Land Use Cover Classification Research Articles

Fine-resolution estimation for urban surface water pollution susceptibility with multi-modal earth observation data

Abstract The sustainability and suitability of water resources are of great importance for maintaining urban populations. The landscapes and environment around urban waters have always been the main focus of maintaining water quality for sustainable water supplies. Early-stage field investigations recognized the influence of land use/land cover (LULC) on water quality. To extend the research scope in spatial and temporal dimensions, remote sensing techniques have been utilized to discover the relationships between LULC and water quality. However, these remote sensing datasets generally had a medium spatial resolution, making them unable to support the fine-detailed land classifications that are critical to explore the water quality in an urban area. Moreover, although more details regarding the land surface are available from the currently-generated high-resolution and very-high-resolution remote sensing images, this land surface information is too complex for the state-of-the-art deep learning approaches and benchmark datasets. This manuscript reports our efforts on developing a framework to explore the fine-resolution relationship between surface water pollution and LULC. To address the cost of computing time and limitations of well-labelled datasets, we employ a foundation model-enhanced approach for water extraction and water-surrounded LULC classification. We propose an estimator of surface water pollution susceptibility to main pollutants based on the surrounding LULCs. Selecting the Future City of Beijing as the study area, based on very-high-resolution remote sensing images, the experiment proved that our proposed approach could effectively map the susceptibility of surface water pollution caused by its surrounding land use and land cover. To our knowledge, the relationship of LULCs and water quality have not been investigated using 0.5m spatial resolution data. We hope our work can provide a prospective fine-detailed water quality analysis in the community of water environment of remote sensing.

Harmonized NDVI time-series from Landsat and Sentinel-2 reveal phenological patterns of diverse, small-scale cropping systems in East Africa

Capturing the phenological responses of crops in diverse, small-scale agricultural systems is one of the most notable remote sensing research gaps in Eastern Africa. Because of the heterogeneous and complex agricultural environments, remote sensing methodologies successfully implemented elsewhere yield poor results in Eastern Africa. The reliance on local rainfall and dry spells can lead to alterations in planting patterns, impacting growing season timing and yield, underscoring the importance of high temporal resolution for capturing crop phenology. We generated a high-resolution land use/land cover (LULC) classification map of 10 m using Sentinel-1 and Sentinel-2 imagery together with a large set of reference data acquired in situ in 2022. The LULC classification yielded an overall accuracy of 87.9% and Cohen's kappa value of 0.83. Based on the LULC map, we investigated the phenological metrics of the most important crops in the Kamuli district of Eastern Uganda by combining a multi-sensor harmonization approach, thresholding method, and cloud computing. Previously unavailable Landsat 9 band adjustment coefficients were derived. By harmonizing Landsat (LS) 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 and 9 Operational Land Imager (OLI), with Sentinel-2 Multispectral Instrument (MSI), the phenological patterns during the March to May growing season were captured using a dense temporal Normalized Difference Vegetation Index (NDVI) time series. The seasonal onset for most crop types was in the end of March to middle of April while the end of the growing season occurred from the end of June to middle of July. Investigation of the 13 crop types using a high-resolution LULC map and dense, harmonized NDVI time series signifies the first field-level phenological metrics estimation of smallholder farming systems in Eastern Africa. Future research should focus on expanding the methodology in space and time to provide insights into past and current growing season development.

Sentinel 1a-2a Incorporating an Object-Based Image Analysis Method for Flood Mapping and Extent Assessment

Abstract. This study presents flood extent extraction and mapping from Sentinel images. Here we suggest an algorithm for extracting flooded areas from object-based image analysis (OBIA) using Sentinel-1A and Sentinel-2A images to map and assess the flood extent from the beginning to one week after the event. This study used multi-scale parameters in OBIA for image segmentation. First, we identified the flooded regions by applying our proposed algorithm on the Sentinel-1A. Then, to evaluate the effects of the flood on each land-use/land cover (LULC) class, Sentinel-2A images is classified using the OBIA after the event. Besides, we also used the threshold method to compare the proposed algorithm applying OBIA to determine the efficiency in computing parameters for change detection and flood extent mapping. The findings revealed the best performance for the segmentation process with an Object Fitness Index (OFI) is 0.92 when the scale parameter of 60 is applied. The results also show that 2099.4 km2 of the study area is flooded at the beginning of the flood. Furthermore, we found that the most flooded LULC classes are agricultural land and orchards with 695.28km2 (32.4%) and 708.63 km2 (33.7%), respectively. In comparison, about 33.9% of the remaining flooded area has occurred in other classes (i.e., fish farm, built-up, bare land and water bodies). The resulting object of each scale parameter was evaluated by Object Pureness Index (OPI), Object Matching Index (OMI), and OFI. Finally, our Overall Accuracy (OA) method incorporated field data using the Global Positioning System (GPS) shows 93%, 90%, and 89% for LULC, flood map (i.e., using our proposed algorithm), and threshold method, respectively.

Analysis of Land Use Land Cover and Land Surface Temperature in Karst Area: A Case Study Wonogiri Regency

<p class="Abstract">Urban development and global climate change drive increased Land Surface Temperature (LST). Wonogiri Regency is an area that is within the development range of Surakarta City. Analysis of land surface temperature has become Important in Wonogiri Regency as a mitigation measure for urban heat islands. This study aims to measure Land Surface Temperature (LST) in Wonogiri Regency and determine the controlling factor in the form of Land Use Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), seasons, and zones by landform. Using the Google Earth Engine platform, LST calculations, LULC classifications, and NDVI calculations can compute imagery. The dataset used is USGS Landsat 8. The analytical technique used in this study is comparative descriptive, which compares other controlling factors. The analysis results show the vital role of LULC, NDVI, seasonality, and landform on LST. LULC types of built-up land, low NDVI values, dry season, and southern zone (karst) tend to have higher LST than other factors.</p>

CA-Markov prediction modeling for the assessment of land use/land cover change in two sub-basins of the Tocantins-Araguaia River Basin.

Due to the anthropogenic pressures of expansion areas for livestock and agricultural production in the Brazilian Cerrado, it is of paramount importance to understand the dynamics of land use/land cover (LULC) changes in this region. Thus, we investigated LULC changes in two sub-basins of the Tocantins-Araguaia River basin from 1997 to 2015 and consequently projected future changes for the timespan between 2030 and 2050. The Formoso sub-basin experienced significant expansion of agricultural and pasture areas, whereas the Sono sub-basin limited farmland expansion (more stable native vegetation) due to substantial protected areas, trends that were also observed for future projections (2030 and 2050). Pastureland in the Formoso sub-basin increased by 5.8%, while the Sono sub-basin saw significant gains in cultivated land, according to change detection analyses during the 1997-2015 period. High stability probabilities of no change (> 70%) for grassland areas in the Sono River sub-basin and pasturelands in the Formoso River sub-basin were computed. The CA-Markov model demonstrated a high consistency level with actual LULC classes for both sub-basins, as indicated by an overall Kappa coefficient above 0.8. Future projections for 2030 and 2050 show a substantial expansion of agriculture and pasture in both sub-basins, driven by specific factors such as soil organic carbon stocks, distance from rural settlements, and proximity to rivers. Short- and mid-term simulations indicate substantial expansion of agriculture and pasture in both basins, with potential adverse impacts on water erosion. Consequently, developing policies for soil management and sustainable land use planning is essential for agroecosystem sustainability, promoting a balanced approach to economic development while addressing climate change and anthropogenic challenges.

Land Use Change Mapping and Analysis Using Remote Sensing and GIS: A Case Study in Tam Ky City, Quang Nam Province, Vietnam

Changes in land use/land cover (LULC) play a critical role in effective natural resource management, monitoring, and development, particularly within the realm of urban planning. In the examination of Tam Ky city, Quang Nam province, Vietnam, spanning from 2000 to 2020, remote sensing and Geographic Information System (GIS) techniques were employed. The Landsat satellite data (Landsat 7 ETM+ for 2000, Landsat 5 TM for 2010, and Landsat 8 OLI for 2022) underwent analysis using the supervised classification method in ArcGIS 10.8 software to identify and categorize six primary LULC classes: water bodies, agriculture, settlements, vegetation, construction, and bare soil/rocks. The reliability of the classification was evaluated through k values, revealing high accuracy with values of 0.951, 0.953, and 0.950 for the years 2000, 2010, and 2020, respectively. Notable shifts in LULC were observed during the period from 2000 to 2020. The areas covered by vegetation and settlements expanded by 53 and 1300 ha, respectively, while water bodies, agriculture, construction, and bare soil/rocks experienced reductions of 466, 48, 413, and 425 ha, respectively. To facilitate a rapid assessment, the study also incorporated the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). The trends identified in this study are consistently aligned with the results of the supervised classification. The identified changes in LULC pose a substantial environmental threat, and the study's outcomes serve as a valuable asset for future land use planning and management in the area. The method's high accuracy enhances the dependability of the results, making them crucial for well-informed decision-making and sustainable development initiatives.

Multi-scenario simulation and evaluation of the impacts of land use change on ecosystem service values in the Chishui River Basin of Guizhou Province, China

Changes in land-use patterns are the main driving factors of ecosystem service values (ESVs). The quantitative evaluation of ESVs is significant for the sustainable development of ecologically fragile areas. In this study, the Chishui River Basin (CRB) in China was selected as the study region, and an intensity map (IM) was used to analyze the transformation of land use/land cover (LULC). The modified equivalent coefficient value method was used to evaluate the ESVs based on the 2000, 2010, and 2020 land use datasets. The spatial and temporal distributions of ESV and its response to changes in LULC were analyzed using the Getis-Ord Gi* statistical method and Spearman’s rank correlation analysis. Moreover, spatial and temporal changes in LULC and ESV over the next 30 years in the four development scenarios were simulated using the Markov-PLUS model. The following results were obtained: 1) The farmland and forestland were the dominant LULC categories, and that the LULC pattern dramatically changed in the study area; 2) the total ESV of the CRB decreased significantly from 23.21 billion yuan in 2000 to 22.29 billion yuan in 2020; and 3) the ecological conservation scenario significantly promoted the total ESV, and the town development scenario was more conducive to the urbanization process. Therefore, although the ESV of the CRB showed a decreasing to slowly increasing trend in the context of a series of ecological restoration measures, scientific ecological protection and development policies must be continuously implemented to achieve harmony between ecological service improvement and urbanization development. Our results provide important information for making land-use decisions and facilitating sustainable development in ecologically protected basins.

Remote Sensing-Enabled Urban Growth Simulation Overlaid with AHP-GIS-Based Urban Land Suitability for Potential Development in Mersin Metropolitan Area, Türkiye

This study delves into the integration of analytic hierarchy process (AHP) and geographic information system (GIS) techniques to identify suitable areas for urban development in six districts within the Mersin Metropolitan Area of Turkey. The specific aim is to generate an urban land use suitability map, in order to facilitate informed decision-making for urban development. Drawing on open Landsat satellite imagery and employing the random forest (RF) algorithm, the study spans a fifteen-year period, over which land use/land cover (LULC) changes are measured. Furthermore, a novel approach is introduced by incorporating the urban land use suitability map into an urban growth simulation model developed using a logistic regression (LR) algorithm. This simulation forecasts urban growth up to 2027, enabling planners to evaluate potential development areas against suitability criteria. Findings reveal spatial patterns of land suitability and projected urban growth, aiding decision-makers in selecting optimal areas for development while preserving ecological integrity. Notably, the study emphasizes the importance of considering various factors such as topography, accessibility, soil capability, and geology in urban planning processes. The results showcase significant proportions of the study area as being moderately to highly suitable for urban development, alongside notable shifts in LULC classes over the years. Additionally, the overlay analysis of simulated urban growth and land suitability maps highlights areas with contrasting suitability levels, offering valuable insights for sustainable urban growth strategies. By overlaying the urban land suitability map with a simulated LULC map for 2027, it is revealed that 2247.3 hectares of potential new urbanization areas demonstrate very high suitability for settlement, while 7440.12 hectares exhibit very low suitability. By providing a comprehensive framework for assessing urban land suitability and projecting future growth, this research offers practical implications for policymakers, urban planners, and stakeholders involved in Mersin’s development trajectory, ultimately fostering more sustainable and resilient urban landscapes.

Assessing the impact of land use land cover changes on soil moisture and vegetation cover in Southern Punjab, Pakistan using multi-temporal satellite data

ABSTRACT Change in land use land cover (LULC) is driven by human activities and drives changes that limit the accessibility of services and products for livestock and humans. This study aimed to develop the spatio-temporal changes in LULC, vegetation cover, and moisture index using multi-temporal satellite data in Southern Punjab, Pakistan. Analysis of satellite data was accomplished using ArcGIS 10.4 software. Based on ground-truthing, the supervised classification technique (maximum likelihood algorithm) was used to achieve the LULC classification. The LULC change analysis revealed that vegetation area converted to the build-up area by 7.17 % and bare soil converted to urban areas by 1.68 % from 2000 to 2021. Average NDVI values were calculated at 0.23, 0.17, 0.19, and 0.14 for 2000, 2007, 2014, and 2021, respectively. In the study area, average NDMI values were observed at 0.28, 0.25, 0.2, and 0.15 for 2000, 2007, 2014, and 2021, respectively. Based on our study, the general trend in Southern Punjab is a decrease in vegetation cover, moisture index, and forest area due to an increase in build-up areas. Assessments of LULC and NDMI changes, as well as estimates of the effect on the environment, are necessary for many policy decisions and planning.

Land Use Land Cover (LULC) Change Dynamics Associated with Mining Activities in Kitwe District and Adequacy of the Legal Framework on Mine Closure in Zambia

Land use land cover (LULC) changes resulting from copper exploration in Kitwe District, Copperbelt Province has adversely impacted the environment. To understand LULC change dynamics associated with mining activities, this study mapped LULC changes using the Google Earth Engine (GEE) from 1990 to 2020. In addition, the Zambian legal framework for mine closure was assessed in terms of adequacy and comprehensiveness. A remote sensing analysis using Landsat TM (1990, 2000, and 2010) and OLI (2020) images was performed and the GEE Random Forest classifier algorithm was employed to detect LULC changes. Then, transition matrices and overall changes were calculated for each LULC class. The LULC classification had an overall accuracy and kappa coefficient of 82.47% and 0.78, respectively. In total, 45.2% of the district area (360.92 km2) experienced LULC changes from 1990 to 2020. The overall change indicates that the areas of built-up area, bare land, and grassland/pasture/agricultural land gained 35.84, 14.67, and 43.53 km2, respectively, while forest lost 95.30 km2, with the major driver being the privatization of mining companies. Several concerns regarding the mine closure process practiced in Zambia have principally been raised to the government. Although the legislation generally conformed to international best practices, a gap involving various pieces of legislation, overlapping requirements, and different interpretations of the laws by different governmental departments makes the system complex and unmanageable. An area of concern is the government’s capability and competence to implement legislation. Ineffective law enforcement, that is, the inadequacy of the legislation, is to blame for LULC changes in mining areas, resulting in mining corporations not paying attention to the changes made, particularly regarding mine closures. This study provides decision-makers and land use planners with baseline knowledge on LULC changes that can be valuable for future mining legislation and how these legislations can be effectively executed to ensure sustainable mine closure.

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.

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.

Analysis of land use/ land cover changes and landscape fragmentation in the Baro-Akobo Basin, Southwestern Ethiopia

This study investigated the relationship between land use/land cover (LULC) changes and forested landscape fragmentation in the southwestern region of Ethiopia. Satellite images from 1986, 2002 and 2019 were collected and analyzed using standard procedures in ERDAS 2015 software. Fragstat 4.2.1 software was utilized to assess landscape fragmentation by examining a raster datasets derived from the classified LULC map over the research period. The study identified seven LULC classes in the study area. Findings revealed a substantial reduction in shrubland by 46.3%, dense forest by 23.75%, open forest by 17.3%, and wetland by 32.63%, while cropland increased by 38.06%, agroforestry by 20.29%, and settlements by 163.8% during the study period. These changes varied across different agroecological zones and slope gradients. Landscape metrics results indicated an increase in the number of patches and patch density for all LULC classes, demonstrating significant fragmentation of the landscape. The largest patch index, mean patch areas, and the percentage of landscape occupied by open forest, dense forest, shrubland, and wetland declined as a result of conversion to cropland, agroforestry, and settlement. Conversely, the largest patch index, the mean patch area and the percentage of the landscape occupied by agroforestry, cropland and settlement increased, indicating their increasing dominance in the landscape over the study periods. The findings highlighted the potential deleterious impacts of ongoing land use change and fragmentation on the environment, ecosystem function and local livelihoods. Therefore, it is crucial to implement appropriate conservation efforts and sustainable land management practices to mitigate the rapid change and fragmentation of land use and its negative impacts on sub-watershed ecosystems.

Performance assessment of machine learning algorithms for mapping of land use/land cover using remote sensing data

The rapid increase in population accelerates the rate of change of Land use/Land cover (LULC) in various parts of the world. This phenomenon caused a huge strain for natural resources. Hence, continues monitoring of LULC changes gained a significant importance for management of natural resources and assessing the climate change impacts. Recently, application of machine learning algorithms on RS (remote sensing) data for rapid and accurate mapping of LULC gained significant importance due to growing need of LULC estimation for ecosystem services, natural resource management and environmental management. Hence, it is crucial to access and compare the performance of different machine learning classifiers for accurate mapping of LULC. The primary objective of this study was to compare the performance of CART (Classification and Regression Tree), RF (Random Forest) and SVM (Support Vector Machine) for LULC estimation by processing RS data on Google Earth Engine (GEE). In total four classes of LULC (Water Bodies, Vegetation Cover, Urban Land and Barren Land) for city of Lahore were extracted using satellite images from Landsat-7, Landsat-8 and Landsat-9 for years 2008, 2015 and 2022, respectively. According to results, RF is the best performing classifier with maximum overall accuracy of 95.2% and highest Kappa coefficient value of 0.87, SVM achieved maximum accuracy of 89.8% with highest Kappa of 0.84 and CART showed maximum overall accuracy of 89.7% with Kappa value of 0.79. Results from this study can give assistance for decision makers, planners and RS experts to choose a suitable machine learning algorithm for LULC classification in an unplanned urbanized city like Lahore.

Urban engineering insights: Spatiotemporal analysis of land surface temperature and land use in urban landscape

In the field of urban environment engineering, understanding the relationship between land surface temperature (LST) and land use land cover (LULC) is essential in rapidly growing and climatically unstable urban landscapes such as Chengdu. It helps alleviate the magnitude and intensity of Urban Heat Islands (UHIs). Toward this aim, summer and winter Landsat images were acquired in four years from 1992 to 2021 and were used to extract LULC classes, LST and three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) and analyze their spatiotemporal associations. Results showed that built-up areas expanded approximately six times (820.82 Km2, 584.96%) from 1992 to 2021. Meanwhile, their mean LST increased in both seasons, by 9.94 °C in summer and by 0.95 °C in winter. The LST-NDBI correlation was significant and positive in both the summer and winter of the studied years (0.437< r <0.874, p=0.00) while a very high variability was observed in the LST-NDVI (-0.835< r <0.255, p=0.00) and LST-MNDWI (-0.632< r <0.628, p=0.00) correlation coefficients. According to the results, NDBI can be used as a good intra- and inter-annual predictor of LST in Chengdu, especially in the context of its fast-paced physical expansion and increasing intensity of UHI.

Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network

The integration of multi-spectral remote sensing and GIS-based analysis is significant for studying land cover changes, providing valuable insights for informed land management and sustainable development. The present study aims to examine land use land cover (LULC) changes of three decades from 1991 to 2021 and predict the future LULC change in Binh Duong province, Vietnam to explore a future research direction on land use change and associated challenges in the study region. Multi-spectral remote sensing data and random forest tree (RFT) were utilized to generate LULC maps. Areal statistics and annual change rate were considered to analyze the categorical land use change detection. Statistical measures such as user's accuracy, producer’s accuracy, kappa coefficient, and confusion matrix were employed to assess the accuracy of LULC classification. To predict future LULC and simulate the spatio-temporal change, we considered previous year LULC maps, independent spatial variables and a combined artificial neural network (ANN) multi-layer perceptron approach. The analysis revealed that there was a huge transition from agricultural lands to residential land with industry and commerce which resulting an expansion of impervious lands and a rapid decline of agricultural land as well as of scrub land and barren lands, and a changeability of forest and plantation, croplands, and waterbodies. Our study revealed that the impervious land has expanded 10 times within 30 years and will increase in the future.

Cognitive land cover mapping: A three-layer deep learning architecture for remote sensing data classification

In many application domains, remotely sensed (RS) data are essential for disaster monitoring, climate forecasting, and remote surveillance. It perceives and gathers environmental, sociological, and transitional data on its own, from a distance, in increasing amounts of spatial and temporal details. While urbanization has been continued to be a worldwide phenomenon, the urban management and planning are needed to satisfy the needs of the Earth system. The operations associated with land management, urban planning, and transportation on Earth have been connected to digital maps and geographic databases. Knowledge of land cover is also significant for managing, conserving, and formulating environmental and urban development policies. Therefore, highly accurate Land Use Land Cover (LULC) assessment and monitoring are of the utmost significance for empirical studies and organizational accomplishment. In this study, we leverage remotely sensed data to formulate a sophisticated deep learning system for comprehensive Earth observation. The core of our approach involves the development of a three-layered Convolution-Convolution-Maxpooling Convolutional Neural Network (CCMCNN) model, preceded by a meticulous pre-processing stage. In the pre-processing stage, images extracted from the NWPU-RESISC dataset undergo resizing, division, and augmentation procedures before being fed into the three-layered CCMCNN architecture for the purpose of classifying them into five different LULC categories. The effectiveness of our proposed model undergoes thorough validation through a comparative examination against various pre-trained deep learning models outlined in existing literature. Presenting performance metrics, our model attains a remarkable accuracy rate of 98.4%. This advancement holds significant promise in the realm of Earth surveillance, offering a valuable tool for enhancing situational awareness and environmental monitoring.

Tripartite relationship of urban planning, city growth, and health for sustainable development in Akure, Nigeria

We live in an urban planet. As the world continues to urbanize, urban development that support the health and wellbeing of city dwellers is far more important than ever before to achieve sustainable development targets. This study explores the complex relationship among urban planning, city growth, and health as critical drivers of sustainable development in the rapidly growing nodal city of Akure, Nigeria. The study provides a four-decade spatio-temporal model of urban Land Use Land Cover (LULC) changes in Akure between the years 1984 and 2023 from acquired Landsat satellite imageries. The result shows more than 20% net change increase in developed LULC classes between the study years. A strong positive correlation exists between the years covered in the analyses and urban development (r = 0.93, p = 0.002), and a strong negative relationship with the forest land use (r = −0.94, p = 0.002) with potential debilitating impacts on residents’ health, green infrastructures and the city’s sustainability in the future. Furthermore, results of key informant interviews (KIIs) of officials of the Ministry of Physical Planning and Urban Development (MPPUD) in Akure, Ondo State, unveil various views on the “place of health” in urban planning practices in Akure. A lack of synergy between urban planners and public health practitioners in the city and limiting scope of functions of urban planning on the impact of health in Akure were observed. Thus, we recommend the integration of a sustainable urban planning approach as a guide to manage the city.

Land Surface Temperature and Urban Heat Island: A Case Study of Faridabad City using RS and GIS techniques

Land use and land surface temperature both plays a crucial role in the global climate change studies, as a little fluctuation in these, can bring about a dramatic impact on the regional as well as global environment. With rapid urbanization many regions across the world are altering the existing land use/land cover (LULC), which is significantly raising the land surface temperature (LST). The present study aims to estimate the land use change and land surface temperature and what changes does both bring about in a span of 20 years over Faridabad MC. Faridabad being a part of Delhi NCR has seen rapid growth of population in last two decades which has severely impacted its Land cover. Landsat imagery of 2002, 2013 and 2022 has been used for estimating both the LULC and LST of the city. Supervised classification with Maximum likelihood Classification (MLC) which is basically a machine learning based algorithm has been adopted for LULC classification and Mono window algorithm has been used for the retrieval of LST. UTFVI (Urban Thermal Field Variance Index) has also been calculated which showed different Urban heat islands across the city. The results reveal a rapid increase in the built-up area and reduction in the vegetation and agricultural land. Similarly, Land surface temperature was highest in highly dense built-up areas as well in the barren area of Faridabad, compared to this LST was comparatively lower over the agriculture and vegetation and least of water bodies. There were several UHI hotspots over the city area, mostly located in the densely populated and industrial parts of city.

Spatio-temporal Landuse/Landcover Dynamics in the Coastal Areas of Limbe and Douala IV Municipalities and Implications on Wetland Regulating Ecosystem Services

Globally, wetlands are ecological hot spots and as a result they supply a plethora provisioning, regulating, supporting and cultural services. Despite their functions, wetlands continue to degrade arising from urban developments (population growth and housing developments, road infrastructural developments, urban agricultures and usage of wetlands as dumpsites). These activities operate via land use/land cover changes at different scales in different areas depending on technology and needs of the population. This paper describes the implications of spatio-temporal land use/ land cover (LULC) dynamics in the Limbe I and III Municipality (L1&amp;3Ms) and Douala IV Municipality (D4M) coastal areas from 1986 to 2022 on wetland ecosystem services. This research made use of Geographic Information System (GIS) technique in describing the land use/cover changes in the study areas. Primary data was obtained through field observations, questionnaires and ground truthing aim at confirming the observations on downloaded satellite imageries. Four Satellite images of different years (1986, 1999, 2013 and 2022) were downloaded from the United State Geologic Survey (USGS) Earth Explorer. Images were imported into Erdas Imagine, 2014 where preprocessing was done and Supervised Classification was adopted for the processing of the image to generate a raster format depicting the designed land cover/land uses; especially the wetlands which constituted one of the themes. Maps were generated to show changes in land use/cover which were transposed into tables and figures to show the trends in magnitude of changes, percentage of change and the rate of change. Within 36 years (1986-2022) built-up increased by 0.75km2 (0.36%) and 1.63km2 (0.45%) while wetlands retreated by -0.2km2 (-.96%) and -0.48km2 (-0.14%) in L1&amp;3M and D4M respectively. Annually, wetlands decreased by - 97.04km2 and -191.54km2 while settlements/infrastructures increased by 294.33km2 and 644.46km2 over the same 36 year periods in L1&amp;3Ms and D4M respectively. Base on wetland regulating services, changes in LULC has exposes neighbourhoods in L1&amp;3Ms to flood vulnerability comprising the vulnerable, less vulnerable, least vulnerable and most vulnerable areas as opposed to two categories of neighbourhoods in D4M (most vulnerable neighbourhood’s and the less vulnerable neighbourhoods). Information on changes in land use-land cover is crucial in delineating impacted areas, understand the type of changes, their spatial patterns and the need for formulating proper urban planning and environmental preservation policies as well as updating these policies with time. GIS analysis of 1986, 1999, 2013, and 2022, indicated that, there have been significant modifications in observed LULC classes (forest cover, farmland and built-up land uses) over L1&amp;3Ms and D4M.