Suivi du développement végétal au cours de l'été 1984 dans le Sahel Malien

A ground data-collection programme was initiated to establish a calibration between the normalized difference vegetation index (NDVI) from the NOAA Advanced Very High Resolution Radiometer (AVHRR) and grassland biomass. Thirty sites were selected representing a range of Sahclian vegetation communities in the Gourma region of Mali and monitored during the 1984 growing season. The sites were 1?km square and located within larger areas of homogeneous terrain. The herbaceous and woody strata were sampled every fourteen days, and above-ground green biomass and rainfall data were collected. Ground and airborne radiometer data were recorded to facilitate interpretation of the satellite data, and aerial photographs were taken to provide estimates of tree and shrub density. AVHRR LAC and GAC data were acquired and a thermal cloud mask was applied to the data. NDVI values were extracted for the ground sites and correlation analysis performed. Low correlation coefficients were calculated for the ground measured green biomass and satellite NDVI (0,67). The correlation between the maximum NDVI and the total biomass produced during the season was 0,73. A value of 0,05 was determined as the NDVI associated with the minimum vegetation cover identifiable by the satellite (100 kg/ha). Explanation is given for the possible causes for such low correlations, including the very low biomass production associated with the 1984 drought conditions, atmospheric haze and dust and poor locational accuracy of the satellite data     

Assessment of ecological conditions associated with the 1980/81 desert locust plague upsurge in West Africa using environmental satellite data

National Oceanic and Atmospheric Administration (NOAA) satellite data from the Advanced Very High Resolution Radiometer (AVHRR) sensor were analysed to document the vegetation biomass dynamics associated with the regional desert-locust upsurge in West Africa during 1980/81, which affected an area of some 600 000 km² in Mali, Niger and Algeria. Comparisons were made among locust population survey reports, rainfall records from eighteen stations in the same area, and the satellite data in vegetation index format. The satellite-recorded temporal and spatial distributions of desert vegetation biomass were closely correlated with both the locust population surveys and the available rainfall data. An attempt was made to develop a quantitative relationship between a satellite-derived potential breeding activity factor (PBAF) and the observed desert locust populations. Analysis of the multitemporal satellite data set indicates that, had the NOAA/AVHRR vegetation index data been operationally available in June 1980, effective preventive control measures would have only been necessary for an area of 600 km².     

Satellite remote sensing of rangelands in Botswana II. NOAA AVHRR and herbaceous vegetation

NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) global-area coverage (GAC) data for the visible and near-infrared bands were used to investigate the relationship between the normalized difference vegetation index (NDVI) and the herbaceous vegetation in three representative rangeland types in eastern Botswana. Regressions between Landsat MSS band-7/band-5 ratios and field measurements of the cover of the live parts of herbaceous plants, above-ground biomass of live herbaceous plants and bare ground were used in conjunction with MSS data in order to interpolate the field data to 144 km² areas for comparison with blocks of nine AVHRR GAC pixels. NOAA NDVI data were formed into 10-day composites in order to remove cloud cover and extreme off-nadir viewing angles. Both individual NDVI composite data and multitemporal integrations throughout the period May 1983-April 1984 were compared with the field data.

In multiple linear regressions, the cover and biomass of live herbaceous plants and bare ground measurements accounted for 42, 56 and 19 per cent respectively of the variation in NDVI. When factors were included in I he regression models to specify the site and date of acquisition of the data, between 93 and 99 per cent of the variation in NDVI was accounted for. The total herbaceous biomass at the end of the season was positively related to integrated NDVI, up lo the maximum biomass observed in a 12km × 12km area (590kgha^?1)- These results give a different regression of herbaceous biomass values on integrated AVHRR NDVI to that reported by Tucker et at. (1985 b) for Senegalese grasslands. The effect of the higher cover of the tree canopy in Botswana on this relationship and on the detection of forage available to livestock is discussed.     

Environmental monitoring and crop forecasting in the Sahel through the use of NOAA NDVI data. A case study: Niger 1986–89

Abstract

Several investigations have shown that NOAA NDVI data accumulated during a rainy season can be related to total rainfall or final primary productivity in the Sahel. However, serious problems can arise when looking for quantitative relations to monitor and forecast crop yield from NDVI values. Geographical variability can affect such relations, while the use of data taken from a whole season is impractical for forecasting. The present paper proposes a complete methodology of NDVI data processing which only utilizes NOAA AVHRR scenes from the first part of successive rainy seasons. A series of basic corrections are first applied to the original data to obtain reliable NDVI maximum value composites at the middle of the rainy seasons considered. Next, the variability in land resources is accounted for by means of a standardization process which normalizes the mean NDVI levels of some areas on the relevant multi-temporal averages and standard deviations. In this way, good estimates of the actual condition of vegetation can be obtained in relation to the local seasonal trend

The methodology was applied to the Sahelian sub-departments of Niger with data from four years (1986–1989). The most interesting result achieved concerns the estimation of final grain (millet and sorghum) yield for the sub-departments by the end of July with a mean error of about 0·08 T ha ^?1. This timely evaluation could be of great utility in the context of an efficient drought early warning system.     

Satellite remote sensing of total herbaceous biomass production in the senegalese sahel: 1980–1984

NOAA-6 and NOAA-7 1-km and 4-km advanced very high resolution radiometer data were obtained at frequent intervals during the 1980, 1981, 1982, 1983, and 1984 rainy or growing seasons in the Sahel zone of northern Senegal. Above-ground herbaceous biomass clippings, visual estimates, and hand-held radiometer measurements of herbaceous vegetation were made during and at the conclusion of the rainy seasons for 4 of the 5 years. The satellite data were compared to sampled above-ground biomass data and the integral of the satellite data over time was compared to end-of-growing-season above-ground total dry biomass. A strong correlation between the integrated NOAA-7 satellite data and end-of-season above-ground dry biomass was found for ground samples collected over a 3-year period. The satellite data documented the highly variable precipitation regime in the Senegalese Sahel both within years and among years and suggest a direct method of monitoring Sahelian total herbaceous biomass production in areas where the percentage cover of woody species is less than 10%. Predicted average total dry biomass production was 1093 kg/ha for 1981, 536 kg/ha for 1982, 178 kg/ha in 1983, and 55 kg/ha in 1984 for the $\text{～ 30,000 km}{}^2$ study area.     

Spatial degradation of satellite data

Abstract

Two aspects of spatial degradation of satellite data are examined. The first describes a technique for spatially degrading high-resolution satellite data to produce comparable data sets over a range of coarser resolutions. In this study seven spatial resolution data sets are produced from Landsat Multispectral Scanner (MSS) data resulting in spatial resolutions ranging from 79 m to 4 km applying a spatial filter designed to simulate sensor response. The simulation is demonstrated for part of the Superior National Forest, Minnesota. The second part of the paper examines spatial degradation of coarse resolution data to provide data compression for the production of global-scale data sets. The on-board sampling approach adopted by the National Oceanographic and Atmospheric Administration (NOAA) to produce the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data from the 1 km Large Area Coverage (LAC) data, is compared to other sampling procedures. Six sampling procedures were compared for seven terrain types. The GAC sampling procedure provided a relatively poor representation of the 1 km data, particularly for heterogeneous areas. Coefficients of determination for the GAC sampling compared to the original data ranged from 0.49?0.76. Sampling procedures incorporating averaging resulted in a decrease in the variance as compared with the original data. Sampling procedures adopting single-value selection had higher variances and produced data values directly comparable with those from the original data. Sampling scheme design should consider data fidelity requirements as well as the engineering constraints of on-board processing.     

Analysis of the temporal and spatial vegetation patterns in a semi-arid environment observed by NOAA AVHRR imagery and spectral ground measurements

The borderline between Israel and Sinai is characterized by a sharp contrast that is caused by the low spectral reflectance on the Israeli side (Negev desert) and the high spectral reflectance on the bare Egyptian side (Sinai desert). This contrast across the political border has been discussed in many publications over the last two decades. In this study, satellite images acquired by NOAA Advanced Very High Resolution Radiometer (AVHRR) over a time period of 3 years (June 1995 to June 1998) were analysed. In addition, extensive field studies were carried out on the Israel side of the border. The current research shows that the reflectance values in Sinai seem to be quite stable over the entire year, however reflectance values in the Negev show a significant difference between the dry and the rainy seasons. Comparison between the AVHRR-derived Normalized Difference Vegetation Index (NDVI ) values and rainfall data from the Negev shows that the highest AVHRR-derived NDVI values occur a few weeks after the main rainfall. Field observations, based on spectrometer measurements of different surface components (bare sands, biological soil crusts, annuals, and perennials) and estimation of vegetation cover on the Israeli side of the border, show that the peak NDVI of the perennials occurs at the same time as the satellite observed peak. The spectral difference between both sides of the border during the dry season is caused by the dense cover of the higher vegetation and the biological soil crusts and by the photosynthetic activity of perennials during the dry season. The highest difference between both sides during the rainy season is caused by the photosynthetic activity and vegetation cover of the annuals and perennials.     

Biomass quantification of Andean wetland forages using ERS satellite SAR data for optimizing livestock management

Spatio-temporal information on the biomass of totora reeds and bofedal water-saturated Andean grasslands, which are a critical forage resource for smallholders in Bolivia's Altiplano, is needed to promote their protection and improve livestock management. Satellite radar data appear well adapted to map biomass and to monitor biomass changes in this environment for two reasons: (a) the C-band (5.3 GHz) radar data is particularly sensitive to vegetation biomass when the canopy is over an underlying water surface or a water-saturated soil; this is through the dominant scattering mechanisms involving vegetation-water surface interaction; (b) the cloud cover during the growing period which corresponds to the rainy season. This paper assesses the potential of ERS satellite radar data for retrieving biomass information, which is spatially highly variable owing to the numerous small, nonuniform areas of totora harvesting and bofedal grazing. Ground data, including vegetation humid and dry biomass, were collected over 18 months during satellite descending passes at 12 sites located between the Eastern Cordillera and Titicaca Lake, representing three vegetation units: shoreline and inland totoras, and Puna bofedales.ERS-SAR data were analysed as a function of plant biomass at homogeneous totora and bofedal areas. Because of the small size of these areas (typically 20×30 m), the SAR data need to be processed using an advanced multitemporal filter which improves radiometric resolution without significant reduction of the spatial resolution. The radar backscattering coefficient (σ° in dB) measured by ERS was found to be sensitive at both per site and per vegetation unit levels to humid and dry biomass of totora reeds and bofedal grasslands. The sensitivity of the signal to biomass variation is high for dry biomass ranges less than 1 kg/m² for totora, and less than 2 kg/m² for bofedal. The corresponding biomass maps provided by inversion of SAR data are valuable information for livestock management for three critical periods: after the calving season (October-November), when animal pressure is most significant; toward the end of the rainy season (March-April), as an indicator of coming trends to promote the adoption of measures aimed at preventing shortages during the winter season; in the middle of the winter dry season (June-July), to adjust animal charge.     

Assessing the biomass dynamics of Andean bofedal and totora high-protein wetland grasses from NOAA/AVHRR

Biomass measurements of totora and bofedal Andean wetland grasses in the Bolivian Northern Altiplano were correlated over a growing season to vegetation indices derived from 1-km visible and near-infrared bands of the advanced very high resolution radiometer (AVHRR) instrument flown on the NOAA-14 polar-orbiting meteorological satellite. This article discusses the potential and limits of these indices for the assessment of the spatial and temporal variation of biomass and of the fraction of the photosynthetic active radiation absorbed by these herbaceous native forages growing in water-saturated environments. Bidirectional reflectance distribution function (BRDF) normalization was also investigated based on simple kernel-driven models. BRDF normalized difference vegetation index (NDVI) performed the best for both totorales and bofedales vegetation associations, followed by the uncorrected maximum-value composite NDVI. BRDF normalized NDVI was shown to be sensitive to the green leaf or photosynthetically active biomass.Estimation of biomass production after Kumar and Monteith (1982) was used to determine the efficiency of solar energy conversion into biomass (ε_b) for the main phenological periods, corresponding to the rainy and dry seasons. Two approaches were investigated for the biomass production estimation: the first one is based on monthly field biomass measurements; the second one is based on estimates from the regression computed previously using Roujean's BRDF normalized NDVI. The values found for these efficiencies for the rainy season agree with those of the literature for grasslands of temperate regions. For the dry season, more accurate information on totora and bofedal senescence and on animal consumption is required to get a reasonable efficiency value. This is not surprising, as other workers have reported biomass estimation with remotely sensed data to be most relevant to the growing season.     

Determination of land surface spectral reflectances using Meteosat and NOAAA/AVHRR Shortwave Channel Data

Abstract

A method to derive surface spectral reflectances from currently available Meteosat geostationary and NOAA/AVHRR polar orbiting satellite data is described. Broadband reflectance was derived from Meteosat measurements while NOAA/AVHRR vegetation index provided a spectral weighting which enabled the spectral reflectances on either side of 0-7 μm to be estimated. The method takes into account satellite calibrations, viewing geometry, and correction of some atmospheric effects. Conversion from narrow-band to broadband reflectances is discussed. The method was applied to a month of data to obtain the surface spectral reflectances of Africa which are compared with some data sets used by climate modellers, in order to assess them and to monitor their seasonal and interannual changes on a global scale.     

Monitoring phenological cycles of desert ecosystems using NDVI and LST data derived from NOAA-AVHRR imagery

The potential of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) for monitoring phenological cycles in semi-arid lands has been demonstrated in this study. Attention was focused on two areas located only a few kilometres apart but across the political border between the Negev (Israel) and Sinai (Egypt). Although the areas are identical from the pedological, geomorphological, and climatic points of view, due to different land management, the Negev is under a continuous rehabilitation process while Sinai is under a desertification process. Four years of digital data were used to compute the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperatures (LST) over two sampling polygons. The NDVI was used to monitor the vegetation reaction to rainfall, while LST proved to be a good indicator of seasonal climatic fluctuations. Using these biological and physical variables, the potential for following the vegetation dynamics throughout the year was demonstrated. Through cluster analysis, it was shown that the movements of the Sinai desertified side in the LST-NDVI space are only due to seasonal climatic fluctuations. On the Israeli recovered side, on the other hand, three different parts of the annual ecological cycle of the indigenous vegetation are evident: the dry season in which plants reduce their activity, the rainy season, and a growing season characterised by relatively intense biological activity. Within the LST-NDVI space it was also shown that Sinai is positioned similarly to the Sahara biome and the Negev similarly to the Sahel biome. Finally, LST-NDVI data were used to estimate phenological parameters that can be exploited for defining protection policies or, on the long term, for climate change studies.     

Assessment of millet yields and production in northern Burkina Faso using integrated NDVI from the AVHRR.

Abstract

It is possible to assess crop yields at the end of the growing season in a semi-arid environment using data from meteorological satellites. This is the result of a work carried out in northern Burkina Faso. The technique used is based on linear correlation between millet yield and the time integral of the Normalized Difference Vegetation Index (iNDVI) derived from NOAA AVHRR data. In contrast to earlier related studies, the correlation has been established using satellite data extracted exclusively within the agricultural domain. The integration period for the iNDVI correponds to the reproductive phase only of the growing period of millet. Furthermore, iNDVI can also be used to estimate the acreage or the agricultural domain, by the application of a suitable threshold to classify areas into agricultural and non-agricultural domains.

It is therefore possible to assess the yield and the acreage of the agricultural domain and to derive an estimate of the millet production of the area by the end of the season, on the basis of NOAA AVHRR data alone.     

Estimation of the evapotranspiration using surface and satellite data

Abstract

A method to derive evapotranspiration from a combination of satellite and conventional data is investigated. For this purpose NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) infrared images on clear days of various seasons are used to derive surface temperatures over France. These temperatures are then compared to the shelter-height temperatures collected at the WMO (World Meteorological Organization) standard meteorological stations at the time of satellite overpass. The difference between the two temperatures varies both with season and latitude. To analyse those results we use a model of the soil-vegetation interface, forced by a reconstruction of the surface fluxes derived from the WMO data. The model simulates reasonably well the diurnal and seasonal variations in the difference between satellite surface temperature and surface-air temperature. The corresponding latitudinal variations which occur in summer may be interpreted in terms of evapotranspiration. The limitations of this method are determined by a model sensitivity study; in particular they are due to the role played by tall vegetation.     

Leaf area index mapping in northern Canada

Leaf area index (LAI) is an important structural vegetation parameter that is commonly derived from remotely sensed data. It has been used as a reliable indicator for vegetation's cover, status, health and productivity. In the past two decades, various Canada-wide LAI maps have been generated by the Canada Centre for Remote Sensing (CCRS). These products have been produced using a variety of very coarse satellite data such as those from SPOT VGT and NOAA AVHRR satellite data. However, in these LAI products, the mapping of the Canadian northern vegetation has not been performed with field LAI measurements due in large part to scarce in situ measurements over northern biomes. The coarse resolution maps have been extensively used in Canada, but finer resolution LAI maps are needed over the northern Canadian ecozones, in particular for studying caribou habitats and feeding grounds.

In this study, a new LAI algorithm was developed with particular emphasis over northern Canada using a much finer resolution of remotely sensed data and in situ measurements collected over a wide range of northern arctic vegetation. A statistical relationship was developed between the in situ LAI measurements collected over vegetation plots in northern Canada and their corresponding pixel spectral information from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. Furthermore, all Landsat TM and ETM+ data have been pre-normalized to NOAA AVHRR and SPOT VGT data from the growing season of 2005 to reduce any seasonal or temporal variations. Various spectral vegetation indices developed from the Landsat TM and ETM?+?data were analysed in this study. The reduced simple ratio index (RSR) was found to be the most robust and an accurate estimator of LAI for northern arctic vegetation. An exponential relationship developed using the Theil–Sen regression technique showed an R ² of 0.51 between field LAI measurement and the RSR. The developed statistical relationship was applied to a pre-existing Landsat TM 250 m resolution mosaic for northern Canada to produce the final LAI map for northern Canada ecological zones. Furthermore, the 250 m resolution LAI estimates, per ecological zone, were almost generally lower than those of the CCRS Canada-wide VGT LAI maps for the same ecozones. Validation of the map with LAI field data from the 2008 season, not used in the derivation of the algorithm, shows strong agreement between the in situ LAI measurement values and the map-estimated LAI values.     

Synergism between NOAA-AVHRR and Meteosat data for studying vegetation development in semi-arid West Africa

Abstract

Rainfall estimates, based on cold cloud duration estimated from Meteosat data, are compared with vegetation development depicted by data of the normalized difference vegetation index (NDVI) from the National Oceanic and Atmospheric Administration's (NOAA) advanced very high resolution radiometer (AVHRR) for part of the Sahel. Decadal data from the 1985 and 1986 growing seasons are examined to determine the synergism of the datasets for rangeland monitoring. There is a general correspondence between the two datasets with a marked lag between rainfall and NDVI of between 10 and 20 days. This time lag is particularly noticeable at the beginning of the rainy season and in the more northern areas where rainfall is the limiting factor for growth. Principal component analysis was used to examine deviations from the general relationship between rainfall and the NDVI. Areas of low NDVI values for a given input of rainfall were identified: at a regional scale, they give an indication of areas of low production potential and possible degradation of ecosystems. This study demonstrates in a preliminary way the synergism of such datasets derived from satellite--borne sensors with coarse spatial resolution, which may provide new information for the improved management of the Sahelian grasslands.     

Agreement assessment of NOAA/AVHRR NDVI with Landsat TM NDVI for mapping burned forested areas

Meteorological satellites are appropriate for operational applications related to early warning, monitoring and damage assessment of forest fires. Environmental or resources satellites, with better spatial resolution than meteorological satellites, enable the delineation of the affected areas with a higher degree of accuracy. In this study, the agreement of two datasets, coming from National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Landsat TM, for the assessment of the burned area, was investigated. The study area comprises a forested area, burned during the forest fire of 21-24 July 1995 in Penteli, Attiki, Greece. Based on a colour composite image of Landsat TM a reference map of the burned area was produced. The scatterplot of the multitemporal Normalized Difference Vegetation Index (NDVI) images, from both Landsat TM and NOAA/AVHRR sensors, was used to detect the spectral changes due to the removal of vegetation. The extracted burned area was compared to the digitized reference map. The synthesis of the maps was carried out using overlay techniques in a Geographic Information System (GIS). It is illustrated that the NOAA/AVHRR NDVI accuracy is comparable to that from Landsat TM data. As a result NOAA/AVHRR data can, operationally, be used for mapping the extent of the burned areas.     

Correction of the effect of Sun-sensor-target geometry in NOAA AVHRR data

Models of determining the effects of the bidirectional reflectance distribution function (BRDF) of different surfaces and of eliminating the effect of Sun-sensor-target geometry from the remotely sensed satellite data are actual. The objective of this study is to develop a simple relation between the Sun-sensor-target geometry and the remotely sensed vegetation index. In this investigation 238 National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images were used over Hungary during the period 1996-98. The greenness vegetation index (the difference between the reflectance values of near-infrared and visible channels) was used between days of the year 140-200, because the greenness values can be considered as constant in this period over the agricultural areas. The so-called 'hot spot effect' can be observed in the variation of reflectance values with different viewing zenith angles of the sensor. A simple quadratic relation was found between the raw AVHRR greenness values and the angle enclosed by the Sun-target and target-sensor directions over the agricultural areas, forests and grasslands. A correction method was developed to eliminate the effect of the Sun-sensor-target geometry, which it is hoped will improve the accuracy of yield forecasting and estimation procedures using NOAA AVHRR data.     

Technical note. Agricultural,hydrologic and oceanographic studies in Bangladesh with NOAA AVHRR data

Abstract

This paper presents some results of studies made with NOAA Advanced Very High Resolution Radiometer (AVHRR) data in the fields of agriculture, hydrology and oceanography in Bangladesh. The techniques of processing the raw image data are also discussed. The data for four different dates in 1984 and 1985 have been used. The 1984 data were obtained from NOAA/NESDIS of U.S. A. and the 1985 data were received in real time at the SPARRSO Meteorological Satellite Ground Station. Data from the AVHRR channels 1 and 2 were used to calculate the vegetation index (normalized) which was found to be a useful tool for monitoring vegetation conditions from time to time. These data have also application in monitoring and studying flood conditions in and outside the country. The thermal channels of the AVHRR (channels 4 and 5) have been used for calculating the surface temperatures of the Bay of Bengal. These channels revealed some noteworthy surface features in the Bay.     

NOAA AVHRR and its uses for rainfall and evapotranspiration monitoring

Abstract

NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) Global Vegetation Indices (GVI) were used during the 1986 rainy season (June-September) over Senegal to monitor rainfall. The satellite data were used in conjunction with ground-based measurements so as to derive empirical relationships between rainfall and GVI. The regression obtained was then used to map the total rainfall corresponding to the growing season, yielding good results. Normalized Difference Vegetation Indices (NDVI) derived from High Resolution Picture Transmission (HRJT) data were also compared with actual evapotranspiration (ET) data and proved to be closely correlated with it with a time lapse of 20 days.