Geography Theses and Dissertations

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    MULTISCALE, MULTITEMPORAL ASSESSMENT OF CHIMPANZEE (Pan troglodytes) HABITAT USING REMOTELY SENSED DATASETS
    (2023) Jantz, Samuel M; Hansen, Matthew C; Geography/Library & Information Systems; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    All four sub-species of our closest living relative, the chimpanzee, are listed as endangered by the International Union for the Conservation of Nature (IUCN), and their populations continue to decline due to human activities. Effective conservation efforts require information on their population status and distribution. Traditional field surveys are expensive and impractical for covering large areas at regular time intervals, making it difficult to track population trends. Given that chimpanzees occupy a large range (2.3 x 106 km2), new cost-effective methods and data are needed to provide relevant information on population status and trends across large geographic and time scales. The objective of this dissertation is to help fill this gap by leveraging freely available and regularly updated remotely sensed datasets to map and monitor chimpanzee habitat across their range. This research begins by first producing annual forest cover and change maps for the Greater Gombe (GGE) and Greater Mahale ecosystems (GME) in western Tanzania using Landsat phenological metrics and machine learning methods. Canopy cover was predicted at 30-meter resolution and the Cumulative Sums (CuSum) algorithm was applied to the canopy cover time series to detect forest loss and gain events between 2000-2020. An accuracy assessment showed the CuSum algorithm was able to detect forest loss well but had more difficulty detecting gradual forest gain events. A total of 276,000 ha (+/- 27,000 ha) of gross forest loss was detected between 2000 and 2020 in the GGE and GME; however, loss was not spread equally among the two ecosystems. The results show widespread forest loss in the GME, contrasted with net forest cover gain in the GGE. Next, the annual forest cover maps, and additional derived variables, were used to train an ensemble model to predict the relative encounter rate of chimpanzee nest sightings in the GGE and GME. Model output exhibited a strong linear relationship to chimpanzee abundances and population density estimated from a recent ground survey, enabling model output to be linearly transformed into population estimates. The model predicted the two ecosystems harbor just over 3,000 individuals, which agrees with the upper limit of population estimates from ground surveys. Most importantly, the model can be applied to annually updated variables enabling the detection of potential population shifts caused by changes in landscape condition. Model output indicates a possible population reduction in portions of the GME, while the GGE is predicted to have increased its ability to sustain a larger population. Finally, Random Forests regression was used to relate predictor variables, primarily derived from Landsat data to a coarse resolution, range-wide habitat suitability map enabling the prediction of habitat suitability at 30 meter resolution. The model showed good agreement with the calibration data; however, there was considerable variation in predictive capability among the four chimpanzee sub-species. Elevation, Landsat ETM+ band 5 and Landsat derived canopy cover were the strongest predictors; highly suitable areas were associated with dense tree canopy cover for all but the Nigeria-Cameroon and Central Chimpanzee sub-species. The model can detect changes in suitability to support monitoring and conservation planning across the chimpanzee range. Results from this dissertation highlight the promise of integrating continuously updated satellite data into habitat suitability models to detect changes through time and inform conservation efforts for chimpanzees at multiple scales.
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    DYNAMICS OF GLOBAL SURFACE WATER 1999 - PRESENT
    (2021) Pickens, Amy; Hansen, Matthew C; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Inland surface waters are critical to life, supplying fresh water and habitat, but are constantly in flux. There have been considerable advances in surface water monitoring over the last decade, though the extent of surface water has not been well-quantified per international reporting standards. Global characterizations of change have been primarily bi-temporal. This is problematic due to significant areas with multi-year cycles of wet and dry periods or anomalous high water or drought years. Many areas also exhibit strong seasonal fluctuations, such as floodplains and other natural wetlands. This dissertation aims to characterize open surface water extent dynamics by employing all of the Landsat archive 1999-present, and to report area estimates with associated uncertainty measures as required by policy guidelines. From 1999 to 2018, the extent of permanent water (in liquid or ice state) was 2.93 (standard error ±0.09) million km2, representing only 60.82 (±1.93)% of the total area that had water for some duration of the period. The unidirectional loss and gain areas were relatively small, accounting for only 1.10 (±0.23)% and 2.87 (±0.58)% of total water area, respectively. The area that transitioned multiple times between water and land states on an annual scale was over four times larger (19.74 (±2.16)%), totaling 0.95 (±0.10) million km2, establishing the need to evaluate the time-series from the entire period to assess change dynamics. From a seasonal perspective, June has over double the amount of open surface water as January, with 3.91 (±0.19) million km2 and 1.59 (±0.21) million km2, respectively. This is due to the vast network of lakes and rivers across the high-latitudes of the northern hemisphere that freeze over during the winter, with a maximum extent of ice over areas of permanent and seasonal water in February, totaling 2.49 (±0.25) million km2. This is the first global study to estimate the areas of extent and change with associated uncertainty measures and evaluate the seasonal dynamics of surface water and ice in a combined analysis. The methods developed here provide a framework for continuing to evaluate past trends and monitoring current dynamics of surface water and ice.
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    Interdisciplinary Geospatial Assessment of Malaria Exposure in Ann Township, Myanmar
    (2020) Hall, Amanda Hoffman; Loboda, Tatiana V; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Despite considerable progress toward malaria elimination in Myanmar, challenges remain owing to the persistence of complex focal transmission reservoirs. Nearly all remaining infections are clinically silent, rendering them invisible to routine monitoring. Moreover, limited knowledge of population distributions and human activity on the landscape in remote regions of Myanmar hinders the development of targeted malaria elimination approaches, as advocated by the World Health Organization (WHO). This is especially true for Ann Township, a remote region of Myanmar with a high malaria burden, where a comprehensive understanding of local exposure, which includes the characterization of environmental settings and land use activities, is crucial to developing successful malaria elimination strategies. In this dissertation, I present an interdisciplinary approach that combines satellite earth observations with two separate on-the-ground surveys to assess human exposure to malaria at multiple scales. First, I mapped rural settlements using a fusion of Landsat imagery and multi-temporal auxiliary data sensitive to human activity patterns with a classification accuracy of 93.1%. A satellite data-based map of land cover and land use was then used to assess landscape-scale malaria exposure as a function of environmental settings for a subset of ten villages where a malaria prevalence survey was carried out. While multiple significant associations were discovered, the relationship found between malaria exposure and satellite-measured village forest cover was the most significant. Finally, a separate detailed survey that explored a variety of land use activities, including their frequency and duration along with testing for clinical or subclinical malaria, was used to identify and quantify factors promoting an individual’s likelihood of malaria infection regardless of the environmental settings. This analysis established strong associations between malaria and individual land use activities that bring respondents into direct contact with forested areas. These results highlight that the current Myanmar malaria elimination strategies, which focus on prevention from within the home (i.e., bednets and indoor spraying), are no longer sufficient to remove remaining malaria reservoirs in the country. A paradigm shift in malaria elimination strategies towards targeted interventions that can disrupt malaria transmission in the settings where the exposure occurs are critical to achieving country-wide malaria elimination.
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    GLOBAL BARE GROUND GAIN BETWEEN 2000 AND 2012 AND THE RELATIONSHIP WITH SOCIOECONOMIC DEVELOPMENT
    (2020) Ying, Qing; Hansen, Matthew C; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Bare ground gain -- the complete removal of vegetation due to land use changes, represents an extreme land cover transition that completely alters the structure and functioning of ecosystems. The fast expansion of bare ground cover is directly associated with increasing population and urbanization, resulting in accelerated greenhouse gas emissions, intensified urban heat island phenomenon, and extensive habitat fragments and loss. While the economic return of settlement and infrastructure construction has improved human livelihoods, the negative impacts on the environment have disproportionally affected vulnerable population, creating inequality and tension in society. The area, distribution, drivers, and change rates of global bare ground gain were not systematically quantified; neither was the relationship between such dynamics and socioeconomic development. This dissertation seeks methods for operational characterization of bare ground expansion, advances our understanding of the magnitudes, dynamics, and drivers of global bare ground gain between 2000 and 2012, and uncovers the implications of such change for macro-economic development monitoring, all through Landsat satellite observations. The approach that employs wall-to-wall maps of bare ground gain classified from Landsat imagery for probability sample selection is proved particularly effective for unbiased area estimation of global, continental, and national bare ground gain, as a small land cover and land use change theme. Anthropogenic land uses accounted for 95% of the global bare ground gain, largely consisting of commercial/residential built-up, infrastructure development, and resource extraction. China and the United States topped the total area increase in bare ground. Annual change rates of anthropogenic bare ground gain are found as a leading indicator of macro-economic change in the study period dominated by the 2007-2008 global financial crisis, through econometric analysis between annual gains in the bare ground of different land use outcomes and economic fluctuations in business cycles measured by detrended economic variables. Instead of intensive manual interpretation of land-use attributes of probability sample, an approach of integrating a pixel- and an object- based deep learning algorithms is proposed and tested feasible for automatic attribution of airports, a transportation land use with economic importance.
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    Spatial and Temporal Dynamics of Disturbance Within and Between Forest Regions of the U.S.
    (2015) Dolan, Katelyn Anne; Hurtt, George C; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Forest disturbances play a critical role in shaping forest structure and influencing the ecosystem services that forests provide. However, the rates, patterns and consequences of disturbance remain largely uncertain. How do disturbance rates vary within and between regions and how vulnerable are forests to changes in disturbance? This research takes a tiered approach to quantifying the spatial and temporal patterns and impacts of disturbance within and between diverse forested landscapes of the contiguous U.S. First an intraregional characterization of the patterns and process of disturbance, as captured by over a quarter century of Landsat imagery was performed over the highly forested northeastern state, New Hampshire U.S. Next an inter- regional comparison of disturbance rates, trends and size distributions were conducted across three regions representing diverse forested landscapes in the U.S. with different dominant disturbance regimes. Finally, a framework was developed to assess the vulnerability of forested ecosystems to disturbance and how vulnerability may change in the future. Results showed that disturbance is not homogenous but varies both spatially and temporally within and between regions. Further ecosystem vulnerability to disturbance varies strongly across the U.S., with western forests generally exhibiting greater sensitivity and vulnerability to disturbance under current climates. Under a potential climate scenario, the majority of U.S. forest area was estimated to increase in resiliency to disturbance, which may buffer some of the impact of intensified forest disturbance. The challenge and opportunities going forward is to continue to quantify and integrate the complex rates, patterns and processes of disturbance into ecosystem models and field study designs that link impact assessment of changes to ecosystem function and services.
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    Estimation of Pan-Tropical Deforestation and Implications for Conservation
    (2015) KIM, DOHYUNG; Townshend, John R; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Reducing tropical deforestation has been a primary focus for the implementation of policies that are aimed at biodiversity conservation, and reducing greenhouse gas emissions, as tropical forests have, biologically, the richest ecosystem on Earth, tropical deforestation is one of the largest sources of anthropogenic carbon emission into the atmosphere, and preventing it is the most inexpensive option, in order to reduce carbon emissions and conserve biodiversity. To set the effective policies and conservation plans to reduce emission from tropical deforestation, the evaluation of effectiveness of both the current and previous efforts for conservation is critical. The three studies in this dissertation describe the development of the methods to accurately monitor pan-tropical forest cover change, using satellite remote sensing data, and their integration with the econometrics approach, to evaluate the effectiveness of the tropical forest conservation practices. The dissertation contributes a method for long-term, global forest cover change estimation from Landsat, and the methods are applied to report the first, pan-tropical forest cover change trends, between the 1990s and the 2000s. The global forest cover change product from 1990 to 2000, which was produced, based on the developed methods which are evaluated to have an overall accuracy of 88%. The results demonstrate that tropical deforestation has accelerated between the 1990s and the 2000s by 62%, which contradicts the assertions of it being decelerating. The results further show that the increased deforestation rate between the 1990s and the 2000s is significantly correlated with the increases in Gross Domestic Product (GDP) growth rate, agricultural production growth, and urban population growth between the two decades. Protected Areas (PA), throughout the tropics, avoided 83,000 ± 22,000 km2 of the deforestation during the 2000s. The effectiveness of international aid can be suppressed by weak governance and the lack of forest change monitoring capacity of each country. The conclusions of this dissertation provide a historical baseline for the estimates of tropical forest cover change, and for the evaluation of effectiveness of such conservation efforts.
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    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.
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    Advancing Indonesian Forest Resource Monitoring Using Multi-Source Remotely Sensed Imagery
    (2014) Margono, Belinda Arunarwati; Hansen, Matthew C; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Tropical forest clearing threatens the sustainability of critically important global ecosystems services, including climate regulation and biodiversity. Indonesia is home to the world's third largest tropical forest and second highest rate of deforestation; as such, it plays an important role in both increasing greenhouse gas emissions and loss of biodiversity. In this study, a method is implemented for quantifying Indonesian primary forest loss by landform, including wetlands. A hybrid approach is performed for quantifying the extent and change of primary forest as intact and degraded types using a per-pixel supervised classification mapping followed by a GIS-based fragmentation analysis. The method was prototyped in Sumatra, and later employed for the entirety of Indonesia, and can be replicated across the tropics in support of REDD+ (Reducing Emissions from Deforestation and forest Degradation) initiatives. Mapping of Indonesia's wetlands was performed using cloud-free Landsat image mosaics, ALOS-PALSAR imagery and topographic indices derived from the SRTM. Results quantify an increasing rate of primary forest loss over Indonesia from 2000 to 2012. Of the 15.79 Mha of gross forest cover loss for Indonesia reported by Hansen et al. (2013) over this period, 38% or 6.02 Mha occurred within primary intact or degraded forests, and increased on average by 47,600 ha per year. By 2012, primary forest loss in Indonesia was estimated to be higher than Brazil (0.84 Mha to 0.47 Mha). Almost all clearing of primary forests (>90%) occurred within degraded types, meaning logging preceded conversion processes. Proportional loss of primary forests in wetlands increased with more intensive clearing of wetland forests in Sumatra compared to Kalimantan or Papua, reflecting a near-exhaustion of easily accessible lowland forests in Sumatra. Kalimantan had a more balanced ratio of wetland and lowland primary forest loss, indicating a less advanced state of natural forest transition. Papua was found to have a more nascent stage of forest exploitation with much of the clearing related to logging activities, largely road construction. Loss within official forest-land uses that restrict or prohibit clearing totaled 40% of all loss within national forest-land, another indication of a dwindling resource. Methods demonstrated in this study depict national scale primary forest change in Indonesia, a theme that until this study has not been quantified at high spatial (30m) and temporal (annual) resolutions. The increasing loss of Indonesian primary forests found in this study has significant implications for climate change mitigation and biodiversity conservation efforts.
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    TOWARDS A BETTTER UNDERSTANDING OF FOREST CHANGE PROCESSES IN THE CONTIGUOUS U.S.
    (2012) Schleeweis, Karen; Goward, Samuel N.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Estimates of forest canopy areal extent, configuration and change have been developed from satellite based imagery and ground based inventories to improve understanding of forest dynamics and how they interact with other earth systems across many scales. The number of these types of studies has grown in recent years. Yet, few have assessed the multiple change processes underlying observed forest canopy dynamics across large spatio-temporal extents. To support these types of assessments, a more detailed and integrated understanding of the geographic patterns of the multiple forest change processes across the contiguous US (CONUS) is needed. This study examined a novel data set from the North American Forest dynamics (NAFD) project that provides a dense temporal record (1984-2005) of forest canopy history across the U.S., United States Forest Service (USFS) ground inventory data, and ancillary geospatial data sets on forest change processes (wind, insect, fire, harvest and conversion to suburban/urban land uses) across the CONUS to develop a more robust understanding of the implications of the shifting dynamics of forest change processes and our ability to measure their effect on forest canopy dynamics. A geodatabase of forest change processes was created to support synoptic and specific quantitative analysis of change processes support through space and time. Using the geodatabase, patterns of forest canopy losses from NAFD and USFS data and the underlying causal process were analyzed across multiple scales. This research has shown that the overlap of multiple disturbance processes leads to complex patterns across the nation's forested landscape that can only be fully understood in relation to forest canopy losses at fine scales. Regional statistics confounded the direction and magnitude of forest canopy loss from multiple change processes operating on the landscape. Data gaps and uncertainty associated with process data prevent a full quantitative analysis of the proportion of forest area affected by each forest change process considered here. Fine scale data were critical for interpreting the highly variable NAFD canopy change observations and their ability to capture the continuously changing spatial and temporal characteristics of forest change processes across the CONUS.
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    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.