Using Multi-Source Data to Assess the Dynamics of Socioeconomic Development in Africa

Frequent and rapid spatially explicit assessment of socioeconomic development is critical for achieving the Sustainable Development Goals (SDGs) at both national and global levels. In the past decades, scientists have proposed many methods for monitoring human activities on the Earth’s surface on various spatiotemporal scales using Defense Meteorological Satellite Program Operational Line System (DMSP-OLS) nighttime lights (NTL) data. However, the DMSP-OLS NTL data and the associated processing methods have limited their reliability and applicability for systematic measuring and mapping of socioeconomic development. This research utilizes Visible Infrared Imaging Radiometer Suite (VIIRS) NTL and the Isolation Forest (iForest) machine learning algorithm for more intelligent data processing to capture human activities. I use machine learning and NTL data to map gross domestic product (GDP) at 1 km2. I then use these data products to derive inequality indexes like GINI coefficients and 20:20 ratios at nationally aggregate levels. I have also conducted a case study based on agricultural production information to estimate subnational GDP in Uganda. This flexible approach processes the data in an unsupervised manner on various spatial scales. Assessments show that this method produces accurate sub-national GDP data for mapping and monitoring human development uniformly in Uganda and across the globe

Improving Nocturnal Fire Detection with the VIIRS Day-Night Band

As an important component in the Earth-atmosphere system, wildfires are a serious threat to life and property that—despite improving warning systems—have exacted greater costs in recent years. In addition, they impact global atmospheric chemistry by releasing potent trace gasses and aerosols. Using the Visible Infrared Imaging Radiometer Suite (VIIRS), this study investigates the adjustment of fire pixel selection criteria to include visible light signatures at night, creating the Firelight Detection Algorithm (FILDA). This allows for greatly improved detection of smaller and cooler fires from satellite observations. VIIRS scenes with coincident Advanced Spaceborne Thermal Emission and Reflection (ASTER) overpasses are examined after applying the operational VIIRS fire product algorithm and including a modified candidate fire pixel selection approach, which lowers the 4 μm brightness temperature threshold from 305 K but includes a minimum day-night band (DNB) radiance. FILDA is tested by applying it to scenes in different environments, including large forest fires like the Rim Fire in California and High Park fire in Colorado, in addition to gas flares. A large increase in the number of detected fire pixels is observed with small non-agricultural wildfires, as verified with the finer-resolution ASTER data (90 m). Quantitative use of the DNB to improve detection of these smaller fires could lead to reduced warning and response times as well as provide more accurate quantification of biomass burning emissions at night. Adviser: Jun Wan

Spacecraft Position Estimation and Attitude Determination using Terrestrial Illumination Matching

An algorithm to conduct spacecraft position estimation and attitude determination via terrestrial illumination matching (TIM) is presented consisting of a novel method that uses terrestrial lights as a surrogate for star fields. Although star sensors represent a highly accurate means of attitude determination with considerable spaceflight heritage, with Global Positioning System (GPS) providing position, TIM provides a potentially viable alternative in the event of star sensor or GPS malfunction or performance degradation. The research defines a catalog of terrestrial light constellations, which are then implemented within the TIM algorithm for position acquisition of a generic spacecraft bus. With the algorithm relying on terrestrial lights rather than the established standard of star fields, a series of sensitivity studies are showcased to determine performance during specified operating constraints, to include varying orbital altitude and cloud cover conditions. The pose is recovered from the matching techniques by solving the epipolar constraint equation using the Essential and Fundamental matrix, and point-to-point projection using the Homography matrix. This is used to obtain relative position change and the spacecraft\u27s attitude when there is a measurement. When there is not, both an extended and an unscented Kalman filter are applied to test continuous operation between measurements. The research is operationally promising for use with each nighttime pass, but filtering is not enough to sustain orbit determination during daytime operations

Mapping regional land cover and land use change using MODIS time series

Coarse resolution satellite observations of the Earth provide critical data in support of land cover and land use monitoring at regional to global scales. This dissertation focuses on methodology and dataset development that exploit multi-temporal data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve current information related to regional forest cover change and urban extent. In the first element of this dissertation, I develop a novel distance metric-based change detection method to map annual forest cover change at 500m spatial resolution. Evaluations based on a global network of test sites and two regional case studies in Brazil and the United States demonstrate the efficiency and effectiveness of this methodology, where estimated changes in forest cover are comparable to reference data derived from higher spatial resolution data sources. In the second element of this dissertation, I develop methods to estimate fractional urban cover for temperate and tropical regions of China at 250m spatial resolution by fusing MODIS data with nighttime lights using the Random Forest regression algorithm. Assessment of results for 9 cities in Eastern, Central, and Southern China show good agreement between the estimated urban percentages from MODIS and reference urban percentages derived from higher resolution Landsat data. In the final element of this dissertation, I assess the capability of a new nighttime lights dataset from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) for urban mapping applications. This dataset provides higher spatial resolution and improved radiometric quality in nighttime lights observations relative to previous datasets. Analyses for a study area in the Yangtze River Delta in China show that this new source of data significantly improves representation of urban areas, and that fractional urban estimation based on DNB can be further improved by fusion with MODIS data. Overall, the research in this dissertation contributes new methods and understanding for remote sensing-based change detection methodologies. The results suggest that land cover change products from coarse spatial resolution sensors such as MODIS and VIIRS can benefit from regional optimization, and that urban extent mapping from nighttime lights should exploit complementary information from conventional visible and near infrared observations

Evaluation of VIIRS Nightfire Product and Comparison with MODIS and VIIRS Active Fire Products in a Russian Gas Flaring Region.

Gas flaring is a commonly used practice for disposing of waste gases emerging from industrial oil drilling and production processes. It is a serious environmental and economic hazard with adverse impacts on air quality, climate, and the public health. Accurate determination of flare locations and estimation of associated emissions are therefore of prime importance. Recently developed Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire product (VNF) has shown remarkable efficiency in detecting gas flares globally, owing primarily to its use of Shortwave Infrared (SWIR) band in its detection algorithm. This study compares and contrast nocturnal hot source detection by VNF to detections by other established fire detection products (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) Terra Thermal Anomalies product (MOD14), MODIS Aqua Thermal Anomalies product (MYD14) and VIIRS Applications Related Active Fire Product (VAFP)) over an extensive gas flaring region in Russia ‑ Khanty Mansiysk Autonomous Okrug, for the time period of April - August 2013. The surface hotspots detected by VNF were found to be much higher in magnitude than detected by other products. An attempt to replicate VNF algorithm locally for better comprehension, revealed threshold related discrepancies in VNF V1.0 in multiple spectral bands. Case studies for reconciliation between VNF‑R (VNF replicated product) and VAFP hotspots showed that convergence in hotspot detection between two products is possible by scaling up VNF-R thresholds, and, VAFP can detect large flares having strong spectral signature in SWIR bands. The efficacy of VNF hotspot detection was evaluated for 10 previously identified flare locations with varying hot source sizes over the period of April‑August 2013. VNF was able to detect all the test sites with frequency of detection varying between 20% to 42% of the days tested. Mean areas of tested gas flares estimated by VNF showed good agreement with areas of flares computed using Google Earth with a linear correlation of 0.91; however, VNF estimated areas were found to be somewhat underestimated. Overall the results indicate significant potential of VNF in characterizing gas flaring from space. Advisor: Mark R. Anderso

Remote sensing of night lights: a review and an outlook for the future

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordRemote sensing of night light emissions in the visible band offers a unique opportunity to directly observe human activity from space. This has allowed a host of applications including mapping urban areas, estimating population and GDP, monitoring disasters and conflicts. More recently, remotely sensed night lights data have found use in understanding the environmental impacts of light emissions (light pollution), including their impacts on human health. In this review, we outline the historical development of night-time optical sensors up to the current state of the art sensors, highlight various applications of night light data, discuss the special challenges associated with remote sensing of night lights with a focus on the limitations of current sensors, and provide an outlook for the future of remote sensing of night lights. While the paper mainly focuses on space borne remote sensing, ground based sensing of night-time brightness for studies on astronomical and ecological light pollution, as well as for calibration and validation of space borne data, are also discussed. Although the development of night light sensors lags behind day-time sensors, we demonstrate that the field is in a stage of rapid development. The worldwide transition to LED lights poses a particular challenge for remote sensing of night lights, and strongly highlights the need for a new generation of space borne night lights instruments. This work shows that future sensors are needed to monitor temporal changes during the night (for example from a geostationary platform or constellation of satellites), and to better understand the angular patterns of light emission (roughly analogous to the BRDF in daylight sensing). Perhaps most importantly, we make the case that higher spatial resolution and multispectral sensors covering the range from blue to NIR are needed to more effectively identify lighting technologies, map urban functions, and monitor energy use.European Union Horizon 2020Helmholtz AssociationNatural Environment Research Council (NERC)Chinese Academy of ScienceLeibniz AssociationIGB Leibniz Institut

Multisource Remote Sensing based Impervious Surface Mapping

Impervious surface (IS) not only serves as a key indicator of urbanization, but also affects the micro-ecosystem. Therefore, it is essential to monitor IS distribution timely and accurately. Remote sensing is an effective approach as it can provide straightforward and consistent information over large area with low cost. This thesis integrates multi-source remote sensing data to interpretate urban patterns and provide more reliable IS mapping results. Registration of optical daytime and nighttime lights (NTL) data is developed in the first contribution. An impervious surface based optical-to-NTL image registration algorithm with iterative blooming effect reduction (IS_iBER) algorithm is proposed. This coarse-to-fine procedure investigates the correlation between optical and NTL features. The iterative registration and blooming effect reduction method obtains precise matching results and reduce the spatial extension of NTL. Considering the spatial transitional nature of urban-rural fringes (URF) areas, the second study proposed approach for URF delineation, namely optical and nighttime lights (NTL) data based multi-scale URF (msON_URF).The landscape heterogeneity and development vitality derived from optical and NTL features are analyzed at a series of scales to illustrate the urban-URF-rural pattern. Results illustrate that msON_URF is effective and practical for not only concentric, but also polycentric urban patterns. The third study proposes a nighttime light adjusted impervious surface index (NAISI) to detect IS area. Parallel to baseline subtraction approaches, NAISI takes advantage of features, rather than spectral band information to map IS. NAISI makes the most of independence between NTL-ISS and pervious surface to address the high spectral similarity between IS and bare soil in optical image. An optical and NTL based spectral mixture analysis (ON_SMA) is proposed to achieve sub-pixel IS mapping result in the fourth study. It integrates characteristics of optical and NTL imagery to adaptively select local endmembers. Results illustrate the proposed method yields effective improvement and highlight the potential of NTL data in IS mapping. In the fifth study, GA-SVM IS mapping algorithm is investigated with introduction of the achieved urban-URF-rural spatial structure. The combination of optical, NTL and SAR imagery is discussed. GA is implemented for feature selection and parameter optimization in each urban scenario

LAND USE AND LAND COVER CHANGE AS A DRIVER OF ECOSYSTEM DEGRADATION ACROSS BIOMES

The expansion and intensification of agricultural production in human-dominated landscapes threaten efforts to sustain natural ecosystems and maintain agricultural production in a changing climate. Long-term use of agricultural lands, combined with conversion of natural ecosystems for agricultural production, can rapidly degrade the health of remaining natural ecosystems. The fundamental goal of this dissertation was to assess the impacts of anthropogenic degradation on stocks and sequestration of carbon. Although degradation alters a range of ecosystem services, case studies of ecosystem degradation in this dissertation focus on reductions in vegetation productivity, carbon stocks, and the extent of natural forest cover as a result of human activity. Time series of satellite remote sensing data were used to track forest and rangeland degradation in the southwestern United States, forest carbon emissions from cropland expansion in the Brazilian Cerrado, and fire-driven forest conversion for oil palm plantations in Southeast Asia. Three major themes link the regional case studies: expansion and intensification of agricultural production, market demand and certification, and agricultural management in response to climate variability. Conclusions from the dissertation underscore the widespread influence of land management on vegetation productivity and forest carbon stocks. In the Southwest United States, reductions in net primary production on managed lands were higher in forested landscapes than other cover types. In contrast, Native American Indian Reservations, often considered to be more degraded, actually had smaller absolute reductions in net primary productivity during 2000-2011. Multi-year droughts in the southwest present new challenges for managing forests and rangelands, and climate projections suggest dry conditions will intensify in the coming century. In Southeast Asia, industry-led efforts to certify sustainable palm oil production were evaluated using satellite data on fires and forest loss. Rates of fire-driven deforestation and total fire activity declined following certification, highlighting the potential for certification to reduce ignitions during El Niño years and protect remaining fragments of lowland and peat forest. Aligning certification criteria for sustainable palm oil with satellite monitoring capabilities may help accelerate compliance with environmental legislation and market demands for deforestation-free products. In Brazil, government and industry actions to limit Amazon deforestation have largely overlooked the neighboring Cerrado biome. Forest carbon emissions from deforestation for soy expansion in the Cerrado increased substantially after the implementation of the Soy Moratorium in the Brazilian Amazon, partially offsetting recent reductions in Amazon deforestation carbon emissions. The success of policies to support sustainable agricultural production therefore depends on efforts to minimize cross-biome leakage and the ability to monitor compliance and unintended consequences. Solutions for management must also confront the growing influence of climate variability. Time series of satellite data may allow early detection of degradation impacts and support efforts to mitigate the influence of sustained agricultural production on natural systems. Changes in vegetation carbon stocks from ecosystem degradation varied across case studies, underscoring the diverse nature of direct and indirect drivers of degradation across different land use systems. Direct human drivers of ecosystem degradation in the southwest United States from management of livestock grazing resulted in gradual changes in vegetation productivity, whereas mining and oil extraction areas showed large and permanent reductions. Forest carbon emissions from agriculture expansion in the Cerrado were a one-time process, as native vegetation is cleared for cropland expansion. In contrast, the carbon emissions from Southeast Asia’s forest and peatland conversion involve both sudden and gradual processes, as carbon accumulation in oil palm plantations partially compensates for emissions from forest conversion. Overall, this research made contributions to understanding of the regional impacts of human activity and the potential for climate change mitigation from sustainable land use practices in human-dominated landscapes

TOWARDS FINE SCALE CHARACTERIZATION OF GLOBAL URBAN EXTENT, CHANGE AND STRUCTURE

Urbanization is a global phenomenon with far-reaching environmental impacts. Monitoring, understanding, and modeling its trends and impacts require accurate, spatially detailed and updatable information on urban extent, change, and structure. In this dissertation, new methods have been developed to map urban extent, sub-pixel impervious surface change (ISC), and vertical structure at national to global scales. First, an innovative multi-level object-based texture classification approach was adopted to overcome spectral confusion between urban and nonurban land cover types. It was designed to be robust and computationally affordable. This method was applied to the 2010 Global Land Survey Landsat data archive to produce a global urban extent map. An initial assessment of this product yielded over 90% overall accuracy and good agreement with other global urban products for the European continent. Second, for sub-pixel ISC mapping, the uncertainty caused by seasonal and phenological variations is one of the greatest challenges. To solve this issue, I developed an iterative training and prediction (ITP) approach and used it to map the ISC of entire India between 2000 and 2010. At 95% confidence, the total ISC for India between 2000 and 2010 was estimated to be 2274.62±7.84 km2. Finally, using an object-based feature extraction approach and the synergy of Landsat and freely available elevation datasets, I produced 30m building height and volume maps for England, which for the first time characterized urban vertical structure at the scale of a country. Overall, the height RMSE was only ±1.61 m for average building height at 30m resolution. And the building volume RMSE was ±1142.3 m3. In summary, based on innovative data processing and information extraction methods, this dissertation seeks to fill in the knowledge gaps in urban science by advancing the fine scale characterization of global urban extent, change, and structure. The methods developed in this dissertation have great potentials for automated monitoring of global urbanization and have broad implications for assessing the environmental impact, disaster vulnerability, and long-term sustainability of urbanization