On the Scale Effect of Relationship Identification between Land Surface Temperature and 3D Landscape Pattern: The Application of Random Forest

Urbanization processes greatly change urban landscape patterns and the urban thermal environment. Significant multi-scale correlation exists between the land surface temperature (LST) and landscape pattern. Compared with traditional linear regression methods, the regression model based on random forest has the advantages of higher accuracy and better learning ability, and can remove the linear correlation between regression features. Taking Beijing’s metropolitan area as an example, this paper conducted multi-scale relationship analysis between 3D landscape patterns and LST using Pearson Correlation Coefficient (PCC), Multiple Linear Regression and Random Forest Regression (RFR). The results indicated that LST was relatively high in the central area of Beijing, and decreased from the center to the surrounding areas. The interpretation effect of 3D landscape metrics on LST was more obvious than that of the 2D landscape metrics, and 3D landscape diversity and evenness played more important roles than the other metrics in the change of LST. The multi-scale relationship between LST and the landscape pattern was discovered in the fourth ring road of Beijing, the effect of the extent of change on the landscape pattern is greater than that of the grain size change, and the interpretation effect and correlation of landscape metrics on LST increase with the increase in the rectangle size. Impervious surfaces significantly increased the LST, while the impervious surfaces located at low building areas were more likely to increase LST than those located at tall building areas. It seems that increasing the distance between buildings to improve the rate of energy exchange between urban and rural areas can effectively decrease LST. Vegetation and water can effectively reduce LST, but large, clustered and irregularly shaped patches have a better effect on land surface cooling than small and discrete patches. The Coefficients of Rectangle Variation (CORV) power function fitting results of landscape metrics showed that the optimal rectangle size for studying the relationship between the 3D landscape pattern and LST is about 700 m. Our study is useful for future urban planning and provides references to mitigate the daytime urban heat island (UHI) effect.

Urban–Rural Gradients Predict Educational Gaps: Evidence from a Machine Learning Approach Involving Academic Performance and Impervious Surfaces in Ecuador

Academic performance (AP) is explained by a multitude of factors, principally by those related to socioeconomic, cultural, and educational environments. However, AP is less understood from a spatial perspective. The aim of this study was to investigate a methodology using a machine learning approach to determine which answers from a questionnaire-based survey were relevant for explaining the high AP of secondary school students across urban–rural gradients in Ecuador. We used high school locations to construct individual datasets and stratify them according to the AP scores. Using the Boruta algorithm and backward elimination, we identified the best predictors, classified them using random forest, and mapped the AP classification probabilities. We summarized these results as frequent answers observed for each natural region in Ecuador and used their probability outputs to formulate hypotheses with respect to the urban–rural gradient derived from annual maps of impervious surfaces. Our approach resulted in a cartographic analysis of AP probabilities with overall accuracies around 0.83–0.84% and Kappa values of 0.65–0.67%. High AP was primarily related to answers regarding the academic environment and cognitive skills. These identified answers varied depending on the region, which allowed for different interpretations of the driving factors of AP in Ecuador. A rural-to-urban transition ranging 8–17 years was found to be the timespan correlated with achievement of high AP.

GISD30: global 30-m impervious surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform

Accurately mapping impervious surface dynamics has great scientific significance and application value for urban sustainable development research, anthropogenic carbon emission assessment and global ecological environment modeling. In this study, a novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral generalization method and time-series Landsat imagery, on the Google Earth Engine cloud-computing platform. Firstly, the global training samples and corresponding reflectance spectra were automatically derived from prior global 30 m land-cover products after employing the multitemporal compositing method and relative radiometric normalization. Then, spatiotemporal adaptive classification models, trained with the migrated reflectance spectra of impervious surfaces from 2020 and pervious surface samples in the same epoch for each 5° × 5° geographical tile, were applied to map the impervious surface in each period. Furthermore, a spatiotemporal consistency correction method was presented to minimize the effects of independent classification errors and improve the spatiotemporal consistency of impervious surface dynamics. Our global 30 m impervious surface dynamic model achieved an overall accuracy of 91.5 % and a kappa coefficient of 0.866 using 18,540 global time-series validation samples. Cross-comparisons with four existing global 30 m impervious surface products further indicated that our GISD30 dynamic product achieved the best performance in capturing the spatial distributions and spatiotemporal dynamics of impervious surfaces in various impervious landscapes. The statistical results indicated that the global impervious surface has doubled in the past 35 years, from 5.116 × 105 km2 in 1985 to 10.871 × 105 km2 in 2020, and Asia saw the largest increase in impervious surface area compared to other continents, with a total increase of 2.946 × 105 km2. Therefore, it was concluded that our global 30 m impervious surface dynamic dataset is an accurate and promising product, and could provide vital support in monitoring regional or global urbanization as well as in related applications. The global 30 m impervious surface dynamic dataset from 1985 to 2020 generated in this paper is free to access at http://doi.org/10.5281/zenodo.5220816 (Liu et al., 2021b).

Spatially Explicit Subpixel-Based Study On The Expansion of Artificial Impervious Surfaces And Its’ Impacts On Soil Organic Carbon

Precise spatiotemporal datasets of artificial impervious surfaces (AISs) are essential for evaluating urbanization processes and associated soil organic carbon (SOC) dynamics. However, spatially explicit studies on SOC stocks based on high-quality AIS data remain deficient, which affects the accuracy of urban SOC budgets. In this study, we used 30-m Landsat images and a subpixel-based model to accurately evaluate and quantify the annual AIS of Kaifeng, an ancient city in China that experienced intensive urbanization from 2000 to 2020. Soil organic carbon (SOC) dynamics were further estimated and spatially exhibited based on the SOC densities (SOCD) of different land covers observed in the field. Our results demonstrate that Kaifeng experienced drastic AIS expansion from 2000–2020, both in total area (an increase of ~154.35%) and density (described by mean AIS abundance, 0.56 vs. 0.72). Spatially, AIS mainly sprawled to the west, and infilling was observed in the old town. Moreover, the expansion of AIS in Kaifeng has resulted in a total of 0.08 Tg of SOC loss over the past 20 years, and the study area has acted as a clear carbon source. The greatest SOC losses occurred during 2010 — 2015, mainly in the west — with >30% (~0.024 Tg) of the total loss occurring between 2010 and 2015. This study provides new insights into urban growth through the mapping of growth patterns in terms of both outward sprawl and infill. We also provide a novel means of presenting the spatial patterns of urbanization-induced SOC dynamics using subpixel AIS maps.

Spatio-Temporal Analysis of Impervious Surface Expansion in the Qinhuai River Basin, China, 1988–2017

Impervious surfaces are key indicators for urbanization monitoring and watershed degradation assessment over space and time. However, most empirical studies only extracted impervious surface from spatial, temporal or spectral perspectives, paying less attention to integrating multiple dimensions in acquiring continuous changes in impervious surfaces. In this study, we proposed a neighborhood-based spatio-temporal filter (NSTF) to obtain the continuous change information of impervious surfaces from multi-temporal Landsat images in the Qinhuai River Basin (QRB), Jiangsu, China from 1988–2017, based on the results from semi-automatic decision tree classification. Moreover, we used the expansion intensity index (EII) and the landscape extension index (LEI) to further characterize the spatio-temporal characteristics of impervious surfaces on different spatial scales. The preliminary results showed that the overall accuracies of the final classification were about 95%, with the kappa coefficients ranging between 0.9 and 0.96. The QRB underwent rapid urbanization with the percentage of the impervious surfaces increasing from 2.72% in 1988 to 25.6% in 2017. Since 2006, the center of urbanization expansion was shaped from the urban built-up areas of Nanjing and Jiangning to non-urban built-up areas of the Jiangning, Lishui, and Jurong districts. The edge expansion occupied 73% on average among the different landscape expansion types, greatly beyond outlying (12%) and infilling (15%). The window size in the NSTF has a direct impact on the subsequent analysis. Our research could provide decision-making references for future urban planning and development in the similar basins.

Mitigating and adapting to climate change with a taxonomy of smart urban surfaces

Rapid urbanization is replacing natural land with dark, impervious surfaces. This has led to dire urban consequences including rising temperatures and stormwater deluge, resulting in significantly higher energy costs, greater stormwater damage, and associated health and comfort impacts. These issues can be mitigated using smart surfaces, those with high reflectivity and permeability, which can achieve sustainable and regenerative cities. The current literature on the benefits of urban surfaces is very segmented, focusing on either one specific surface type or one property of surfaces. A smart surface taxonomy with correlated heat, and water metrics has been developed to fill this gap. A range of city surfaces in three broad categories - roofs, streets and sidewalks, and parking lots - have been identified with various levels of reflectivity, permeability. Through literature review, the taxonomy reveals surface temperatures that range from 29.7°C for a green roof to 74.3°C for a black roof. Also, the taxonomy reveals Rainfall retention potential ranging from 1.27 mm for impervious pavement to 86.4 mm for bioswales. The development of a smart surface taxonomy with quantified benefits for mitigating or adapting to climate change will be critical for decision-makers to make informed decisions on city surface choices.

Landsat-Based Monitoring of the Heat Effects of Urbanization Directions and Types in Hangzhou City from 2000 to 2020

Rapid urbanization has produced serious heat effects worldwide. However, the literature lacks a detailed study on heat effects based on the directions and types of urban expansion. In this work, a typical city with an extremely hot summer climate, Hangzhou, was selected as a case study to determine the relationships between the urban heat-effect dynamics and spatiotemporal patterns of impervious surface expansion. Based on long-term Landsat imagery, this study characterized the spatiotemporal patterns of urban expansion and normalized surface temperatures in Hangzhou City from 2000 to 2020 using object-based backdating classification and a generalized single-channel algorithm with the help of a land-use transfer matrix, expansion index, and spatial centroids. Relevant policies, industries, and traffic networks were discussed to help explain urban expansion and thermal environment changes. The results demonstrated that in 2020, the area of impervious surfaces covered 1139.29 km2. The majority of the gains were in farmland, water, and forests, and the annual growth rate was 32.12 km2/year beginning in 2000. During the expansion of impervious surfaces, the city warmed at a slower rate, and more thermal contributions came from sub-urban areas. The southeast-oriented expansion of impervious surfaces was the key reason for the spatiotemporal dynamics of the urban heat effects. The dominant urban edge expansion intensified the local heat effects. This research provides a Landsat-based methodology for better understanding the heat effects of urban expansion.

Automatically Monitoring Impervious Surfaces Using Spectral Generalization and Time Series Landsat Imagery from 1985 to 2020 in the Yangtze River Delta

Accurately monitoring the spatiotemporal dynamics of impervious surfaces is very important for understanding the process of urbanization. However, the complicated makeup and spectral heterogeneity of impervious surfaces create difficulties for impervious surface monitoring. In this study, we propose an automatic method to capture the spatiotemporal expansion of impervious surfaces using spectral generalization and time series Landsat imagery. First, the multitemporal compositing and relative radiometric normalization methods were used to extract phenological information and ensure spectral consistency between reference imagery and monitored imagery. Second, we automatically derived training samples from the prior MSMT_IS30-2020 impervious surface products and migrated the surface reflectance of impervious surfaces in the reference period of 2020 to other periods (1985–2015). Third, the random forest classification method, trained using the migrated surface reflectance of impervious surfaces and pervious surface training samples at each period, was employed to extract temporally independent impervious surfaces. Further, a temporal consistency check method was applied to ensure the consistency and reliability of the monitoring results. According to qualitative and quantitative validation results, the method achieved an overall accuracy of 90.9% and kappa coefficient of 0.859 in identifying the spatiotemporal expansion of impervious surfaces and performed better in capturing the impervious surface dynamics when compared with other impervious surface datasets. Lastly, our results indicate that a rapid increase of impervious surfaces was observed in the Yangtze River Delta, and the area of impervious surfaces in 2000 and 2020 was 1.86 times and 4.76 times that of 1985, respectively. Therefore, it could be concluded that the proposed method offered a novel perspective for providing timely and accurate impervious surface dynamics.