Land Use And Land Cover Research Articles

Comparison of various models for multi-scenario simulation of land use/land cover to predict ecosystem service value: A case study of Harbin-Changchun Urban Agglomeration, China

Understanding the intricate changes in land use and land cover (LULC) transformations, as well as accurately quantifying the ecosystem services value (ESV), holds paramount importance in achieving sustainable development goals. However, previous studies have notably neglected conducting empirical evaluations across various LULC prediction models under identical conditions within the same geographical region. Additionally, the majority of relevant studies primarily concentrate on local scales. In this study, we used CA-Markov, Future Land Use Simulation (FLUS) and Patch-generating Land Use Simulation (PLUS) models to simulate the dynamics of LULC, respectively. Subsequently, we successfully projected the future LULC patterns and corresponding ESV within the Harbin-Changchun Urban Agglomeration (HCUA), China. During the period spanning from 2000 to 2020, a persistent reduction of 2067 km2 in farmland was witnessed within the HCUA, while an accompanying expansion of 4081 km2 in built-up land occurred concurrently. The ESV in HCUA experienced a fluctuating trend. There was an initial decline of 7.443 × 109 yuan during the first decade, which was subsequently followed by an increase of 4.615 × 109 yuan during the latter decade. PLUS, FLUS, and CA-Markov models can all simulate the LULC of HCUA, but in decreasing order of accuracy and ease of use, with OA values of 0.8987, 0.8944 and 0.8651, and Kappa values of 0.8217, 0.8150 and 0.7860, respectively. The PLUS model was chosen to predict the future LULC. Then four development scenarios were set. Our optimization results indicate that it is advisable to restrict the area of built-up land within 17000 km2 in 2030. Moreover, under the economic and ecological balance (EEB) scenario, both ESV and economic benefits are projected to increase compared to the business as usual (BAU) scenario. This study offers valuable insights and serves as a significant reference for future research endeavors focused on optimizing land use.

Impacts of land use and land cover change on the landscape pattern and ecosystem services in the Poyang Lake Basin, China

ContextDecades of intensifying human activities have led to drastic changes in land use and land cover (LULC) in the Poyang Lake Basin (PLB), resulting in significant changes in landscape pattern and ecosystem service value (ESV), thereby affecting regional sustainability.ObjectivesWe focused on understanding the impact of LULC changes on the landscape pattern and ESV of the PLB and used the Patch-generating Land Use Simulation model (PLUS) to predict LULC changes in 2050.MethodsWe evaluated landscape patterns using landscape metrics and calculated ESV using the ecosystem service equivalent factor method. The Pearson correlation coefficient was used to analyze the correlation between landscape patterns and ESV from 1990 to 2020. Then, we combined the PLUS model and the ecosystem service equivalent factor method to calculate the ESV under multiple scenarios from 2020 to 2050.ResultsFrom 1990 to 2020, the LULC of the PLB changed to varying degrees. The PLB has undergone a rapid process of landscape fragmentation, and the total ESV of the PLB has decreased. The total ESV was positively correlated with the CONTAG index and negatively correlated with the SHDI index. Between 2020 and 2050, the ESV of the PLB is projected to decrease under the NDS (nature development scenario) and EDS (economic development scenario) and increase under the EPS (ecological protection scenario).ConclusionsESV responded to changes in landscape pattern. We recommend that the PLB should increase patch connectivity. Additionally, future development in the PLB should prioritize ecological protection to prevent further declines in ESV.

Anthropogenic threats in the border zone of the Amazon Forest: a case study of land use and land cover in RESEX Ciriaco, Maranhão, Brazil

The state of Maranhão is located in northeastern Brazil and covers three biomes, the Amazon Forest, Cerrado (savannah), and Caatinga (semi-arid), and an endemic ecosystem known as the Mata de Cocais (coconut forest). This study aimed to identify the land use and land cover (LU/LC) in RESEX Ciriaco, located in the border zone of the Amazon Forest of Maranhão, where occurs the Mata de Cocais. The database used in this study was obtained from the Brazilian Annual Land Use and Land Cover Mapping Project (Mapbiomas). The LU/LC in RESEX Ciriaco was carried out across 8,106.7 ha and a buffer zone with a 3 km radius was adopted. A map representing the class changes in the study area was created for the years 1985, 1992, 1999, 2006, 2013, 2020, and 2021. Despite the findings indicate a troubling particularly driven by the presence of pasturelands, the results demonstrate that RESEX Ciriaco has fulfilled its function of guaranteeing the sustainable use of natural resources without deforestation. However, the advancement of livestock and agriculture around RESEX Ciriaco poses a risk to the ecosystem. Creating a buffer zone for RESEX Ciriaco is vital for stopping the advancement of anthropogenic activities in the conservation unit.

Land Use and Land Cover Change in East Java Indonesia from 1972 to 2021: Learning from Landsat

This study analyses land use and land cover (LULC) changes during the last five decades (1970–2021) in East Java Province, Indonesia. The changes are analysed by comparing four maps interpreted from Landsat images (MSS 1972, TM 1997, OLI 2013, and OLI 2021). The main research procedures consist of (1) data collection, (2) field survey, (3) image classification, and (4) LULC change interpretation. The classification uses the maximum likelihood algorithm, achieving an overall kappa accuracy of > 75%. The classification produces eight classes, i.e., built-up land (BU), heterogeneous agricultural land (HAL), bare soil (BS), paddy fields (PF), open water (OW), vegetation (VG), shrubland (SH), and wetlands (WL). The analysis shows a significant shift in land use from 1972 to 2021, with HAL declining by 46%, SH by 91%, and BS by 88%. In contrast, PF has increased by 105%, VG (forest and plantation) by 64%, and built-up areas by a remarkable 366%. These changes show a significant shift from dryland agriculture, shrublands, and barren lands to irrigated regions, vegetated areas, and urban growth. Furthermore, there are increases in water bodies due to the construction of several reservoirs to support water availability. In coastal regions, the development of inland aquaculture leads to a proliferation of wetlands (salt evaporation ponds, fish ponds, and rice-fish integrated farming).

A Comparative Analysis Machine Learning Techniques for LULC Classification Using Landsat-8 Satellite Imagery

This study compares various categorization methods to assign land use and land cover (LULC) classes. Using Geographic Information Systems (GIS) and Remote Sensing (RS) to leverage the dynamic and complex area of LULC, this study examines the potential of different machine learning classification methods. Precise differentiation and classification of various land cover categories, such as green vegetation, urban areas, water bodies, dark green vegetation, and bare terrain, are made possible by the great spatial and spectral resolution of Landsat imagery. For efficient land management and planning, the integration of Landsat data with GIS and RS approaches provides insightful information about the distribution and temporal changes in LULC. This study uses four classifiers to explore the principles of supervised machine learning techniques and identify their drawbacks and advantages. Testing results show that the Support Vector Machine with four kernel linear-99.17%, radial basis (RBF)-99.11%, Sigmoid-99.11% and Polynomial-99.11% is a reliable option for LULC classification, outperforming than other classifiers in terms of accuracy, including the Minimum Distance Classifier (MD-93.47%), Maximum Likelihood Classifier (MLC- 98.98%), and Mahalanobis Distance Classifier (MH-97.83%). Among the tested classifiers, SVM with four kernels notably shows the highest accuracy. With their essential insights for well-informed decision-making towards sustainable development and resource utilization, our findings add to a thorough understanding of LULC dynamics. For accurate mapping and long-term monitoring of deviations in land cover (LC), the study emphasizes the value of using front-line classification systems in remote sensing applications.

Land cover and drought risk assessment in Türkiye's mountain regions using neutrosophic decision support system.

Earth observation (EO) provides dynamic scientific methods for tracking and defining ecological parameters in mountainous regions. Open-source platforms are frequently utilized in this context to efficiently collect and evaluate spatial data. In this study, we used Collect Earth (CE), an open-source land monitoring platform, to reveal and assess land cover, land cover change, and relevant ecological parameters such as drought risk. Mountain ecosystems were subject to an evaluation for the first time by combining remote sensing with a hybridization of Decision-Making Trial and Evaluation Laboratory (DEMATEL), analytic hierarchy process (AHP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for neutrosophic sets in risk assessment problems of several connected criteria. The high and dispersed high alpine environment of Türkiye accommodates land with relatively less human influence, making it suitable to observe climate change impacts. In the framework of the study, we evaluated more than two decades (2000-2022) of land use and land cover (LULC) changes in the mountain regions of the country. Using nine identified ecological parameters, we also evaluated drought risk. The parameters included were the LULC classes and their change, elevation, slope, aspect, precipitation, temperature, normalized difference vegetation index (NDVI), water deficit, and evapotranspiration (ET). The risk map we produced revealed a high to very high drought risk for almost throughout the Türkiye's mountainous areas. We concluded that integrating geospatial techniques with hybridization is promising for mapping drought risk, helping policymakers prepare effective drought mitigation measures to reasonably adapt to climate change impacts.

Flash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios

Flash floods are highly destructive, and their frequency and intensity are expected to escalate due to climatic changes. This study thus investigated flash flood susceptibility (FFS) by applying machine learning algorithms and climate projection to predict both present and future hazard scenarios in the southeastern hilly regions of Bangladesh. To predict FFS, we evaluated twelve flood-influencing variables: elevation (EL), slope (SL), aspect (AS), drainage density (DD), distance to stream (DS), topography roughness index (TRI), stream power index (SPI), topographic wetness index (TWI), soil permeability (SP), precipitation (PR), land use and land cover (LULC) and normalized difference vegetation index (NDVI). Earth observation data, field surveys, and past flood records were used to create a detailed flood inventory. Among the machine learning models tested, the random forest (RF) algorithm outperformed others, including support vector machine (SVC), logistic regression (LR), and extreme gradient boosting (XGBoost), and was subsequently used for flood susceptibility mapping based on future precipitation projections under two Sixth Coupled model intercomparison project (CMIP6) climate change scenarios: SSP1-2.6 and SSP5-8.5. Our findings indicated that the areas at high to very high risk of flooding are projected to increase significantly under both the SSP1-2.6 and SSP5-8.5 scenarios. Initially, around 38 % of the studied region had high to very high flood susceptibility, but this is expected to rise to 40–42 % over the projected time periods. These spatial delineations of flood-prone areas can provide guidance for developing effective mitigation and adaptation strategies to address the adverse impacts of flash flooding in the hilly river basins of Bangladesh.

Assessing the relationship between river water pollution and the LULC composition of a basin in the Isthmus of Tehuantepec in Oaxaca, Mexico

Water pollution originating from land use and land cover (LULC) can disrupt river ecosystems, posing a threat to public health, safety, and socioeconomic sustainability. Although the interactions between terrestrial and aquatic systems have been investigated for decades, the scale at which land use practices, whether in the entire basin or separately in parts, significantly impact water quality still needs to be determined. In this research, we used multitemporal data (field measurements, Sentinel 2 images, and elevation data) to investigate how the LULC composition in the catchment area (CA) of each water pollution measurement station located in the river course of the Los Perros Basin affects water pollution indicators (WPIs). We examined whether the CAs form a sequential runoff aggregation system for certain pollutants from the highest to the lowest part of the basin. Our research applied statistical (correlation, time series analysis, and canonical correspondence analysis) and geo-visual analyses to identify relationships at the CA level between satellite-based LULC composition and WPI concentrations. We observed that pollutants such as nitrogen, phosphorus, coliforms, and water temperature form a sequential runoff aggregation system from the highest to the lowest part of the basin. We concluded that the observed decrease in natural cover and increase in built-up and agricultural cover in the upper CAs of the study basin between the study period (2016 to 2020) are related to elevated WPI values for suspended solids and coliforms, which exceeded the allowed limits on all CAs and measured dates.

Prediction and Simulation for Land Use and Land Cover Change of Paddy Field Influence by Salinization in Coastal Demak Regency

The extent of coastal rice paddy agricultural land is vulnerable to land use and land cover (LULC) changes to non-agricultural uses due to land degradation, one of which is caused by salinity. This study aims to detect and project LULC changes up to 2031, particularly in coastal rice paddy areas affected by salinity, by comparing LULC in 2017, 2019, and 2021. Sentinel-2 Imagery is used for LULC classification, with recordings selected during the generative phase of rice growth to obtain the most optimal rice paddy area. There are six LULC classifications: water, wetland, low-medium-high vegetation cover, and built-up area. To understand the impact of salinity on crops, several vegetation indices (VIs) such as NDVI, SAVI, EVI, and ARVI are used. The LULC changes classified according to VIs are compared with the MOLUSCE plugin based on artificial neural networkmultilayer perceptron (ANN-MLP) and Cellular Automata (CA). The comparison of VIs results shows that NDVI is better at describing LULC changes due to the influence of salinity, with a kappa value of 0.63 and a Correctness of 72.565. The LULC projection using CA in all VIs indicates that wetland areas are more likely to convert into water bodies, suggesting that high salinity land tends to be unproductive for rice paddies, making it prone to conversion.

Fusion of spectral and topographic features for land use mapping using a machine learning framework for a regional scale application.

This study investigated the dynamics of land use and land cover (LULC) modelling, mapping, and assessment in the Kegalle District ofSri Lanka, where policy decision-making is crucial in agricultural development where LULC temporal datasets are not readily available.Employing remotely sensed datasets and machine learning algorithms, the work presented here aims to compare the accuracy of three classification approaches in mapping LULC categories across the time in the study area primarily using the Google Earth Engine (GEE). Three classifiers namely random forest (RF), support vector machines (SVM), and classification and regression trees (CART) were used in LULC modelling, mapping, and change analysis. Different combinations of input features were investigated to improve classification performance. Developed models were optimised using the grid search cross-validation (CV) hyperparameter optimisation approach. It was revealed that the RF classifier constantly outstrips SVM and CART in terms of accuracy measures, highlighting its reliability in classifying the LULC. Land cover changes were examined for two periods, from 2001 to 2013 and 2013 to 2022, implying major alterations such as the conversion of rubber and coconut areas to built-up areas and barren lands. For suitable classification with higher accuracy, the study suggests utilising high spatial resolution satellite data, advanced feature selection approaches, and a combination of several spatial and spatial-temporal data sources. The study demonstrated practical applications of derived temporal LULC datasets for land management practices in agricultural development activities in developing nations.

Remote Sensing Mapping and Analysis of Spatiotemporal Patterns of Land Use and Cover Change in the Helong Region of the Loess Plateau Region (1986–2020)

Land use and cover change (LUCC) is directly linked to the sustainability of ecosystems and the long-term well-being of human society. The Helong Region in the Loess Plateau has become one of the areas most severely affected by soil and water erosion in China due to its unique geographical location and ecological environment. The long-term construction of terraces and orchards is one of the important measures for this region to combat soil erosion. Despite the important role that terraces and orchards play in this region, current studies on their extraction and understanding remain limited. For this reason, this study designed a land use classification system, including terraces and orchards, to reveal the patterns of LUCC and the effectiveness of ecological restoration projects in the area. Based on this system, this study utilized the Random Forest classification algorithm to create an annual land use and cover (LUC) dataset for the Helong Region that covers eight periods from 1986 to 2020, with a spatial resolution of 30 m. The validation results showed that the maps achieved an average overall accuracy of 87.54% and an average Kappa coefficient of 76.94%. This demonstrates the feasibility of the proposed design and land coverage mapping method in the study area. This study found that, from 1986 to 2020, there was a continuous increase in forest and grassland areas, a significant reduction in cropland and bare land areas, and a notable rise in impervious surface areas. We emphasized that the continuous growth of terraces and orchards was an important LUCC trend in the region. This growth was primarily attributed to the conversion of grasslands, croplands, and forests. This transformation not only reduced soil erosion but also enhanced economic efficiency. The products and insights provided in this study help us better understand the complexities of ecological recovery and land management.

Comparing Sentinel-2 and Landsat 9 for land use and land cover mapping assessment in the north of Congo Republic: a case study in Sangha region

ABSTRACT A study of land use and land cover (LULC) mapping using satellite data was conducted in the Congo Basin rainforest in the Sangha area of the northern Congo Republic, which is crucial for sustainable land management. However, the heterogeneous nature of LULC features makes data classification difficult in this area because persistent cloud cover is challenging for observing the land surface and tropical humid conditions characterizing the study area. This study compares and evaluates the potential of two of the most widely used and popular satellite image sources, the recently launched Landsat 9 and Sentinel-2, to generate land-use land cover (LULC) maps in the Sangha area using pixel-based supervised machine learning algorithms, Random Forest (RF), for classification. All procedures were performed using ArcGIS Pro. The potential to improve Landsat 9 performance was tested using the nearest resampling technique, and different bands were resampled from 30 × 30 to 10 × 10 m. Owing to the heterogeneous characteristics of the Sangha study area, six classes were defined: dense forest, open forest, wetland forest, water bodies, urban areas, and bare soils. To classify and compare Sentinel-2 and Landsat 9, the shortwave infrared (SWIR), Near Infrared (NIR), and Red, Green, Blue (RGB) bands were used. These two satellite images were compared to test the quality of their results, particularly for assessing the accuracy of the LULC classifications and identifying which dataset had the highest accuracy. Results show that overall accuracy was 93.80% for Sentinel-2 while it was 91.60% for Landsat 9, similarly, the Kappa coefficient was calculated 0.89 and 0.85 for Sentinel-2 and Landsat 9, respectively. Therefore, Sentinel-2 exhibits significant classification ability compared to the nearest resample technique Landsat 9, despite the improved resolution of the latter, which offers a scientific basis for choosing the appropriate satellite imagery to create accurate LULC maps.

Mapping anthropogenic impacts on natural resources in the northeastern Sahara region: A case study on the past 26 years over the Ziban region (Algeria)

The objective of this research was to map and analyze anthropogenic impacts on natural resources in the Ziban region of Algeria using remote sensing imagery from 1995 to 2021. Medium-resolution satellite images from Landsat TM and OLI were utilized, with image processing techniques such as colorful compositions and supervised classification to map land cover. Seven land use and land cover (LULC) classes were identified, distinguishing human-derived categories (palm, agriculture, urban, and greenhouses) from natural surfaces (nebka, natural vegetation, and bare soil). The results indicated significant increases in human-driven elements like palm, urban areas, agriculture, greenhouses, and nebka, and notable decreases in natural components like vegetation and bare soil. Specifically, the rate of change (Tc) showed increases of 0.9% for palm, 1.14% for urban, 1.55% for agriculture, 1.91% for greenhouses, and 9.37% for nebka, and decreases of -3.68% for vegetation and -11.19% for bare soil. These findings highlight the impact of agricultural policies, population growth, and natural and human-induced conditions on natural resources. Palm areas remained largely unchanged due to state policies, whereas agriculture saw significant conversions to bare soil and palm. Vegetation declined substantially due to adverse climatic conditions and agricultural expansion. Nebka and urban areas had moderate to high conversion rates, and bare soil saw notable changes due to sand movement and urban/agricultural development. The study highlights the role of remote sensing and land cover analysis in managing natural resources sustainably, considering factors like agricultural policies and population growth. It emphasizes developing data-driven strategies for effective land use and management.

Government Policy Influence on Land Use and Land Cover Changes: A 30-Year Analysis

This study investigated land use and land cover (LULC) patterns and changes in the Bandon Bay area of Thailand from 1991 to 2021 using satellite imagery, the first comprehensive effort to assess historical LULC trends over the past 30 years and forecast future LULC scenarios using the CA-Markov model for 2031, 2041, and 2051. Results showed the predominant LULC during 1991-2001 was the abandoned paddy fields, and during 2006-2021 was the oil palm plantations. During 1991-2001, the abandoned paddy fields changed significantly, with a net gain of 59.28 km2. From 2001-2011 and 2011-2021, the oil palm plantations experienced the most crucial change, with a net gain of 292.94 km2 and 70.06 km2. In 2031, 2041, and 2051, the LULC was predicted to be oil palms, shrimp farms, mangroves, and urban and built-up lands. The LULC changes were consistent with the government policies implemented and indicated government policy as a driving force in LULC dynamics on Bandon Bay area forestry, aquaculture, and agriculture, particularly on oil palm cultivation. Government management and regulation on land use is crucial for reducing the expansion of agricultural areas, especially oil palm plantations and aquaculture areas, to mitigate negative impacts on the Bandon Bay ecosystem. Doi: 10.28991/ESJ-2024-08-05-06 Full Text: PDF

A multi-objective optimization framework for regional land-use allocation: Fully utilizing terrestrial vegetation to mitigate carbon emissions

Human-induced changes in land use and land cover (LULC) considerably impact the global carbon cycle. Rational LULC optimization with full utilization of terrestrial vegetation facilitates reconciling carbon reduction and socio-economic development. To this end, this study develops a multi-objective framework for low-carbon LULC spatial optimization that incorporates carbon suitability, historical suitability, and socio-economic needs. The framework is applied to Hubei Province, China. The key findings include: (1) Considering spatial variations in vegetation carbon sinks could lead to remarkable carbon reductions of 2.75 × 106 t C by 2030 and 7.68 × 106 t C by 2060. It would also lower carbon emissions per unit of gross domestic product (GDP) and enhance carbon sequestration per unit of vegetation by 2030, signifying enhanced carbon reduction efficiency. (2) Integrating carbon sequestration potential and historical suitability can considerably minimize emissions while sustaining socio-economic growth. Our method, compared with strategies based solely on historical development laws, can reduce carbon emissions by approximately 1.3–1.6 × 106 t C·a−1. (3) We also propose targeted low-carbon development suggestions for different LULCs. This study provides optimal spatial LULC allocations and strategic advice for regional LULC planning aimed at low-carbon development, demonstrating the feasibility of reconciling the tension between carbon reduction and socio-economic growth at the regional scale.

Two decades of land cover change and anthropogenic pressure around Bontioli Nature Reserve in Burkina Faso

Protected areas (PAs) are critical for ecosystem maintenance and providing services that benefit both wildlife and people. Nevertheless, climate change and anthropogenic pressures are posing an increasing challenge. Surrounded by high human population densities, there is still a paucity of information on how the land cover in Burkina Faso's PAs is changing, and what kinds of human activities are the main drivers. In this study, we examined the change in land use and land cover (LULC) in the Bontioli Nature Reserve (NR), one of Burkina Faso's most important protected areas, and assessed anthropogenic pressure within and around. Landsat imagery (ETM+ and OLI-TIRS) is used to categorise and estimate the change in LULC in 2000, 2010, and 2022 with the Random Forest algorithm on the Google Earth Engine platform. Regression analysis was applied to examine the relationship between the LULC categories and population increase. We found significant changes and correlations in LULC trends and population growth over time. From 2000 to 2022, wooded savanna, tree savanna, and shrub savanna decreased by 20.8 %, 6.8 %, and 4.5 %, respectively, while cropland increased by 26.3 %, along with grass savanna by 5 %. Population growth correlated with increased agriculture and decreased vegetative area with R2 of 0.903 and 0.793, respectively. Efforts should be made to create harmony between humans and nature through various approaches such as nature-based solutions to enable efforts for the reserve sustainable management (SDG15).

Human-induced landscape modification in the in the last two centuries in the Po delta plain (northern Italy)

Historical maps with high degree of accuracy permit quantitative reconstructions of past land use and land cover (LULC), crucial to assess the impact of human activities on landscape evolution. After georeferencing in a modern reference system, the Carta del Ferrarese commissioned by Napoleon Bonaparte and completed in 1814, has been used to quantify the changes in LULC, occurred in the last two centuries in the Po delta plain. The map depicts a palaeo-landscape dominated by wetlands (49% of the study area) and agricultural areas (41%), whereas forests appear already largely depleted (5%). The Piantata Padana, a traditional agroforestry system with live trees used to support grapevines, is dominant (62% of the agricultural areas). The comparison with the 2014 LULC map highlights a dramatic reduction (85%) of wetland areas and the replacement of the Piantata Padana with bare arable lands, with the consequent removal of 4-40 million trees. Soils of areas formerly occupied by wetlands show high organic-carbon content, highlighting the potential of humid areas in carbon sequestration. Land reclaiming, prompted by the introduction of steam pumps, favoured the economic development of the area, but concurred to CO2 emissions through the oxidation of soil organic substances, energy consumption from pumping stations, and the extensive use of hydrocarbon fuels in agriculture. Although urbanisation is limited in the Po delta plain, this area appears nowadays largely shaped by human activities, with the dominance of lands devoted to agriculture, dissected by a dense network of draining channels. The landscape changes recorded in the last two centuries in the Po coastal plain have been uniquely driven by human activities, like in several coastal plains worldwide.

Assessing the impact of Land Use/Land Cover Change on Land Surface Temperatures and SUHI: Case of Pune, India

The emergence of urban heat islands and rising land surface temperatures in developing countries like India is becoming a crucial concern for urban planners and policymakers. This study attempts to assess the impact of Land use and land cover (LULC) change on the Land Surface Temperature (LST) and Surface Urban Heat Islands (SUHI) in Pune, India, using remote sensing and Geographic information systems (GIS). A spatiotemporal analysis of the Landsat satellite imagery from 2000 – 2023 has been used to trace the LULC trends and compute LST variations across the years in land cover types. The thermal band, 10 of the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), is used for computing LST. Spatial indicators such as normalized difference vegetation index (NDVI) and normalized differentiated built index (NDBI) are also calculated. Results indicate that there has been a 30% decline in vegetation across the city over the years, alongside an increase of 16.64°C in LST due to urbanization-induced LULC changes. Furthermore, this study attempts to identify the thermal hotspots in the city. The results of this study can enable the assessment of urbanization and the formulation of informed climate-sensitive urban planning strategies. Keywords: Land Surface Temperature, Pune, Remote Sensing, GIS, Local Climate Zone, SUHI

Time-series studies of land surface temperature in Damascus, Syria through MODIS by Google Earth Engine

Urbanisation and changes in land use and land cover (LULC) significantly impact Urban Heat Islands (UHI). Despite extensive research on UHI, regional variations demand localised studies. This research assesses the UHI effect in Damascus, Syria, where rapid urbanisation threatens environmental balance. Leveraging Google Earth Engine, Landsat 8, and MODIS data, LULC changes, temperature patterns, NDVI, and NDBI, have been analysed with a focus on the summers of 2013, 2017, and 2022. Results revealed a substantial 12 % increase in urban areas between 2013–2022, accompanied by an 11 % decrease in green spaces. Land surface temperatures (LST) rose from 47.1 °C in 2013 to 48 °C in 2022. NDBI analysis confirmed built-up area expansion, while NDVI indicated dwindling vegetation cover. The pronounced UHI effect (48.7 °C) in industrial and densely populated areas during summer were observed. A strong positive correlation existed between LST and NDBI 90 %, and a moderately negative correlation between LST and NDVI. These findings highlight the adverse impact of urbanisation on Damascus’ climate. They underscore the need for urban planning strategies prioritising green space preservation and sustainable development to mitigate the UHI effect and ensure a healthier urban environment.

Simulation and prediction of daytime surface urban heat island intensity under multiple scenarios via fully connected neural network

The intensification of the Surface Urban Heat Island (SUHI), driven by urbanization, land use and land cover (LULC) changes, and population growth, presents significant environmental and public health risks in urban areas. Simulating and predicting SUHI, particularly through the identification of future high SUHI intensity (SUHII) zones, has been recognized as a critical step in mitigating these effects. This study employs a Fully Convolutional Neural Network (FCNN) model, trained on data from four research sites, to simulate the current daytime SUHII across six validation sites in Singapore, utilizing 15 key independent variables identified in previous studies. The model exhibits high validation accuracy, achieving 87.45%. Three projection scenarios, based on projected population growth and LULC changes, predict a decrease in High SUHII across all validation sites, ranging from 98.3% to 9%. This reduction is attributed to the LULC improvements proposed in the 2019 Master Plan. Spatial analysis of the predicted SUHII maps indicates that the majority of High SUHII locations across scenarios remain consistent with the current situation. This research also suggests that the model could be a valuable tool for urban planners, allowing them to assess whether new urban development plans will effectively reduce High SUHII to desired thresholds, thereby mitigating SUHII in urban environments.