ارزیابی آماری از انتقالات کاربری اراضی و پوشش زمین در حوضة آبخیز دریاچة ارومیه با استفاده از ماتریس انتقال

The major impact on land use change and peatland degradation was directly from human activities and inappropriate management. Changing of land use and land cover (LULC) influence directly on peat land drained and degraded. Nathong and Saming Village, Champhone district, Champasack Province, Lao PDR is one of proposed peatland area and rapid LULC changes from 2016 due to a social-economic growth. Therefore, the assessment of land use change is required for sustainable peatland management. The main objectives of this study were to assess LULC and its change between 2011 to 2016 and 2016 to 2021. In this study, remote sensing data has been used for LULC classification using random forest classifier in Google Earth Engine (GEE). Then, the post-comparison change detection algorithm was applied for detected LULC changes. The result revealed that mixed deciduous area was continually decrees 523.44 ha and 607.05 ha from 2011 to 2016 and 2016 to 2021. Additionally, cassava and dry dipterocarp were increase from 138.24 ha in 2011 to 2016 to 386.1 ha in 2016 to 2021 and 93.15 ha in 2011 to 2016 to 156.42 ha in 2016 to 2021 respectively. Additionally, the derived overall accuracy and Kappa hat coefficients of the land use and land cover map in 2011,2016 and 2021 were higher than 85%. The average producer’s accuracy and user’s accuracy for all land use and land cover types were also more than 83%. The derived information of land use and land cover data of this study can be used as information to support decision-makers and land use planners for to awareness and sustainable peatland management.

Identifying the mechanism through which changes in land use and land cover (LULC) modulate hydrological flows is vital for water resource planning and management. To examine the impact of LULC change on the hydrology of the Gaborone Dam catchment within the upper Limpopo basin, where Notwane river is the major river within the catchment, three LULC maps for the years 1997, 2008, and 2017 were established based on a mosaic of Landsat 5 for 1997 and 2008 and Landsat 8 for 2017. The 10 m-resolution Version 200 ESA World Land Cover Map for 2021 is used as a ground truth to train the random forest (RF) classifier to identify land cover classes from Landsat 8 imageries of 2021 using the Google Earth Engine (GEE) Python API. The overall accuracy/kappa coefficient of the RF classifier is 0.99/0.99 for the training and 0.73/0.68 for the validation data sets, which indicate excellent and substantial agreements with the ground truth, respectively. With this confidence in the LULC classification, the impact of LULC change on the hydrological flow within the catchment was estimated by employing the Soil and Water Assessment Tool (SWAT) and indicator of hydrological alteration (IHA). The SWAT model calibration and validation were first performed, and the ability of the model to capture the observed stream flow was found to be good. The LULC maps from Landsat images during the 1997–2017 period show a decrease in forests and shrubland in contrast to an increase in pasture land. The expansion of pasture and cropland and the reduction in forests and shrubland led to a decline in the amount of evapotranspiration and groundwater recharge. Furthermore, the LULC change also caused a reduction in low flow during dry periods and an increase in high flow during the rainy season. The findings clearly demonstrate that LULC changes can affect the water table by altering soil water recharge capacity. The study highlighted the importance of LULC for catchment water resource management through land use planning to regulate the water level in the Gaborone Dam against the impact of climate change and growing water demands by the city of Gaborone due to population growth.

The effects of irregular population growth, migration mobility, and vegetation dynamics by humans can lead to changes in Land Use and Land Cover (LULC). Changes in LULC are particularly significant in coastal areas associated with industrial activities. The South-Eastern Marmara region, which is one of Turkey's important industrial coastal areas, is also affected by the surrounding changes. The study area was selected on purposes to determine LULC change and accuracy of classification using Sentinel-2 based vegetation indices combinations. In study area, the Gemlik-Bursa Northern Interchange investments area and newly construction of the TOGG (Turkey's Automobile Initiative Group) factory are located. The study area was determined by creating a 5 km buffer zone from the coast to the mainland in the area covering the Armutlu district of Yalova province and the Osmangazi, Mudanya, and Gemlik districts of Bursa province. The RF (Random Forest) classification technique has been applied to original bands and combination of 21 new bands using indices obtained from satellite images for 3 seasons in 2016 and 2020. The highest accuracy results for the winter, spring, and summer seasons of 2016 were observed in OI12 (%82,93), ORF (%84,44), and again ORF (%84,67) indices, while in 2020, they were observed in OI5 (%85,89), ORF (%84,75), and OI6 (%84,63) indices. The main reason for changes in classification accuracy due to the influence of different bands is understood to be the interaction between the LULC on the ground in the season when the image was acquired and the characteristics in the bands. It was determined that the changes in LULC in the South-Eastern Marmara would be due to investment decisions taken in the region, resulting population growth, and pixel-based classification errors due to spectral reflections in some areas.

The change detection and land use and land cover (LULC) maps are more important powerful forces behind numerous ecological systems and fallow land. The current research focuses on demarcating the spatiotemporal LULC changes, NDVI and change detections maps. These effects directly affect the ecosystem, land resources, cropping pattern and agriculture. LULC assessment and surveillance are essential for long-term planning and sustainable use of natural resources. However, we have developed the soft computing machine learning algorithm for mapping land use and land cover based on the Google earth engine (GEE) platform and change detection mapping done by SAGA GIS software. It is significantly used for ecological safety and planning under various climate variations. To accurately describe the land use and land cover classes with changes are identified in the area. This area exclusively uses the multitemporal Landsat-5 (30 m) and Sentinel-2 (10 m) imageries in LULC mapping. The GEE is a cloud-computing platform with the prevailing classification ability of random forest (RF) models to make five-year interval LULC maps for 2010, 2015 and 2020. To unique multiple RF models established as a classifier in the algorithm created by JavaScript and GEE. SAGA GIS has provided the best platform for detecting changes in land use and land cover classes. NDVI maps are created based on the cloud-based platform. These maps value ranges between −0.68 to −0.15, 0.76 to −0.29 and 0.66 to −0.11 in 2010, 2015 and 2020. Experimental outcomes indicate five classes such as water bodies, built up, barren, cropland and fallow land during 2010, 2015 and 2020. The overall accuracy of User and Producer for 2010, 2015 and 2019 years in between 86.23%, 88.34%, 85.53% and 92.51%, 94.34% and 91.54%, respectively. We have observed that (2010, 2015 − 2020) agriculture and built-up land increased by 1040.76 ha, 1246.32 ha, 1500.93 ha and 34.96 ha, 37.08 ha, 42.58 ha, respectively. Other side degraded land, fallow land, waterbodies areas (953.19 ha, 679.23 ha, 937.24 ha and 1385.73 ha, 1513.53 ha, 991.08 ha and 32.85 ha, 21.33 ha, 25.66 ha) are increased during the year of 2010, 2015 and 2020, respectively. While results have been done by GEE cloud platform and remote sensing data, this developed algorithm easily classified the land use maps from Landsat-5 and Sentinel-2 TM imagery in the machine learning approach. The determined 30-m and 10-m three-year LULC maps are made-up to deliver vital data on the changes, monitoring and understanding of which types of LULC classes and changes have occupied a place in the Rahuri area.

The change in land use and land cover (LULC) due to human activities is a major cause of hydrological process changes in watersheds. This study assesses the effects of LULC changes on the hydrological processes in the Bilate catchment, located in southern Ethiopia. Landsat images of 1986, 2002, and 2018 were used to classify LULC through supervised classification. The Soil and Water Assessment Tool (SWAT) evaluated the hydrological responses to LULC changes. Results indicate an increase in built-up areas (0.70%), water bodies (0.19%), cultivated land (10.54%), and barren land (1.265%) from 1986 to 2018. Conversely, the forest (8.52%) and grazing (3.18%) areas declined. An increase in water leaving the root zone (962.02 mm/year), potential evapotranspiration (593.85 mm/year), actual evapotranspiration (201.71 mm/year), and surface runoff (514.86 mm/year) was observed, while soil water content (1423.25 mm/year) decreased at the catchment level. The declining soil water content and increasing actual and potential evapotranspiration could result in a water shortage for crop production. The impact of LULC changes on hydrology provides vital knowledge for integrated LULC and water resource management. Water resource development planning must consider LULC changes to achieve sustainable development in the catchment.

In a world experiencing rapid urbanization, the phenomenon of land surface temperature (LST) variation has invited substantial attention due to its profound impact on the environment and human well-being. Changes in land use and land cover (LULC) within urban areas significantly influence the dynamics of LST and are a major driver of urban eco-environmental change. The complex connections between LULC dynamics, LST, and climate change are investigated in this systematic review, with a focus on the combined effects of these variables and the use of Machine Learning (ML) techniques. The data in this study, based on peer-reviewed publications from the past 25 years, were obtained from Science Direct and Web of Science databases. Based on our findings, Landsat is the most widely used dataset for analyzing the impacts of LULC on LST. Additionally, built-up areas, vegetation, and population density had the biggest effects on LST values based on focused studies. This systematic review reveals that Artificial Neural Networks (ANNs), Cellular Automata-Markov (CA-Markov), and Random Forest (RF) are the most used ML techniques in predicting LULC and LST. The study finds that NDBI and NDVI are recognized as the key LULC indices that have strong correlations with LST. We also highlight key LULC classes that have the most impact on LST variation. To validate the results, these studies employ Pearson correlation, the NDVI and NDBI index, and other linear regression methods. This review concludes by highlighting future research directions and the current need for interdisciplinary efforts to address the intricate dynamics of LULC and the Earth’s surface temperature.

Water is a primary element for human life on Earth. Fresh water, which includes rivers, lakes, streams, and ponds, contributes less than one thousandth of a percent of the total water on Earth, but it is critical for the environment and human life. Change in land use and land cover (LULC) is a foremost concern in global environment change. Rapid changes in LULC lead to the degradation of ecosystems and have adverse effects on the environment. There is an urgent need to monitor changes in LULC and evaluate the effects of these changes in order to inform decision makers on how to support sustainable development. This study used Moderate Resolution Imaging Spectroradiometry images to detect and investigate changes in LULC patterns in Gilgit-Baltistan, Pakistan, between 2008 and 2017. Six types of LULC were used to explain the major changes of LULC in the study area. The results showed that there was a reduction of barren lands and an increase of urban areas. It also showed an inconsistent behavior of water bodies during the study. Snow area, which also increased, needs further investigation.

Assessing and modeling the changes in land use and land cover (LULC) patterns and associated ecosystem service values (ESV) has become an important global agenda for formulating sustainable land management policies. Taking the coastal region of Bangladesh (CRB) as a case study, we utilized remote sensing and GIS techniques to analyze Landsat data of 1999 and 2019 to estimate the effect of LULC changes on the ESVs. The LULC classification, ESV quantification, and spatial variations were performed by semi-automated classification, per-unit value transfer, and spatial autocorrelation techniques, respectively. First, between 1999 and 2019, LULC altered dramatically (agricultural land and coastal wetlands decreased, while all other LULC types increased). Second, the total ESV decreased by 1.87%, which was mostly attributed to a 70% growth in both rural settlement and aquaculture/saltpan, and a 30% decline in both agricultural land and wetlands. Third, significant spatial correlation and moderately high spatial clustering were observed, which consisted mostly of mangrove forests, waterbodies, and wetland zones. Both high-high and low-low values increased, but spatial outliers remained unchanged. Conserving agricultural land should be prioritized in future land use plans of CRB to meet the ever-increasing food demands, control natural land conversion, and make land use sustainable.

This study addresses the significant factors contributing to warming during the 20th century, namely Greenhouse Gases (GHG) and Land Use (LU), emphasizing the need for localized hydrological impact assessments. Recognizing the limitations of Global Climate Models (GCMs) in predicting local-scale phenomena, the research employs a downscaling approach for hydrological impact studies. Observed datasets and downscaled GCM data are utilized to analyze temperature, precipitation, and potential evapotranspiration (PET) trends. The study integrates downscaled GCM data from the Coupled Model Inter-comparison Project 6 (CMIP6) to project future climate scenarios under Representative Concentration Pathways (RCPs) 4.5 and 8.5. Runoff data from three stations within the Jhelum Basin is collected and analyzed over various time scales, providing a comprehensive understanding of historical and future hydrological trends. Future climate projections are corrected using the Daily Bias Correction (DBC) method). The study then employs the Soil & Water Assessment Tool (SWAT) model, given its suitability for hydrological studies with limited data availability. SWAT is calibrated and validated using SWAT CUP, incorporating observed river discharge. The impact assessment on runoff considers different climate change and land use change scenarios. Future Land Use and Land Cover (LULC) predictions are made for 2025 to 2100, and the model is rerun to analyze the combined impact of changing climate and LULC on runoff. The study aims to achieve a robust understanding of future water resource dynamics for runoff generation; the integrated assessment of climate and land use impact on the hydrological dynamics of the Jhelum Basin uncovers substantial shifts in runoff patterns. The combination of changing climate conditions and evolving Land Use/Land Cover (LULC) scenarios reveals intricate interactions, influencing the basin's hydrological response. Future projections highlight the nuanced interplay between climate scenarios and LULC changes, offering valuable insights into the complex dynamics of water resource management. These results provide essential information for policymakers and decision-makers, guiding the formulation of adaptive strategies to address the evolving challenges in runoff generation within the Jhelum Basin. The research explores runoff responses to LULC and climate change through scenario-based setups. By quantitatively analyzing the effects on runoff and peak discharge across different periods, the study provides valuable insights for policymakers and decision-makers in water resources. This integrated assessment contributes to a more informed and sustainable approach to water resource management in the Jhelum Basin amidst changing climatic and land use conditions.

Nas últimas décadas Portugal sofreu grandes alterações no uso e cobertura do solo (LULC). No entanto, essas mudanças não foram quantificadas para anos antes de 1985, que é um teste inovador nessa perspectiva, que apresenta uma estimativa das mudanças entre 1980 e 2010 em escala continental, com base em amostras de LULC. As informações utilizadas pela LULC nestas avaliações resultaram de uma combinação de diferentes metodologias, desde a seleção de fotografias aéreas, georreferenciamento, vetorização de LULC, até extrapolar para Portugal Continental. O presente trabalho apresenta estes métodos e variantes de LULC estimados em três épocas (décadas de 1980/95, 1995/2010 e 1980/2010). Os resultados evidenciam a ocupação de terras florestais e agrícolas nos três momentos, demonstrando o aumento ao longo do tempo do segundo tipo, devido ao detrimento das perdas ocorridas no primeiro tipo.

Population growth and agricultural expansion cause major changes in land use and land cover (LULC) in Ethiopia. Cultivated lands are mostly expanding without land suitability evaluation. Consequently, crop yields are not increasing as expected. This is particularly the case in the highland catchments draining toward Lake Tana, where severe consequences such as deforestation and the degradation of soil and land can be observed. In this study, the impacts of long-term LULC dynamics on the land suitability potential for selected major crops in three sub-catchments of Lake Tana, Ethiopia (Gilgelabay, Gumara and Ribb), were evaluated. Time series of Landsat images from three periods (1988, 1998, and 2017) were classified. Land suitability was analyzed via a multi criteria approach based on spatial input data such as elevation, soil, and slope maps. The overall accuracy for all LULC classifications was good to very good (89.7% to 91.6%). Five major LULC classes were distinguished: agriculture, forest, shrub/bushland, grassland, and water. In all three catchments, the results revealed that agricultural land was the dominant land cover that expanded at the expense of the other land cover types to 80%-90% in all catchments in 2017. The rate of change in agricultural land in the Gilgelabay catchment (4041.3 ha/yr) was greater than that in the Gumara (1374.5 ha/yr) and Ribb (1362.3 ha/yr) catchments. This is possibly due to the availability of other LULC classes. The natural vegetation of Gilgelabay, Gumara, and Ribb has decreased by 16.0%, 10.5%, and 1.1%, respectively, over the past three decades. However, the present LULC change trends are unsustainable, and any remaining natural vegetation should be maintained. The results from the land suitability analysis revealed that the land suitability for teff, corn, and rice is likely to change with climate change in the future. To ensure sustainable land use management, modifying land use on the basis of land suitability should be preferred over traditional practices to improve crop production. This can be achieved in close collaboration with all stakeholders, including local communities, the government, and NGOs.

Abstract. Groundwater, a critical freshwater resource, is under increasing pressure from rapid urbanization and environmental changes, especially in India, where declining levels threaten water and food security. These challenges are made worse by a lack of integrated management strategies and the overarching impacts of climate change on hydrological patterns. This research investigates the dynamic changes in land use and land cover (LULC) within parts of the Pune and Satara districts of Maharashtra. We used remote sensing data and geospatial techniques from 2015 to 2024. Our study used multitemporal Landsat 8 Operational Land Imager (OLI) data, processed and analyzed on the Google Earth Engine (GEE) cloud computing platform. We classified five main LULC categories: Water Body, Vegetation (Forest), Agricultural Area, Barren Land, and Built-up. The accuracy of the LULC maps was carefully assessed. The overall accuracy improved from 85% in 2015 to 89% in 2024, and the Kappa coefficient increased from 0.768 to 0.867, showing robust classification performance. The findings show a substantial LULC transformation. Over the nine-year period, Built-up areas expanded significantly by 7.99%, reflecting rapid urbanization. At the same time, Agricultural areas also increased by 4.99%. In contrast, both Forest cover and Barren Land declined by 2.38% each, while Water bodies had a minor net decrease of 0.49%. These significant conversions highlight the impact of factors such as population growth, accelerated urban expansion, economic development, and the growth of industrial and commercial activities. These findings highlight an urgent need for formulating and rigorously implementing effective land-use policies and regulations. These policies are essential for ensuring sustainable urban development and balancing the competing demands on finite land resources. The GEE-based approach proved to be a powerful and efficient method for examining LULC changes, offering actionable insights for urban planning and land-use management strategies. This research emphasizes the critical need for robust policies to foster sustainable development amidst the complex interplay of competing land demands and the ongoing challenges of urbanization.

The increasing pressures of urban development and agricultural expansion have significant implications for land use and land cover (LULC) dynamics, particularly in ecologically sensitive regions like the Murree and Kotli Sattian tehsils of the Rawalpindi district in Pakistan. This study's primary objective is to assess spatial variations within each LULC category over three decades (1992–2023) using cross-tabulation in ArcGIS to identify changes in LULC and investigates into forest fragmentation analysis using the Landscape Fragmentation Tool (LFTv2.0) to classify forest into several classes such as patch, edge, perforated, small core, medium core, and large core. Utilizing remote sensing data from Landsat 5 and Landsat 9 satellites, the research focuses on the temporal dynamics in various land classes including Coniferous Forest (CF), Evergreen Forest (EF), Arable Land (AR), Buildup Area (BU), Barren Land (BA), Water (WA), and Grassland (GL). The Support Vector Machine (SVM) classifier and ArcGIS software were employed for image processing and classification, ensuring accuracy in categorizing different land types. Our results indicate a notable reduction in forested areas, with Coniferous Forest (CF) decreasing from 363.9 km2, constituting 45.0 % of the area in 1992, to 291.5 km2 (36.0 %) in 2023, representing a total decrease of 72.4 km2. Similarly, Evergreen Forests have also seen a significant reduction, from 177.9 km2 (22.0 %) in 1992 to 99.8 km2 (12.3 %) in 2023, a decrease of 78.1 km2. The study investigates into forest fragmentation analysis using the Landscape Fragmentation Tool (LFTv2.0), revealing an increase in fragmentation and a decrease in large core forests from 20.3 % of the total area in 1992 to 7.2 % in 2023. Additionally, the patch forest area increased from 2.4 % in 1992 to 5.9 % in 2023, indicating significant fragmentation. Transition matrices and a Sankey diagram illustrate the transitions between different LULC classes, providing a comprehensive view of the dynamics of land-use changes and their implications for ecosystem services. These findings highlight the critical need for robust conservation strategies and effective land management practices. The study contributes to the understanding of LULC dynamics and forest fragmentation in the Himalayan region of Pakistan, offering insights essential for future land management and policymaking in the face of rapid environmental changes.

<p>The land use and land cover (LULC) change induces hydrologic variability in a catchment and studying this variability is central to efficient water management practice in a catchment. The assessment of the alteration in hydrological processes due to LULC change and its influence on overall river ecosystem functioning is even more pertinent to developing nations that face the issue of water scarcity and pollution. In this work, we investigate the influence of the LULC change over a period of ~40 years (1970-2013) on the variability of natural or virgin flow in the Ramganga river, a major tributary of the Ganga river, India. For LULC change data, object-based image classification was performed on high-resolution satellite imageries acquired for the Ramganga river basin – CORONA (1970) and LISS IV (2013) images. The natural or virgin flows (i.e., the flow in the river without regulation practices such as construction of dams or barrages) were estimated by performing hydrological modeling using the Soil and Water Assessment Tool (SWAT). Initially, the SWAT model was set up, calibrated, and validated for the present flow scenario (i.e. with all management practices present) using LULC data of the year 2013. Natural flows were derived by removing all interventions and keeping agricultural practices only rain-fed. Next, keeping all parameters unchanged, the LULC data of the year 2013 was replaced by the LULC data of the year 1970. This enabled us to study the effects of LULC change on river hydrology between the period 1970-2013. The model showed good agreement between the observed and simulated flows with R<sup>2</sup> values of 0.82 for the calibration period (2002-2014) and 0.68 for the validation period (1990-1999). The Nash-Sutcliffe efficiency values were 0.81 and 0.66 for calibration and validation periods respectively. The comparison of LULC data between the study period (1970 and 2013) reveals that land cover classes of agriculture, built-up, mixed forest, barren land, shrubs and bushes, and water areas were altered by nearly 6%, 102%, -7%, -59%, -75%, and -2% respectively (‘-’ sign indicates decrement in the land cover area). The influence of this LULC change was evident in the results from the hydrological model. For the years 2002-2013 (calibration period), the natural flows estimated using the LULC map of 2013 at the basin outlet were observed to be higher by 3-12% compared to flows estimated using the LULC input of 1970. The estimates of mean monthly flows for the years 2002-2013 at the basin outlet reveal that while the natural flows estimated using the LULC map of 2013 were higher compared to flow estimates using the LULC map of 1970 for most of the months, the flows during the dry months (May-July) were observed to be lower for the former compared to the latter. Our work provides valuable insights into hydrological variability in a major sub-basin of the Ganga river induced due to LULC changes and we advocate that alterations associated with LULC must be incorporated into water management strategies.</p>

Human‐induced land use and land cover (LUC) changes threaten the ecosystem services of the vulnerable tropical afro‐alpine vegetation. Several LUC change studies are available for the Ethiopian highlands, but relatively little is known about LUC change in the afro‐alpine zones. In this study, LUC changes between 1964 and 2012 were mapped for the afro‐alpine zone of Lib Amba Mountain, part of the Abune Yosef Mountains in North Ethiopia. Historical LUC was derived from georeferenced aerial photographs of 1964 and 1982, and the present LUC (2012) from Bing Map satellite imagery. Based on these successive LUC maps a time‐depth map, LUC proportions, LUC transition matrices and LUC change trajectories were calculated. Two main phases of LUC change could be distinguished linked to the neo‐Boserupian perspective. (i) Between 1964 and 1982, there were large‐scale deforestation and general degradation of the vegetation above 3500 m, in a period of low population pressure; (ii) Between 1982 and 2012, an intensification of land use prevailed accompanied with a slight regeneration of the vegetation and the Erica arborea L. forest, under increased population pressure. Depth interviews indicated that local and governmental land management measures are very important for the protection against vegetation depletion and soil degradation. Quick recovery of the forest on Lib Amba provides confidence that degraded afro‐alpine areas would benefit in a short time from complete protection, given the vicinity of remaining patches of afro‐alpine vegetation. Management interventions are thus vital to restore the important ecosystem services of the afro‐alpine vegetation belt. Copyright © 2015 John Wiley & Sons, Ltd.