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The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980s to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR surface reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geolocation, improvement of cloud masking, and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream leaf area index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by Becker-Reshef et al. (2010) and Franch et al. (2015) are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980s, the results have errors equivalent to those derived from MODIS.

Forest fires are a significant source of air pollution in Asia. In this study, we integrate satellite remote sensing data and ground-based measurements to infer fire-air pollution relationships in selected regions of Vietnam. We first characterized the active fires and burnt areas at a regional scale from MODIS satellite data. We then used satellite-derived active fire data to correlate the resulting atmospheric pollution. Further, we analyzed the relationship between satellite atmospheric variables and ground-based air pollutant parameters. Our results show peak fire activity during March in Vietnam, with hotspots in the Northwest and Central Highlands. Active fires were significantly correlated with UV Aerosol Index (UVAI), aerosol extinction absorption optical depth (AAOD), and Carbon Monoxide. The use of satellite aerosol optical thickness improved the prediction of Particulate Matter (PM) concentration significantly.

Surface reflectance is one of the key products fromVIIRS and as withMODIS, is used in developing several higherorder land products. The VIIRS Surface Reflectance (SR) Intermediate Product (IP) is based on the heritageMODIS Collection 5 product (Vermote, El Saleous, & Justice, 2002). The quality and character of surface reflectance depend on the accuracy of the VIIRS Cloud Mask (VCM), the aerosol algorithms and the adequate calibration of the sensor. The focus of this paper is the early evaluation of the VIIRS SR product in the context of the maturity of the operational processing system, the Interface Data Processing System (IDPS). After a brief introduction, the paper presents the calibration performance and the role of the surface reflectance in calibration monitoring. The analysis of the performance of the cloud mask with a focus on vegetation monitoring (no snow conditions) shows typical problems over bright surfaces and high elevation sites. Also discussed is the performance of the aerosol input used in the atmospheric correction and in particular the artifacts generated by the use of the Navy Aerosol Analysis and Prediction System. Early quantitative results of the performance of the SR product over the AERONET sites showthatwith the fewadjustments recommended, the accuracy iswithin the threshold specifications. The analysis of the adequacy of the SR product (Land PEATE adjusted version) in applications of societal benefits is then presented. We conclude with a set of recommendations to ensure consistency and continuity of the JPSS mission with the MODIS Land Climate Data Record.

In order to secure the necessary image acquisitions for global agricultural monitoring applications, we must first articulate Earth observation (EO) requirements for diverse agricultural landscapes and cropping systems. Crucial to this task is the identification of agricultural growing season timing at a meaningful spatial scale, so as to better define the necessary periods of image acquisition. To this end, 10 years of MODIS Terra Surface Reflectance imagery have been used to determine phenological transition dates including start of season, peak period, and end of season at 0.5° globally. This is the first set of global, satellite-derived, cropland-specific calendar dates for major field crops within a 0.5°, herein called agricultural growing season calendars Preliminary comparison against ground-based crop-specific calendars is performed, highlighting the utility of this approach for articulating growing season timing and its interannual and within-region variability. This research provides critical in...

The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (S-NPP) satellite incorporates fire-sensitive channels, including a dual-gain high-saturation temperature 4 µm channel, enabling active fire detection and characterization. The active fire product, based on the 750 m moderate resolution “M” bands of VIIRS, is one of the standard operational products generated by the Interface Data Processing Segment of the S-NPP ground system. The product builds on an earlier “Collection 4” version of the algorithm used for processing Moderate Resolution Imaging Spectroradiometer (MODIS) data. Following postlaunch quality assessments and corrections in the input VIIRS Sensor Data Record data processing, an initial low detection bias was removed and the product achieved Beta quality in April 2012. Daily spurious detections along-scan lines were also significantly reduced as a result of further processing improvements in October 2012. Direct product comparison with MODIS over 4 months of data in 2013 has shown that VIIRS produces approximately 26% more detections than MODIS within the central 3 pixel VIIRS aggregation zone of approximately ±31° scan angle range and 70% more detections outside of that zone, mainly as a result of the superior VIIRS scanning and sampling characteristics. Further development is in progress to ensure high-quality VIIRS fire products that continue the MODIS data record and better serve the user community by delivering a full image classification product and fire radiative power retrievals. Research is also underway to take advantage of the radiometric signal from the 375 m VIIRS imager “I” bands.

Remotely sensed Earth observations (EO) have their history firmly rooted in agricultural monitoring, and more recently with applications in food production, food security, and sustainable agriculture. Still, after more than 45 years of observing the Earth's land surface, usage of EO data by operational monitoring entities concerned with global agriculture is uneven. One reason for this is a gap in continuous communication and collaboration between those who undertake research and development of methods for cropland assessment and monitoring, and those who have the mandate to report on agricultural indicators at a national, regional, and global scales. The recent international policy focus on the United Nations 2030 Agenda for Sustainable Development via its Sustainable Development Goals (SDGs) is giving increased attention to measurements and indicators for monitoring and measuring progress for meeting these goals. Satellite EO provide a source of measurements beyond traditional census data collection and statistical reporting. In this vein, this overview paper describes the current and potential uses of EO data and tools that can support the SDGs, particularly highlighting the activities of the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) Initiative. GEOGLAM is composed of agricultural ministries, intergovernmental organizations, research entities, universities, space agencies, and members of industry concerned with agricultural monitoring. This GEOGLAM community has a broad portfolio of activities which provide information on the state and changes in agricultural production and land use that can be considered as contributions to both supporting the attainment of several of the 17 SDGs and many of their 169 Targets, as well as monitoring their achievement via the Global Indicator Framework. GEOGLAM contributes in particular to Goal 2: Zero Hunger, but also has less immediately apparent contributions in the realms of water (Goal 6), responsible consumption and production (Goal 12), climate action (Goal 13), life on land (Goal 15), and global partnerships for sustainable development (Goal 17). We further characterize the applicability and use of EO data products and tools as they correspond with the United Nations Interagency Expert Group on Sustainable Development Goals (IAEG-SDGs) Global Indicator Framework. This inventory will be complemented by a discussion of the intersection of other policy mandates with the SDGs in the agriculture and food security contexts, and will conclude with a discussion of approaches to improving awareness of EO value and bridging the gap between policy and EO communities, to the societal benefit of all with no one left behind.

Forests in Flux Forests worldwide are in a state of flux, with accelerating losses in some regions and gains in others. Hansen et al. (p. 850 ) examined global Landsat data at a 30-meter spatial resolution to characterize forest extent, loss, and gain from 2000 to 2012. Globally, 2.3 million square kilometers of forest were lost during the 12-year study period and 0.8 million square kilometers of new forest were gained. The tropics exhibited both the greatest losses and the greatest gains (through regrowth and plantation), with losses outstripping gains.

In many regions of the world, fire is an integral part of land-use practices. The accurate spatio-temporal characterization of the fire regime can, therefore, inform land-use policy at many scales. Satellite-based fire detections can be manipulated with GIS methodologies to investigate the spatio-temporal patterns of fire across a landscape. However, caveats and accuracy limitations of data and analysis methodologies must be understood in order to avoid misrepresentation of the fire regime and its impacts. This research uses moderate resolution imaging spectroradiometer (MODIS) active fire detections (MCD14ML) together with land cover data (MOD12), (MOD44B), population data (Afripop) and information on land use drawn from the literature. A case study is presented for Ethiopia reporting on a 7-year period. Results show that 91% of fires occur in the woody savanna and savanna biomes, and fire activity is inversely correlated with population density. A 0.05° latitude/longitude grid is used to report fire den...

In this study, we quantify vegetation fire activity in India using the MODerate resolution Imaging Spectroradiometer (MODIS) active fire datasets. We assessed different fire regime attributes, i.e., fire frequency, seasonality, intensity and the type of vegetation burnt in diverse geographical regions. MODIS data from 2002–2010 revealed an average of 63696 fire counts per year with the highest during 2009. Fire season in India extends from October to June with the peak during March. The K-means algorithm identified hotspot regions of fire clusters in diverse regions of India. We examined fire radiative power (FRP) data in the hotspot regions to address which fires burn intensively than others based on the vegetation type. We first assessed the best statistical fit distributions for the FRP data using the probability density functions (PDFs) and ranked them based on Kolmogorov-Smirnov statistic. We then described the fire intensities using empirical cumulative distribution functions (CDFs). Results suggest diverse pdfs for the FRP data that included Burr, Dagum, Johnson as well as Pearson distribution and they varied based on the vegetation type burnt. Analysis from empirical CDFs suggested relatively high fire intensity for closed broadleaved evergreen/ semi-deciduous forests than the other vegetation types. Although, annual sum of FRP for agricultural fires was less than the closed broadleaved evergreen forests, the values were higher than the mosaic vegetation category and broadleaved deciduous forests. These results on fire hotspots and FRP will be useful to address the impact of vegetation fires on air pollution and climate in India.

Carbon monoxide (CO) is an important greenhouse gas that is emitted during the incomplete combustion of biomass burning. In this study, we assessed the Measurements Of Pollution In the Troposphere (MOPITT) CO retrievals from two different biomass burning regions, fires in the evergreen forests of Northeast India and agriculture residue fires, Punjab, India. We analyzed long-term trends (2003–2011) in CO retrievals and fire–CO relationships including CO profiles at nine different atmospheric levels. Over a ten year period, the mean monthly CO for Northeast India ranged from 140.86 ppmv (−1 σ ) to 348.85 ppbv (+1 σ ) with a mean CO of 244.85 ppbv. We observed a clear increase in CO signal from February to March followed by a decrease in May coinciding with the fire signal. In Punjab, the mean monthly CO ranged from 158.21 ppbv (−1 σ ) to 286.40 ppbv (+1 σ ) with a mean CO of 222.30 ppbv. Comparison of mean CO during the peak fire months suggested relatively higher CO (439.06 ppbv) during March (evergreen forest burning) than October (194.83 ppbv) agricultural residue burning. We found MODIS fire radiative power (FRP) as a stronger predictor of surface CO signal than the fire counts in the evergreen forest fires. The segmented regression model fitted using nine years of FRP–CO data was useful in finding the FRP threshold impact on CO concentrations in the evergreen forests. To explain the low correlation between fires and MOPITT CO signal from the agricultural residue fires, we used the CALIPSO data to infer the smoke plume heights. Results suggested an average smoke plume height of 2.2 km during the peak biomass burning month from agricultural fires, compared to 4.61 km from evergreen forest fires. Overall, the MODIS FRP and CALIPSO data were useful in understanding the MOPITT CO sensitivity to fires.

This paper presents a generic approach developed to derive surface reflectance over land from a variety of sensors. This technique builds on the extensive dataset acquired by the Terra platform by combining MODIS and MISR to derive an explicit and dynamic map of band ratio's between blue and red channels and is a refinement of the operational approach used for MODIS and LANDSAT over the past 15 years. We will present the generic approach and the application to MODIS and LANDSAT data and its validation using the AERONET data [1].

The surface reflectance, i.e., satellite derived top of atmosphere (TOA) reflectance corrected for the temporally, spatially and spectrally varying scattering and absorbing effects of atmospheric gases and aerosols, is needed to monitor the land surface reliably. For this reason, the surface reflectance, and not TOA reflectance, is used to generate the greater majority of global land products, for example, from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. Even if atmospheric effects are minimized by sensor design, atmospheric effects are still challenging to correct. In particular, the strong impact of aerosols in the Visible and Near Infrared spectral range can be difficult to correct, because they can be highly discrete in space and time (e.g., smoke plumes) and because of the complex scattering and absorbing properties of aerosols that vary spectrally and with aerosol size, shape, chemistry and density. This paper presents the Landsat 8 Operational Land Imager (OLI) atmospheric correction algorithm that has been developed using the Second Simulation of the Satellite Signal in the Solar Spectrum Vectorial (6SV) model, refined to take advantage of the narrow OLI spectral bands (compared to Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+)), improved radiometric resolution and signal-to-noise. In addition, the algorithm uses the new OLI Coastal aerosol band (0.433-0.450μm), which is particularly helpful for retrieving aerosol properties, as it covers shorter wavelengths than the conventional Landsat, TM and ETM+ blue bands. A cloud and cloud shadow mask has also been developed using the "cirrus" band (1.360-1.390 μm) available on OLI, and the thermal infrared bands from the Thermal Infrared Sensor (TIRS) instrument. The performance of the surface reflectance product from OLI is analyzed over the Aerosol Robotic Network (AERONET) sites using accurate atmospheric correction (based on in situ measurements of the atmospheric properties), by comparison with the MODIS Bidirectional Reflectance Distribution Function (BRDF) adjusted surface reflectance product and by comparison of OLI derived broadband albedo from United States Surface Radiation Budget Network (US SURFRAD) measurements.

In- land surface models, which are used to evaluate the role of vegetation in the context of global climate change and variability, LAI and FAPAR play a key role, specifically with respect to the carbon and water cycles. The AVHRR-based LAI/FAPAR dataset offers daily temporal resolution, an improvement over previous products. This climate data record is based on a carefully calibrated and corrected land surface reflectance dataset to provide a high-quality, consistent time-series suitable for climate studies. It spans from mid-1981 to the present. Further, this operational dataset is available in near real-time allowing use for monitoring purposes. The algorithm relies on artificial neural networks calibrated using the MODIS LAI/FAPAR dataset. Evaluation based on cross-comparison with MODIS products and in situ data show the dataset is consistent and reliable with overall uncertainties of 1.03 and 0.15 for LAI and FAPAR, respectively. However, a clear saturation effect is observed in the broadleaf forest biomes with high LAI (>4.5) and FAPAR (>0.8) values.

Environmental degradation and natural resource scarcity are cited as two of the underlying causes of conflict in Sudan. In a study of the rangelands (albaja) of Kordofan and White Nile States, we use biophysical data, interviews, and satellite data to reveal how owners of large-scale, mechanized agriculture operations are progressively modifying the landscape at the expense of pastoral nomads. Our analysis shows that annual vegetation fires are used by the farmers to create buffers around the expanding mechanized farming operations to keep nomads' livestock away. This annual burning in the short term is depriving pastoralists of fodder and in the longer term is degrading the rangeland by favoring tree and grass species unpalatable to livestock. These changes in the landscape render pastoralists and traditional small-scale farmers increasingly marginalized. The competing demands for land are resulting in heightened tensions with parallels to the underlying causes of conflict in the adjacent region of Darfur.

Central Asian dry lands are grass- and desert shrub-dominated ecosystems stretching across Northern Eurasia. This region supports a population of more than 100 million which continues to grow at an average rate of 1.5% annually. Dry steppes are the primary grain and cattle growing zone within Central Asia. Degradation of this ecosystem through burning and overgrazing directly impacts economic growth and food supply in the region. Fire is a recurrent disturbance agent in dry lands contributing to soil erosion and air pollution. Here we provide an overview of inter-annual and seasonal fire dynamics in Central Asia obtained from remotely sensed data. We evaluate the accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) global fire products within Central Asian dry lands and use these products to characterize fire occurrence between 2001 and 2009. The results show that on average ∼15 million ha of land burns annually across Central Asia with the majority of the area burned in August and September in grasslands. Fire is used as a common crop residue management practice across the region. Nearly 89% of all burning occurs in Kazakhstan, where 5% and 3% of croplands and grasslands, respectively, are burned annually.

In this study, we investigate the potential of multi-satellite datasets for quantifying the biomass burning emissions from the Himalayan region. A variety of satellite products were used for characterizing fire events including active fire counts, burnt areas, aerosol optical depth (AOD) variations, aerosol index and smoke plume heights. Results from the MODerate-resolution Imaging Spectroradiometer (MODIS) fire product suggest March–June as the major fire season with the peak during the April. An average of 3908 fire counts per year were recorded with sixty four percent of the fires occurring in the low elevation areas in the Himalayan Region. We estimate average burnt areas of 1129 sq. km, with the black carbon emissions of 431 Mg, per year. The mean AOD (2005–2010) was 0.287 ± 0.105 (one sigma) with peak values in May. Correlation analysis between the fire counts and AOD resulted in a Pearson correlation coefficient of 0.553; the correlation between the FRP and AOD is relatively weaker (r = 0.499). Planetary boundary layer height retrieved from the Modern Era Retrospective-Analysis For Research And Applications (MERRA) product suggests typical PBL height of 1000–1200 m during the April–May peak biomass burning season. Cloud-Aerosol Lidar Orthogonal Polarisation (CALIOP) retrievals show the extent of smoke plume heights beyond the planetary boundary layer during the peak biomass burning month of April. However, comparison of fires in the Himalayan region with other regions and comparisons to aerosol index data from the Ozone Monitoring Instrument (OMI) suggest smoke plumes reaching less than 3 km. Our results on fires and smoke plume height relationships provide valuable information for addressing aerosol transport in the region.

Wheat is one of the key cereal crops grown worldwide, providing the primary caloric and nutritional source for millions of people around the world. In order to ensure food security and sound, actionable mitigation strategies and policies for management of food shortages, timely and accurate estimates of global crop production are essential. This study combines a new BRDF-corrected, daily surface reflectance dataset developed from NASA's Moderate resolution Imaging Spectro-radiometer (MODIS) with detailed official crop statistics to develop an empirical, generalized approach to forecast wheat yields. The first step of this study was to develop and evaluate a regression-based model for forecasting winter wheat production in Kansas. This regression-based model was then directly applied to forecast winter wheat production in Ukraine. The forecasts of production in Kansas closely matched the USDA/NASS reported numbers with a 7% error. The same regression model forecast winter wheat production in Ukraine within 10% of the official reported production numbers six weeks prior to harvest. Using new data from MODIS, this method is simple, has limited data requirements, and can provide an indication of winter wheat production shortfalls and surplus prior to harvest in regions where minimal ground data is available.

In recent years there has been a dramatic increase in the demand for timely, comprehensive global agricultural intelligence. Timely information on global crop production is indispensable for combating the growing stress on the world’s crop production and for securing both short-term and long-term stable and reliable supply of food. Global agriculture monitoring systems are critical to providing this kind of intelligence and global earth observations are an essential component of an effective global agricultural monitoring system as they offer timely, objective, global information on croplands distribution, crop development and conditions as the growing season progresses. The Global Agriculture Monitoring Project (GLAM), a joint NASA, USDA, UMD and SDSU initiative, has built a global agricultural monitoring system that provides the USDA Foreign Agricultural Service (FAS) with timely, easily accessible, scientifically-validated remotely-sensed data and derived products as well as data analysis tools, for crop-condition monitoring and production assessment. This system is an integral component of the USDA’s FAS Decision Support System (DSS) for agriculture. It has significantly improved the FAS crop analysts’ ability to monitor crop conditions, and to quantitatively forecast crop yields through the provision of timely, high-quality global earth observations data in a format customized for FAS alongside a suite of data analysis tools. FAS crop analysts use these satellite data in a ‘convergence of evidence’ approach with meteorological data, field reports, crop models, attaché reports and local reports. The USDA FAS is currently the only operational provider of timely, objective crop production forecasts at the global scale. These forecasts are routinely used by the other US Federal government agencies as well as by commodity trading companies, farmers, relief agencies and foreign governments. This paper discusses the operational components and new developments of the GLAM monitoring system as well as the future role of earth observations in global agricultural monitoring.

article i nfo This study reports results of a classification tree approach to mapping the wetlands of the Congo Basin, focusing on the Cuvette Centrale of the Congo River watershed, an area of 1,176,000 km 2 . Regional expert knowledge was used to train passive optical remotely sensed imagery of the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors, JERS-1 active radar L-band imagery, and topographical indices derived from 3 arc sec elevation data of the Shuttle Radar Topography Mission (SRTM). All data inputs were resampled to a common 57 m resolution grid. A classification tree bagging procedure was employed to produce a final map of per-grid cell wetland probability. Thirty bagged trees were ranked and the median result was selected to produce the final wetland probability map. Thresholding the probability map at < 0.5 yielded a proportion of wetland cover for the study area of 32%, equivalent to 360,000 km 2 . Wetlands predominate in the CARPE Lake Tele-Lake Tumba landscape located in the western part of the Democratic Republic of the Congo and the south-eastern Republic of Congo, where they constitute 56% of the landscape. Local topography depicting relative elevation for sub-catchments proved to be the most valuable discriminator of wetland cover. However, all sources of information (i.e. optical, radar and topography) featured prominently in contributing to the classification tree procedure, reinforcing the idea that multi-source data are useful in the characterization of wetland land cover. The method employed freely available data and a fully automated process, except for training data collection. Comparisons to existing maps and in situ field observations indicate improvements compared to previous efforts.

Burning crop residue before and/or after harvest is a common farming practice however; there is no baseline estimate for cropland burned area in the contiguous U.S. (CONUS). We present the results of a study, using five years of remotely sensed satellite data to map the location and areal extent of crop residue burning in the CONUS. Our burned area approach combines 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Burn Ratio (dNBR) data, with 1 km MODIS active fire counts calibrated using coincident high resolution satellite data to generate area estimates. Our results show that cropland burning is an extensive and recurring annual event in several states in the CONUS. On average, 1,239,000 ha of croplands burn annually, which is equivalent to 43% of the annual average area of wildland fires in the U.S., as reported by the United States Forest Service for the same period. Several states experience high levels (> 30,000 ha yr − 1 ) of crop residue burning, including Arkansas, California, Colorado, Florida, Idaho, Kansas, Louisiana, North Dakota, Oklahoma, Oregon, South Dakota, Texas, and Washington. Validation with high resolution burn scar imagery and GPS data collected during targeted field campaigns showed a moderate to high-level accuracy for our burned area estimates, ranging from 78 to 90%. Our approach provides a more consistent methodology for quantifying cropland burned area at regional scales than the previously available U.S. national and state-level statistics on crop residue burning.

We present an automated method for mapping burned areas using 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) imagery coupled with 1-km MODIS active fire observations. The algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive vegetation index and a measure of temporal texture. Cumulative active fire maps are used to guide the selection of burned and unburned training samples. An accuracy assessment for three geographically diverse regions (central Siberia, the western United States, and southern Africa) was performed using high resolution burned area maps derived from Landsat imagery. Mapped burned areas were accurate to within approximately 10% in all regions except the high-tree-cover sub-region of southern Africa, where the MODIS burn maps underestimated the area burned by 41%. We estimate the minimum detectable burn size for reliable detection by our algorithm to be on the order of 120 ha.

Technological advances have driven all aspects of Earth observation data, including improvements realized in sensor characteristics and capabilities, global data processing, near real-time monitoring, value-added products, and the distribution of global products. In particular, the growth of the World Wide Web is contributing to an increase in the global user base. The synergy of remote sensing, geographic information systems (GIS), Internet, and mobile phone technologies is revolutionizing the way in which satellite-derived information is archived and distributed to users. The Fire Information for Resource Management System (FIRMS), a NASA-funded application, is just one of many examples that illustrate the increasing ease with which Earth observation data are accessible to a broad range of users. This paper describes how the delivery of satellite-derived fire information has evolved over the last six years. By understanding user requirements and taking advantage of recent developments in areas such as information management, search, access, visualization, and enabling technologies, FIRMS has expanded the number and range of users that are able to access and utilize satellite-derived fire information. Specifically, we describe how satellite remote sensing and GIS technologies have been integrated to deliver MODIS active fire data to natural resource managers using Internet mapping services and customized e-mail alerts to users in more than 90 countries. We also describe how this web-based desktop application has been transitioned to a mobile service in South Africa to deliver fire information to field staff to warn of fires that may be potentially damaging to both natural resources and infrastructure.

Landsat remote sensing of the central African humid tropics is confounded by persistent cloud cover and, since 2003, missing data due to the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) scan line corrector (SLC) malfunction. To quantify these limitations and their effects on contemporary forest cover and change characterization, a comparison was made of multiple Landsat-7 image mosaics generated for a six Landsat path/row study site in central Africa for 2000 and 2005. Epoch 2000 mosaics were generated by compositing (i) two to three Landsat acquisitions per path/row, (ii) using the best single GeoCover 2000 acquisition for each path/row. Epoch 2005 composites were generated by compositing SLC-off data using (iii) five to seven acquisitions per path/row, (iv) three acquisitions per path/row. Eighty per cent of pixels were of suitable quality for change detection between (ii) and (iv), emulating that which is possible with current GeoCover and planned Global Land Survey (GLS) inputs. In a more data intensive change detection analysis using mosaics (i) and (iii), 96% of pixels had suitable quality. Compositing more acquisitions per path/row for the study area systematically reduced the percentage of SLC-off gaps and, when more than three acquisitions were composited, reduced the percentage of pixels with high likelihood of cloud, haze or shadow. The results indicate that additional input imagery to augment both the Geocover and GLS data may be required to enable forest cover and change analyses for regions of the humid tropics.

The results of the first consecutive 12 months of the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) global burned area product are presented. Total annual and monthly area burned statistics and missing data statistics are reported at global and continental scale and with respect to different land cover classes. Globally the total area burned labeled by the MODIS burned area product is 3.66 × 10 6 km 2 for July 2001 to June 2002 while the MODIS active fire product detected for the same period a total of 2.78 × 10 6 km 2 , i.e., 24% less than the area labeled by the burned area product. A spatio-temporal correlation analysis of the two MODIS fire products stratified globally for pre-fire leaf area index (LAI) and percent tree cover ranges indicate that for low percent tree cover and LAI, the MODIS burned area product defines a greater proportion of the landscape as burned than the active fire product; and with increasing tree cover (> 60%) and LAI (> 5) the MODIS active fire product defines a relatively greater proportion. This pattern is generally observed in product comparisons stratified with respect to land cover. Globally, the burned area product reports a smaller amount of area burned than the active fire product in croplands and evergreen forest and deciduous needleleaf forest classes, comparable areas for mixed and deciduous broadleaf forest classes, and a greater amount of area burned for the non-forest classes. The reasons for these product differences are discussed in terms of environmental spatio-temporal fire characteristics and remote sensing factors, and highlight the planning needs for MODIS burned area product validation.

ARTICLE I NFO We present an automated fire detection algorithm for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor capable of mapping actively burning fires at 30-m spatial resolution. For daytime scenes, our approach uses near infrared and short-wave infrared reflectance imagery. For nighttime scenes a simple short wave infrared radiance threshold is applied. Based on a statistical analysis of 100 ASTER scenes, we established omission and commission error rates for nine different regions. In most regions the probability of detection was between 0.8 and 0.9. Probabilities of false alarm varied between 9×10 −8 (India) and 2×10 −5 (USA/Canada). In most cases, the majority of false fire pixels were linked to clusters of true fire pixels, suggesting that most false fire pixels occur along ambiguous fire boundaries. We next consider fire characterization, and formulate an empirical method for estimating fire radiative power (FRP), a measure of fire intensity, using three ASTER thermal infrared channels. We performed a preliminary evaluation of our retrieval approach using four prescribed fires which were active at the time of the Terra overpass for which limited ground-truth data were collected. Retrieved FRP was accurate to within 20%, with the exception of one fire partially obscured by heavy soot.

Three-dimensional virtual globes are radically changing the way geographic information is perceived by the public. This article describes how NASA World Wind, an open source virtual globe, is currently being used for visualization of the MODIS burned area product. The procedures adopted for converting the product into a format compatible with World Wind, as well as the spatial generalization of these data at different scales, are described. Directions to instructions on how to obtain the MODIS burned area product visualization imagery and use it in World Wind are included. This article highlights the potential benefits of integrating the visualization capability of virtual globes into the next generation of remotely sensed product internet analysis and distribution systems.

In this paper we demonstrate a new approach that uses regional/continental MODIS (MODerate Resolution Imaging Spectroradiometer) derived forest cover products to calibrate Landsat data for exhaustive high spatial resolution mapping of forest cover and clearing in the Congo River Basin. The approach employs multi-temporal Landsat acquisitions to account for cloud cover, a primary limiting factor in humid tropical forest mapping. A Basin-wide MODIS 250 m Vegetation Continuous Field (VCF) percent tree cover product is used as a regionally consistent reference data set to train Landsat imagery. The approach is automated and greatly shortens mapping time. Results for approximately one third of the Congo Basin are shown. Derived high spatial resolution forest change estimates indicate that less than 1% of the forests were cleared from 1990 to 2000. However, forest clearing is spatially pervasive and fragmented in the landscapes studied to date, with implications for sustaining the region's biodiversity. The forest cover and change data are being used by the Central African Regional Program for the Environment (CARPE) program to study deforestation and biodiversity loss in the Congo Basin forest zone. Data from this study are available at http://carpe.umd.edu.

Devastating fires affected Greece in the summer 2007, with the loss of more than 60 human lives, the destruction of more than 100 villages and hundreds of square kilometres of forest burned. This Letter presents a map of the extent burned and the approximate day of burning in Greece mapped by the MODIS burned area product for 22 June to 30 August 2007 and the burned areas mapped independently by the European Forest Fires Information Service (EFFIS). The characteristics of the two datasets, and an evaluation of the areas burned comparing the MODIS and EFFIS data for the same temporal interval are described.

There is interest in the global community on how fire regimes are changing as a function of changing demographics and climate. The ground-based data to monitor such trends in fire activity are inadequate at the global scale. Satellite observations provide a basis for such a monitoring system. In this study, a set of metrics were developed from 6 years of MODIS active fire data. The metrics were grouped into eight classes representing three axes of fire activity: density, season duration and interannual variability. These groups were compared with biophysical and human explanatory variables on a global scale. We found that more than 30% of the land surface has a significant fire frequency. The most extensive fire class exhibited high fire density, low duration and high variability and was found in boreal and tropical wet and dry environments. A high association was found between population distribution and fire persistence. Low GDPkm � 2 was associated with fire classes with high interannual variability and low seasonal duration. In areas with more economic resources, fires tend to be more regular and last longer. High fire duration and low interannual variability were associated with croplands, but often with low fire density. The study was constrained by the limited length of satellite data record but is a first step toward developing a comprehensive global assessment of fire regimes. However, more attention is needed by the global observing systems to provide the underpinning socio-economic observations to better quantify and analyze the human characteristics of fire regimes.

The southeastern United States, including the states of Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, and Virginia, had a high occurrence of fire activity as detected by the 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA Active Fire Product (MOD 14). The analysis of the satellite data from 2001 to 2004 showed that agricultural burning in the southeastern United States accounted for an average of 16% of annual fire activity. The southeastern region contributed an average of 33% of all agricultural burning detected in the contiguous United States. Crop residues that burned in the southeast included rice, winter wheat, sugarcane, soybean and cotton. Much of the agricultural burning occurred in June and from October to January and was related to the harvest of winter wheat and rice in the spring and the harvest of sugarcane, soybean and cotton in the fall and winter. The results showed that cropland burning was spatially dependent on crop type and temporally dependent on management practices (planting/harvesting). Arkansas, Florida, and Louisiana contributed more than 75% of all agricultural burning in the southeast. A 250 m MODIS land cover map cover for 2004 was developed for these three states using a decision tree classification and validation from a field campaign in the fall of 2004. Compared to the standard MODIS 1 km Land Cover Dataset (MOD 12) product ([Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D., Strahler, A. H., Woodcock, C. E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., Schaaf, C. (2002), Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of the Environment, 83, 287–302.]), the 250 m classified images contained on average 50% more cropland area and improved the estimation of cropland area based on validation from ground control sites of croplands. Results from the decision tree classification for each state revealed that in 2004 agricultural burning contributed 73%, 54%, and 33% of total fires for Arkansas, Florida, and Louisiana, respectively.

Soil moisture is a good indicator of agricultural drought. The Advanced Scatterometer Surface Soil Moisture (ASCAT SSM) product provides daily estimates of surface soil moisture and shows high potential for drought monitoring at global scale. However, its regional application still needs further investigation. In this paper, the effectiveness of ASCAT SSM product for drought monitoring in 3 major winter wheat producing states of the US (Oklahoma, Kansas and Nebraska) was evaluated using intensive in situ measurements from North America Soil Moisture Database (NASMD). The results indicate ASCAT SSM product is generally effective for characterizing drought conditions across different stations, with better representation of soil moisture at 5cm than 10cm depth. ASCAT SSM product shows varying capability for drought monitoring across the entire year, with a better capability observed around March-July and October-November. Also, better drought monitoring capability is found for the ASCAT descending SSM than for the ascending SSM.

Global agricultural monitoring utilizes a variety of Earth observations (EO) data spanning different spectral, spatial, and temporal resolutions in order to gather information on crop area, type, condition, calendar, and yield, among other applications. Categorical requirements for space-based monitoring of major agricultural production areas have been articulated based on best practices established by the Group on Earth Observation’s (GEO) Global Agricultural Monitoring Community (GEOGLAM) of Practice, in collaboration with the Committee on Earth Observation Satellites (CEOS). We present a method to transform generalized requirements for agricultural monitoring in the context of GEOGLAM into spatially explicit (0.05°) Earth observation (EO) requirements for multiple resolutions of data. This is accomplished through the synthesis of the necessary remote sensing-based datasets concerning where (crop mask, when (growing calendar, and how frequently imagery is required (considering cloud cover impact throughout the agricultural growing season. Beyond this provision of the framework and tools necessary to articulate these requirements, investigated in depth is the requirement for reasonably clear moderate spatial resolution (10–100 m) optical data within 8 days over global within-season croplands of all sizes, a data type prioritized by GEOGLAM and CEOS. Four definitions of “reasonably clear” are investigated: 70%, 80%, 90%, or 95% clear. The revisit frequency required (RFR) for a reasonably clear view varies greatly both geographically and throughout the growing season, as well as with the threshold of acceptable clarity. The global average RFR for a 70% clear view within 8 days is 3.9–4.8 days (depending on the month), 3.0–4.1 days for 80% clear, 2.2–3.3 days for 90% clear, and 1.7–2.6 days for 95% clear. While some areas/times of year require only a single revisit (RFR = 8 days) to meet their reasonably clear requirement, generally the RFR, regardless of clarity threshold, is below to greatly below the 8 day mark, highlighting the need for moderate resolution optical satellite systems or constellations with revisit capabilities more frequent than 8 days. This analysis is providing crucial input for data acquisition planning for agricultural monitoring in the context of GEOGLAM.

NASA’s Land, Atmosphere Near real-time Capability for EOS (Earth Observing System) (LANCE) provides global data and imagery from the Terra, Aqua and Aura satellites in less than 3 h from satellite observation to meet the needs of the near real-time (NRT) applications community. Science quality, or higher-level “standard” products are made available within 8–40 h of observation but application users, operational agencies, and even researchers often need data much sooner than what routine science processing offers. This chapter describes the architecture of LANCE and modifications made to achieve the nominal 3-h latency requirement.

Fire is an important disturbance agent in Myanmar impacting several ecosystems. In this study, we quantify the factors impacting vegetation fires in protected and non-protected areas of Myanmar. Satellite datasets in conjunction with biophysical and anthropogenic factors were used in a spatial framework to map the causative factors of fires. Specifically, we used the frequency ratio method to assess the contribution of each causative factor to overall fire susceptibility at a 1km scale. Results suggested the mean fire density in non-protected areas was two times higher than the protected areas. Fire-land cover partition analysis suggested dominant fire occurrences in the savannas (protected areas) and woody savannas (non-protected areas). The five major fire causative factors in protected areas in descending order include population density, land cover, tree cover percent, travel time from nearest city and temperature. In contrast, the causative factors in non-protected areas were population density, tree cover percent, travel time from nearest city, temperature and elevation. The fire susceptibility analysis showed distinct spatial patterns with central Myanmar as a hot spot of vegetation fires. Results from propensity score matching suggested that forests within protected areas have 11% less fires than non-protected areas. Overall, our results identify important causative factors of fire useful to address broad scale fire risk concerns at a landscape scale in Myanmar.

This paper examines the vulnerability of the Congo Basin's forests through a GIS platform, taking into consideration the variables of population growth, road density, logging concession, and forest fragmentation. The assessment indicates that the forests will continue to shrink towards the interior over the next 50 years. Current contiguous forests will fragment into three large blocks, including one on the west side of the Congo River and two in the Democratic Republic of Congo, while a large number of small forest patches will retain in the periphery of the large blocks. The study shows that integrated GIS assessment of the driving forces of tropical deforestation can shed light on the future forest distribution and provide a tool to address the broader implications of social and economic development for tropical deforestation.

The development of appropriate validation techniques is critical to assess uncertainties associated with satellite-data-based products, to identify needed product improvements and to allow products to be used appropriately. At regional to global scales, there are several outstanding issues in the development of robust validation methodologies, including the need to increase the quality and economy of product validation by developing and promoting international validation standards and protocols. This paper describes a protocol developed to validate a regional southern Africa burned-area product derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m time series data. The protocol is based upon interpretations by members of the Southern Africa Fire Network (SAFNet) of multitemporal Landsat Enhanced Thematic Mapper plus (ETM + ) data to derive maps of the location and approximate date of burning. The validation data are derived using Landsat ETM + scenes distributed to

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...

The remote sensing of Earth surface changes is an active research field aimed at the development of methods and data products needed by scientists, resource managers, and policymakers. Fire is a major cause of surface change and occurs in most vegetation zones across the world. The identification and delineation of fire-affected areas, also known as burned areas or fire scars, may be considered a change detection problem. Remote sensing algorithms developed to map fire-affected areas are difficult to implement reliably over large areas because of variations in both the surface state and those imposed by the sensing system. The availability of robustly calibrated, atmospherically corrected, cloud-screened, geolocated data provided by the latest generation of moderate resolution remote sensing systems allows for major advances in satellite mapping of fire-affected area. This paper describes an algorithm developed to map fire-affected areas at a global scale using Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance time series data. The algorithm is developed from the recently published Bi-Directional Reflectance Model-Based Expectation change detection approach and maps at 500 m the location and approximate day of burning. Improvements made to the algorithm for systematic global implementation are presented and the algorithm performance is demonstrated for southern African, Australian, South American, and Boreal fire regimes. The algorithm does not use training data but rather applies a wavelength independent threshold and spectral constraints defined by the noise characteristics of the reflectance data and knowledge of the spectral behavior of burned vegetation and spectrally confusing changes that are not associated with burning. Temporal constraints are applied capitalizing on the spectral persistence of fire-affected areas. Differences between mapped fire-affected areas and cumulative MODIS active fire detections are illustrated and discussed for each fire regime. The results reveal a coherent spatio-temporal mapping of fire-affected area and indicate that the algorithm shows potential for global application.

This paper uses three recently generated southern African satellite burned area products for the month of September 2000 in a sensitivity study of regional biomass burning emissions for a number of trace gases and particulates. Differences in the extent and location of areas burned among products generated from Moderate Resolution Imaging Spectroradiometer (MODIS), Systeme Pour l'Observation de la Terre (SPOT-VEGETATION), and Along Track Scanning Radiometer (ATSR-2) data are significant and result in different emissions estimates for woodland and grassland land cover types. Due to the different emission profiles in woodlands and grasslands, favoring relatively more products of incomplete combustion in woodlands compared with products of complete combustion in grasslands in the late dry season, these changes are not proportional to the differences in the burned area amounts. The importance of accurate burned area information not just in terms of the total area but also in terms of its spatial distribution becomes apparent from our modeling results. This paper highlights the urgent need for satellite data producers to provide accuracy assessments associated with satellite-derived products. Preferably, these accuracy data will be spatially explicit, or defined in a way that can be applied in a spatially explicit modeling context, to enable emissions uncertainties to be defined with respect to different landscape units in support of greenhouse gas emissions reporting.

Experience with the first 2 years of high quality data from the Moderate Resolution Imaging Spectroradiometer (MODIS) through quality control and validation has suggested several improvements to the original MODIS active fire detection algorithm described by Kaufman, Justice et al. [Journal of Geophysical Research 103 (1998) 32215]. In this paper, we present an improved replacement detection algorithm that offers increased sensitivity to smaller, cooler fires as well as a significantly lower false alarm rate. Performance of both the original and improved algorithm is established using a theoretical simulation and high-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes. In general, the new algorithm can detect fires roughly half the minimum size that could be detected with the original algorithm while having an overall false alarm rate 10–100 times smaller.

Fires in African savannas produce emissions contributing to changes in global biogeochemical processes. In the Southern African Regional Science Initiative (SAFARI-2000), fuels characteristics were measured before and after experimental burns in two different western Zambian grassland types (dambo and flood plain) situated on Kalahari Sands. The two land use types did not differ in terms of fuel characteristics, fire behavior or combustion completeness. As a consequence of a significantly wet year, fuel loads and moisture content were higher than those found during SAFARI-92. Use of moisture content and fire behavior component could increase the emission estimation through the fuel consumption calculation. Ranges of estimated emissions for CH 4 and NMHC were lower than previously stated.

Biomass burning is an important ecosystem process in southern Africa, with significant implications for regional and global atmospheric chemistry and biogeochemical cycles. In this paper, representative Landsat path-row scene locations, distributed over southern Africa, were used to quantify the area burned and to understand the coupled role of the timing and the extent of burning on regional emissions. The total area burned and the scar size distribution were found to vary between semi-arid and humid scenes and dry and average rainfall years. Analysis of images from the start and end of the burning season resulted in a modest underestimate of the annual area burned, as compared to using a monthly time-series approach. However, at the regional level the start/end method is likely to yield acceptable annual burned area estimates and total carbon dioxide estimates. On the other hand, combustion factors and emission factors vary sufficiently during the burning season to result in large errors in emission estimates of products of incomplete combustion, when using the start/end method. This study indicates that in southern Africa, the timing in addition to the extent of burning must be considered and that time-series satellite burned area products are needed to quantify pyrogenic emissions accurately.

The MODIS sensor, launched on NASA's Terra satellite at the end of 1999, was designed with 36 spectral channels for a wide array of land, ocean, and atmospheric investigations. MODIS has a unique ability to observe fires, smoke, and burn scars globally. Its main fire detection channels saturate at high brightness temperatures: 500 K at 4 microns and 400 K at 11 microns, which can only be attained in rare circumstances at the I kin fire detection spatial resolution. Thus, unlike other polar orbiting satellite sensors with similar thermal and spatial resolutions, but much lower saturation temperatures (e.g. AVHRR and ATSR), MODIS can distinguish between low intensity ground surface fires and high intensity crown forest fires. Smoke column concentration over land is for the first time being derived from the MOMS solar channels, extending from 0.41 microns to 2.1 microns. The smoke product has been provisionally validated both globally and regionally over southern Africa and central and south America. Burn scars are observed from MODIS even in the presence of smoke, using the 1.2 to 2.1 micron channels. MODIS burned area information is used to estimate pyrogenic emissions. A wide range of these fire and related products and validation are demonstrated for the wild fires that occurred in northwestern United States in the summer of 2000. The MODIS rapid response system and direct broadcast capability is being developed to enable users to obtain and generate data in near real time. It is expected that health and land management organizations will use these systems for monitoring the occurrence of fires and the dispersion of smoke within two to six hours after data acquisition.

The MODIS instrument provides major advances in moderate resolution earth observation. Improved spatial resolution for land observation at 250 and 500 m and improved spectral band placement provide new remote sensing opportunities. NASA has invested in the development of improved algorithms for MODIS, which will provide new data sets for global change research. Surface reflectance is one of the key products from MODIS and is used in developing several higher-order land products. The surface reflectance algorithm builds on the heritage of the Advanced Very High Resolution Radiometer (AVHRR) and SeaWiFS algorithms, taking advantage of the new sensing capabilities of MODIS. Atmospheric correction by the removal of water vapor and aerosol effects provides improvements over previous coarse resolution products and the basis for a new time-series, which will extend through to the NPOESS generation imagers. This paper summarizes the first evaluation of the MODIS surface reflectance product accuracy, in comparison with other data products and in the context of the MODIS instrument performance since launch. The MODIS surface reflectance product will provide an important time-series data set for quantifying global environmental change.

While remote sensing offers the capability for monitoring land surface changes, extracting the change information from satellite data requires effective and automated change detection techniques. The majority of change detection techniques rely on empirically derived thresholds to differentiate changes from background variations, which are often considered noise. Over large areas, reliable threshold definition is problematic due to variations in both the surface state and those imposed by the sensing system. A new approach to change detection, applicable to high-temporal frequency satellite data, that maps the location and approximate day of change occurrence is described. The algorithm may be applied to a range of change detection applications by using appropriate wavelengths. The approach is applied here to the problem of mapping burned areas using moderate spatial resolution satellite data. A bi-directional reflectance model is inverted against multi-temporal land surface reflectance observations, providing an expectation and uncertainty of subsequent observations through time. The algorithm deals with angular variations observed in multi-temporal satellite data and enables the use of a statistical measure to detect change from a previously observed state. The algorithm is applied independently to geolocated pixels over a long time series of reflectance observations. Large discrepancies between predicted and measured values are attributed to change. A temporal consistency threshold is used to differentiate between temporary changes considered as noise and persistent changes of interest. The algorithm is adaptive to the number, viewing and illumination geometry of the observations, and to the amount of noise in the data. The approach is demonstrated using 56 days of Moderate Resolution Imaging Spectroradiometer (MODIS) land surface reflectance data generated for Southern Africa during the 2000 burning season. Qualitatively, the results show high correspondence with contemporaneous MODIS active fire detection results and reveal a coherent spatio-temporal progression of burning. Validation of these results is in progress and recommendations for future research, pending the availability of independent validation data sets, are made. This approach is now being considered for the MODIS burned area algorithm.

Data from the first Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the NASA Terra Platform are being used to provide a new generation of land data products in support of the National Aeronautics and Space Administration (NASA)'s Earth Science Enterprise, global change research and natural resource management. The MODIS products include global data sets heretofore unavailable, derived from new moderate resolution spectral bands with spatial resolutions of 250 m to 1 km. A partnership between Science Team members and the MODIS Science Data Support Team is producing data sets of unprecedented volume and number for the land research and applications. This overview paper provides a summary of the instrument performance and status, the data production system, the products, their status and availability for land studies.

The MODIS Land team is producing a suite of global land products whose uncertainty will be estimated through validation activities. The MODIS Land team will base its validation work on the comparison of its products to similar products derived from independent sources. The independent products will be derived from a combination of in situ data and imagery from airborne and spaceborne sensors. Since in situ and image data can often serve to validate more than one product and sensor, the MODIS Land Discipline Team's validation strategy has focused on data collection and analysis at the EOS Land Validation Core Sites. Initial characterization of these sites is presented, as well as an overview of the on-line access to imagery and field data collected over these sites. The data and resources available through this work are available to the science community for continued validation and scientific investigations. This paper describes the results of a 4-year effort to develop the infrastructure to allow timely and comprehensive validation of EOS land products.

The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor is providing a series of products of unparalleled quality and sophistication for the observation and biophysical monitoring of the terrestrial environment. The capabilities of the MODIS sensor of NASA's Terra and Aqua platforms, in observing the terrestrial environment, are a precursor to the operational capabilities of the National Polar Operational Environmental Satellite System (NPOESS) series of missions, which will succeed the current polar operational capabilities of the NOAA series of satellites. Prior to NPOESS, the NPOESS Preparatory Project (NPP) will be launched, acting as a bridge between the two MODIS instruments and the longer term advanced operational capability. The result of these missions will be the establishment of long-term systematic, operational capabilities. Notwithstanding the improvements arising from these missions, there are many lessons to be learnt from current experiences in using MODIS data. Moreover, the timing of the implementation of these missions will provide challenges to users. Additionally, agencies must anticipate a rapid evolution of requirements associated with burgeoning terrestrial programs such as Global Observations of Forest Cover/Global Observations of Landcover Dynamics (GOFC/GOLD) and Terrestrial Carbon Observations (TCO).

Research on shifting cultivation in the tropics has focused primarily on the issues of agricultural productivity and soil degradation. Few have attempted to quantify its impact on forest losses and the carbon cycle in the context of the global change debate. A simulation, which incorporated historic and projected rural population growth, was conducted to investigate the impacts of shifting cultivation on deforestation and the carbon stocks of the humid tropical forests of central Africa. The study demonstrated that in 1950, the central African forest region subjected to shifting cultivation was composed of 92% forest (including primary and secondary forests) and that in 1990 the forest cover had been reduced to 80% of the total simulated land area. The results of the simulation showed that with the current level of technology and consumptive habitats, shifting cultivation under projected rates of rural population growth would increase the annual rate of deforestation to 1.3%, converting 94% of the land area into cropland, fallow and secondary forests by 2050. At that time, only 40% of the potential above-ground biomass carbon (i.e., assuming all the simulated area is occupied by dense forests) will remain, compared to 85 and 74% in 1950 and 1990, respectively. The simulation suggests that there is a need for alternatives to shifting cultivation such as integration of cultivation intensification, fallow management and agroforestry if population pressure on the tropical forests of central Africa is to be alleviated. This preliminary regional study shows that modeling could provide an important tool to explore alternative scenarios of land use and to assist decision makers with forest management.