The acquisition of impervious surface area from LANDSAT 8 satellite sensor data using urban indices: a comparative analysis

Monitoring land use and land cover change near a nuclear power plant construction site: Akkuyu case, Turkey

Land use and land cover (LULC) change analysis of the construction site and its surroundings of the Akkuyu Nuclear Power Plant project in southern Turkey was undertaken in this case study, which was supported by remotely sensed Landsat 8 image composites. The composite images compiled in 2017 and 2021 were prepared on the Google Earth Engine platform. The Random Forest algorithm was used as the classifier model. A high classification performance was obtained for both images (kappa > 0.88, overall accuracy > 90%). After the classification process, LULC maps for both years were generated, and statistical calculations for the LULC change were computed for both the entire study area (15 × 25 km) and a buffer zone with a radius of 1 km around the power plant. In the whole study area, artificial surfaces significantly increased (78.46%), whereas forests (- 8.31%) and barren lands experienced a considerable decrease (- 6.11%). In the 1 km buffer, artificial surfaces predominantly increased (113.94%), while forests and barren lands decreased dramatically (- 69.13% and - 74.28%, respectively). The agricultural areas in the study area were changed into other LULC classes: 9.1% to artificial surfaces, 27.6% to barren lands, and 21.7% to forest. The rise in the area of artificial surfaces was especially noticeable within the 1 km buffer zone: construction activities converted 36.1% of agricultural fields, 54.1% of forests, and 23.2% of barren lands into artificial surfaces. The filling activities on the seashore resulted in a loss of water bodies of up to 26.5%. The study provides an overview of how the LULC classes have evolved on the construction site and in the region. In the end, the study discusses how the current land use preferences in the region contradict the issues and concerns mentioned in the existing body of literature.

Sentinel-Based Adaptation of the Local Climate Zones Framework to a South African Context

The LCZ framework has become a widely applied approach to study urban climate. The standard LCZ typology is highly specific when applied to western urban areas but generic in some African cities. We tested the generic nature of the standard typology by taking a two-part approach. First, we applied a single-source WUDAPT-based training input across three urban areas that represent a gradient in South African urbanization (Cape Town, Thohoyandou and East London). Second, we applied a local customized training that accounts for the unique characteristics of the specific area. The LCZ classification was completed using a random forest classifier on a subset of single (SI) and multitemporal (MT) Sentinel 2 imagery. The results show an increase in overall classification accuracy between 17 and 30% for the locally calibrated over the generic standard LCZ framework. The spring season is the best classified of the single-date imagery with the accuracies 7% higher than the least classified season. The multi-date classification accuracy is 13% higher than spring but only 9% higher when a neighborhood function (NF) is applied. For acceptable performance of the LCZ classifier in an African context, the training must be local and customized to the uniqueness of that specific area.

Explore the Mitigation Mechanism of Urban Thermal Environment by Integrating Geographic Detector and Standard Deviation Ellipse (SDE)

The urban surface temperature is a complex integrated natural-human geographic phenomena; with the development of geostatistical methods and the application of multisource data, its research has gradually shifted from a single perspective to a study that integrates multiple factors such as nature and humanity. However, based on the context of the integration of natural and human factors and mutual constraints of each factor, the research on the mechanism of influence on urban habitat thermal environment needs to be further deepened. Therefore, this paper explores the spatial and temporal heterogeneity of urban surface temperature in Zhengzhou City during the summer of 2013–2020 from the perspective of multi-source data fusion, and uses the Geodetector model to quantitatively reveal the main influencing factors of urban surface temperature and the impact of superimposed factors on the compound effect of surface temperature. The results show that: (1) the urban thermal environment in the central of Zhengzhou city (region within the first ring) is obvious, and it is mainly concentrated in commercial and densely populated areas. (2) According to trend analysis, the northwest-southeast direction of the city continues to increase in temperature from 2013–2020, coupled with the direction of urban development. (3) Among the factors affecting urban surface temperature, normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), tasseled cap wetness (TCW), and human elements are particularly typical. NDVI and TCW are strongly negatively correlated with the urban thermal environment, while NDBI and human elements are strongly positively correlated. (4) Mitigation of the urban thermal environment can start with the interaction mechanism of positive and negative factors. This study provides new ideas for the mechanism analysis of spatial and temporal evolution patterns of the urban thermal environment under multifactorial constraints, and provides suggestions and decisions for promoting green and sustainable urban development.

The Influence of Urbanization on the Development of a Convective Storm—A Study for the Belém Metropolitan Region, Brazil

One of the main problems faced by the Belém Metropolitan Region (BMR) inhabitants is flash floods caused by precarious infrastructure and extreme rainfall events. The objective of this article is to investigate whether and how the local urban characteristics may influence the development of thunderstorms. The Weather Research and Forecasting (WRF) model was used with three distinct configurations of land use/cover to represent urbanization scenarios in 2017 and 1986 and the forest-only scenario. The WRF model simulated reasonably well the event. The results showed that the urban characteristics of the BMR may have an impact on storm systems in the urban areas close to the Northern Coast of South America. In particular, for the urban characteristics in the BMR in 2017, the intensification of the storm may be linked to a higher value of energy available for convection (over 1000 J kg−1) and favorable wind convergence and vertical shear in the urban area (where the wind speed at the surface was more than 3 m s−1 slower than in the forest-only scenario). Meanwhile, the other land cover scenarios could not produce a similar storm due to lack of moisture, wind convergence/shear, or convective energy.

Determination of impervious area of Saroor Nagar Watershed of Telangana using spectral indices, MLC, and machine learning (SVM) techniques

Urbanization affects the local wind and water cycle by changing the natural surface and atmospheric conditions, which further changes the local climate and climate system. Assessment of built-up-area changes in a rapidly growing urban area within a short time is a prime factor for administrators for better environmental assessment and sustainable development of urban areas. Traditional survey approaches, on the other hand, are unable to meet the demand for rapid urban land management development, and there is a pressing need to develop new methods to address the limitations of existing ones. This study compares various urban spectral indices to other existing approaches in order to determine which index provides a better representation of the impervious area in the urban watershed. Landsat 8 OLI (Operational Land Imager) satellite images acquired on 15 March 2014 and 31 March 2020 are used in the present study. Indices, namely Modified Built-up Index (MBUI), SwiRed Index (SwiRed), and Enhanced Built-up and Bareness Index (EBBI), were utilized to extract impervious areas. Thresholding of indices is done by comparing them with 1000 reference points taken from Google Earth imagery of the respective years. The accuracy of the urban indices is assessed by comparing the results with high-resolution Google Earth Satellite Images. The impervious area is extracted from spectral indices and other remote sensing techniques such as maximum likelihood classification and support vector machine classification techniques. The overall accuracy of SVM, MLC, MBUI, EBBI, and SwiRed for the 2014 dataset was found to be 95.1%, 90.8%, 83.9%, 78.9%, and 87.3%, respectively, and the overall accuracy of SVM, MLC, MBUI, EBBI, and SwiRed was found to be 96%, 89.2%, 89.1%, 85.5%, and 92.6%, respectively. Impervious areas in the heterogeneous urban environment can be monitored in a better way and within lesser time by using spectral indices generated using Landsat 8 OLI (Operational Land Imager) satellite data.

Human visceral leishmaniasis in Central-Western Brazil: Spatial patterns and its correlation with socioeconomic aspects, environmental indices and canine infection

In this ecological study, we investigated spatial patterns of human visceral leishmaniasis (VL) incidence, its correlation with socioeconomic aspects, environmental indices (obtained through remote sensing) and canine VL during 2011-2016 in the municipality of Rondonópolis, a relevant endemic area for VL in Central-Western Brazil. Human VL cases were georeferenced and point patterns were analyzed by univariate Ripley's K function and Kernel density estimation (KDE). Poisson-based scan statistics were used to investigate spatial and spatiotemporal clusters of human VL incidence at the neighborhood level. Socioeconomic and environmental characteristics were compared between neighborhoods within and outside spatial human VL clusters. Also, we assessed the correlation between smoothed human VL incidence and canine VL seropositivity rates within and between neighborhoods. Human VL cases were clustered up to 2000 m; four hotspots were identified by KDE in peripheral areas. Spatial and spatiotemporal low-risk clusters for human VL were identified in central and southern areas. Neighborhoods within spatial low-risk cluster presented higher mean income, literacy rate, sanitary sewage service coverage and lower altitude, compared to the rest of the municipality. A positive correlation was found between the occurrence of human and canine VL. On the northern outskirts, high human VL incidence was spatially correlated with high canine VL seropositivity in surrounding neighborhoods. In conclusion, human VL demonstrated a heterogeneous, aggregated and peripheral spatial pattern. This distribution was correlated with intra-urban socioeconomic differences and canine VL seropositivity at the neighborhood level.

Spatiotemporal Continuous Impervious Surface Mapping by Fusion of Landsat Time Series Data and Google Earth Imagery

The monitoring of impervious surfaces in urban areas using remote sensing with fine spatial and temporal resolutions is crucial for monitoring urban development and environmental changes in urban areas. Spatiotemporal super-resolution mapping (STSRM) fuses fine-spatial-coarse-temporal remote sensing data with coarse-spatial-fine-temporal data, allowing for urban impervious surface mapping at both fine-spatial and fine-temporal resolutions. The STSRM involves two main steps: unmixing the coarse-spatial-fine-temporal remote sensing data to class fraction images, and downscaling the fraction images to sub-pixel land cover maps. Yet, challenges exist in each step when applying STSRM in mapping impervious surfaces. First, the impervious surfaces have high spectral variability (i.e., high intra-class and low inter-class variability), which impacts the accurate extraction of sub-pixel scale impervious surface fractions. Second, downscaling the fraction images to sub-pixel land cover maps is an ill-posed problem and would bring great uncertainty and error in the predictions. This paper proposed a new Spatiotemporal Continuous Impervious Surface Mapping (STCISM) method to deal with these challenges in fusing Landsat and Google Earth imagery. The STCISM used the Multiple Endmember Spectral Mixture Analysis and the Fisher Discriminant Analysis to minimize the within-class variability and maximize the between-class variability to reduce the spectral unmixing uncertainty. In addition, the STCISM adopted a new temporal consistency check model to incorporate temporal contextual information to reduce the uncertainty in the time-series impervious surface prediction maps. Unlike the traditional temporal consistency check model that assumed the impervious-to-pervious conversion is unlikely to happen, the new model allowed the bidirectional conversions between pervious and impervious surfaces. The temporal consistency check was used as a post-procession method to correct the errors in the prediction maps. The proposed STCISM method was used to predict time-series impervious surface maps at 5 m resolution of Google Earth image at the Landsat frequency. The results showed that the proposed STCISM outperformed the STSRM model without using the temporal consistency check and the STSRM model using the temporal consistency check based on the unidirectional pervious-to-impervious surface conversion rule.

Continuous Monitoring of Urban Land Cover Change Trajectories with Landsat Time Series and LandTrendr-Google Earth Engine Cloud Computing

Producing accurate land cover maps is time-consuming and estimating land cover changes between two generated maps is affected by error propagation. The increased availability of analysis-ready Earth Observation (EO) data and the access to big data analytics capabilities on Google Earth Engine (GEE) have opened the opportunities for continuous monitoring of environment changing patterns. This research proposed a framework for analyzing urban land cover change trajectories based on Landsat time series and LandTrendr, a well-known spectral-temporal segmentation algorithm for land-based disturbance and recovery detection. The framework involved the use of baseline land cover maps generated at the beginning and at the end of the considered time interval and proposed a new approach to merge the LandTrendr results using multiple indices for reconstructing dense annual land cover maps within the considered period. A supervised support vector machine (SVM) classification was first performed on the two Landsat scenes, respectively, acquired in 1987 and 2019 over Kigali, Rwanda. The resulting land cover maps were then imported in the GEE platform and used to label the interannual LandTrendr-derived changes. The changes in duration, year, and magnitude of land cover disturbance were derived from six different indices/bands using the LandTrendr algorithm. The interannual change LandTrendr results were then combined using a robust estimation procedure based on principal component analysis (PCA) for reconstructing the annual land cover change maps. The produced yearly land cover maps were assessed using validation data and the GEE-based Area Estimation and Accuracy Assessment (Area2) application. The results were used to study the Kigali’s urbanization in the last three decades since 1987. The results illustrated that from 1987 to 1998, the urbanization was characterized by slow development, with less than a 2% annual growth rate. The post-conflict period was characterized by accelerated urbanization, with a 4.5% annual growth rate, particularly from 2004 onwards due to migration flows and investment promotion in the construction industry. The five-year interval analysis from 1990 to 2019 revealed that impervious surfaces increased from 4233.5 to 12116 hectares, with a 3.7% average annual growth rate. The proposed scheme was found to be cost-effective and useful for continuously monitoring the complex urban land cover dynamics, especially in environments with EO data affordability issues, and in data-sparse regions.

Impact Quantification of Decentralization in Urban Growth by Extracting Impervious Surfaces Using ISEI in Model Maker

Decentralization problems in Africa have caused some infrastructure disparity between country capitals and distant districts. In Ghana, less public investment has created a gap between implementation results and theoretical benefits. Spectral indices are a good approach to extracting impervious surfaces, which is a good method of measuring urbanization. These are restricted by complexity, sensor limitation, threshold values, and high computational time. In this study, we measure the urbanization dynamics of Wa District in Ghana by applying a proposed method of impervious surface extraction index (ISEI), to evaluate the decentralization policy using Landsat images from 1984–2018 and a single S2A data. Comparing our proposed method with five other existing indexes, ISEI provided good discriminated results between target feature and background, with pixel values ranging between 0 and +1. Other indexes produced negative values. ISEI accuracy varied from 84.62–94.00% while existing indexes varied from 73.85–90.00%. Our results also showed increased impervious surface areas of 83.26 km2, which is about 7.72% of total area while the average annual urban growth was recorded as 4.42%. These figures proved that the quantification of decentralization is very positive. The study provides a foundation for urban environment research in the context of decentralization policy.

Driving forces of impervious surface in a world metropolitan area, Shanghai: threshold and scale effect

Shanghai is one of the largest metropolitan areas in the world, during the rapid urbanization of the past decades, impervious surface expanded dramatically and became a main factor influencing surface water quality. Thus, exploring the driving forces of impervious surface has great implications in such metropolitan area. In this study, an impervious surface coefficient method (ISC) was used to measure the percentage of total impervious area (PTIA) of Shanghai; regression analysis was conducted to define the relationship between PTIA and three socio-economic factors, population density, unit area gross domestic product, and unit area industrial output at the city and district scale. Results showed that the industrial land use generated the highest ISC value, followed by high-density residential. Strong correlations were showed between PTIA and socio-economic indicators, in which population density was the most significant. Threshold effect was presented that when population density was higher than 15000 per/km², this relationship would become less significant and PTIA remained stable. Similar effects were found when unit area gross domestic product exceeded 125 million yuan/km². Scale effect was also discussed that the relationship was more significant at city scale than district. An improved understanding of the threshold effect and scale effect will help guide future urban planning and design new urban ecosystem policies.

Quantifying and simulating landscape composition and pattern impacts on land surface temperature: A decadal study of the rapidly urbanizing city of Beijing, China

The increase in impervious surfaces due to the urbanization has caused many adverse effects on urban ecological systems, including the urban heat environmental risk. Revealing the relationship between landscape composition and pattern and land surface temperature (LST) gives insight into how to effectively mitigate the urban heat island (UHI) effect. It is also essential to simulate and optimize the distribution of impervious surfaces in urban planning. In this study, the multi-scale relationship between impervious surface and LST in Beijing was analyzed. Different distributions of land cover types and the corresponding LSTs were simulated under two development scenarios. Various geospatial approaches, including geographic information system (GIS), remote sensing, and the Conversion of Land Use and its Effects at Small regional extent (CLUE-S), were used to facilitate the analysis. The results showed that (1) impervious surfaces increased from 36.76% to 44.95% of the total area between 2005 and 2015 and the mean LST of impervious surfaces was approximately 2 °C higher than that of the areas with vegetation cover; (2) impervious surfaces had a positive logarithmic correlation with LST, while the vegetation coverage had a negative linear correlation with LST; (3) as the grid size increased, the correlation coefficients between the impervious surface density and mean LST increased at different magnitudes, and the correlation coefficients stabilized after the scale of 900 × 900 m; (4) large and contiguous patches of impervious surfaces aggravated the UHI effect when the total percentage of impervious surface remained the same; and (5) to achieve an improved and healthier urban living environment, populations controls should be considered to decrease future impervious surface demands by 7.69%-which corresponds to an average LST decrease of 1.1 °C. Landscape distribution and configuration should also be better integrated into landscape and urban planning.