Natural Land Cover Research Articles

Spatial Assessment of Urban Heat Island (UHI) in Kütahya using Landsat-8 Satellite Data

The Urban Heat Island (UHI) effect refers to the phenomenon where urban areas experience significantly higher temperatures than their rural surroundings due to human activities and modifications, such as replacing natural land cover with impervious surfaces, reducing vegetation, and increasing heat-absorbing materials. Kütahya central district has a dense population and building layout, which contributes to the intense UHI. Geographic Information System (GIS) and its tools are widely used in assessing UHI in urban areas, providing insights for both researchers and local authorities. Landsat-8 images acquired on August 8, 2023 (Path: 179, Row: 33, Cloud Cover: 0%) were utilized to create the Urban Heat Island (UHI) map for the study area. Through spatial analysis using Landsat-8 data for Kütahya city center, six different classes were defined, with the UHI map created using land surface temperature values. The highest and lowest UHI values vary between 4.245°C and -3.457°C. It has been observed that the average UHI increases by 12% in areas lacking dense building structures, characterized by rock surfaces and limited green areas. Conversely, in areas with parks, forests, and sparse settlements, the UHI decreases by 10%. With this study, a comprehensive evaluation of UHI was conducted for Kütahya city center. While the results obtained are expected to be an important resource for researchers and local authorities, they are also anticipated to be beneficial in the fields of engineering geology, urban geology, and urban planning.

Historical expansion of tea plantations over 150years (1876-2023) in North Bengal, India.

The Camellia sinensis (L.) Kuntze agroforestry system has been a dominant driver of land transformation for over a century. However, most previous studies have not captured the dynamics of tea plantations since their inception. To address this research gap, this study investigates 150years (1876 to 2023) of tea area dynamics and two decades (2001-2022) of tree loss in the Bengal-Dooars region (Jalpaiguri and Alipurduar districts). Various data sources were employed, including Sentinel-2A imagery (10m), early twentieth century topographic maps at a 1:50,000 scale, and historical records from the British colonial period. Results revealed that the tea area expanded from 331 to 95,800ha (a 70% increase) during the study period. The 1-km grid-wise spatial analysis indicated that approximately 70% of the increase was attributed to the 60-90% and > 90% grid categories, signifying the expansion of large tea estates. Most tea gardens(TG) are sustained with trees, which can significantly contribute to carbon stock. Tree loss within and outside TG for 2001-2022 was analyzed using annual tree loss data at 30m from the Global Forest Watch (GFW) platform. Between 2001 and 2022, about 52.7% of tree loss was attributed to TG, although tea covers only 15.3% of the geographic area of the study region. Our findings highlight the historical expansion of tea plantations and their impact on natural land cover. We suggest that future studies address the mean age of TG along with tree fractions for improved carbon estimates.

A U-Net Model for Urban Land Cover Classification Using VHR Satellite Images

Urban settings are dynamic, constantly changing, and presenting a wide range of surface materials with high diversity in both spatial and spectral variation. As a result, mapping urban growth, evaluating infrastructure, managing water resources, and monitoring natural land cover become more complex tasks. Urban applications have made considerable progress thanks to the abundance of VHR orbital data and the recent development of artificial intelligence strategies especially neural networks. Convolutional neural networks have the potential to significantly enhance the analysis of urban land cover by addressing the limitations of traditional techniques. U-Net is a popular neural network for land cover analysis in remote sensing images. The current research presents a CNN model employing U-Net for image semantic segmentation in urban study area using both spectral and spatial context of VHR satellite data. The proposed model is trained, validated, and tested for VHR satellite image classification into five urban classes: water, vegetation, bare soil, road, and building. The CNN semantic segmentation results are compared to maximum likelihood image classification outcomes for validation and stability evaluation. A confusion matrix is applied to the classified scenes to determine the overall accuracy, producer's and user's accuracy, and Kappa coefficient using 400 random points with their corresponding ground truth. The U-Net image semantic segmentation technique achieved an overall accuracy of 87.50% and Kappa coefficient of 0.8395 which outperforms the maximum likelihood classification method with an overall accuracy of 83.25% and Kappa coefficient of 0.7812.

Spatio-temporal analysis of land use and land cover dynamics in a part of Southern Nigeria

Agriculture, industrialization, transportation, urbanization and other anthropogenic activities have impacted on the natural cover of the earth surface, hence the dual concepts of land use and land cover (LULC). This study analyses spatio-temporal dynamics in LULC in part of southern Nigeria (path 189 and row 056) between 2002 and 2023using satellite imageries: namely, Landsat Enhanced Thematic Mapper Plus ETM+ of 2023, 2014 and 2002. The classification of land coverwas done with the aid of ArcGIS.The study revealed spatial dynamics in LULC over the study period. The percentage of the study area under cultivation increased from 7.96% in 2002, 17.79% in 2014 to 30.67% in 2023. Also, built-up area increased from 4.36% in 2002, 8.29% in 2014 but decreased to 7.06% in 2023. While areas of dense forest were 34.59% in 2002, 21.42% in 2014 and 11.57% in 2023, bare surface was 4.94% in 2014, 1.19% in 2002 and 25.34% in 2023.Oil spill was 6.13% in 2002, 6.42% in 2014 and 2.07% in 2023. Secondary regrowth/Plantation was 0.19%, 4.22% and 5.53% in 2002, 2014 and 2023 respectively. Water bodieswere 3.37% (2002), 7.06% (2002) and 11.27% (2023). The study recommends enactment of an enabling law or enforcement of existing laws governing forest protection and raising awareness and education of inhabitants on the need to preserve the natural land cover.

The urban air quality nexus: Assessing the interplay of land cover change and air pollution in emerging South Asian cities

Air quality degradation presents a significant public health challenge, particularly in rapidly urbanizing regions where changes in land use/land cover (LULC) can dramatically influence pollution levels. This study investigates the association between LULC changes and air pollution (AP) in the five fastest-growing cities of Bangladesh from 1998 to 2021. Leveraging satellite data from Landsat and Sentinel-5P, the analysis reveals a substantial increase in urban areas and sparse vegetation, with declines in dense vegetation and water bodies over this period. Urban expansion was most pronounced in Sylhet (22–254%), while Khulna experienced the largest increase in sparse vegetation (2–124%). Dense vegetation loss was highest in Dhaka (20–77%) and water bodies (9–59%) over this period. Concentrations of six major air pollutants (APTs) - aerosol index, CO, HCHO, NO2, O3, and SO2 - were quantified, showing alarmingly high levels in densely populated industrial and commercial zones. Pearson's correlation indicates strong positive associations between APTs and urban land indices (R > 0.8), while negative correlations exist with vegetation indices. Geographically weighted regression modeling identifies city centers with dense urban built-up as pollution hotspots, where APTs exhibited stronger impacts on land cover changes (R2 > 0.8) compared to other land classes. The highest daily emissions were observed for O3 (1031 tons) and CO (356 tons) at Chittagong in 2021. In contrast, areas with substantial green cover displayed weaker pollutant-land cover associations. These findings underscore how unplanned urbanization drives AP by replacing natural land cover with emission sources, providing crucial insights to guide sustainable urban planning strategies integrating pollution mitigation and environmental resilience.

Intra-Urban Induced Heating Assessment in Kuwait’s Desert Metropolis Using Explainable Machine Learning

Intra-urban induced heating (IUIH) in hot desert cities exhibits distinct patterns and complex diurnal interactions with built environment features, differing significantly from those in temperate areas and remains not fully understood. Understanding how various built environment features contribute to intra-urban thermal variability is an essential first step in developing sub-diurnal targeted heat mitigation strategies. This study presents a data-driven examination of IUIH dynamics in Kuwait’s desert metropolis. Near-surface air temperature observations were collected using high-resolution loop-type traverses at selected hours during a representative summer day to determine IUIH variability. The diurnal impacts of built environment features were modeled using an ensemble learning approach and interpreted with SHapley Additive exPlanations. Among several candidate machine learning regressors evaluated, Random Forest demonstrated strong predictive power (R2 = 0.954) with acceptable error (RMSE = 0.096, MAPE = 0.001) and least bias (MBE = 0.008). The study’s significance lies in its assessment framework that emphasizes explainability of sub-diurnal dynamics, offering detailed insights that challenge traditional assumptions and inform both immediate local climate interventions and strategic urban planning. The findings reveal that simple day-evening comparatives might overlook nuanced sub-diurnal dynamics, such as potential irrigation-induced warming by shrubs observed at mid-day and the complex trade-offs between radiative and transpirative processes by trees in the afternoon and evening. Additionally, the study identifies cooling effects associated with natural land cover, presenting a critical optimization challenge between compact and open urban forms to effectively modulate near-surface air temperatures.

Spatiotemporal changes in LULC and associated impact on urban Heat Islands over Pakistan using geospatial techniques

Urban Heat Islands arise when the natural land cover changes with dense concentrations of pavement, buildings, roads, and other surfaces that absorb and preserve heat. The current study aimed to assess Land Use Land Cover(LULC) changes and associated impact on Urban Heat Island (UHI) from 2000 to 2020 using Geospatial technologies for four metropolitan cities (Islamabad, Lahore, Multan and Muzaffarabad) of Pakistan. A Supervised Classification algorithm was performed on satellite imageries using ArcGIS software for analyzing LULC changes. The study areas were classified into 4 classes such as built-up, barren land, vegetation, and water bodies. Thermal bands of Landsat 4–5 and 8 were used for retrieved Land Surface Temperature (LST) and UHI. The findings of this study indicate that the urban area in all four districts i.e. Islamabad, Lahore, Multan and Muzaffarabad has increased by 21.3 %, 20.9 %, 6.1 % and 15 % respectively during 2000–2020. The built-up and barren land revealed the highest recorded LST, while vegetation and water bodies showed the lowest measured LST values. From 2000 to 2020, the annual maximum LST in Islamabad rose by 1.1 °C, Lahore by 2.1 °C, and Multan and Muzaffarabad by 1.9 °C and 1.5 °C. The expansion of built-up areas and the reduction in vegetation contribute to a favorable contribution to UHI. The study's findings showed that appropriate action plans are required to manage urban heat and promote sustainable city development.

Land Surface Temperature Anomalies as Indicators of Urban Land Cover Change—A Study of Two Indian Cities

Rapid urbanisation over the years has led to the loss of natural land cover, thereby affecting Land Surface Temperature (LST) distribution in urban areas. This study aims to analyse LST anomalies (calculated as the deviation from the normal) over selected Indian cities and check if critical land cover changes can be identified. LST from Landsat Thermal Infrared (TIR) images acquired in March, April and May from 1988 to 2020 were used to estimate LST anomalies. Positive LST anomalies were observed mainly over barren and impervious areas; however, some areas showed a negative anomaly where the barren lands were converted to vegetated areas. The study has demonstrated that while some developed areas exhibit a positive anomaly indicative of significant changes or development, there are instances where the conversion of barren land to developed (i.e. built up) areas has resulted in a negative anomaly. Developed areas that are closer to the water creek or mangroves were associated with lower anomaly values indicating the cooling effect of the water body and vegetation. Conversely, the core urban areas generally exhibited higher LST values with positive anomalies indicating a warming effect. These findings can be used by city planners to identify hotspot areas and develop more effective strategies and policies to address the challenges of urban heat. They also highlight the regions that require infrastructural resources and policy changes to reduce the temperature.

Land transformation across agroecological zones reveals expanding cropland and settlement at the expense of tree-cover and wetland areas in Nigeria

ABSTRACT Evaluating how land cover is being transformed is essential to identify patterns necessary to infer the change trajectories and the driving factors. This study considers the case of Nigeria, where various natural ecosystems are being converted and for which a current national scale assessment at high spatial resolution is lacking. Producing 30 m Landsat-based time-series data, we analyze change among land cover types (i.e. tree-covered area, grassland, wetland, waterbody, cropland, artificial surface, and otherland) across seven agroecological zones. The annual change intensity was assessed at multi-levels across two time-intervals (i.e. 2000–2013, 2013–2022). Distinguishing between natural land cover and human activity-related land-use, we estimate the extent of change signifying how humans have appropriated natural land cover. Insights from analysis at the interval level reveal that land transformation accelerated from 3.3% in 2000–2013 to 4.5% during 2013–2022 in all agroecological zones (e.g. rainforest, mangrove), except in Sudan savannah and Sahel savannah where speed was higher in 2000–2013 as grasslands were increasingly cultivated. Cropland expanded almost two-fold (22% to 37%), whereas tree-cover declined from 50% to 31% and wetland from 7% to 3.7% over the 23 years. Much loss of natural land cover (e.g. tree-cover, grassland, and wetland) to cropland occurred in 2000–2013 (22%) when most irrigation schemes in Nigeria were established. In contrast, the loss of natural land cover to settlement (0.9%) during 2000–2013 increased to 2.0% in 2013–2022. Of all agroecological zones, the mangrove zone was most disturbed as its persisting land cover areas reduced from 69% to 5% between 2000–2013 and 2013–2022. The amount of persisting land cover was highest in the Sudan savannah at 44% in 2000–2013 and 49% in 2013–2022. Processes of human-appropriated natural land cover in Nigeria are related to urbanization and cropland expansion into natural areas with some instances of natural regeneration, especially in croplands and abandoned settlement areas.

Exploring Built and Natural Landscapes, Land Cover, and Environmental Justice Using Web-Based Tools

Exploring Built and Natural Landscapes, Land Cover, and Environmental Justice Using Web-Based Tools

Expanding the habitat quality assessment of rivers in Greece using an updated River Habitat Survey toolbox

Although most of the EU members have adopted methods for assessing the hydromorphological quality of lotic ecosystems in line with the Water Framework Directive, until now, Greece lacked an integrated approach. In this article, we present the results of the first nationwide assessment of habitat quality for rivers in Greece, using the River Habitat Survey (RHS) alongside a typology of Greek rivers. The RHS toolbox software has been updated with data from the Greek monitoring program which allowed for type-specific assessments using the Habitat Quality Assessment (HQA) score. The HQA scores from 366 monitoring sites were analyzed to identify spatial patterns and significant variations. Sites were classified in five HQA classes based on the position of their HQA score relative to sites of similar types. We also investigated whether Ecological Quality Ratios (EQRs) for three biological quality elements (benthic diatoms, benthic macroinvertebrates and fish) differed significantly among HQA classes. Correlations showed that sites at mid and high altitude catchments with natural land cover had better HQA scores than lowland sites with agricultural land cover. In addition, large urbanized districts (e.g. Attica), and central Greece displayed lower habitat quality than Epirus and Western Macedonia. Our findings revealed that EQR values varied significantly among the HQA classes, with these differences being more prominent for small and medium lowland streams. This study shows that this approach can identify sites that are prone to habitat degradation and require further management, whilst providing a consistent framework for exploring the relationships between biota and hydromorphological conditions.

Mesocarnivore sensitivity to natural and anthropogenic disturbance leads to declines in occurrence and concern for species persistence.

Understanding mesocarnivore responses to both natural and anthropogenic disturbance is crucial for understanding species' potential to maintain landscape persistence into the future. We examined the response of five mesocarnivore species (bobcat, coyote, fisher, gray fox, and red fox) to both types of disturbances and climatic conditions. The Northeastern U.S. has experienced multiple large-scale disturbances, such as a mass defoliation event following larval spongy moth outbreak and high densities of infrastructure that divide the natural landcover into roadless zones where these species inhabit. Using dynamic occupancy models in a Bayesian framework, we aimed to (1) examine variation in species' responses over a 4-year study by estimating variation in site-level occupancy, colonization and extirpation of each species in the state of Rhode Island relative to natural disturbance (i.e., defoliation event), anthropogenic disturbance (i.e., parceling of natural landcover bounded by roads, distance to roads), and climate (i.e., seasonal precipitation) and (2) compare current occurrence trends to predicted asymptotic occupancy to identify key variables contributing to distribution instability. Our findings indicated declines in the occurrence of both fox species, and fisher. There was variation in mesocarnivore response to disturbance among the species. We found gray fox and fisher occupancy dynamics to be sensitive to all forms of disturbance and coyote occurrence was positively associated with anthropogenic disturbance. Although bobcat and red fox were predicted to respond positively to future climate scenarios, fisher and gray fox were not, and persistence of fisher and gray fox in a landscape of disturbance relies on large areas with high forest and shrubland cover. With the wide-spread spongy moth outbreak across much of southern New England, our findings indicate that efforts to conserve forested lands may be crucial in maintaining the persistence of several mesocarnivore species in this region experiencing large-scale disturbance.

The synergy between protected area effectiveness and economic growth

Protected areas conserve biodiversity and ecosystem functions but might impede local economic growth. Understanding the global patterns and predictors of different relationships between protected area effectiveness and neighboring community economic growth can inform better implementation of the Kunming-Montreal Global Biodiversity Framework. We assessed 10,143 protected areas globally with matched samples to address the non-random location of protected areas. Our results show that protected areas resist human-induced land cover changes and do not limit nightlight increases in neighboring settlements. This result is robust, using different matching techniques, parameter settings, and selection of covariates. We identify four types of relationships between land cover changes and nightlight changes for each protected area: "synergy," "retreat," and two tradeoff relationships. About half of the protected areas (47.5%) retain their natural land cover and do so despite an increase of nightlights in the neighboring communities. This synergy relationship is the most common globally but varies between biomes and continents. Synergy is less frequent in the Amazon, Southeast Asia, and some developing areas, where most biodiversity resides and which suffer more from poverty. Smaller protected areas and those with better access to cities, moderate road density, and better baseline economic conditions have a higher probability of reaching synergy. Our results are promising, as the expansion of protected areas and increased species protection will rely more on conserving the human-modified landscape with smaller protected areas. Future interventions should address local development and biodiversity conservation together to achieve more co-benefits.

Habitat connectivity of threatened ungulate species in a native savanna landscape of northern South America

AbstractThe native savanna ecosystem of the Orinoquia region is the habitat of 50% of the wild ungulate species reported for Colombia. Over the last 20 years, this high species diversity has been strongly threatened by the human transformation of the natural land cover causing connectivity loss of the habitats. The Orinoquia region lacks a biological connectivity analysis with a multi-species approach involving species groups that are representative of the savanna ecosystem such as the ungulates. Understanding the spatial distributions of suitable areas and the main habitats that act as primary habitats for ungulate species in these landscapes is fundamental for the design of conservation strategies. We use an occurrence dataset for lowland tapir (Tapirus terrestris), white-tailed deer (Odocoileus virginianus), and white-lipped peccary (Tayassu pecari) in the development of species’ potential distribution models, binarization process, and morphological spatial pattern analysis. This information was used for the modeling of dispersal corridors connecting the core habitats of the ungulate focal species using the randomized shortest path algorithm and quantifying the weighted global connectivity metrics. Our results suggest an integral corridor with potential least-cost dispersal routes between the native savanna landscape on the middle basins of the savanna rivers and the Meta River. These areas associated with the fluvial dendritic systems are connected, while the core habitats in the eastern part of the savanna landscape are disconnected. We discuss how the application of such knowledge on the spatial ecology of ungulate focal species might improve the management of the metapopulations in the Orinoquia region.

Changes in natural forest land cover of the Eucalyptus urophylla type in the Mutis Timau area in 2002-2022, East Nusa Tenggara Province

Anthropogenic activities carried out by communities around forests in production forests, protected forests, and conservation forests have an impact on changes in the land cover of these areas. This impact is difficult to avoid because, on the other hand, production forests and protected forests are in direct contact with the lives of local communities. Changes in land cover have a significant impact on the contribution of Folu Net Sink, which is set by the government at 31.89% in 2030 with its efforts. This research aims to provide an overview of land cover changes that occurred in natural forests of the Eucalyptus urophylla type from 2002 to 2022. The method used in this research is a supervised/guided digital classification technique. Ground checks were also carried out in this research to match the accuracy of the image data with the actual conditions in the field. The research results show that changes in land cover in 2012 and 2017 were very extreme. Forest fires were the cause of changes in land cover during this period. Meanwhile, in 2002, 2007, and 2022, the land cover was still good, in a positive direction. The causes of changes in land cover are encroachment activities, encroachment, illegal logging, firewood theft, rice fields, forest fires, plantations, and farming. Forest preservation provides very high benefits for life on earth. Therefore, it is important to pay attention to the welfare of local communities so that they do not damage the forest, as well as increase the capacity to educate the community regarding zoning or blocks in conservation and protected forests. Apart from that, good forest management is needed to ensure forest sustainability in the future.

Native prey, not landscape change or novel prey, drive cougar (Puma concolor) distribution at a boreal forest range edge.

Many large carnivores, despite widespread habitat alteration, are rebounding in parts of their former ranges after decades of persecution and exploitation. Cougars (Puma concolor) are apex predator with their remaining northern core range constricted to mountain landscapes and areas of western North America; however, cougar populations have recently started rebounding in several locations across North America, including northward in boreal forest landscapes. A camera-trap survey of multiple landscapes across Alberta, Canada, delineated a range edge; within this region, we deployed an array of 47 camera traps in a random stratified design across a landscape spanning a gradient of anthropogenic development relative to the predicted expansion front. We completed multiple hypotheses in an information-theoretic framework to determine if cougar occurrence is best explained by natural land cover features, anthropogenic development features, or competitor and prey activity. We predicted that anthropogenic development features from resource extraction and invading white-tailed deer (Odocoileus virgianius) explain cougar distribution at this boreal range edge. Counter to our predictions, the relative activity of native prey, predominantly snowshoe hare (Lepus americanus), was the best predictor of cougar occurrence at this range edge. Small-bodied prey items are particularly important for female and sub-adult cougars and may support breeding individuals in the northeast boreal forest. Also, counter to our predictions, there was not a strong relationship detected between cougar occurrence and gray wolf (Canis lupus) activity at this range edge. However, further investigation is recommended as the possibility of cougar expansion into areas of the multi-prey boreal system, where wolves have recently been controlled, could have negative consequences for conservation goals in this region (e.g. the recovery of woodland caribou [Rangifer tarandus caribou]). Our study highlights the need to monitor contemporary distributions to inform conservation management objectives as large carnivores recover across North America.

The Analysis of Lawn Rebate Programs and Drought Tolerant Lawn Recommendations in California

California faces persistent cyclical droughts that are expected to worsen due to climate change. The state currently operates in an overall annual water deficit, and projections suggest a 10% reduction in water availability by 2040. To combat these water shortages, Governor Newsom's administration has unveiled a water supply strategy with substantial investments in water conservation policies, including rebate initiatives to encourage residents to replace lawns with drought-tolerant (DT) plants and non-living hardscapes. However, questions have arisen about the overall effectiveness and environmental impact of these costly programs. This study delves into the potential consequences of replacing living lawns with non-living hardscapes, specifically whether it might elevate local microclimate temperatures, akin to urban heat islands (UHI) caused by the substitution of natural land cover with man-made, impermeable surfaces. The research examines existing rebate programs across the state, assessing their reimbursement structures and variations. Surface temperatures of commonly recommended lawn alternatives are measured and compared, including comparisons between different alternatives and drought-tolerant ground cover plants. Additionally, the study includes an experiment involving a drought-tolerant ground cover seed mix to replace a traditional lawn. The findings indicate that hardscape lawn alternatives generate significantly higher temperatures than any of the drought-tolerant ground cover plants analyzed. Furthermore, there is notable temperature variability among the hardscapes studied. Consequently, this paper proposes modifications to the existing rebate programs and emphasizes the importance of ensuring that environmental solutions introduced to address one issue do not inadvertently exacerbate related problems.

Stream Chemistry and Forest Recovery Assessment and Prediction Modeling in Coal-Mine-Affected Watersheds in Kentucky, USA

Kentucky is one of the largest coal-producing states; surface coal mining has led to changes in natural land cover, soil loss, and water quality. This study explored relationships between actively mined and reclaimed areas, vegetation change, and water quality parameters. The study site evaluated 58 watersheds with Landsat-derived variables (reclamation age and percentage of mining, reclaimed forest, and reclaimed woods) as well as topographic variables (such as elevation, slope, drainage density, and infiltration). Water samples were collected in spring (n = 9), summer (n = 14), and fall (n = 58) 2017 to study changes in water quality variables (SO42−, alkalinity, conductivity, Ca2+, Mg2+, Mn2+, Al3+, and Fe2+, Fe3+) in response to changes in land cover. Pearson correlation analyses indicated that conductivity has strong to very strong relationships with water quality variables related to coal mining (except Al3+, Fe2+, Fe3+, Mn2+, elevation, slope, and drainage density) and land cover variables. In addition, separate regression analyses were performed, with conductivity values based on samples collected in the fall. First, conductivity responses to mining percentage, reclamation age and topographic variables were examined (adjusted R2 = 0.818, p < 0.01). Next, vegetation cover change parameters were added to the same model, which yielded slightly improved R2 (adjusted R2 = 0.826, p < 0.01). Finally, reclamation age and mining percentages were used to explain the quantity of reclaimed forested areas as a percentage of watersheds. The model was significant (p < 0.01), with an adjusted R2 value of 0.641. Results suggest that the quantity (area as a percentage) of reclaimed forests may be a predictor of the mining percentage and reclamation age. This study indicated that conductivity is a predictable water quality indicator that is highly associated with Coal-Mine-Related Stream Chemistry in areas where agriculture and urban development are limited. Water quality is not suitable for various purposes due to the presence of contaminants, especially in mined sites. These findings may help the scientific community and key state and federal agencies improve their understanding of water quality attributes in watersheds affected by coal mining, as well as refine land reclamation practices more effectively while such practices are in action.

UAS-Based Thermal Photogrammetry for Microscale Surface Urban Heat Island Intensity Assessment in Support of Sustainable Urban Development (A Case Study of Lyulin Housing Complex, Sofia City, Bulgaria)

The urban heat island (UHI) and its intensity is one of the phenomena that are of determining importance for the comfort of living in cities and their sustainable development in the face of deepening climate change. The study is objectively difficult due to the large dynamics like land cover and the considerable diversity of land use patterns in urban areas. Most of the frequently used research practice approaches provide information with problematic spatial and temporal resolution, making them difficult to apply for sustainable urban planning purposes. This paper proposes to calculate SUHI intensity as the difference between the temperature of a given point within a city and the average minimum temperature of the land cover class with the lowest surface temperatures within the same urban area. The study presents the results of the application of thermal photogrammetry based on the use of unmanned aerial systems (UAS), combined with geographic information systems (GIS), in the study of surface urban heat island intensity (SUHI), at the local level for the largest housing complex in Bulgaria–Lyulin district of the capital of Sofia city. The studies were carried out during a heat wave in July 2023. A difference of 16.5 °C was found between locations with SUHI occurrence and of the peripheral non-build and natural land cover types within the urbanized area. The information benefits of locally addressed data and their direct applicability are discussed to support decision-making processes in the planning and management of urban areas, including their climate adaptation and sustainable development.

Assessment of the Spatiotemporal Variability of Urban Heat Island Intensity in Colombo Metropolitan Area


 
 
 During the last few decades, the Colombo Metropolitan Area (CMA) has experienced rapid population growth and unplanned urbanization, resulting in the replacement of natural landcover with more impervious surfaces. The primary approach of current study was to determine the gradients of air temperature and assess the spatiotemporal variation of atmospheric urban heat island (AUHI) intensity in the CMA considering a range of rural sites extending in different radial directions from the urban center. A total of sixteen rural observation sites that extend from an urban center in four directions were selected within the CMA, with four sites 3.5 km apart from each other in any direction. The air temperatures at each site were measured using HOBO UX-100-003 temperature data loggers for an 8-day period from 21st to 28th, of February 2022. The normalized difference vegetation index (NDVI) and normalized difference building index (NDBI) were used to determine the influence of land use/land cover on air temperatures. The results revealed that air temperature had a strong positive correlation with NDBI. Thus, the current study suggests that the impervious surface/s is a significant predictor of air temperature over the study area. The higher air temperatures were observed at the urban center in relation to the selected suburban and rural observation sites. Hence, the current study confirms that AUHI exists within the selected study area over the CMA. It was found that AUHI tends to be present throughout the day in the CMA. However, it becomes strong between 12.00 A.M. and 6.00 A.M and mostly weakens in the late afternoon hours. Furthermore, this study found that the timing and magnitudes of AUHI vary from direction to direction across the study area depending on the site-specific characteristics of the rural observation sites. The current study emphasizes that site-specific land use/land cover characteristics can have a significant impact on the interpretation of UHI study results even within a single metropolitan area. Therefore, taking only one rural site might not be effective in determining the UHI intensity and a range of sites need to be considered (to select from) for in-depth analysis of UHI in any given area. The findings of the current study will provide firsthand knowledge for urban planners to identify future trends in UHI in the CMA and will promote further UHI research in this area.
 Keywords: Rural observation site, Atmospheric urban heat island, Normalized Difference Vegetation Index