Evaluating NDVI-based emissivities of MODIS bands 31 and 32 using emissivities derived by Day/Night LST algorithm

Surface emissivity estimation is a significant factor for the land surface temperature estimation from remotely sensed data. For fully vegetated surfaces, the emissivity estimation is performed in a simple manner since the emissivity is relatively uniform. However, for arid land with sparse vegetation, the estimation is more complicated since the emissivity of the exposed soil and rock is highly variable. In this study, mean and difference emissivity for bands 31 and 32 of MODIS sensor have been derived based on NDVI values. First, the NDVI thresholds have been determined to separate bare soil, partially vegetated soil and fully vegetated land. Then regression relations have been derived to estimate mean and difference emissivity of the bare soil samples and partially vegetated surfaces. A constant emissivity is also used for fully vegetated area. Along with the correlations, standard deviations of the regression relations have been examined for a set of representative soil types. Standard deviations smaller than 0.003 in mean emissivity and smaller than 0.004 in difference emissivity are resulted in regression linear relations. Evaluation of the NDVI derived regression relations has been performed using the results of MODIS Day/Night Land Surface Temperature (LST) algorithm on a pair of MODIS images. Using around 45,500 pixels with different soil and land cover types, emissivity of each pixel in bands 31 and 32 have been estimated. The calculated emissivities have been compared with emissivities calculated by MODIS Day/Night LST algorithm. Biases and standard deviations of NDVI-based relations show relatively high agreement for mean and difference emissivity relations with Day/Night method results. It may be concluded that the proposed algorithm can be used as a rather simple alternative to complex emissivity estimation algorithms.     

Boosted decision tree classifications of land cover over Turkey integrating MODIS,climate and topographic data

This study investigates the impact of using different combinations of Moderate Resolution Imaging Spectroradiometer (MODIS) and ancillary datasets on overall and per-class classification accuracies for nine land cover types modified from the classification system of the International Geosphere Biosphere Programme (IGBP). Twelve land cover maps were generated for Turkey using boosted decision trees (BDTs) based on the stepwise addition of 14 explanatory variables derived from a time series of 16-day MODIS composites between 2000 and 2006 (Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and four spectral bands) and ancillary climate and topographic data (minimum and maximum air temperature, precipitation, potential evapotranspiration, aspect, elevation, distance to sea and slope) at 500-m resolution. Evaluation of the 12 BDTs indicated that the BDT built as a function of all the MODIS and climate variables, aspect and elevation produced the highest degree of overall classification accuracy (79.8%) and kappa statistic (0.76) followed by the BDTs that additionally included distance to sea (DtS), and both DtS and slope. Based on an independent validation dataset derived from a pre-existing national forest map and Landsat images of Turkey, the highest overall accuracy (64.7%) and kappa coefficient (0.58) among the 12 land cover maps was achieved by using MODIS-derived NDVI time series only, followed by NDVI and EVI time series combined; NDVI, EVI and four MODIS spectral bands; and the combination of all MODIS and climate data, aspect, elevation and distance to sea, respectively. The largest improvements in producer's accuracies were observed for grasslands (+50%), barrenlands (+46%) and mixed forests (+39%) and in user's accuracies for grasslands (+53%), shrublands (+30%) and mixed forests (+28%), in relation to the lowest producer's accuracy. The results of this study indicate that BDTs can increase the accuracy of land cover classifications at the national scale.     

Studies on land surface temperature over heterogeneous areas using AVHRR data

The study addresses the use of the split-window method in tropical regions for estimation of surface temperature over heterogeneous surfaces from satellite sensor data. An attempt has been made to derive emissivity in the thermal channels using the NDVI in conjunction with fractional vegetation cover at pixel level. The estimated surface temperature values are compared with the in situ data over the region and are found to be within error limits of +/- 1.8°C. The utility of fractional vegetation cover in controlling surface temperature has been studied for the selected features over the area. The results suggest the utility of emissivity estimated from the NDVI in land surface temperature estimation.     

城市热岛效应地表通量空间分布研究

城市化进程的加快促使更多大中城市的产生以及城市面积的扩展,导致更加严重的城市热岛现象。为了更加深入理解城市热岛效应产生根源,以西安市城区为例采用美国陆地卫星遥感数据反演或估算地表温度、植被指数以及地表通量等变量,不仅采用传统的地表温度参数理解城市热岛现象,还着重分析城市建成区和郊区的地表通量空间分布格局及其与地表温度的关系。研究发现西安城市建成区与郊区之间热环境存在显著的差异,地表温度不仅与植被覆盖状况具有密切的关系,还与地表潜热通量和实际蒸散发变量存在显著的反相关关系。详细分析表明拥有众多工厂企业的西安市莲湖区热岛效应尤为显著,而位于市中心的新城区次之,具有较大面积郊区的灞桥区热岛效应并不明显。因此城市绿地不仅影响城市建成区的地表温度空间分布,还对地表通量以及实际蒸散发的空间格局产生重要的影响,在调节城市热岛效应方面具有重要的作用。
     

Vegetation cover changes and their relationship to climate variation in the source region of the Yellow River,China, 1990–2000

The change history of vegetation cover and its relations to growing season precipitation (GSP) and average growing season temperature (AGST) in the source region of the Yellow River (SRYR) during 1990–2000 was retrieved based on the 1 km Advanced Very High‐Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data and meteorological records. The results show an overall warming and drying trend of the climate and a common degradation tendency of the ecosystem, with a greening trend in higher rugged regions. The pixel‐by‐pixel correlations between NDVI and climate factors indicate that a decrease in GSP mainly affects ecosystems with low precipitation and worse vegetation condition, and superimposes on the effects of increasing AGST which further deteriorate the climate background of these ecosystems. However, the positive correlations between AGST and NDVI in some higher/rugged regions suggest that the raising temperature can ameliorate vegetation growth conditions in these areas. Comparison and combination of the results of three change detection algorithms, i.e. post‐classification comparison (PCC), principal components analysis (PCA) and a newly developed multi‐temporal image difference (MTID) method, show that the integration of different methods can give a more comprehensive understanding of vegetation changes than any single method.     

A spatial regression procedure for evaluating the relationship between AVHRR-NDVI and climate in the northern Great Plains

The relationship between vegetation and climate in the grassland and cropland of the northern US Great Plains was investigated with Normalized Difference Vegetation Index (NDVI) (1989–1993) images derived from the Advanced Very High Resolution Radiometer (AVHRR), and climate data from automated weather stations. The relationship was quantified using a spatial regression technique that adjusts for spatial autocorrelation inherent in these data. Conventional regression techniques used frequently in previous studies are not adequate, because they are based on the assumption of independent observations. Six climate variables during the growing season; precipitation, potential evapotranspiration, daily maximum and minimum air temperature, soil temperature, solar irradiation were regressed on NDVI derived from a 10-km weather station buffer. The regression model identified precipitation and potential evapotranspiration as the most significant climatic variables, indicating that the water balance is the most important factor controlling vegetation condition at an annual timescale. The model indicates that 46% and 24% of variation in NDVI is accounted for by climate in grassland and cropland, respectively, indicating that grassland vegetation has a more pronounced response to climate variation than cropland. Other factors contributing to NDVI variation include environmental factors (soil, groundwater and terrain), human manipulation of crops, and sensor variation.     

Land cover classification in SE Asia using near and short wave infrared bands

A land cover classification map is necessary for modelling interactions between the land surface and the atmosphere, monitoring the environment and estimating food production. In order to classify land cover in SE Asia in 2000, Normalized Difference Vegetation Index (NDVI), reflectance of near-infrared (NIR) band, and reflectance of short wave infrared (SWIR) band of Systeme pour l'Observation de la Terre (SPOT) VEGETATION data were used in this study. First, ground data were collected for training data. In addition, supervised classification was performed on twelve months of NDVI data. As a result, some deserts and peripheral sparse vegetative areas were classified into urban, compared with the world atlas. Secondly, the number of months when the reflectance of the SWIR band is higher than that of the NIR band was counted (SWIR>NIR month-count condition) in each pixel, and pixels with counts of 10 were classified as Sparse Herbaceous/Shrub and of 11 or 12 were classified as Bare Areas, respectively. Finally, land cover was classified based on the SWIR>NIR month-count condition combined with NDVI, and it was compared with the existing land cover map. It was found that the SWIR>NIR month-count condition gives a better result for areas of non- or sparsely vegetative classification than when using only NDVI.     

Interannual covariability between actual evapotranspiration and PAL and GIMMS NDVIs of northern Asia

This study examined the covariability between interannual changes in the normalized difference vegetation index (NDVI) and actual evapotranspiration (ET). To reduce possible uncertainty in the NDVI time series, two NDVI datasets derived from Pathfinder AVHRR Land (PAL) data and the Global Inventory Monitoring and Modeling Studies (GIMMS) group were used. Analyses were conducted using data over northern Asia from 1982 to 2000. Interannual changes over 19 years in the PAL-NDVI and GIMMS-NDVI were compared with interannual changes in ET estimated from model-assimilated atmospheric data and gridded precipitation data. For both NDVI datasets, the annual maximum correlation with ET occurred in June, which is the beginning of the vegetation growing season. The PAL and GIMMS datasets showed a significant, positive correlation between interannual changes in the NDVI and ET over most of the vegetated land area in June. These results suggest that interannual changes in vegetation activity predominantly control interannual changes in ET in June. Based on analyses of interannual changes in temperature, precipitation, and the NDVI in June, the study area can be roughly divided into two regions, the warmth-dominated northernmost region and the wetness-dominated southern region, indicating that interannual changes in vegetation and the resultant interannual changes in ET are controlled by warmth and wetness in these two regions, respectively.     

Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies

Remote sensing of urban heat islands (UHIs) has traditionally used the Normalized Difference Vegetation Index (NDVI) as the indicator of vegetation abundance to estimate the land surface temperature (LST)-vegetation relationship. This study investigates the applicability of vegetation fraction derived from a spectral mixture model as an alternative indicator of vegetation abundance. This is based on examination of a Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City, IN, USA, acquired on June 22, 2002. The transformed ETM+ image was unmixed into three fraction images (green vegetation, dry soil, and shade) with a constrained least-square solution. These fraction images were then used for land cover classification based on a hybrid classification procedure that combined maximum likelihood and decision tree algorithms. Results demonstrate that LST possessed a slightly stronger negative correlation with the unmixed vegetation fraction than with NDVI for all land cover types across the spatial resolution (30 to 960 m). Correlations reached their strongest at the 120-m resolution, which is believed to be the operational scale of LST, NDVI, and vegetation fraction images. Fractal analysis of image texture shows that the complexity of these images increased initially with pixel aggregation and peaked around 120 m, but decreased with further aggregation. The spatial variability of texture in LST was positively correlated with those in NDVI and in vegetation fraction. The interplay between thermal and vegetation dynamics in the context of different land cover types leads to the variations in spectral radiance and texture in LST. These variations are also present in the other imagery, and are responsible for the spatial patterns of urban heat islands. It is suggested that the areal measure of vegetation abundance by unmixed vegetation fraction has a more direct correspondence with the radiative, thermal, and moisture properties of the Earth's surface that determine LST.     

基于MODIS温度和植被指数产品的山东省土地覆盖变化研究

地表温度(LST)与归一化植被指数(NDVI)构成的NDVI-Ts特征空间具有丰富的地学和生态学内涵。MODIS数据因其优越的时间分辨率、波谱分辨率,已被广泛地运用于各个领域。在本研究中,运用遥感技术和GIS技术相结合的手段,利用NASA提供的MODIS温度产品和NDVI产品,以山东省土地利用图、山东省TM遥感影像图和基于3S技术的山东省森林资源调查项目的外业调查数据为参考和评价标准,以NDVI-Ts时间序列为指标,在进行土地覆盖分类的基础上,分析比较了山东省土地覆盖从2000年到2006年的变化情况。研究结果表明,利用MODIS产品将NDVI-Ts时间序列作为分类特征,在较大尺度范围的土地覆盖分类中具有较高的分类精度,有利于对土地覆盖变化进行动态监测。     

Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect

Day and night airborne thermal infrared image data at 5 m spatial resolution acquired with the 15-channel (0.45mum-12.2mum) Advanced Thermal and Land Applications Sensor (ATLAS) over Alabama, Huntsville on 7 September, 1994 were used to study changes in the thermal signatures of urban land cover types between day and night. Thermal channel number 13 (9.60 mum-10.2mum) data with the best noise-equivalent temperature change (NEDeltaT) of 0.25 C after atmospheric corrections and temperature calibration were selected for use in this analysis. This research also examined the relation between land cover irradiance and vegetation amount, using the Normalized Difference Vegetation Index (NDVI), obtained by ratioing the difference and the sum of the red (channel number 3: 0.60-0.63mum) and reflected infrared (channel number 6: 0.76-0.90mum) ATLAS data. Based on the mean radiance values, standard deviations, and NDVI extracted from 351 pairs of polygons of day and night channel number 13 images for the city of Huntsville, a spatial model of warming and cooling characteristics of commercial, residential, agricultural, vegetation, and water features was developed using a GIS approach. There is a strong negative correlation between NDVI and irradiance of residential, agricultural, and vacant/transitional land cover types, indicating that the irradiance of a land cover type is greatly influenced by the amount of vegetation present. The predominance of forests, agricultural, and residential uses associated with varying degrees of tree cover showed great contrasts with commercial and services land cover types in the centre of the city, and favours the development of urban heat islands. The high-resolution thermal infrared images match the complexity of the urban environment, and are capable of characterizing accurately the urban land cover types for the spatial modeling of the urban heat island effect using a GIS approach.     

The influence of temperature and precipitation climate regimes on vegetation dynamics in the US Great Plains: a satellite bioclimatology case study

Advanced Very High Resolution Radiometer (AVHRR)‐derived Normalized Difference Vegetation Index (NDVI) data are widely used in global‐change research, yet relationships between the NDVI and ecoclimatological variables are not fully understood. This study attempts to model climate‐driven vegetation dynamics through the integration of satellite‐derived NDVI data with climate data collected from ground‐based meteorological stations in the US Great Plains. Monthly maximum value composites of NDVI data (8‐km resolution) and monthly temperature and precipitation records from 305 stations were collected from 1982 to 2001. Analyses involving deseasonalized datasets supported temperature as the dominant climate regime, demonstrating a higher average NDVI–temperature correlation (r = 0.73) than the NDVI–precipitation relationship (r = 0.38). Cluster analysis was used to develop a climate regionalization scheme based primarily on temperature, and NDVI characteristics of each subregion were compared. In the context of global climate change, findings from this study emphasize the influence of temperature and precipitation variability over vegetation cover in the Great Plains region.     

中国西北地区植被时空演变特征及其对气候变化的响应

利用GIMMS/NDVI数据分析了1982~2006年我国西北地区植被覆盖时空变化特征及其对气温和降水变化的响应。结果表明：近25 a来,中国西北地区年均植被NDVI增速为0.5%/10a,7月、8月和10月份增加趋势最显著。天山、阿尔泰山、祁连山、青海的中东部等地区植被覆盖显著增加;青海的格尔木至玉树一线、陕西的南部地区、新疆的塔里木盆地、吐鲁番、塔河、托里等地区植被退化。植被覆盖与气温、降水的年际关系都呈弱的正相关。但年内关系则都呈显著的线性关系,植被覆盖随月均温升高而增加,当月均温超过20℃时,植被NDVI呈下降趋势;月降水量在0~100 mm之间,植被NDVI随降水呈线性增长,当月降水量超过100 mm之后,不再有明显的增长趋势。     

Estimating sub-pixel temperatures using the triangle algorithm

In water-deficient areas, water resource management requires evapotranspiration at high spatiotemporal resolution – an impossible situation given the trade-off between spatial and temporal resolutions in space-borne systems. Some researchers have suggested sharpening the Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature product with a resolution from 1 km to 250 m and a functional relationship between surface temperature (T _r) and normalized difference vegetation index (NDVI). Evapotranspiration at 250 m resolution can be obtained once every few days using this technique. Based on the interpretation of the triangular T _r–NDVI space and assuming uniform soil moisture conditions in a coarse pixel, this paper suggests an alternative algorithm – the triangle algorithm – for sharpening. The triangle algorithm was tested using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data from an arid zone. Sharpened surface temperatures and reference temperatures were compared at 60 m and 240 m resolutions. Root mean square errors with the triangle algorithm are smaller than those with a functional relationship between T _r and NDVI. This paper will also discuss the impact of soil moisture variations in the coarse pixel on the triangle algorithm. Finally we should mention that the triangle algorithm only applies to regions with non-stressed vegetation canopies.     

基于GLASS数据的青藏高原2001—2018年蒸散发时空变化分析

准确认知青藏高原蒸散发时空变化特征,为当地可持续农业的水资源规划及理解高原气候变化具有重要现实意义。研究基于GLASS陆表潜热通量产品,采用Mann-Kendall趋势分析方法,结合青藏高原生态地理分区方案,分析了2001—2018年青藏高原蒸散发的时空变化特征及其与气温、降水和植被的关系。结果表明：(1)GLASS ET产品可以较好地表征青藏高原蒸散发的时空分布特征;(2)青藏高原多年平均蒸散发为296.52 mm,整体上呈现出东南高西北低的空间格局,其中东喜马拉雅南翼最高（690.94 mm）,柴达木盆地最低（163.47 mm）;(3)近18 a来,青藏高原蒸散发年际变化呈波动性上升,只有东喜马拉雅南翼在下降;(4)研究期间,青藏高原蒸散发以显著性增长趋势为主,占47.44%,主要位于高原东部边缘和中西部腹地,呈显著性减小趋势的地区占3.82%,主要集中于东喜马拉雅南翼;(5)蒸散发的空间分布在干旱区与气温呈负相关,在湿润区呈正相关,与降水空间格局总体呈正相关;(6)蒸散发与NDVI的空间分布呈较好的正相关,与NDVI的变化趋势相关性较为复杂,大部分呈正相关,小部分呈负相关。     

NDVI time series for monitoring RUSLE cover management factor in a tropical watershed

Land cover, an important factor for monitoring changes in land use and erosion risk, has been widely monitored and evaluated by vegetation indices. However, a study that associates normalized difference vegetation index (NDVI) time series to climate parameters to determine soil cover has yet to be conducted in the Atlantic Rainforest of Brazil, where anthropogenic activities have been carried out for centuries. The objective of this paper is to evaluate soil cover in a Brazilian Atlantic rainforest watershed using NDVI time series from Thematic Mapper (TM) Landsat 5 imagery from 1986 to 2009, and to introduce a new method for calculating the cover management factor (C-factor) of the Revised Universal Soil Loss Equation (RUSLE) model. Twenty-two TM Landsat 5 images were corrected for atmospheric effects using the 6S model, georeferenced using control points collected in the field and imported to a GIS database. Contour lines and elevation points were extracted from a 1:50,000-scale topographic map and used to construct a digital elevation model that defined watershed boundaries. NDVI and RUSLE C-factor values derived from this model were calculated within watershed limits with 1 km buffers. Rainfall data from a local weather station were used to verify NDVI and C-factor patterns in response to seasonal rainfall variations. Our proposed method produced realistic values for RUSLE C-factor using rescaled NDVIs, which highly correlated with other methods, and were applicable to tropical areas exhibiting high rainfall intensity. C-factor values were used to classify soil cover into different classes, which varied throughout the time-series period, and indicated that values attributed to each land cover cannot be fixed. Depending on seasonal rainfall distribution, low precipitation rates in the rainy season significantly affect the C-factor in the following year. In conclusion, NDVI time series obtained from satellite images, such as from Landsat 5, are useful for estimating the cover management factor and monitoring watershed erosion. These estimates may replace table values developed for specific land covers, thereby avoiding the cumbersome field measurements of these factors. The method proposed is recommended for estimating the RUSLE C-factor in tropical areas with high rainfall intensity.     

Approaches to fractional land cover and continuous field mapping: A comparative assessment over the BOREAS study region

Subpixel land cover mapping involves the estimation of surface properties using sensors whose spatial sampling is coarse enough to produce mixtures of the properties within each pixel. This study evaluates five algorithms for mapping subpixel land cover fractions and continuous fields of vegetation properties within the BOREAS study area. The algorithms include a conventional “hard”, per-pixel classifier, a neural network, a clustering/look-up-table approach, multivariate regression, and linear least squares inversion. A land cover map prepared using a Landsat TM mosaic was adopted as the source of fine scale calibration and validation data. Coarse scale mixtures of five basic land cover classes and continuous vegetation fields, both corresponding to the field of view of SPOT-VEGETATION imagery (1.15-km pixel size), were synthesised from the TM mosaic using a modelled point spread function. Two measures of land cover distribution were used, fractions of fine scale land cover categories and continuous fields of vegetation structural characteristics. The subpixel algorithms were applied using both proximate (<100 km) and distant (>400 km) separation between training and validation regions. “Hard” classification performed poorly in estimating proportions or continuous fields. The neural network, look-up-table and multivariate regression algorithms produced good matches of spatial patterns and regional land cover composition for the proximate treatment. However, all three methods exhibited substantial biases with the distant treatment due to the characteristics of the training data. Linear least squares inversion offers a relatively unbiased but less precise alternative for subpixel proportion and fraction mapping as it avoids calibration to the a priori distribution of land cover in the training data. In general, a combination of multivariate regression for proximate training data and linear least squares inversion for distant training data resulted in woody fraction estimates within 20% of the Landsat TM classification-based estimates.     

Evapotranspiration estimation in the Yellow River Basin,China using integrated NDVI data

It is important to estimate land surface evapotranspiration (ET) for water resources evaluation, drought monitoring and crop production simulation. In this paper, a relationship between annual ET, integrated Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) and Relative Moisture Index (RMI) was established. Based on this relationship, the spatial distribution and dynamic change of annual ET were estimated for the Yellow River Basin, China from 1982 to 2000. Our analyses involved the use of integrated NDVI data, monthly mean air temperature, and precipitation. Our results showed that the integrated AVHRR NDVI can be used to effectively estimate annual ET in the Yellow River Basin, with an accuracy over 90% for the whole basin.     

Global analysis of the relationships among a vegetation index,precipitation and land surface temperature

Abstract. This study is designed to examine the spatial variability of the relationships among global NDVI (Normalized Difference Vegetation Index) data, remotely-sensed land surface temperature data, and gridded station precipitation data as well as to investigate the potential for the combined use of NDVI and temperature data for global bioclimate monitoring. The relationships among the three variables are examined using single and multiple temporal correlations and the analysis is augmented by the computation of the first annual harmonic of each parameter. In addition, the global variability of growing season liming is analysed using a proxy for the onset and conclusion of the growing season, based upon slopes of the NDVI time series.

The NDVI data set as processed for this study has significant sources of systematic error, which include aerosol and cloud contamination, orbital drift, and instrument degradation. This analysis provides insight into the manner in which the relationships among NDVI, precipitation and remotely-sensed land surface temperature vary geographically, in spite of the data noise. Due to excessive systemic error, anomalies of this NDVI data set are not highly correlated with precipitation, or multiply correlated with temperature to precipitation. Greater immediate promise for interannual bioclimate monitoring is contained in the proxies presented here for the growing season onset and length.     

Vegetation spatial variability and its effect on vegetation indices

Abstract

Landsat MSS data were used to simulate low resolution satellite data, such as NOAA AVHRR, to quantify the fractional vegetation cover within a pixel and relate the fractional cover to the normalized difference vegetation index (NDVI) and the simple ratio (SR). The MSS data were converted to radiances from which the NDVI and SR values for the simulated pixels were determined. Each simulated pixel was divided into clusters using an unsupervised classification programme. Spatial and spectral analysis provided a means of combining clusters representing similar surface characteristics into vegetated and non-vegetated areas. Analysis showed an average error of 12·7 per cent in determining these areas. NDVI values less than 0·3 represented fractional vegetated areas of 5 per cent or less, while a value of 0·7 or higher represented fractional vegetated areas greater than 80 per cent. Regression analysis showed a strong linear relation between fractional vegetation area and the NDVI and SR values; correlation values were 0·89 and 0·95 respectively. The range of NDVI values calculated from the MSS data agrees well with field studies.