An assessment of land degradation in the Save catchment of Zimbabwe

The lack of reliable baseline information on land degradation is a hindrance towards its monitoring and mitigation. Of particular interest is the identification of areas susceptible to degradation. In this study, remote sensing and GIS technologies were applied to detect and map susceptibility to land degradation in Buhera district, in Save catchment, Zimbabwe. Data used included Landsat TM and ETM imagery for 1992 and 2002, agro-ecological zones, vegetation cover and population density. The study identified five preliminary categories of degradation susceptibility ranging from very high to low.     

20世纪80年代以来西南喀斯特地区植被变化对气候变化的响应

气候变化与植被变化的关系已经在全球和区域尺度上得到了研究证明。在前人研究的基础上,基于AVHRR GIMMS NDVI和AVHRR GloPEM NPP数据集,通过对逐个像元信息的提取和分析,研究了20世纪80年代以来,我国西南喀斯特地区植被变化对气候变化的响应。研究结果显示:(1)20世纪80年代以来,西南喀斯特地区植被覆盖度和净初级生产量总体均呈增加的趋势,但不显著。植被指数的年际变化存在着明显的区域差异;(2)植被指数年际变化与气候因子年际变化的相关系数区域分异比较明显;(3)不同的植被类型对气候变化有着不同的响应特征;在该研究区气温变化对植被变化的影响要高于降水量变化对其影响;(4)植被指数年际变化与气候因子年际变化的相关系数在不同气候条件下分布的规律性比较明显。本研究将会增进对影响喀斯特生态系统稳定性的自然过程的认识,同时也会为喀斯特生态系统的管理提供科学依据。     

近15a陕西省植被时空变化与影响因素分析

利用2000—2014年MODIS/NDVI时间序列数据,采用栅格像元趋势分析、稳定性评价的方法,研究了陕西省近15 a植被的时空变化特征和规律;利用Hurst指数对陕西省植被未来变化趋势进行了预测;并利用相关性分析法分析了NDVI与年均温度和降雨量的关系。结果表明,2000年、2015年陕西省NDVI均值分别为0.4273、0.4942, 15 a来增加了0.067,增长了16.0%,其中陕北地区NDVI增加明显,关中部分地区出现负增长,陕南地区NDVI总体依旧维持在较高水平。陕西省植被变化趋势具有明显的空间差异性,全省植被未变化的占52.0 %,改善部分占44.27 %,退化部分占3.73%,说明15 a间陕西省植被覆盖改善面积大于退化面积,植被状况有所改善;其中陕北地区植被呈明显改善区域面积较大,关中地区植被覆盖面积有所减少,陕南地区植被变化幅度较小。陕西省植被稳定区域占50%以上（0 0.2),说明15a间陕西省植被较为稳定,变化程度不大;其中陕西省植被最稳定地区主要集中在陕南、延安南部,榆林部分、西安、渭南少部地区变化幅度较大。Hurst指数分析表明陕西省44.54%面积的植被未来有可能面临退化,主要分布在陕北和关中地区的北部,植被未来有可能退化也有可能改善的面积占49.78%,主要分布在延安和陕南地区。陕西省近15 a气温和降水量总体呈增加趋势,增加速率分别为0.48 ℃·(10 a)-1和69.5 mm ( a)-1;相关性分析结果表明,年均降雨量是影响NDVI的主要气象因子,同时陕西省植被变化也受到了退耕还林还草、防沙治沙、生态政治等人为因素的影响。     

3个时期骆马湖大型水生植物的分布及变化

大型水生植物对湖泊的生物地球化学循环具有重要影响.一方面,大型水生植物在生长过程吸收营养;另一方面,其通过向水体释放氧气而影响磷元素以及其他相关因子,进而影响磷元素的生物地球化学循环.为了从宏观上了解骆马湖生态系统变化,以1990年9月20日、2000年5月2日和2008年10月15日Landsat TM/ETM+影像为主要数据源,以大型水生植物的归一化植被指数(NDVI)为测试变量,运用分类回归树(classifica-tion and regression tree,CART)方法确定分割阈值,通过构建知识决策树的方法识别骆马湖大型水生植物动态变化特征.3个时期的遥感分类的总体精度与kappa系数分别为92.28％和0.87、91.73％和0.86、93.38％和0.88,因此,该方法的分类精度完全满足骆马湖水生植物分布及变化的研究.研究结果表明,骆马湖大型水生植物分布面积由1990年的55.461 6 km2,减少到2000年的41.801 4 km2,2008年又增加到79.065 km2;大型水生植物主要分布在骆马湖北部河湖交汇区;人类活动干扰是造成骆马湖大型水生植物分布面积发生变化的主要原因.     

MODIS NDVI和AVHRR NDVI 对草原植被变化监测差异

以草地作为研究载体,对比分析草原植被AVHRR NDVI和MODIS NDVI两种NDVI序列的年内、年际变化特征,讨论两种NDVI序列对降水量、平均气温和水汽压3种气候因子的响应差异,为合理选择NDVI序列对植被进行监测研究提供参考。结果表明：(1)两种NDVI序列所反映的草原植被年内变化趋势相似,但MODIS NDVI对各类草原的区分度优于AVHRR NDVI;(2)两种NDVI序列所反映的2000年—2003年草原植被年际变化差异明显。较之于MODIS NDVI,AVHRR NDVI变化趋势分类图表现出更强的植被改善趋势,植被改善面积在AVHRR NDVI变化趋势分类图中占94.25%,在MODIS NDVI中为83.33%;两种NDVI变化趋势分类图反映的植被变化趋势吻合度为52.88%。(3)两种NDVI序列与水汽压、降水量相关性差异显著。MODIS NDVI与各站点平均气温的相关系数均大于GIMMS NDVI;而MODIS NDVI与水汽压的相关系数83%(10个站点)小于GIMMS NDVI,与降水量的相关系数67%(8个站点)小于GIMMS NDVI。     

基于NDVI变化的三江源生态环境演变分区研究

在对1 km分辨率的1993年NOAA/AVHRR、2000年和2006年MODIS NDVI数据进行归一化处理的基础上,通过变化强度参量分析三江源地区NDVI的空间变化规律,结合源区内温度、降水、湖泊和湿地变化等调查资料,利用GIS空间分析技术综合研究三江源地区生态环境变化.结果表明:在1993-12000年和2000-2006年,三江源地区NDVI下降区域面积分别占源区总面积的50.73%和23.85%.三江源地区在20世纪90年代环境恶化严重,NDVI下降强烈,随着2000年8月三江源自然保护区的建立,该区环境治理取得良好效果,源区NDVI呈现稳定、轻微增加趋势,但局部地区仍存在NDVI下降趋势.综合分析温度、降水、湖泊、湿地和NDVI的变化状况表明,三江源环境变化存在显著区域特征.     

基于NDVI的新疆荒漠地区植被覆盖度遥感估算经验模型研究

新疆荒漠地区植被覆盖度遥感估算模型十分缺乏,给荒漠化监测等相关工作带来很大不便,开展植被覆盖度遥感估算经验模型研究,对于促进和完善相关地区的生态监测及研究工作具有积极的现实意义。通过对阜康市北部沙漠南缘和克拉玛依市中部平原荒漠进行无人机航拍,利用无人机遥感提取(光合)植被信息,并将无人机航拍影像的植被覆盖度统计单元与高分辨率卫星影像像元在空间上直接相对应,获取在高分辨率卫星影像像元尺度上的植被盖度,然后通过植被覆盖度和空间上与其相对应的源自高分辨率卫星影像的NDVI数据的拟合关系,建立基于源自高分二号影像的NDVI的阜康北部沙漠植被覆盖度遥感估算线性模型以及基于源自ZY1-02C影像的NDVI的克拉玛依平原荒漠植被覆盖度遥感估算二次多项式模型。研究中所采用的无人机遥感与卫星遥感相结合、植被覆盖度统计单元与卫星像元在空间上直接对应的方法,可避免以往相关工作中常以点位测量数据代表卫星像元数据所带来的不确定性。由于所用卫星影像的NDVI数据稳定性相对不足等原因,所建立的遥感估算模型的估算精度尚相对偏低,有待于今后进一步的工作加以改进。     

Vegetation cover variation in the Qilian Mountains and its response to climate change in 2000–2011

An understanding 0f variati0ns in vegetati0n c0ver in resp0nse t0 climate change is critical f0r predicting and managing future terrestrial ec0system dynamics. Because scientists anticipate that m0untain ec0systems will be m0re sensitive t0 future climate change c0mpared t0 0thers, 0ur 0bjectives were t0 investigate the impacts 0f climate change 0n variati0n in vegetati0n c0ver in the Qilian M0untains （QLM）, China, between 2000 and 2011. T0 acc0mplish this, we used linear regressi0n techniques 0n 250-m MODIS N0rmalized Difference Vegetati0n Index （NDVI） datasets and mete0r0l0gical rec0rds t0 determine spati0temp0ral variability in vegetati0n c0ver and climatic fact0rs （i.e. temperature and precipitati0n）. Our results sh0wed that temperatures and precipitati0n have increased in this regi0n during 0ur study peri0d. In additi0n, we f0und that gr0wing seas0n mean NDVI was mainly distributed in the vertical z0ne fr0m 2,700 m t0 3,600 m in elevati0n. In the study regi0n, we 0bserved significant p0sitive and negative trends in vegetati0n c0ver in 26.71% and 2.27% 0f the vegetated areas. C0rrelati0n analyses indicated that rising precipitati0n fr0m May t0 August was resp0nsible f0r increased vegetati0n c0ver in areas with p0sitive trends in gr0wing seas0n mean NDVI. H0wever, there was n0 similar significant c0rrelati0n between gr0wing seas0n mean NDVI and precipitati0n in regi0ns where vegetati0n c0ver declined thr0ugh0ut 0ur study peri0d. Using spatial statistics, we f0und that veeetati0n c0ver freauentlvdeclined in areas within the 2,500-3,100 m vertical z0ne, where it has steep sl0pe, and is 0n the sunny side 0f m0untains. Here, the p0sitive influences 0f increasing precipitati0n c0uld n0t 0ffset the drier c0nditi0ns that 0ccurred thr0ugh warming trends. In c0ntrast, in higher elevati0n z0nes （3,900-4,500 m） 0n the shaded side 0f the m0untains, rising temperatures and increasing precipitati0n impr0ved c0nditi0ns f0r vegetati0n gr0wth. Increased precipitati0n als0 facilitated vegetati0n gr0wth in areas experiencing warming trends at l0wer elevati0ns （2,000-2,400 m） and 0n l0wer sl0pes where water was m0re easily c0nserved. We suggest that spatial differences in variati0n in vegetati0n as the result 0f climate change depend 0n l0cal m0isture and thermal c0nditi0ns, which are mainly c0ntr0lled by t0p0graphy （e.g. elevati0n, aspect, and sl0pe）, and 0ther fact0rs, such as l0cal hydr0l0gy.     

Predicting agricultural drought in eastern Rajasthan of India using NDVI and standardized precipitation index

In the present study, prediction of agricultural drought has been addressed through prediction of agricultural yield using a model based on NDVI-SPI. It has been observed that the meteorological drought index SPI with different timescale is correlated with NDVI at different lag. Also NDVI of current fortnight is correlated with NDVI of previous lags. Based on the correlation coefficients, the Multiple Regression Model was developed to predict NDVI. The NDVI of current fortnight was found highly correlated with SPI of previous fortnight in semi-arid and transitional zones. The correlation between NDVI and crop yield was observed highest in first fortnight of August. The RMSE of predicted yield in drought year was found to be about 17.07 kg/ha which was about 6.02 per cent of average yield. In normal year, it was 24 kg/Ha denoting about 2.1 per cent of average yield.     

Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation

Abstract

While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties, the temporal resolution of the data is rather low, which can be easily made worse by cloud contamination. In contrast, although Moderate Resolution Imaging Spectroradiometer (MODIS) can only achieve a spatial resolution of 250 m in its normalised difference vegetation index (NDVI) product, it has a high temporal resolution, covering the Earth up to multiple times per day. To combine the high spatial resolution and high temporal resolution of different data sources, a new method (Spatial and Temporal Adaptive Vegetation index Fusion Model [STAVFM]) for blending NDVI of different spatial and temporal resolutions to produce high spatial–temporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). STAVFM defines a time window according to the temporal variation of crops, takes crop phenophase into consideration and improves the temporal weighting algorithm. The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution. An application of the generated NDVI dataset in crop biomass estimation was provided. An average absolute error of 17.2% was achieved. The estimated winter wheat biomass correlated well with observed biomass (R ² of 0.876). We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail. There is potential to apply the approach in many other studies, including crop production estimation, crop growth monitoring and agricultural ecosystem carbon cycle research, which will contribute to the implementation of Digital Earth by describing land surface processes in detail.