UMD Theses and Dissertations
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Item The Ecological Velocity of Climate Change(2020) O'Leary, Donal Sean; Hurtt, George C; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Vegetation productivity and distributions are largely driven by climate, and increasing variability in seasonal and interannual climate is both changing the spatiotemporal patterns of resource availability across the landscape, and driving species’ migrations towards climate refugia. Climate and vegetation dynamics take place along the time dimension (e.g. earlier snowmelt and arrival of spring in temperate mountains), but they also occur throughout space, where changes in climate can be expressed as a movement across the landscape (e.g. warm temperatures and migratory animals moving uphill in spring, or tree species distributions moving uphill and towards the poles under climate change). Here, we present new methods to track the movement of climate and vegetation, quantifying the ecological velocity of climate change at the landscape scale. Our focus is on national parks of the USA, which are important study areas because of their great conservation and social value, protection from anthropogenic disturbances, and longstanding research and monitoring records. First, we explore the spatio-temporal relationships between snowmelt timing and vegetation phenology in Crater Lake National Park. We find that snowmelt timing is closely linked to spring greenup, but has far weaker influence on later season phenology, such as the senescence or growing season length. Second, we extend our comparison of snowmelt timing with vegetation phenology across space and time together as we track the speed and direction of receding seasonal snowpack (snowmelt velocity) with the ‘green wave velocity’ of spring greenness that follows. We find that snowmelt velocity has a moderate predictive power for green wave velocity in areas with steep slopes, where both phenomena are controlled by strong spatial gradients relating to elevation. Third, we extend our analysis into the future as we forecast the climate velocity of air temperature and precipitation in and surrounding national parks from 2019-2099. Here, we identify possible corridors and velocities of future climate migration across park boundaries, highlighting locations of ecological concern and climate vulnerability. Taken together, our analysis of the ecological velocity of climate change forms new connections among climate, conservation, and spatial sciences while prioritizing management-relevant deliverables.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.Item A STUDY OF REMOTELY SENSED AEROSOL PROPERTIES FROM GROUND-BASED SUN AND SKY SCANNING RADIOMETERS(2012) Giles, David Matthew; Dickerson, Russell R; Thompson, Anne M.; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Aerosol particles impact human health by degrading air quality and affect climate by heating or cooling the atmosphere. The Indo-Gangetic Plain (IGP) of Northern India, one of the most populous regions in the world, produces and is impacted by a variety of aerosols including pollution, smoke, dust, and mixtures of them. The NASA Aerosol Robotic Network (AERONET) mesoscale distribution of Sun and sky-pointing instruments in India was established to measure aerosol characteristics at sites across the IGP and around Kanpur, India, a large urban and industrial center in the IGP, during the 2008 pre-monsoon (April-June). This study focused on detecting spatial and temporal variability of aerosols, validating satellite retrievals, and classifying the dominant aerosol mixing states and origins. The Kanpur region typically experiences high aerosol loading due to pollution and smoke during the winter and high aerosol loading due to the addition of dust to the pollution and smoke mixture during the pre-monsoon. Aerosol emissions in Kanpur likely contribute up to 20% of the aerosol loading during the pre-monsoon over the IGP. Aerosol absorption also increases significantly downwind of Kanpur indicating the possibility of the black carbon emissions from aerosol sources such as coal-fired power plants and brick kilns. Aerosol retrievals from satellite show a high bias when compared to the mesoscale distributed instruments around Kanpur during the pre-monsoon with few high quality retrievals due to imperfect aerosol type and land surface characteristic assumptions. Aerosol type classification using the aerosol absorption, size, and shape properties can identify dominant aerosol mixing states of absorbing dust and black carbon particles. Using 19 long-term AERONET sites near various aerosol source regions (Dust, Mixed, Urban/Industrial, and Biomass Burning), aerosol absorption property statistics are expanded upon and show significant differences when compared to previous work. The sensitivity of absorption properties is evaluated and quantified with respect to aerosol retrieval uncertainty. Using clustering analysis, aerosol absorption and size relationships provide a simple method to classify aerosol mixing states and origins and potentially improve aerosol retrievals from ground-based and satellite-based instrumentation.Item ESTIMATING LAND SURFACE ALBEDO FROM SATELLITE DATA(2012) He, Tao; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Land surface albedo, defined as the ratio of the surface reflected incoming and outgoing solar radiation, is one of the key geophysical variables controlling the surface radiation budget. Surface shortwave albedo is widely used to drive climate and hydrological models. During the last several decades, remotely sensed surface albedo products have been generated through satellite-acquired data. However, some problems exist in those products due to instrument measurement inaccuracies and the failure of current retrieving procedures, which have limited their applications. More significantly, it has been reported that some albedo products from different satellite sensors do not agree with each other and some even show the opposite long term trend regionally and globally. The emergence of some advanced sensors newly launched or planned in the near future will provide better capabilities for estimating land surface albedo with fine resolution spatially and/or temporally. Traditional methods for estimating the surface shortwave albedo from satellite data include three steps: first, the satellite observations are converted to surface directional reflectance using the atmospheric correction algorithms; second, the surface bidirectional reflectance distribution function (BRDF) models are inverted through the fitting of the surface reflectance composites; finally, the shortwave albedo is calculated from the BRDF through the angular and spectral integration. However, some problems exist in these algorithms, including: 1) "dark-object" based atmospheric correction methods which make it difficult to estimate albedo accurately over non-vegetated or sparsely vegetated area; 2) the long-time composite albedo products cannot satisfy the needs of weather forecasting or land surface modeling when rapid changes such as snow fall/melt, forest fire/clear-cut and crop harvesting occur; 3) the diurnal albedo signature cannot be estimated in the current algorithms due to the Lambertian approximation in some of the atmospheric correction algorithms; 4) prior knowledge has not been effectively incorporated in the current algorithms; and 5) current observation accumulation methods make it difficult to obtain sufficient observations when persistent clouds exist within the accumulation window. To address those issues and to improve the satellite surface albedo estimations, a method using an atmospheric radiative transfer procedure with surface bidirectional reflectance modeling will be applied to simultaneously retrieve land surface albedo and instantaneous aerosol optical depth (AOD). This study consists of three major components. The first focuses on the atmospheric radiative transfer procedure with surface reflectance modeling. Instead of executing atmospheric correction first and then fitting surface reflectance in the previous satellite albedo retrieving procedure, the atmospheric properties (e.g., AOD) and surface properties (e.g., BRDF) are estimated simultaneously to reduce the uncertainties produced in separating the entire radiative transfer process. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua are used to evaluate the performance of this albedo estimation algorithm. Good agreement is reached between the albedo estimates from the proposed algorithm and other validation datasets. The second part is to assess the effectiveness of the proposed algorithm, analyze the error sources, and further apply the algorithm on geostationary satellite - the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). Extensive validations on surface albedo estimations from MSG/SEVIRI observations are conducted based on the comparison with ground measurements and other satellite products. Diurnal changes and day-to-day changes in surface albedo are accurately captured by the proposed algorithm. The third part of this study is to develop a spatially and temporally complete, continuous, and consistent albedo maps through a data fusion method. Since the prior information (or climatology) of albedo/BRDF plays a vital role in controlling the retrieving accuracy in the optimization method, currently available multiple land surface albedo products will be integrated using the Multi-resolution Tree (MRT) models to mitigate problems such as data gaps, systematic bias or low information-noise ratio due to instrument failure, persistent clouds from the viewing direction and algorithm limitations. The major original contributions of this study are as follows: 1) this is the first algorithm for the simultaneous estimations of surface albedo/reflectance and instantaneous AOD by using the atmospheric radiative transfer with surface BRDF modeling for both polar-orbiting and geostationary satellite data; 2) a radiative transfer with surface BRDF models is used to derive surface albedo and directional reflectance from MODIS and SEVIRI observations respectively; 3) extensive validations are made on the comparison between the albedo and AOD retrievals, and the satellite products from other sensors; 4) the slightly modified algorithm has been adopted to be the operational algorithm of Advanced Baseline Imager (ABI) in the future Geostationary Operational Environmental Satellite-R Series (GOES-R) program for estimating land surface albedo; 5) a framework of using MRT is designed to integrate multiple satellite albedo products at different spatial scales to build the spatially and temporally complete, continuous, and consistent albedo maps as the prior knowledge in the retrieving procedure.Item SEASONAL AND INTERANNUAL VARIABILITY OF EMISSIONS FROM CROP RESIDUE BURNING IN THE CONTIGUOUS UNITED STATES(2009) McCarty, Jessica; Justice, Chrisopher O; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Crop residue burning is a global agricultural practice used to remove excess residues before or after harvest. Crop residue burning in the contiguous United States (CONUS) has been documented at the regional and state-level by governmental organizations and in the scientific literature. Emissions from crop residue burning in the CONUS have been found to impair local and regional air quality, leading to serious health impacts and legal disputes. Currently, there is no baseline estimate for the area and emissions of crop residue burning in the CONUS. A bottom-up model for emissions calculations is employed to calculate CO2, CO, CH4, NO2, SO2, PM2.5, PM10, and Pb emissions from crop residue burning in the CONUS for the years 2003 through 2007. These atmospheric species have negative impacts on air quality and human health and are important to the carbon cycle. Spatially and temporally explicit cropland burned area and crop type products for the CONUS, necessary for emissions calculations, are developed using remote sensing approaches. The majority of crop residue burning and emissions in the CONUS are shown to occur during the spring (April - June) and fall harvests (October - December). On average, 1,239,000 ha of croplands burn annually in the CONUS with an average interannual variability of ± 91,200 ha. In general, CONUS crop residue burning emissions vary less than ±10% interannually. The states of Arkansas, California, Florida, Idaho, Texas, and Washington emit 50% of PM10, 51% of CO2, 52% of CO, and 63% of PM2.5 from all crop residue burning in the CONUS. Florida alone emits 17% of all annual CO2, CO, and PM2.5 emissions and 12% of annual PM10 emissions from crop residue burning. Crop residue burning emissions in the CONUS account for as little as 1% of global agricultural emissions and as much as 15% of all agricultural burning emissions estimates in North America, including Mexico and Canada. The results have implications for international, federal, and state-level reporting and monitoring of air quality and greenhouse gas and carbon emissions aimed at protecting human health, mitigating climate change, and understanding the carbon cycle.Item Changes in Amazon Forest Structure from Land-Use Fires: Integrating Satellite Remote Sensing and Ecosystem Modeling(2008-11-17) Morton, Douglas; DeFries, Ruth S; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Fire is the dominant method of deforestation and agricultural maintenance in Amazonia, and these land-use fires frequently escape their intended boundaries and burn into adjacent forests. Initial understory fires may increase forest flammability, thereby creating a positive fire feedback and the potential for long-term changes in Amazon forest structure. The four studies in this dissertation describe the development and integration of satellite remote sensing and ecosystem modeling approaches to characterize land-use fires and their consequences in southern Amazon forests. The dissertation contributes three new methods: use of the local frequency of satellite-based active fire detections to distinguish between deforestation and maintenance fires, use of satellite data time series to identify canopy damage from understory fires, and development of a height-structured fire sub-model in Ecosystem Demography, an advanced ecosystem model, to evaluate the impacts of a positive fire feedback on forest structure and composition. Conclusions from the dissertation demonstrate that the expansion of mechanized agricultural production in southern Amazonia increased the frequency and duration of fire use compared to less intensive methods of deforestation for pasture. Based on this increase in the frequency of land-use fires, fire emissions from current deforestation may be higher than estimated for previous decades. Canopy damage from understory fires was widespread in both dry and wet years, suggesting that drought conditions may not be necessary to burn extensive areas of southern Amazon forests. Understory fires were five times more common in previously-burned than unburned forest, providing satellite-based evidence for a positive fire feedback in southern Amazonia. The impact of this positive fire feedback on forest structure and composition was assessed using the Ecosystem Demography model. Scenarios of continued understory fires under current climate conditions show the potential to trap forests in a fire-prone structure dominated by early-successional trees, similar to secondary forests, reducing net carbon storage by 20-46% within 100 years. In summary, satellite and model-based results from the dissertation demonstrate that fire-damaged forests are an extensive and long-term component of the frontier landscape in southern Amazonia and suggest that a positive fire feedback could maintain long-term changes in forest structure and composition in the region.Item Estimating High Spatial Resolution Clear-Sky Land Surface Longwave Radiation Budget from MODIS and GOES Data(2008-05-06) Wang, Wenhui; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The surface radiation budget (SRB) is important in addressing a variety of scientific and application issues related to climate trends, hydrological and biogeophysical modeling, and agriculture. The three longwave components of SRB are surface downwelling, upwelling, and net longwave radiation (LWDN, LWUP, and LWNT). Existing surface longwave radiation budget (SLRB) datasets have coarse spatial resolution and their accuracy needs to be greatly improved. This study develops new hybrid methods for estimating instantaneous clear-sky high spatial resolution land LWDN and LWUP from the Moderate Resolution Imaging Spectroradiometer (MODIS, 1km) and the Geostationary Operational Environmental Satellites (GOES, 2-10 km) data. The hybrid methods combine extensive radiation transfer (physical) and statistical analysis (statistical) and share the same general framework. LWNT is derived from LWDN and LWUP. This study is the first effort to estimate SLRB using MODIS 1 km data. The new hybrid methods are unique in at least two other aspects. First, the radiation transfer simulation accounted for land surface emissivity effect. Second, the surface pressure effect in LWDN was considered explicitly by incorporating surface elevation in the statistical models. Nonlinear models were developed using the simulated databases to estimate LWDN from MODIS TOA radiance and surface elevation. Artificial Neural Network (ANN) models were developed to estimate LWUP from MODIS TOA radiance. The LWDN and LWUP models can explain more than 93.6% and 99.6% of variations in the simulated databases, respectively. Preliminary study indicates that similar hybrid methods can be developed to estimate LWDN and LWUP from the current GOES-12 Sounder data and the future GOES-R data. The new hybrid methods and alternative methods were evaluated using two years of ground measurements at six validation sites from the Surface Radiation Budget Network (SURFRAD). Validation results indicate the hybrid methods outperform alternative methods. The mean RMSEs of MODIS-derived LWDN, LWUP, and LWNT using the hybrid methods are 16.88, 15.23, and 17.30 W/m2. The RMSEs of GOES-12 Sounder-derived LWDN and LWUP are smaller than 23.70 W/m2. The high spatial resolution MODIS and GOES SLRB derived in this study is more accurate than existing datasets and can be used to support high resolution numerical models.Item Inferring Radiative Fluxes from a New Generation of Satellites: Model Updates(2007-08-05) Wang, Hengmao; Pinker, Rachel T.; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this study an inference scheme is developed to derive surface, Top of the Atmosphere (TOA), and atmospheric spectral shortwave (SW) radiative fluxes for implementation with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) type of sensors. The model takes into account all atmospheric constituents and addresses the characteristics of water and ice clouds and the variation of cloud particle effective radius. The near infrared spectrum is divided into three bands to better represent the spectral variation of cloud optical properties and water vapor absorption. A multi-layered structure allows for the treatment of surface elevation effects and for the representation of the vertical distribution of the radiative fluxes. Spectral fluxes such as Photosynthetically Active Radiation (PAR) and near-infrared radiation (NIR) are also estimated. The new inference scheme is implemented with MODIS one degree products as well as with the 5 km swath products. The derived fluxes are evaluated against the globally distributed Baseline Radiation Network (BSRN) measurements and compared with products from independent satellites. It was demonstrated that the MODIS products are in good agreement with ground observations and provide improved estimates of radiative fluxes than the other evaluated satellite products. In problematic areas for most satellite retrievals, such as the Tibet Plateau and Antarctica, the MODIS results have shown a substantial improvement. Availability of the high resolution swath based estimates of surface radiative fluxes allow, for the first time, to address unique space-time coupling issues.