Land Cover Research Articles

Comparative analysis of land cover changes on outdoor thermal comfort in Doha, Dubai, Kuwait City, Manama, Muscat, and Riyadh

ABSTRACT Rapid urbanization in Gulf cities has driven significant land cover changes, influencing outdoor thermal comfort and land surface temperatures (LST). This study investigates land cover dynamics from 1998 to 2023 across six cities – Doha, Dubai, Kuwait City, Manama, Muscat, and Riyadh – using Landsat imagery to assess LST, Normalized Difference Vegetation Index (NDVI) and approximated wet-bulb globe temperature (AWGBT). Results reveal an increase in urban areas, with Manama and Kuwait City experiencing the largest expansions (47.50% and 47.02%). Vegetation patterns varied, with cities like Dubai and Riyadh showing consistent increases, while Doha stagnated from 2013 to 2023. LST ranged from 42°C to 55°C, with desert areas showing the highest temperatures. Built-up areas had LST comparable to desert land, highlighting a reverse urban heat island effect. Dubai’s LST decreased between 2013 and 2023 due to successful green initiatives, contrasting with rising temperatures in other cities. The mean LST difference between the desert and urban areas was 2.5°C, and vegetation displayed a cooling effect, with a 3.5°C difference between vegetated and desert areas. Thermal comfort maps aligned with LST data, showing increasing heat stress, particularly in Doha and Kuwait City, while Dubai maintained stable comfort levels. This study underscores the critical role of vegetation and sustainable urban planning in mitigating heat stress and enhancing outdoor thermal comfort across Gulf cities.

Vegetation-climate feedbacks across scales.

Vegetation is often viewed as a consequence of long-term climate conditions. However, vegetation itself plays a fundamental role in shaping Earth's climate by regulating the energy, water, and biogeochemical cycles across terrestrial landscapes. It exerts influence by consuming water resources through transpiration and interception, lowering atmospheric CO2 concentration, altering surface roughness, and controlling net radiation and its partitioning into sensible and latent heat fluxes. This influence propagates through the atmosphere, from microclimate scales to the entire atmospheric boundary layer, subsequently impacting large-scale circulation and the global transport of heat and moisture. Understanding the feedbacks between vegetation and atmosphere across multiple scales is crucial for predicting the influence of land use and land cover changes, and for accurately representing these processes in climate models. This review discusses the biophysical and biogeochemical mechanisms through which vegetation modulates climate across spatial and temporal scales. Particularly, we evaluate the influence of vegetation on circulation patterns, precipitation, and temperature, considering both long-term trends and extreme events, such as droughts and heatwaves. Our goal is to highlight the state of science and review recent studies that may help advance our collective understanding of vegetation feedbacks and the role they play in climate.

Small forest patches and landscape-scale fragmentation exacerbate forest fire prevalence in Amazonia.

Over recent decades, forest fire prevalence has increased throughout the tropics, necessitating improved understanding of the landscape-scale drivers of fire occurrence. Here, we use MapBiomas land-cover and fire scar data to evaluate relationships between forest fragmentation, land-use, and forest fire prevalence in a typically consolidated Amazonian agricultural frontier: Portal da Amazonia, Mato Grosso, Brazil. Using zero-/zero-one-inflated Beta regressions, we investigate effects of forest patch (area, shape, surrounding forest cover) and landscape-scale variables (forest edge length, land-cover composition) on forest fire occurrence and density between 1985 and 2021. We show that fire density was greatest in small, complex forest patches. Small patches (≤100ha) were also the dominant contributors to annual, regional forest fire cover. At the landscape-scale (100km2), forest edge length and urban land cover were positively associated with forest fire occurrence and density. Furthermore, forest fires were most likely to occur in landscapes consisting of ∼45% pasture cover, while fire density increased roughly linearly with pasture cover. Cropland cover was negatively associated with forest fire occurrence and density. Our findings indicate clear links between forest fragmentation and increased forest fire prevalence. This is cause for global concern, given that fragmentation rates throughout Amazonia are increasing, and fires are eroding the Amazon's capacity to act as a carbon sink. Efforts to minimise further fragmentation within Amazonia would likely help reduce forest fire prevalence. Within already fragmented regions, the conversion of pasture into crops, alongside targeted efforts to suppress fires within small forest patches and urbanized areas, may also limit fire prevalence.

Correction: Evaluation of land use land cover dynamics and urban heat island effects over Mumbai metropolitan Region, India

Correction: Evaluation of land use land cover dynamics and urban heat island effects over Mumbai metropolitan Region, India

Positioning Ecocities within the planetary system: ecological performance of China’s National New Areas

Conceptualised initially as ‘ecologically healthy cities,’ Ecocities sought to accommodate human habitation within the planet’s resource constraints. However, as this concept evolved, concerns emerged with regard to its broader impact on the global ecosystem beyond local landscapes. This study revisits the Ecocity concept and its synonyms, grounded on an empirical assessment of China’s 19 National New Areas (NNAs). This research examines two indicators, Ecosystem Service Value (ESV) and Ecological Footprint (EF), to assess the NNAs’ influences on local landscape and global ecosystems from land cover and resource consumption perspectives. Findings reveal that ESV increased in 15 NNAs, while EF decreased in 8, suggesting that most projects contribute positively to local landscapes but have limited influence on global overshoot mitigation. This study further discusses the intrinsic nature of Ecocities and advocates for a One-Planet-Living worldview when developing Ecocities to foster sustainability for the collective well-being of humanity.

Green sanctuaries: residential green and garden space and the natural environment mitigate mental disorders risk of diabetic patients

BackgroundThe co-occurrence of diabetes and mental disorders is an exceedingly common comorbidity with poor prognosis. We aim to investigate the impact of green space, garden space, and the natural environment on the risk of mental disorders among the population living with diabetes.MethodsWe performed a longitudinal analysis based on 39,397 participants with diabetes from the UK Biobank. Residential green and garden space modeled from land use data and the natural environment from Land Cover Map were assigned to the residential address for each participant. Cox proportional hazards model was used to analyze the associations between nature exposures and mental disorders of diabetes. Casual mediation analysis was used to quantify indirect effect of air pollution.ResultsDuring a mean follow-up of 7.55 years, 4513 incident mental disorders cases were identified, including 2952 depressive disorders and 1209 anxiety disorders. Participants with natural environment at 300 m buffer in the second and third tertiles had 7% (HR = 0.93, 95%CI: 0.86–0.99) and 12% (HR = 0.88, 95%CI: 0.82–0.94) lower risks of incident mental disorders compared with those in the first tertile, respectively. The risk of mental disorders incidence among diabetes patients will decrease by 13% when exposed to the third tertile of garden space at 300 m buffer. The natural environment and garden space individually prevented 6.65% and 10.18% of mental disorders incidents among diabetes patients. The risk of incident mental disorders was statistically decreased when exposed to the third tertile of green space at 1000 m buffer (HR = 0.84, 95% CI: 0.78–0.90). Protective effects of three nature exposures against depressive and anxiety disorders in diabetes patients were also observed. Air pollution, particularly nitrogen dioxide, nitrogen oxides, and fine particulate matter, significantly contributed to the associations between nature exposures and mental disorders, mediating 48.3%, 29.2%, and 62.4% of the associations, respectively.ConclusionsResidential green and garden space and the natural environment could mitigate mental disorders risk in diabetes patients, with air pollution playing a vital mediator. This highlights the potential for local governments to enhance the sustainability of such interventions, grounded in public health and urban planning, through strategic planning initiatives.Graphical

Land use land cover dynamics and its impact on SWL fluctuation in shallow alluvial aquifers of the Purba Bardhaman plain, West Bengal, India

Land use land cover dynamics and its impact on SWL fluctuation in shallow alluvial aquifers of the Purba Bardhaman plain, West Bengal, India

Harnessing complexity: integrating remote sensing and fuzzy expert system for evaluating land use land cover changes and identifying mangrove forest vulnerability in Bangladesh

Purpose: This study analyzes Landsat images to examine the alterations in land cover within the Sundarbans and its surrounding regions in Bangladesh, spanning twenty-one years from 2000 to 2021. Furthermore, we develop a mangrove vulnerability map considering the combined effect of eight socioeconomic, geophysical, and climatic factors. Methods: Land use land cover (LULC) changes in the study area over a 21-year period were assessed using a random forest model, and the vulnerability analysis employed a fuzzy expert-based multicriteria decision-making (MCDM) approach. Results: The results show that a significant portion of the mangrove forest has been transformed into aquaculture practices because of the expansion of high-value shrimp cultivation. A decrease in forest areas and the expansion of aquaculture zones suggest a livelihood shift among the local population over time. This transition has adversely affected human activities within the ecosystem and the biodiversity of mangrove forests. Consequently, it is imperative to implement suitable measures to enhance the state of mangrove forests and safeguard their biodiversity. The vulnerability analysis shows that the highly vulnerable, moderately vulnerable, and low vulnerable areas cover 35.66%, 26.86%, and 19.42%, respectively. Conclusion: The vulnerability maps generated in this research could serve as a valuable resource for coastal planners seeking to ensure the sustainable stewardship of these coastal mangrove forests. These results offer a detailed understanding of coastal mangrove LULC patterns and vulnerability status, which will be useful for policymakers and resource managers to urgently incorporate into coastal land use and environmental management practices.

Utilizing GIS, remote sensing, and AHP-multi-criteria decision analysis for optimal landfill site selection in Kenitra Province, Morocco: a step towards sustainable development goals

The aim of this study is to identify suitable landfill sites in Kenitra Province, Morocco, using ArcGIS 10.8, Remote Sensing, and the Analytic Hierarchy Process (AHP) to enhance site selection and improve environmental and public health outcomes. Additionally, the study seeks to enhance decision-making through multicriteria analysis, align landfill site selection with Sustainable Development Goals (SDGs) to promote long-term sustainability, and provide a model that can be replicated in other Moroccan regions or countries facing similar waste management challenges. Ten criteria were considered in selecting new landfill sites, including distance from surface water, soil permeability, land use land cover (LULC), topographic wetness index, distance from the shoreline, slope, aspect, proximity to urban areas, transportation networks, and villages. Maps for each criterion were generated, and pairwise comparisons were used to assign weights to these factors. A Weighted Overlay Analysis (WOA) was then applied to create a landfill suitability map, classifying areas into high, moderate, low, and unsuitable categories. The results reveal a limited availability of highly suitable landfill areas within the province. To address this, the study proposes two strategically located waste management sites, A and B, to mitigate land constraints. Establishing these sites as centralized landfills for multiple municipalities aligns with Sustainable Development Goals (SDGs) 3, 6, 14, 15, and 17, promoting public health, preventing water pollution, protecting ecosystems, and fostering partnerships for sustainable waste management. This comprehensive approach emphasizes the need for collaboration in achieving long-term sustainability and environmental conservation.

Geomodeling soil quality using remote sensing and land use metrics in Abeokuta, Nigeria

Remote sensing techniques offer potent and accurate geospatial modeling of Soil quality (SQ) for sustainable food security and development. The study aims to model SQ and landscape changes in Federal University of Agriculture Abeokuta, Nigeria using Landsat images of 2014 and 2024. We applied support vector machine and land use cover metrics like Number patches (NP), Largest-path (LP), and Effective—MESH combined with spectra Bare-soil (BSI), Normalized Difference Vegetation (NDVI), and Soil Adjusted Vegetation (SAVI) Indexes to assess land use and land cover, focusing on built-up, vegetation and wetlands of the area. A composite of 70 soil samples (0 to 30 cm) were analyzed for their physical, chemical, and biological soil properties. The additive, weighted-additive, and proposed models were used to estimate SQ of the studied area. Results showed that NP, LP, and MESH revealed substantial discontinuity and landscape fragmentation especially in the built-up areas. At the same time, BSI, NDVI, and SAVI signify shifts in wetland areas and significant decrease in vegetation cover of the area respectively. The models ranked SQ of the area as high (199 ha), moderate (1333 ha), and low (126 ha) hectares, respectively. The findings suggest valuable ways for SQ improvement and could contribute to efforts on achieving food security, well-being and meet sustainable development goal 2.

Chilean Mediterranean forest on the verge of collapse? Evidence from a comprehensive risk analysis.

World forests are experiencing significant modification due to the confluence of climate change and deforestation, with Mediterranean forests facing particularly acute threats. The Chilean Sclerophyllous Mediterranean Forest is considered a world biodiversity hotspot, a restricted ecosystem type that is highly affected by global change drivers. Despite the high ecological and environmental importance of this ecosystem, an integrated assessment of its risk derived from climate and land-use change is lacking. This study estimates the level of risk of all sclerophyll forest stands in Chile, by assessing their exposure to several factors linked with climate change, land cover change, wildfires, change in the vegetation functional properties, and habitat fragmentation. We also generated a spatially explicit estimate of the main factors underlying the risk of each sclerophyllous forest stand. We constructed a multifactorial risk index based on the different analyzed variables, exploring the main drivers associated with the risk of each forest stand using principal components analysis with agglomerative hierarchical clustering. We found that 39.8% of sclerophyll forest stands reached a high or very high-risk index, with the highest levels concentrated between 32°S and 34°S. The vigor and net primary production of sclerophyll forest stands decreased in 90.9% and 86.6%, respectively. We identified three groups of forest stands: A) northern group (29.9°S to 33.5°S), which has experienced high decrease of net primary production and vegetation vigor; B) central group (32°S to 35°S), also affected by degradation and replacement by croplands; and C) southern group (34°S to 39°S), affected by drought and exotic forest plantations. The results of this study provided clarity on the current state of the sclerophyll forest, allowing for the identification of spatial differences in the risk and their underlying factors. Our results and data can be useful for informing policy and supporting sustainable management of government agencies and practitioners.

Unveiling hydrological responses in Madagascar’s major river basins: addressing data scarcity and land-use change impacts

Insufficient observed hydro-climatological data and studies on land use and land cover change (LULCC) hinder the understanding of environmental changes at the Major River Basins in Madagascar (MRBM). This paper assesses the impact of LULCC on water resources across the MRBM. The ability of the Soil and Water Assessment Tool (SWAT) model to simulate streamflow is evaluated to obtain long-term and consistent hydrological information across the country. Moreover, this study emphasized the process of single-gauge calibration and dealing with uncertainty on a large river basin scale. Despite the lack of data, the model efficiently simulated streamflow, achieving high predictive accuracy in 8 basins out of 12 during calibration and validation. By 2050, a major change in LULC is predicted across the MRBM, which is expected to significantly alter the water balance. Major causes of LUCC and measures to address these changes are also investigated. We recommend basin-specific water management plans.

Design-based mapping of errors in remote sensing-based land use/land cover maps

Abstract For the first time, spatially explicit representation of classification errors of land use/land cover (LULC) maps is approached from a design-based perspective. Since LULC maps are typically derived from non-probabilistic training samples, these maps, like the true LULC map, are fixed in a design-based scenario so that the error maps achieved by comparing the satellite-based and true maps are fixed. Based on a probabilistic sample of locations where the true or “reference” class is obtained (i.e., the “reference” class is considered the best representation of the true class), errors can be assessed at these sample locations by comparing the map classes to the reference classes. Then, the presence or absence of errors is interpolated across the entire survey area using the nearest neighbour technique. Under very common sampling schemes used to collect reference sample data, the interpolated error maps are design consistent. A simulation study confirms the design consistency of the interpolated error maps, which converge to the true error map as the reference sample size increases. The U.S. land cover map from the LCMAP program and the Italian forest/non forest map serve as case studies.

Land use and cover changes and sand fly (Diptera: Psychodidae) assemblages in an emerging focus of leishmaniasis.

Land use and cover changes lead to fragmentation of the natural habitats of sand flies and modify the epidemiological profile of leishmaniasis. This process contributes to the infestation of adjacent rural settlements by vector sand fly species with different degrees of adaptation, promoting leishmaniasis outbreaks. This study aimed to assess land use and cover changes over a 12-year period and investigate the diversity and abundance of sand fly assemblages in the rural area of Codó, Maranhão State, Brazil. Temporal analysis of land use and cover changes was conducted using Sentinel-2 satellite imagery treated in QGIS software (free version 3.10) and classified using Orfeo Toolbox. Sand flies were sampled in alternate months between August 2022 and June 2023 using Centers for Disease Control and Prevention (CDC) light traps and white and black Shannon traps installed in peridomestic and extradomestic environments. Map images showed that the predominant land covers in 2012 and 2014 were dense and sparse vegetation, with few buildings. In 2021 and 2023, areas of sparse and dense vegetation were fragmented as new settlements were established. The entomological survey resulted in the capture of 3375 sand flies (CDC = 856, white Shannon = 650, black Shannon = 1969) belonging to 20 species. The most abundant species were Psychodopygus wellcomei Fraiha, Shaw & Lainson, 1971 (78.19%), followed by Nyssomyia whitmani (Antunes & Coutinho, 1939) (7.53%). Additionally, Ny. whitmani was the most abundant species (84.97%) in peridomestic environments, whereas Ps. wellcomei was the most abundant species (96.51%) in extradomestic environments. The sand fly assemblage was highly diverse, with a high abundance of competent vectors of Leishmania spp. These findings can promote community participation in surveillance and control efforts to prevent leishmaniasis cases.

Enhancing prediction and inference of daily in-stream nutrient and sediment concentrations using an extreme gradient boosting based water quality estimation tool - XGBest.

Estimating constituent loads in streams and rivers is a crucial but challenging task due to low-frequency sampling in most watersheds. While predictive modeling can augment sparsely sampled water quality data, it can be challenging due to the complex and multifaceted interactions between several sub-watershed eco-hydrological processes. Traditional water quality prediction models, typically calibrated for individual sites, struggle to fully capture these interactions. This study introduces XGBest, a machine learning-based tool, that integrates hydrological data, land cover, and physical watershed attributes at a regional scale to predict daily concentrations of Total Nitrogen (TN), Total Phosphorus (TP), and Total Suspended Solids (TSS). XGBest leverages 29 environmental variables, including daily and antecedent discharge, temporal features, and landscape characteristics, to comprehensively evaluate water quality dynamics across a large hydrologic region. To explore the robustness of the developed tool, XGBest was validated using observed water quality data in three different hydrologic regions in the eastern United States, encompassing 499 water quality monitoring sites characterized by diverse hydro-climatic conditions and watershed attributes. This study also employed the legacy United States Geological Survey (USGS) tools - LOADEST and WRTDS as benchmarks to evaluate the performance of XGBest in these regions. The results demonstrated that XGBest outperformed LOADEST and WRTDS in predictive accuracy and revealed critical insights into the spatial and temporal variability of nutrient and sediment loads. In addition, SHapley Additive exPlanations (SHAP) values highlighted the importance of integrating static and dynamic watershed attributes, such as land cover, antecedent discharge, and seasonality, in capturing the complex concentration-discharge (C-Q) relationships. This study positions XGBest as a robust and scalable water quality prediction tool that bridges the gap between hydrology and broader environmental management. By combining multiple environmental factors into a unified predictive framework, XGBest enhances our understanding of water quality and supports more effective environmental monitoring and management strategies.

Predictive modeling of land cover changes in round-1 smart cities of India using cellular automata and GIS

The present study dealt with the Land Use Land Cover (LULC) and built-up dynamics for four round-1 smart cities within a period of 20 years (2001–2021) using satellite-based remote sensing and Geographic Information System (GIS) techniques. The study also presented predictive aspects of built-up dynamics by the year 2031. Various layers contribute to urban growth viz. slope, population density, distance to Central Business District (CBD), major roads, and drainage were analyzed and prepared. The Cellular Automata (CA) model was applied using the previous years’ layers and the thematic ones to predict the spatial extent of the year 2031. Thresholds for all thematic layers based on spatial proximity were selected for various classes of these factors. Built-up change rate for all the four round-1 smart cities viz. Ahmedabad (the year 2001 ~ 183.79 km2, the year 2021 ~ 252.3 km2), Chennai (the year 2001 ~ 102.45 km2, the year 2021 ~ 137.07 km2), Jaipur (the year 2001 ~ 132.73 km2, the year 2021 ~ 221.71 km2) and Surat (the year 2001 ~ 97.73 km2, the year 2021 ~ 158.79 km2) has shown a dynamic growth from 2001–2021. The study also revealed transition states of land cover classes between 2021 and 2031. The comparison between actual and simulated changes for the year 2021 showed the model’s accuracy assessment. The estimates also revealed a continuous increment in the impervious state of the urban surface due to urban expansion. Thus, satellite image-based LULC classification, built-up mapping, and model-based prediction could be utilized for planning infrastructure development.

GIS-based Classification of Habitat, Land Use, and Land Cover in Several Creeks within Sindh Province's Indus Delta

GIS-based Classification of Habitat, Land Use, and Land Cover in Several Creeks within Sindh Province's Indus Delta

Edaphic Diversity, Polychemical Soil Status of the Prinevskaya Lowland and Prospects for Soils Use

There will be a significant increase in anthropogenic load on the soils of the Prinevskaya lowland in the nearest decade due to the fact that a significant territory is occupied by St. Petersburg. The main objective is a study of the sanitary-hygienic state and soil diversity of the Prinevskaya lowland in case of a high degree of agricultural soil development there and the significant role of the lithological factor. Soils were studied at the following land use and land cover: agricultural and fallow soils of agrolandscapes; forest soils; and soils of industrial areas. Studies were carried out using morphological descriptions and analyses of chemical, physical, and biological properties. The most vulnerable land use are forest and agricultural and fallow zones, where active accumulation of priority toxicants of anthropogenic origin can occur. Geochemical peculiarities of studied soils are deficit of Mn, Cu, Mo, and Zn in soil-forming rock materials and accumulation of strontium and lead in arable horizons. The soils examined show minimal contamination with trace elements, as verified by a range of individual and combined ecotoxicological indicators. Urban development planning, particularly in St. Petersburg, should prioritize the preservation of biodiversity and soil resources.

Spatiotemporal analysis of urban expansion and its impact on farmlands in the central Ethiopia metropolitan area

Urban growth in sub-Saharan Africa presents significant challenges to sustainable development, food security, and environmental conservation. The rapid urban expansion and impact on agricultural land reduction in central Ethiopian metropolitan areas (Addis Ababa and Sheger city) exemplify these issues while simultaneously offering opportunities for sustainable development. This study aims to quantify and characterize the spatiotemporal dynamics of urban expansion in Addis Ababa and the surrounding Sheger city, explicitly focusing on understanding the impact of urban expansion on farmlands. The supervised random forest (RF) classification in the Google Earth Engine platform was used to prepare land use and land cover (LULC) for 1990, 2000, 2010, and 2023. The study employed an analytical framework incorporating multiple methodologies: intensity analysis at interval, categorical, and transitional levels to quantify urban growth trajectories; gradient direction and distance analyses to examine spatial expansion patterns; and Land Expansion Index (LEI) and Landscape Dynamic Typology (LDT) metrics to characterize the urban morphology and spatial dynamics of the study area. The results revealed that edge expansion is the predominant mode of urban development, primarily affecting farmlands in the eastern section. Built-up areas quadrupled between 1990 and 2023, whereas arable land declined. Intensity analysis revealed significant changes, particularly affecting farmlands. Our LDT analysis showed reduction in stable areas and increased in LULC changes from 1990 to 2023. The findings highlight the need for revised urban development strategies in Ethiopia to focus on compact and efficient growth while safeguarding agricultural lands, aligning with SDGs 2, 11, and 15 to promote balanced development that ensures urban and agricultural sustainability.

The role of host plants, land cover and bioclimate in predicting the invasiveness of Aromia bungii on a global scale

Aromia bungii is an invasive Cerambycidae of major concern at the global scale because of the damage caused to Rosaceae. Given the major phytosanitary relevance of A. bungii, predicting its spread in invaded areas and identifying possible new suitable regions worldwide remains a key action to develop appropriate management practices and optimise monitoring and early detection campaigns. To improve the predictive power of the modelling framework, a habitat suitability model (HSM), which includes host plants, was combined with a bioclimatic suitability model (BSM), both of which were calibrated on native occurrences. The range of A. bungii was substantially limited by the bioclimate, while habitat conditions acted as limiting factors in the species’ distribution. Host plants were the most important variable that positively influenced habitat suitability. Bioclimatic suitability improved as rainfall in the warmest quarter and average temperatures in the wettest quarter increased and as isothermality decreased. According to the combination of HSM and BSM, Japan is the most suitable area outside the native range of the species. In Europe, despite its high habitat suitability, it is difficult to expect a species to expand its range except through a substantial change in its bioclimatic niche.