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

Mapping 2000–2010 Impervious Surface Change in India Using Global Land Survey Landsat Data

Partial funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund.Understanding and monitoring the environmental impacts of global urbanization requires better urban datasets. Continuous field impervious surface change (ISC) mapping using Landsat data is an effective way to quantify spatiotemporal dynamics of urbanization. It is well acknowledged that Landsat-based estimation of impervious surface is subject to seasonal and phenological variations. The overall goal of this paper is to map 2000–2010 ISC for India using Global Land Survey datasets and training data only available for 2010. To this end, a method was developed that could transfer the regression tree model developed for mapping 2010 impervious surface to 2000 using an iterative training and prediction (ITP) approachAn independent validation dataset was also developed using Google Earth™ imagery. Based on the reference ISC from the validation dataset, the RMSE of predicted ISC was estimated to be 18.4%. At 95% confidence, the total estimated ISC for India between 2000 and 2010 is 2274.62 +/- 7.84 km2

30m Building Height/Volume of England by Fusing Landsat and Global Elevation Data

The lack of three-dimensional (3D) urban structure information has been a major limiting factor in important urban applications such as population mapping, disaster vulnerability assessment, and climate change adaptation. The 30m England building height and volume data set was produced by fusing of freely available Landsat and global elevation data. Based on an object-based machine learning approach, it is the first freely available data set to provide 30m wall-to-wall coverage of urban structure at the scale of a country. This data set has been validated using Lidar measurements and achieved an RMSE of 1.61 meters for building height and an RMSE of 1,142 cubic meters for building volume

Integrating Pixels, People, and Political Economy to Understand the Role of Armed Conflict and Geopolitics in Driving Deforestation: The Case of Myanmar

Armed conflict and geopolitics are a driving force of Land Use and Land Cover Change (LULCC), but with considerable variation in deforestation trends between broader and finer scales of analysis. Remotely-sensed annual deforestation rates from 1989 to 2018 are presented at the national and (sub-) regional scales for Kachin State in the north of Myanmar and in Kayin State and Tanintharyi Region in the southeast. We pair our multiscaled remote sensing analysis with our multisited political ecology approach where we conducted field-based interviews in study sites between 2018 and 2020. Our integrated analysis identified three common periods of deforestation spikes at the national and state/region level, but with some notable disparities between regions as well as across and within townships and village tracts. We found the rate and geography of deforestation were most influenced by the territorial jurisdictions of armed authorities, national political economic reforms and timber regulations, and proximity to national borders and their respective geopolitical relations. The absence or presence of ceasefires in the north and southeast did not solely explain deforestation patterns. Rather than consider ceasefire or war as a singular explanatory variable effecting forest cover change, we demonstrate the need to analyze armed conflict as a dynamic multisited and diffuse phenomenon, which is simultaneously integrated into broader political economy and geopolitical forces

Patterns of regional site index across a North American boreal forest gradient

Forest structure—the height, cover, vertical complexity, and spatial patterns of trees—is a key indicator of productivity variation across forested extents. During the 2017 and 2019 growing seasons, NASA’s Arctic-Boreal Vulnerability Experiment collected full-waveform airborne LiDAR using the land, vegetation and imaging sensor, sampling boreal and tundra landscapes across a variety of ecological regions from central Canada westward through Alaska. Here, we compile and archive a geo-referenced gridded suite of these data that include vertical structure estimates and novel horizontal cover estimates of vegetation canopy cover derived from vegetation’s vertical LiDAR profile. We validate these gridded estimates with small footprint airborne LiDAR, and link >36 million of them with stand age estimates from a Landsat time-series of tree-canopy cover that we confirm with plot-level disturbance year data. We quantify the regional magnitude and variability in site index, the age-dependent rates of forest growth, across 15 boreal ecoregions in North America. With this open archive suite of forest structure data linked to stand age, we bound current forest productivity estimates across a boreal structure gradient whose response to key bioclimatic drivers may change with stand age. These results, derived from a reduction of a large archive of airborne LiDAR and a Landsat time series, quantify forest productivity bounds for input into forest and ecosystem growth models, to update forecasts of changes in North America’s boreal forests by improving the regional parametrization of forest growth rates

Are Essential Women’s Healthcare Services Fully Covered? A Comparative Analysis of Policy Documents in Shanghai and New York City from 1978–2017

This study aimed to analyze the changes in the 10 major categories of women’s healthcare services (WHSs) in Shanghai (SH) and New York City (NYC) from 1978 to 2017, and examine the relationship between these changes and maternal mortality ratio (MMR). Content analysis of available public policy documents concerning women’s health was conducted. Two indicators were designed to represent the delivery of WHSs: The essential women’s healthcare service coverage rate (ESCR) and the assessable essential healthcare service coverage rate (AESCR). Spearman correlation was used to analyze the relationship between the two indicators and MMR. In SH, the ESCR increased from 10% to 90%, AESCR increased from 0% to 90%, and MMR decreased from 24.0/100,000 to 1.01/100,000. In NYC, the ESCR increased from 0% to 80%, the AESCR increased from 0% to 60%, and the MMR decreased from 24.7/100,000 to 21.4/100,000. The MMR significantly decreased as both indicators increased (p < 0.01). Major advances have been made in women’s healthcare in both cities, with SH having a better improvement effect. A common shortcoming for both was the lack of menopausal health service provision. The promotion of women’s health still needs to receive continuous attention from governments of SH and NYC. The experiences of the two cities showed that placing WHSs among policy priorities is effective in improving service status