Land Use Cover Change Research Articles

Predicting the impacts of urban development on urban thermal environment using machine learning algorithms in Nanjing, China

The urban thermal environment undergoes significant influences from changes in land use/land cover (LULC). This article uses CA-ANN and ANN algorithms to forecast LULC and changes in the urban thermal environment in Nanjing for the years 2030 and 2040. It investigates the interplay between LULC changes, land surface temperature (LST), and the urban thermal field variance index (UTFVI). The findings reveal that urban land exhibited a significant expansion trend from 2000 to 2019, reaching 1083.43 km2 in 2019. The forecast indicates that urban land may increase by 8.79% and 10.92% by 2030 and 2040, respectively. Conversely, vegetation and bare land may decrease. The LST is likely to continue to rise, accompanied by a significant expansion of the high temperature range and a contraction of the low temperature range. By 2030 and 2040, the area with LST<20 °C is likely to decrease by 2.17% and 3.19%, while the area with LST>30 °C is likely to expand by 5.68% and 8.08%, respectively. The UTFVI area of urban land may decrease at none and middle levels but may notably expand at stronger and strongest levels. The areas with UTFVI at none, weak, and middle levels show a declining trend, while the increase in UTFVI at the strong level may exceed 46.29% and the strongest level of UTFVI may continue to expand. This study offers new insights into urban sustainable development and thermal environment governance.

Impacts of Spatio-Temporal Changes in Anthropogenic Disturbances on Landscape Patterns in the Nandu River Basin, China

We explored the characteristics of landscape pattern (LP) changes in the Nandu River Basin (NRB) and its dynamic response mechanism to anthropogenic disturbance (AD). This is important for ecological protection and for land use decision-making in the basin in the context of the construction of a free trade port. Land use and land cover change (LULCC) data of 1990, 2000, 2010, and 2020 were analyzed with the help of the LP index, moving window method, hemeroby index, geo-information atlas, and geographic information system (GIS) spatial analysis to reveal the dynamic changes in LP characteristics in the NRB. Furthermore, this paper discusses into the correlation between LP and AD. The results indicate that over the past three decades, the NRB showed a staggered LP, dominated by forestland and cropland, whose total area has decreased by 25.27 km2 and 62.75 km2, respectively. On the other hand, the built-up land increased by 91.37%. The overall landscape fragmentation, landscape patch homogeneity, and landscape diversity have increased in the NRB. AD is the main reason for the dramatic changes in the LP of urban agglomerations centered in Haikou city. The area of minor disturbance (over 34%) occupies the largest proportion of the land, followed by higher (about 13~25%) and moderate disturbances (about 17~22%). The area of minor and strong disturbances has increased significantly, whereas those of moderate and higher disturbances have significantly decreased. The spatial distribution pattern of AD is gradually increasing from the southwest (natural landscape) to the northeast (human landscape). A transition in areas of low disturbance levels to higher levels is obvious. The area of the atlas that has transitioned from “minor → lighter disturbance (12)” and “higher → minor disturbance (41)” changed changed most significantly during 2000 to 2010 and from 2010 to 2020, occupying 26.79% and 11.99% of the transfer atlas, respectively. All regions encountering disturbances were significantly correlated with the largest patch index (LPI) from 1990 to 2020. The overall AD in the NRB has increased, especially in Haikou. Urbanization is the main factor for an increase in AD. The ecological and environmental management and monitoring in the basin need to be strengthened.

EXPERIMENTAL RELATIONSHIP BETWEEN LAND USE/LAND COVER AND UNDERGROUND FLOW IN AL BA COMMUNE IN THE CENTRAL REGION OF VIETNAM

Rapid urbanization and intensive agricultural practices in Vietnam's Central Highlands have substantially transformed land use/land cover (LULC) patterns, with implications for groundwater resources necessitating measurement. The objective of this investigation is to examine the links between multi-temporal variations in LULC and changes in underground water flow in Al Ba Commune, Gia Lai Province. Supervised object-based classification of Landsat and Sentinel imagery from the past 25 years has produced consistent LULC maps as inputs for the model. Geospatial data of LULC, meteorological data, and in-situ data of groundwater flow (Q) were used to calibrate and validate the integrated hydrological MIKE SHE model and simulate past Q conditions for correlation analysis. With these data on LULC and groundwater flow, this study investigates the relationship between LULC changes and Q using Multivariate Linear Regression (MLR) and Ridge Regression models. The MLR model showed high R² (0.91) but positive impacts of all LULC classes on Q, contrary to expectations. Further analysis with Variance Inflation Factor (VIF) revealed multicollinearity among LULC variables, which was addressed using Ridge Regression with a bias factor to correct for this issue. The revised model demonstrated that non-vegetative covers negatively affect Q while vegetative covers have a positive impact. These results highlight the complex dynamics of LULC changes on groundwater resources, which can be detected by statistical approaches. The results of study could be important for sustainable land management.

Intensity analysis applied to land use and land cover change and transitions in a fragile tropical mountain environment: a case of sironko catchment on Mt. Elgon, Eastern Uganda

ABSTRACT Intensity Analysis (IA) of land use and land cover change (LULCC) is important to support policy and practice. We applied IA to analyse LULCC in Sironko catchment, Uganda. Results show that agricultural land, forest, and wetland reduced by 8%; 32%; and 20% between 1986-2000. Between 2000-2016, forest, wetland, and built-up areas increased by 84%, 5%, and 57%. Active gainers between 1986-2000 were bare land, built-up areas, grassland and woodland. Between 2000-2016, bare lands and woodlands were the active losers. Transition to agricultural land in both intervals suggests that subsistence agriculture is fuelling deforestation and wetland encroachment in the region.

Synergizing google earth engine and earth observations for potential impact of land use/ land cover on air quality

Changes in land use and land cover are imperative drivers of climate change in urbanization. The conversion of land use modifies the physical and thermal characteristics of the land surface and also has an impact on the air quality. This study aims to assess the potential impact of land use and land cover (LULC) on the air quality in Gujarat state, India for 6 years, 2018, 2020, and 2023. Six land-use types, water bodies, forest, agricultural land, built-up, barren land, and scrubland obtained from the Landsat 8 land cover product processed in Google Earth Engine (GEE), where LULC of each category was estimated. The analysis findings indicated that variations in pollution response to LULC change exhibit distinct differences across different regions, influenced by natural factors or human activities like deforestation and urbanization. Over the years 2018–2023, agricultural land seems to consistently decrease in area, but urban areas saw exponential growth. The combined percentage of agricultural land and forest area slightly decreased from 61.08% to 60.7%, while the water spread area increased from 4.07% to 5.13%. The category of bare land decreased from 29.59% to 27.56%, mainly due to urbanization converting bare soil into built-up areas. The Sentinel-5P satellite data was used to estimate atmospheric pollution i.e., carbon mono-oxide (CO), Nitrogen dioxide (NO2), Methane (CH4), Sulfur Dioxide (SO2), and formaldehyde (HCHO). Over the past decade, a significant portion of land use land cover has transitioned from forest to vegetation areas in the western region, while urbanization has rapidly expanded in the eastern and central-western parts.

Can Landuse Landcover changes influence the success of India's national clean air plans ?

India implemented a range of multifarious strategies to address the issue of substandard air quality. One such flagship scheme of government of India is National Clean Air Programme (NCAP), which recommends sector specific reduction in emissions and increase in forest cover etc. To reduce particulate matter concentrations by 40% in 2026 compared to 2019. The present study aims to gauge the impact of Land Use Land Cover (LULC) changes alone on success of NCAP, using weather research forecasting model with chemistry (WRF-Chem) and integrated geographical information system and remote sensing software Terrset. The findings elucidate that, by the year 2026, the Ventilation Coefficient (VC) in India's eastern, central, northern, and north-eastern regions is anticipated to register a decline ranging from 18% to 50% compared to the baseline year of 2019. Conversely, an increase of 17% is expected in the southern region. The alterations in Fallow Land, Barren and sparsely vegetated land, Urban and Built-up Land, and Tundra, contribute to these shifts, displaying varying percentage changes across distinct zones. Simulations indicate that these LULC changes are impeding the planned reduction in PM2.5 levels. Projections suggest an increase in PM2.5 levels as high as 13% in the eastern, central, northern, and north-eastern regions, accompanied by a decrease of 33% in the Southern zone of the country. Significantly, non-attainment cities in Himachal Pradesh and Maharashtra are expected to witness a substantial rise in PM2.5-induced premature mortality, with Pune city projected to experience over 24,525 additional premature deaths by 2026. A comparable examination conducted for the year 2022, utilizing actual LULC data, suggests that if the NCAP fails to effectively implement LULC changes, it may reduce this anticipated trade-off. Addressing this concern, the study employed WRF-Chem to simulate 60 combinations, proposing LULC enhancements conducive to improving VC. The results underscore the critical importance of preserving at least 36% of the LULC category of mixed forest land, encompassing plantations, orchards, and areas under shifting agriculture. Additionally, a reduction in barren land and fallow land emerges as pivotal for enhancing the ventilation coefficient. The study accentuates the necessity of refraining from further expansion in densely populated areas to counter these anticipated VC trends. This study provides valuable insights, highlighting the need to prioritize LULC management to effectively combat the alarming air pollution.

Incorporating Spatio-Temporal Changes of Well Irrigation into a Distributed Hydrologic Model to Improve Groundwater Anomaly Estimations for Basins with Expanding Agricultural Lands

This paper seeks to address the deficiency of utilizing satellite-based GRACE observations and model-based GLDAS water budget components in estimating the changes in the groundwater storage in Konya Endorheic Basin (KEB), a basin experiencing considerable land use land cover (LULC) change, primarily agricultural expansion. Cereal cultivation in the basin has a slight decreasing trend, however, the cultivation of crops with high water consumption, such as maize and sunflower, is increasing substantially. And total agricultural areas are increasing. GRACE-GLDAS approach does not accurately give the long-term groundwater decline in the basin, mainly because the land surface models employed in GLDAS cannot realistically simulate variations in water budget components as they do not consider the changes in LULC and do not possess an elaborated irrigation scheme. Here, we used a fully-distributed mesoscale hydrologic model, mHM, that can handle multiple LULC maps from different years. The model was modified to incorporate the spatio-temporal changes of agricultural fields in KEB and an explicit irrigation scheme since we hypothesized that the groundwater depletion is mainly caused by well irrigation. mHM was calibrated against streamflow observations for the period 2004–2019. The simulations show that the use of mHM with the incorporated features gives groundwater storage changes that are more consistent with the well-based observations than those obtained from the GRACE-GLDAS approach. On the other hand, the mHM simulation with a static LULC map, as in GLDAS models but with a better representation of irrigated fields, provides groundwater anomaly changes that are more consistent with the GRACE-GLDAS results, a further justification of insufficiency of the GLDAS-based approach in estimating groundwater variations for basins with considerable landscape change.

Evaluating the Climate Change Trends and Spatiotemporal Variations of Evapotranspiration: A Case Study in El-Beheira Governorate, Egypt

Aims: Evapotranspiration (ET) is crucial for determining crop water requirements, while climate change and global warming are major concerns. Therefore, in this study, we aimed at evaluating the climate change trends and the spatiotemporal variations in ET in a selected area located in El-Beheira Governorate, Egypt, while accounting for potential land use/land cover (LULC) changes. Methodology: We analyzed three Landsat images acquired in 1984, 2001, and 2020 to assess LULC changes in the study area. Climate change was studied from 1984 to 2021 using data from NASA's Prediction of Worldwide Energy Resource (POWER) project. We also used the Terra MODIS MOD16A3GF Version 6.1 ET product from 2000 to 2021 to evaluate the spatiotemporal variations of ET. Results: LULC change detection showed that the unchanged agricultural area from 1984 to 2020 accounted for 29.3% of the study area and was used to evaluate the changes of ET. Evaluating climate change revealed that there was an increased trend in temperature, relative humidity (RH), and precipitation, while no change was observed in wind speed. Similarly, the anomalies changes of temperature, RH, and precipitation had an increasing trend while wind speed was constant. On the other hand, the yearly ET values calculated from 2000 to 2021 had an increasing trend. There was a moderate correlation between RH and precipitation, as well as between ET and precipitation. Conclusion: The increased trends in climatic parameters such as temperature will eventually require changes in crop patterns to adjust to these changes and maintain a profitable yield. Furthermore, the increase in ET will result in an increase in crop water requirements, which is problematic, especially considering the limited water resources in Egypt.

Assessment of land use/ land cover change derived catchment hydrologic response: An integrated parsimonious hydrological modeling and alteration analysis based approach

Land use/land cover (LULC) change, often a consequence of natural or anthropogenic drivers, plays a decisive role in governing global catchment dynamics, and subsequent impact on regional hydrology. Insight into the complex relationship between the drivers of LULC change and catchment hydrology is of utmost importance to decision makers. Contemplating the dynamic rainfall-runoff response of the Indian catchments, this study proposes an integrated modeling-based approach to identify the drivers and relative contribution to catchment hydrology. The proposed approach was evaluated in the tropical climate Nagavali River Basin (NRB) (9512 km2) of India. The Soil and Water Assessment Tool (SWAT) hydrological model, which uses daily-scale rainfall, temperature, wind speed, relative humidity, solar radiation, and streamflow information was integrated with the Indicators of Hydrologic Alteration (IHA) technique to characterize the plausible changes in the flow regime of the NRB. Subsequently, the Partial Least Squares Regression (PLSR) based modeling analysis was performed to quantify the relative contribution of individual LULC components on the catchment water balance. The outcomes of the study revealed that forest land has been significantly converted to agricultural land (45–59%) across the NRB resulting in mean annual streamflow increase of 3.57 m3/s during the monsoon season. The affinity between land use class and streamflow revealed that barren land (CN = 83–87) exhibits the maximum positive response to streamflow followed by the built-up land (CN = 89–91) and fallow land (CN = 88–93). The period 1985–1995 experienced an increased ET scenario (911–1050 mm), while the recent period (2005–2020) experienced reduced ET scenario owing to conversion of forest to agricultural land. Certainly, the study endorses adopting the developed methodology for understanding the complex land use and catchment-scale hydrologic interactions across global-scales for early watershed management planning.

Cultural heritage and urban morphology: land use transformation in ‘Kumbh Mela’ of Prayagraj, India

A Hindu pilgrimage site where millions of people gather to seek solutions to problems related to everyday life, the “Kumbh Mela” is recognised as one of India’s intangible cultural heritages. The site of the observance rotates between four cities, Haridwar, Ujjain, Nashik and Prayagraj. The infrastructure and services in the city that is hosting such a gathering undergo numerous changes as a result of the sizeable gathering. The goal of the study is to monitor the changes in Land Use and Land Cover (LULC) in the city of Prayagraj before and after the “Kumbh Mela” and to evaluate the effect on infrastructure changes in the citry. Analyzing the changes that such pilgrimages and events bring about in cities are fascinating. In 2019, the city of Prayagraj hosted the “Kumbh”, hence, Pre-Kumbh (2016), Kumbh (2019), and Post-Kumbh (2022) phases were taken under consideration for LULC change assessment. Supervised classification was conducted using Maximum Likelihood Classifier to prepare LULC maps. It was observed that urbanisation has significantly increased at the expense of vegetation, fallow land, and agricultural land. The success of the local agroforestry policy and plantation drive is demonstrated by the interesting increase in vegetation that has been observed. The construction of a civil airport, the widening of existing roads, the installation of new railroad tracks, and the adornment of roundabouts on existing roads are the major infrastructure changes. The findings offer fresh insights of increasing urban infrastructure from a cultural perspective. The findings will aid in better planning and policy development for the resilient and sustainable infrastructure, as well as in the development of a long-term strategy for the temporary habitation in the “Kumbh” area of such cities.

TEMPORAL PATTERN OF AEDES SOLLICITANS AND AEDES TAENIORHYNCHUS IN AN INTERTIDAL WETLAND SYSTEM, NORTHEASTERN FLORIDA: A LITERATURE REVIEW

Temporal and spatial patterns of mosquito species distributions are influenced by weather conditions and climate, changes in water quality, and many other factors. Aedes taeniorhynchus and Aedes sollicitans are common salt marsh mosquitoes that cause many nuisance problems for human populations. St. Johns County (north-eastern Florida) has, over the past decades, been one of the fastest growing counties in the U.S.A. This is related to changes in land use and land cover, and local climate variations. It has been recently noticed that two species, Ae. taeniorhynchus and Ae. sollicitans, switch their temporal pattern in St. Johns County. To explore factors influencing potential changes between the temporal distribution of the two species of salt marsh mosquitoes, a literature search was conducted. Based on the literature, there are many factors influencing the potential species switch or temporal patterns of the two species. These include meteorological and tidal patterns as well as land use and land cover changes. The results of the literature analysis indicate that the switch and change in temporal pattern between the two species needs to be further explored, especially with respect to temperature, tidal flooding, water quality (especially salinity), sea level rise and land use/cover.

Spatio-Temporal Analysis of Land Use/Land Cover Change and the Implications on Sustainable Development Goals in the Vea Catchment of Ghana

Globally, land use and land cover change (LULCC) is recognized as posing a substantial environmental challenge with lasting and severe consequences. This study quantified LULCC within the Vea catchment area spanning 24 years, from 1998 to 2022. Landsat TM imagery from 1998, ETM+ imagery from 2006, and Landsat 8 OLI imagery from 2014 to 2022 were used. A supervised classification algorithm was employed to classify the LULC classes. The findings reveal a notable transformation in the Vea catchment area from 1998 to 2022, primarily by converting grassland to cropland. Agricultural activities emerged as a significant contributor to the observed LULCC trend. Notably, cropland expanded from 10.9% to 51.98% between 1998 and 2022, while grassland and mixed vegetation/forest areas decreased from 54.8% to 18.14% and 31.7% to 22.73%, respectively. These results, it is argued, underscore the potential implications for achieving the targets set by the UN's Sustainable Development Goals.

Analyzing Flood Damage and Mapping Flood Hazard Zones Using AHP Model: A Case Study of Pol-e-Alam, Logar Province, Afghanistan

This research aims to evaluate the impact of the most recent floods that occurred on August 20, 2022, in Logar province in southern Afghanistan. For this purpose, changes in land use and land cover (LULC) of the study area were created from the Sentinel-2 image with a spatial resolution of 10 meters. To achieve this, the study utilized Sentinel-2 images to analyze LULC changes before and after the flood event and employed a support vector machine for supervised classification. The study also applied the analytical hierarchy process (AHP) to evaluate the future risks of flooding in the study area, focusing on factors related to hydrological phenomena. Overall, the study demonstrates the effectiveness of geospatial technologies and remote sensing in assessing the impacts of floods and creating flood risk maps. This can significantly reduce the consequences of flooding and inform decision-making for disaster management and mitigation.

Scenario-Based Land Use and Land Cover Change Detection and Prediction Using the Cellular Automata–Markov Model in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia

Land use and land cover (LULC) change detection and prediction studies are crucial for supporting sustainable watershed planning and management. Hence, this study aimed to detect historical LULC changes from 1985 to 2019 and predict future changes for 2035 (near future) and 2065 (far future) in the Gumara watershed, Upper Blue Nile (UBN) Basin, Ethiopia. LULC classification for the years 1985, 2000, 2010, and 2019 was performed using Landsat images along with vegetation indices and topographic factors. The random forest (RF) machine learning algorithm built into the cloud-based platform Google Earth Engine (GEE) was used for classification. The results of the classification accuracy assessment indicated perfect agreement between the classified maps and the validation dataset, with kappa coefficients (K) of 0.92, 0.94, 0.90, and 0.88 for the LULC maps of 1985, 2000, 2010, and 2019, respectively. Based on the classified maps, cultivated land and settlement increased from 58.60 to 83.08% and 0.06 to 0.18%, respectively, from 1985 to 2019 at the expense of decreasing forest, shrubland and grassland. Future LULC prediction was performed using the cellular automata–Markov (CA–Markov) model under (1) the business-as-usual (BAU) scenario, which is based on the current trend of socioeconomic development, and (2) the governance (GOV) scenario, which is based on the Green Legacy Initiative (GLI) program of Ethiopia. Under the BAU scenario, significant expansions of cultivated land and settlement were predicted from 83.08 to 89.01% and 0.18 to 0.83%, respectively, from 2019 to 2065. Conversely, under the GOV scenario, the increase in forest area was predicted to increase from 2.59% (2019) to 4.71% (2065). For this reason, this study recommends following the GOV scenario to prevent flooding and soil degradation in the Gumara watershed. Finally, the results of this study provide information for government policymakers, land use planners, and watershed managers to develop sustainable land use management plans and policies.

QGIS-based modeling and analysis of urban dynamics affecting land surface temperature towards climate hazards in coastal zones of Portugal

Climate risk creates considerable concern due to the density of natural and socio-economic assets in coastal areas. Monitoring land use/cover changes, detecting population growth, and analyzing their impact on land surface temperature (LST) are necessary for effective urban management. In this study, land use/land cover (LULC), population, and LST changes in coastal regions of Portugal. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery was examined using World Bank population and CORINE data. Changes in land use types and LST values from 1990 to 2018 were analyzed. At the same time, LULC predictions were made using the Modules for Land Use Change Simulation (MOLUSCE) plug-in included in the QGIS software, and population projections were analyzed with LULC predictions in 2046. The results show the significant impact of land use on temperatures. It has been demonstrated that green and water areas can effectively cool cities. In the LULC changes between 1990 and 2018, the Leiria region stands out, with an annual increase of 4.04% in built areas from 121.58 to 259.06 km2. According to the simulations between 2018 and 2046, it was predicted that 18.74% of agricultural areas and 14.43% of forest areas would be transformed into built environments. The study is also essential as it confirms that the MOLUSCE plug-in can be effectively applied to land cover simulation on a large regional scale.

VST-PCA: A Land Use Change Simulation Model Based on Spatiotemporal Feature Extraction and Pre-Allocation Strategy

Land use/cover change (LUCC) refers to the phenomenon of changes in the Earth’s surface over time. Accurate prediction of LUCC is crucial for guiding policy formulation and resource management, contributing to the sustainable use of land, and maintaining the health of the Earth’s ecosystems. LUCC is a dynamic geographical process involving complex spatiotemporal dependencies. Existing LUCC simulation models suffer from insufficient spatiotemporal feature learning, and traditional cellular automaton (CA) models exhibit limitations in neighborhood effects. This study proposes a cellular automaton model based on spatiotemporal feature learning and hotspot area pre-allocation (VST-PCA). The model utilizes the video swin transformer to acquire transformation rules, enabling a more accurate capture of the spatiotemporal dependencies inherent in LUCC. Simultaneously, a pre-allocation strategy is introduced in the CA simulation to address the local constraints of neighborhood effects, thereby enhancing the simulation accuracy. Using the Chongqing metropolitan area as the study area, two traditional CA models and two deep learning-based CA models were constructed to validate the performance of the VST-PCA model. Results indicated that the proposed VST-PCA model achieved Kappa and FOM values of 0.8654 and 0.4534, respectively. Compared to other models, Kappa increased by 0.0322–0.1036, and FOM increased by 0.0513–0.1649. This study provides an accurate and effective method for LUCC simulation, offering valuable insights for future research and land management planning.

Assessing the impact of anthropogenic activities on land use and land cover changes in the semi-arid and arid regions of Algeria.

Land use and land cover are critical factors that influence the environment and human societies. The dynamics of LULC have been constantly changing over the years, and these changes can be analyzed at different spatial and temporal scales to evaluate their impact on the natural environment. This study employs multitemporal satellite data to investigate the spatial and temporal transformations that occurred in Sidi Bel Abbes province, situated in the northwestern region of Algeria, spanning from the early 1990s to 2020. Notably, this province is marked by semi-arid and arid climates and hosts a wide range of areas susceptible to gravitational hazards, especially concerning alterations in land use and forest fires. The interactive supervised classification tool utilized multiple machine learning algorithms including Random Forest, Support Vector Machine, Classification and Regression Tree, and Naïve Bayes to produce land cover maps with six main classes: forest, shrub, agricultural, pasture, water, and built-up. The findings showed that the LULC in the research area is undergoing continuous change, particularly in the forest and agricultural lands. The forest area has decreased significantly from 10.80% in 1990 to 5.25% in 2020, mainly due to repeated fires. Agricultural land has also undergone fluctuations, with a decrease between 1990 and 2000, followed by a fast increase and near stabilization in 2020. At the same time, pasture lands and built-up areas grew steadily, increasing by 11% and 13% respectively. This research highlights the significant impact of anthropogenic activities on LULC changes in the study area and can provide valuable insights for promoting sustainable land use policies.

Advanced CMD predictor screening approach coupled with cellular automata-artificial neural network algorithm for efficient land use-land cover change prediction

The prediction of future land use and land cover (LULC) change is essential for natural resource management and the development of environmental monitoring strategies, especially for a non-resilient system. Thus, a novel LULC prediction model was proposed by coupling a hybrid predictor screening approach with the Cellular Automata coupled Artificial Neural Network (CA-ANN) approach. The hybrid predictor screener was formed by integrating the outcomes of Correlation Coefficient (CC), Mutual Information (MI), and Decision Tree (DT) methods and termed as the CMD technique. The CA-ANN model was trained by LULC maps of 1995 and 2005 and tested by the map of 2020 for these four sets of variables (i.e., CC, MI, DT, and CMD). The results revealed that the CMD predictors were the most influential variables for LULC prediction, followed by the DT-based variables. In contrast, the CC-based predictors were found to be the least effective, followed by the MI variables. Thus, the CMD-CA-ANN model was established as the benchmark model in this study. The study showed that elevation and distance from roads were more critical variables for LULC prediction compared to slope and aspect parameters. The proposed model demonstrated that by 2080, there would be an increase in the area of built-up land (5.79%), cropland (4.35%), shrubland (1.62%), and water bodies (0.3%) and a decrease in the area of forested land (1.34%) and barren land (10%) in the study area. The research has the potential to significantly contribute to the identification and management of significant environmental changes.

A comprehensive research on open surface drinking water resources in Istanbul using remote sensing technologies.

Istanbul is a megacity with a population of 15.5 million and is one of the fastest-growing cities in Europe. Due to the rapidly increasing population and urbanization, Istanbul's daily water needs are constantly increasing. In this study, eight drinking water basins that supply water to Istanbul were comprehensively examined using remote sensing observations and techniques. Water surface area changes were determined monthly, and their relationships with meteorological parameters and climate change were investigated. Monthly water surface areas of natural lakes and dams were determined with the Normalized Difference Water Index (NDWI) applied to Sentinel-2 satellite images. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in months when optical images were unavailable. The study was carried out using 3705 optical and 1167 SAR images on the Google Earth Engine (GEE) platform. Additionally, to determine which areas of water resources are shrinking, water frequency maps of the major drinking water resources were produced. Land use/land cover (LULC) changes that occurred over time were determined, and the effects of the increase in urbanization, especially on drinking water surface areas, were investigated. ESRI LULC data was used to determine LULC changes in watersheds, and the increase in urbanization areas from 2017 to 2022 ranged from 1 to 91.43%. While the basin with the least change was in Istranca, the highest increase in the artificial surface was determined to be in the Büyükçekmece basin with 1833.03ha (2.89%). While there was a 1-12.35% decrease in the surface areas of seven water resources from 2016 to 2022, an increase of 2.65-93% was observed in three water resources (Büyükçekmece, Sazlıdere, and Elmalı), each in different categories depending on their size. In the overall analysis, total WSA decreased by 62.33ha from 2016 to 2022, a percentage change of 0.70%. Besides the areal change analysis, the algae contents of the drinking water resources over the years were examined for the major water basins using the Normalized Difference Chlorophyll Index (NDCI) and revealed their relationship with meteorological factors and urbanization.

Quantifying LULC Changes in Constantine, Algeria Using Google Earth Engine

The objective of this research is to analyze land use changes in the municipality of Constantine over a thirty year period (1993 – 2023) using Landsat satellite data provided through the Google Earth Engine (GEE) platform and a supervised classification approach. Five land cover classes were mapped at six key dates: water bodies, green spaces, built-up areas, bare soil and agricultural land. The quantified changes highlight a considerable reduction in bare soil in favor of an increase in cultivated land, as well as an expansion of artificial surfaces during certain periods (2008 to 2023). These dynamics reflect the processes of urban sprawl and peri-urbanization at work in this territory. The results obtained demonstrate the potential of remote sensing through GEE for detailed spatio-temporal monitoring of land use. Coupled with other data, this approach contributes to a better understanding of territorial evolutions and provides valuable information for sustainable land use planning.