Land Cover Research Articles

Land Use and Land Cover Change Analysis in Brazilian Urban centres: an approach using Cellular Automata and Neural Networks

Abstract. During the 1970s, Brazil experienced most of its population living in cities for the first time. Historically, urbanization has been intensely related to land use and cover changes, amplifying climatic and ecological stress in recently established metropolises. This study aims to analyze, through classified Remote Sensing images and the Cellular Automata and Artificial Neural Networks (CA-ANN) machine learning model, land use and land cover trends in regions that have experienced accentuated demographic growth in the decades 2000 and 2010. The methodology consists of: i) identifying areas with a high density of buildings, ii) defining the variables that drive land use change, and iii) proposing a methodology for predicting changes in the urban area. The results indicate that the urban class prediction presented high precision (≥ 0.74) and recall (≥ 0.86) indices. Forest class also presented a high precision score (≥0.72), showing an elevated prediction hit rate. Furthermore, the proposed methodology improved the results obtained in previous works for the same cities, presenting higher Kappa values in all cases.

Assessment of land restoration from optical satellite image time-series

Abstract. Ecosystem conservation and restoration is recognized by the international community as a key strategy for human well-being as well as our planet’s health. Assessing the efficiency of actions implemented in this context is essential, as their application on the ground may prove challenging. In the present paper, we present a methodology for characterising vegetation regeneration. This methodology has been developed and tested at the scale of the Sao Paulo state. It is based on two successive steps. Firstly, annual land cover maps are produced with breakpoints detection and Random Forest classification. This process is performed on biophysical variables derived from more than 10 000 optical images (2016 – 2021 included) and 4774 reference data. Secondly, we applied an expert-based rules algorithm to derive land regeneration map from annual land cover time series. The distinction is made between natural and anthropogenic regeneration. The accuracy assessment shows an overall accuracy of more than 80% for both the annual land cover maps and the regeneration map.

Quantifying the Effect of Land Use and Land Cover Changes on Spatial-Temporal Dynamics of Water in Hanjiang River Basin

As a vital part of the geo-environment and water cycle, ecosystem health and human development are dependent on water resources. Water supply and demand are influenced significantly by land use and cover change (LUCC) which shapes the surface ecosystems by altering their structure and function. Under future climate change scenarios, LUCC may greatly impact regional water balance, yet the impact is still not well understood. Therefore, examining the spatial relationship between LUCC and water yield services is crucial for optimizing land resources and informing sustainable development policies. In this study, we focused on the Hanjiang River Basin and used the patch-generating land use simulation (PLUS) model, coupled with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, to assess water yield services under three Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios. For the first time, we considered the impact of future changes in socio-economic and water use indicators on water demand using correction factors and ARIMA projections. The relationship between water supply and demand was explored using this approach, and LUCC’s effects on this balance are also discussed. Results indicate that: (1) The patterns of LUCC are similar for the three scenarios from 2030 to 2050, with varying levels of decrease for cropland and significant growth of built-up areas, with increases of 6.77% to 19.65% (SSP119), 7.66% to 22.65% (SSP245), and 15.88% to 46.69% (SSP585), respectively, in the three scenarios relative to 2020; (2) The future supply and demand trends for the three scenarios of produced water services are similar, and the overall supply and demand risks are all on a downward trend. Water demand continues to decline, and by 2050, the water demand of the 3 scenarios will decrease by 96.275×108t, 81.210×108t, and 84.13×108t relative to 2020, respectively; while supply decreases from 2030 to 2040 and rises from 2040 to 2050; (3) Both water supply and demand distributions exhibit spatial correlation, and the distribution of hotspots is similar. The water supply and demand are well-matched, with an overall supply-demand ratio greater than 1.5; (4) LUCC can either increase or decrease water yield. Built-up land provides more water supply compared to other land types, while forest land has the lowest average water supply. Limiting land use type conversions can enhance the water supply.

Analisis Potensi Kawasan Pengembangan Lahan Pertanian Tanaman Padi Sawah di Kabupaten Tanah Datar

This study aims to determine 1) the level of suitability of wet-rice land on food agricultural land in Tanah Datar Regency, 2) existing land cover in Tanah Datar Regency and 3) potential wet-rice land development areas in Tanah Datar Regency. The method used is a descriptive approach with quantitative research. Data was processed using fuzzy logic, supervised classification, and spatial data analysis. The results of this study show 1) Tanah Datar Regency has 3 levels of land suitability with an index value of 0-0.59 which is classified at the level of unsuitable to less suitable for rice paddy agricultural crops, with an area of 7,583.9 Ha with a percentage of 5.5%. The second category of land suitability with an index value of 0.6-0.79 is classified as moderately suitable to suitable, with an area of 98,955.7 Ha with a percentage of 71.8%. The third category of land suitability with an index value of 0.8-1.0 which is classified as moderately - highly suitable for wetland agricultural crops, with an area of 31,267.7 Ha with a percentage of 22.7% of the total area of the study site. 2) Analysis of existing land cover using Landsat 8 satellite images in 2023 in Tanah Datar District shows that the structure of land cover in 2023 is as follows: dense vegetation cover covering 93,385.5 Ha with a percentage of 67.9%, followed by agricultural land covering 35,630.6 Ha with a percentage of 25.8%, settlements covering 1,828.3 Ha with a percentage of 1.4%, open land covering 137.6 Ha with a percentage of 0.09%, and waters.

Are Neutrality Targets Alone Sufficient for Protecting Nature? Learning From Land Cover Change and Land Degradation Neutrality Targets in Mauritius

ABSTRACTLand degradation contributes to loss of land productivity, climate change, and biodiversity loss worldwide, making the monitoring of land cover change a fundamental component of sustainable land use planning. To facilitate this task, international research organizations publish increasingly high‐quality global land cover data, yet those are still proving too imprecise at the scale of Small Island Developing States (SIDS) such as Mauritius. Though rapidly developing, Mauritius does not have a locally produced land cover map which is consistently updated and made available to the public in the scope of participatory planning processes. This study sought to bridge this gap by revising a 2010 land cover map of Mauritius island and producing a new map for 2020, which can serve as a basis for future updates through a consistent methodology that combines photointerpretation and semi‐automated classification from various datasets. These maps enabled the observation of key changes in the island's landscape, which are consistent with socio‐economic events, namely the decline of agricultural coverage (12.7%) due to the phasing out of preferential trade agreements for sugar, and the expansion of real estate (14.3% increase in built area) following new investment promotion policies. Considering the small size and high population density of the island, as is the case for SIDS, these developments and the Land Degradation Neutrality Targets of Mauritius bring the feasibility of area‐based land degradation neutrality into question and call for the analysis of land and ecosystem conditions through additional indicators.

Scale-specific effects of urban landscape pattern on the COVID-19 epidemic in Hangzhou, China

Abstract Context Understanding the scale-specific effects of different landscape variables on the COVID-19 epidemics is critical for developing the precise and effective prevention and control strategies within urban areas. Objective Based on the landscape epidemiology framework, we analyzed the scale-specific effects of urban landscape pattern on COVID-19 epidemics in Hangzhou, China. Methods We collected COVID-19 cases in Hangzhou from 2020‒2022 and combined the datasets of land use and land cover (LULC) and social gathering point (SGP) to quantify the urban landscape pattern. Optimal general linear model with stepwise regression was applied to explore the dominant landscape factors driving the COVID-19 transmission in the city. Furthermore, multi-scale geographically weighted regression illustrated the spatial heterogeneity and scale specificity of these landscape variables’ effects to COVID-19 epidemic. Results Eight landscape variables of LULC and SGP patterns were identified which explained 68.5% of the variance in spatial risk of COVID-19. Different optimal bandwidths across these variables in MGWR indicated their scale-specific effects. LSI of green space enhanced the spatial risk across the entire region. The effects of landscape contagion, the number of water bodies, LSI of cropland and built-up areas, and the density of commercial houses were detected to vary between urban and suburban areas. The effects of LSI of water bodies and the density of shopping malls were found to vary among different districts. Conclusions In this study, we firstly discriminated the scale-specific effects of different landscape variables on the COVID-19 epidemic in the urban region. These findings can help to optimize the differentiated zoning prevention and control strategies for COVID-19 in cities and guide policy-making and urban planning at a multi-scale hierarchical perspective to improve public health and urban sustainability.

Land-Use-Change-Driven Erosion and Sediment Transport in the Yaqui River Sub-Basin (Mexico): Insights from Satellite Imagery and Hydraulic Simulations

Soil erosion and sediment transport are significant concerns in the Yaqui River sub-basin in northwest Mexico, driven by land use changes and environmental degradation. This study aims to evaluate erosion processes between 2000 and 2020 using a combination of satellite imagery and numerical simulations with Iber software (Version 2.5.2). The primary objective is to assess the impacts of land use changes, particularly the conversion of forest to grassland, on erosion rates and sediment transport. Satellite images from 2000 and 2020 were analyzed to detect land cover changes, while Iber’s sediment transport module was used to simulate erosion patterns based on the Meyer–Peter and Müller equation for bedload transport. Hydrological and topographical data were incorporated to provide accurate simulations of flow velocity, depth, and erosion potential. The results reveal a 35.3% reduction in forest cover, leading to increased erosion and sediment transport in steep areas. Simulation predictions highlighted areas with high future erosion potential, which are at risk of further soil loss if current trends continue. Flow velocity increased, contributing to riverbank destabilization and higher sediment yield, posing a risk to infrastructure such as the Álvaro Obregón Dam. This study underscores the need for targeted erosion control measures and sustainable land management practices to mitigate future risks and protect vital infrastructure in the Yaqui River Basin.

A new strategy based on multi-source remote sensing data for improving the accuracy of land use/cover change classification.

Land Use/Cover Change (LUCC) plays a crucial role in sustainable land management and regional planning. However, contemporary feature extraction approaches often prove inefficient at capturing critical data features, thereby complicating land cover categorization. In this research, we introduce a new feature extraction algorithm alongside a Segmented and Stratified Principal Component Analysis (SS-PCA) dimensionality reduction method based on correlation grouping. These methods are applied to UAV LiDAR and UAV HSI data collected from land use types (e.g., residential areas, agricultural lands) and specific species (e.g., tree species) in urban, agricultural, and natural environments to reflect the diversity of the study area and to demonstrate the ability of our methods to be applied in different classification scenarios. We utilize LiDAR and HSI data to extract 157 features, including intensity, height, Normalized Digital Surface Model (nDSM), spectral, texture, and index features, to identify the optimal feature subset. Subsequently, the best feature subset is inputted into a random forest classifier to classify the features. Our findings demonstrate that the SS-PCA method successfully enhances downscaled feature bands, reduces hyperspectral data noise, and improves classification accuracy (Overall Accuracy = 91.17%). Additionally, the CFW method effectively screens appropriate features, thereby increasing classification accuracy for LiDAR (Overall Accuracy = 78.10%), HIS (Overall Accuracy = 89.87%), and LiDAR + HIS (Overall Accuracy = 97.17%) data across various areas. Moreover, the integration of LiDAR and HSI data holds promise for significantly improving ground fine classification accuracy while mitigating issues such as the 'salt and pepper noise'. Furthermore, among individual features, the LiDAR intensity feature emerges as critical for enhancing classification accuracy, while among single-class features, the HSI feature proves most influential in improving classification accuracy.

Spatiotemporal variations in land surface temperature within an urban ecosystem: A comprehensive assessment of land cover change in Düzce, Türkiye

Spatiotemporal variations in land surface temperature within an urban ecosystem: A comprehensive assessment of land cover change in Düzce, Türkiye

Understanding the relationship between land use/land cover changes and air quality: A GIS-based fuzzy inference system approach.

Air pollution is a global issue that demands urgent attention due to its detrimental effects on human health and the environment. Land Use and Land Cover (LULC) change is an essential factor that significantly impacts ambient air quality through alterations in emission sources, vegetation cover, natural processes, and urban design. This study investigates the spatio-temporal variation of key air pollutants resulting from urban LULC changes in the Delhi region. Findings reveal a notable increase in pollutant concentrations, particularly particulate matter, in 2019 (PM10: 318.65 ± 45.80 µg/m3) and 2023 (PM10: 383.70 ± 61.49 µg/m3), compared to 2008 (PM10: 246.76 ± 30.66). LULC change analysis demonstrates a rise in built-up areas 24.59%(2008 to 2019), 33.62% (2008 to 2023) and a decline in vegetation cover 27.49% (2008 to 2019),32.37% (2008 to 2023). Correlation analysis indicates a positive correlation between PM10 and urban indices (+ 0.63) and a negative correlation between PM10 and vegetation indices (- 0.61), highlighting the impact of LULC on air quality deterioration. Subsequently, a fuzzy inference system model integrates LULC information to develop an air quality index (AQI). Incorporating LULC changes in AQI assessment offers a realistic approach to address the complexity arising from combined air pollutant effects, surpassing conventional AQI calculation methods. The findings underscore the significance of understanding the impact of Land Use and Land Cover (LULC) change on ambient air quality in formulating effective air quality management programs and policies. Integrating this knowledge into policymaking is crucial for the successful abatement of air pollution in urbanized areas.

Evolving road networks and urban landscape transformation in the Himalayan foothills, India.

The amalgamation of Himalayan and Indo-Burmese biodiversity has made the state of Manipur, India, a unique ecosystem. In addition, the region is a strategic place for the country to establish an economic corridor to Southeast Asia. In recent times, the region has witnessed tremendous infrastructural/road development. Subsequently, forest fragmentation related to urbanization and road expansion has emerged in the Himalayan foothills. The development of roads brought rapid changes in land use and land cover (LULC) and thus subsequent environmental degradation. The current study attempts to understand how the development of road networks has impacted the natural cover in Manipur, India. A spatio-temporal analysis was performed to assess the relationship between the development of road networks and LULC changes using the Landsat satellite images over a decade (2012-2022). The results showed significant changes in the area coverage of LULC categories such as agricultural land, built-up areas, forest, and water bodies with the increase in road density. To have a holistic view, the study area was segregated into three functional zones based on their urban land use pattern, i.e., urban center, peri-urban, and urban peripheral fringes. Urban sprawl in the urban center has led to the rapid conversion of forested lands into built-up areas and agricultural lands in the peri-urban and urban peripheral fringes, respectively. The decline of forest areas to urbanization in peri-urban and urban peripheral fringes calls for conservation and restoration initiatives. The study also emphasizes how different stakeholders can be involved and empowered to strengthen public-private partnerships for conservation and restoration in such sensitive ecosystems. Urban planners and developers should be critical in making informed decisions through understanding ecological concerns in tandem with infrastructural development.

Holocene Floodplain Transformation Through Catchment‐Scale Human‐Environment Interactions: An Interdisciplinary Case Study of the Gete Catchment (Belgium)

ABSTRACTFloodplains across the European loess region transformed from nature‐ to human‐dominated environments during the Holocene. A general framework of this evolution is well established, but it is less clear how differences in timing—observed within and between catchments—can be explained. Although human impact is an important driver, little attention has thus far been paid to the actual human activities involved, their intensities, and spatiotemporal patterns. By adopting an interdisciplinary approach, this study provides insight into the evolution of both environmental (e.g., local floodplain geoecohydrology and regional land cover) and human dynamics (e.g., demography and land use) in the Belgian Gete catchment. Results show that the observed changes in land cover and floodplain geoecology can indeed be attributed to spatiotemporal variations in human impact. Human‐induced vegetation change, driven by population growth and associated agrarian production, resulted in hydrological changes and localized colluviation. Catchment‐scale improvement of hillslope‐channel connectivity initiated alluviation in the valleys and completed the transformation from forested marsh to open floodplain with overbank sedimentation. In turn, this allowed people to settle in the floodplains in the Early Medieval period, laying the foundations for our present‐day landscape.

Spatiotemporal Evolution and Drivers of Ecological Quality in the Tengger Desert (2001–2021)

Desert ecosystems, particularly in arid regions like the Tengger Desert, are highly sensitive to both anthropogenic activities and climate change, making the monitoring and evaluation of ecological quality critical for sustainable management and restoration efforts. This study analyses the spatiotemporal evolution of ecological quality in the Tengger Desert from 2001 to 2021 using the Remote Sensing Ecological Index (RSEI), incorporating meteorological factors (temperature, precipitation, wind speed), topographical factors (elevation, slope, relief) and anthropogenic indices (land use and land cover). The mean RSEI fluctuated between 0.1542 and 0.2906, indicating poor ecological quality, with a peak in 2008 attributed to national ecological projects. Despite initial improvements, overall ecological quality declined at a rate of 0.0008 a−1 from 2008 to 2021. Spatially, degradation was most pronounced in the central and southern areas. Due to sand-binding engineering in the Tengger Desert in 2008 and the mountain climate suitable for vegetation growth, improvements occurred in the northeast and southwest. Moran’s I and Hurst index analyses revealed significant spatial clustering of ecological quality and persistence of degradation trends, with over 49.53% of the area projected to experience further deterioration. Geodetector analysis identified land use and land use cover as the most influential factors on RSEI, especially in combination with wind speed, temperature, and precipitation, underscoring the role of both human activities and climate. The study highlights the need for sustained ecological management, particularly in areas showing continuous degradation, to prevent further ecological deterioration.

Changes in the soil and vegetation cover of the Kochubey biosphere station under global warming in recent years

Objectives: The study and long-term observations of changes in the spatial structure of vegetation cover of pasture landscapes of the Kochubey Biosphere Station (KBS) due to global climate warming. Arid pasture landscapes, due to the peculiarities of their spatial structure (the presence of semi-desert vegetation groupings) are a good indicator of modern climatic changes. To achieve this goal, the spatial structure and the current state of the arid landscapes of the Northwestern Caspian Sea in the territory of the KBS have been studied. The analysis of the biological diversity of species was carried out. Also the characteristics of the common features of seasonal dynamics of landscapes were studied. Moreover, trends of climatic changes in the landscape structure of the territory and trends in anthropogenic transformation of arid landscapes of the region were identified. It has been revealed that the main reasons for the reduction of biodiversity are habitat fragmentation, the introduction of alien species, the settlement of native species outside the range, ecotonization and “islandization” of pasture ecosystems, and the displacement of range boundaries. Since the goal of preserving the components of vegetation biodiversity is to reduce the pressures caused by climate change, adaptation measures should be aimed at reducing the rate of fragmentation and degradation of pasture landscapes. Monitoring observations have shown that over the last 20-century period there have been rhythmically repeating changes in climate and soil and vegetation cover, which indicates high mobility of vegetation. It has been established that the contribution of anthropogenic land degradation to desertification is confirmed by a significant linear trend in interannual fluctuations in indicators of pasture digression in the region. It has been established that the contribution of anthropogenic land degradation to desertification is confirmed by a significant linear trend in interannual fluctuations in indicators of pasture digression in the region. Due to excessive pasture digression, “islands” of anthropogenic desertification are formed here, the lifespan of which is determined by human influence and precipitation fluctuations.

MMS-EF: A Multi-Scale Modular Extraction Framework for Enhancing Deep Learning Models in Remote Sensing

The analysis of land cover using deep learning techniques plays a pivotal role in understanding land use dynamics, which is crucial for land management, urban planning, and cartography. However, due to the complexity of remote sensing images, deep learning models face practical challenges in the preprocessing stage, such as incomplete extraction of large-scale geographic features, loss of fine details, and misalignment issues in image stitching. To address these issues, this paper introduces the Multi-Scale Modular Extraction Framework (MMS-EF) specifically designed to enhance deep learning models in remote sensing applications. The framework incorporates three key components: (1) a multiscale overlapping segmentation module that captures comprehensive geographical information through multi-channel and multiscale processing, ensuring the integrity of large-scale features; (2) a multiscale feature fusion module that integrates local and global features, facilitating seamless image stitching and improving classification accuracy; and (3) a detail enhancement module that refines the extraction of small-scale features, enriching the semantic information of the imagery. Extensive experiments were conducted across various deep learning models, and the framework was validated on two public datasets. The results demonstrate that the proposed approach effectively mitigates the limitations of traditional preprocessing methods, significantly improving feature extraction accuracy and exhibiting strong adaptability across different datasets.

Identification of Global Extended Pseudo Invariant Calibration Sites (EPICS) and Their Validation Using Radiometric Calibration Network (RadCalNet)

This study introduces a global land cover clustering using an unsupervised algorithm, incorporating the novel step of filtering data to retain only temporally stable pixels before applying K-means clustering. Unlike previous approaches that did not assess the pixel-level temporal stability, this method provides more reliable clustering results. The K-means identified 160 distinct clusters, with Cluster 13 Global Temporally Stable (Cluster 13-GTS) showing significant improvements in temporal stability. Compared to Cluster 13 Global (Cluster 13-G) from earlier research, Cluster 13-GTS reduced the coefficient of variation by up to 1% and increased the number of calibration locations from 23 to over 50. This study also validated these clusters using TOA reflectance from ground-truth measurements collected at the Radiometric Calibration Network (RadCalNet) Gobabeb (RCN-GONA) site, incorporating data from Landsat 8, Landsat 9, Sentinel-2A, and Sentinel-2B. The GONA Extended Pseudo Invariant Calibration Sites (EPICS) GONA-EPICS cluster used for the validation provided statistically comparable mean TOA reflectance to RCN-GONA, with a reduced chi-square test indicating minimal differences within the cluster’s uncertainty range. Notably, the difference in reflectance between RCN-GONA and GONA-EPICS was less than 0.023 units across all the bands. Although GONA-EPICS exhibited slightly higher uncertainty (6.4% to 10.3%) compared to RCN-GONA site (<5%), it offered advantages such as 80 potential calibration points per Landsat cycle and reduced temporal instability, and it provided alternatives to reduce the reliance on single sites like traditional PICS or RCN-GONA, making it a valuable tool for calibration efforts. These findings highlight the potential of the newly developed EPICS for radiometric calibration and stability monitoring of optical satellite sensors. Distributed across diverse regions, these global targets increase the number of calibration points available for any sensor in any orbital cycle, reducing the reliance on traditional PICS and offering more robust targets for radiometric calibration efforts.

A Study on the Spatiotemporal Dynamics of Land Cover Change and Carbon Storage in the Northern Gulf Economic Zone of Guangxi Based on the InVEST Model

In recent years, the international community has increasingly focused on the “dual carbon” issue, as human-induced land use changes significantly impact ecosystem structure and carbon cycling. This study analyzes land use changes in the economic zone of the northern Gulf of Guangxi from 1980 to 2020, utilizing the InVEST model to simulate spatiotemporal changes in carbon storage and conducting zoning studies through spatial analysis. The findings reveal that ① forest land and arable land dominate the northern Gulf of Guangxi’s land use, with notable changes observed in forest land, unused land, and construction land areas. Forest land and construction land have increased by 1761.5 km2 and 1001.19 km2, respectively, while unused land has decreased by 1881.18 km2 from 2000 to 2020. ② The total carbon storage values in the northern Gulf of Guangxi in 1980, 2000, and 2020 were, respectively, 504.91 × 106/t, 487.29 × 106/t, and 500.31 × 106/t, with the expansion of construction land and conversion of forest land being the main reasons for the decrease in carbon storage. ③ In the northern Gulf of Guangxi, there is a slight upward trend in total carbon storage values over time. Spatially, higher carbon storage values are observed in mountainous and hilly areas at high altitudes, while the central and southern coastal areas exhibit lower carbon storage values. ④ The local spatial autocorrelation results reveal that Pu Bei County exhibits high–high clustering of carbon storage, while He Pu County undergoes a transition from high–low to low–low clustering, and several other administrative areas in Beihai demonstrates low–low clustering. Due to the imperative of economic development, the expansion of urban construction land encroaches upon ecological land, resulting in a decline in carbon storage. Therefore, in the Northern Gulf of Guangxi, it is essential to implement measures such as reforestation and establish ecological protection areas such as forests, grasslands, and wetlands to develop effective carbon sequestration methods and compensate for the carbon loss caused by the expansion of construction land.

Assessing Land Use and Cover Changes arising from the 2022 water crisis in Southeast China: A comparative analysis of Remote Sensing Imagery classifications and Machine Learning algorithms

Abstract. The water crisis in the southeast region of China in 2022, caused by one of the worst heatwaves on record, was characterized by severe shortages of water resources, leading to challenges for local communities, agriculture, and industry. To analyze changes in land use and land cover (LULC) in the Jialing River region, Chongqing, China, we compared Remote Sensing (RS) imagery classifications before and after the intense heat waves of 2022. We evaluated the performance of two machine learning algorithms, KDTree KNN and Random Forest (RF), in LULC classifications. The classifications were carried out based on the RS images from the OLI/Landsat 8 system, NDWI index, and SRTM data. The model performances were similar, the classification accuracy showed that the RF algorithm was superior to KDTree KNN. The RF LULC classification and area calculation corroborate with the visual analysis, reaffirming the superiority of RF, which shows a decrease in water surface area, unlike DKTree KNN.

Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, Mexico

Abstract. This study evaluates four Machine Learning Algorithms—Random Forest (RF), K-Means Clustering, Support Vector Machine (SVM), and Classification and Regression Trees (CART)—for precise land use and land cover (LULC) classification in the Monterrey Metropolitan Area. During the period 2016-2019, and with alternating wet and dry season classifications, the research addresses challenges in identifying narrow rivers, using geospatial tools and it does notably the Pesqueria River, which is specially the most narrow and shallow river in the area. Five classes—Water, Vegetation, Urban, and Soil—were classified, achieving precision rates above 85%. Remarkably, SVM exhibited an excellent accuracy, particularly for narrow rivers, showcasing its utility in complex urban landscapes. The study utilizes high resolution satellite imagery with a spatial resolution of 4.7m, contributing to the reliability of the results. Emphasizing temporal dynamics, the research links LULC changes to urbanization, infrastructure, and seasonal variations, offering vital insights for sustainable urban development.

A Study on the Cooling Impact of the River Yamuna and its Surrounding LULC: A Case Study of New Delhi, India

Abstract. The unplanned growth of urban cities has adversely affected the thermal conditions. The Land Use and Land Cover (LULC) present in urban areas significantly impacts the thermal conditions of the surrounding environment. This study investigated the cooling effect of river in dense urban environments. It is conducted on the Yamuna river that flows through New Delhi, the capital city of India. The River Cooling Intensity (RCI) and River Cooling Range (RCR) assessment has been done for the summer season of May 2023 with the help of Landsat 8 images. The RCI ranged from 3.58°C to 1.16°C while the RCR varied from 1320 m to 360 m. It is observed that the river cooling effect is higher in the built-up class while the vegetation class weakens the effect of river cooling intensity and range. Zonal mean temperature varies from 32.02°C to 34.82°C according to the LULC distribution. This study provides valuable insights into the relationship between river cooling and temperature regulation according to the LULC classes. It also establishes the presence of an urban cool island inside the city region due to the river, as the mean temperature of the river surrounding (33.83°C) was lower than that of the city (35.73°C).