科尔沁沙地不同沙地景观区植被指数对水热因素响应

在全球气候变化背景下,不同尺度下植物对环境条件的响应机制是生态学研究的核心。为了更好理解不同沙地景观区域植被指数(NDVI)对气象水热环境因子的响应机制,以2000-2013年7-9月MODIS遥感影像为数据源,采用3S空间分析技术和通径分析方法,对科尔沁沙地围封保护区、人工造林固沙区和流动沙丘区等3种景观类型区NDVI的变化趋势和影响其变化的同期气象水热因子年降水、关键期降水、关键期最大一次性降水、重要期降水、年均温、关键期均温、关键期最高气温、关键期最低气温等8个因素进行了分析。结果表明:不同景观类型区NDVI整体上呈现增长的态势。降水的影响明显高于气温,且降水主要起正相关作用,气温主要起负相关作用。促进沙地围封保护景观区NDVI增长的气象因子是关键期降水,决策系数最大且为正(0.445);人工造林固沙景观区受到人工造林更新和农业开垦的影响,削弱了水热条件对NDVI的影响,相比较而言,重要期降水影响较大;流动沙丘景观类型区主要受到关键期气温极值的影响,其他因子影响不显著。     

近10年重庆市归一化植被指数变化分析

运用1 km分辨率经过Savitzky-Golay滤波技术平滑处理的SPOT/VGT数据,通过对1998~2007年的归一化植被指数(NDVI)时间序列数据、NDVI与气象因子的关系、10 a来NDVI变化率的空间分布以及NDVI与海拔高度的关系等进行分析,研究重庆市植被变化及空间分布情况。结果表明,重庆市NDVI年变化相对稳定,在空间上变化较大,东北及东南部分地区植被退化,而西部地区NDVI变化率增加,表明植被覆盖有所增多。另外引起NDVI时空变化的原因中,温度远大于降水量,海拔高度也与NDVI有较好的相关性。     

植被覆盖状况影响中国地表气温变化的观测事实

利用NOAA/AVHRR归一化植被指数（NDVI）及观测气温与再分析地表气温的差值（Observation Minus Reanalysis, OMR）分析了植被覆盖状况对中国地表气温变化的影响.结果表明,地表气温OMR趋势值与NDVI在空间上呈现出显著的负相关关系,植被覆盖状况差（NDVI小于0.1）的区域地表升温较为显著,气温OMR趋势值超过0.2℃/10a,而植被覆盖度高（NDVI大于0.5）的区域气温OMR趋势值则变化不大,甚至出现降温.气温OMR趋势值对植被的季节变化还有着敏感的响应.不同区域植被覆盖状况的差异可能导致中国地表气温变化对全球变暖的响应不同,预测中国未来气候变化需要考虑植被覆盖状况及其动态变化的影响.     

基于卫星遥感数据的地表信息特征--NDVI-Ts空间描述

介绍了基于卫星遥感数据的可操作NDVI、Ts和Ts／NDVI计算方法，讨论了NDVI、Ts和Ts／NDVI数据对植被覆盖信息表达的差异，分析了不同地表覆盖在NDVI—Ts空间的年内变化特征。利用信息熵和平均梯度定量分析了NDVI、Ts和Ts／NDVI数据在信息表达丰富度方面的差异，讨论了在不同地表植被覆盖下，Ts／NDVI数据信息提高程度的敏感性。     

火烧迹地信息遥感快速提取方法研究

利用福建省福州地区ETM图像，在分析火烧迹地光谱特征的基础上，研究了火烧迹地信息遥感快速提取方法。文中设置了4种提取方案，即监督分类法、NDVI参与的监督分类法、NDVI差值法及谱间关系法，最后，对提取结果进行了精度评价。     

Mapping soil salinity using a combined spectral response index for bare soil and vegetation: A case study in the former lake Texcoco, Mexico

Mapping soil salinity is difficult due to its large spatial and temporal variability. Remote sensing is widely used to lower survey costs, but existing studies usually analyze bare soils and make little reference to the halophytic plants and their role as salinity indicators.This paper aims to correlate soil characteristics (electric conductivity in saturation extract (EC_e) and sodium absorption ratio (SAR) with the spectral response of plant species and bare soils, integrating an algorithm to allow multi-scale mapping using remote sensors.Ground radiance was measured on different plant species and bare soils. A Combined Spectral Response Index (COSRI) was calculated for bare soils and vegetation by adjusting the normalized difference vegetation index (NDVI). EC_e and SAR were determined in surface soil samples. Correlation coefficients between COSRI and soil salinity were obtained and a model was adjusted to predict soil salinity. Landsat-ETM and airborne digital images were used to calculate raster maps of COSRI, and EC_e and SAR were estimated using adjusted models. Correlation between COSRI and EC_e and SAR was of −0.885 and −0.857, respectively. Variance accounted for by exponential models for EC_e and SAR was of 82.6% and 75.1%, respectively. It may be concluded that the method is an easy, low-cost procedure to map salt-affected areas.     

Seasonal characters of regional vegetation activity in response to climate change in West China in recent 20 years

Using NDVI data of NOAA-AVHRR in recent 20 years and the temperature and precipitation data of West China, the vegetation activity is discussed by adopting the EOF and REOF decomposed functions. Results show that the overall increasing trend of vegetation activity in different seasons reflects an advanced and prolonged growth period of vegetation under the circumstance of climate warming, but the vegetation evolvement has much inconsistency between different regions and seasons. There are four notable regions, eight sub-areas for vegetation evolvement in spring and summer, and nine sub-areas in autumn. The vegetation activity in most sub-areas is increasing. The most notable region is represented by Lhaze station on the Tibetan Plateau. Two other marked stations are represented by Altay station in Xinjiang Uygur Autonomous Region and Pengshui station in Sichuan Province. But the time series analysis of NDVI makes clear that the trends of the other two sub-areas, Turpan station in Xinjiang and Huashan station in Shaanxi Province, are descending. It is an important reason for vegetation evolvement that temperature ascends in most of the regions and descends in the east region in some seasons. But another important reason for vegetation evolvement is that precipitation is ascending in the west and descending in the east of the region.     

基于GEE的1987—2019年民勤绿洲NDVI变化特征及趋势分析

Google Earth Engine（GEE）是目前最先进的地理大数据分析和可视化平台,使遥感监测地表植被突破了数据获取难、本地存储量大、处理效率低的限制。基于GEE云平台计算Landsat系列卫星影像,研究了1987—2019年民勤绿洲NDVI的时空变化,并通过一元线性趋势分析、稳定性分析、Hurst指数对变化趋势进行了分析和预测。结果表明：（1）1987—2019年民勤绿洲NDVI年均值从0.172上升到0.230,多年平均值为0.244,1989—2007、2010—2016年呈上升趋势,1987—1989、2007—2010、2016—2019年呈下降趋势。年内NDVI-0.017—0.333,高值集中在每年4—10月。空间上来看,绿洲外围NDVI明显增大,绿洲向荒漠扩张。（2）1987—2019年民勤绿洲大部分区域NDVI基本不变,坝区、泉山区绿洲外围、湖区南部和青土湖呈增大趋势,坝区中部民勤县城区、湖区北部绿洲边缘呈减小趋势,基本不变、增大、减小区域分别占总面积的81.90%、16.25%和1.85%。（3）1987—2019年民勤绿洲NDVI变化稳定性高的区域主要位于坝区东南部、泉山区东部、湖区北部荒漠区域;波动性高的区域主要位于坝区、泉山区绿洲外围、湖区南部和青土湖。（4）未来民勤绿洲大部分区域NDVI变化趋势保持基本不变,坝区、泉山区绿洲外围、湖区北部和青土湖,NDVI未来有可能增大,坝区中部民勤县城区、湖区北部向荒漠过渡地带,NDVI未来有可能减小,基本不变、未来有可能增大、减小的区域面积分别占71.62%、12.96%、15.42%。     

青海高寒草甸退化演替中的植被指数

随着气候变化和人为活动干扰,高寒草甸退化已成为青藏高原严重的生态环境问题,精准识别其退化程度并制定相应恢复策略,对实现高寒草甸可持续发展具有重要意义。目前,低空间分辨率MODIS数据为草地遥感监测的主要数据源,但难以满足景观破碎度或异质性较强地区的应用。本研究基于野外调查资料,利用多源遥感数据（MODIS、Landsat、Sentinel-2）研究不同空间分辨率归一化植被指数（NDVI）对高寒草甸退化演替的响应,为准确评估青藏高原高寒草甸退化程度提供依据。结果表明：（1）随着高寒草甸退化,植被群落优势种演化趋势为禾草—矮嵩草—小嵩草—杂草群落;植被高度和生物量先快速下降,然后缓慢下降或趋于稳定,植被覆盖度和NDVI的变化呈相反特征。（2）随着湿地草甸旱化,植被群落优势种从藏嵩草演变为矮嵩草或小嵩草,湿地旱化初期植被高度、生物量和覆盖度平均值略低于原生湿地,NDVI略大于原生湿地,差异不显著。（3）植被高度、覆盖度和生物量与Sentinel-2或Landsat的NDVI相关性均优于MODIS,说明Sentinel-2和Landsat的NDVI对高寒草甸退化演替过程更加敏感,采用该数据能更准确评估高寒草甸退化程度。