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The recent thrust on digital agriculture (DA) has renewed significant research interest in the automated delineation of agricultural fields. Most prior work addressing this problem has focused on detecting medium to large fields, while there is strong evidence that around 40% of the fields worldwide and 70% of the fields in Asia and Africa are small. The lack of adequate labelled images for small fields, huge variations in their colour, texture, and shape, and faint boundary lines separating them make it difficult to develop an end-to-end learning model for detecting such fields. Hence, in this paper, we present a multi-stage approach that uses a combination of machine learning and image processing techniques. In the first stage, we leverage state-of-the-art edge-detection algorithms such as holistically nested edge detection (HED) to extract first-level contours and polygons. In the second stage, we propose image-processing techniques to identify polygons that are non-fields, over-segmentations, or noise and eliminate them. The next stage tackles under-segmentations using a combination of a novel 'cut-point' based technique and localized second-level edge detection to obtain individual parcels. Since a few small, non-cropped but vegetated or constructed pockets can be interspersed in areas that are predominantly croplands, in the final stage, we train a classifier for identifying each parcel from the previous stage as an agricultural field or not. In an evaluation using high-resolution 1.19 m imagery sourced from DigitalGlobe, we show that our approach has a high F-Score of 0.84 in areas with large fields and reasonable accuracy with an F-Score of 0.73 in areas with small fields, which is encouraging. We also apply our techniques without any additional specialized processing to coarser resolution open satellite imagery and find the approach to be reasonable for delineating large fields with such imagery. [ABSTRACT FROM AUTHOR]

A concentration of surface mass has a distinct, localized signature in Gravity Recovery and Climate Experiment (GRACE) K-band range rate (KBRR) data. This fact is exploited in the regional solutions for mass concentration parameters (mascons) made at the Goddard Space Flight Center (GSFC). In this paper we explore an experimental set of regionally constrained mascon blocks over Southern Africa where a system of 1.25° × 1.5° and 1.5° × 1.5° blocks has been designed. The blocks are divided into hydrological regions based on drainage patterns of the largest river basins, and are constrained in different ways. We show that the use of regional constraints, when solving mascon parameters of different hydrological regions independently, yields more detail and variation than comparable spherical harmonic solutions and mascon solutions using isotropic constraints. We validate our results over Lake Malawi with water level from altimetry. Results show that weak constraints across regions in addition to intra-regional constraints are necessary, to reach reasonable mass variations. [ABSTRACT FROM AUTHOR]

Achieving food security in particular in Africa continues to pose a major challenge to humankind. It is clear that the future agricultural potential of Africa plays a critical role in meeting this challenge. Although crop yield can be estimated with a degree of reliability using a limited sample of ground observations, the exact crop acreage and the spatial distribution are rarely available. Even though remote sensing offers the ability to produce a rapid and up-to-date land use and land cover database for agricultural monitoring, there are only a few countries in Africa where higher resolution satellite data such as Landsat have been used for land cover map production at the national level. However a number of global products have been produced which contain information on cropland extent. This paper will outline a comparison of four sources of land cover data to determine which product is the most suitable for agricultural monitoring and for the subsequent development of a crop mask. The land cover products used are the Global Land Cover Map (GLC-2000), the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MOD12V1), the SAGE (Center for Sustainability and the Global Environment) and the AFRICOVER dataset from the Food and Agriculture Organisation (FAO). Both the GLC-2000 and MODIS land cover products are at a resolution of 1 km2 while AFRICOVER, based on visual interpretation of 30-m resolution Landsat images, is available at a much finer resolution. The four land cover products are first aggregated to the same resolution so that they can be compared. The legend categories of the four land cover products in this study are reconciled using the method developed in Fritz and See [Fritz, S. and See, L., 2005, Comparison of land cover maps using fuzzy agreement. International Journal of Geographic Information Science, 19, pp. 787-807.] and See and Fritz [See, L.M. and Fritz, S., 2005, A user-defined fuzzy logic approach to comparing global land cover products. In 14th European Colloquium on Theoretical and Quantitative Geography, 9-12 September 2005, Lisbon, Portugal.] that allows overlap between legend definitions to be taken into account. Once the legend definitions between the different land cover products are reconciled, the maps are then compared with national and sub-national statistics. Analysis is undertaken at both continental and national scales as well as sub-national for Sudan and Eritrea. The study generally concludes that MODIS has the tendency to underestimate cropland cover when compared with FAO statistics or AFRICOVER data, whereas GLC-2000 tends to overestimate cropland cover in those countries that are located at the northern transition zone of subtropical shrubland and semi-desert areas. In this area MODIS and SAGE show a relatively similar cropland distribution. Even though the SAGE database has been calibrated with national statistics, it does not perform better than the other two datasets overall, and has highlighted the fact that the SAGE data show regional weaknesses and should be replaced in certain regions by more recent datasets such as GLC-2000 and MODIS, or ideally by a hybrid product that combines the best of the three products, depending upon the region and country. [ABSTRACT FROM AUTHOR]

The analysis of feedbacks between continental surfaces and the atmosphere is one of the key factors to understanding African Monsoon dynamics. For this reason, the monitoring of surface parameters, in particular soil moisture, is very important. Satellite remote sensing appears to be the most suitable means of obtaining data relevant to such parameters. The present paper presents a methodology applied to the mapping and monitoring of surface soil moisture over the Kori Dantiandou region in Niger, using data provided by the ASAR/ENVISAT radar instrument. The study is based on 15 sets of ASAR/ENVISAT C-band radar data, acquired during the 2004 and 2005 rainy seasons. Simultaneously with radar acquisitions, ground soil moisture measurements were carried out in a large number of test fields. Soil moisture was estimated only for fields with bare soil or low-density vegetation, using low-incidence-angle radar data (IS1 configuration). A mask was developed, using SPOT/HRV data and DTM, for use over areas characterized by high-density vegetation cover, pools, and areas with high slopes. Soil moisture estimations are based on horizontal- and vertical-polarization radar data. In order to double the temporal frequency of soil moisture estimations, IS2 data were used with IS1 data, with all data normalized to a single incidence angle. A high correlation is observed between in situ measurements and processed radar data. An empirical inversion technique is proposed, to estimate surface soil moisture from dual-polarization data with a spatial resolution of approximately 1 km. Surface soil moisture maps are presented for all the studied sites, at various dates in 2004 and 2005. Of particular interest, these maps reveal convective precipitation scales associated with strong spatial variations in surface soil moisture. [ABSTRACT FROM AUTHOR]

This paper describes the use of high-spatial-resolution ASTER data to determine the accuracy of the moderate-resolution MODIS active fire product. Our main objective was to develop a methodology to use ASTER data for quantitative evaluation of the MODIS active fire product and to apply it to fires in southern Africa during the 2001 burning season. We utilize 18 ASTER scenes distributed throughout the Southern Africa region covering the time period 5 August 2001 to 6 October 2001. The MODIS fire product is characterized through the use of logistic regression models to establish a relationship between the binary MODIS 'fire'/'no fire' product and summary statistics derived from ASTER data over the coincident MODIS pixel. Probabilities of detection are determined as a function of the total number of ASTER fires and Moran's I, a measure of the spatial heterogeneity of fires within the MODIS pixel. The statistical analysis is done for versions 3 and 4 of the MODIS fire-detection algorithm. It is shown that the algorithm changes have a positive effect on the fire-product accuracy. [ABSTRACT FROM AUTHOR]

The goal of this research was to establish inter-sensor relationships between IKONOS and Landsat-7 ETM+ data. Dry and wet season images were acquired on the same date or about the same date from IKONOS and ETM+ sensors to enable direct comparison between the two distinctly different data types. The images were from three distinct ecoregions located in African rainforests and savannas that encompass a wide range of land use/land cover classes and ecological units. The IKONOS NDVI had a high degree of correlation with ETM+ NDVI with R² values between 0.67 and 0.72. Intersensor model equations relating IKONOS NDVI with ETM+ NDVI were determined. The characteristics that contribute to the increased sensitivity in dynamic ranges of IKONOS NDVI relative to ETM+ NDVI were attributed to: (1) radiometric resolution that adds more bits per data point (11-bit IKONOS data as opposed to 8-bit ETM+); and (2) spatial resolution that helped in resolving spectral characteristics at micro landscape units. Spectral bandwidths of the two sensors had no effect on the dynamic ranges of NDVIs. Overall, the IKONOS data showed greater sensitivity to landscape units and ecological characteristics when compared with Landsat-7 ETM+ data. Across ecoregions and land use/land cover classes, the IKONOS NDVI dynamic range (-0.07 to 0.71) was considerably greater than the ETM+ NDVI dynamic range (-0.24 to 0.46). IKONOS data explained greater variability (R²=0.73) in agroforest biomass when compared with ETM+ data (R²=0.66). The inter-sensor relationships presented in this paper are expected to facilitate better understanding and proper interpretation of terrestrial characteristics studied using multiple sensors over time periods. [ABSTRACT FROM AUTHOR]

Large-scale land cover information is a crucial input in numerical climatic change modelling. Traditionally, this information is encoded as vegetation maps based on ground surveys made at selected points, and is of poor resolution. Nowadays, improvements in land cover mapping/monitoring are obtained by using the Normalized Difference Vegetation Index (NDVI) calculated from NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite sensor data. In this paper, a Digital Vegetation Model (DVM) was derived in an attempt to get an accurate and upgradable digital image of African land cover types, using 1985-1991 NOAA AVHRR data. First, the main seasonal features of Africa are extracted from the NDVI data. Then (unsupervised) classification, using only the principal components analysis (PCA) first components of the NOAA multi-spectral data (the visible channels 1 and 2, and the thermal channel 4) on the IDRISI32 GIS platform, yields our macro-scale seven-category African DVM, consistent with Köppen climatic classification. Subsequent reclassifications, referring to known African sub-regional or local features (vegetation, eco-region maps, altitude, geomorphology), finally lead us to our fine-tuned regional-scale 30 category DVM. Including channel 4 data in our scheme has been pivotal for allowing full-scale classification from dense forests to sparsely or non-vegetated areas. Usually, this is barely possible in NDVI-only classifications. [ABSTRACT FROM AUTHOR]

The total and individual aerosol direct radiative effects (ADREs) were estimated for clear-sky conditions using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) observations and radiative transfer model. In this study, a parametric sensitivity analysis was performed for the Santa Barbara DISORT Atmospheric Radiative Transfer model using a global sensitivity analysis method. Uncertainties in the ADREs due to aerosol optical properties and surface albedo errors were evaluated. The single-scattering albedo and asymmetry factor for marine, dust, pollution, and smoke aerosols required for the model calculations were obtained from the classified AErosol RObotic NETwork observations. The estimated average global ADREs and errors at the top-of-atmosphere (TOA) and the surface were −2.36 ± 0.54 and −4.78 ± 2.2 Wm−2, respectively. In regions with higher dust, pollution, and smoke aerosol loading, the ADREs exhibited significant seasonal variability. In the Sahara and Arabian deserts, during the June-July-August season with higher dust aerosol loading, the seasonal average ADREs in the region were −3.23 and −21.37 Wm−2 at the TOA and surface, respectively. In the Indian region, during the March–April–May season with higher pollution aerosol loading, the ADREs were −10.33 and −28.04 Wm−2 at the TOA and surface, respectively. In Southern Africa, the smoke aerosol with a single-scattering albedo of 0.87 caused negative radiative effects at the TOA, and during the September-October-November season with higher smoke aerosol loading, the seasonal average ADREs were −2.34 and −6.36 Wm−2 at the TOA and surface, respectively. [ABSTRACT FROM AUTHOR]

Rural demographic studies have not led much in the field of remote sensing. In Africa, as well as in some developed countries, the existing demographic data are not suitable for planners' needs. To create accurate data, our paper explores a practical modelling technique, as well as less expensive solution techniques. We used data from an official demographic survey carried out in 1988 and a HRV1 satellite image of 1990. With a pixel-by-pixel textural basis analysis, we selected meaningful measure pixels for land cover units classification (mixed pixels were automatically removed). We then computed linear regression models. In the first model, the endogenous and heterogenous variables are respectively dwelling unit and cultivated unit. Furthermore, we calculated a regression from Census data (endogenous variable) and cultivated unit (exogenous data extracted from a HRV image). Finally we adjusted demographic data with two laws of distributions: exponential law and Pareto law. This article opens up the possibility of using remote sensing to estimate population sizes through environmental indicators more accurately. We obtained consistent adjusted correlation coefficients. En Afrique, et dans certains pays developpes, la pratique de recensements, amplement mise en cause par les bailleurs de fonds, est menacee de disparition. Nous proposons des pistes nouvelles pour la production d'une information demographique adaptee aux besoins des planificateurs et orientee vers la realisation de la comptabilite sociale. Pour cela, nous avons utilise une image HRV et les donnees du recensement officiel de 1988. Apres des traitements preliminaires bases sur la texture de notre image, nous avons extrait les villages dont nous disposons des statistiques officielles. Nous avons etabli des modeles de regression lineaire ou dans un premier temps la surface habitee est la variable endogene et la surface cultivee la variable exogene. Dans un deuxieme temps la population (recensement de 1988) est la variable endogene et la surface cultivee (extaite de l'image) la variable exogene. Enfin nous terminons par un ajustement des donnees de population par deux lois de distribution: loi exponentielle et loi de Pareto. [ABSTRACT FROM AUTHOR]

Temporal relations between AVHRR NDVI and rainfall data over East Africa at 10-day and monthly time scales have been analysed using distributed lag models. On average, only 10 per cent of the variation in 10-day NDVI values could be explained by concurrent and preceding rainfall. Corresponding values for monthly data was 36 per cent. If it is assumed that rainfall data can be used as an indicator of vegetation development the study indicates that AVHRR NDVI may have limitations for temporal vegetation monitoring in these environments. [ABSTRACT FROM AUTHOR]

Accurate and up-to-date land use land cover (LULC) mapping has long been a challenge in Africa. Recently, three LULC maps with moderate spatial resolution (20 m to 100 m) have been developed using multiple Earth observation datasets for 2015–2016 for the whole continent, which provide unprecedented spatial detail of the land surface for Africa. This study aimed to compare these three recent African LULC maps (i.e. the Copernicus Global Land Service Land Cover (CGLS-LC100, 100 m), European Space Agency Sentinel-2A Land Cover (ESA-S2-LC20, 20 m) and Finer Resolution Observation and Monitoring of Global Land Cover for Africa version 2 (FROM-GLC-Africa30, 30 m)) using a validation sample set and statistics from the FAO. The results indicated that the accuracy of the three datasets was unevenly distributed in spatial extent and area estimation. All the three datasets achieve an accuracy of above 60% and the fraction layer of CGLS-LC100 showed the best consistency with FAO statistics in the area. However, great disagreement in spatial details was found among three products, with 43.12% of the total area in Africa was in low agreement. The LULC mapping regions with the highest uncertainty were southeast Africa, the Sahel region and the Eastern Africa Plateau. Uncertainty was most closely related to elevation and precipitation changes along latitude/longitude. [ABSTRACT FROM AUTHOR]

Monitoring land surface phenology (LSP) trends is important in understanding how both climatic and non-climatic factors influence vegetation growth and dynamics. Controlling for land-cover changes in these analyses has been undertaken only rarely, especially in poorly studied regions like Africa. Using regression models and controlling for land-cover changes, this study estimated LSP trends for Africa from the enhanced vegetation index (EVI) derived from 500 m surface reflectance Moderate-Resolution Imaging Spectroradiometer (MOD09A1), for the period from 2001 to 2015. Overall end of season showed slightly more pixels with significant trends (12.9% of pixels) than start of season (11.56% of pixels) and length of season (LOS) (5.72% of pixels), leading generally to more 'longer season' LOS trends. Importantly, LSP trends that were not affected by land-cover changes were distinguished from those that were influenced by land-cover changes such as to map LSP changes that have occurred within stable land-cover classes and which might, therefore, be reasonably associated with climate changes through time. As expected, greater slope magnitudes were observed more frequently for pixels with land-cover changes compared to those without, indicating the importance of controlling for land cover. Consequently, we suggest that future analyses of LSP trends should control for land-cover changes such as to isolate LSP trends that are solely climate-driven and/or those influenced by other anthropogenic activities or a combination of both. [ABSTRACT FROM AUTHOR]

Maize (Zea mays L.) is the second most commonly grown crop worldwide and number one staple food in Africa where it accounts for more than 50% of the energy requirements. However, despite its widespread cultivation and the significance of maize information in Africa, maize crop maps and yield forecasts are hardly available. Yet, systematic area, spatial distribution, and maize yield estimates are important in understanding and addressing food security in Africa. Objective monitoring of maize yield statisics in a systematic way is possible with remotely sensed data. However, absence of maize yield forecasts using remote sensing in Africa has been attributed to the cost of acquiring satellite imagery and the heterogeneity of agricultural landscapes. The recent advances in sensors technology and availability of free high-resolution (spatial and temporal) multispectral satellite images afford an opportunity to forecast maize yield as well as mapping its spatial distribution in near real-time basis. This review gives an overview of maize yield estimation using remotely sensed information and its potential application in a fragmented and highly granular agricultural landscapes in Africa, including inherent challenges and research needs. The review was motivated by challenges faced by researchers and national agricultural statistical services agents when forecasting maize yield using conventional ground-based survey methods. These problems include, but are not limited to, restricted accuracy, and cost and time spent resulting in missed opportunities in food security early warning systems and proper developmental interventions. We conclude that by picking multispectral sensors with high spatial, temporal, and spectral resolution, as well as appropriate classification techniques and accurate ground-truthing data, remote sensing can be a practical option for estimating maize grain yield and its spatio-temporal dynamics in heterogeneous African agricultural landscapes for designing appropriate developmental interventions and technological out scaling. [ABSTRACT FROM AUTHOR]

We report on the first systematic ground-based validation of the US Air Force Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) night lights imagery to detect rural electrification in the developing world. Drawing upon a unique survey of villages in Senegal and Mali, this study compares night-time light output from the DMSP-OLS against ground-based survey data on electricity use in 232 electrified villages and additional administrative data on 899 unelectrified villages. The analysis reveals that electrified villages are consistently brighter than unelectrified villages across annual composites, monthly composites, and a time series of nightly imagery. Electrified villages appear brighter because of the presence of streetlights, and brighter villages tend to have more streetlights. By contrast, the correlation of light output with household electricity use and access is low. We further demonstrate that a detection algorithm using data on night-time light output and the geographic location of settlements can accurately classify electrified villages. This research highlights the potential to use night lights imagery for the planning and monitoring of ongoing efforts to connect the 1.4 billion people who lack electricity around the world. [ABSTRACT FROM AUTHOR]

The accurate quantification of the three-dimensional (3-D) structure of mangrove forests is of great importance, particularly in Africa where deforestation rates are high and the lack of background data is a major problem. The objectives of this study are to estimate (1) the total area, (2) canopy height distributions, and (3) above-ground biomass (AGB) of mangrove forests in Africa. To derive the 3-D structure and biomass maps of mangroves, we used a combination of mangrove maps derived from Landsat Enhanced Thematic Mapper Plus (ETM+), lidar canopy height estimates from ICESat/GLAS (Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System), and elevation data from SRTM (Shuttle Radar Topography Mission) for the African continent. The lidar measurements from the large footprint GLAS sensor were used to derive local estimates of canopy height and calibrate the interferometric synthetic aperture radar (InSAR) data from SRTM. We then applied allometric equations relating canopy height to biomass in order to estimate AGB from the canopy height product. The total mangrove area of Africa was estimated to be 25,960 km2 with 83% accuracy. The largest mangrove areas and the greatest total biomass were found in Nigeria covering 8573 km2 with 132 × 106 Mg AGB. Canopy height across Africa was estimated with an overall root mean square error of 3.55 m. This error includes the impact of using sensors with different resolutions and geolocation error. This study provides the first systematic estimates of mangrove area, height, and biomass in Africa. [ABSTRACT FROM AUTHOR]

To assess the potential of high-resolution satellite data for land-cover monitoring in the Greater Horn of Africa, we used a regular sampling grid of 170 sites (each measuring 20 km × 20 km) located at the confluence of the latitudes and meridians across the study area. For each of these sites, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) satellite data were acquired for the years 1990 and 2000. A dot grid visual interpretation was used to assess land-cover change between the two dates in each of the sites. With only two acquisition dates, we found that these data were insufficient for accurately determining land-cover change and degradation in arid areas where non-woody biomass dominates. We were nevertheless able to detect land-cover modifications in three areas: increases in agriculture on the coastal plain near Mogadishu, increases in agriculture at the western fringes of our study area where there is higher rainfall, and finally a reduction in woodlands and shrublands in areas close to refugee camps on the Somali–Kenya border. [ABSTRACT FROM AUTHOR]

This study presents a predictive modelling technique to map population distribution and abundance for rural areas in Africa. Prediction models were created using a generalized regression analysis and spatial prediction (GRASP) method that uses the generalized additive model (GAM) regression technique. Dwelling unit presence–absence was mapped from airborne images covering 98 km2 (30% of the study area) and used as a response variable. Remote-sensing-based (reflectance, texture and land cover) and geospatial (topography, climate and distance) data were used as predictors. For the rest of the study area (228 km2; 70%), GAM models were extrapolated, and prediction maps constructed. Model performance was measured as explanatory power (adj.D 2, adjusted deviance change), predictive power (area under the receiver operator curve, AUC) and kappa value (κ). GAM models explained 19–31% of the variation in dwelling-unit occurrence and 28–47% of the variation in human population abundance. The predictive power for population distribution GAM models was good (AUC of 0.80–0.86). This study shows that for the prediction of dwelling-unit distribution and for human population abundance, the best modelling performance was achieved using combined geospatial- and remote-sensing-based predictor variables. The best predictors for modelling the variability in human population distribution using combined predictors were angular second moment image-texture measurement, precipitation, mean elevation, surface reflectance for Satellite Pour l'Observation de la Terre (SPOT) red and near-infrared (NIR) bands, correlation image-texture measurement and distance to roads, respectively. The population-abundance modelling result was compared with two existing global population datasets: Gridded Population of the World version 3 (GPWv3) and LandScan 2005. The result showed that for regional and local-scale population-estimation probability, models created using remotely sensed and geospatial data were superior compared to GPWv3 or LandScan 2005 data products. Population models had high correlation with Kenyan population census data for 1999 in mountainous sub-locations and low correlation for sub-locations that also extended into the lowlands. [ABSTRACT FROM AUTHOR]

The upwelling index (UI) obtained from sea surface temperature (SST) images for the period 1987-2006 and remote sensing wind stress were used to analyse the features of the coastal upwelling region off northwest Africa. The seasonal distribution shows a persistent upwelling throughout the year from 20° N to 33° N, seasonal behaviour from 12° N to south of 20° N, and an almost total lack of upwelling throughout the year from 5° N to 12° N. The major centres of active upwelling are located around Cape Ghir, Cape Juby and Cape Blanc. The UI shows an intensification of the upwelling system off northwest Africa during the 20-year period while the alongshore wind stress remains almost stable. During this period, upwelled waters off Cape Blanc have increased their offshore spreading. [ABSTRACT FROM AUTHOR]

Two databases of solar surface irradiance (SSI) derived from satellites were compared to ground measurements in Algeria, Egypt, Libya and Tunisia. We found that it was possible to accurately derive the SSI from geostationary meteorological satellites, even with a coarse spatial resolution. The two databases HelioClim-1 (HC1) and SSE exhibited similar and good performances. The bias was generally lower for SSE than for HC1; however, HC1 exhibited a smaller scattering of data compared to ground measurements (smaller standard deviation) than the SSE, allowing better performance when mapping the long-term variations in SSI. The long-term variations in SSI from 1985 to 2005 show that these four countries as a whole experienced dimming. Detailed analyses of the range of dimming at sites with long-term records and of its spatial distribution were performed. We found that the analysis of SSI from HC1 supports the findings for the individual sites. Dimming may be explained by: (1) transportation of sand dust northwards from the Sahel, (2) an increase in urbanization, and (3) an increase in cloud cover and aerosol loading. [ABSTRACT FROM AUTHOR]

Very high resolution (VHR) Ikonos images were analysed to assess the state of activity of a diamond mine extraction site in Africa. The methodology uses a dynamic approach, based on an object-oriented classification of two sets of satellite remote sensing data that were acquired four months apart. The data was processed according to a supervised maximum likelihood classification system, using fuzzy sets of membership functions. Additionally, field information gathered from experts in the sector of diamond mining was used. The results confirm the usefulness of VHR satellite imagery for the identification of both artisanal and industrial diamond mining. The bi-temporal dataset allows information on the evolution of the activity to be derived during a first period. [ABSTRACT FROM AUTHOR]

We tested the utility of imaging spectroscopy and neural networks to map phosphorus concentration in savanna grass using airborne HyMAP image data. We also sought to ascertain the key wavelengths for phosphorus prediction using hyperspectral remote sensing. The remote sensing of foliar phosphorus has received very little attention as compared to nitrogen, yet it plays an equally important role in explaining the distribution and feeding patterns of herbivores. Band depths from two continuum-removed absorption features as well as the red edge position (REP) were input into a backpropagation neural network. Following a series of experiments to ascertain the optimum wavelengths, the best trained neural network was used to predict and ultimately to map grass phosphorus concentration in the Kruger National Park. The results indicate that the best trained neural network could predict phosphorus distribution with a coefficient of determination of 0.63 and a root mean square error (RMSE) of 0.07 (28% of the mean observed phosphorus concentration) on an independent test data set. Our results also show that the absorption feature located in the shortwave infrared (R 2015-2199) contains more information on phosphorus distribution, a region that has hardly been explored before in most spectroscopic experiments for phosphorus as compared to the visible bands. Overall, the study demonstrates the potential of imaging spectroscopy in mapping grass phosphorus concentration in savanna rangelands. [ABSTRACT FROM AUTHOR]

An extensive evaluation of 10 different satellite rainfall products was performed using station network over a complex topography, where elevation varies from below sea level to 4620 m. Evaluation was for two groups of products. The first group had low spatial (2.5°) and temporal (monthly) resolution and included the Global Precipitation Climatology Project (GPCP), the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA-CPC) merged analysis (CMAP), and the Tropical Rainfall Measuring Mission (TRMM-3B43). The second group comprised products with relatively high spatial (0.1° to 1°) and temporal (3-hourly to 10-daily) resolution. These included the NOAA-CPC African rainfall estimation algorithm, GPCP one-degree-daily (1DD), TRMM-3B42, Tropical Applications of Meteorology using SATellite and other data (TAMSAT) estimates, and the CPC morphing technique (CMORPH). These products were aggregated to a 10-day total and remapped to spatial resolutions of 1°, 0.5° and 0.25°. TRMM-3B43 and CMAP from the first group and CMORPH, TAMSAT and TRMM-3B42 from the second group performed reasonably well. [ABSTRACT FROM AUTHOR]

Bi-monthly normalized difference vegetation index (NDVI) at an 8 km spatial resolution from the advanced very high resolution radiometers (AVHRR) was used from 1981 to 1995 to analyse the vegetation response to rainfall supply in semi-arid regions of Africa. Within the 200-600 mm annual rainfall belt, for which the apparent NDVI response to rainfall was the strongest, three regions were selected which exhibited different patterns in their NDVI regimes and/or relationships with rainfall. The regions, located in western, southern and eastern Africa, were split into coherent sub-regions in terms of mean regime of photosynthetic activity through a cluster analysis. Overall, intra-regional differences were found to be of relatively minor importance compared with inter-regional differences. The mean annual rain-use efficiency (RUEa) was the highest in southern Africa, followed by that in eastern Africa, and it was the lowest in western Africa. In eastern Africa, a distinctive feature was found which consisted of the dissimilar vegetation response to the two rainy seasons. For a given rainfall amount, the NDVI peak was comparatively higher for the short versus the long rains. Then, in all three regions, the correlation between rainfall and NDVI regimes was found to be significantly high. Moreover, the lag between the rainfall and NDVI peaks, which was found to be the smallest in western Africa (1 month), and the highest in southern Africa (over 1.5 month), was determined to be related to the increased rainfall rate before the peak. In view of these results, it is shown that the dominant cause for the inter-regional discrepancies, at the spatial and temporal scales considered, is the structure of the rainy season (distribution, concentration) more than the potential evapotranspiration, vegetation cover or soil type. [ABSTRACT FROM AUTHOR]

This study examines the utility of NASA's circa 1990 and circa 2000 global orthorectified Landsat dataset for land cover and land use change mapping and monitoring across Africa. This is achieved by comparing the temporal and spatial variation of NDVI, measured independently by the NOAA‐AVHRR at the time of Landsat scene acquisition, against the seasonal mean for each Landsat scene extent. Decadal sequences of drift‐corrected NOAA‐AVHRR imagery were used to calculate NDVI means and standard deviations for the periods covered by the scenes composing the c.1990 and c.2000 Landsat datasets. The specific NOAA‐AVHRR NDVI values at the acquisition date of each individual Landsat scene were also calculated and the differences, both from the mean and scaled by standard deviation, were mapped for the Landsat scene footprints in the c.1990 and c.2000 datasets. The resulting maps show the temporal position of each Landsat scene within the seasonal NDVI cycle, and provide a valuable guide to assist in quantifying uncertainty and interpreting land cover and land use changes inferred from these Landsat data. [ABSTRACT FROM AUTHOR]

Daily daytime Advanced Very High Resolution Radiometer (AVHRR) 4-km global area coverage data have been processed to produce a Normalized Difference Vegetation Index (NDVI) 8-km equal-area dataset from July 1981 through December 2004 for all continents except Antarctica. New features of this dataset include bimonthly composites, NOAA-9 descending node data from August 1994 to January 1995, volcanic stratospheric aerosol correction for 1982-1984 and 1991-1993, NDVI normalization using empirical mode decomposition/ reconstruction to minimize varying solar zenith angle effects introduced by orbital drift, inclusion of data from NOAA-1 6 for 2000-2003 and NOAA- 17 for 2003-2004, and a similar dynamic range with the MODIS NDVI. Two NDVI compositing intervals have been produced: a bimonthly global dataset and a 10- day Africa-only dataset. Post-processing review corrected the majority of dropped scan lines, navigation errors, data drop outs, edge-of-orbit composite discontinuities, and other artefacts in the composite NDVI data. All data are available from the University of Maryland Global Land Cover Facility (http:// glcf.umiacs.umd.edu/data/gimms/). [ABSTRACT FROM AUTHOR]

Lake Malawi is the second largest lake in Africa by volume and an important regional source of food. Seasonal fluctuations in the primary production of the lake are principally controlled by the lake's thermal structure, which modulates the mixing of nutrient-rich deep water with that of the phytoplanktonrich near-surface layer. Satellites potentially offer an efficient, low cost method of providing information on the lakes thermal structure over the longer term via remote sensing observations of lake surface temperature. Here we investigate the accuracy of remotely sensed lake surface temperatures derived using data from the NOAA-11 AVHRR over a two-year period (1992-1993). Optimised triple window atmospheric correction algorithms are shown to provide an accuracy of around 0.5°C when compared to in situ water temperatures. The effect of the 1994 switch in operational night-time satellite from NOAA-11 to NOAA-14 is assessed using modelling of the transfer of radiation through the Malawian atmosphere, combined with detail on the differences in the satellite spectral response functions. These simulations indicate that lake surface temperatures derived from NOAA-14 are warmer than those that would be derived from NOAA-11 under the same conditions. The magnitude of the temperature difference is estimated at 0.27°±0.07°C, depending on the viewing zenith angle. Finally, we illustrate the ability of the remotely derived surface temperature maps to provide information relevant to the lakes 3-D thermal structure. Evaluations of the annual mixing regime of the lake can be based on this information, this mixing being directly relevant to the seasonal variations in lake primary production. [ABSTRACT FROM AUTHOR]

Fire suppression associated with decades of cattle grazing can result in bush encroachment in savannas. Textural analyses of historical, high resolution images was used to characterize bush densities across a South African study landscape. A control site, where vegetation was assumed to have changed minimally for the duration of the image record (1955-1996), was used to standardize textural values between multidate images. Standardized textural values were then converted to estimates of percent woody canopy cover using a simple linear regression model. Results indicate a 30% relative increase in percent woody cover between 1955 and 1996. [ABSTRACT FROM AUTHOR]

Abstract. The accuracy of NOAA AVHRR NDVI data can be poor because of interference from several sources, including cloud cover. A parameter of the variogram model can be used to estimate the contribution of noise from the total variation in an image. However, remotely sensed information over large areas incorporates non-stationary (regional) trend and directional effects, resulting in violation of the assumptions for noise estimation. These assumptions were investigated at five sites across Africa for a range of ecological environments over several seasons. An unsupervised spectral classification of multi-temporal NDVI data partially resolved the problem of non-stationarity. Quadratic polynomials removed the remaining regional trend and directional effects. Isotropic variograms were used to estimate the noise contributing variation to the image. Standardized estimates of noise ranged from a minimum of 18.5% in west Zambia to 68.2% in northern Congo. Cloud cover and atmospheric particulates (e.g. dust) explained some of the regional and seasonal variations in noise levels. Image artifacts were also thought to contribute noise to image variation. The magnitude of the noise levels and its temporal variation appears to seriously constrain the use of AVHRR NDVI data. [ABSTRACT FROM AUTHOR]