Landsat Satellite Research Articles

Classification of Flood Disaster Risk Areas Using Vegetation Index Calculation and Landsat-8 Satellite Imagery in West Sumatra

One of the Indonesian provinces most vulnerable to flooding catastrophes is West Sumatra. West Sumatra is a province that spans 98°36' to 101°53' East Longitude and 00°54' North Latitude to 03°30' South Latitude. Its total size is roughly 42,297.30 km2, or 4,229,730 hectares, and it is surrounded by around 391 major and small islands. Flash floods and landslides have affected a number of regencies and cities in West Sumatra, including Agam Regency, Tanah Datar Regency, Padang Panjang City, Padang Pariaman Regency, and Padang City. The objective of this study is to use vegetation index estimates based on Landsat-8 satellite images to categorise flood catastrophe risk zones in West Sumatra. NDVI = -0.106 and NDWI = -0.03999, respectively, are examples of vegetation indices that define land as either non-vegetated or sparsely vegetated, and SAVI = 0.0002447 classify areas with water bodies such as rivers/floods.

Spatial Evaluation of Waste Disposal Site Selection Using GIS-Based Multi-Criteria Analysis: A Case Study of Dera Ghazi Khan District, Pakistan

The rapid population growth and rising per capita incomes have caused the city to generate massive amounts of municipal waste, indicating a serious environmental threat. Solid waste disposal has become a crucial problem for several municipalities. The selection of suitable waste disposal sites is a crucial problem in the urban areas of developing countries like Pakistan due to unsatisfactory urban planning and management to decrease human being’s health risks. Particularly, Dera Ghazi (D.G.) Khan district is facing the issue of identifying suitable sites for solid waste disposal. This research aims to select appropriate potential sites suitable for solid waste disposal purposes in the D.G. Khan. Primary datasets used for this study are Landsat-8 satellite imagery, digital elevation model (DEM) with 30-meter resolution for slope extraction. Other criteria included roads, railroads, and rivers digitized using the topographical map of the study area. The maps are prepared to incorporate overlay and suitability analysis using Geographic Information System (GIS), Remote Sensing techniques, and Analytical Hierarchy Process (AHP), a multi-criteria analysis. The final suitability map of study area is prepared using a GIS software suite and categorized as highly, moderately, least, and unsuitable regions, of which 3% is entirely unsuitable area, 70% less suitable, 26.16% moderately suitable, and 0.84% highly suitable area. The suggested disposal sites have been carefully selected to assist policymakers in determining the most sensitive areas and resolving waste management issues with the slightest contamination of water bodies and the environment.

Addressing the impact of land use land cover changes on land surface temperature using machine learning algorithms

Over the past two and a half decades, rapid urbanization has led to significant land use and land cover (LULC) changes in Kabul province, Afghanistan. To assess the impact of LULC changes on land surface temperature (LST), Kabul province was divided into four LULC classes applying the Support Vector Machine (SVM) algorithm using the Landsat satellite images from 1998 to 2022. The LST was assessed using Landsat data from the thermal band. The Cellular Automata-Logistic Regression (CA-LR) model was applied to predict the future patterns of LULC and LST for 2034 and 2046. Results showed significant changes in LULC classes, as the built-up areas increased about 9.37%, while the bare soil and vegetation cover decreased 7.20% and 2.35%, respectively, from 1998 to 2022. The analysis of annual LST revealed that built-up areas showed the highest mean LST, followed by bare soil and vegetation. The future simulation results indicate an expected increase in built-up areas to 17.08% and 23.10% by 2034 and 2046, respectively, compared to 11.23% in 2022. Similarly, the simulation results for LST indicated that the area experiencing the highest LST class (≥ 32 °C) is expected to increase to 27.01% and 43.05% by 2034 and 2046, respectively, compared to 11.21% in 2022. The results indicate that LST increases considerably as built-up areas increase and vegetation cover decreases, revealing a direct link between urbanization and rising temperatures.

Spatiotemporal changes of urban heat island effect relative to land surface temperature: a case study of Jinan City, China.

The urban heat island (UHI) effect has become increasingly prevalent and significant with the accelerated pace of urbanization, posing challenges for urban planners and policymakers. To reveal the spatiotemporal variations of the urban heat island effect in Jinan City, this study utilized Landsat satellite images from 2009, 2014, and 2019, employing the classic Mono-Window algorithm to extract land surface temperature (LST). Additionally, Geodetector was introduced to conduct a detailed analysis of the relationship between LST in Jinan City and land cover types (vegetation, water bodies, and buildings). The results indicate a significant increase in the severity of the urban heat island effect in Jinan from 2009 to 2019, with the central urban area consistently exhibiting a high-intensity core heat island. Suburban areas of Jinan show a clear trend of merging their heat island effects with the central urban area. The combined area of strong cool island effect zones and cool island effect zones within water bodies reaches 89.7%, while the combined proportion of heat island and strong heat island effect zones in building areas is 62.2%. Vegetation cover (FVC) exerts the greatest influence among all factors on the intensity level of the urban heat island effect. These findings provide a reliable basis for decision-making related to urban planning and construction in Jinan City.

Harnessing Machine Learning Algorithms to Model the Association between Land Use/Land Cover Change and Heatwave Dynamics for Enhanced Environmental Management

As we navigate the fast-paced era of urban expansion, the integration of machine learning (ML) and remote sensing (RS) has become a cornerstone in environmental management. This research, focusing on Silchar City, a non-attainment city under the National Clean Air Program (NCAP), leverages these advanced technologies to understand the urban microclimate and its implications on the health, resilience, and sustainability of the built environment. The rise in land surface temperature (LST) and changes in land use and land cover (LULC) have been identified as key contributors to thermal dynamics, particularly focusing on the development of urban heat islands (UHIs). The Urban Thermal Field Variance Index (UTFVI) can assess the influence of UHIs, which is considered a parameter for ecological quality assessment. This research examines the interlinkages among urban expansion, LST, and thermal dynamics in Silchar City due to a substantial rise in air temperature, poor air quality, and particulate matter PM2.5. Using Landsat satellite imagery, LULC maps were derived for 2000, 2010, and 2020 by applying a supervised classification approach. LST was calculated by converting thermal band spectral radiance into brightness temperature. We utilized Cellular Automata (CA) and Artificial Neural Networks (ANNs) to project potential scenarios up to the year 2040. Over the two-decade period from 2000 to 2020, we observed a 21% expansion in built-up areas, primarily at the expense of vegetation and agricultural lands. This land transformation contributed to increased LST, with over 10% of the area exceeding 25 °C in 2020 compared with just 1% in 2000. The CA model predicts built-up areas will grow by an additional 26% by 2040, causing LST to rise by 4 °C. The UTFVI analysis reveals declining thermal comfort, with the worst affected zone projected to expand by 7 km2. The increase in PM2.5 and aerosol optical depth over the past two decades further indicates deteriorating air quality. This study underscores the potential of ML and RS in environmental management, providing valuable insights into urban expansion, thermal dynamics, and air quality that can guide policy formulation for sustainable urban planning.

Urban sprawl and thermal response in the Guangdong-Hong Kong-Macao Greater Bay Area, 2000–2020

The urban heat island (UHI) environment is closely related to the daily life of residents, and the impact of UHI is further expanding in the era of rapid urbanization, which is particularly obvious in the Beijing-Tianjin-Hebei urban agglomeration and Guangdong-Hong Kong-Macao Greater Bay Area (GBA). To explore the connection between UHI and urban expansion, this study takes GBA as the study area, based on Google Earth Engine platform and Landsat satellite data. The urban-rural temperature difference method was used to analyze the heat island zoning in the GBA from 2000 to 2020, and the direction and type of urban expansion in the GBA were investigated by standard deviation ellipse and LEI, to explore the link between the type of urban expansion and the heat island region through geospatial analysis methods. The results of the study show that: (1) The proportion of the area of strong and extreme heat islands in the GBA was 37.64% in 2000; 38.61% in 2005; 47.07% in 2010; 55.76% in 2015; and finally 59.69% in 2020. The proportion of the area of heat island regions in the period from 2000 to 2020 showed an overall increasing trend, with the largest increase in heat island regions in the period from 2015 to 2020. (2) The intensity of urban expansion in the GBA is 32% in both 2000–2005 and 2005–2010, which is significantly higher than the 9% in 2010–2015, and then increases to 24 per cent in 2015–2020. The speed of movement of urban centroids decreases in 2000–2015 and increases in 2015–2020. The direction of urban expansion in the first 5 years is dominated by the northeast, and the direction of urban expansion in the latter 15 years is generally dominated by the southwest, and the type of urban expansion is dominated by the fringe type, with the enclave type of expansion accounting for a smaller proportion. (3) Pearson correlation analysis further confirms that there is a significant positive correlation between urban sprawl and the urban heat island effect, with a correlation coefficient of 0.78 and a p-value of 0.041. There is a high degree of spatial consistency between the distribution of built-up urban areas and the distribution of heat island areas in GBA, and the direction of the evolution of the urban heat island areas is the same as the direction of the evolution of the built-up urban areas in general, and the thermal response of the marginal expansion to the ground surface temperature is stronger than that of enclave expansion. The thermal response to the surface temperature is stronger than that of the enclave type of expansion.

Mapping Vegetation Types and Land Use Dynamics in Kanyabaha Wetland from 1990 to 2021

Wetlands are crucial ecosystems providing essential ecological services, yet they face increasing threats from human activities. This study focuses on Kanyabaha Wetland in Uganda, examining its vegetation dynamics over three decades (1990-2021) using Landsat satellite imagery. The research characterizes land use and cover types including papyrus, grasslands, farmlands, tree plantations, built-up areas, and woodlands. Remote sensing data was processed and classified using ArcMap software, validated through field verification, resulting in high overall accuracy (>75%) across all study years. The images were analyzed using a hybrid of unsupervised (ISO data) and supervised (Maximum Likelihood) classification techniques. Findings reveal significant shifts in vegetation cover, with papyrus dominating initially but declining over time due to expansion in farmlands and settlements. Grasslands also decreased, while areas under farming and built-up structures expanded. Transition matrices illustrate these changes, highlighting stable and shifting landscape dynamics. Statistical analyses indicate a decrease in papyrus cover from 51.5% in 1990 to 39.1% in 2021, while farmland and built-up areas increased from 3.0% to 31.6% and 3.2% to 5.1%, respectively. This study highlights the vulnerability of Kanyabaha Wetland to anthropogenic impacts, necessitating targeted conservation strategies to sustain its ecological integrity amid ongoing land use changes.

Artificial Intelligence for Computational Remote Sensing: Quantifying Patterns of Land Cover Types around Cheetham Wetlands, Port Phillip Bay, Australia

This paper evaluates the potential of using artificial intelligence (AI) and machine learning (ML) approaches for classification of Landsat satellite imagery for environmental coastal mapping. The aim is to identify changes in patterns of land cover types in a coastal area around Cheetham Wetlands, Port Phillip Bay, Australia. The scripting approach of the Geographic Resources Analysis Support System (GRASS) geographic information system (GIS) uses AI-based methods of image analysis to accurately discriminate land cover types. Four ML algorithms are applied, tested and compared for supervised classification. Technical approaches are based on using the ‘r.learn.train’ module, which employs the scikit-learn library of Python. The methodology includes the following algorithms: (1) random forest (RF), (2) support vector machine (SVM), (3) an ANN-based approach using a multi-layer perceptron (MLP) classifier, and (4) a decision tree classifier (DTC). The tested methods using AI demonstrated robust results for image classification, with the highest overall accuracy exceeding 98% and reached by the SVM and RF models. The presented scripting approach for GRASS GIS accurately detected changes in land cover types in southern Victoria over the period of 2013–2024. From our findings, the use of AI and ML algorithms offers effective solutions for coastal monitoring by analysis of change detection using multi-temporal RS data. The demonstrated methods have potential applications in coastal and wetland monitoring, environmental analysis and urban planning based on Earth observation data.

Utilizing Multi-Source Datasets for the Reconstruction and Prediction of Water Temperature in Lake Miedwie (Poland)

Water temperature is a fundamental parameter of aquatic ecosystems. It directly influences most processes occurring within them. Hence, knowledge of this parameter’s behavior, based on long-term (reliable) observations, is crucial. Gaps in these observations can be filled using contemporary methodological solutions. Difficulties in reconstructing water temperature arise from the selection of an appropriate methodology, and overcoming them involves the proper selection of input data and choosing the optimal modeling approach. This study employed the air2water model and Landsat satellite imagery to reconstruct the water temperature of Lake Miedwie (the fifth largest in Poland), for which field observations conducted by the Institute of Meteorology and Water Management—National Research Institute ended in the late 1980s. The approach based on satellite images in this case yielded less accurate results than model analyses. However, it is important to emphasize the advantage of satellite images over point measurements in the spatial interpretation of lake thermal conditions. In the studied case, due to the lake’s shape, the surface water layer showed no significant thermal contrasts. Based on the model data, long-term changes in water temperature were determined, which historically (1972–2023) amounted to 0.20 °C per decade. According to the adopted climate change scenarios by the end of the 21st century (SSP245 and SSP585), the average annual water temperature will be higher by 1.8 °C and 3.2 °C, respectively. It should be emphasized that the current and simulated changes are unfavorable, especially considering the impact of temperature on water quality. From an economic perspective, Lake Miedwie serves as a reservoir of drinking water, and changes in the thermal regime should be considered in the management of this ecosystem.

Characteristics of Changes in Typical Mountain Wetlands in the Middle and High Latitudes of China over the Past 30 Years

Analysis of the driving mechanisms of wetland change can help identify spatial differences in the mechanisms affecting various elements, enabling a more scientific approach to the conservation and utilization of wetlands. This study investigated the impacts of natural and anthropogenic factors on the spatiotemporal evolution of the Altay and Greater and Lesser Khingan Mountains areas using Landsat satellite image data from 1980 to 2018 and fieldwork data from 2019 to 2020. A transfer matrix, correlation analysis, and dynamic characteristics were applied to calculate and analyze the transformation types and areas of wetland resources across all consecutive periods. Finally, the dominant factors influencing the spatiotemporal evolution of the wetland were explored and revealed using the drought index (Standardized Precipitation Index, SPEI) and statistical almanacs. The results showed: (1) From 1980 to 2018, the wetlands area in the Altay Mountains exhibited a decreasing trend, whereas the wetlands area in the Greater and Lesser Khingan Mountains showed an increasing trend. The primary type of wetland transfer in the Altay Mountains was grassland, whereas in the Greater and Lesser Khingan Mountains regions, the primary types of wetland transfer were grassland and forestland. The wetlands area transferred out of the Altay Mountain region was larger than the area of wetland types transferred into during 2010–2018, whereas the wetland areas of the Greater and Lesser Khingan Mountain areas showed the opposite trend. (2) From 1980 to 2018, the wetland ecosystem types in the Altay Mountains exhibited the highest dynamic and conversion degrees of the channels. Similarly, the mountain areas of the Greater Khingan Mountains showed the highest dynamic and conversion degrees of marshes and channels among the wetland types. In addition, the mountainous areas of the Lesser Khingan Mountains showed the highest dynamic and conversion degrees for reservoirs and rivers. (3) Natural driving factor analysis revealed that the SPEI values in the Altay Mountains and the Greater and Lesser Khingan Mountains areas exhibited an increasing trend, indicating that the climate has been warm and humid over the past 30 years and that the expansion of cropland and human-made wetland areas has been significantly influenced by human activities. Therefore, the wetland areas of the Greater and Lesser Khingan Mountains in the northeast are strongly influenced by human activities, whereas the wetland in the Altay Mountains in the northwest is strongly influenced by the climate.

Assessing Normalized Difference Vegetation Index as a proxy of urban greenspace exposure

The Normalized Difference Vegetation Index (NDVI) is a popular proxy of urban greenspace (UGS). However, it’s unclear how NDVI approximates physical characteristics of UGS in the context of urban health studies, causing ambiguities in translating research findings to UGS management. Therefore, we collected data from Landsat and MODIS satellites and Lidar 3D scans in New York City as of circa 2013, and we evaluated linear and non-linear relationships between NDVI and UGS characteristics. We found that: (1) % UGS was the best predicted UGS characteristic by NDVI (R2: 0.35–0.90, varies by data source and unit of analysis), whereas average tree height was the worst (R2: 0.09–0.46). The predictive power on % canopy cover, tree density, and crown volume density was in a similar range (R2: 0.10–0.67). Prediction improved with finer-resolution NDVI sources and larger units of analysis at the cost of losing useful variations; (2) There was a saturation effect where a linear relationship underestimated UGS characteristics in areas of high NDVI. These areas typically had NAIP-NDVI greater than the range of 0.08–0.25, Landsat-NDVI greater than the range of 0.42–0.65, and MODIS-NDVI greater than the range of 0.49–0.75; (3) Smaller absolute errors from a linear NDVI-UGS relationship were often found in more developed locations. We therefore recommend NDVI as a reliable predictor of UGS coverage and its use in longitudinal studies. Future studies should also consider fine resolution land cover maps and Lidar, which are increasingly available to derive detailed UGS characteristics.

Prediction of surface urban heat island based on predicted consequences of urban sprawl using deep learning: A way forward for a sustainable environment

The present work aimed at the spatiotemporal analysis of Land Surface Temperature (LST) and several land-use land-cover spectral indices, namely Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Soil Adjusted Vegetation Index (SAVI), and Modified Normalized Difference Water Index (MNDWI) and to develop a Convolutional Neural Network (CNN) model to predict the LST and the indices for Surface Urban Heat Island (SUHI) evaluation based on the impervious surface area. The predictions were made only for LST and two indices (NDVI and NDBI) because of their strong correlation with LST. Landsat-8 satellite imageries acquired for 2013, 2015, 2017, 2019, and 2021 were utilized, and the Sialkot district, a swiftly urbanizing and advancing industrial city, was selected as a case study area. In the study, first, the indices (NDVI and NDBI) were predicted for 2019, 2021, and 2023 following a sequential procedure, and then based on the predicted indices, the LST for the mentioned years was predicted. Ultimately, to analyze the impact of impervious area on SUHI, impervious and SUHI were extracted for 2019, 2021, and 2023 using the predicted NDBI and LST, respectively, by employing Otsu's Thresholding technique. The predicted LST and indices for 2019 and 2021 were compared with the obtained 2019 and 2021 LST and indices through several statistical measures, such as the kappa index, to evaluate the performance of the CNN model. The kappa index of different kappa statistics for 2019 and 2021 varied between 0.81 and 0.96, which resembles several previous studies, thus indicating sufficient future predictions. SUHI evaluation was conducted based on the observed (2016) and simulated (2021) impervious area and normalized LST images. The results showed that the distribution of the SUHI was highly related to the impervious area. Suppose the urbanization rate in the study area continues at its current pace. In that case, this expansion will cause a dramatic increase in SUHI distribution unless decision-makers consider any proper urban planning model for the study area.

Spatio-temporal analysis land use land cover changes in South Kashmir region of North-western Himalayas using Landsat data

This paper presents a comprehensive analysis of the spatial patterns and temporal dynamics of land use and land cover changes in South Kashmir from 2000 to 2022 leveraging remote sensing technologies revealing significant transformations in various land cover classes. The study used maximum likelihood classification, a supervised classification method, to analyze Landsat satellite imagery and identify ten major land use categories. The findings demonstrate notable increases in, barren land by 18.78 km2 (0.35%), built-up areas by 72.28 km2 (1.33%), forests by 274.76 km2 (5.05%), grasslands by 68.06 km2 (1.25%), scrubland by 307.82 km2 (5.66%). horticulture experienced a significant rise of 419.17 km2 (7.70%), Conversely, several land use classes reported decline, agriculture by 757.21 km2 (13.91%), exposed rockmass by 258.58 km2 (4.75%), glaciers and snow by 136.83 km2 (2.51%), and water bodies contracted by 8.23 km2 (0.15%). The primary drivers of land use change in the region are identified as climate change, population growth, and economic factors. Climate change has altered precipitation patterns affecting agricultural productivity and leading to the retreat of glaciers. Population growth and economic reasons, including the rise of horticulture and changes in land use policies, have also played a significant role in shaping the landscape dynamics of South Kashmir. These changes underscore the dynamic nature of land use in South Kashmir, with significant implications for regional planning and environmental management. The study underscores the cost-effectiveness and efficacy of geospatial technologies in conducting spatiotemporal analyses and formulating evidence-based policies for the sustainable management of natural resources.

LULC changes in the region of the proposed Pwalugu hydropower project using GIS and remote sensing technique

<p>Proper understanding of LULC changes is considered an indispensable element for modeling. It is also central for planning and management activities as well as understanding the earth as a system. This study examined LULC changes in the region of the proposed Pwalugu hydropower project using remote sensing (RS) and geographic information systems (GIS) techniques. Data from the United States Geological Survey's Landsat satellite, specifically the Landsat Thematic Mapper (TM), the Enhanced Thematic Mapper (ETM), and the Operational Land Imager (OLI), were used. The Landsat 5 thematic mapper (TM) sensor data was processed for the year 1990; the Landsat 7 SLC data was processed for the year 2000; and the 2020 data was collected from Operation Land Image (OLI). Landsat images were extracted based on the years 1990, 2000, and 2020, which were used to develop three land cover maps. The region of the proposed Pwalugu hydropower project was divided into the following five primary LULC classes: settlements and barren lands; croplands; water bodies; grassland; and other areas. Within the three periods (1990–2000, 2000–2020, and 1990–2020), grassland has increased from 9%, 20%, and 40%, respectively. On the other hand, the change in the remaining four (4) classes varied. The findings suggest that population growth, changes in climate, and deforestation during this thirty-year period have been responsible for the variations in the LULC classes. The variations in the LULC changes could have a significant influence on the hydrological processes in the form of evapotranspiration, interception, and infiltration. This study will therefore assist in establishing patterns and will enable Ghana's resource managers to forecast realistic change scenarios that would be helpful for the management of the proposed Pwalugu hydropower project.</p>

Hydroecological limitations of the use of water resources of transboundary rivers of the steppe zone (through the example of the Ural and Tobol Rivers, Russia)

To identify the main types of hydroecological restriction on the use of water resources and their spatial patterns in the Ural and Tobol river basins within the steppe zone.A comprehensive assessment of the water‐environmental situation in the regions of the studied basins studied was carried out on the basis of the calculation of water‐environmental stress – the ratio of water intake and free flow (average long‐term flow minus ecological). To assess the environmental restrictions on water use, data from the State reports, “On the State and Protection of the Environment" were used. Exposure to the risk of flooding of settlements was analysed according to information from the registers of settlements at risk of flooding (flooding). Analysis of the dynamics of channel processes was carried out using Landsat satellite images. For the rivers of the Tobol River basin, an approach based on taking into account the nature of channel transformations in the sectors of the state border of the Russian Federation and the Republic of Kazakhstan was used.As a result of the research, the main types of hydroecological restriction in the transboundary basins of the Ural and Tobol were identified. In particular, the key factor limiting water use is the availability of a guaranteed volume and adequate quality of water resources. Hydrological restrictions associated with the negative impact of water include the risks of flooding of settlements during the passage of spring or summer floods, as well as intensive riverbed transformations (in the Ural River basin).The Ural and Tobol river basins (within the steppe zone) are characterised by the development of a rather complex hydroecological situation in some areas. Taking into account the long cycle of low water content in these rivers, the general hydroecological situation has aggravated in recent decades, the primary problem being that of guaranteed provision of water resources of standard quality. As a result, an urgent task is to develop an algorithm for a comprehensive assessment of hydroecological restrictions on water use and their consequences for the population and economy of the regions of the steppe zone.

Spatiotemporal Dynamics and Scenario Simulation of Regional Green Spaces in a Rapidly Urbanizing Type I Large City: A Case Study of Changzhou, China

The rapid urbanization observed in major Chinese cities has resulted in the degradation of both urban and rural environments. In response to this challenge, the concept of regional green spaces has emerged as an innovative approach to coordinate and manage green space resources across urban and rural areas. This study focuses on conducting a comprehensive analysis of the evolution, driving factors, and future scenarios of regional green spaces in Changzhou, which serves as a representative Type I large city in China. To accomplish this analysis, Landsat satellite images from 1992, 2002, 2012, and 2022 were utilized. Various methodologies, including landscape pattern indices for quantitative evaluation, the CLUE-S model, logistic regression for qualitative evaluation, and the Markov–FLUS model, were employed. The findings indicate a continuous decline in the area of regional green spaces in Changzhou, decreasing from 248.23 km2 in 1992 to 204.46 km2 in 2022. Landscape pattern analysis reveals an increase in fragmentation, complexity, irregularity, and human interference within these green spaces. Logistic regression analysis identifies key driving factors influencing regional green spaces, including elevation, urban population, and proximity to water bodies and transportation. The scenario simulations provide valuable insights into potential future trends of regional green spaces. According to the economic priority scenario, a modest increase in regional green spaces is anticipated, while the ecological priority scenario indicates substantial growth. Conversely, the inertial development scenario predicts a continued decline in regional green spaces. This research emphasizes the significance of achieving a harmonious coexistence between economic progress and environmental preservation. It emphasizes the necessity of optimizing the arrangement of green areas within a region while fostering public engagement in the conservation of these spaces. The findings contribute to the protection and sustainable development of the urban environment in the Yangtze River Delta region.

Forecasting of Landuse and Land Cover Changes by Applying Support Vector Machine Using Landsat Satellite Imagery

Forecasting of Landuse and Land Cover Changes by Applying Support Vector Machine Using Landsat Satellite Imagery

Integrated use of the CA–Markov model and the Trends.Earth module to enhance the assessment of land cover degradation

This study aims to demonstrate the potential of assessing future land cover degradation status by combining the forecasting capabilities of the Cellular-Automata and Markov chain (CA-Markov) models in Idris Selva with the land cover degradation (LCD) model in the Trends.Earth module. The study focuses on the upper Zambezi Basin (UZB) in southern Africa, which is one of the regions with high rates of land degradation globally. Landsat satellite imagery is utilised to generate historical (1993–2023) land cover and land use (LCLU) maps for the UZB, while the global European Space Agency Climate Change Initiative (ESA CCI) LCLU maps are obtained from the Trends.Earth module. The CA-Markov chain model is employed to predict future LCLU changes between 2023 and 2043. The LCD model in the Trends.Earth module in QGIS 3.32.3 is then used to assess the historical and forecasted land cover degradation status. The findings reveal that land cover degradation maps produced from local LCLU classifications provide more detailed information compared to those produced from the global ESA CCI LCLU product. Between 2023 and 2043, the UZB is predicted to experience a net reduction of approximately 3.2 million hectares of forest cover, with an average annual reduction rate of − 0.13%. In terms of land cover degradation, the UZB is forecasted to remain generally stable, with 87% and 96% of the total land cover area expected to be stable during the periods 2023–2033 and 2033–2043, respectively, relative to the base years 2023 and 2033. Reduction in forest cover due to the expansion of grassland, human settlements, and cropland is projected to drive land cover degradation, while improvements in forest cover are anticipated through the conversion of grassland and cropland into forested areas. It appears that using locally produced LCLU with high-resolution images provides better assessments of land degradation in the Trends.Earth module than using global LCLU products. By leveraging the opportunities offered by models with capacity to predict LCLU such as the CA–Markov model and the capabilities of the LCD model, as evidenced in this study, valuable forecasted information can be effectively obtained for monitoring land cover degradation. This information can then be used to implement targeted interventions that align with the objective of realising the United Nations' land degradation neutral world target by 2030.

The effect of natural factors on changing soil uses in the marshes: An experimental study using Landsat satellite data

AbstractThe study aimed to analyze the effect of meteorological factors (rainfall rate and temperature) on the change in land use in the marshes of the Al‐Majar Al‐Kabir region in southern Iraq. Satellite images from Landsat 7 for 2012 and Landsat 8 for 2022 were used to monitor changes in the land coverings, the images taken from the Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) sensors of the Landsat satellite. Geometric correction was used to convert images into a format with precise geographic coordinates using ArcMap 10.5. The maximum likelihood classification method was used to examine satellite image data using a supervised approach, and the data were analyzed statistically. We obtained clear images of the area, through which we identified five types of lands (agriculture, bare, dry, wet, and water bodies) and calculated the differences that occurred in the land areas between 2012 and 2022. Then, we studied the change in atmospheric conditions (rainfall rate and temperature average) for the same period. The results showed that temperature increases negatively affected the types of ground cover, as the area of agricultural lands, water bodies, and wetlands decreased inversely with the temperature and directly with the rainfall. It also showed that the area of dry and bare land increased directly with the temperature rates and inversely with the rainfall, which makes these areas more susceptible to desertification. In addition, decreased rainfall highlights how crucial it is to manage water resources in marsh ecosystems. Water conservation and sustainable water use practices are two strategies that can encourage healthy plant growth, maintain appropriate soil moisture levels, and preserve the ecological functions that marshes offer.

Spatio-temporal analysis of land use and land cover changes in a wetland ecosystem of Bangladesh using a machine-learning approach

This study investigates quantifiable and explicable changes in Land Use and Land Cover (LULC) within the context of a freshwater wetland, Hakaluki Haor, in Bangladesh. The haor is a vital RAMSAR site and Ecologically Critical Area (ECA), which needs to be monitored to investigate LULC change patterns for future management interventions. Leveraging Landsat satellite data, the Google Earth Engine Database, CART algorithm, ArcGIS 10.8 and the R programming language, this study analyses LULC dynamics from 2000 to 2023. It focuses explicitly on seasonal transitions between the rainy and dry seasons, unveiling substantial transformations in cumulative LULC change patterns over the study period. Noteworthy changes include an overall reduction (~51%) in Water Bodies. Concurrently, there is a significant increase (~353%) in Settlement areas. Moreover, vegetation substantially declines (71%), while Crop Land demonstrates varying coverage. These identified changes underscore the dynamic nature of LULC alterations and their potential implications for the environmental, hydrological, and agricultural aspects within the Hakaluki Haor region. The outcomes of this study aim to provide valuable insights to policymakers for formulating appropriate land-use strategies in the area.