Urban Class Research Articles

Dynamics in impervious urban and non-urban areas and their effects on run-off, nutrient emissions, and macroinvertebrate communities

Urbanization is a key factor driving water quality and biological communities in freshwater systems. While urbanization impacts on water resources were well-studied in urban areas, few studies have examined the growing impacts in non-urban areas. Here we investigate the spatiotemporal trends of urbanization using high-resolution impervious surface data of both urban and non-urban areas across Germany, and evaluate the urbanization impacts on changes of run-off, nutrient emissions, and macroinvertebrate communities. The emission changes were modelled using the MONERIS-PCRaster tool, while the macroinvertebrate community responses to urbanization were determined using Generalized Least Square models. We find that urbanization expanded by 3.2% from 2006 to 2015 nationwide, and primarily occurred in less-populated regions. The non-urban areas contributed 19.3–19.6% of the imperviousness expansion nationwide, which were equivalent to the ‘unaccounted’ increases of 15.5–15.9% of run-off, 10.6–11.0% of total nitrogen, and 12.5–12.9% of total phosphorus when only emissions from urban areas were modelled. Incorporating the emissions from both urban and non-urban areas revealed significant impacts of urbanization on macroinvertebrate community composition changes, with increases in more pollution-resistant and non-native species (particularly in large rivers), and reductions of more sensitive taxa. The community responses were jointly determined by local catchment characteristics and urbanization stressors. Our results suggest neglected effects of non-urban impervious surfaces and the importance of using impervious surface data instead of urban land classes to better represent urbanization processes and impacts in long-term planning and management of freshwater systems.

Species Composition and Diversity of Middle-Aged Trees among Different Urban Green Space Types and Tree Age Classes in Changchun, Northeast China

Middle-aged trees refer to trees aged between 50 and 99 years, which are the reserve resources of old trees (trees ≥ 100 years of age). They are vital parts of the urban ecosystem, with important ecological, landscape, cultural, and historical value. Conservation of middle-aged trees in urban areas is important for the development of large old trees in the future. In this study, we investigated the middle-aged trees in Changchun city and analyzed the species composition and diversity characteristics of different urban green space types and tree age classes. The results showed that there were 72 species and 22,376 plants of middle-aged trees in Changchun city, and the coniferous species prevailed. The top five species with a high importance value (IV) were Pinus tabuliformis var. mukdensis, Lavix olgensis, Salix matsudana, Ulmus pumila, and Abies holophylla. Green space type and tree age were important factors influencing the richness and diversity of middle-aged trees. Tree growth spaces were relatively sufficient, and land use was stable for park green spaces (PGS) and attached green spaces (AGS), which resulted in the abundant, richer, and diverse species richness (SR) of middle-aged trees. Road green spaces (RGS) and square green spaces (SGS) had fewer trees and lower species richness, Margalef richness index (dMa), Shannon–Wiener index (He) and evenness index (Je) which could be attributed to the high intensity of human interference and poor environmental quality. The SR of middle-aged trees decreased with an increase in age class, and the values of SR in Age Class 80–89 years and Age Class 90–99 years were lower than in Age Class 50–59 years. Age Class 70–79 years had the lowest values of dMa, He, and Je, which need to be protected urgently. The results of this study can provide a basis for the conservation and management of middle-aged trees in urban areas and the choice of species for urban greening.

More than Civil Engineering and Civic Reasoning: World-Building in Middle School STEM

This narrative essay describes a project in an urban sixth grade science class that began as an effort to link civic engagement with disciplinary learning in chemistry. The ways in which students took up this project prompted the authors to see urban infrastructures as engineered sites of learning with world-making possibilities. By interrogating the ways in which science and engineering practices are imbued with values and happen in places, teachers can engage young learners in critical examinations of their built worlds. The authors argue that there is an opportunity in K-8 engineering education to avoid reproducing some of the pathologies of (post)secondary STEM education by making the ethical and political dimensions of the STEM enterprise explicit. In fact, elementary school teachers’ ability to prioritize the whole child over disciplinary norms may allow them to engage students in realistic assessments of the built world while also imagining hopeful present-futures.

Building-scale flood loss estimation through vulnerability pattern characterization: application to an urban flood in Milan, Italy

Abstract. The vulnerability of flood-prone areas is determined by the susceptibility of the exposed assets to the hazard. It is a crucial component in risk assessment studies, both for climate change adaptation and disaster risk reduction. In this study, we analyse patterns of vulnerability for the residential sector in a frequently hit urban area of Milan, Italy. The conceptual foundation for a quantitative assessment of the structural dimensions of vulnerability is based on the modified source–pathway–receptor–consequence model. This conceptual model is used to improve the parameterization of the flood risk analysis, describing (i) hazard scenario definitions performed by hydraulic modelling based on past event data (source estimation) and morphological features and land-use evaluation (pathway estimation) and (ii) the exposure and vulnerability assessment which consists of recognizing elements potentially at risk (receptor estimation) and event losses (consequence estimation). We characterized flood hazard intensity on the basis of variability in water depth during a recent event and spatial exposure also as a function of a building's surroundings and buildings' intrinsic characteristics as a determinant vulnerability indicator of the elements at risk. In this sense the use of a geographic scale sufficient to depict spatial differences in vulnerability allowed us to identify structural vulnerability patterns to inform depth–damage curves and calculate potential losses from mesoscale (land-use level) to microscale (building level). Results produces accurate estimates of the flood characteristics, with mean error in flood depth estimation in the range 0.2–0.3 m and provide a basis to obtain site-specific damage curves and damage mapping. Findings show that the nature of flood pathways varies spatially, is influenced by landscape characteristics and alters vulnerability spatial distribution and hazard propagation. At the mesoscale, the “continuous urban fabric” Urban Atlas 2018 land-use class with the occurrence of at least 80 % of soil sealing shows higher absolute damage values. At microscale, evidence demonstrated that even events with moderate magnitude in terms of flood depth in a complex urbanized area may cause more damage than one would expect.

Comparison of Land Use Land Cover Classifiers Using Different Satellite Imagery and Machine Learning Techniques

Accurate land use land cover (LULC) classification is vital for the sustainable management of natural resources and to learn how the landscape is changing due to climate. For accurate and efficient LULC classification, high-quality datasets and robust classification methods are required. With the increasing availability of satellite data, geospatial analysis tools, and classification methods, it is essential to systematically assess the performance of different combinations of satellite data and classification methods to help select the best approach for LULC classification. Therefore, this study aims to evaluate the LULC classification performance of two commonly used platforms (i.e., ArcGIS Pro and Google Earth Engine) with different satellite datasets (i.e., Landsat, Sentinel, and Planet) through a case study for the city of Charlottetown in Canada. Specifically, three classifiers in ArcGIS Pro, including support vector machine (SVM), maximum likelihood (ML), and random forest/random tree (RF/RT), are utilized to develop LULC maps over the period of 2017–2021. Whereas four classifiers in Google Earth Engine, including SVM, RF/RT, minimum distance (MD), and classification and regression tree (CART), are used to develop LULC maps for the same period. To identify the most efficient and accurate classifier, the overall accuracy and kappa coefficient for each classifier is calculated throughout the study period for all combinations of satellite data, classification platforms, and methods. Change detection is then conducted using the best classifier to quantify the LULC changes over the study period. Results show that the SVM classifier in both ArcGIS Pro and Google Earth Engine presents the best performance compared to other classifiers. In particular, the SVM in ArcGIS Pro shows an overall accuracy of 89% with Landsat, 91% with Sentinel, and 94% with Planet. Similarly, in Google Earth Engine, the SVM shows an accuracy of 87% with Landsat 8 and 92% with Sentinel 2. Furthermore, change detection results show that 13.80% and 14.10% of forest areas have been turned into bare land and urban class, respectively, and 3.90% of the land has been converted into the urban area from 2017 to 2021, suggesting the intensive urbanization. The results of this study will provide the scientific basis for selecting the remote sensing classifier and satellite imagery to develop accurate LULC maps.

Impacts of urban forms and socioeconomic factors on CO2 emissions: A spatial econometric analysis

Planning and development of urban is one of the key factors that affect CO2 emissions. Sustainable urban planning and development is crucial to achieving carbon neutrality. However, there are relatively less studies in quantifying the impacts of urban forms and socioeconomic development on CO2 emissions with considering its spatial autocorrelation effects at the city-level. Thus, this study aimed to analyze the impacts of urban forms and socioeconomic factors on CO2 emissions of cities in Guangdong province during 2000–2017 based on a spatial panel econometric model. The results showed that CO2 emissions of Guangdong province increased significantly with a growth rate of 151% and had obvious spatial clustering characteristics. Urban form and socioeconomic development had a significant impact on CO2 emissions. Specifically, the results indicated that increasing the continuity of urban areas and improving the traffic coupling factor can help reduce CO2 emissions. The economic growth, population increase and economic openness degree increase would lead to stronger CO2 emission. In addition, the spatial spillover effects would enhance such impacts of the driving factors. Specifically, when the spillover effect of spatial Dubin model was taken into account, every 1% increase in urban class area, mean perimeter-area ratio, GDP, population, technology level and urbanization rate would lead a total CO2 emissions change by −0.53%, 1.3%, 1.1%, 0.18%, 12% and 1.4%, respectively. The results suggested that cities in Guangdong province should develop a more compact pattern, urban development with a scattered or disperse pattern should transform towards one continuous core city cluster and city belt, thus enhancing integration and sharing of infrastructure and related policies for low carbon development.

Family-Level Multimorbidity among Older Adults in India: Looking through a Syndemic Lens.

Most evidence on multimorbidity is drawn from an individual level assessment despite the fact that multimorbidity is modulated by shared risk factors prevailing within the household environment. Our study reports the magnitude of family-level multimorbidity, its correlates, and healthcare expenditure among older adults using data from the Longitudinal Ageing Study in India (LASI), wave-1. LASI is a nationwide survey amongst older adults aged ≥45 years conducted in 2017–2018. We included (n = 22,526) families defined as two or more members coresiding in the same household. We propose a new term, “family-level multimorbidity”, defined as two or more members of a family having multimorbidity. Multivariable logistic regression was used to assess correlates, expressed as adjusted odds ratios with a 95% confidence interval. Family-level multimorbidity was prevalent among 44.46% families, whereas 41.8% had conjugal multimorbidity. Amongst siblings, 42.86% reported multimorbidity and intergenerational (three generations) was 46.07%. Family-level multimorbidity was predominantly associated with the urban and affluent class. Healthcare expenditure increased with more multimorbid individuals in a family. Our findings depict family-centred interventions that may be considered to mitigate multimorbidity. Future studies should explore family-level multimorbidity to help inform programs and policies in strategising preventive as well as curative services with the family as a unit.

The Change in the Land Cover of Mahmudiyah City in Iraq for the Last Three Decades

The land cover of Mahmudiyah city, located south of the capital, Baghdad - Iraq, was studied for the period from 1986 to 2021 with five years between every two successive scenes, where Landsat scenes were used downloaded from the US Geological Survey (USGS ) website with low cloud cover for sensors TM and OLI. The land cover of the study area was classified. The total accuracy of the classification was calculated, as well as the analysis of the user accuracy and the classifier accuracy (maximum likelihood) and its impact on the overall classification accuracy. The lowest accuracy value in 2009 was (85.101% (and the highest accuracy value in 1995) was 95.654%). The constancy percentage of the class for the adopted years was calculated and compared to 1986 as a reference year to determine the changes in the land cover of the study area. It was found that there were changes in the classes influencing one the other, and the constancy percentage of the class was low due to environmental influences and human factors. The constancy percentage of the urban class was recorded at 50%, while the other classes did not exceed this rate since they suffered from the overlap of their spectral response. The low spatial resolution of the Landsat scenes (30 meters per pixel) led to recording omissions and commission, which decreased the overall classification accuracy.

LiSurveying: A high-resolution TLS-LiDAR benchmark

Outdoor point-cloud object localization is an essential processing step for urban scene analysis and modeling in numerous applications, especially in land surveying and site analysis. Given the increasingly use of Terrestrial Laser Scanning (TLS) and LiDAR 3D data acquisition, numerous annotated point-cloud datasets are available and can be used to evaluate computer vision and machine learning based algorithms. Nevertheless, current point-cloud datasets mainly focus on object detection and classification in autonomous driving or urban planning type of applications, and share redundant object classes, e.g., trees, vehicles and pedestrians, which limit their usefulness for land surveying and site analysis. This paper introduces a novel 3D benchmark dataset LiSurveying, which is a large-scale point-cloud dataset with over a billion points and uncommon urban object categories in complex outdoor environments. Our dataset incorporates more urban object classes than existing datasets. Its instances have diverse point densities, shapes and dimensions, which also impose a challenge for point-cloud detection and classification algorithms. We conducted baselines experiments for point-cloud classification using machine learning classifiers and deep learning methods on different subsets of the LiSurveying dataset, and we are able to demonstrate that the various types of object classes, number of instances per class, distribution of object points, and variety of complex scenes, make this LiSurveying benchmark dataset suitable for evaluating 3D point-cloud classification, semantic segmentation, and object detection algorithms.

Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient

Urbanization may significantly alter the abundance, composition and phenology of natural communities of plants and pollinators. However, how such alterations eventually affect the structure of plant-pollinator interaction networks is still poorly known. Here, we investigate how the structure of plant-pollinator networks changes along an urbanization gradient, which coincides with a phenological mismatch between plants and pollinators. We examined changes in plant-pollinator network structure in 12 sites sown with standardized native flower mixes along an urbanization gradient in a metropolis in Northern France. We used network-level metrics in combination with more detailed methodologies to identify changes in network structure, species clustering, and species roles through urban classes and time. We also evaluated the temporal trajectories of α- and β-diversity of species and interactions along the gradient. Network-level metrics showed limited spatial–temporal variability in the connectance, distribution of interactions and network-level specialization. Finer-scale analyses showed that generalist plant and pollinator species with long phenology were the most central and played key roles in defining the composition of cohesive groups of interacting species in all networks. Network motifs and species positions showed higher temporal variability in less urbanized areas, and interactions were more dissimilar between urbanization classes earlier in the season. We showed evidence of alterations in plant-pollinator network structure across space and time along an urbanization gradient, likely driven by the significant advancement in flowering phenology observed in the more urbanized areas. Our results emphasize the importance of targeted measures to maintain functional plant-pollinator communities, especially early in the season in highly urbanized areas.

Impact of urbanization on land surface temperature and surface urban heat Island using optical remote sensing data: A case study of Jeju Island, Republic of Korea

Urbanization changes the existing form of land use and cover (LULC) which can influence the land surface temperature (LST). Therefore, it is important to present the causes of urban heat island (UHI) which is usually linked with anthropogenic activities. There are very few studies on thermal behavior of Korean cities in literature. Hence, in this study we have estimated the LULC of Jeju island and analyzed LST using Landsat (TM/ETM+/OLI) images for the last two decades. The supervised image classification method employed with maximum likelihood classifier algorithm was used to classify the images and thermal band was used to calculate the LST. We have used simplified urban extent (SUE) algorithm to calculate the surface urban heat island (SUHI) and eventually we have correlated the SUHI with mean wind speed of Jeju Island. The results of LULC revealed that urban area increased from 8.69% in 2002 to 20.81% in 2021 and in this period barren land decreased by 34.88% due to urban expansion. Interestingly forest region has been slightly increased which is influenced by decreasing barren land. The accuracy was assessed using confusion matrix for the classified images and results revealed an overall accuracy of 0.87, 0.85 and 0.92 with kappa coefficient of 79.02%, 76.45% and 88.05% for the years of 2002, 2011, and 2021 respectively. The LST of all LULC classes were calculated which revealed the highest temperature for urban class which is followed by barren land. However, the forest cover and water body have the lowest temperature in the island. The intensity of surface urban heat island (SUHI) was increased from 2.47 °C (2002) to 3.10 °C (2021). We have correlated the wind speed and SUHI which revealed that SUHI and wind speed has inverse relationship. The outcome of this research can be utilized by the policymakers, urban planners, architects, and climatologist to develop policy related to climate-resilient cities.

Digital Mapping of Land Cover Changes Using the Fusion of SAR and MSI Satellite Data

The aim of this article is to choose the most appropriate method for identifying and managing land cover changes over time. These processes intensify due to human activities such as agriculture, urbanisation and deforestation. The study is based in the remote sensing field. The authors used four different methods of satellite image segmentation with different data: Synthetic Aperture Radar (SAR) Sentinel-1 data, Multispectral Imagery (MSI) Sentinel-2 images and a fusion of these data. The images were preprocessed under segmentation by special algorithms and the European Space Agency Sentinel Application Platform (ESA SNAP) toolbox. The analysis was performed in the western part of Lithuania, which is characterised by diverse land use. The techniques applied during the study were: the coherence of two SAR images; the method when SAR and MSI images are segmented separately and the results of segmentation are fused; the method when SAR and MSI data are fused before land cover segmentation; and an upgraded method of SAR and MSI data fusion by adding additional formulas and index images. The 2018 and 2019 results obtained for SAR image segmentation differ from the MSI segmentation results. Urban areas are poorly identified because of the similarity of spectre signatures, where urban areas overlap with classes such as nonvegetation and/or sandy territories. Therefore, it is necessary to include the field surveys in the calculations in order to improve the reliability and accuracy of the results. The authors are of the opinion that the calculation of the additional indexes may help to enhance the visibility of vegetation and urban area classes. These indexes, calculated based on two or more different bands of multispectral images, would help to improve the accuracy of the segmentation results.

Social classes and political Islam: a comparative ecological approach of post-Arab Spring elections in Northern Africa (2011- 2014)

ABSTRACT Starting from the debate on the sociology of political Islam, opposing interpretations centred on identity and on specific class alliances, the paper proposes a comparative analysis of the socio-geographies of mainstream Islamist parties in the post-Arab spring period in Morocco, Tunisia, and Egypt. The paper shows that socio-geographies of political Islam are very pronounced, making unlikely an interpretation of Islamist parties as having a purely non class-based identity. These results challenge the conception of political Islam as a hegemonic ideology among Arab populations, as such an ideology would be built on their cultural heritage, repressed both by colonialism and by post-colonial elites. This conception denies the complexity of modern Arab societies, the importance of minorities, the diversity of social trajectories and the capacity of other movements to penetrate into some deprived rural or urban areas. This analysis neither validates conclusions that political Islam is an alliance between the deprived urban classes and the traditional bourgeoisie politically excluded from the ruling post-colonial classes. Rather, one finding is that the social grounds of Islamists are very dependent on the national contexts.

The limits of spatial data? Sense-making within the development and different uses of Finnish urban-rural classification

In order to formulate relevant understanding of key sustainability challenges, evidence-based decision-making relies on comprehensive data. While the complexity of producing and processing spatial data and the potential for biases are well recognised, the social process of making sense of data and its implications for societal uses is less analysed. In this article, insights of critical data studies are applied to study the production of, as well as uses and misuses of, the Finnish urban-rural classification. The classification structures Finland into three urban classes and four rural classes and offers an alternative to classifications that utilise administrative, municipal, and regional boundaries. The classification acts as a boundary object, functioning as a common reference for parties with varying information needs and interests. Using document analysis, as well as an insider action research methodology and our own experiences as data producers, this article aims to understand the processes of sense-making of data in the context of urban-rural classification and identify ways of improving related information systems and data practices. Intended and realised uses of the classification are analysed in order to identify different ways in which data producers and users make sense of data and justify the utilisation of the classification. The process of sense-making starts from the planning of data production and shapes how data and eventually information system are formulated throughout the data cycle. Communication about the limitations of the classification remains an issue and highlights the nature of sense-making as a collective process wherein users are actively shaping data practices as they translate information systems into their own contexts. This also draws attention to the nature of information systems as inherently unneutral, inevitably affected by negotiations shaped by the various information needs.

Wells and Boreholes: Resilient Water Provision in Nairobi

Abstract In most Global South cities, the majority of urban residents, especially those in informal settlements, continue to survive off the main infrastructural grid. In Nairobi for instance, over 60% of residents live in informal settlements, defined by widespread squalor and shortage of key infrastructures for everyday living. Despite their existence as unplanned, these informal settlements have witnessed some forms of innovation around alternative technologies for water provision. Through oral and archival sources, this article shows that although large infrastructural systems are critical to urban dwellers, Nairobi’s waterscape has always found its resilience in a quilted landscape of water supply technologies. As part of this quilt, boreholes and wells have long been essential, either as key solutions or as complements to the main supply system. The author explores the development of Nairobi’s centralised formal water supply system from 1899 to date locating inherently built vulnerabilities that are born out of the dependency on large infrastructural systems. He concludes that the centralised piped water supply system is critical hence vulnerable, and that urban resilience for both the poor and rich urban class, is built on alternatives that ensure multiplicity of access and usage.

Fusing Earth observation and socioeconomic data to increase the transferability of large-scale urban land use classification

Monitoring and understanding urban development requires up-to-date information on multiple urban land-use classes. Manual classification and deep learning approaches based on very-high resolution imagery have been applied successfully, but the required resources limits their capacity to map urban land use at larger scales. Here, we use a combination of open-source satellite imagery, constituting of data from Sentinel-1 and Sentinel-2, and socioeconomic data, constituting of points-of-interest and spatial metrics from road networks to classify urban land-use at a national scale, using a deep learning approach. A related challenge for large-scale mapping is the availability of ground truth data. Therefore, we focus our analysis on the transferability of our classification approach, using ground truth labels from a nationwide land-use dataset for the Netherlands. By dividing the country into four regions, we tested whether a combination of satellite data and socioeconomic data increases the transferability of the classification approach, compared to using satellite data only. The results indicate that socioeconomic data increases the overall accuracy of the classification for the Netherlands by 3 percentage points. In a transfer learning approach we find that adding socioeconomic data increases the accuracy between 3 and 5 percentage points when trained on three regions and tested on the independent fourth one. In the case of training and testing on one region and testing on another, the increase in overall accuracy increased up to 9 percentage points. In addition, we find that our deep learning approach consistently outperforms a random forest model, used here as benchmark, in all of the abovementioned experiments. Overall, we find that socioeconomic data increases the accuracy of urban land use classification, but variations between experiments are large.

The Imperial Power and Russian Society in the First Quarter of the XVIII Century: The Problem of Gaining Trust

The article analyzes the process of changes in the sphere of state and local government during the first quarter of the XVIII century. Considering the features of the city government reform, the introduction of new institutions and procedures in the field of management, the author concludes that during this period the boundaries of trust between the tsarist/imperial authorities and persons involved in process of management were changed. As a result of a deliberate rejection of the aristocratic model of public service, a significant number of representatives of the urban and noble classes were involved in the process of management. The author used problem-chronological, historical-typological, historical-critical methods, as well as the method of content analysis. It is concluded that although many innovations of the first quarter of the XVIII century were not of a long-term nature, they served as the basis for subsequent transformations of the second half of the XVIII century.

GeoInFuse - A data-driven information fusion for intra-urban form classification in data-scarce heterogeneous cities

Understanding the variability in intra-urban forms is essential for protecting future-proofing cities from climate volatilities. However, the classification of urban forms is costly due to its reliance on high-resolution datasets, limited in the Global South's low-income cities. Additionally, the current classifiers are constrained in characterizing urban forms in heterogeneous cities where the built environment has an isomorphic footprint but vary in urban canopy parameters. Existing methodologies have constraints on using unstructured low-resolution datasets like images and portable document formats to classify intra-urban forms. This study breaks ground by forwarding a novel end-to-end multi-channel supervised data fusion framework, GeoInFuse, to classify different urban form typologies in heterogeneous hyper-dense cities. It uses a state-of-the-art deep learning network on open-source image-based datasets to generate urban form classification. Three datasets (two for ground truth and one containing multi-dimensional urban canopy parameters) are used to demonstrate the framework. The parameters as channels were fused to generate urban form classes. Results show that the GeoInFuse could extract the heterogeneous urban form classes with 74.9.

STATE-WIDE WETLAND INVENTORY MAP OF MINNESOTA USING MULTI-SOURCE AND MULTI-TEMPORALREMOTE SENSING DATA

Abstract. Carbon sequestration coupled with flood mitigation and other functions of wetlands, such as water filtration, coastal protection, biodiversity, and providing recreational spots, make wetland mapping and monitoring important for different countries. Google Earth Engine (GEE) cloud computing platform is becoming a very important tool for lots of environmental studies as it provides a suite of tools and access to data that facilitate large-scale environmental monitoring projects through its powerful parallel processing capabilities. In this study, we use GEE to access multi-source remote sensing datasets and implement an object-based image analysis, and random forest algorithm for the classification of wetlands in the state of Minnesota. Emergent, forested, and scrub-shrub wetland classes, water, as well as urban, forest, and agriculture land cover types were classified using Sentinel-2, Sentinel-1, USGS 3D Elevation Program 10-meter DEM, and gridded soil data. NDVI, EVI, BSI, NDBI, and NDWI spectral indices were calculated from Sentinel-2 imagery, VV and VH polarization channels, and their ratio, as well as span parameters, were calculated from Sentinel-1 imagery, and slope and aspect features were extracted from DEM. Simple Non-Iterative Clustering (SNIC), Gray-Level Co-occurrence Matrix (GLCM), Principal Components Analysis (PCA), and random forest algorithms were implemented to classify wetlands from the GEE platform. Emergent wetlands, water, urban, and agriculture classes performed well with producer accuracies greater than 90%. Sentinel-1, DEM, and soil datasets improve the identification of wetland classes and highlight the importance of multi-source approaches for wetland mapping.

Characterizing the Patterns and Trends of Urban Growth in Saudi Arabia’s 13 Capital Cities Using a Landsat Time Series

Development and a growing population in Saudi Arabia have led to a substantial increase in the size of its urban areas. This sustained development has increased policymakers’ need for reliable data and analysis regarding the patterns and trends of urban expansion throughout the country. Although previous studies on urban growth in Saudi cities exist, there has been no comprehensive research that focused on all 13 regional capitals within the country. Our study addressed this gap by producing a new annual long-term dataset of 30 m spatial resolution that covered 35 years (1985–2019) and maintained a high overall accuracy of annual classifications across the study period, ranging between 93 and 98%. Utilizing the continuous change detection and classification (CCDC) algorithm and all available Landsat data, we classified Landsat pixels into urban and non-urban classes with an annual frequency and quantified urban land cover change over these 35 years. We implemented a stratified random sampling design to assess the accuracy of the annual classifications and the multi-temporal urban change. The results revealed that Saudi capitals experienced massive urban growth, from 1305.28 ± 348.71 km2 in 1985 to 2704.94 ± 554.04 km2 in 2019 (±values represent the 95% confidence intervals). In addition to the high accuracy of the annual classifications, the overall accuracy of the multi-temporal urban change map was also high and reached 91%. The urban expansion patterns varied from city to city and from year to year. Most capital cities showed clear growth patterns of edge development, that is, a continuous expansion of built-up lands radiating from existing urban areas. This study provides distinct insights into the urban expansion characteristics of each city in Saudi Arabia and a synoptic view of the country as a whole over the past four decades. Our results provided a dataset that can be used as the foundation for future socioeconomic and environmental studies.