Geography
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Item A Sample-Based Forest Monitoring Strategy Using Landsat, AVHRR and MODIS Data to Estimate Gross Forest Cover Loss in Malaysia between 1990 and 2005(MDPI, 2013-04-15) Giree, Namita; Stehman, Stephen V.; Potapov, Peter; Hansen, Matthew C.Insular Southeast Asia is a hotspot of humid tropical forest cover loss. A sample-based monitoring approach quantifying forest cover loss from Landsat imagery was implemented to estimate gross forest cover loss for two eras, 1990–2000 and 2000–2005. For each time interval, a probability sample of 18.5 km × 18.5 km blocks was selected, and pairs of Landsat images acquired per sample block were interpreted to quantify forest cover area and gross forest cover loss. Stratified random sampling was implemented for 2000–2005 with MODIS-derived forest cover loss used to define the strata. A probability proportional to x (πpx) design was implemented for 1990–2000 with AVHRR-derived forest cover loss used as the x variable to increase the likelihood of including forest loss area in the sample. The estimated annual gross forest cover loss for Malaysia was 0.43 Mha/yr (SE = 0.04) during 1990–2000 and 0.64 Mha/yr (SE = 0.055) during 2000–2005. Our use of the πpx sampling design represents a first practical trial of this design for sampling satellite imagery. Although the design performed adequately in this study, a thorough comparative investigation of the πpx design relative to other sampling strategies is needed before general design recommendations can be put forth.Item Quantification of Impact of Orbital Drift on Inter-Annual Trends in AVHRR NDVI Data(MDPI, 2014-07-22) Nagol, Jyoteshwar R.; Vermote, Eric F.; Prince, Stephen D.The Normalized Difference Vegetation Index (NDVI) time-series data derived from Advanced Very High Resolution Radiometer (AVHRR) have been extensively used for studying inter-annual dynamics of global and regional vegetation. However, there can be significant uncertainties in the data due to incomplete atmospheric correction and orbital drift of the satellites through their active life. Access to location specific quantification of uncertainty is crucial for appropriate evaluation of the trends and anomalies. This paper provides per pixel quantification of orbital drift related spurious trends in Long Term Data Record (LTDR) AVHRR NDVI data product. The magnitude and direction of the spurious trends was estimated by direct comparison with data from MODerate resolution Imaging Spectrometer (MODIS) Aqua instrument, which has stable inter-annual sun-sensor geometry. The maps show presence of both positive as well as negative spurious trends in the data. After application of the BRDF correction, an overall decrease in positive trends and an increase in number of pixels with negative spurious trends were observed. The mean global spurious inter-annual NDVI trend before and after BRDF correction was 0.0016 and −0.0017 respectively. The research presented in this paper gives valuable insight into the magnitude of orbital drift related trends in the AVHRR NDVI data as well as the degree to which it is being rectified by the MODIS BRDF correction algorithm used by the LTDR processing stream.Item Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics(MDPI, 2014-10-13) Dempewolf, Jan; Adusei, Bernard; Becker-Reshef, Inbal; Hansen, Matthew; Potapov, Peter; Khan, Ahmad; Barker, BrianPolicy makers, government planners and agricultural market participants in Pakistan require accurate and timely information about wheat yield and production. Punjab Province is by far the most important wheat producing region in the country. The manual collection of field data and data processing for crop forecasting by the provincial government requires significant amounts of time before official reports can be released. Several studies have shown that wheat yield can be effectively forecast using satellite remote sensing data. In this study, we developed a methodology for estimating wheat yield and area for Punjab Province from freely available Landsat and MODIS satellite imagery approximately six weeks before harvest. Wheat yield was derived by regressing reported yield values against time series of four different peak-season MODIS-derived vegetation indices. We also tested deriving wheat area from the same MODIS time series using a regression-tree approach. Among the four evaluated indices, WDRVI provided more consistent and accurate yield forecasts compared to NDVI, EVI2 and saturation-adjusted normalized difference vegetation index (SANDVI). The lowest RMSE values at the district level for forecast versus reported yield were found when using six or more years of training data. Forecast yield for the 2007/2008 to 2012/2013 growing seasons were within 0.2% and 11.5% of final reported values. Absolute deviations of wheat area and production forecasts from reported values were slightly greater compared to using the previous year’s or the three- or six-year moving average values, implying that 250-m MODIS data does not provide sufficient spatial resolution for providing improved wheat area and production forecasts.Item Long-Term Record of Sampled Disturbances in Northern Eurasian Boreal Forest from Pre-2000 Landsat Data(MDPI, 2014-06-27) Chen, Dong; Loboda, Tatiana; Channan, Saurabh; Hoffman-Hall, AmandaStand age distribution is an important descriptor of boreal forest structure, which is directly linked to many ecosystem processes including the carbon cycle, the land–atmosphere interaction and ecosystem services, among others. Almost half of the global boreal biome is located in Russia. The vast extent, remote location, and limited accessibility of Russian boreal forests make remote sensing the only feasible approach to characterize these forests to their full extent. A wide variety of satellite observations are currently available to monitor forest change and infer its structure; however, the period of observations is mostly limited to the 2000s era. Reconstruction of wall-to-wall maps of stand age distribution requires merging longer-term site observations of forest cover change available at the Landsat scale at a subset of locations in Russia with the wall-to-wall coverage available from coarse resolution satellites since 2000. This paper presents a dataset consisting of a suite of multi-year forest disturbance samples and samples of undisturbed forests across Russia derived from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus images from 1985 to 2000. These samples provide crucial information regarding disturbance history in selected regions across the Russian boreal forest and are designed to serve as a training and/or validation dataset for coarse resolution data products. The overall accuracy and Kappa coefficient for the entire sample collection was found to be 83.98% and 0.83%, respectively. It is hoped that the presented dataset will benefit subsequent studies on a variety of aspects of the Russian boreal forest, especially in relation to the carbon budget and climate.Item Surface Shortwave Net Radiation Estimation from FengYun-3 MERSI Data(MDPI, 2015-05-19) Wang, Dongdong; Liang, Shunlin; He, Tao; Cao, Yunfeng; Jiang, BoThe Medium-Resolution Spectral Imager (MERSI) is one of the major payloads of China’s second-generation polar-orbiting meteorological satellite, FengYun-3 (FY-3), and it is similar to the Moderate-Resolution Imaging Spectroradiometer (MODIS). The MERSI data are suitable for mapping terrestrial, atmospheric and oceanographic variables at continental to global scales. This study presents a direct-estimation method to retrieve surface shortwave net radiation (SSNR) data from MERSI top-of-atmosphere (TOA) reflectance and cloud mask products. This study is the first attempt to use the MERSI to retrieve SSNR data. Several critical issues concerning remote sensing of SSNR were investigated, including scale effects in validating SSNR data, impacts of the MERSI calibration update on the estimation of SSNR and the dependency of the retrieval accuracy of SSNR data on view geometry. We also incorporated data from twin MODIS sensors to assess how time and the number of satellite overpasses affect the retrieval of SSNR data. Validation against one-year data over seven Surface Radiation Budget Network (SURFRAD) stations showed that the presented algorithm estimated daily SSNR at the original resolution of the MERSI with a root mean square error (RMSE) of 41.9 W/m2 and a bias of −1.6 W/m2. Aggregated to a spatial resolution of 161 km, the RMSE of MERSI retrievals can be reduced by approximately 10 W/m2. Combined with MODIS data, the RMSE of daily SSNR estimation can be further reduced to 22.2 W/m2. Compared with that of daily SSNR, estimation of monthly SSNR is less affected by the number of satellite overpasses per day. The RMSE of monthly SSNR from a single MERSI sensor is as small as 13.5 W/m2.Item A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation(MDPI, 2016-03-22) Claverie, Martin; Matthews, Jessica L.; Vermote, Eric F.; Justice, Christopher O.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.Item Evaluating characterization of fire extent and fire spread in boreal and tundra fires of Alaska from coarse and moderate resolution MODIS and VIIRS data(2017-04-04) Loboda, Tatiana; O'Neal, Kelley; Yang, QiSatellite observations of fire occurrence, extent, and spread have become a routine source of information for fire scientists and managers worldwide. In remote regions of arctic and boreal zones, satellite observations frequently represent the primary and at times the only source of information about fire occurrence. While a large suite of observations have been shown to provide beneficial and important information about fire occurrence, coarse and moderate resolution data from polar orbiting satellites in optical and thermal ranges of the electromagnetic spectrum provide the most widely-used observations that characterize on-going burning processes and consistent estimates of fire-affected areas. The reliance of the global community on active fire detections and burned area estimates delivered from the Moderate Resolution Imaging Spectroradiometer (MODIS) raises concerns about the continuity of the data record beyond the lifetime of this mission. The Visible Infrared Imaging Radiometer Suite (VIIRS) operated by National Oceanic and Atmospheric Administration (NOAA) represents the future of satellite fire monitoring within US-designed and operated missions. While some advancements have been introduced into the VIIRS fire detection capabilities, including enhanced spatial resolution of spectral bands aimed at active fire detection, the reduced number of orbital overpasses (only one VIIRS instrument is currently in orbit compared to two MODIS instruments) and other differences in data acquisition open the potential for substantial differences in future fire monitoring and mapping capacity and long-term record compatibility between MODIS and VIIRS observations. This study aims to assess and quantify the differences in characterization of on-going burning processes (including in time of detection, spatial fidelity and extent of fire detection coverage, fire spread rate, and fire radiative power) and post-fire extent within fire events (i.e. burned area mapping) in boreal forests and tundra regions of North America delivered by the MODIS Terra and Aqua collection 6 and VIIRS 750m and 375m active fire products and derived burned area maps. Since VIIRS standard data suite does not include burned area estimates, we used VIIRS and MODIS collection 6 surface reflectance products to generate an annual burned area record using the Regionally Adapted Burned Area algorithm developed specifically for high northern latitudes. Our initial results indicate that despite higher spatial resolution of VIIRS observations, the MODIS record (even from a single satellite) delivers a more comprehensive coverage of on-going burning within the large fire events of the 2014 fire season in the Northwest Territories, Canada. However, while substantial differences in fire characterization exist between the satellite data, there is strong potential for calibration of the data records (particularly for the burned area and fire radiative power estimates) for the two instruments necessary to achieve a consistent long-term record of fire occurrence in the high northern latitudes that would support long-term scientific studies and management decision-making processes.Item Impact of Satellite Geometric Distortions on Landscape Analysis: Effects on Albedo(2015) Montano, Enrique Lugardo; Justice, Christopher O; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Data from wide field-of-view sensors have been providing information about the Earth's surface since the early 1980's. This manuscript is the result of investigations designed to determine the effective resolution and geometric variability of the NASA Earth Observing System MODerate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imager Radiometer Suite (VIIRS) gridded data. Although the wide field-of-view and high temporal frequency of MODIS provide near-daily global coverage, inconsistent observation assignment in geolocated MODIS pixels measurably demonstrates how spatial accuracy is affected by pixel-size growth (up to 4.8x) along-scan. For studying the effective resolution, the point spread function of nominal 250m MODIS gridded surface reflectance products (L2G) was estimated from [man-made] large size targets. The findings indicate that in near-optimal locations the resolution of (sinusoidal grid) gridded products varies between 344m-835m along-scan for a range of viewing angles, but also indicate location-dependent variability with along-scan and along-track ranges of 314m-1363m and 284m-501m respectively. Albedo was identified as a well-known physical metric to study the effects of geometric variability, thus a broadband albedo using MODIS-like geometry was simulated for five EOS validation sites. Results of each site simulation exhibit compounded uncertainty attributable to the geometric distortion in ranges sufficient to influence climate models (i.e. ranges from 0.01-0.045 albedo). A second series of broadband albedo simulations was developed for the same five EOS validation sites using VIIRS-like geometries and aggregation zones. Spatially heterogeneous land cover demonstrated a marginally significant difference in the mean albedo between aggregation zones (< 0.015). Results from data simulating temporal compositing, demonstrate the influence of geometric artifacts through differing levels of uncertainty between periods (i.e. ranges from 0.01-0.05 albedo). The variability in both MODIS and VIIRS L2G questions the standard application of a global fixed grid, and indicates that regional projections combined with a representative grid cell 4x the nominal detector size (i.e. 1000m and 1500m for MODIS and VIIRS, respectively) are potentially useful for products using off-nadir views. This work ultimately resolves the surface-feature representation of temporo-spatial wide field-of-view instrument observations and quantifies the results of associating inherently-variable observations into an artificially-fixed and geometrically-regular space.Item Estimating the fraction of absorbed photosynthetically active radiation from multiple satellite data(2015) Tao, Xin; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical input parameter in many climate and ecological models. The accuracy of satellite FAPAR products directly influences estimates of ecosystem productivity and carbon stocks. The targeted accuracy of FAPAR products is 10%, or 0.05, for many applications. This study evaluates satellite FAPAR products, presents a new FAPAR estimation model and develops data fusion schemes to improve the FAPAR accuracy. Five global FAPAR products, namely MODIS, MISR, MERIS, SeaWiFS, and GEOV1 were intercompared over different land covers and directly validated with ground measurements at VAlidation of Land European Remote sensing Instruments (VALERI) and AmeriFlux sites. Intercomparison results show that MODIS, MISR, and GEOV1 agree well with each other and so do MERIS and SeaWiFS, but the difference between these two groups can be as large as 0.1. The differences between the products are consistent throughout the year over most of the land cover types, except over the forests, because of the different assumptions in the retrieval algorithms and the differences between green and total FAPAR products over forests. Direct validation results show that the five FAPAR products have an uncertainty of 0.14 when validating with total FAPAR measurements, and 0.09 when validating with green FAPAR measurements. Overall, current FAPAR products are close to, but have not fulfilled, the accuracy requirement, and further improvements are still needed. A new FAPAR estimation model was developed based on the radiative transfer for horizontally homogeneous continuous canopy to improve the FAPAR accuracy. A spatially explicit parameterization of leaf canopy and soil background reflectance was derived from a thirteen years of MODIS albedo database. The new algorithm requires the input of leaf area index (LAI), which was estimated by a hybrid geometric optic-radiative transfer model suitable for both continuous and discrete vegetation canopies in this study. The FAPAR estimates by the new model was intercompared with reference satellite FAPAR products and validated with field measurements at the VALERI and AmeriFlux experimental sites. The validation results showed that the FAPAR estimates by the new method had slightly better performance than the MODIS and the MISR FAPAR products when using corresponding satellite LAI product values as input. The FAPAR estimates can be further improved with the LAI estimates from the presented model as input. The improvements are apparent at grasslands and forests with an 8% reduction of uncertainty. The new model can successfully identify the growing seasons and produce smooth time series curves of estimated FAPAR over years. The root mean square error (RMSE) was reduced from 0.16 to 0.11 for MODIS and from 0.18 to 0.1 for MISR overall. Application of the presented model at a regional scale generated consistent FAPAR maps at 30 m, 500 m, and 1100 m spatial resolutions from the Landsat, MODIS, and MISR data. As an alternative method to improve FAPAR accuracy, in addition to developing FAPAR estimation models, two data fusion schemes were applied to integrate multiple satellite FAPAR products at two scales: optimal interpolation at the site scale and multiple resolution tree at the regional scale. These two fusion schemes removed the bias and resulted in a 20% increase in the R2 and a 3% reduction in the RMSE as compared with the average of the individual FAPAR products. The regional scale fusion filled in the missing values and provided spatially consistent FAPAR distributions at different resolutions. The original contribution of this study is that multiple FAPAR products have been assessed with a comprehensive set of measurements from two field experiments at the global scale. This study improved the accuracy of FAPAR using a new model and local pixel based soil background and leaf canopy albedos. High FAPAR accuracy was achieved through integration at both the temporal and spatial domains. The improved accuracy of FAPAR values from this study by 5% would help to decrease an equal amount of uncertainty in the estimation of gross and net primary production and carbon fluxes.Item Developing Earth Observations Requirements for Global Agricultural Monitoring: Toward a Multi-Mission Data Acquisition Strategy(2014) Whitcraft, Alyssa Kathleen; Justice, Christopher O; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Global food supply and our understanding of it have never been more important than in today's changing world. For several decades, Earth observations (EO) have been employed to monitor agriculture, including crop area, type, condition, and yield forecasting processes, at multiple scales. However, the EO data requirements to consistently derive these informational products had not been well defined. Responding to this dearth, I have articulated spatially explicit EO requirements with a focus on moderate resolution (10-70m) active and passive remote sensors, and evaluate current and near-term missions' capabilities to meet these EO requirements. To accomplish this, periods requiring monitoring have been identified through the development of agricultural growing season calendars (GSCs) at 0.5 degrees from MODIS surface reflectance. Second, a global analysis of cloud presence probability and extent using MOD09 daily cloud flags over 2000-2012 has shown that the early-to-mid agricultural growing season (AGS) - an important period for monitoring - is more persistently and pervasively occluded by clouds than is the late and non-AGS. Third, spectral, spatial, and temporal resolution data requirements have been developed through collaboration with international agricultural monitoring experts. These requirements have been spatialized through the incorporation of the GSCs and cloud cover information, establishing the revisit frequency required to yield reasonably clear views within 8 or 16 days. A comparison of these requirements with hypothetical constellations formed from current/planned moderate resolution optical EO missions shows that to yield a scene at least 70% clear within 8 or 16 days, 46-55% or 10-32% of areas, respectively, need a revisit more frequent than Landsat 7 & 8 combined can deliver. Supplementing Landsat 7 & 8 with missions from different space agencies leads to an improved capacity to meet requirements, with Resourcesat-2 providing the largest incremental improvement in requirements met. No single mission/observatory can consistently meet requirements throughout the year, and the only way to meet a majority (77-94% for ≥70% clear; 47-73% for 100% clear) of 8 day requirements is through coordination of multiple missions. Still, gaps exist in persistently cloudy regions and periods, highlighting the need for data coordination and for consideration of active EO for agricultural monitoring.
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