黑河实验区地表植被指数的区域分布及季节变化

利用具有较高空间分辨率的Ｌａｎｄｓａｔ ＴＭ卫星资料估算了黑河实验区夏季和近冬季地表标准化差值植被指数ＮＤＶＩ,分析了ＮＤＶＩ的区域分布特征和季节变化。结果表明,由于该实验区下垫面的复杂性,ＮＤＶＩ表现出明显的空间和季节变化,ＮＤＶＩ的图像能够很好地反映出地表植被的分布状况。     

基于降水条件的城镇化与植被覆盖协调发展研究

为探讨城镇化发展与植被覆盖状况的协调发展关系,本文以黄河中上游的144个县域单元为研究对象,基于2000、2005、2010及2015年4期的县域经济发展数据及MODIS-NDVI数据,通过门槛回归模型,以影响研究区域植被覆盖最主要的降水因素作为门槛变量,具体分析不同降水条件下城镇化发展对植被覆盖的影响,并提出以保护植被覆盖为基础的差异化城镇化发展策略。结果表明：①随着城镇化的推进,植被覆盖会进一步遭到破坏,但降水因素在城镇化影响植被覆盖的过程中可发挥显著的正向调节作用,从而促进城镇化与植被覆盖的协调发展;②实现城镇化与植被覆盖的协调发展,不仅取决于自身的降水条件,还依赖于当地的生态工程建设及产业结构优化升级等多种因素。其中,退耕还林(草)政策的实施可加速植被覆盖的修复进程,提高区域环境的生态承载力,有效缓解城镇化发展带给植被覆盖的负面影响;③第二产业在经济发展中的比重增加会给植被覆盖带来严重威胁,阻碍城镇化与植被覆盖的协调发展,尤其是在年均降水量较少且生态脆弱的地区。     

不同数据源归一化植被指数在中国北方草原区的应用比较

中国北方草原区生产力在区域碳水循环、农牧业发展中举足轻重。归一化植被指数（Normalized Difference Vegetation Index,NDVI）广泛应用于生产力的计算,然而目前来源众多的NDVI数据反映中国北方草原植被时空动态的一致性仍未可知。本研究利用2000—2015年3个来源NDVI数据集（MODIS NDVI、GIMMS NDVI和SPOT NDVI）并以国际上公认的数据准确性较高的MODIS NDVI为基准对比分析了中国北方草原区NDVI时空动态的一致性,并选取适宜的NDVI产品揭示研究区NDVI长期的时空格局。结果表明：整个中国北方草原区以及部分草原类型（高寒草甸、高寒草原、高寒荒漠、温带荒漠草原）GIMMS NDVI和MODIS NDVI 2套数据集无论是数值范围,还是年际波动和变化趋势具有较高一致性（二者在高寒草甸、高寒草原、高寒荒漠、温带荒漠草原的相关系数分别为0.60、0.47、0.51、0.74）,而SPOT NDVI数值远高于其他2个数据集,尤其是在青藏高原草原区,SPOT NDVI数值每年较另外两套数据集约偏高0.15,表明该区域使用SPOT数据应慎重。部分温带草原类型（典型草原和草甸草原）GIMMS NDVI和SPOT NDVI数据集在年际波动以及变化趋势上具有较高的一致性（相关系数分别为0.85和0.60）,但温带草原区3种数据集NDVI数值范围整体相差不大,小于0.06。基于上述结果,本研究进一步采用时间序列最长且与MODIS NDVI一致性最好的GIMMS NDVI分析了研究区NDVI的时空动态,发现1982—2015年中国北方草原区NDVI整体呈增加趋势,25%的区域达显著水平（p<0.05）,主要集中在温带草原区;高寒草原区NDVI大部分区域变化不显著且有一定比例的区域NDVI呈显著下降趋势。本研究可以为模型数据集选择和预测中国北方草原区植被对未来气候变化的响应提供科学依据。     

Assessing vegetation dynamics impacted by climate change in the southwestern karst region of China with AVHRR NDVI and AVHRR NPP time-series

The relationship between climate change and vegetation dynamics in the southwestern karst region of China has been identified by recent studies. Based on previous researches and AVHRR (Advanced Very High Resolution Radiometer) GIMMS (Global Inventory Monitoring and Modeling Studies) NDVI (Normalized Difference Vegetation Index) (1982–2003) and AVHRR GloPEM (Global Production Efficiency Model) NPP (Net Primary Production) (1981–2000) datasets, vegetation dynamics impacted by climate change in the southwestern karst region of China were assessed. The results show that: (1) since the early 1980s, both vegetation cover density and net primary production have insignificant ascending tendencies. However, the inter-annual variation rates of vegetation indexes have apparent spatial differentiations; (2) the correlation coefficients between the inter-annual variations of vegetation indexes and the inter-annual variations of climate factors vary geographically; (3) as indicated by NDVI and NPP, various vegetation types have different responses to climate change, and the annual mean temperature variation has more significant impact on vegetation dynamics than the annual precipitation variation in the study area; (4) distribution laws of correlation coefficients between the inter-annual variations of vegetation indexes and the inter-annual variations of climate factors in different climate conditions are apparent. All these findings will enrich our knowledge of the natural forces which impact the stability of the karst ecosystems and provide scientific basis for the management of the karst ecosystems.     

Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data

Crop identification is the basis of crop monitoring using remote sensing. Remote sensing the extent and distribution of individual crop types has proven useful to a wide range of users, including policy-makers, farmers, and scientists. Northern China is not merely the political, economic, and cultural centre of China, but also an important base for grain production. Its main grains are wheat, maize, and cotton. By employing the Fourier analysis method, we studied crop planting patterns in the Northern China plain. Then, using time-series EOS-MODIS NDVI data, we extracted the key parameters to discriminate crop types. The results showed that the estimated area and the statistics were correlated well at the county-level. Furthermore, there was little difference between the crop area estimated by the MODIS data and the statistics at province-level. Our study shows that the method we designed is promising for use in regional spatial scale crop mapping in Northern China using the MODIS NDVI time-series.     

Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest

Unmanned Aerial Vehicle (UAV) remote sensing has opened the door to new sources of data to effectively characterize vegetation metrics at very high spatial resolution and at flexible revisit frequencies. Successful estimation of the leaf area index (LAI) in precision agriculture with a UAV image has been reported in several studies. However, in most forests, the challenges associated with the interference from a complex background and a variety of vegetation species have hindered research using UAV images. To the best of our knowledge, very few studies have mapped the forest LAI with a UAV image. In addition, the drawbacks and advantages of estimating the forest LAI with UAV and satellite images at high spatial resolution remain a knowledge gap in existing literature. Therefore, this paper aims to map LAI in a mangrove forest with a complex background and a variety of vegetation species using a UAV image and compare it with a WorldView-2 image (WV2).In this study, three representative NDVIs, average NDVI (AvNDVI), vegetated specific NDVI (VsNDVI), and scaled NDVI (ScNDVI), were acquired with UAV and WV2 to predict the plot level (10 × 10 m) LAI. The results showed that AvNDVI achieved the highest accuracy for WV2 (R² = 0.778, RMSE = 0.424), whereas ScNDVI obtained the optimal accuracy for UAV (R² = 0.817, RMSE = 0.423). In addition, an overall comparison results of the WV2 and UAV derived LAIs indicated that UAV obtained a better accuracy than WV2 in the plots that were covered with homogeneous mangrove species or in the low LAI plots, which was because UAV can effectively eliminate the influence from the background and the vegetation species owing to its high spatial resolution. However, WV2 obtained a slightly higher accuracy than UAV in the plots covered with a variety of mangrove species, which was because the UAV sensor provides a negative spectral response function(SRF) than WV2 in terms of the mangrove LAI estimation.     

南方丘陵区植被覆盖度遥感估算的地形效应评估

植被覆盖变化是生态环境领域的核心研究内容之一,但其估算精度常受到地形效应、土壤背景、大气效应等各种因素影响。以Landsat 8 OLI为遥感数据源,基于像元二分模型,分别利用归一化差值植被指数(NDVI)、经Cosine-C校正的归一化差值植被指数(NDVI)和归一化差值山地植被指数(NDMVI)建立植被覆盖度估算模型,以评估南方丘陵区植被覆盖度的地形效应。结果表明,3种植被覆盖度估算模型均能削弱地形效应,但消除或抑制地形效应影响的能力不同。比较而言,基于NDMVI指数构建的植被覆盖度估算模型的地形效应最小,更适合地形复杂区域的植被覆盖度遥感估算;基于Cosine-C校正的NDVI植被指数构建的植被覆盖度估算模型的地形效应次之,但存在一定的过度校正现象;基于NDVI植被指数构建的植被覆盖度估算模型的地形效应最大,尤其当坡度≥10°时,阴坡植被覆盖度比阳坡明显偏低。     

基于CHRIS数据的HDVI植被指数的植被覆盖度估算研究

选取江西省余干县的一景CHRIS/PROBA影像5个观测角度(±55°、0°和±36°)的反射率,在CHRIS数据处理后得到的NDVI基础上得到HDVI,并与野外实测的草地、灌木、针叶林、针阔林和阔叶林的VFC建立相关性分析,以及对于模型拟合度的决定系数R2的影响因素进行分析。结果表明,在±55°下,针阔林的多项式拟合度最高,植被类型模型的拟合度均值也最高。其次是灌木拟合度较高。最差的是针叶林指数模型,植被类型的整体拟合度最不理想。在±36°下,针阔林多项式拟合度最高,植被类型的整体拟合度也最高,其次是阔叶林拟合度较高。最差的是针叶林的指数模型,植被类型的整体拟合度也是最不理想的。     

不同排放情景下鄂尔多斯沙漠化风险预估

气候变化可以对沙漠化的扩张和逆转产生影响,综合预估未来多气候情景下沙漠化风险是制定防沙治沙策略的基础依据。基于鄂尔多斯1981—2015年NDVI数据和气候数据,利用最小二乘法对各年NDVI值、年降水量和年积温构成的时间序列进行线性倾向估计,对每个像元的值进行线性回归模型拟合,获取了未来35 a不同气候情景下的NDVI预测值,并在IPCC提出的4种气候变化情景下预测沙漠化风险状况,结果表明:(1)鄂尔多斯未来35 a沙漠化风险呈西北高、东南低的特点;(2)从气候变化情景看,在RCP8.5情景下,鄂尔多斯未来35 a的沙漠化风险最大,RCP6.0情景下风险最小;(3)从未来不同时期看,除RCP4.5情景外,未来15 a鄂尔多斯沙漠化风险最大,未来25~35 a沙漠化风险相对较小。     

NDVI、NDMI与地表温度关系的对比研究

通过研究归一化植被指数(NDVI)、归一化水汽指数(NDMI)与地表温度(LST)的相关关系,对比NDVI和NDMI定量分析LST的适宜程度。以Landsat 8遥感影像为数据源,以郑汴都市区为例,反演LST,计算NDVI和NDMI。从整体、分区、像元等不同层面分析NDVI和NDMI与LST的相关关系,并利用GEO-Da软件,分析150 m、300 m、450 m三种不同采样间隔数据的NDVI、NDMI与LST的空间相关性。主要结论为：①NDVI与LST的线性拟合度较差,而NDMI与LST具有很强的线性关系,剖面分析显示NDMI与LST呈显著负相关关系。②缓冲区分析结果表明,随着距城市中心距离的增加,用地类型增多,LST和NDMI之间的相关性逐渐增强。③在Moran's I空间相关性分析中,不同采样间隔下两指数与LST的负相关关系均比较明显,但由于水体在2个指数中数值的差异,使NDMI与LST表现出更强的空间负相关性,而NDVI与LST空间相关性则相对较弱。因此,总的来说NDMI是热环境研究的有效指标,与NDVI相比,NDMI与LST的负相关关系更强、更稳定,也更适宜于对LST的定量分析。