Geography

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30+ YEARS OF LAND COVER AND LAND USE CHANGE IN SOUTH AMERICA
(2020) Zalles Ballivian, Viviana; Hansen, Matthew C.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The modification of the Earth’s surface constitutes the most impactful way in which humans affect their surrounding environment, with broad and lasting consequences. Changes in land cover accelerate biodiversity loss, contribute to climate change, and affect the provisioning of ecosystem services. Such negative environmental impacts can have important effects on human health and livelihoods. The South American continent, in particular, has undergone significant transformations over the past decade, due in large part to the conversion of natural land to more economically productive land uses, such as crops, pastures, and tree plantations. The agricultural commodities produced in South America are traded and consumed globally, and land will likely continue to be converted if demand for these commodities continues to rise. Despite the environmental and commercial importance of land cover and land use change dynamics in South America, the extent and rates of land change have not yet been thoroughly characterized and quantified. This dissertation aims to advance scientific knowledge on the extent and rates of change of important land covers and land uses, especially as they relate to the production of agricultural commodities, by leveraging the 34-year Landsat archive of Earth observation data. The general approach employed throughout follows a two-step process of mapping and sampling, in order to provide spatially explicit information on the patterns of land cover/land use change, as well as associated unbiased area estimates. This approach is first employed for the use-case of Brazilian cropland expansion from 2000 to 2014, and results show a near doubling of cropland area, the majority of which (80%) came about through the conversion of existing pastures. The methodology is then repeated at broader thematic, temporal, and geographic scales, resulting in area estimates of changes in cropland, pasture, plantation, natural tree regrowth, semi-natural land, tree cover and degraded tree cover from 1985 to 2018. Altogether, these changes amount to a 60% increase in human impact on natural land over the study period. Finally, an analysis and evaluation of the methodology employed for mapping and sampling when there is a multitude of target classes instead of a single one is provided as an assessment of methodological approaches.
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.
A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index
(MDPI, 2021-04-01) Hosseini, Mehdi; McNairn, Heather; Mitchell, Scott; Robertson, Lauren Dingle; Davidson, Andrew; Ahmadian, Nima; Bhattacharya, Avik; Borg, Erik; Conrad, Christopher; Dabrowska-Zielinska, Katarzyna; de Abelleyra, Diego; Gurdak, Radoslaw; Kumar, Vineet; Kussul, Nataliia; Mandal, Dipankar; Rao, Y. S.; Saliendra, Nicanor; Shelestov, Andrii; Spengler, Daniel; Verón, Santiago R.; Homayouni, Saeid; Becker-Reshef, Inbal
The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m−2 and mean absolute error (MAE) of 0.51 m2m−2 . The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m−2 and MAE of 0.61 m2m−2 ) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m−2 and MAE of 0.30 m2m−2 ). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance.
A Disease Control-Oriented Land Cover Land Use Map for Myanmar
(MDPI, 2021-06-13) Chen, Dong; Shevade, Varada; Baer, Allison; He, Jiaying; Hoffman-Hall, Amanda; Ying, Qing; Li, Yao; Loboda, Tatiana V.
Malaria is a serious infectious disease that leads to massive casualties globally. Myanmar is a key battleground for the global fight against malaria because it is where the emergence of drug-resistant malaria parasites has been documented. Controlling the spread of malaria in Myanmar thus carries global significance, because the failure to do so would lead to devastating consequences in vast areas where malaria is prevalent in tropical/subtropical regions around the world. Thanks to its wide and consistent spatial coverage, remote sensing has become increasingly used in the public health domain. Specifically, remote sensing-based land cover/land use (LCLU) maps present a powerful tool that provides critical information on population distribution and on the potential human-vector interactions interfaces on a large spatial scale. Here, we present a 30-meter LCLU map that was created specifically for the malaria control and eradication efforts in Myanmar. This bottom-up approach can be modified and customized to other vector-borne infectious diseases in Myanmar or other Southeastern Asian countries.
A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)
(MDPI, 2015-01-29) Whitcraft, Alyssa K.; Becker-Reshef, Inbal; Justice, Christopher O.
Global agricultural monitoring utilizes a variety of Earth observations (EO) data spanning different spectral, spatial, and temporal resolutions in order to gather information on crop area, type, condition, calendar, and yield, among other applications. Categorical requirements for space-based monitoring of major agricultural production areas have been articulated based on best practices established by the Group on Earth Observation’s (GEO) Global Agricultural Monitoring Community (GEOGLAM) of Practice, in collaboration with the Committee on Earth Observation Satellites (CEOS). We present a method to transform generalized requirements for agricultural monitoring in the context of GEOGLAM into spatially explicit (0.05°) Earth observation (EO) requirements for multiple resolutions of data. This is accomplished through the synthesis of the necessary remote sensing-based datasets concerning where (crop mask, when (growing calendar, and how frequently imagery is required (considering cloud cover impact throughout the agricultural growing season. Beyond this provision of the framework and tools necessary to articulate these requirements, investigated in depth is the requirement for reasonably clear moderate spatial resolution (10–100 m) optical data within 8 days over global within-season croplands of all sizes, a data type prioritized by GEOGLAM and CEOS. Four definitions of “reasonably clear” are investigated: 70%, 80%, 90%, or 95% clear. The revisit frequency required (RFR) for a reasonably clear view varies greatly both geographically and throughout the growing season, as well as with the threshold of acceptable clarity. The global average RFR for a 70% clear view within 8 days is 3.9–4.8 days (depending on the month), 3.0–4.1 days for 80% clear, 2.2–3.3 days for 90% clear, and 1.7–2.6 days for 95% clear. While some areas/times of year require only a single revisit (RFR = 8 days) to meet their reasonably clear requirement, generally the RFR, regardless of clarity threshold, is below to greatly below the 8 day mark, highlighting the need for moderate resolution optical satellite systems or constellations with revisit capabilities more frequent than 8 days. This analysis is providing crucial input for data acquisition planning for agricultural monitoring in the context of GEOGLAM.
A Mapping Framework to Characterize Land Use in the Sudan-Sahel Region from Dense Stacks of Landsat Data
(MDPI, 2019-03-16) Sedano, Fernando; Molini, Vasco; Azad, M. Abdul Kalam
We developed a land cover and land use mapping framework specifically designed for agricultural systems of the Sudan-Sahel region. The mapping approach extracts information from inter- and intra-annual vegetation dynamics from dense stacks of Landsat 8 images. We applied this framework to create a 30 m spatial resolution land use map with a focus on agricultural landscapes of northern Nigeria for 2015. This map provides up-to-date information with a higher level of spatial and thematic detail resulting in a more precise characterization of agriculture in the region. The map reveals that agriculture is the main land use in the region. Arable land represents on average 52.5% of the area, higher than the reported national average for Nigeria (38.4%). Irrigated agriculture covers nearly 2.2% of the total area, reaching nearly 20% of the cultivated land when traditional floodplain agriculture systems are included, above the reported national average (0.63%). There is significant variability in land use within the region. Cultivated land in the northern section can reach values higher than 75%, most land suitable for agriculture is already under cultivation and there is limited land for future agricultural expansion. Marginal lands, not suitable for permanent agriculture, can reach 30% of the land at lower altitudes in the northeast and northwest. In contrast, the southern section presents lower land use intensity that results in a complex landscape that intertwines areas farms and larger patches of natural vegetation. This map improves the spatial detail of existing sources of LCLU information for the region and provides updated information of the current status of its agricultural landscapes. This study demonstrates the feasibility of multi temporal medium resolution remote sensing data to provide detailed and up-to-date information about agricultural systems in arid and sub arid landscapes of the Sahel region.
A Method for Landsat and Sentinel 2 (HLS) BRDF Normalization
(MDPI, 2019-03-15) Franch, Belen; Vermote, Eric; Skakun, Sergii; Roger, Jean-Claude; Masek, Jeffrey; Ju, Junchang; Villaescusa-Nadal, Jose Luis; Santamaria-Artigas, Andres
The Harmonized Landsat/Sentinel-2 (HLS) project aims to generate a seamless surface reflectance product by combining observations from USGS/NASA Landsat-8 and ESA Sentinel-2 remote sensing satellites. These satellites’ sampling characteristics provide nearly constant observation geometry and low illumination variation through the scene. However, the illumination variation throughout the year impacts the surface reflectance by producing higher values for low solar zenith angles and lower reflectance for large zenith angles. In this work, we present a model to derive the bidirectional reflectance distribution function (BRDF) normalization and apply it to the HLS product at 30 m spatial resolution. It is based on the BRDF parameters estimated from the MODerate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product (M{O,Y}D09) at 1 km spatial resolution using the VJB method (Vermote et al., 2009). Unsupervised classification (segmentation) of HLS images is used to disaggregate the BRDF parameters to the HLS spatial resolution and to build a BRDF parameters database at HLS scale. We first test the proposed BRDF normalization for different solar zenith angles over two homogeneous sites, in particular one desert and one Peruvian Amazon forest. The proposed method reduces both the correlation with the solar zenith angle and the coefficient of variation (CV) of the reflectance time series in the red and near infrared bands to 4% in forest and keeps a low CV of 3% to 4% for the deserts. Additionally, we assess the impact of the view zenith angle (VZA) in an area of the Brazilian Amazon forest close to the equator, where impact of the angular variation is stronger because it occurs in the principal plane. The directional reflectance shows a strong dependency with the VZA. The current HLS BRDF correction reduces this dependency but still shows an under-correction, especially in the near infrared, while the proposed method shows no dependency with the view angles. We also evaluate the BRDF parameters using field surface albedo measurements as a reference over seven different sites of the US surface radiation budget observing network (SURFRAD) and five sites of the Australian OzFlux network.
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, Aolin
Surface 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.
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.
ADDRESSING GEOGRAPHICAL CHALLENGES IN THE BIG DATA ERA UTILIZING CLOUD COMPUTING
(2020) Lan, Hai; Stewart, Kathleen; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Processing, mining and analyzing big data adds significant value towards solving previously unverified research questions or improving our ability to understand problems in geographical sciences. This dissertation contributes to developing a solution that supports researchers who may not otherwise have access to traditional high-performance computing resources so they benefit from the “big data” era, and implement big geographical research in ways that have not been previously possible. Using approaches from the fields of geographic information science, remote sensing and computer science, this dissertation addresses three major challenges in big geographical research: 1) how to exploit cloud computing to implement a universal scalable solution to classify multi-sourced remotely sensed imagery datasets with high efficiency; 2) how to overcome the missing data issue in land use land cover studies with a high-performance framework on the cloud through the use of available auxiliary datasets; and 3) the design considerations underlying a universal massive scale voxel geographical simulation model to implement complex geographical systems simulation using a three dimensional spatial perspective. This dissertation implements an in-memory distributed remotely sensed imagery classification framework on the cloud using both unsupervised and supervised classifiers, and classifies remotely sensed imagery datasets of the Suez Canal area, Egypt and Inner Mongolia, China under different cloud environments. This dissertation also implements and tests a cloud-based gap filling model with eleven auxiliary datasets in biophysical and social-economics in Inner Mongolia, China. This research also extends a voxel-based Cellular Automata model using graph theory and develops this model as a massive scale voxel geographical simulation framework to simulate dynamic processes, such as air pollution particles dispersal on cloud.
ADDRESSING THE IMPACT ON SOIL DEGRADATION OF CHANGE FROM GRASSLAND TO CROPLAND: A CASE STUDY IN THE URUGUAYAN GRASSLANDS
(2017) Castano-Sanchez, Jose P; Prince, Stephen D; Izaurralde, Roberto C; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Globally, there has been large-scale conversion of natural grassland to cropland ecosystems which this has led to land degradation that could reduce future food security, other ecosystem services and even climate. Currently, there is a dearth of quantitative information assessing the severity, distribution, and causes of this land degradation. For practical purposes, this information is needed to develop improved methods of land use (LU) conversion. Uruguay, in contrast with many other regions, still has a high proportion of unimproved grasslands but, during the last 15 years, there has been extensive conversion to grow grain crops. The fundamental goal of this dissertation was to quantify soil degradation resulting from this LU change. Two aspects of soil degradation were studied, soil organic carbon (SOC) and erosion by water. The Environmental Policy Integrated Climate biophysical simulation model (EPIC) was used to model the grassland and cropping systems. The study consisted of three steps: (1) calibration and validation of the model for the Uruguayan agroecosystems, and development of a spatial version, (2) identification of the LU change areas, and (3) quantification of soil degradation as a result of the LU changes. The EPIC model adequately reproduced the field-scale SOC dynamics and erosion in field validation sites. Further, the spatial version of the model was found to simulate spatial and temporal performance adequately. LU change areas during 2000-2013 were mapped and found to cover an area of 410,000 ha, about 13% of potential area for commercial agriculture. LU greatly affected soil degradation. It was greatest for continuous Soybean cultivation with no crop rotation, and lowest for grassland (no conversion to cropping). In addition to LU, slope and initial SOC had significant effects on degradation. The main conclusions were that the recent and continuing conversion from grassland to cropland has caused significant soil degradation, but that some modifications of LU can reduce the risk of degradation.
ADOPTION, IMPACT EVALUATION, AND TECHNICAL EFFICIENCY OF IRON BIOFORTIFIED BEAN PRODUCTION IN RWANDA
(2020) Funes, Jose Elias; Sun, Laixiang; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Micronutrient malnutrition, also known as hidden hunger, is a public health problem in many developing countries. Hidden hunger limits cognitive and physical development of children and increases both children’s and adults’ susceptibility to infectious diseases. The most common outcome of iron deficiency is anemia and in Rwanda, iron micronutrient malnutrition is highly pervasive. Thirty seven percent of children under five years of age and nearly 20 percent of women of childbearing age suffer from anemia in the country. Since 2012, HarvestPlus and its partners have been intensively disseminating iron biofortified common beans (Phaseolus Vulgaris) (IBB) varieties to help alleviate iron deficiency in Rwanda. On one hand, Rwandan farmers may be uncertain about the economic returns of this new technology owing to insufficient knowledge about the types and costs of inputs needed, the yield distribution, expected market prices, and the demand for the produce. On the other hand, policy makers and donors cannot observe the outcomes that bean farmers would experience under all treatments of the IBB program. The counterfactual outcomes that a bean farming household would have experienced under other treatments are not observable. In this context, this dissertation uses a multiprong analytical framework to: 1) analyze how peer interactions, households and farm characteristics, as well as regional factors influence smallholder farming households’ decisions to grow IBB varieties, 2) evaluate the impact of the IBB program on Rwandan farmer’s livelihoods, focusing on the outcomes of yields and incomes for beneficiary households, and 3) estimate the impact of the IBB program on smallholder farming households’ technical efficiency. The spatial econometric results indicate spatial interdependence in smallholder farming households' decisions to adopt IBB. In addition to the directly targeted beneficiaries, the spatial parameters from the econometric analysis suggest that the biofortification program affected non-beneficiaries as well. This finding indicates that (1) a household is more likely to grow IBB if the household is near other early IBB adopters who informed them about the nutritional and yield benefits of IBB technology and (2) the propensity of a household to grow IBB varies with the characteristics of neighboring farmers. The impact evaluation analysis supports the hypothesis that IBB growers had significantly higher yields and incomes, as compared to farmers that grew non-biofortified beans, whether improved or traditional. In addition, the impact assessment shows that farmers who grew iron biofortified varieties were relatively more efficient and obtained greater bean production than the control group.
Advanced Modeling Using Land-use History and Remote Sensing to Improve Projections of Terrestrial Carbon Dynamics
(2021) Ma, Lei; Hurtt, George; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Quantifying, attributing, and projecting terrestrial carbon dynamics can provide valuable information in support of climate mitigation policy to limit global warming to 1.5 °C. Current modeling efforts still involve considerable uncertainties, due in part to knowledge gaps regarding efficient and accurate scaling of individual-scale ecological processes to large-scale dynamics and contemporary ecosystem conditions (e.g., successional states and carbon storage), which present strong spatial heterogeneity. To address these gaps, this research aims to leverage decadal advances in land-use modeling, remote sensing, and ecosystem modeling to improve the projection of terrestrial carbon dynamics at various temporal and spatial scales. Specifically, this research examines the role of land-use modeling and lidar observations in determining contemporary ecosystem conditions, especially in forest, using the latest land-use change dataset, developed as the standard forcing for CMIP6, and observations from both airborne lidar and two state-of-the-art NASA spaceborne lidarmissions, GEDI and ICESat-2. Both land-use change dataset and lidar observations are used to initialize a newly developed global version of the ecosystem demography (ED) model, an individual-based forest model with unique capabilities to characterize fine-scale processes and efficiently scale them to larger dynamics. Evaluations against multiple benchmarking datasets suggest that the incorporation of land-use modeling into the ED model can reproduce the observed spatial pattern of vegetation distribution, carbon dynamics, and forest structure as well as the temporal dynamics in carbon fluxes in response to climate change, increased CO2, and land-use change. Further, the incorporation of lidar observations into ED, largely enhances the model’s ability to characterize carbon dynamics at fine spatial resolutions (e.g., 90 m and 1 km). Combining global ED model, land-use modeling and lidar observation together can has great potential to improve projections of future terrestrial carbon dynamics in response to climate change and land-use change.
Advances in Mapping Forest Biomass and Old-Growth Conditions Using Waveform Lidar
(2023) Bruening, Jamis; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne waveform lidar sys- tem that has transformed scientific understanding of the world’s forests through billions of pre- cise measurements of ecosystem structure. Relative to forest processes that operate on decadal to millennial timescales, the four year period during which GEDI collected these measurements is short, and GEDI’s ability to analyze how forest structure changes over time is mostly unproven. However, fusion efforts that integrate GEDI data with forest inventory measurements and ecosys- tem models hold immense potential for discovery. In this dissertation, I explore the limitations and capabilities of GEDI data for inference into structural and successional dynamics within east- ern US forests. First, I used a forest gap model to quantify uncertainty in biomass predictions for individual GEDI waveforms, and discovered a relationship between biomass uncertainty and successional stage. Next, I investigated uncertainties and errors in large-scale GEDI biomass estimates relative to unbiased estimates from the US forest inventory. I developed a novel mod- eling framework based on fusion between GEDI and the US forest inventory data that corrected these errors, and I produced unbiased and precise maps of forest biomass for the continental US. Lastly, I assessed GEDI’s ability to identify and map different types of old-growth forests, and discovered that GEDI can detect some old forests more effectively than others. This research identified key limitations associated with using GEDI to study forest dynamics, and I leveraged these discoveries to develop new ways of using GEDI data for ecological and successional in- ference. These discoveries will inform new uses of GEDI data and its integration with inventory data and ecosystem modeling to better characterize changes within forest ecosystems.
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.
Aerosol Retrieval over Land from the Directional Polarimetric Camera Aboard on GF-5
(MDPI, 2022-11-11) Wang, Shupeng; Gong, Weishu; Fang, Li; Wang, Weihe; Zhang, Peng; Lu, Naimeng; Tang, Shihao; Zhang, Xingying; Hu, Xiuqing; Sun, Xiaobing
The DPC (Directional Polarization Camera) onboard the Chinese GaoFen-5 (GF-5) satellite is the first operational aerosol monitoring instrument capable of performing multi-angle polarized measurements in China. Compared with POLDER (Polarization and Directionality of Earth’s Reflectance) which ended its mission in December 2013, DPC has similar band design, with a maximum of 12 imaging angles and a relatively higher spatial resolution of 3.3 km. The global aerosol optical depth (AOD) over land from October to December in 2018 was retrieved with multi-angle polarization measurements of DPC. Comparisons with MODIS (Moderate Resolution Imaging Spectroradiometer) AOD products show relatively good agreement over fine-aerosol-particle-dominated areas such as northern China and Huanghuai areas in eastern China, the southern foothills of the Himalayas and India. AERONET (Aerosol Robotic Network) measurements over Beijing, Xianghe and Kanpur were used to evaluate the accuracy of DPC AOD retrievals. The correlation coefficients are greater than 0.9 and the RMSE are lower than 0.08 for Beijing and Xianghe stations. For Kanpur, a relatively lower correlation of 0.772 and larger RMSE of 0.082 are found.
AGRICULTURAL LAND USE, DROUGHT IMPACTS AND VULNERABILITY: A REGIONAL CASE STUDY FOR KARAMOJA, UGANDA
(2017) Nakalembe, Catherine Lilian; Justice, Christopher O; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The increasing frequency of extreme climate events brings into question the sustainability of agriculture in marginal lands, especially those already experiencing drought such as the Karamoja region in northeastern Uganda. A significant amount of research often qualitative has been conducted documenting drought and its impact on Karamoja. Taking a mixed methods approach, this study combined remotely-sensed satellite data, national agricultural surveys, census, and field data to expand on empirical knowledge on agricultural drought, land use and human perceptions of drought necessary for comprehensive drought forecasting, monitoring, and management. Results from this study showed that Karamoja is at least twice more vulnerable to drought than any other region in Uganda. This is because of its very low adaptive capacity in part due to high poverty rates and a higher dependency on the natural environment for livelihood. Analysis of satellite data quantified a 229 percent increase in cropland area in Karamoja between 2000 and 2011/12, driven largely by agricultural development programs. Underlying forces (e.g., cropland expansion programs and controlled grazing) originating from land use policy and development programs, more than proximate causes (direct local level actions) remain the major drivers of this expansion. Although the cultivated area has dramatically increased, there is no quantifiable overall increase in yield or per-capita production as evidenced by the recurrent poor food security. This status quo, (poor yields and dependence on food aid) is likely to continue as more land is put to crop cultivation by poor households and meager investments are made in livestock-based livelihood opportunities. The cropland area mask developed in this research facilitated the characterization of drought within agricultural areas. The drought information developed by this study is spatially and temporally explicit, showing differences in severity between years and between districts. Overall Abim District showed the least variation and is the least impacted while, Moroto District had the highest inter-annual variability and was often the most severely impacted. This research presents an approach to predict the number of people who would require food aid during the lean season in Karamoja (December to March) within a reasonable margin of error (less than 10\%) at the peak of the growing season (August/September), although the need for more extensive testing is recognized. The method takes advantage of readily available satellite data and can contribute to planning for a timely and appropriate response. A case study of farmer's perceptions of drought in Moroto District found that many farmers feel helpless and have no control of their future. For the majority of farmers in the district, past experiences of drought do not necessarily impact on future expectations of drought and many have no long-term adjustment plans. Quite often the majority of the population depends on emergency food assistance, building a culture of dependency. The analysis indicates that factors such as; conflict (insecurity) and interventions by government and international agencies intermingle with culture to have a profound direct influence on farmers' perception of drought amongst communities in Moroto district. This research shows that satellite data can provide the much-needed information to fill the gaps that inhibit long-term drought monitoring, at a significantly lower cost than traditional climate station-based monitoring in data scarce regions like Karamoja. It also points to a way forward for proactive assessment, planning, and response.
An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products
(MDPI, 2021-02-16) Li, Xiuxia; Liang, Shunlin; Jin, Huaan
Leaf area index (LAI) and normalized difference vegetation index (NDVI) are key parameters for various applications. However, due to sensor tradeoff and cloud contaminations, these data are often temporally intermittent and spatially discontinuous. To address the discontinuities, this study proposed a method based on spectral matching of 30 m discontinuous values from Landsat data and 500 m temporally continuous values from Moderate-resolution Imaging Spectroradiometer (MODIS) data. Experiments have proven that the proposed method can effectively yield spatiotemporally continuous vegetation products at 30 m spatial resolution. The results for three different study areas with NDVI and LAI showed that the method performs well in restoring the time series, fills in the missing data, and reasonably predicts the images. Remarkably, the proposed method could address the issue when no cloud-free data pairs are available close to the prediction date, because of the temporal information “borrowed” from coarser resolution data. Hence, the proposed method can make better use of partially obscured images. The reconstructed spatiotemporally continuous data have great potential for monitoring vegetation, agriculture, and environmental dynamics.
An Analytic BRDF Model of Canopy Radiative Transfer and Its Inversion
(Institute of Electrical and Electronics Engineers, 1993-09) Liang, Shunlin; Strahler, Alan H.
Radiative transfer modeling of the bidirectional reflectance distribution function (BRDF) of leaf canopies is a powerful tool to relate multiangle remotely sensed data to biophysical parameters of the leaf canopy and to retrieve such parameters from multiangle imagery. However, the approximate approaches for multiple scattering that are used in the inversion of existing models are quite limited, and the sky radiance frequently is simply treated as isotropic. This paper presents an analytical model based on a rigorous canopy radiative transfer equation in which the multiple-scattering component is approximated by asymptotic theory and the single-scattering calculation, which requires numerical integration to properly accommodate the hotspot effect, is also simplified. Because the model is sensitive to angular variation in sky radiance, we further provide an accompanying new formulation for directional radiance in which the unscattered solar radiance and single-scattering radiance are calculated exactly, and multiple-scattering is approximated by the well-known two-stream Dirac delta function approach. A series of validations against exact calculations indicates that both models are quite accurate, especially when the viewing angle is smaller than 55 degrees. The Powell algorithm is then used to retrieve biophysical parameters from multiangle observations based on both the canopy and the sky radiance distribution models. The results using the soybean data of Ranson et al. to recover four of nine soybean biophysical parameters indicate that inversion of the present canopy model retrieves leaf area index well. Leaf angle distribution was not retrieved as accurately for the same dataset, perhaps because these measurements do not describe the hotspot well. Further experiments are required to explore the applicability of this canopy model.
Analyze Municipal Annexations: Case Studies in Frederick and Caroline Counties of Maryland, 1990-2010
(2012) Pomeroy, Jennifer Yongmei; Geores, Martha E; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Municipal annexations play an important role in converting undeveloped land to development, influencing landscape change. However, the existing literature does not explore the links between annexation and development. An additional inadequacy is the failure to consider environment/landscape aspect of annexation. Therefore, this dissertation proposes a new theoretical framework that is drawn upon political ecology and structuration theory to examine annexation phenomenon processes: environmental/landscape sensitivity and its causal social structures. Frederick and Caroline counties in Maryland from 1990 to 2010 were the two case-study areas because both counties experience increased annexation activities and are representative of suburban and exurban settings at rural - urban continuum of the United States. The data used in this qualitative research were collected from multiple data sources, including key-person interviews, a review of Maryland's annexation log, annexation applications and meeting minutes, and observations at public meetings. Triangulating content analysis, discourse analysis, and social network analysis, this research finds that environmental/landscape is not considered more widely in annexation practices. Although environmental mitigation measures are considered at site level if a property has site environmental elements, the overall environmental/landscape sensitivity is low. It is also found that the economic-centered space remains dynamic in the annexation processes determining annexation approvals and low-density zoning. In addition, the triangulated analyses reveal that current social structures are not conducive to environmental-conscious landscape planning because environmentally oriented non-profit organizations and residents are injected at a later stage of annexation process and is not being fully considered in the evaluation process. Power asymmetry in current annexation structures is due to a lack of environmental voice in annexation processes. The voice of such groups needs to be institutionalized to facilitate more tenable annexation practices.

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