Coupling Coordination Relationship between Urban Sprawl and Urbanization Quality in the West Taiwan Strait Urban Agglomeration, China: Observation and Analysis from DMSP/OLS Nighttime Light Imagery and Panel Data

Urban sprawl is the most prominent characteristic of urbanization, and increasingly affects local and regional sustainable development. The observation and analysis of urban sprawl dynamics and their relationship with urbanization quality are essential for framing integrative urban planning. In this study, the urban areas of the West Taiwan Strait Urban Agglomeration (WTSUA) were extracted using nighttime light imagery from 1992 to 2013. The spatio-temporal characteristics and pattern of urban sprawl were quantitatively analyzed by combining an urban expansion rate index and a standard deviation ellipse model. The urbanization quality was assessed using an entropy weight model, and its relationship with urban sprawl was calculated by a coupling coordination degree model. The results showed that the urban area in the WTSUA experienced a significant increase, i.e., 18,806.73 km2, during the period 1992–2013. The central cities grew by 11.08% and noncentral cities by 27.43%, with a general uneven city rank-size distribution. The urban sprawl showed a circular expansion pattern, accompanied by a gradual centroid migration of urban areas from the southeast coast to the central-western regions. The coupling coordination level between urban expansion and urbanization quality increased from serious incoordination in 1992 to basic coordination in 2013. Dual driving forces involving state-led policies and market-oriented land reform had a positive influence on the harmonious development of urban sprawl and urbanization quality of the WTSUA. This research offers an effective approach to monitor changes in urban sprawl and explore the coupling coordination relationship between urban sprawl and urbanization quality. The study provides important scientific references for the formulation of future policies and planning for sustainable development in urban agglomerations

Evolution of Urban Spatial Clusters in China: A Graph-Based Method Using Nighttime Light Data

An urban spatial cluster (USC) describes one or more geographic agglomerations and the linkages among cities. USCs are conventionally delineated based on predefined administrative boundaries of cities, without considering the dynamic and evolving nature of the spatial extent of USCs. This study uses Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light (NTL) satellite images to quantitatively detect and characterize the evolution of USCs. We propose a dynamic minimum spanning tree (DMST) and a subgraph partitioning method to identify the evolving USCs over time, which considers both the spatial proximity of urban built-up areas and their affiliations with USCs at the previous snapshot. China is selected as a case study for its rapid urbanization process and the cluster-based economic development strategy. Four DMSTs are generated for China using the urban built-up areas extracted from DMSP/OLS NTL satellite images collected in 2000, 2004, 2008, and 2012. Each DMST is partitioned into various subtrees and the urban built-up areas connected by the same subtree are identified as a potential USC. By inspecting the evolution of USCs over time, three different types of USCs are obtained, including newly emerging, single-core, and multicore clusters. Using the rank-size distribution, we find that large-sized USCs have greater development than medium- and small-sized USCs. A clear directionality and heterogeneity are observed in the expansions of the ten largest USCs. Our study provides further insight for the understanding of urban system and its spatial structures, and assists policymakers in their planning practices at national and regional scales

A global North-South division line for portraying urban development

Rapid urbanization has tremendously changed the global landscape with profound impacts on our society. Nighttime light (NTL) data can provide valuable information about human activities and socioeconomic conditions thus has become an effective proxy to measure urban development. By using NTL-derived urban measures from 1992 to 2018, we analyzed the spatiotemporal patterns of global urban development from country to region to city scales, which presented a distinct North-South divergence characterized by the rising and declining patterns. A global North-South division line was identified to partition the globe into the Line-North and the Line-South geographically, which accorded with the socioeconomic difference from the aspects of urban population and economy. This line may keep a certain degree of stability deriving from the trends of population and economic information but also bears uncertainties in the long term

Evaluating Urbanization and Spatial-Temporal Pattern Using the DMSP/OLS Nighttime Light Data: A Case Study in Zhejiang Province

The application of DMSP/OLS nighttime light data provides an effective measure for characterizing urbanization and its spatial-temporal changes. Combined with the social economic statistics and calibrated nighttime data, the nighttime light imagery of Zhejiang province was fully intercalibrated during the period 1992–2013. The backgrounds were explained and the model of region light index (RLI) was built to make further research. The methods of mutation detection, regression analysis, and spatial analysis were adopted in this study. The results show that the urbanization progress of Zhejiang experienced a transformation from rapid development to steady improvement and was accompanied by a changing direction of urban expansion from coastal to inland areas from 2000. Further research indicated that Zhejiang province possessed a relative high level of urbanization, where a spatial pattern of urbanization with one center and four axes was initially formed. It is a novel attempt to investigate the urbanization of Zhejiang province on the basis of the DMSP/OLS night-lighting data, which may provide a significant guideline for the urban planning and development

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

Nighttime Lights as a Proxy for Economic Performance of Regions

Studying and managing regional economic development in the current globalization era demands prompt, reliable, and comparable estimates for a region’s economic performance. Night-time lights (NTL) emitted from residential areas, entertainment places, industrial facilities, etc., and captured by satellites have become an increasingly recognized proxy for on-ground human activities. Compared to traditional indicators supplied by statistical offices, NTLs may have several advantages. First, NTL data are available all over the world, providing researchers and official bodies with the opportunity to obtain estimates even for regions with extremely poor reporting practices. Second, in contrast to non-standardized traditional reporting procedures, the unified NTL data remove the problem of inter-regional comparability. Finally, NTL data are currently globally available on a daily basis, which makes it possible to obtain these estimates promptly. In this book, we provide the reader with the contributions demonstrating the potential and efficiency of using NTL data as a proxy for the performance of regions

It’s not big, it’s large: Mapping and characterizing urban landscapes of a different magnitude based on EO-data

The United Nation’s “World Urbanization Prospects” numeralise a migration process of a huge dimension – from rural to urban areas. While in 1975 only 37.7% of the world’s global population were urban dwellers, in 1990 already 43.0% and today little over 50% of all earth-dwellers are living in urban areas. For the year 2050 the expected number is even 67.2% (UN, 2011). This recent and prospective urbanization trend leads to new spatial dimensions of urban landscapes. One new trend is the spatial evolution of once polynuclei urban areas to so-called ‘mega-regions’. Because in literature clear definitions for the term ‘mega-region’ are missing or at least fuzzy and only qualitative we aim to derive quantitative physical spatial characteristics possibly defining mega-regions. For this purpose we use multi-temporal and multi-source satellite data to classify urbanized areas for an exemplary mega-region – the Hong Kong-Shenzhen-Guangzhou mega-region in Southern China – for the years 1975, 1990, 2000 and 2011. Furthermore, we suggest a set of spatial features potentially characteristic for the evolution of mega-regions. In particular we apply a multitude of spatial metrics at a defined spatial unit for the entire mega-region. The result is a novel spatial approach to capture, measure and analyze new dimensions and shapes of urban landscapes

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

Mapping of nighttime light trends and refugee population changes in Ukraine during the Russian–Ukrainian War

The nighttime lights accurately and coherently depict how humans live. This study uses nighttime light measurements to quantify changes in nighttime lighting and refugee population in Ukraine before and after the war. We combined the Theil–Sen estimator with the M-K test to explore the trends of nighttime light. In addition, we constructed a linear model using nighttime light data and a portion of the UNHCR refugee data. Our results reveal that 1 week after the start of the Russo-Ukrainian War, the nighttime light area and the average nighttime light DN value in Ukraine exhibited a steep decline of about 50 percent. Our findings showed taht refugee population changes calculated through models and nighttime light data were mostly consistent with UNHCR data. We thought that the nighttime light data might be used directly to dynamically estimate changes in the refugee movement throughout the war. Nighttime light changes has significant implications for international humanitarian assistance and post-war reconstruction