The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI

The green vegetation fraction (Fg) is an important climate and hydrologic model parameter. A common method to calculate Fg is to create a simple linear mixing model between two NDVI endmembers: bare soil NDVI (NDVI_o) and full vegetation NDVI (NDVI_∞). Usually it is assumed that NDVI_o is close to zero (NDVI_o ∼ 0.05) and is generally chosen from the lowest observed NDVI values. However, the mean soil NDVI computed from 2906 samples is much larger (NDVI = 0.2) and is highly variable (standard deviation = 0.1). We show that the underestimation of NDVI_o yields overestimations of Fg. The largest errors occur in grassland and shrubland areas. Using parameters for NDVI_o and NDVI_∞ derived from global scenes yields overestimations of Fg that are larger than 0.2 for the majority of U.S. land cover types when pixel NDVI values are 0.2 < NDVI_pixel < 0.4. When using conterminous U.S. scenes to derive NDVI_o and NDVI_∞, the overestimation is less (0.10-0.17 for 0.2 < NDVI_pixel < 0.4). As a result, parts of the conterminous U.S. are affected at different times of the year depending on the local seasonal NDVI cycle. We propose using global databases of NDVI_o along with information on historical NDVI_pixel values to compute a statistically most-likely estimate of Fg. Using in situ measurements made at the Sevilleta LTER, we show that this approach yields better estimates of Fg than using global invariant NDVI_o values estimated from whole scenes. At the two studied sites, the Fg estimate was adjusted by 52% at the grassland and 86% at the shrubland. More significant advances will require information on spatial distribution of soil reflectance.     

Variation in NDVI values with change in spatial resolution for semi-arid savanna vegetation: a case study in northwestern South Africa

Natural vegetation and crop-greening patterns in semi-arid savannas are commonly monitored using normalized difference vegetation index (NDVI) values from low spatial resolution sensors such as the Advanced Very High Resolution Radiometer (AVHRR) (1 km, 4 km) and Moderate Resolution Imaging Spectroradiometer (MODIS) (250 m, 500 m). However, because semi-arid savannas characteristically have scattered tree cover, the NDVI values at low spatial resolution suffer from the effect of aggregation of near-infrared and red energy from adjacent vegetated and non-vegetated cover types. This effect is seldom taken into consideration or quantified in NDVI analyses of the vegetation of semi-arid lands. This study examined the effect of pixel size on NDVI values of land-cover features for a semi-arid area, using the 1000 m, 250 m and 10 m pixel sizes. A rainy season Système Pour l'Observation de la Terre 5 (SPOT 5) High Resolution Geometric (HRG) image at 10 m spatial resolution was utilized. Following radiometric and geometric preprocessing, the 10 m pixel size of the image was aggregated to 250 m and 1000 m to simulate imagery at these pixel sizes, and then NDVI images at the spatial resolution scales of 10 m (NDVI_{10 m}), 250 m (NDVI_{250 m}), and 1000 m (NDVI_{1000 m}) derived from the respective images. The simulation of the NDVI_{250 m} image was validated against a concurrent 16 day MODIS NDVI composite (MOD13Q1) image, and the accuracy derived from the validation was generalized to the NDVI_{1000 m} image. With change from low to high spatial resolution, extreme magnitude NDVI values shifted towards the centre (mode) of the resulting approximately Gaussian NDVI distributions. There was a statistically significant difference in NDVI values at the three pixel sizes. Low spatial magnitude vegetation sites (woodland, cropland) had reductions of up to 28% in NDVI value between the NDVI_{10 m} and NDVI_{1000 m} scales. The results indicate that vegetation monitoring using low spatial resolution imagery in semi-arid savannas may only be indicative and needs to be supplemented by higher spatial resolution imagery.     

The influence of land-cover type on the relationship between NDVI–LST and LST-Tair

Air temperature (T_2m or T_air) measurements from 20 ground weather stations in Berlin were used to estimate the relationship between air temperature and the remotely sensed land surface temperature (LST) measured by Moderate Resolution Imaging Spectroradiometer over different land-cover types (LCT). Knowing this relationship enables a better understanding of the magnitude and pattern of Urban Heat Island (UHI), by considering the contribution of land cover in the formation of UHI. In order to understand the seasonal behaviour of this relationship, the influence of the normalized difference vegetation index (NDVI) as an indicator of degree of vegetation on LST over different LCT was investigated. In order to evaluate the influence of LCT, a regression analysis between LST and NDVI was made. The results demonstrate that the slope of regression depends on the LCT. It depicts a negative correlation between LST and NDVI over all LCTs. Our analysis indicates that the strength of correlations between LST and NDVI depends on the season, time of day, and land cover. This statistical analysis can also be used to assess the variation of the LST–T_2m relationship during day- and night-time over different land covers. The results show that LST_Day and LST_Night are correlated significantly (p = 0.0001) with T_2mDay (daytime air temperature) and T_2mNight (night-time air temperature). The correlation (r) between LST_Day and T_Day is higher in cold seasons than in warm seasons. Moreover, during cold seasons over every LCT, a higher correlation was observed during daytime than during night-time. In contrast, a reverse relationship was observed during warm seasons. It was found that in most cases, during daytime and in cold seasons, LST is lower than T_2m. In warm seasons, however, a reverse relationship was observed over all land-cover types. In every season, LST_Night was lower than or close to T_2mNight.     

The assessment of different vegetation indices for spatial disaggregating of thermal imagery over the humid agricultural region

ABSTRACT

Land surface temperature (LST) plays a significant role in surface water circulation and energy balance at both global and regional scales. Thermal disaggregation technique, which relies on vegetation indices, has been widely used due to its advantage in producing relatively high resolution LST data. However, the spatial enhancement of satellite LST using soil moisture delineated vegetation indices has not gained enough attention. Here we compared the performances of temperature vegetation dryness index (TVDI), normalized difference vegetation index (NDVI), and fractional vegetation coverage (FVC), in disaggregating LST over the humid agriculture region. The random forest (RF) regression was used to depict the relationship between LST and vegetation indices in implementing thermal disaggregating. To improve the model performance, we used the thin plate spline (TPS) approach to calibrate the RF residual estimation. Results suggested that the models based on TVDI performed better than those based on NDVI and FVC, with a reduced average root mean square error and mean absolute error of 0.20 K and 0.16 K, respectively. Moreover, based on the surface energy balance model, we found the surface evapotranspiration (ET) derived with the TVDI disaggregated LST as inputs achieved higher accuracy than those derived with NDVI and FVC disaggregated LST. It is indicated that TVDI, a soil moisture delineated vegetation indices, can improve the performance of LST enhancement and ET estimation over the humid agriculture region, when combining random forest regression and TPS calibration. This work is valuable for terrestrial hydrology related research.     

Scaling of impervious surface area and vegetation as indicators to urban land surface temperature using satellite data

Vegetation and impervious surface as indicators of urban land surface temperature (LST) across a spatial resolution from 30 to 960 m were investigated in this study. Enhanced thematic mapper plus (ETM+) data were used to retrieve LST in Nanjing, China. A land cover map was generated using a decision tree method from IKONOS imagery. Taking the normalized difference vegetation index (NDVI) and percent vegetation area (V) to present vegetated cover, and the normalized difference building index (NDBI) and percent impervious surface area (I) to present impervious surface, the correlation coefficients and linear regression models between the LST and the indicators were simulated. Comparison results indicated that vegetation had stronger correlation with the LST than the impervious surface at 30 and 60 m, a similar magnitude of correlation at 120 and 240 m, and a much lower correlation at 480 and 960 m. In total, the impervious surface area was a slightly better indicator to the LST than the vegetation because all of the correlation coefficients were relatively high (>0.5000) across the spatial resolution from 30 to 960 m. The indicators of LST, V and I are slightly better than the NDVI and NDBI, respectively, based on the correlation coefficients between the LST and the four indices. The strongest correlation of the LST and vegetation at the resolution of 120 m, and the strongest correlation between the LST and impervious surface at 120, 480 and 960 m, denoted the operational scales of LST variations.     

The role of directional LAI in determining the fAPAR–NDVI relationship when using active optical sensors in tall fescue (Festuca arundinacea) pasture

Remote sensing based spectral indices, such as the normalized difference vegetation index (NDVI), are often used to estimate the fraction of absorbed photosynthetically active radiation (fAPAR) in plant canopies. Owing to similar changes in both the NDVI and fAPAR as functions of varying solar illumination angle when using entirely passive sensors, the fAPAR–NDVI relationships are often stable, appearing insensitive to solar illumination angle. Active optical sensors (AOS) on the other hand, which have their own illuminating light source and are increasingly being used to measure NDVI (NDVI_AOS), do not respond to solar illumination geometry. Yet, the passive sensor-derived fAPAR component of the fAPAR–NDVI_AOS relationship remains affected by solar illumination angle. In this paper, a simple two-stream canopy model has been used to predict the fAPAR–NDVI_AOS relationships of a pasture canopy (tall fescue; Festuca arundinacea) for a nadir-viewing active optical NDVI sensor under conditions of varying solar elevation angle. Both the model derived and subsequent experimental measurements of the fAPAR–NDVI_AOS relationship in this pasture confirmed a strong dependence of the linear fAPAR–NDVI_AOS relationships on solar illumination angle. The modelled fAPAR–NDVI_AOS relationship only agreed with the field measurements when the ‘solar angle–dependent directional leaf area index’ (LAI_θs) of the canopy, as presented to the incoming solar photons, was used as opposed to the traditionally used ‘nadir version’ of the leaf area index (LAI). Users of AOS to measure indices such as NDVI must account for the solar illumination angle dependent LAI_θs when considering any fAPAR–NDVI_AOS relationship.     

Satellite-based and mesoscale regression modeling of monthly air and soil temperatures over complex terrain in Turkey

Simple regression algorithms were developed to quantify spatio-temporal dynamics of minimum and maximum air temperatures (T_min and T_max, respectively) and soil temperature for a depth of 0-5 cm (T_soil-5cm) across complex terrain in Turkey using Moderate Resolution Imaging Spectroradiometer (MODIS) data at a 500-m resolution. A total of 762 16-day MODIS composites (127 images × 6 bands) between 2000 and 2005 were averaged over a monthly basis to temporally match monthly T_min, T_max, and T_soil-5cm from 83 meteorological stations. A total of 60 (28 temporally averaged plus 32 time series-based) linear regression models of T_min, T_max, and T_soil-5cm were developed using best subsets procedure as a function of a combination of 12 explanatory variables: six MODIS bands of blue, red, near infrared (NIR), middle infrared (MIR), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI); four geographical variables of latitude, longitude, altitude, and distance to sea (DtS); and two temporal variables of month, and year. The best multiple linear regression models elucidated 65% (RMSE = 5.9 °C), 65% (RMSE = 5.1 °C), and 57% (RMSE = 6.9 °C) of variations in T_min, T_max, and T_soil-5cm, respectively, under a wide range of T_min (−34 to 25 °C), T_max (0.2-47 °C) and T_soil-5cm (−9 to 40 °C) observed at the 83 stations.     

Estimating northern peatland CO2 exchange from MODIS time series data

Studies using satellite sensor-derived data as input to models for CO₂ exchange show promising results for closed forest stands. There is a need for extending this approach to other land cover types, in order to carry out large-scale monitoring of CO₂ exchange. In this study, three years of eddy covariance data from two peatlands in Sweden were averaged for 16-day composite periods and related to data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled photosynthetic photon flux density (PPFD). Noise in the time series of MODIS 250 m vegetation indices was reduced by using double logistic curve fits. Smoothed normalized difference vegetation index (NDVI) showed saturation during summertime, and the enhanced vegetation index (EVI) generally gave better results in explaining gross primary productivity (GPP). The strong linear relationships found between GPP and the product of EVI and modeled PPFD (R² = 0.85 and 0.76) were only slightly stronger than for the product of EVI and MODIS daytime 1 km land surface temperature (LST) (R² = 0.84 and 0.71). One probable reason for these results is that several controls on GPP were related to both modeled PPFD and daytime LST. Since ecosystem respiration (ER) was largely explained by diurnal LST in exponential relationships (R² = 0.89 and 0.83), net ecosystem exchange (NEE) was directly related to diurnal LST in combination with the product of EVI and modeled PPFD in multiple exponential regressions (R² = 0.81 and 0.73). Even though the R² values were somewhat weaker for NEE, compared to GPP and ER, the RMSE values were much lower than if NEE would have been estimated as the sum of GPP and ER. The overall conclusion of this study is that regression models driven by satellite sensor-derived data and modeled PPFD can be used to estimate CO₂ fluxes in peatlands.     

A climatological study of the spatio-temporal variability of land surface temperature and vegetation cover of Vadodara district of Gujarat using satellite data

In the present study, a comprehensive assessment of the spatio-temporal variation of day-time and night-time land surface temperature (LST) and normalized difference vegetation index (NDVI) of Vadodara district of Gujarat in India from 2001 to 2012 has been carried out using satellite data. A significant cooling trend was observed in the day-time LST, whereas the night-time LST showed a distinct warming trend. The entire geographical extent of Vadodara was classified into different night-time LST classes to quantify the extent of the hot pockets, and it showed a clear-cut warming pattern for all the months of the year with an increase in the geographical areas under higher temperature range. Further analysis of Diurnal Temperature Range (DTR) also revealed a strong impact of the urbanization process, with annual DTR showing a decreasing trend at the rate of 0.29°C year^?1. An analysis of the vegetation cover of the district showed that on an average, the NDVI of the district increased during the study period. However, a micro-level examination of NDVI values depicted that the type of vegetation cover had drastically changed. The maximum NDVI values for months from May to December for 2012 were much lower than those of 2001 and 2006, indicating a change in vegetation pattern of the district. An assessment of the area under different NDVI values exhibited that for all the months of the year (except September), the total area with NDVI values of higher range (i.e. +0.5 and above) had substantially decreased from 2001 to 2012. The analysis revealed that for some of the months like February, while in 2001, 45% of district exhibited NDVI values above +0.5, but by 2012, it had decreased to only 18%, showing a drastic change in vegetation type and deterioration of the extent of thick dense vegetation.     

Downscaling MODIS LST in the East African mountains using elevation gradient and land-cover information

Land-surface temperature (LST) is strongly affected by altitude and surface albedo. In mountain regions where steep slopes and heterogeneous land cover are predominant, LST can vary significantly within short distances. Although remote sensing currently provides opportunities for monitoring LST in inaccessible regions, the coarse resolution of some sensors may result in large uncertainties at sub-pixel scales. This study aimed to develop a simple methodology for downscaling 1 km Moderate Resolution Spectroradiometer (MODIS) LST pixels, by accounting for sub-pixel LST variation associated with altitude and land-cover spatial changes. The approach was tested in Mount Kilimanjaro, Tanzania, where changes in altitude and vegetation can take place over short distances. Daytime and night-time MODIS LST estimates were considered separately. A digital elevation model (DEM) and normalized difference vegetation index (NDVI), both at 250 m spatial resolution, were used to assess altitude and land-cover changes, respectively. Simple linear regressions and multivariate regressions were used to quantify the relationship between LST and the independent variables, altitude and NDVI. The results show that, in Kilimanjaro, altitude variation within the area covered by a 1 km MODIS LST pixel can be up to ±300 m. These altitude changes can cause sub-pixel variation of up to ±2.13°C for night-time and ±2.88°C for daytime LST. NDVI variation within 1 km pixels ranged between –0.2 and 0.2. For night-time measurements, altitude explained up to 97% of LST variation, while daytime LST was strongly affected by land cover. Using multivariate regressions, the combination of altitude and NDVI explained up to 94% of daytime LST variation in Kilimanjaro. Finally, the downscaling approach proposed in this study allowed an improved representation of the influence of landscape features on local-scale LST patterns.     

Quantification of aboveground rangeland productivity and anthropogenic degradation on the Arabian Peninsula using Landsat imagery and field inventory data

The productivity of semi-arid rangelands on the Arabian Peninsula is spatially and temporally highly variable, and increasing grazing pressure as well as the likely effects of climatic change further threatens vegetation resources. Using the Al Jabal al Akhdar mountains in northern Oman as an example, our objectives were to analyse the availability and spatial distribution of aboveground net primary production (ANPP) and the extent and causes of vegetation changes during the last decades with a remote sensing approach. A combination of destructive and non-destructive biomass measurements by life-form specific allometric equations was used to identify the ANPP of the ground vegetation (< 50 cm) and the leaf and twig biomass of phanerophytes. The ANPP differed significantly among the life forms and the different plant communities, and the biomass of the sparsely vegetated ground was more than 50 times lower (mean = 0.22 t DM ha^− 1) than the biomass of phanerophytes (mean = 12.3 t DM ha^− 1). Among the different vegetation indices calculated NDVI proved to be the best predictor for rangeland biomass.Temporal trend analysis of Landsat satellite images from 1986 to 2009 was conducted using a pixel-based least square regression with the annual maximum Normalized Differenced Vegetation Index (NDVI_max) as a dependent variable. Additionally, linear relationships of NDVI_max and annual rainfall along the time series were calculated. The extent of human-induced changes was analysed using the residual trends method. A strongly significant negative biomass trend detected for 83% of the study area reflected a decrease in annual rainfall but even without clear evidence of deforestation of trees and shrubs, human-induced vegetation degradation due to settlement activities were also important.     

Assessing viewing and illumination geometry effects on the MODIS vegetation index (MOD13Q1) time series: implications for monitoring phenology and disturbances in forest communities in Queensland,Australia

Time series analysis of satellite data can be used to monitor temporal dynamics of forested environments, thus providing spatial data for a range of forest science, monitoring and management issues. The moderate resolution imaging spectroradiometer (MODIS) vegetation index (MOD13Q1) product has potential for monitoring forest dynamics and disturbances. However, the suitability of the product to accurately measure temporal changes due to phenology and disturbances is questionable as the effects of variable solar and viewing geometry have not been removed from these data. This study aimed to examine the impact that viewing and illumination geometry differences had on MOD13Q1 vegetation index values, and their subsequent ability to map changes arising from phenology and disturbances in a number of forest communities in Queensland, Australia. MOD13Q1 normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were compared to normalized NDVI and EVI (NDVI_normalized and EVI_normalized), which were derived from the reflectance modelled from a bidirectional reflectance distribution function (BRDF)/albedo parameters product (MCD43A1) using fixed viewing and illumination geometry. Time series plots of the vegetation index values from a number of pixels representing different forest types and known disturbances showed that the NDVI_normalized time series was more effective at capturing the changes in vegetation than the NDVI. MOD13Q1 NDVI showed higher seasonal amplitude, but was less accurate at capturing phenology and disturbances compared to the NDVI_normalized. The EVI was less affected by variable viewing and illumination geometry in terms of amplitude, but was affected in terms of time shift in periodicities providing erroneous information on phenology. More studies in different environments with appropriate vegetation phenology reference data will be needed to confirm these observations.     

Enhancing estimation accuracy of daily maximum,minimum, and mean air temperature using spatio-temporal ground-based and remote-sensing data in southern Iran

ABSTRACT

In this research, a model was proposed for improving the estimation of air temperature (T_a), which enhanced computation accuracy by combining remote sensing, station data, and spatio-temporal interpolation methods. Stepwise linear regression model was used to find the relationship between daily mean, maximum, and minimum air temperature (T_mean, T_max, and T_min, respectively) with daytime and night-time land surface temperature (LST), normalized difference vegetation index (NDVI) from the Moderate Resolution Imaging Spectroradiometer sensor, elevation, geometric temperature (T_geom, as a function of the day of the year and latitude), and solar radiation (R_s). Measured T_a from 75 stations in the Fars province in southern Iran were used during 2003–2012. The results of stepwise linear regression showed that among the considered variables daytime and night-time LST, NDVI, T_geom, R_s, and elevation were significant in the final model. For increasing the accuracy of estimation, four interpolation methods were considered and analysed for the residual errors of multiple linear regression model consisting regression kriging, spatio-temporal regression kriging (STRK), regression inverse distance weighting, and spatio-temporal regression inverse distance weighting (STRIDW). The result showed that the STRIDW method had the best accuracy among the considered methods and a significant improvement in the accuracy was achieved with this method comparing to the others. The accuracy of estimations was less than 2°C for T_max, T_min, and T_mean (root mean square error = 1.6°C, 1.84°C, and 1.43°C, respectively) for the validation year (2012). Finally, using the proposed models, it was possible to estimate daily air temperatures in the Fars province with 1 km resolution, which is higher than methods that used purely station-based or purely remote-sensing data.     

Using earth observation-based dry season NDVI trends for assessment of changes in tree cover in the Sahel

The co-existence of trees and grasses is a defining feature of savannah ecosystems and landscapes. During recent decades, the combined effect of climate change and increased demographic pressure has led to complex vegetation changes in these ecosystems. A number of recent Earth observation (EO)-based studies reported positive changes in biological productivity in the Sahelian region in relation to increased precipitation, triggering an increased amount of herbaceous vegetation during the rainy season. However, this ‘greening of the Sahel’ may mask changes in the tree–grass composition, with a potential reduction in tree cover having important implications for the Sahelian population. Large-scale EO-based evaluation of changes in Sahelian tree cover is assessed by analysing long-term trends in dry season minimum normalized difference vegetation index (NDVI_min) derived from three different satellite sensors: Système Pour l’Observation de la Terre (SPOT)-VEGETATION (VGT), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) dataset. To evaluate the reliability of using NDVI_min as a proxy for tree cover in the Sahel, two factors that could potentially influence dry season NDVI_min estimates were analysed: the total biomass accumulated during the preceding growing season and the percentage of burned area observed during the dry season. Time series of dry season NDVI_min derived from low-resolution satellite time series were found to be uncorrelated to dry grass residues from the preceding growing season and to seasonal fire frequency and timing over most of the Sahel (88%), suggesting that NDVI_min can serve as a proxy for assessing changes in tree cover. Good agreement (R² = 0.79) between significant NDVI_min trends (p < 0.05) derived from VGT and MODIS was found. Significant positive trends in NDVI_min were registered by both MODIS and VGT dry season NDVI_min time series over the Western Sahel, whereas trends based on GIMMS data were negative for the greater part of the Sahel. EO-based trends were generally not confirmed at the local scale based on selected study cases, partly caused by a temporal mismatch between data sets (i.e. different periods of analysis). Analysis of desert area NDVI_min trends indicates less stable values for VGT and GIMMS data as compared with MODIS. This suggests that trends in dry season NDVI_min derived from VGT and GIMMS should be used with caution as an indicator for changes in tree cover, whereas the MODIS data stream shows a better potential for tree-cover change analysis in the Sahel.     

Relationship between satellite-derived land surface temperatures, arctic vegetation types, and NDVI

Arctic vegetation distribution is largely controlled by climate, particularly summer temperatures. Summer temperatures have been increasing in the Arctic and this trend is expected to continue. Arctic vegetation has been shown to change in response to increases in summer temperatures, which in turn affects arctic fauna, human communities and industries. An understanding of the relationship of existing plant communities to temperature is important in order to monitor change effectively. In addition, variation along existing climate gradients can help predict where and how vegetation changes may occur as climate warming continues. In this study we described the spatial relationship between satellite-derived land surface temperature (LST), circumpolar arctic vegetation, and normalized difference vegetation index (NDVI). LST, mapped as summer warmth index (SWI), accurately portrayed temperature gradients due to latitude, elevation and distance from the coast. The SWI maps also reflected NDVI patterns, though NDVI patterns were more complex due to the effects of lakes, different substrates and different-aged glacial surfaces. We found that for the whole Arctic, a 5 °C increase in SWI along the climate gradient corresponded to an increase in NDVI of approximately 0.07. This result supports and is of similar magnitude as temporal studies showing increases of arctic NDVI corresponding to increases in growing season temperatures over the length of the satellite record. The strongest positive relationship between NDVI and SWI occurred in partially vegetated and graminoid vegetation types. Recently deglaciated areas, areas with many water bodies, carbonate soil areas, and high mountains had lower NDVI values than predicted by SWI. Plant growth in these areas was limited by substrate factors as well as temperature, and thus is likely to respond less to climate warming than other areas.     

Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI

Current models of vegetation dynamics using the normalized vegetation index (NDVI) time series perform poorly for high-latitude environments. This is due partly to specific attributes of these environments, such as short growing season, long periods of darkness in winter, persistence of snow cover, and dominance of evergreen species, but also to the design of the models. We present a new method for monitoring vegetation activity at high latitudes, using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI. It estimates the NDVI of the vegetation during winter and applies a double logistic function, which is uniquely defined by six parameters that describe the yearly NDVI time series. Using NDVI data from 2000 to 2004, we illustrate the performance of this method for an area in northern Scandinavia (35 × 162 km², 68° N 23° E) and compare it to existing methods based on Fourier series and asymmetric Gaussian functions. The double logistic functions describe the NDVI data better than both the Fourier series and the asymmetric Gaussian functions, as quantified by the root mean square errors. Compared with the method based on Fourier series, the new method does not overestimate the duration of the growing season. In addition, it handles outliers effectively and estimates parameters that are related to phenological events, such as the timing of spring and autumn. This makes the method most suitable for both estimating biophysical parameters and monitoring vegetation phenology.     

Retrieval and validation of land surface temperature (LST) from NOAA AVHRR thermal images of Gujarat,India

A study was undertaken to retrieve land (soil-vegetation complex) surface temperature (LST) over a 100 km 2 100 km area in Gujarat (India) using thermal bands (channel 4 and 5) and estimated emissivity from atmospherically corrected NDVI, derived from NOAA-14 AVHRR data. The LST values were compared with near synchronous soil and air temperature measurements over five sites in December and May 1997 during Land Surface Processes Experiment (LASPEX) in Gujarat, India. The estimated LST of a semi-arid mixed agricultural barren landscape at 10.00 GMT was found to vary from 302 to 305.6 K on 13 December 1997 (winter) and from 317.5 to 328.5 K at 08.30 GMT on 15 May 1997 (Summer). During December, the LST values were near midway between air temperature (AT) and soil surface temperature (ST) with mean bias of m 2.9 K and 7.0 K respectively. However, in May, the LST values were found to be closer to ST, which may be due to lower fractional vegetation cover and NDVI.     

Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China

Urbanization is taking place at an unprecedented rate around the world, particularly in China in the past few decades. One of the key impacts of rapid urbanization on the environment is the effect of urban heat island (UHI). Understanding the effects of landscape pattern on UHI is crucial for improving the ecology and sustainability of cities. This study investigated how landscape composition and configuration would affect UHI in the Shanghai metropolitan region of China, based on the analysis of land surface temperature (LST) in relation to normalized difference vegetation index (NDVI), vegetation fraction (Fv), and percent impervious surface area (ISA). Two Landsat ETM+ images acquired on March 13 and July 2, 2001 were used to estimate LST, Fv, and percent ISA. Landscape metrics were calculated from a high spatial resolution (2.5 × 2.5 m) land-cover/land-use map. Our results have showed that, although there are significant variations in LST at a given fraction of vegetation or impervious surface on a per-pixel basis, NDVI, Fv, and percent ISA are all good predictors of LST on the regional scale. There is a strong negative linear relationship between LST and positive NDVI over the region. Similar but stronger negative linear relationship exists between LST and Fv. Urban vegetation could mitigate the surface UHI better in summer than in early spring. A strong positive relationship exists between mean LST and percent ISA. The residential land is the biggest contributor to UHI, followed by industrial land. Although industrial land has the highest LST, it has limited contribution to the overall surface UHI due to its small spatial extend in Shanghai. Among the residential land-uses, areas with low- to-middle-rise buildings and low vegetation cover have much high temperatures than areas with high-rise buildings or areas with high vegetation cover. A strong correlation between the mean LST and landscape metrics indicates that urban landscape configuration also influences the surface UHI. These findings are helpful for understanding urban ecology as well as land use planning to minimize the potential environmental impacts of urbanization.     

Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa

The estimation of near surface air temperature (Ta) is useful for a wide range of applications such as agriculture, climate related diseases and climate change studies. Air temperature is commonly obtained from synoptic measurements in weather stations. In Africa, the spatial distribution of weather stations is often limited and the dissemination of temperature data is variable, therefore limiting their use for real-time applications. Compensation for this paucity of information may be obtained by using satellite-based methods. However, the derivation of near surface air temperature (Ta), from the land surface temperature (Ts) derived from satellite is far from straight forward. Some studies have tried to derive maximum Ta from satellites through regression analysis but the accuracy obtained is quite variable according to the study. The main objective of this study was to explore the possibility of retrieving high-resolution Ta data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Ts products over different ecosystems in Africa. First, comparisons between night MODIS Ts data with minimum Ta showed that MODIS nighttime products provide a good estimation of minimum Ta over different ecosystems (with (ΔTs − Ta) centered at 0 °C, a mean absolute error (MAE) = 1.73 °C and a standard deviation = 2.4 °C). Secondly, comparisons between day MODIS Ts data with maximum Ta showed that (ΔTs − Ta) strongly varies according to the seasonality, the ecosystems, the solar radiation, and cloud-cover. Two factors proposed in the literature to retrieve maximum Ta from Ts, i.e. the Normalized Difference Vegetation Index (NDVI) and the Solar Zenith Angle (SZA), were analyzed. No strong relationship between (ΔTs − Ta) and (i) NDVI and (ii) SZA was observed, therefore requiring further research on robust methods to retrieve maximum Ta.     

Reflectance assessment of seasonal and annual changes in biomass and CO2 uptake of a Mediterranean shrubland submitted to experimental warming and drought

We aimed to evaluate how the remote sensing vegetation indices NDVI and PRI responded to seasonal and annual changes in an early successional stage Mediterranean coastal shrubland canopy that was submitted to experimental warming and drought simulating predicted climate change for the next decades. These conditions were obtained by using a new non-intrusive methodological approach that increases the temperature and prolongs the drought period by using roofs that automatically cover the vegetation after the sunset or when it rains. On average, warming increased air temperature by 0.7 °C and soil temperature by 1.6 °C, and the drought treatment reduced soil moisture by 22%. We measured spectral reflectance at the canopy level and at the individual plant level seasonally during 4 years. Shrubland NDVI tracked the community development and activity. In control and warming treatments, NDVI increased with the years while it did not change in the drought treatment. There was a good relationship between NDVI and both community and individual plant biomass. NDVI also decreased in summer seasons when some species dry or decolour. The NDVI of E. multiflora plant individuals was lower in autumn and winter than in the other seasons, likely because of flowering. Shrubland PRI decreased only in winter, similarly to the PRI of the most dominant species, G. alypum. At this community scale, NDVI was better related than PRI to photosynthetic activity, probably because photosynthetic fluxes followed canopy seasonal greening in this complex canopy, which includes brevideciduous, annual and evergreen species and variable morphologies and canopy coverage. PRI followed the seasonal variations in photosynthetic rates in E. multiflora and detected the decreased photosynthetic rates of drought treatment. However, PRI did not track the photosynthetic rates of G. alypum plants which have lower LAIs than E. multiflora. In this community, which is in its early successional stages, NDVI was able to track biomass, and indirectly, CO₂ uptake changes, likely because LAI values did not saturate NDVI. Thus, NDVI appears as a valid tool for remote tracking of this community development. PRI was less adequate for photosynthetic assessment of this community especially for its lower LAI canopies. PRI usefulness was also species-dependent and could also be affected by flowering. These results will help to improve the interpretation of remote sensing information on the structure and physiological status of these Mediterranean shrublands, and to gain better insight on ecological and environmental controls on their ecosystem carbon dioxide exchange. They also show the possibility of assessing the impacts of climate change on shrubland communities.