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。     

Malaria incidence in Nairobi,Kenya and dekadal trends in NDVI and climatic variables

The primary objective of this research was to determine if the remotely-sensed metric, Normalised Difference Vegetation Index (NDVI) and ground-collected dekadal climatological variables were useful predictors of future malaria outbreaks in an epidemic-prone area of Nairobi, Kenya. Data collected consisted of 36 dekadal (10-day) periods for the variables rainfall, temperature and NDVI along with yearly documented malaria admissions in 2003 for Nairobi, Kenya. Linear regression models were built for malaria cases reported in Nairobi, Kenya, as the dependent variable and various time-based groupings of temperature, rainfall and NDVI data from the dekads in both the current and the previous month as the independent variables. Data from 2003 show that malaria incidence in any given month is best predicted (R²  = 0.881, p < 0.001) by the average NDVI for the 30 days including the final two dekads of the previous month and first dekad of the current month, and by the average rainfall for the 30 days including the three dekads of rainfall data from the prior month. Forecasting an outbreak in an epidemic zone would allow public health entities to plan for and disseminate resources to the general public such as antimalarials and insecticide impregnated bed nets. In addition, vector control measures could be implemented to slow the rate of transmission in the impacted population.     

基于傅立叶变换的混合分类模型用于NDVI时序影像分析

应用2004年MODIS的时序NDVI数据,在分析湖北省不同地物类型的NDVI曲线季节性变化特征的基础上,设置对应的阈值,先后将水体、居民地与其他地物类型分离开。将去除了水体和居民地影响的剩余的NDVI序列影像傅立叶变换的1/12频率分量引入到地表覆盖分类的特征空间中,与其最大值影像和平均值影像组合,经过归一量化处理后合成一个类似具有三波段的卫星影像。在合成后的影像上利用最大似然法对其他地类进行分类。研究表明,引入傅立叶变换的特殊频率分量是分析多时相MODIS数据及提取地表植被覆盖信息的有效工具。     

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。     

肾综合征出血热疫情与NDVI的时间关系——以内蒙古自治区大杨树镇为例

利用月度肾综合征出血热发病人数和长时序月度NDVI值的相互关系, 对肾综合征出血热的发病趋势及发病人数进行预测。研究区大杨树镇2001—2005年共有144例完整的HFRS病人资料, 以及同期详细的宿主动物捕获数据。基于Landsat TM影像以及Google earth 影像, 大杨树地区土地利用分为4种类型, 山地、林地、农田以及居民点。各类土地利用类型的NDVI数据由SPOT-4 卫星的 VGT-S10数据集(10d最大化合成的NDVI数据)提供。对HFRS病例与NDVI之间的关系进行图解分析、相关分析和回归分析。研究表明, NDVI的峰值多出现于8月, 而HFRS发病人数的峰值多出现在11月。前朔3个月的农田NDVI值与HFRS病例数之间的相关系数为0.67(P值<0.001)。农田NDVI峰值比HFRS病例的峰值提前了3个月。研究量化了NDVI与HFRS之间的关系, 为HFRS早期预警系统的建立提供了依据。     

MODIS植被指数时间序列Savitzky-Golay滤波算法重构

利用Savitzky-Golay(S-G)滤波方法对若尔盖高原湿地区2000—2009年MODIS16d最大值合成的NDVI时间序列数据进行了重构,并与中值迭代滤波法、傅里叶变换法进行了比较。结果表明,基于S-G滤波的时间序列重构方法重构后的NDVI时间序列在直观及像元的时间序列曲线上均取得了较好的效果,对提高该数据产品质量有很大帮助,通过该方法重构后的高质量的NDVI时间序列对利用该数据源对若尔盖湿地生态系统监测提供了良好的基础。     

Multi-scale remote sensing to support insurance policies in agriculture: from mid-term to instantaneous deductions

ABSTRACT

Climate change is today one of the biggest issues for farmers. The increasing number of natural disasters and change of seasonal trends is making insurance companies more interested in new technologies that can somehow support them in quantifying and mapping risks. Remotely sensed data, with special focus on free ones, can certainly provide the most of information they need, making possible to better calibrate insurance fees in space and time. In this work, a prototype of service based on free remotely sensed data is proposed with the aim of supporting insurance companies’ strategies. The service is thought to calibrate annual insurance rates, longing for their reduction at such level that new customers could be attracted. The study moves from the entire Piemonte region (NW Italy), to specifically focus onto the Cuneo province (Southern Piemonte), which is mainly devoted to agriculture. MODIS MOD13Q1-v6 and Sentinel-2 L2A image time series were jointly used. NDVI maps from MODIS data were useful to describe the midterm phenological trends of main crops at regional level in the period 2000–2018; differently, Sentinel-2 data permitted to map local crop differences at field level in 2016 and 2017 years. With reference to MODIS data, the average phenological behavior of main crop classes in the area, obtained from the CORINE Land Cover map Level 3, was considered using a time series decomposition approach. Trend analyses showed that the most of the crop classes alternated three phases (about 7 years) suggesting that, presently, this is probably the time horizon to be considered to tune mid-term algorithms for risk estimates in the agricultural context. Crop classes trends were consequently split into three phases and each of them modeled by a first-order polynomial function used to update correspondent insurance risk rate. Sentinel-2 data were used to map phenological anomalies at field level for the 2016 and 2017 growing seasons; shifts from class average behavior were considered to locally and temporarily tune insurance premium around its average trend as described at the previous step. Synthesizing, one can say that this approach, integrating MODIS and Sentnel-2 data, makes possible to locally and temporarily calibrate premiums of indexed insurance policies by describing the average trends of crop performance (NDVI) at regional level by MODIS data and refining it at field and specific crop level by Sentinel-2 data.     

AMSR-E微波极化指数与MODIS植被指数关系研究

首先，利用辐射传输方程对微波极化指数（MPI，Microwave Polarization Index)进行推导，以AMSR-E像元经纬度为控制条件，采集与之对应的MODIS植被指数（ LAI/NDVI），并将其平均值作为AMSR-E对应像元的值； 然后，对AMSR-E微波极化指数与LAI/NDVI进行相关分析。结果表明，MPI与LAI/NDVI之间存在着指数关系，而且频率越低，相关性越好。     

Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco

In Morocco, no operational system actually exists for the early prediction of the grain yields of wheat (Triticum aestivum L.). This study proposes empirical ordinary least squares regression models to forecast the yields at provincial and national levels. The predictions were based on dekadal (10-daily) NDVI/AVHRR, dekadal rainfall sums and average monthly air temperatures. The Global Land Cover raster map (GLC2000) was used to select only the NDVI pixels that are related to agricultural land. Provincial wheat yields were assessed with errors varying from 80 to 762 kg ha⁻¹, depending on the province. At national level, wheat yield was predicted at the third dekad of April with 73 kg ha⁻¹ error, using NDVI and rainfall. However, earlier forecasts are possible, starting from the second dekad of March with 84 kg ha⁻¹ error, at least 1 month before harvest. At the provincial and national levels, most of the yield variation was accounted for by NDVI. The proposed models can be used in an operational context to early forecast wheat yields in Morocco.     

采用6S模型的多时相MODIS植被指数NDVI归一化方法

目的 基于6S辐射传输模型,对连续6个时相的 MODIS影像进行逐像元大气校正获取地表反射率,以此实现 MODIS NDVI的时相归一化。归一化后,地物 NDVI的时相趋势与校正前相比差异显著,能更加准确地描述地物随时间变化的规律,在浓密植被区域表现更为明显。开展对 NDVI时相归一化的研究,对全球变化、作物物候监测等遥感时序分析相关应用具有着重要的意义和实用价值。     

Mapping land cover gradients through analysis of hyper-temporal NDVI imagery

The green cover of the earth exhibits various spatial gradients that represent gradual changes in space of vegetation density and/or in species composition. To date, land cover mapping methods differentiate at best, mapping units with different cover densities and/or species compositions, but typically fail to express such differences as gradients. Present interpretation techniques still make insufficient use of freely available spatial-temporal Earth Observation (EO) data that allow detection of existing land cover gradients. This study explores the use of hyper-temporal NDVI imagery to detect and delineate land cover gradients analyzing the temporal behavior of NDVI values. MODIS-Terra MVC-images (250 m, 16-day) of Crete, Greece, from February 2000 to July 2009 are used. The analysis approach uses an ISODATA unsupervised classification in combination with a Hierarchical Clustering Analysis (HCA). Clustering of class-specific temporal NDVI profiles through HCA resulted in the identification of gradients in landcover vegetation growth patterns. The detected gradients were arranged in a relational diagram, and mapped. Three groups of NDVI-classes were evaluated by correlating their class-specific annual average NDVI values with the field data (tree, shrub, grass, bare soil, stone, litter fraction covers). Multiple regression analysis showed that within each NDVI group, the fraction cover data were linearly related with the NDVI data, while NDVI groups were significantly different with respect to tree cover (adj. R² = 0.96), shrub cover (adj. R² = 0.83), grass cover (adj. R² = 0.71), bare soil (adj. R² = 0.88), stone cover (adj. R² = 0.83) and litter cover (adj. R² = 0.69) fractions. Similarly, the mean Sorenson dissimilarity values were found high and significant at confidence interval of 95% in all pairs of three NDVI groups. The study demonstrates that hyper-temporal NDVI imagery can successfully detect and map land cover gradients. The results may improve land cover assessment and aid in agricultural and ecological studies.     

宁夏不同植被类型归一化指数与气象因子分析

针对植被动态对气候变化响应的问题,提出了从小尺度范围研究植被指数与气象因子的相关性,采用2000—2010年MODIS归一化植被指数数据集和宁夏10个气象站2000—2010年逐月气象资料,分析了气象站点周围10km缓冲区内不同植被类型NDVI与气象因子的相关性。结果表明:2000—2010年宁夏不同植被类型NDVI均呈上升趋势;极端最低气温、最高气温、平均气温、平均相对湿度以及日照时数对宁夏地区植被的生长有明显的滞后效应;植被NDVI与极端最低气温的相关性系数最大,其次是平均气温;不同植被类型的NDVI与极端最高气温、极端最低气温以及平均气温的相关性由南向北呈现波动性增长,与降水量的相关性由南向北呈现明显的减小趋势;且耕地NDVI与各气象因子的相关性最大。     

Directional effects on NDVI and LAI retrievals from MODIS: A case study in Brazil with soybean

The Moderate Resolution Imaging Spectroradiometer (MODIS) is largely used to estimate Leaf Area Index (LAI) using radiative transfer modeling (the “main” algorithm). When this algorithm fails for a pixel, which frequently occurs over Brazilian soybean areas, an empirical model (the “backup” algorithm) based on the relationship between the Normalized Difference Vegetation Index (NDVI) and LAI is utilized. The objective of this study is to evaluate directional effects on NDVI and subsequent LAI estimates using global (biome 3) and local empirical models, as a function of the soybean development in two growing seasons (2004–2005 and 2005–2006). The local model was derived from the pixels that had LAI values retrieved from the main algorithm. In order to keep the reproductive stage for a given cultivar as a constant factor while varying the viewing geometry, pairs of MODIS images acquired in close dates from opposite directions (backscattering and forward scattering) were selected. Linear regression relationships between the NDVI values calculated from these two directions were evaluated for different view angles (0–25°; 25–45°; 45–60°) and development stages (<45; 45–90; >90 days after planting). Impacts on LAI retrievals were analyzed. Results showed higher reflectance values in backscattering direction due to the predominance of sunlit soybean canopy components towards the sensor and higher NDVI values in forward scattering direction due to stronger shadow effects in the red waveband. NDVI differences between the two directions were statistically significant for view angles larger than 25°. The main algorithm for LAI estimation failed in the two growing seasons with gradual crop development. As a result, up to 94% of the pixels had LAI values calculated from the backup algorithm at the peak of canopy closure. Most of the pixels selected to compose the 8-day MODIS LAI product came from the forward scattering view because it displayed larger LAI values than the backscattering. Directional effects on the subsequent LAI retrievals were stronger at the peak of the soybean development (NDVI values between 0.70 and 0.85). When the global empirical model was used, LAI differences up to 3.2 for consecutive days and opposite viewing directions were observed. Such differences were reduced to values up to 1.5 with the local model. Because of the predominance of LAI retrievals from the MODIS backup algorithm during the Brazilian soybean development, care is necessary if one considers using these data in agronomic growing/yield models.     

归一化植被指数在遗址探测中的应用

针对目前遗址探测研究多基于单时相遥感数据开展,存在偶然性,对最佳探测时间的研究较少等问题,该文以洛阳盆地为研究对象,利用多时相遥感数据,通过时间序列谐波分析算法(HANTS)重构时间序列植被指数数据集,去噪的同时对比分析出利用冬小麦长势信息进行地下遗址遥感监测的最佳时间区间。研究表明,受地下遗址的胁迫,在分蘖期,冬小麦长势明显比非遗址区的长势差,表明该时期是进行地下遗址探测的最佳时期,进而对最佳探测时期内NDVI积分,有效增强了遗址区和非遗址区之间差异,突出地下遗址的位置和轮廓信息。利用该研究成果,文章成功探测到汉魏洛阳故城以及古伊洛河异常区,与现有的考古资料相吻合。     

澜沧江流域2000-2017年植被NDVI时空特征及其对气候因子的响应

基于MODIS-NDVI数据分析澜沧江流域生长季植被NDVI时空特征和变化趋势,结合地形数据、气象站点数据和植被类型数据,利用趋势分析和相关性分析法研究植被NDVI变化对气候因子的响应。结果表明:1)2000-2017年澜沧江流域生长季植被NDVI均值为0.592,整体呈现出由西北向东南波动增加趋势,增长速率为0.09%/10年;2) 2000-2017年澜沧江流域气温呈上升趋势,降水呈下降趋势,植被NDVII总体与平均气温的相关性高于累积降水量;3)澜沧江流域生长季植被NDVI驱动因子分析表明,气候驱动中以气温降水联合驱动为主,流域植被NDVI变化整体为非气候驱动。     

基于NDVI先验知识的LAI地面采样方法

针对非均质中低分辨率像元的叶面积指数LAI验证中如何布设基本采样单元ESU的问题,提出基于NDVI先验知识的ESU布设方法,并采用不同植被类型、不同均匀程度的地表作为模拟场,分析对比了方法的精度及稳定性。结果显示,本文方法用NDVI先验知识描述植被的生长空间分布信息,能相对准确地划分植被的不同生长水平,有效降低层内方差。在草地和森林地区的试验中,精度与稳定性均优于传统的随机采样、均匀采样和基于分类图的3种采样方法。因此,本文提出的采样方法为大尺度非均质区域LAI地面验证的采样方案提供了新的设计思路。     

山西省西山煤田长时间序列NDVI时空变化趋势及对气候响应

研究采矿扰动区内植被变化规律,能够为矿区生态修复提供理论依据.本文以山西省西山煤田为研究区,通过设立对比试验区,利用MODIS/NDVI(2001—2019年)结合同期的气温、降水气候因子,分别从植被指数的时空变化及与气象因子之间的关系等方面展开对比,用于探究采矿扰动区内植被变化情况.研究结果表明:①19年来西山煤田与...     

Vegetation response to intensive commercial horticulture and environmental changes within watersheds in central highlands,Kenya, using AVHRR NDVI data

Expansion and heterogeneous clustering of commercial horticulture within the central highlands of Kenya after the mid-1990s impact watersheds and the sustainable resource management. This is distressing since climate conditions for world horticultural regions are projected to change, making such farming extremely difficult and costly to the environment. To understand the scope of impact on vegetation, the study evaluated (1) interannual variability in averaged normalized difference vegetation index (NDVI); (2) trends in average annual NDVI before and after 1990 – the presumed onset of rapid horticulture; and (3) relationship between the average annual NDVI and large-scale commercial farms, population density, and mean annual rainfall in subwatersheds. Time-series analysis of long-term Global Inventory Modeling and Mapping Studies NDVI data were analyzed as indicator of vegetation condition. NDVI trends before 1990s (1982–1989) and after 1990s (1990–2006) were evaluated to determine the slope (sign), and the Spearman’s correlation coefficient (strength). Overall, results show considerable variations in vegetation condition due largely to mixed factors including intensive farming activities, drought, and rainfall variation. Statistical analysis shows significant differences in slopes before 1990 and after 1990 (p < 0.05 and p < 0.1 respectively). Negative (decline) trends were common after 1990, linked to increased commercial horticulture and related anthropogenic disturbances on land cover. There was decline in vegetation over densely populated subwatersheds, though low NDVI values in 1984 and 2000 were the effect of severe droughts. Understanding the linkage between vegetation responses to the effects of human-induced pressure at the subwatershed scale can help natural resource managers approach conservation measures more effectively.     

Investigating rural poverty and marginality in Burkina Faso using remote sensing-based products

Poverty at the national and sub-national level is commonly mapped on the basis of household surveys. Typical poverty metrics like the head count index are not able to identify its underlaying factors, particularly in rural economies based on subsistence agriculture. This paper relates agro-ecological marginality identified from regional and global datasets including remote sensing products like the normalized difference vegetation index (NDVI) and rainfall to rural agricultural production and food consumption in Burkina Faso. The objective is to analyze poverty patterns and to generate a fine resolution poverty map at the national scale. We compose a new indicator from a range of welfare indicators quantified from Georeferenced household surveys, indicating a spatially varying set of welfare and poverty states of rural communities. Next, a local spatial regression is used to relate each welfare and poverty state to the agro-ecological marginality. Our results show strong spatial dependency of welfare and poverty states over agro-ecological marginality in heterogeneous regions, indicating that environmental factors affect living conditions in rural communities. The agro-ecological stress and related marginality vary locally between rural communities within each region. About 58% variance in the welfare indicator is explained by the factors of rural agricultural production and 42% is explained by the factor of food consumption. We found that the spatially explicit approach based on multi-temporal remote sensing products effectively summarizes information on poverty and facilitates further interpretation of the newly developed welfare indicator. The proposed method was validated with poverty incidence obtained from national surveys.     

基于NDVI背景场的雪盖制图算法探索

NDSI算法提取MSS雪盖面积时,受到MSS影像缺少短波红外波段的局限。为充分精确提取MSS影像的雪盖面积,本文探索一种以NDVI为背景场的雪盖制图新思路。该方法首先在辐射校正时利用6S模型反演地表反射率,然后根据各地物的光谱特性差异和NDVI特性差异,在ENVI软件SPECTRAL模块中创建冰雪光谱阈值查找表。通过ETM＋和TM影像的三个例证,详细阐明该算法流程以及查找表的创建,并以NDSI对其雪盖制图进行精度验证。结果一致表明,与常规的分类方法（最大似然法）相比较,本文探索的NDVI背景场算法有更高的总体精度和Kappa系数。