Geography Research Works
Permanent URI for this collectionhttp://hdl.handle.net/1903/1641
Browse
17 results
Search Results
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 A New Set of MODIS Land Products (MCD18): Downward Shortwave Radiation and Photosynthetically Active Radiation(MDPI, 2020-01-03) Wang, Dongdong; Liang, Shunlin; Zhang, Yi; Gao, Xueyuan; Brown, Meredith G. L.; Jia, AolinSurface downward shortwave radiation (DSR) and photosynthetically active radiation (PAR), its visible component, are key parameters needed for many land process models and terrestrial applications. Most existing DSR and PAR products were developed for climate studies and therefore have coarse spatial resolutions, which cannot satisfy the requirements of many applications. This paper introduces a new global high-resolution product of DSR (MCD18A1) and PAR (MCD18A2) over land surfaces using the MODIS data. The current version is Collection 6.0 at the spatial resolution of 5 km and two temporal resolutions (instantaneous and three-hour). A look-up table (LUT) based retrieval approach was chosen as the main operational algorithm so as to generate the products from the MODIS top-of-atmosphere (TOA) reflectance and other ancillary data sets. The new MCD18 products are archived and distributed via NASA’s Land Processes Distributed Active Archive Center (LP DAAC). The products have been validated based on one year of ground radiation measurements at 33 Baseline Surface Radiation Network (BSRN) and 25 AmeriFlux stations. The instantaneous DSR has a bias of −15.4 W/m2 and root mean square error (RMSE) of 101.0 W/m2, while the instantaneous PAR has a bias of −0.6 W/m2 and RMSE of 45.7 W/m2. RMSE of daily DSR is 32.3 W/m2, and that of the daily PAR is 13.1 W/m2. The accuracy of the new MODIS daily DSR data is higher than the GLASS product and lower than the CERES product, while the latter incorporates additional geostationary data with better capturing DSR diurnal variability. MCD18 products are currently under reprocessing and the new version (Collection 6.1) will provide improved spatial resolution (1 km) and accuracy.Item Intercomparison of Machine-Learning Methods for Estimating Surface Shortwave and Photosynthetically Active Radiation(MDPI, 2020-01-23) Brown, Meredith G. L.; Skakun, Sergii; He, Tao; Liang, ShunlinSatellite-derived estimates of downward surface shortwave radiation (SSR) and photosynthetically active radiation (PAR) are a part of the surface radiation budget, an essential climate variable (ECV) required by climate and vegetation models. Ground measurements are insufficient for generating long-term, global measurements of surface radiation, primarily due to spatial limitations; however, remotely sensed Earth observations offer freely available, multi-day, global coverage of radiance that can be used to derive SSR and PAR estimates. Satellite-derived SSR and PAR estimates are generated by computing the radiative transfer inversion of top-of-atmosphere (TOA) measurements, and require ancillary data on the atmospheric condition. To reduce computational costs, often the radiative transfer calculations are done offline and large look-up tables (LUTs) are generated to derive estimates more quickly. Recently studies have begun exploring the use of machine-learning techniques, such as neural networks, to try to improve computational efficiency. Here, nine machine-learning methods were tested to model SSR and PAR using minimal input data from the Moderate Resolution Imaging Spectrometer (MODIS) observations at 1 km spatial resolution. The aim was to reduce the input data requirements to create the most robust model possible. The bootstrap aggregated decision tree (Bagged Tree), Gaussian Process Regression, and Neural Network yielded the best results with minimal training data requirements: an 𝑅2 of 0.77, 0.78, and 0.78 respectively, a bias of 0 ± 6, 0 ± 6, and 0 ± 5 W/m2, and an RMSE of 140 ± 7, 135 ± 8, and 138 ± 7 W/m2, respectively, for all-sky condition total surface shortwave radiation and viewing angles less than 55°. Viewing angles above 55° were excluded because the residual analysis showed exponential error growth above 55°. A simple, robust model for estimating SSR and PAR using machine-learning methods is useful for a variety of climate system studies. Future studies may focus on developing high temporal resolution direct and diffuse estimates of SSR and PAR as most current models estimate only total SSR or PAR.Item Missing Burns in the High Northern Latitudes: The Case for Regionally Focused Burned Area Products(MDPI, 2021-10-16) Chen, Dong; Shevade, Varada; Baer, Allison; Loboda, Tatiana V.Global estimates of burned areas, enabled by the wide-open access to the standard data products from the Moderate Resolution Imaging Spectroradiometer (MODIS), are heavily relied on by scientists and managers studying issues related to wildfire occurrence and its worldwide consequences. While these datasets, particularly the MODIS MCD64A1 product, have fundamentally improved our understanding of wildfire regimes at the global scale, their performance may be less reliable in certain regions due to a series of region- or ecosystem-specific challenges. Previous studies have indicated that global burned area products tend to underestimate the extent of the burned area within some parts of the boreal domain. Despite this, global products are still being regularly used by research activities and management efforts in the northern regions, likely due to a lack of understanding of the spatial scale of their Arctic-specific limitations, as well as an absence of more reliable alternative products. In this study, we evaluated the performance of two widely used global burned area products, MCD64A1 and FireCCI51, in the circumpolar boreal forests and tundra between 2001 and 2015. Our two-step evaluation shows that MCD64A1 has high commission and omission errors in mapping burned areas in the boreal forests and tundra regions in North America. The omission error overshadows the commission error, leading to MCD64A1 considerably underestimating burned areas in these high northern latitude domains. Based on our estimation, MCD64A1 missed nearly half the total burned areas in the Alaskan and Canadian boreal forests and the tundra during the 15-year period, amounting to an area (74,768 km2) that is equivalent to the land area of the United States state of South Carolina. While the FireCCI51 product performs much better than MCD64A1 in terms of commission error, we found that it also missed about 40% of burned areas in North America north of 60° N between 2001 and 2015. Our intercomparison of MCD64A1 and FireCCI51 with a regionally adapted MODIS-based Arctic Boreal Burned Area (ABBA) shows that the latter outperforms both MCD64A1 and FireCCI51 by a large margin, particularly in terms of omission error, and thus delivers a considerably more accurate and consistent estimate of fire activity in the high northern latitudes. Considering the fact that boreal forests and tundra represent the largest carbon pool on Earth and that wildfire is the dominant disturbance agent in these ecosystems, our study presents a strong case for regional burned area products like ABBA to be included in future Earth system models as the critical input for understanding wildfires’ impacts on global carbon cycling and energy budget.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.