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

Uncertainty Assessment of Spectral Mixture Analysis in Remote Sensing Imagery

Spectral mixture analysis (SMA), a scheme of sub-pixel-based classifications, is one of the widely used models to map fractional land use and land cover information in remote sensing imagery. It assumes that: 1) a mixed pixel is composed by several pure land cover classes (endmembers) linearly or nonlinearly, and 2) the spectral signature of each endmember is a constant within the entire spatial extent of analysis. SMA has been commonly applied to impervious surface area extraction, vegetation fraction estimation, and land use and land cover change (LULC) mapping. Limitations of SMA, however, still exist. First, the existence of between- and within-class variability prevents the selection of accurate endmembers, which results in poor accuracy of fractional land cover estimates. Weighted spectral mixture analysis (WSMA) and transformed spectral mixture analysis (TSMA) are alternate means to address the within- and between- class variability. These methods, however, have not been analyzed systematically and comprehensively. The effectiveness of each WSMA and TSMA scheme is still unknown, in particular within different urban areas. Second, multiple endmember SMA (MESMA) is a better alternative to address spectral mixture model uncertainties. It, nonetheless, is time consuming and inefficient. Further, incorrect endmember selections may still limit model performance as the best-fit endmember model might not be the optimal model due to the existence of spectral variability. Therefore, this study aims 1) to explore endmember uncertainties by examining WSMA and TSMA modeling comprehensively, and 2) to develop an improved MESMA model in order to address the uncertainties of spectral mixture models. Results of the WSMA examination illustrated that some weighting schemes did reduce endmember uncertainties since they could improve the fractional estimates significantly. The results also indicated that spectral class variance played a key role in addressing the endmember uncertainties, as the better performing weighting schemes were constructed with spectral class variance. In addition, the results of TSMA examination demonstrated that some TSMAs, such as normalized spectral mixture analysis (NSMA), could effectively solve the endmember uncertainties because of their stable performance in different study areas. Results of Class-based MEMSA (C-MESMA) indicated that it could address spectral mixture model uncertainties by reducing a lot of the calculation burden and effectively improving accuracy. Assessment demonstrated that C-MEMSA significantly improving accuracy. Major contributions of this study can be summarized as follow. First, the effectiveness of addressing endmember uncertainties have been fully discussed by examining: 1) the effectiveness of ten weighted spectral mixture models in urban environments; and 2) the effectiveness of 26 transformed spectral mixture models in three locations. Constructive guidance regarding handling endmember uncertainties using WSMA and TSMA have been provided. Second, the uncertainties of spectral mixture model were reduced by developing an improved MESMA model, named C-MESMA. C-MESMA could restrict the distribution of endmembers and reduce the calculation burden of traditional MESMA, increasing SMA accuracy significantly

The use of satellite data, meteorology and land use data to define high resolution temperature exposure for the estimation of health effects in Italy

Introduction. Despite the mounting evidence on heat-related health risks, there is limited evidence in suburban and rural areas. The limited spatial resolution of temperature data also hinders the evidence of the differential heat effect within cities due to individual and area-based characteristics. Methods. Satellite land surface temperature (LST), observed meteorological and spatial and spatio-temporal land use data were combined in mixed-effects regression models to estimate daily mean air temperature with a 1x1km resolution for the period 2000-2010. For each day, random intercepts and slopes for LST were estimated to capture the day-to-day temporal variability of the Ta–LST relationship. The models were also nested by climate zones to better capture local climates and daily weather patterns across Italy. The daily exposure data was used to estimate the effects and impacts of heat on cause-specific mortality and hospital admissions in the Lazio region at municipal level in a time series framework. Furthermore, to address the differential effect of heat within an urban area and account for potential effect modifiers a case cross-over study was conducted in Rome. Mean temperature was attributed at the individual level to the Rome Population Cohort and the urban heat island (UHI) intensity using air temperature data was calculated for Rome. Results. Exposure model performance was very good: in the stage 1 model (only on grid cells with both LST and observed data) a mean R2 value of 0.96 and RMSPE of 1.1°C and R2 of 0.89 and 0.97 for the spatial and temporal domains respectively. The model was also validated with regional weather forecasting model data and gave excellent results (R2=0.95 RMSPE=1.8°C. The time series study showed significant effects and impacts on cause-specific mortality in suburban and rural areas of the Lazio region, with risk estimates comparable to those found in urban areas. High temperatures also had an effect on respiratory hospital admissions. Age, gender, pre-existing cardiovascular disease, marital status, education and occupation were found to be effect modifiers of the temperature-mortality association. No risk gradient was found by socio-economic position (SEP) in Rome. Considering the urban heat island (UHI) and SEP combined, differential effects of heat were observed by UHI among same SEP groupings. Impervious surfaces and high urban development were also effect modifiers of the heat-related mortality risk. Finally, the study found that high resolution gridded data provided more accurate effect estimates especially for extreme temperature intervals. Conclusions. Results will help improve heat adaptation and response measures and can be used predict the future heat-related burden under different climate change scenarios.Open Acces

An Evaluation of Surface Urban Heat Islands in Two Contrasting Cities

This thesis presents a comparative study on surface urban heat islands effects in Baghdad and Perth. The first part evaluates expansion of built-up areas and quantifies its effects on land surface temperature patterns. The second part examines the extent to which the urban thermal environment is influenced by spatial patterns of land use and land cover (LULC) categories. The final part investigates the thermophysical behaviour of various urban LULC categories using albedo and LST parameters

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

High-resolution maps of material stocks in buildings and infrastructures in Austria and Germany

The dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Two main types of data are currently used to map stocks, night-time lights (NTL) from Earth-observing (EO) satellites and cadastral information. We present an alternative approach for broad-scale material stock mapping based on freely available high-resolution EO imagery and OpenStreetMap data. Maps of built-up surface area, building height, and building types were derived from optical Sentinel-2 and radar Sentinel-1 satellite data to map patterns of material stocks for Austria and Germany. Using material intensity factors, we calculated the mass of different types of buildings and infrastructures, distinguishing eight types of materials, at 10 m spatial resolution. The total mass of buildings and infrastructures in 2018 amounted to ∼5 Gt in Austria and ∼38 Gt in Germany (AT: ∼540 t/cap, DE: ∼450 t/cap). Cross-checks with independent data sources at various scales suggested that the method may yield more complete results than other data sources but could not rule out possible overestimations. The method yields thematic differentiations not possible with NTL, avoids the use of costly cadastral data, and is suitable for mapping larger areas and tracing trends over time

Assessing sustainable development in industrial regions towards smart built environment management using Earth observation big data

This thesis investigates the sustainability of nationwide industrial regions using Earth observation big data, from environmental and socio-economic perspectives. The research contributes to spatial methodology design and decision-making support. New spatial methods, including the robust geographical detector and the concept of geocomplexity, are proposed to demonstrate the spatial properties of industrial sustainability. The study delivers scientific decision-making advice to industry stakeholders and policymakers for the post-construction assessment and future planning phases. The research has been published in prestigious geography journals, demonstrating its success

Potential contributions of remote sensing to ecosystem service assessments

Ecological and conservation research has provided a strong scientific underpinning to the modeling of ecosystem services (ESs) over space and time, by identifying the ecological processes and components of biodiversity (ecosystem service providers, functional traits) that drive ES supply. Despite this knowledge, efforts to map the distribution of ESs often rely on simple spatial surrogates that provide incomplete and non-mechanistic representations of the biophysical variables they are intended to proxy. However, alternative data sets are available that allow for more direct, spatially nuanced inputs to ES mapping efforts. Many spatially explicit, quantitative estimates of biophysical parameters are currently supported by remote sensing, with great relevance to ES mapping. Additional parameters that are not amenable to direct detection by remote sensing may be indirectly modeled with spatial environmental data layers. We review the capabilities of modern remote sensing for describing biodiversity, plant traits, vegetation condition, ecological processes, soil properties, and hydrological variables and highlight how these products may contribute to ES assessments. Because these products often provide more direct estimates of the ecological properties controlling ESs than the spatial proxies currently in use, they can support greater mechanistic realism in models of ESs. By drawing on the increasing range of remote sensing instruments and measurements, data sets appropriate to the estimation of a given ES can be selected or developed. In so doing, we anticipate rapid progress to the spatial characterization of ecosystem services, in turn supporting ecological conservation, management, and integrated land use planning

Tree Cover Variability in the District of Columbia

Urban forests are increasingly a focus of interest as urbanized populations grow and urban areas expand. Urban forests change as trees are planted, grow, die, and are removed. These processes alter a city's tree cover over time, but this inherent dynamism is poorly understood. Better understanding of how tree cover is a variable land cover component will enhance knowledge of the urban environment and provide new perspectives for management of urban resources. In this study, tree cover variability within a major urban center was observed over a 20 year period. Changes in tree cover proportion were measured in the District of Columbia between 1984-2004 utilizing highly calibrated satellite remote sensing data. Testing of alternate methodologies demonstrated that an approach utilizing support vector regression provided most consistent accuracy across land use types. Tree cover maps were validated using aerial photography imagery and data from field surveys. Between 1984-2004, the city-wide tree cover remained between 22.1(+/-2.9)% and 28.8(+/-2.9)% of total land surface area. The District of Columbia did not experience an overall increase or decrease in total tree canopy area. Spatial patterns of tree cover variability were investigated to identify local scale changes in tree cover and connections with urban land use. Within the city, greatest variability was observed in low density residential zones. Tree cover proportion in these zones declined 7.4(+/-5.4)% in the years between 1990-1996 and recovered after 1996. Changes in tree cover were observed with high resolution aerial photography to determine relative contribution from fluctuation in the number of standing trees and changes in crown sizes. Land cover conversion removed dense tree cover from 50.2 hectares of the city's land surface between 1984-2004. The results demonstrate that tree cover variability in the District of Columbia occurred primarily within low population density residential areas. Neighborhoods within these zones were analyzed to identify factors correlated with tree cover. Implications of the results include enhanced understanding of the possible impact of urban forest management, and how a focus on low density residential zones is appropriate in setting goals for expansion of urban tree cover