月下旬到8月下旬快速增长并达到最大,之后又迅速降低;2004年耕地植被指数旬变化趋势与6 a平均植被指数旬变化趋势基本相同,但作物生长前期NDVI较6 a平均值低,主要原因是受到2004年春夏之交严重干旱的影响。     

基于城市地表温度与归一化植被指数对北京市建成区热环境分析

针对研究城市热环境的过程中,利用归一化植被指数(NDVI)进行地表温度(LST)反演,再将LST和NDVI结合说明地物变迁与城市热环境的影响的现状,利用Landsat-8多时相遥感影像、高分辨率影像、公开GIS等多源数据,通过人工交互判读和量化统计分析,实现了2013—2017年北京建成区NDVI变化及其对地表热环境影响分析,再对分析结果进行差值拟合评价。对NDVI阈值分割按照大小为LC1、LC2、LC3、LC4,对LST分为高温区(TH)、常温区(TR)和低温区(TL)。结果表明,2013年至2017年:1)建成区的平均NDVI增加0.03,其中LC1增加1.0%,LC2减少11.6%,LC3区域减少1.7%,LC4区域增加12.3%。2)建成区平均LST增加2.55 K,TH百分比增加0.6%;TR百分比减少1.1%,TL百分比增加0.5%。3)NDVI相对增加,地表温度相对下降以及NDVI相对减少,地表温度相对上升占60%,NDVI相对下降,地表温度相对下降以及NDVI相对增加,地表温度相对上升的占40%。     

Landsat时序数据支持下的藏东南植被时空变化监测

本文利用GEE平台和1990—2019年巴宜区Landsat遥感影像,采用像元二分模型、相关性分析等方法分析了巴宜区植被覆盖度的时空变化特征与驱动力。研究结果表明:①1990—2019年巴宜区植被覆盖度总体呈稳中有增的趋势,其中,河谷区域增加明显,而高海拔区域相对稳定;②1990—2019年巴宜区气温呈显著升高,降水略有下降,总体呈"暖干化",气温较降水量对植被覆盖变化更明显,但气候变化对植被覆盖变化影响总体不明显;③1990—2019年巴宜区植被覆盖变化与人类活动有很好的相关性,其中,低、中低、中、中高植被覆盖区域,呈显著的负相关,而高植被覆盖区域呈正相关。本文基于遥感大数据和地理云计算的植被覆盖监测动态监测和定量分析方法,能对高山峡谷区生态评估和演替分析提供一定的技术支撑和科学数据。     

Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center routinely produces and distributes a remote sensing phenology (RSP) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) 1-km data compiled from a series of National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11, −14, −16, −17, −18, and −19). Each NOAA satellite experienced orbital drift during its duty period, which influenced the AVHRR reflectance measurements. To understand the effect of the orbital drift on the AVHRR-derived RSP dataset, we analyzed the impact of solar zenith angle (SZA) on the RSP metrics in the conterminous United States (CONUS). The AVHRR weekly composites were used to calculate the growing-season median SZA at the pixel level for each year from 1989 to 2014. The results showed that the SZA increased towards the end of each NOAA satellite mission with the highest increasing rate occurring during NOAA-11 (1989–1994) and NOAA-14 (1995–2000) missions. The growing-season median SZA values (44°–60°) in 1992, 1993, 1994, 1999, and 2000 were substantially higher than those in other years (28°–40°). The high SZA in those years caused negative trends in the SZA time series, that were statistically significant (at α = 0.05 level) in 76.9% of the CONUS area. A pixel-based temporal correlation analysis showed that the phenological metrics and SZA were significantly correlated (at α = 0.05 level) in 4.1–20.4% of the CONUS area. After excluding the 5 years with high SZA (>40°) from the analysis, the temporal SZA trend was largely reduced, significantly affecting less than 2% of the study area. Additionally, significant correlation between the phenological metrics and SZA was observed in less than 7% of the study area. Our study concluded that the NOAA satellite orbital drift increased SZA, and in turn, influenced the phenological metrics. Elimination of the years with high median SZA reduced the influence of orbital drift on the RSP time series.     

春末夏初青藏高原植被对全球变暖响应的区域特征

利用1982-2002年Pathfinder NDVI遥感数据, 采用REOF和倾向度趋势分析方法, 研究了5～6月青藏高原地表植被变化区域特征及与全球变暖的关系。21年来高原区域春末夏初植被变化存在明显的空间差异, 且存在一个位于高原南北呈带状分布的植被显著变化区域。该区域内植被对全球气温变暖响应显著, 与前期5月北半球平均气温相关系数达到0.7675, 通过0.001显著性水平检验; 植被NDVI随气温升高呈现出显著一致的增加趋势, 增长速率超过10%/10 a, 是全球变暖响应的显著区和敏感区。进一步的分析表明, 对植被全球变暖响应显著的区域基本上处于高山山脉或半荒漠NDVI值低于0.12覆盖度较低的区域。不同植被类型对变暖响应的对比表明, 草地对全球变暖响应明显高于林地, 其植被NDVI 21年约增加10%。     

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。