Classification Of Landsat Images Research Articles

Assessing and predicting changes of ecosystem service values in response to land use/land cover dynamics in Ibb City, Yemen: a three-decade analysis and future outlook

ABSTRACT Assessing ecosystem services values (ESV) within land use/land cover (LULC) changes is crucial for promoting human well-being and sustainable development of regional ecosystems. Yet, the spatial relationship between LULC changes and ecosystem services is still unclear in Yemen. This study aimed to assess the impacts of LULC changes on ESV in Ibb City, over three decades (1990–2020), and to predict the ESV changes in 2050. The hybrid land use classification technique for classifying Landsat images, the CA-Markov model for LULC prediction, and the benefit transfer method (BTM) for assessment ESV were employed. Our findings revealed that there was a continuous increase in built-up areas and barren land, with a decrease in cultivated land and grassland, which are predicted to continue for the next 30 years. Consequently, the total ESV has decreased from US$ 68.5 × 106 in 1990 to US$ 65.2 × 106 in 2020 and is expected to further reduce to US$ 61.2 × 106 by 2050, reflecting the impact of urban expansion and socio-economic activities on ESV. The study provides insights for future ESV monitoring, which will contribute to the formulation of effective land-use strategies for more sustainable ecosystem services, particularly in rapidly urbanizing data-limited regions.

Hydrological responses to historical and predicted land use/land cover changes in the Welmel watershed, Genale Dawa Basin, Ethiopia: Implications for water resource management

Study regionWelmel watershed, Genale-Dawa Basin, Ethiopia. Study focusThis study aimed to analyze the hydrological impacts of land use/land cover (LULC) changes in the Welmel watershed for 1990–2020 and to predict the LULC change impact on the hydrological status in the year 2050. The hybrid land use classification technique for classifying Landsat images; Cellular-Automata Markov (CA-Markov) for prediction of the 2035 and 2050 LULC; and the Soil and Water Assessment Tool (SWAT) for hydrological modeling were employed in the analyses. New hydrological insights for the regionThe findings revealed that from 1990 to 2020, significant alterations in the hydrological regime occurred due to historical LULC changes in the Welmel watershed, which are expected to continue in the 2035 and 2050 periods. Forest cover loss and expansion of agricultural areas, which occurred from 1990 to 2020, have resulted in increased annual flow (6.18%), wet seasonal flow (10.21%), and surface runoff (25.32%), and reduced dry seasonal flow (6.34%), groundwater flow (17.17%, and evapotranspiration (0.36%). The 2035 and 2050 LULC indicate further increased annual flow, wet seasonal flow, and surface runoff, and reduced dry seasonal flow, groundwater flow, and evapotranspiration. The study also stated the hydrological impacts of the LULC changes at the sub-watershed scales. More noticeable changes in hydrological components have been observed at the sub-watershed scale, mainly associated with the uneven spatial distribution of LULC changes. The findings provide valuable insights for policymakers, water resource managers, and local communities in formulating strategies to enhance water resource management practices in the Welmel watershed and similar regions. It could also be for researchers and Academia who can be benefited and it can be used to supplement global goals like Sustainable Development Goal 6 (SDG 6): ensure the availability and sustainable management of water and sanitation for all.

Analysis and Prediction of Land Use/Land Cover Changes in Korgalzhyn District, Kazakhstan

Changes occurring because of human activity in protected natural places require constant monitoring of land use (LU) structures. Therefore, Korgalzhyn District, which occupies part of the Korgalzhyn State Natural Reserve territory, is of considerable interest. The aim of these studies was to analyze changes in the composition of the land use/land cover (LULC) of Korgalzhyn District from 2010 to 2021 and predict LU transformation by 2030 and 2050. Landsat image classification was performed using Random Forest on the Google Earth Engine. The combined CA-ANN model was used to predict LULC changes by 2030 and 2050, and studies were carried out using the MOLUSCE plugin. The results of these studies showed that from 2010 to 2021, there was a steady increase in the share of ploughable land and an adequate reduction in grassland. It is established that, in 2030 and 2050, this trend will continue. At the same time, there will be no drastic changes in the composition of other land classes. The obtained results can be helpful for the development of land management plans and development policies for the Korgalzhyn District.

The effect of landuse dynamics on water balance components in the Upper Oum Er Rabia Basin, Morocco

The Upper Oum Er-Rbia Basin (UOERB) has been facing significant land use/land cover change and urban sprawl dynamics in recent years, in addition to significant climatic variability leading to disruption of precipitation intensity and quantity. Our approach is to identify the morphometric characteristics of the study region and monitor changes in land use and land cover by analyzing Landsat images. These factors form the input parameters for the HEC-HMS (Hydrologic Engineering Center - Hydrologic Modeling System) hydrological model for simulating daily historical hydro-climatic events. The classification of Landsat images shows that the Upper Oum Er-Rbia Basin has experienced significant landuse dynamics over the past 30 years, resulting in disrupted rainfall and recurrent flooding events. Overall, the hydrological simulation shows good agreement between observed and simulated flow hydrographs, with Nash criteria between 0.6 and 0.8. This knowledge enables us to better estimate the return flows in the catchment area, and to take appropriate action in terms of hydraulic management and development in the Upper Oum Er-Rbia Basin.

Land Use and Land Cover (LULC) Change in Eastern Areas of East Java From 1972 To 2021: Learning From Landsat Image

Urban development, population growth, high traffic jams, and intensive disaster events are indicators of changing the landscape of the eastern area of East Java. Investigating these changes is vital for planning and environmental protection in the future. This study examines changes in land use and land cover (LULC) during the past 50 years in the eastern part of East Java from 1972 to 2021. The changes are examined by contrasting four maps derived from Landsat images (1972, 1997, 2013, and 2021). The following are the main study procedures: (1) data inventory, (2) field survey, (3) image processing and classification, and (4) interpretation of LULC changes. With Google Earth Engine, all photos are downloaded (GEE). Landsat image classification was completed using the maximum likelihood algorithm with an overall Kappa accuracy of>85%. Eight (8) major classifications are therefore produced by the classification: (1) the pavement or urban area (PUA); (2) heterogeneous agricultural land (HAL); (3) bare soil (BS); (4) paddy field (PF); (5) open water body (OWB); (6) vegetation/plantation (VG); (7) shrubland (SL); and (8) wetlands (WL). In the areas with rapid development, the LULC change is more pronounced, i.e., Pasuruan, Jember, and Banyuwangi Regencies. LULC change in and near mid-regency and the rural regions comes next. Over the past fifty years, regional growth has resulted in increases in PUA (+4.4 percent), PF (+12.2 percent), and VG (+17.9 percent). On the other hand, the development has decreased SL, BS, and HAL by 5.8 and 15.9 percent, respectively (-13.1 percent ). LULC alterations for human activities have profoundly altered the natural landscape. Keywords: East Java, GEE, Image Processing, Landsat, LULC

Changing land cover: Clustering of agents per moral responsibility and culpability

Are all humans morally responsible and deserving of blame for land cover change? Land cover change research has yet to provide an answer to this moral question. The purpose of this study was to answer the moral question of land cover change by conducting a land cover change analysis in six sampled metropolitan/municipal/districts [MMDAs] in Ghana, identify the agents of land cover change, and cluster the agents based on their moral responsibility and culpability. Supervised classification of Landsat images from 1987 to 2022 for the six MMDAs indicated that farmlands increased the most in area size although one-third of the respondents (agents) were into farming and agro-processing occupation. Based on respondants actions, knowledge and norms they were clustered into seven groups (“responsible and culpable”-25.1 %, “responsible due to culpable ignorance”-17.7 %, “responsible although lack knowledge”-14.1 %, “responsible due to weak norms”-9.1 %, “not responsible”-11 % and “not responsible due to culpable ignorance”-9.3 %) using Mason moral responsibility and culpability decision tree. The large number of respondants found to be responsible and culpable was because they had less knowledge about the consequences of land cover change, which limited their participation in environmental protection initiatives. The findings give sufficient evidence to promote educational programs for responsible land cover conservation and alternative livelihoods programs in Ghana.

Digital image processing (DIP) applied to the analysis of the use and coverage of São Sebastião, federal district, Brazil

The article explores the application of Digital Image Processing (DIP) and Remote Sensing (RS) techniques in analyzing land use and land cover changes in the Administrative Region of São Sebastião, Federal District, Brazil. DIP plays a pivotal role in automated data analysis and interpretation, particularly in the context of RS, where it efficiently processes Earth's surface data collected by sensors on satellites, aircraft, and Unmanned Aerial Vehicles (UAVs). The study covers the period from 2000 to 2020, utilizing data from satellites such as Landsat to provide a continuous view of the region. The spatial and temporal analyses incorporate considerations of hypsometry, slope, and surface temperature, aiming to understand the dynamics of land use and its impact on the microclimate. The methodology involves a comprehensive literature review, acquisition of spatial data from government agencies, and the use of computational tools such as ArcGIS and ENVI. Classification of Landsat images employs false-color compositions and Maximum Likelihood Classification to identify urban areas, water bodies, vegetation, shrubland, and exposed soil. The analysis also includes generating land surface temperature maps. Results indicate significant urban expansion, particularly in the Southeast direction, with a concentration of urban activities. The study highlights a continuous growth trend over the years, emphasizing the importance of understanding and monitoring these transformations for sustainable planning. The article contributes to the field by providing valuable insights into the dynamics of urban growth, offering a clear spatial distribution of changes over time. The detailed methodology ensures the accuracy and reliability of the analysis, supporting informed decision-making in the context of environmental exploration and regional development planning.

Soil Erosion in a Changing Environment over 40 Years in the Merguellil Catchment Area of Central Tunisia

Soil degradation and erosion in semi-arid regions can significantly impact agricultural development, environmental sustainability, and hydrological balance. Understanding the impacts of land use changes and soil and water conservation (SWC) technique implementation on soil erosion and sediment yield is critical to planning effective watershed management. This study aims to evaluate the impacts of environmental changes in the Merguellil watershed (Central Tunisia) over the last forty years. To achieve this, remote sensing techniques and a geographic information system (GIS) will be employed to classify Landsat images from 1980 to 2020. Additionally, the Revised Universal Soil Loss Equation model will be utilized to estimate soil erosion rates, while the sediment delivery distributed model will be employed for sediment yield modeling. Spatiotemporal changes in land use and land cover and in areas treated with SWC techniques were analyzed as the main factors influencing changes in erosion and sediment yield. The combined impact of land use change and SWC techniques resulted in a decrease in the annual soil erosion rate from 18 to 16 t/ha/year between 1980 and 2020 and in sediment yield from 9.65 to 8.95 t/ha/year for the same period. According to the model’s predictions, both soil erosion and sediment yield will experience a slight increase with further degradation of natural vegetation and a reduction in the efficiency of SWC works. This emphasizes the importance of continued efforts in adopting and sustaining SWC techniques, as well as preserving natural vegetation cover, to proactively combat soil degradation and its adverse effects on the environment and communities. Continuous dedication to these measures is crucial to preserving our ecosystem, promoting sustainable practices, and protecting the well-being of both the environment and society.

Evaluation of Land Use/Land Cover Changes due to Urban Sprawl in Bengaluru Rural, Karnataka, India

The availability of productive land is significantly impacted by the global phenomenon of urbanization. The amount of land available for food production and other essential activities decreases as cities grow because the urban perimeter encroaches on rural and natural areas. Conducting research on urban sprawl analysis and land use land cover (LULC) change assessment is essential in ensuring sustainable urban growth. Bengaluru, a rapidly expanding metropolitan city, has a significant impact on the area around it, making it a prime location for this kind of study. In this study, authors sought to assess how urban sprawl affected LULC in the Bengaluru rural district that surrounds the city of Bengaluru. The study evaluated changes in LULC over a two-decade period using remote sensing data and GIS tools. Five LULC classes were used to categorize the study area: settlement, waterbody, vegetation, agriculture, and barren land. The maximum likelihood technique was used to classify Landsat images from three different time periods using the supervised image classification method in the ERDAS software. Accuracy assessment was used to gauge the classified images’ accuracy. The study’s important findings showed how the LULC classes in the study area have been negatively impacted by the urban sprawl. The study emphasizes the significance of ongoing research in LULC change assessment and urban sprawl analysis to ensure sustainable urban growth and safeguard the availability of productive land.

Ecological risk assessment of Kaeng Krachan National Park emphasis on Land Use/Land Cover (LULC)

Land use/land cover (LULC) changes with time, signify disturbances and conservation patterns of landscape types in an environment. Kaeng Krachan National Park (KKNP) is a UNESCO world heritage site with rich biodiversity and provides habitat for indigenous communities who are relying on farming. The study was conducted by calculating landscape ecological risk indices of classified Landsat images based on five landscape classes (Water, Grassland, Built-up-land, Forest, and Cultivated land). Landscape loss indices were calculated based on classified Landsat images. Assigning landscape loss indices in grid cells calculated the ecological risk indices of the area. Ecological risk indices were lowest (ERI ≤ 0.0005), lower (ERI ≤ 0.001), medium (ERI ≤ 0.005), higher (ERI ≤ 0.01), and highest (ERI ≤ 0.015). Ecological risk indices were higher on the east border of KKNP proximate to the semi-urban settlements. The highest risk area demarcates waterbodies and extensively cultivated land. The LULC areas which show the higher variation of changes from 2013 to 2022 are grasslands and forest cover. The changes in the LULC pattern have increased the risk in cultivated land areas and grasslands due to farming obligations. The provision of land permits and land use limitations are addressed issues to conserve KKNP for conservation.

Modeling and Predicting Land Use/Land Cover Change Using the Land Change Modeler in the Suluh River Basin, Northern Highlands of Ethiopia

Land use and land cover change are among the drivers of environmental change. The Suluh River Basin’s land use and land cover are modeled in this study using a land change modeler. To accomplish the goals of this study, Landsat images and ancillary data sources were utilized. In eCognition Developer 9.2 software, nearest neighbor fuzzy classification was used to classify Landsat images. With the IDRISI Selva 17.3 software, change detection and modeling were carried out. Both qualitative and quantitative analyses of the data were conducted. The results showed that, despite a drop in forest land of 97.2%, grazing land of 89.8%, plantation land of 89.1%, shrub-bush land of 1.5%, and water bodies of 84.8% from 1990 to 2002, bare land increased by 10.6%, built-up land by 29.4%, and cultivated land by 65.4%. The model projects, bare, built-up, and cultivated land will increase at the cost of water bodies, grazing, forest, shrub-bush, and plantation land between the years 2028 and 2048. Rainfall, slope, height, distance to rivers, distance to highways, distance from towns, and population density were the main determinants of LULC change in the study area. Therefore, in order to promote sustainable development, safeguard the river basin, and lessen the severity of the changes, appropriate management and timely action must be taken by policymakers and decision makers.

A multi-layer perceptron–Markov chain based LULC change analysis and prediction using remote sensing data in Prayagraj district, India

Land use land cover (LULC) mapping and temporal observations are indispensable drivers for sustainable development. This research showed the growth trends and land use transition for the Prayagraj district in the last three decades. Supervised classification of Landsat images was performed on 5-year temporal intervals using a maximum likelihood classifier. All satellite images were organized into six major LULC feature classes viz agriculture/open land, barren land, built-up, forest, sand, and water. The overall accuracy of LULC classification was achieved by more than 89% in all seven temporal points. Furthermore, the accuracy of the classified maps was estimated through area-based error matrix. The Land Change Modeler tool of TerrSet 2020 software was used to analyze the transition of classes and to incorporate the multi-layer perceptron-Markov chain (MLP-MC) technique. The transition potentials were included in MLP-MC with the help of sensitive explanatory variables and significant transitions of classes. Furthermore, these transition potentials and the Markov chain transition matrix were used to predict the future LULC dynamics and vulnerability. The change analysis revealed that a significant portion of the agriculture/open land gradually decreased and got converted to built-up land. The results depicted that agriculture/open land was reduced by 8.03% in the last three decades while the built-up region was grown by 199.61%. Forest area was continuously decreasing while the sand area increased due to river meandering. Overall, more than 75% of accuracy was achieved in MLP. The prediction model was first validated with observed data, and then the LULC scenario of 2035 and 2050 was simulated. LULC of 2050 showed that the built-up area would likely reach 13.90% of district area whereas the forest area would remain only 0.79%. The prediction model has given the output in the form of future LULC map along with projected potential transition maps. This would be useful for sustainable urban planning to deal with the alarming rate of built-up growthand agriculture/open landshrinkage.

The Spatiotemporal Changing Dynamics of Miombo Deforestation and Illegal Human Activities for Forest Fire in Kundelungu National Park, Democratic Republic of the Congo

In the Kundelungu National Park (KNP), southeast of the Democratic Republic of Congo, illicit human activities including recurrent bushfires contribute to constant regression of forest cover. This study quantifies the landscape dynamics and analyses the spatio-temporal distribution of bushfire occurrence within KNP. Based on classified Landsat images from 2001, 2008, 2015 and 2022, the evolutionary trend of land cover was mapped and quantified through landscape metrics. The spatial transformation processes underlying the observed landscape dynamics were identified based on a decision tree. Finally, the spatio-temporal fire risk assessment was carried out after defining the burnt areas for each year between 2001 and 2022. The obtained results, expressed by the process of dissection and attrition of patches, show that the forest cover has regressed from 2339 km2 to 1596 km2 within the PNK, with an annual deforestation rate varying from 0.8% to 3.4% between 2001 and 2022. Over the same period, the average distance between forest patches has increased significantly, indicating fragmentation and spatial isolation. On the other hand, savannahs as well as field and fallow mosaics have expanded within KNP through the creation of new patches. In addition, several active fires affected more savannahs between 2001 (70 km2 in Integral Zone, 239 km2 in Annex Zone and 309 km2 in KNP) and 2022 (76 km2 in Integral Zone, 744 km2 in Annex Zone and 819 km2 in KNP), limiting their capacity to evolve into forests. Overall, anthropogenic pressure is higher in the Annex Zone of the KNP. Illegal agricultural development and vegetation fires have thus doubled the level of landscape disturbance in 21 years. Our observations justify the need to strengthen protection measures for KNP by limiting repeated human intrusions.

Land use-land cover change in the Tano basin, Ghana and the implications on sustainable development goals

The changes in land use and land cover (LULC) contribute to carbon emissions that lead to climate change and global warming. Obtaining information on LULC change is of necessity to ensure land transformation planning, and anthropogenic and natural impact evaluation. The objective of this study is to analyze the historical changes in LULC in the Tano River Basin of Ghana, to provide scientific information for decision-making on achieving sustainable development. A supervised classification of Landsat images (1986, 2010, and 2020) using the Random Forest algorithm was performed, as well as a post-classification comparison of LULC maps in terms of area and size. A “from–to” matrix was used to identify the changes in LULC that occurred between the periods 1986–2010, 2010–2020, and 1986–2020. The results show an overall classification accuracy of 88.9, 88.5, and 88% for LULC maps for the years 1986, 2010, and 2020, respectively. Between the period 1986 and 2020, there is a conversion of the greater part of dense forest to open forest, and subsequently to settlement and cropland as a major historical LULC change pattern in the Tano basin. Cropland and Settlement increased at the rate of 24.8 km/yr and 1.5 km/yr respectively, while Dense forest and Open forest decreased at the rate of 298.4 km/yr and 173.9 km/yr over the period 1986 to 2020. The study outputs are not only useful for formulating and implementing national policies and programs but also, can contribute to assessing and monitoring progress towards attaining Sustainable Development Goal 13 (climate action).

Intensive land-use is associated with development status in port cities of Southeast Asia

Port cities in Southeast Asia (SEA) have been undergoing intensive land use and land cover change (LUCC) because of rapid socio-economic development in recent decades. However, various development statuses among these cities could result in divergent LUCC transitions and therefore affect the local ecosystem at different levels. This study investigated the historic land-use transition in the SEA port cities by classifying Landsat images from 1990 to 2020 with the random forest algorithm, and further integrated these patterns with land-use modeling to inform the future land uses under the current developing mode. DIVISON was used to assess land-use fragmentation and investigated its relationship with development levels represented by the average nighttime light index (ANLI). The results showed an intensive LUCC in SEA port cities in 1990–2020, with artificial surface increasing by 9.2% (175.9 km2) of the total area, mainly converted from cultivated land which decreased by 136.9 km2. An inverted V-shaped relationship between fragmentation and development level was found at the landscape level and for cultivated land, in which both turning points (TPs) occurred in the mid-developed stage (ANLI = 41.1 and 20.0, respectively). Artificial surfaces tended to be more aggregated in later developed stages, showing a TP of ANLI around 53.2. Under the current developing mode, the aggregation trend will be generally continued for the next three decades, mainly resulting from the increased connection in artificial surfaces. The findings also highlighted a tradeoff between city development and agricultural production in SEA port cities, leading to increased food insecurity in more developed stages. Recent developments also negatively affected aqua ecosystems, such as aquaculture, mangroves, and natural waterbodies in several cities. This study underscored the importance of the sustainable LUCC strategy in SEA port cities and provides insights into rationalizing land-use policies for other port cities worldwide.

The impact of wildfires on air pollution and health across land use categories in Brazil over a 16-year period

Forest fires cause many environmental impacts, including air pollution. Brazil is a very fire-prone region where few studies have investigated the impact of wildfires on air quality and health. We proposed to test two hypotheses in this study: i) the wildfires in Brazil have increased the levels of air pollution and posed a health hazard in 2003–2018, and ii) the magnitude of this phenomenon depends on the type of land use and land cover (e.g., forest area, agricultural area, etc.). Satellite and ensemble models derived data were used as input in our analyses. Wildfire events were retrieved from Fire Information for Resource Management System (FIRMS), provided by NASA; air pollution data from the Copernicus Atmosphere Monitoring Service (CAMS); meteorological variables from the ERA-Interim model; and land use/cover data were derived from pixel-based classification of Landsat satellite images by MapBiomas. We used a framework that infers the “wildfire penalty” by accounting for differences in linear pollutant annual trends (β) between two models to test these hypotheses. The first model was adjusted for Wildfire-related Land Use activities (WLU), considered as an adjusted model. In the second model, defined as an unadjusted model, we removed the wildfire variable (WLU). Both models were controlled by meteorological variables. We used a generalized additive approach to fit these two models. To estimate mortality associated with wildfire penalties, we applied health impact function. Our findings suggest that wildfire events between 2003 and 2018 have increased the levels of air pollution and posed a significant health hazard in Brazil, supporting our first hypothesis. For example, in the Pampa biome, we estimated an annual wildfire penalty of 0.005 μg/m3 (95%CI: 0.001; 0.009) on PM2.5. Our results also confirm the second hypothesis. We observed that the greatest impact of wildfires on PM2.5 concentrations occurred in soybean areas in the Amazon biome. During the 16 years of the study period, wildfires originating from soybean areas in the Amazon biome were associated with a total penalty of 0.64 μg/m3 (95%CI: 0.32; 0.96) on PM2.5, causing an estimated 3872 (95%CI: 2560; 5168) excess deaths. Sugarcane crops were also a driver of deforestation-related wildfires in Brazil, mainly in Cerrado and Atlantic Forest biomes. Our findings suggest that between 2003 and 2018, fires originating from sugarcane crops were associated with a total penalty of 0.134 μg/m3 (95%CI: 0.037; 0.232) on PM2.5 in Atlantic Forest biome, resulting in an estimated 7600 (95%CI: 4400; 10,800) excess deaths during the study period, and 0.096 μg/m3 (95%CI: 0.048; 0.144) on PM2.5 in Cerrado biome, resulting in an estimated 1632 (95%CI: 1152; 2112) excess deaths during the study period. Considering that the wildfire penalties observed during our study period may continue to be a challenge in the future, this study should be of interest to policymakers to prepare future strategies related to forest protection, land use management, agricultural activities, environmental health, climate change, and sources of air pollution.

Spatiotemporal Monitoring of Urban Sprawl in a Coastal City Using GIS-Based Markov Chain and Artificial Neural Network (ANN)

Over the last two decades, globally coastal areas have urbanized rapidly due to various socioeconomic and demographic driving forces. However, urban expansion in towns and cities of the developing world has been characterized by entangled structures and trends exacerbating numerous negative consequences such as pollution, ecological degradation, loss of agricultural land and green areas, and deprived settlements. Substantially, spatial simulation of urban growth and their consequences on coastal areas particularly in Egypt is still very rare. Geospatial modelling coastal urban growth is crucial and has enormous potential for coastal land use transformation and urban sustainability. The key aim of this study was to analyze spatiotemporal changes (2010–2020) and simulate future dynamics (2030 to 2050) of land use/land cover (LULC) in Alexandria Governorate, Egypt. Artificial Neural Network–Multiple Layer Perceptron (ANN-MLP) and Markov Chain techniques were employed within the GIS platform to assess processes of land transitions and predict urban growth trends, patterns and dimensions. The forecasting process was based on three maps of LULC derived from classified Landsat images of 2000, 2010 and 2020. In addition, topographical, demographic, accessibility, proximity factors were generated and developed in the form of raster spatial parameters of urbanization driving forces. The findings revealed that the observed expansion of the built-up area during one decade (2010–2020) was 12,477.51 ha, with a decline in agricultural area (7440.39 ha) and bare land (4904.91 ha). The projected change was forecasted to be 71,544 ha by 2030 and 81,983 ha in 2040 with a total of 35,998 ha increase in the built-up area and residential expansion by 2050. Despite this expected pattern of rapid changes, urban growth will be shaped by the key drivers of proximity to coastline and agricultural land transformation. The analysis indicates that the vertical urban growth will be most likely dominant along the coastal zone due to the lack of vacant lands, whereas the horizontal urban expansion will primarily take place towards the east-northeastern and south-southeastern directions of the city. The present work provides a holistic framework for establishing initial coastal land use plans not only for planners and urban administrators in Alexandria but also for policymakers and coastal municipalities in developing nations.

Monitoring the population change and urban growth of four major Pakistan cities through spatial analysis of open source data

ABSTRACT Cities are complex and dynamic entities in close proximity of people, implying multi temporal observations to analyse and understand the urban context. At present, open-source data and geospatial intelligence are becoming the important means of exploring, monitoring and predicting urban status of area growth and population increase. In last few decades, unemployment and absence of infrastructures in the rural areas promoted the unplanned and haphazard urbanization across the urban centres in Pakistan. This study focuses on exploring the potential of open-source/freely available datasets for city mapping and monitoring spatially. The study gives a spatial perspective of rapidly growing cities of Pakistan using Google Earth Engine to classify Landsat images over last four decades, and discovers sprawl patterns across cities. The study works out that the built-up area is significantly increasing with population growth over four decades and there is a strong positive correlation between population growth and built-up expansion. Using Open-Source Data (Landsat images and LandScan data), this study has offered a technical solution of Google Earth Engine-supported analysis of statistics and machine learning to spatially monitoring the population change and urban growth of four major Pakistan cities. It is undoubted that our working results will provide the timely and cost-effective information to policymakers, Govt Officials and citizens for more sustainable urbanization.

Using Landsat satellite imagery for assessment and monitoring of long-term forest cover changes in Dak Nong province, Vietnam

Forests are essential in regulating climate and protecting land resources from natural disasters. In Vietnam's Dak Nong province, forest cover has changed significantly between 1989 and 2021. This study applies remote sensing and geographic information systems (GIS) approaches to detect negative changes in forest cover as well as other land cover types. The maximum likelihood classification tool was used to classify Landsat images for the years 1989, 2001, 2011, and 2021, with post-classification accuracy evaluated through kappa coefficient statistics. The potential to based classification on Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) to detect changes in forest cover compared with supervised classification was also evaluated. The land use and land cover change detection results show that the forest area decreased from 77.54% of the study area in 1989 to 33.97% in 2021, with a total forest loss of 2,953.48 km 2 and only 117.12 km 2 of newly planted forest during this period. Broadly, forest cover in the area has been severely reduced, often due to indiscriminate logging and expansion of agricultural land on the forest edge.

Spatial Dynamics and Predictive Analysis of Vegetation Cover in the Ouémé River Delta in Benin (West Africa)

The vegetation cover of the Ouémé Delta constitutes a biodiversity hotspot for the wetlands in southern Benin. However, the overexploitation of natural resources in addition to the intensification of agricultural practices led to the degradation of the natural ecosystems in this region. The present work aims to reconstruct, using remote sensing, the spatial dynamics of land use in the Ouémé Delta in order to assess the recent changes and predict the trends in its vegetation cover. The methodology was based on remote sensing and GIS techniques. Altogether, this process helped us carry out the classification of Landsat images for a period of 30 years (stating year 1990, 2005, and 2020) via the Envi software. The spatial statistics resulting from this processing were combined using ArcGIS software to establish the transition matrices in order to monitor the conversion rates of the land cover classes obtained. Then, the prediction of the plant landscape by the year 2035 was performed using the “Land Change Modeler” extension available under IDRISI. The results showed seven (07) classes of occupation and land use. There were agglomerations, mosaics of fields and fallow land, water bodies, dense forests, gallery forests, swamp forests, and shrubby wooded savannahs. The observation of the vegetation cover over the period of 15 years from 1990 to 2005 showed a decrease from 71.55% to 63.42% in the surface area of the Ouémé Delta. A similar trend was noticed from 2005 to 2020 when it reached 55.19%, entailing a loss of 16.37% of the surface area of natural habitats in 30 years. The two drivers of such changes are the fertility of alluvial soils for agriculture along and urbanization. The predictive modeling developed for 2035 reveals a slight increase in the area of dense forests and shrubby wooded savannas, contrary to the lack of significant decrease in the area of gallery forests and swamp forests. This is key information that is expected to be useful to both policy and decision makers involved in the sustainable management and conservation of natural resources in the study area.