Recording Urban Land Dynamic and Its Effects during 2000–2019 at 15-m Resolution by Cloud Computing with Landsat Series

Comparison of the CASA and InVEST models' effects for estimating spatiotemporal differences in carbon storage of green spaces in megacities

Urban green space is a direct way to improve the carbon sink capacity of urban ecosystems. The carbon storage assessment of megacity green spaces is of great significance to the service function of urban ecosystems and the management of urban carbon zoning in the future. Based on multi-period remote sensing image data, this paper used the CASA model and the InVEST model to analyze the spatio-temporal variation and driving mechanism of carbon storage in Shenzhen green space and discussed the applicability of the two models to the estimation of carbon storage in urban green space. The research results showed that, from 2008 to 2022, in addition to the rapid expansion of construction land, the area of green space and other land types in Shenzhen showed a significant decrease trend. The estimation results of the carbon storage model showed that the carbon storage of green space shows a significant trend of reduction from 2008 to 2022, and the reduction amounts are 0.8 × 106 t (CASA model) and 0.64 × 106 t (InVEST model), respectively. The evaluation results of the model show that, in megacities, the spatial applicability of InVEST model is lower than that of CASA model, and the CASA model is more accurate in estimating the carbon storage of urban green space. The research results can provide a scientific basis for the assessment of the carbon sink capacity of megacity ecosystems with the goal of "dual carbon".

Spatiotemporal patterns of urban forest carbon sequestration capacity: Implications for urban CO2 emission mitigation during China's rapid urbanization

Urban forests provide ecological functions and human well-being. However, spatiotemporal changes in urban forest carbon sequestration (CS) under rapid urbanization remain poorly understood. We established a model to predict the annual CS dynamics in urban forests based on plot-measured CS and Landsat images. Our results showed that the urban forest coverage in Changchun increased from 18.09 % to 24.01 % between 2000 and 2019, especially in the urban suburbs. However, urban forest patches became more fragmented and less connected, particularly in the urban center. The NDVI is better than other vegetation indices for mapping urban forest CS. We observed a gradual increase in urban forest CS capacity from 2000 to 2019, with higher CS capacity found in urban suburbs compared to urban centers. The class distribution of urban forest CS capacity was skewed toward low values (0-2 g·m-2·d-1), but this tendency diminished gradually. In 2000, the urban forest in Changchun offset approximately 2.11 % of carbon emissions but declined to 0.88 % by 2019 due to increased carbon emissions. Rapid urbanization was the main factor affecting CS, with impervious surface area accounting for 48.7 % of the variation. Urban landscape pattern indices also influenced the CS, with higher forest patch connectivity and lower patch density leading to greater CS capacity. Our study helps urban managers develop urban greening strategies for carbon neutrality and low-carbon city.

A 10-m resolution impervious surface area map for the greater Mekong subregion from remote sensing images

High-resolution and multi-temporal impervious surface area maps are crucial for capturing rapidly developing urbanization patterns. However, the currently available relevant maps for the greater Mekong subregion suffer from coarse resolution and low accuracy. Addressing this issue, our study focuses on the development of accurate impervious surface area maps at 10-m resolution for this region for the period 2016-2022. To accomplish this, we present a new machine-learning framework implemented on the Google Earth Engine platform that merges Sentinel-1 Synthetic Aperture Radar images and Sentinel-2 Multispectral images to extract impervious surfaces. Furthermore, we also introduce a training sample migration strategy that eliminates the collection of additional training samples and automates multi-temporal impervious surface area mapping. Finally, we perform a quantitative assessment with validation samples interpreted from Google Earth. Results show that the overall accuracy and kappa coefficient of the final impervious surface area maps range from 92.75% to 92.93% and 0.854 to 0.857, respectively. This dataset provides comprehensive measurements of impervious surface coverage and configuration that will help to inform urban studies.

Driving Forces behind Land Use and Land Cover Change: A Systematic and Bibliometric Review

This paper is based on reviewing the literature in the past 10 years on the drivers of land use and land cover change (LULCC) in urban areas. It combines quantitative and qualitative keyword analysis of papers drawn out from the Scopus database. The analysis is primarily based on the number of mentions of keywords in the titles and abstracts of the papers, in addition to the number of keywords appearing in the papers. On the basis of content analysis, a three-level structural categorization of the driving factors was developed. These are presented in a schematic diagram, where the contextual factors are shown as influencing economic and financial factors and policy and regulation, which in turn influences transportation investments and availability, and industrial and residential location choices. Transportation availability was seen as the most frequent factor identified in the literature. This research contends that LULCC is mostly determined by interactions among these four themes in a three-level structure, and on this basis, a model is presented that illustrates LULCC drivers based on local circumstances across the globe.

Comprehensive Risk Assessment of Urban Waterlogging Disaster Based on MCDA-GIS Integration: The Case Study of Changchun, China

Urban waterlogging will harm economic development and people’s life safety; however, the waterlogging risk zoning map provides the necessary decision support for the management of urban waterlogging, urban development and urban planning. This paper proposes an urban waterlogging risk assessment method that combines multi-criteria decision analysis (MCDA) with a geographic information system (GIS). The framework of urban waterlogging risk assessment includes four main elements: hazard, exposure, vulnerability, and emergency response and recovery capability. Therefore, we selected the urban area of Changchun City, Jilin Province as the study area. The Analytic Hierarchy Process (AHP) is a generally accepted MCDA method, it is used to calculate the weight and generate a result map of hazards, exposure, vulnerability, and emergency responses and recovery capability. Based to the principle of natural disaster risk formation, a total of 18 parameters, including spatial data and attribute data, were collected in this study. The model results are compared with the recorded waterlogging points, and the results show that the model is more reliable.

Spatiotemporal Patterns and Driving Force of Urbanization and Its Impact on Urban Ecology

Urbanization inevitably poses a threat to urban ecology by altering its external structure and internal attributes. Nighttime light (NTL) has become increasingly extensive and practical, offering a special perspective on the world in revealing urbanization. In this study, we applied the Normalized Impervious Surface Index (NISI) constructed by NTL and MODIS NDVI to examine the urbanization process in the Yangtze River Delta (YRD). Geographical detectors combined with factors involving human and natural influences were utilized to investigate the drive mechanism. Urban ecology stress was evaluated based on changes in urban morphological patterns and fractional vegetation cover (FVC). The results showed that the NISI can largely overcome the obstacle of directly coupling NTL data in performing urbanization and has efficient applicability in the long-term pixel scale. Built-up areas in the YRD increased by 2.83 times during the past two decades, from 2053.5 to 7872.5 km2. Urbanization intensity has saturated the city center and is spilling over into the suburbs, which show a “cold to hot” spatial clustering distribution. Economic factors are the primary forces driving urbanization, and road network density is becoming essential as factor that reflects urban infrastructure. Urban geometry pattern changes in fractal dimension (FD) and compactness revealed the ecological stress from changing urban external structure, and internal ecological stress was clear from the negative effect on 63.4% FVC. This impact gradually increased in urban expanded area and synchronously decreased when urbanization saturated the core area. An analysis of ecological stress caused by urbanization from changing physical structure and social attributes can provide evidence for urban management and coordinated development.

Decrease in the residents' accessibility of summer cooling services due to green space loss in Chinese cities

Urban green spaces (UGSs) reduce the surrounding temperature and create cooling areas as a buffer between people and high temperatures, thus helping residents adapt to the warming climate. However, the accessibility of UGS cooling services to the residents of cities remains largely unknown, which hinders decision-making regarding the formulation of climate adaptation and urban greening schemes. In the present study, we estimated the number of residents who accessed UGSs for cooling by analyzing the annual changes in such cooling areas during summer across 315 Chinese cities from 2003 to 2015. Approximately 93.3% of the cities showed significant decreasing trends (p < 0.05) of the total UGS area; as such the UGS coverage dropped from 12.23 ± 0.32% in 2003 to 7.69 ± 0.22% in 2015. Consequently, with the prevalent loss of UGS, the coverage of cooling spaces decreased from 32.55 ± 0.76% in 2003 to 24.39 ± 0.60% in 2015. This has formed a spatial mismatch between the growing urban population and the remaining UGSs. Accordingly, the number of residents of areas outside these cooling spaces increased by 4.23 million per year. In particular, the shortage of cooling services was more significant in cities with < 20,000 USD gross domestic product per capita and < 5 million residents than in the rest of the cities. To minimize the adverse impacts of increasing temperatures, focused greening plans are warranted, specifically in underdeveloped cities.

Remarkable Effects of Urbanization on Forest Landscape Multifunctionality in Urban Peripheries: Evidence from Liaoyuan City in Northeast China

Forest landscape multifunctionality (FLM) provides multiple benefits, such as climate regulation, water storage, and biodiversity maintenance. However, the external factors limiting FLM have not been fully identified, although addressing them could contribute to sustainable development. The present study aimed to identify and quantify the role of urbanization as an external factor that affects FLM. To this end, impervious area changes in Liaoyuan, China, were observed from 2000 to 2018, and 10 buffer zones at 500 m intervals were established outside the city. Within each buffer zone, we analyzed changes in forest landscape functions, including habitat maintenance, carbon sequestration, and water yield, as well as changes in the multifunctionality of their composition. The urbanization of Liaoyuan was significant in 2000–2018. The functions of the forest landscape became stronger and more stable as they were located further away from the urban edge. We refer to this pattern as the gradient effect of urbanization. Specifically, urbanization affected the investigated functions at a distance of 1000–2500 m. The FLM showed a more significant gradient effect of urbanization. The impact distance of urbanization on the FLM increased from 3000 m in 2000 to over 5000 m in 2018. This impact distance increased significantly whenever urbanization strengthened significantly (i.e., in 2005–2010 and 2015–2018). These findings are instructive for forest and urban managers working to achieve multiple Sustainable Development Goals.

An Improved Version of the Generalized Laplacian Pyramid Algorithm for Pansharpening

The spatial resolution of multispectral data can be synthetically improved by exploiting the spatial content of a companion panchromatic image. This process, named pansharpening, is widely employed by data providers to augment the quality of images made available for many applications. The huge demand requires the utilization of efficient fusion algorithms that do not require specific training phases, but rather exploit physical considerations to combine the available data. For this reason, classical model-based approaches are still widely used in practice. We created and assessed a method for improving a widespread approach, based on the generalized Laplacian pyramid decomposition, by combining two different cost-effective upgrades: the estimation of the detail-extraction filter from data and the utilization of an improved injection scheme based on multilinear regression. The proposed method was compared with several existing efficient pansharpening algorithms, employing the most credited performance evaluation protocols. The capability of achieving optimal results in very different scenarios was demonstrated by employing data acquired by the IKONOS and WorldView-3 satellites.

Quantifying Urban Vegetation Dynamics from a Process Perspective Using Temporally Dense Landsat Imagery

Urban vegetation can be highly dynamic due to the complexity of different anthropogenic drivers. Quantifying such dynamics is crucially important as it is a prerequisite to understanding its social and ecological consequences. Previous studies have mostly focused on the urban vegetation dynamics through monotonic trends analysis in certain intervals, but not considered the process which provides important insights for urban vegetation management. Here, we developed an approach that integrates trends with dynamic analysis to measure the vegetation dynamics from the process perspective based on the time-series Landsat imagery and applied it in Shenzhen, a coastal megacity in southern China, as an example. Our results indicated that Shenzhen was turning green from 2000–2020, even though a large-scale urban expansion occurred during this period. Approximately half of the city (49.5%) showed consistent trends in greening, most of which were located in the areas within the ecological protection baseline. We also found that 35.3% of the Shenzhen city experienced at least a one-time change in urban greenness that was mostly caused by changes in land cover types (e.g., vegetation to developed land). Interestingly, 61.5% of these lands showed trends in greening in the recent change period and most of them were distributed in build-up areas. Our approach that integrates trends analysis and dynamic process reveals information that cannot be discovered by monotonic trends analysis alone, and such information can provide insights for urban vegetation planning and management.

Impact of Land Use and Land Cover Changes on Urban Ecosystem Service Value in Dhaka, Bangladesh

Urban ecosystem services provide a wide range of services to sustain life, social relation, health, etc., and address most of the challenges, including climate change and environmental pollution. While it is recognized that the urban ecosystem substantially contributes to human well-being in cities, there is less attention to consider the value of urban ecosystem service in urban planning and policymaking. This study analyzed the land use and land cover (LULC) dynamics of city of Dhaka over the past three decades (1990–2020) to evaluate the impact of LULC on ecosystem services value (ESV). The estimation of ESV in relation to LULC has been done using the globally used benefits transfer method (BTM). Findings of the study show that built-up area has increased by 188.35% from 1990 to 2020, with an average annual growth rate is about 6.28%The analysis of ESV shows that it has decreased by 59.55% (85 million USD) from 142.72 million USD in 1990 to 57.72 million USD in 2020 due to the development of the built-up area through conversion of agricultural land, waterbodies, and forest and vegetation land. This study also identified that waterbodies are the greatest contributor to ESV. The result on the elasticity of ESV in relation to LULC implies that about 1% transition in LULC would result in about 0.33% change in total ESV during the study period. We believe that the findings of this study would serve as a reference for the policy maker and urban planner to devise appropriate land use decision to ensure sustainable urban development of Dhaka.