Soil Impact on Satellite Based Vegetation Monitoring in Sahelian Mali

Advanced very high resolution radiometer (AVHRR) data provide the longest available time series of global satellite observations and have been extensively used. The Land Long-Term Data Record (LTDR) project has generated daily surface reflectance and normalized difference vegetation index (NDVI) products from AVHRR. However, residual cloud and aerosol contamination in the LTDR AVHRR surface reflectance and NDVI products significantly limits their applications and results in temporal and spatial inconsistencies in subsequent downstream products. Based on the LTDR AVHRR surface reflectance, a temporally continuous vegetation indices-based land-surface reflectance reconstruction (VIRR) method was refined in this study to generate Global LAnd Surface Satellite (GLASS) AVHRR NDVI and surface reflectance products from 1982 to 2015. The daily LTDR AVHRR surface reflectance data were first aggregated into eight-day intervals. The aggregated surface reflectance data were used to calculate NDVI, and a robust smoothing algorithm was used to reconstruct continuous and smooth NDVI upper envelopes, which were used to identify cloud-contaminated surface reflectance values. Then the surface reflectance time series was reconstructed from cloud-free surface reflectance values by incorporating the upper envelopes of the NDVI time series as constraints. The results show that the refined VIRR method successfully removes NDVI and surface reflectance values contaminated by clouds and can reconstruct temporally continuous NDVI and land-surface reflectance time series. Comparison of the GLASS AVHRR NDVI product with the third-generation Global Inventory Monitoring and Modeling System (GIMMS3g) and the moderate resolution imaging spectroradiometer (MODIS) NDVI products indicates that these NDVI products exhibit similar spatial patterns, but the GIMMS3g NDVI values were clearly higher than the GLASS AVHRR and MODIS NDVI values in tropical forest regions and the 50°N−60°N latitude band, particularly in July. Comparisons with the MODIS NDVI values over the BELMANIP (Benchmark Land Multisite Analysis and Intercomparison of Products) sites demonstrate that the GLASS AVHRR NDVI product provides better performance (RMSE = 0.1007 and Bias = 0.0518) than the GIMMS3g NDVI product (RMSE = 0.1288 and Bias = 0.0852). The temporal profiles of all these NDVI products exhibited consistent seasonal variations, but the temporal smoothness of the GLASS AVHRR NDVI product was superior to that of the GIMMS3g and MODIS NDVI products. The GLASS AVHRR and GIMMS3g NDVI products show consistent trends in most situations, but the trends of the GLASS AVHRR NDVI product were slightly more pronounced than those of the GIMMS3g NDVI product for each biome type. Comparison of the GLASS AVHRR surface reflectance product with MODIS surface reflectance product indicates the GLASS AVHRR and MODIS surface reflectance showed similar seasonal and interannual variations and the GLASS AVHRR surface reflectance was in good agreement with the MODIS surface reflectance, especially in the red band.

Normalized Difference Vegetation Index (NDVI) is generally recognized as a good indicator of terrestrial vegetation productivity. Understanding climatic influences, in particular precipitation and temperature, on NDVI enables prediction of productivity changes under different climatic scenarios. We examined temporal responses of remotely sensed NDVI to precipitation and temperature during a nine-year period (1989-97) in Kansas. Biweekly (every two weeks) and monthly precipitation data were derived from 410 weather stations and biweekly temperature data were derived from 17 weather stations inside and around the borders of Kansas. Biweekly and monthly climate maps were derived by interpolation. Biweekly growing season (March-October) NDVI values for Kansas were calculated using National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) NDVI images. Average growing season NDVI values were highly correlated with precipitation received during the current growing season and seven preceding months (15-month duration); biweekly NDVI values were correlated with precipitation received during 2-4 preceding biweekly periods; and response time of NDVI to a major precipitation event was typical 1-2 biweekly periods (2-4 weeks). Temperature was positively correlated with NDVI early and late in the growing season, and there was a weak negative correlation between temperature and NDVI in the mid growing season. Precipitation has the primary influence on NDVI and, by inference, on productivity. The relationship between precipitation and NDVI is strong and predictable when viewed at the appropriate spatial scale.

Abstract. We analysed vegetation dynamics in Tierra del Fuego steppes using Normalized Difference Vegetation Index (NDVI) data provided by advanced very high‐resolution radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) polar satellite. Our objective, at a regional scale, was to analyse the spatial variability of NDVI dynamics in relation to parent material and geographic location, representing the fertility and climate gradients respectively; at a local scale, it was to analyse the inter‐annual variability associated with climate and its relation with sheep production indices. The general pattern of NDVI dynamics was analysed with Principal Component Analysis. We found that the geographic location was more important than landscape type in explaining NDVI dynamics despite the fact that the variation in landscape type reflects a fertility gradient strongly associated with floristic composition and secondary productivity.Discriminant Analysis was performed to identify the variables that better distinguish geographic units. The Northern region (with the lowest precipitation and the highest temperatures) had lower NDVI values over the year. In the Central region, NDVI reached the highest value of the season, surpassing both other regions. The Southern region (the coldest and moistest) had its growth pattern displaced towards the summer. For the Central region we analysed 10 years of monthly NDVI data with PCA. We found that precipitation from August to December and winter temperature are the most important determinants of overall NDVI values. Lamb production was correlated with spring and early summer NDVI values. Sheep mortality is affected by low NDVI values in late summer and high annual amplitude. Satellite information allowed us to characterize the vegetation dynamics of three ecological areas across the Fuegian steppe.

The article used two NOAA-14 Advanced Very High Resolution Radiometer (AVHRR) datasets to assess flood damage in the middle and lower reaches of China's Changjiang River (Yangtze River) in 1998. As the AVHRR is an optical sensor, it cannot penetrate the clouds that frequently cover the land during the flood season, and this technology is greatly limited in flood monitoring. However the widely used normalized difference vegetation index (NDVI) can be used to monitor flooding, since water has a much lower NDVI value than other surface features. Though many factors other than flooding (e.g. atmospheric conditions, different sun-target-satellite angles, and cloud) can change NDVI values, inundated areas can be distinguished from other types of ground cover by changes in the NDVI value before and after the flood after eliminating the effects of other factors on NDVI. AVHRR data from 26 May and 22 August, 1998 were selected to represent the ground conditions before and after flooding. After accurate geometric correction by collecting GCPs, and atmospheric and angular corrections by using the 6S code, NDVI values for both days and their differences were calculated for cloud-free pixels. The difference in the NDVI values between these two times, together with the NDVI values and a land-use map, were used to identify inundated areas and to assess the area lost to the flood. The results show a total of 358,867 ha, with 207,556 ha of cultivated fields (paddy and non-irrigated field) inundated during the flood of 1998 in the middle and lower reaches of the Changjiang River Catchment; comparing with the reported total of 321,000 and 197,000 ha, respectively. The discrimination accuracy of this method was tested by comparing the results from two nearly simultaneous sets of remote-sensing data (NOAA's AVHRR data from 10 September, 1998, and JERS-1 synthetic aperture radar (SAR) data from 11 September, 1998, with a lag of about 18.5 hr) over a representative flooded region in the study area. The results showed that 67.26% of the total area identified as inundated using the NOAA data was also identified as inundated using the SAR data.

The relationship between normalized difference vegetation index (NDVI) patterns obtained from high spatial resolution aircraft and low spatial resolution satellite data (Advanced Very High Resolution Radiometer (AVHRR)) was investigated with the intent of using multilevel data to scale carbon flux models in Arctic tundra ecosystems. Despite variable illumination conditions during the aircraft missions and maximum value compositing of the AVHRR data, the difference between 3 km average aircraft and AVHRR NDVI values was generally constant along each flight transect. However, the magnitude of the offset differed between flight dates and small lakes had a greater effect on area averaged aircraft NDVI values than on the satellite values. A cloud index was calculated using incident solar radiation measured by the aircraft and this index was used to identify periods when the aircraft NDVI values may have been biased by cloud cover. Removal of NDVI values based on a cloud index threshold did not appear to be justified given the marginal improvement in the relationship between the two NDVI datasets. If the systematic difference between AVHRR and aircraft NDVI values can be determined, then the scaling of carbon flux models based on the NDVI should be a viable approach in Arctic ecosystems.

The processing and analysis of a set of NOAA-14/AVHRR images, obtained from the Satellite Active Archive (SAA) for 1995 and 1999, is discussed. The NDVI values were calculated using channel 1 and channel 2 reflection values after standard radiometric and geometric corrections. The average Normalized Difference Vegetation Index (NDVI) of the main irrigation systems in the Zayandeh Rud Basin, Isfahan, Iran, was then determined for each image. This made it possible to analyse the temporal evolution of these NDVI values for the individual irrigated areas during 1999. The National Oceanic and Atmospheric Administration (NOAA) NDVI values can, in principle, also be used for the determination of the size of the actual irrigated areas of the principal systems. However, calibration of the method proved necessary, because in most NOAA pixels the vegetation is only a fraction of the pixel area. Through the use of Geographic Information System (GIS) operations on a Landsat 7 image (1 August 1999), the actual irrigated areas in Borkhar, Abshar Left and Right, Nekouabad Left and Right, were determined for that particular date. Five NOAA images were then selected for the period July-August 1999. This made it possible to establish regression relations linking NOAA NDVI values to the size of the net irrigated systems, which were then applied to the 1995 dataset.

Fire mapping science can benefit from standard techniques for analyzing time‐series data. The Z transform produces the z‐score, termed a standardized variable because the units are dimensionless standard deviations. The Z transform represents a simple, older way to characterize non‐image data, but is presented here as a new way to enhance fire scar anomalies in image time‐series data. The transform is invertible, and can be applied to any continuous, gridded data. Time series data from the Advanced Very High Resolution Radiometer (AVHRR) are featured. When applied in the x, y (i.e., spatial) plane of AVHRR Normalized Difference Vegetation Index (NDVI) data, the Z transform places each NDVI value in the statistical context at the time of image formation, producing Z‐standardized NDVI (ZNDVI) values. Invoking the Z transform across the z (time series) plane of ZNDVI images produces a multitemporal Z (MTZ) score for each pixel in a ZNDVI target image for a selected time step. The MTZ image thus depicts the deviation or departure of a given pixel, for a specific step in the series, relative to the mean for that pixel across the time series. A case study demonstrates an MTZ enhancement of AVHRR NDVI data that enhances in the Rincon Mountains east of Tucson, Arizona unusually low NDVI values associated with a 1994 wildfire scar. Enhancements are confirmed using higher‐resolution remote sensor data from the Landsat Thematic Mapper (TM).

Wetness and warmth are the principal factors that control global vegetation distribution. This paper investigates climate–vegetation relationships at a global scale using the normalized difference vegetation index (NDVI), warmth index (WAI), and wetness index (WEI).The NDVI was derived from a global, 20‐year Advanced Very High Resolution Radiometer (AVHRR) dataset with 4‐min resolution. The WEI was defined as the ratio of precipitation to potential evaporation. The WAI was defined as the cumulative monthly mean temperature that exceeds 5 °C annually. Meteorological data from the International Satellite Land‐Surface Climatology Project Initiative II (ISLSCP II) dataset were used to calculate the WEI and WAI. All analyses used annual values based on averages from 1986 to 1995 at 1 × 1 degree resolution over land. Relationships among NDVI, WEI, and WAI values were examined using a vegetation‐climate diagram with the WEI and WAI as orthogonal coordinates.The diagram shows that large NDVI values correspond to areas of tropical and temperate forests and large WEI and WAI values. Small WEI and WAI values are associated with small NDVI values that correspond to desert and tundra, respectively.Two major regimes are revealed by the NDVI vegetation‐climate diagram: wetness dominant and warmth dominant. Wetness dominates mid‐ and low latitudes. Warmth dominates high latitudes north of 60°N or elevated land such as the Tibetan Plateau. The boundary between the two regimes roughly corresponds to the vegetation boundary between taiga forest and southern vegetation. Over northern Eurasia, the boundary occurs in areas where the NDVI is large and the maximum monthly temperature is around 18 °C. Copyright © 2006 Royal Meteorological Society.

Average integrated National Oceanic and Atmospheric Administration (NOAA) advanced very‐high resolution radiometer (AVHRR) local area coverage (LAC) normalized difference vegetation index (NDVI) was used to extrapolate ground truth herbaceous biomass estimates. Calibration of average integrated NDVI to herbaceous biomacs was via inverse predictions of NDVI regressed on herbaceous biomass linear regressions. Weighted least squares regression analysis were typically employed with R2 values ranging from 0.25 to 0.80. The herbaceous biomass average estimates of 200 and 1,000 kg/ha had a standard error of less than 200 kg/ha. A pastoral zone mask was developed and applied to obtain arrondissement (county) pastoral zone herbaceous biomass estimates. Total pastoral zone herbaceous biomass indicated that 1987 and 1990 were drought years and that 1986 and 1988 were favorable years for livestock and herders.

Over the past two decades, a key indicator of climate change has been the long time series of global maps of the normalized difference vegetation index (NDVI), derived from remotely sensed data acquired with a series of NOAA advanced very high resolution radiometer (AVHRR) instruments from space. These NDVI values are calculated from relatively broad AVHRR channels in the red and near-infrared regions. Continuation of this long term data set is extremely valuable for climate-related research, However, sometime in the coming decade, the AVHRR time series measurements will no longer be continued. Instead, the measurements will be made using newer generation satellite instruments having narrower channels and improved spatial resolution. For example, the moderate resolution imaging spectroradiometer (MODIS) onboard the Terra spacecraft has several narrow channels in the 0.4-1.0 spectral range. The NDVI values derived from the MODIS red channel and near-IR channel will be biased compared to those derived from the broader AVHRR channels because of differences in channel positions and widths for the two instruments. The narrow MODIS near-IR channel is only slightly affected by atmospheric water vapor absorption, while the broad AVHRR near-IR channel is strongly affected by water vapor absorption. As a result, the largest bias comes from the near-IR channels on the two instruments. To a lesser extent, the bias also comes from the differences between the red channel positions and the widths of MODIS and AVHRR instruments. In this paper, the authors describe a practical method for simulating AVHRR NDVI values using several narrower MODIS channels in the 0.4-1.0 /spl mu/m spectral range, including the MODIS green channel and the water vapor absorption channel.

Normalized Difference Vegetation Index (NDVI) data derived from Advanced Very High Resolution Radiometer (AVHRR) data are influenced by cloud contamination, which is common in individual AVHRR scenes. Maximum value compositing (MVC) of NDVI data has been employed to minimize cloud contamination. Two types of weekly NDVI composites were built for crop seasons in summer: one from all available AVHRR data (named the traditional NDVI composite) and the other from solely cloud-free AVHRR data (named the conditional NDVI composite). The MVC method was applied to both composites. The main objective of this study was to compare the two types of NDVI composites using Texas data. The NDVI seasonal profiles produced from the conditional NDVI composites agreed with the field measured leaf area index (LAI) data, reaching maximum values at similar times. However, the traditional NDVI composites showed irregular patterns, primarily due to cloud contamination. These study results suggest that cloud detection for individual AVHRR scenes should be strongly recommended before producing weekly NDVI composites. Appropriate AVHRR data pre-processing is important for composite products to be used for short-term vegetation condition and biomass studies, where the traditional NDVI composite data do not eliminate cloud-contaminated pixels. In addition, this study showed that atmosphere composition affected near-infrared reflectance more than visible reflectance. The near-infrared reflectance was increasingly adjusted through atmospheric correction.

The NDVI3g time series is an improved 8-km normalized difference vegetation index (NDVI) data set produced from Advanced Very High Resolution Radiometer (AVHRR) instruments that extends from 1981 to the present. The AVHRR instruments have flown or are flying on fourteen polar-orbiting meteorological satellites operated by the National Oceanic and Atmospheric Administration (NOAA) and are currently flying on two European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar-orbiting meteorological satellites, MetOp-A and MetOp-B. This long AVHRR record is comprised of data from two different sensors: the AVHRR/2 instrument that spans July 1981 to November 2000 and the AVHRR/3 instrument that continues these measurements from November 2000 to the present. The main difficulty in processing AVHRR NDVI data is to properly deal with limitations of the AVHRR instruments. Complicating among-instrument AVHRR inter-calibration of channels one and two is the dual gain introduced in late 2000 on the AVHRR/3 instruments for both these channels. We have processed NDVI data derived from the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) from 1997 to 2010 to overcome among-instrument AVHRR calibration difficulties. We use Bayesian methods with high quality well-calibrated SeaWiFS NDVI data for deriving AVHRR NDVI calibration parameters. Evaluation of the uncertainties of our resulting NDVI values gives an error of ± 0.005 NDVI units for our 1981 to present data set that is independent of time within our AVHRR NDVI continuum and has resulted in a non-stationary climate data set.

A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986–89 using upper-air data from the European Centre for Medium-Range Weather Forecasts to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data—the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness data—to represent the urban and rural landscape. Local NDVI and DMSP values were calculated for each station using the mean NDVI and DMSP values from a 3 km × 3 km area centered over the given station. Regional NDVI and DMSP values were calculated to represent a typical rural value for each station using the mean NDVI and DMSP values from a 1° × 1° latitude–longitude area in which the given station was located. Models for the United States were then developed for monthly maximum, mean, and minimum temperatures using data from over 1000 stations in the U.S. Cooperative Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network. Local biases, or the differences between the model predictions using the observed NDVI and DMSP values, and the predictions using the background regional values were calculated and compared with the results of other research. The local or urban bias of U.S. temperatures, as derived from all U.S. stations (urban and rural) used in the models, averaged near 0.40°C for monthly minimum temperatures, near 0.25°C for monthly mean temperatures, and near 0.10°C for monthly maximum temperatures. The biases of monthly minimum temperatures for individual stations ranged from near −1.1°C for rural stations to 2.4°C for stations from the largest urban areas. There are some regions of the United States where a regional NDVI value based on a 1° × 1° latitude–longitude area will not represent a typical “rural” NDVI value for the given region, Thus, for some regions of the United States, the urban bias of this study may underestimate the actual current urban bias. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available. It is anticipated that results from global application will provide insights into the urban bias of the global temperature record.

Monitoring of global vegetation dynamics for assessment of primary productivity using NOAA advanced very high resolution radiometer

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 (NDVI10 m), 250 m (NDVI250 m), and 1000 m (NDVI1000 m) derived from the respective images. The simulation of the NDVI250 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 NDVI1000 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 NDVI10 m and NDVI1000 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.