Land Use And Land Cover Patterns Research Articles

DYNAMICS OF URBAN LANDSCAPE AND ITS THERMAL INTERACTIONS WITH SELECTED LAND COVER TYPES: A CASE OF BENIN CITY, NIGERIA

In this study, the spatiotemporal dynamics of the urban environment and thermal environment of Benin City are analysed. The maximum likelihood algorithm for land use and land cover (LULC) analysis was used to categorise Landsat images. The relative transfer equation (RTE) and land surface emissivity (LSE) approaches were used to retrieve the land surface temperature (LST), whereas the Cellular Automata-Markov (CA-Markov) algorithm was used to forecast the LULC for 2030. The findings reveal evolving LULC patterns over time. Built-up areas made up 19.66% of the total area in 1990, bare ground made up 9.25%, and vegetation made up 71.08%. Built-up areas reached 23.40% in 2000, bare land reached 12%, and the vegetation cover dropped to 64.16%. In 2010, there was an increase in the proportion of built-up areas to 44.38%, the proportion of bare land increased to 22.20%, and the proportion of vegetation decreased to 33.42%. Built-up areas reached 61.79% in 2020, compared to 22.29% for bare land and 61.79% for vegetation. Regarding the relationship between the fractional vegetation cover (FVC) and LST, for the years 1992, 2002, 2012, and 2022, R2 is equal to 0.87097, 0.84598, 0.83957, and 0.71838, respectively. Conversely, for the LST and the normalised difference built-up index (NDBI), the R2 values were 0.5975, 0.73876, 0.86615, and 0.90368 for 1992, 2002, 2012, and 2022 respectively. In conclusion, this study evaluates Benin City's metropolitan setting and thermal environment. According to the LULC study, there are more built-up areas and less vegetation. The impact of the changing land cover on urban thermal features is shown through correlation analysis, which links more built-up regions to higher LSTs. These results can support urban design efforts to lessen the effects of climate change. Examining the distribution of the LST and its associations with particular land cover types was the major goal of this study. Future research will undoubtedly use this study as a useful reference when modelling urban terrain and temperature variations.

Assessment of Land Use Land Cover Changes and Future Predictions Using CA-ANN Simulation for Gazipur City Corporation, Bangladesh

Anthropogenic activities have a significant influence on land use and land cover (LULC) changes, especially in rapidly growing areas. Among several models, the combination of a cellular automata–artificial neural network (CA-ANN) model is being widely used for assessing future LULC changes using satellite images. This study aimed to investigate LULC changes in Gazipur City Corporation (GCC), Bangladesh, and the changes in LULC patterns over the last two decades (2002 to 2022). In this study, the maximum likelihood supervised classification technique was used for processing the available satellite images. The results show that the urban area and vegetation coverage increased by 150% and 22.78%, whereas the bare land and waterbody decreased by 7.02% and 78.9%, respectively, from 2002 to 2022 inside the GCC area. For future LULC predictions, the CA-ANN model was developed, the accuracy percentage of which was 86.49%, and the kappa value was 0.83. The future LULC prediction model results show that the urban area will increase by 47.61%, whereas the bare land and waterbody are supposed to decrease by 24.17% and 67.23%, respectively, by 2042. The findings of this study could be useful for future sustainable urban planning and management, as well as enabling decision making by authorities for improvements in environmental and ecological conditions in the study area.

Land use changes impact on selected chemical denudation element and components of water cycle in small mountain catchment using SWAT model

Land use changes are extremely important in the context of land and water management. This study examines impact of land use and land cover (LULC) changes on nitrate nitrogen, outflow and surface runoff in the agriculture catchment in the Polish Carpathians using the Soil and Water Assessment Tool (SWAT). The study covers 25-year (1995–2019) and was divided into three periods relating to different LULC patterns (1995–2004, 2005–2012, 2013–2019). The LULC maps used were based on aerial photos from 1997, 2009, and 2016. The SWAT was calibrated and applied to periods with variable LULC (in simulation 1) and by keeping the LULC constant during the period 1995–2019 (simulation 2; the LULC from 1997) to determine the impact of LULC changes on the analysed variables. The ongoing LULC changes were found to have resulted in a reduction of cultivated land area (by 72 %) and an increase of grassland (by 45 %), forest (by 2 %) and urbanised areas (by 17 %). The SWAT model achieved reasonable accuracy. The model results tend to overestimate the load of nitrate nitrogen and underestimate outflow and surface runoff. The LULC changes resulted in a decrease in outflow between 1995 and 2004 of 24 % in comparison to 2013–2019, and a decrease in surface runoff of 12 %. At the same time, there was a significant decrease in nitrate nitrogen load of 68 % (from 13.0 kg ha−1 in 1995–2004 to 4.2 kg ha−1 in 2013–2019). If the LULC of 2013–2019 was restored to 1997 conditions (as in simulation 2), the outflow and load of NO3-N would be higher by 16 % (12 % in simulation 1) and 67 % (55 % in simulation 1) in relation to the observed values. The impact of LULC on the analysed parameters was spatially differentiated between subbasins depending on the scale of the land use changes. The research conducted here indicates that planning and managing the effects of LULC changes is of key importance for sustainable development in terms of the quantity and quality of water.

Spatiotemporal modeling of the value of carbon sequestration under changing land use/land cover using InVEST model: a case study of Nour-rud Watershed, Northern Iran

The current paper aims to assess the effects of landscape change in a mountain river basin in the north of Iran through quantifying, mapping, and assessing carbon storage. The analyses were performed based on previous alterations in land use and land cover (LULC) (1988–2018) and on expected changes determined by three LULC alteration setups for 2048. The Landsat imagery from 2018, 2008, 1998, and 1988 was used for evaluating and predicting the spatiotemporal distributions of LULC changes. The future LULC image prediction has been generated using Land Change Modeler (LCM) module of TerrSet software for the years 2028, 2038, and 2048. Validation was carried out by overlaying the actual and projected to 2018 map. We integrated the Markov Chain (MC) and InVEST Carbon Storage and Sequestration (InVEST-CSS) models for simulating the ecosystem carbon storage and the long-term monetary valuation. In this process, we considered social costs/economic value because of the area’s loss and gain of stored carbon. The results show that forests and rangelands with good and poor conditions decreased by 631.2, 10,374, and 10,254 ha, respectively, from 1988 to 2018. Overall, modeling and mapping LULC changes showed a descending trend in forests (0.66%), agriculture (0.1%), and rangelands (4.1%) in 2048. In addition, carbon storage has already been lost by 9.9 million tons (76.98 ha−1) from 1988 to 8.8 million tons (68.86 ha−1) in 2018 and is expected to have an 8.4 million tons (65.25 ha−1) loss by 2048. Monitoring the economic value of carbon storage from 1988 to 2018 shows a loss of $US 15684338 (121.8 ha−1) and estimates a loss of $US6972622 (54.18 ha−1) by 2048. Therefore, spatiotemporal design of InVEST model by estimating the carbon value over time focuses on continuous monitoring actions for both the carbon pools dynamic and LULC pattern. This consideration causes reduction the uncertainty of estimated models and also increases the continuous cost of those changes. This will help government and decision makers for long-term and accurate carbon sequestration strategies for ecosystem.

Monitoring Land Use & Land Cover Change in an Urban Landscape: A Case Study of Siliguri, India

Information related to Land use and Land Cover (LULC) change is important for conservation of natural resources and sustainable development of an area. Siliguri is one of the populated metropolitan of West Bengal, India that has witnessed rapid urbanization. The major challenge is to identify the reason and pattern of urbanization due to inadequate and insufficient data in small cities like Siliguri. This paper has mainly assessed the LU & LC change of Siliguri municipality for the period of 1991-2001, 2001-2011 and 2011-2021. The main aim of the study is to determine the pattern and direction of future expansion. Satellite images from Landsat 4-5(1991-2011) and Landsat 8(2021) were used to extract LU & LC map of the municipality. Supervised classification using maximum likelihood classification was used to developed LULC maps. The supervised classification produced good results with overall accuracy of more than 80%. The change of the LULC pattern was obtained using change detection method. Four major classes viz. Vegetation, water body, sand and built-up have been identified. The analysis shows that maximum transformation has happened in the buildup environment from other land surface features.

Temporal changes in Land Use and Land Cover (LULC) and local climate in the Krueng Peusangan Watershed (KPW) area, Aceh, Indonesia

Watersheds are important sources of various ecosystem services. The Krueng Peusangan Watershed (KPW), one of the watersheds in Aceh Province, is expected to become the "lungs" of the ecology of the north-central part of Aceh. Currently, the KPW is one of the watersheds that suffers from severe or critical damage, especially that caused by changes in forest cover. Spatiotemporal monitoring of changes in landscape patterns (composition and configuration) is needed to inform policy and support planning for sustainable watershed management. This study aims to: 1) determine the pattern of changes in LULC in KPW in two decades (1999–2009 and 2009–2019, and 2) determine the impact of changes in the LULC pattern on the local climate. Landsat Satellite Imagery for three years (1999, 2009, and 2019) is used to classify LULC. Geographic Information System (GIS) technology and remote sensing are used to analyze it. Each satellite image is classified into six categories: built-up area, forest, agriculture, bare land, wetland, and water body. This classification resulted in LULC maps for 1999, 2009, and 2019 with kappa coefficients of 0.84, 0.88, and 0.84. It is found that between 1999–2009 and 2009–2019, there has been a consistent decrease in forest cover area and an increase in built-up area. Local climate change is also occurring in this KPW. Continuous monitoring of LULC changes in KPW is also necessary to keep management planning up to date.

Climate Change Impacts on LULC in the Jarmet Wetland and its Surrounding Areas in Western Ethiopia

The concerns over land use and land cover (LULC) change have emerged on the global stage due to realization that changes occurring on the land surface also influence climate, ecosystem and its services. This study aimed to map the temporal dynamic of LULC patterns and LST in the Jarmet wetland in Ethiopia. The dynamics and pattern of changes for a period of 21 years (2000-2021) were analyzed using geospatial techniques. Multi-temporal satellite images from Landsat ETM+ and Landsat-8 OLI sensor data were used to extract land-cover maps. The Land Surface Temperature (LST) trend of the study areas was computed using MODIS satellite imagery (2000-2021). Supervised classification using a Maximum Likelihood Classifier (MLC) was applied to prepare LULC maps of the watershed. The accuracy of the classified map was assessed using high-resolution data, and ground realities have been verified and ascertained through field observations. The results revealed a decreased trend in wetland, forest, shrubland and grassland in the period of 21 years (2000-2021) by -1148.71ha, -1073.26 ha, -1480.1 ha, and -87.73 ha, respectively. On the other hand, farmland and plantation areas followed an increasing trend. LST revealed decreasing trend in terms of mean and minimum with a fraction change of -0.018 and -0.073, whereas the maximum LST value shows an increasing trend with 0.021. The overall accuracy was 84.41%, with Kappa index of 76.13%. The analysis and findings of the study highlight important policy implications for sustainable LULC management in the study area. The study suggests the design and implementation of a guided natural resource policy, stopping the illegal expansion of farmland and educating society about the value of the sustainable management of habitat reserves.

Land Use/ Cover Change Detection in High-Altitude Mountain Landscapes: A Case of Pangi Valley, Western Himalaya (India).

Recently, mountains are going to widely threatened by developmental activities and global climate change leading to changing shift of land uses/covers across the globe. The present study area has witnessed very significant changes in land use and land cover (LULC) especially after the implementation of Mahatma Gandhi National Rural Employment Guarantee Scheme (MNREGS) in the form of rapid developmental and constructional activities. As a result, rapid local roads and infrastructure construction has lead to increased hazards further putting pressure on the fragile physical landscape. Therefore, the present study is an attempt to evaluate changes and transformation in the LULC patterns in the Pangi valley, Western Himalaya (India). The LULC has been analyzed using remote sensing imagery from LANDSAT for the period 1992-2021. We conduct a supervised classification by using Maximum Likelihood Classifier (MLC) to prepare LULC maps. In short, seven LULC classes have been delineated. The result shows that barren land, built-up land, water bodies and agriculture land have increased by 97.5 per cent, 98.5 per cent, 14.4 per cent and 13.2 per cent, while snow cover pasture land and forest cover have drastically decreased by 68.1 per cent, 21.1 per cent and 20.9 per cent respectively. The LULC transformation result illustrates that the area under snow cover, pasture land and forest cover converted into barren land by 217.8 km2, 175.3 km2 and 47.3 km2 respectively out of the total 1520 km2 area of the Pangi valley. It reflects human influence in terms of developmental activities and impact of global climate change in the transformation of the physical landscape in the study area. The outcome derived from the current research will be helpful for policy makers and spatial planners for sustainable landscape planning and management of the high-altitude mountain landscapes.

Land Use Pattern Changes and the Driving Forces in the Shiyang River Basin from 2000 to 2018

Thorough understanding of the evolution processes and drivers behind the formation of and changes in land use and land cover (LULC) is essential for maintaining the balance between humans and fragile nature in arid regions. This quantitative driving analysis provides in-depth insight into the driving mechanisms behind the formation of and changes in LULC through a case study of the Shiyang River Basin in Northwest China. Based on land use, meteorological, topographic, and socioeconomic data from 2000 to 2018 (2000, 2005, 2010, 2015, and 2018), this study employed land use transfer matrices and the GeoDetector model to explore the evolution and driving forces behind the formation of and variations in the LULC patterns. The results demonstrated that anthropic factors mainly drove the spatial distributions of cropland and settlement. The spatial distributions of the forest, grassland, and bare land were determined by the mutual influence of natural and anthropic factors. The LULC patterns exhibited consequential variations throughout the study period. Through the occupation of the surrounding cropland and grassland, urbanization expanded rapidly. The ecological environment had been improved, but there were still considerable areas of degraded land, characterized by the grassland degradation downstream and the forest degradation upstream. Geographical differentiation was the primary driver for the transformation of bare land to grassland. The main driving forces behind urban expansion and forest loss were socioeconomic development and geographical differentiation. The degree of a certain LULC change varied among different levels of its driving factor. This research can provide scientific advice for administrators and policymakers to formulate scientific, rational, and targeted land use planning and policies in the future to achieve the sustainable development of endorheic river basins.

Land use as an important indicator for water quality prediction in a region under rapid urbanization

Land use and land cover (LULC) have significant impacts on river water quality, particularly in regions subjected to rapid urbanization. However, it is unclear whether LULC (LULC type and pattern index) can be used as an effective indicator to predict water quality over the rapid urbanization regions. Here, we investigated the spatiotemporal changes of LULC and their impacts on the water quality of a river flowing through a rapidly developed area in China. Then, a cellular automata-Markov model was established to predict the LULC, which was used as a key indicator to predict future water quality by a multiple linear regression model. The results showed that construction land experienced rapid growth between 2000 and 2010 taking over arable land to a great extent, and the number of patch (NP) showed a significant downward trend during 2000–2010. The biochemical oxygen demand in five days (BOD5), total nitrogen (TN), and total phosphorus (TP) exhibited significantly positive correlations with construction land, while dissolved oxygen (DO) showed a significantly negative correlation with construction land. The DO exhibited a significantly positive correlation with the number of patch (NP), but TN and TP showed significantly negative correlations with NP. The water quality prediction model based on LULC performed well, especially TN prediction has a coefficient of determination of 0.691 and a mean relative error of 12.14%. The prediction of water quality in 2030 indicated that TN will not increase further, but TP will exhibit a remarkable increase in Zhenjiang city if the current development trend continues and no extra pollution control measures are taken.

Monitoring of LULC Changes and Forest Loss Using Geospatial Technique: A Case Study from Northern Region of Bangladesh

Monitoring changes in land use and land cover (LULC) is essential for determining the state of the environment. This study is an attempt to assessing changes in LULC patterns and forest cover at northern region (Dinajpur district) of Bangladesh for the periods 1989–1999, 1999–2010, 2010–2020 and 1989-2020. Landsat satellite images were applied and supervised classification was done. There is a noticeable change found in LULC classes (Built-up, crop/fallow, forest, homestead and water). The built-up and homestead increased by 210.36% and 134.71%; whereas the crop/fallow, forest, and water decreased by 12.27%, 74.99% and 39.77%, respectively between 1989 to 2020. Forest was narrowed as majority of forest land transferred to homestead (8089.02ha) and crop/fallow (5965.74ha) land in last three decades (1989-2020). The findings of the study help in important policy implications for the sustainable LULC management in Dinajpur region of Bangladesh.

Landscape ecological modeling to identify ecologically significant regions in Tumkur district, Karnataka

Abstract Temporal land use and land cover (LULC) information of a landscape provide an overview of the drivers of change, and impacts on the socio-ecological system. This necessitates organizing diverse data of a landscape, which provides insights into sustainable management. Spatial heterogeneity with landscape dynamics influences biotic and abiotic processes. The knowledge of LULC dynamics aids in assessing the feedback between socio-ecological systems across the urban and rural environments. Visualizing likely landscape changes through modeling help in the decision-making for sustainable landscape management. The current chapter accounts for changes in LULC patterns of the agrarian district Tumkur in Karnataka State, considering temporal remote sensing data of three decades, using geospatial techniques and modeling. Land use (LU) analyses indicate an increase in horticulture area from 0.94 (1989) to 1.02% (2019) due to an increase in commercial cropping. An upsurge of built-up cover from 0.02 to 2.11% (1989–2019) with the enhanced socio-economic activities with the industrialization and infrastructure development across the Tumkur to Bangalore highway. Spatial patterns of landscape dynamics assessed through spatial matrices highlight of increase in urbanization with land conversion from agriculture and forest cover in the outskirts of the Tumkur city center. Ecologically significant regions (ESR) were identified at disaggregated levels through aggregate metrics incorporating bio-geo-climatic, social, hydrological, and ecological aspects. The study region is divided into 9 × 9 km grids for computing metrics at disaggregated levels. ESR is delineated based on the composite metric of all variables, depicts 17 grids (11%) under ESR 1, indicating the highest sensitivity, 29% area (46 grids) as ESR 2 (higher sensitivity), 45% (70 grids) as ESR 3 (high sensitivity), and the rest is 15% (24 grids) in ESR 4 (moderate sensitivity). The outcome of the current research would provide critical management approaches required for managing natural resources and will be valuable for policy and planning purposes in pursuing Sustainable Development Goals (SDGs) at the regional scale.

Assessing the impact of modified LULC on extreme hydrological event over a complex terrain: A case study for kodagu 2018 flood event

The livelihoods and biodiversity in the Kodagu district of Karnataka are greatly altered by the growing urbanization through huge deforestation. The changes in the Land Use and Land Cover (LULC) patterns have important implications on the heavy rainfall activity due to significant alterations in the spatial distribution of existing land-surface properties. Hence, in order to make region (like Kodagu) more climate resilient; it is utmost important to construct a novel coupled framework for hydro-meteorological prediction to assess the impact of changing LULC (degradation of forest cover owing to urban expansion) on extreme rainfall events. Only a well-calibrated, validated, and optimized coupled high-resolution hydrological-atmospheric modelling system can be one of the best tools to study such complex problems. Thus in the present study, we have enhanced the forecasting capability of the state-of-the-art Weather Research and Forecasting (WRF) model by coupling with a physically based, fully distributed, WRF-Hydro. The evaluation of the proposed model configurations (stand-alone and coupled mode) is done with respect to two LULC datasets (USGS, 1992: less urbanized scenario and ISRO, 2017: more urbanized scenario) to explore the underlying causes and hydrological responses behind the 2018 Kodagu flood event. The simulations have been contended with both the stand-alone and coupled WRF/WRF-hydro models with different LULC datasets and a comparative evaluation have been carried out. The simulated variables (rainfall, runoff, surface fluxes, and peak stream discharge) are evaluated and compared with observation to assess the impact of changing LULC pattern during the flood event.

Optimizing the Land Use and Land Cover Pattern to Increase Its Contribution to Carbon Neutrality

Land use and land cover (LULC) contribute to both carbon storage and carbon emissions. Therefore, regulating the LULC is an important means of achieving carbon neutrality under global environmental change. Here, the West Liaohe River Basin, a semiarid watershed, was taken as a case study. Based on the assessment of the carbon storage and emissions induced by LULC from 2000–2020, we set up three different coupled shared socioeconomic pathway (SSP) and representative concentration pathway (RCP) scenarios (SSP119, SSP245, and SSP585), from 2030–2060, to optimize the LULC. Then, the LULC patterns under each scenario were simulated using the patch-generating land use simulation (PLUS) model, and the corresponding changes in carbon storage and emissions were compared and analyzed. It was found that, since 2000, with the expansion of forest, cropland, and construction land, as well as the degradation of grassland, the carbon storage and emissions induced by LULC have significantly increased, but the increase in storage was lower than that of emissions. The scenario simulations revealed that, when we optimize the LULC, mainly including the protection and expansion of ecological land such as forest and grassland in the western and southern edges of the basin, as well as the control and management of cropland land and construction land in the northeast and central parts of the basin, there will be a significant increase in the carbon storage and a significant reduction in carbon emissions from 2030–2060. This indicates that zone-based management measures with rational LULC regulation can contribute to the achievement of carbon neutrality in the study area. Supported by the results of this study, a direct decision-making basis for land use policy regulation to promote regional sustainable development can be undertaken in the basin. This study also provides a reference for low-carbon development in other regions.

DECADAL TRANSFORMATION OF LAND USE - LAND COVER AND FUTURE SPATIAL EXPANSION IN BANGALORE METROPOLITAN REGION, INDIA: OPEN-SOURCE GEOSPATIAL MACHINE LEARNING APPROACH

Abstract. Land use and land cover (LULC) are the terms by which we can denote how the people are utilising the land to serve different purposes and what covers the earth's surface, respectively. This study will present LULC changes and future spatial expansion of the Bangalore Metropolitan Region (BMR), which comprises of Bangalore urban district, Bangalore rural district, and Ramanagara district. The Bangalore metropolitan region is spread across an area of 8005 sq. km and is undergoing a phase of rapid population expansion, which causes increasing concrete space and decreasing green space. This study will focus on achieving the following objectives: Comparing the Spatio-temporal changes, accessing the pattern of expansion, future projection of LULC pattern in Bangalore Metropolitan Region, and finding how the population growth impacts the land use changes. The study is based on an urban expansion model for the years 2030 and 2050 in BMR. The study has used two add-on tools, i.e., the Dzetsaka classification tool and Modules for Land Use Change Simulations (MOLUSCE) in QGIS, to generate the land cover transformation. The core upshots of this technique show the land use and land cover transformation, and urban expansion pattern in the Bangalore Metropolitan Region. It also illustrates the Spatio-temporal changes and how dispersion or expansion has occurred in the last five decades and predicts the future three decades changes. The results are expected to aid society and several organisations such as city planners and policymakers.

Interactive influences of meteorological and socioeconomic factors on ecosystem service values in a river basin with different geomorphic features

Ecosystem service value (ESV) is influenced by land use and land cover (LULC), and is closely related to natural conditions and human activities. However, the interactions between human and natural systems and ESV remain unclear, especially concerning widely discussed meteorological and socioeconomic factors. In this study, three periods of LULC patterns (2000, 2010, and 2020) in the Haihe River Basin, northern China, were collected to determine the relationship between changes in LULC and ESV over time. Natural and socioeconomic data associated with ESV were obtained and the structural equation model was used to decouple interactions between these factors. Results showed that the total value of regional ecosystem services has decreased as cultivated land shrunk and artificial surfaces increased over the past two decades. The ESV was significantly decreased in the middle of the basin. The direct effects of meteorological factors and socioeconomic factors on ESV were positive (0.094) and negative (−0.203), respectively. The indirect effect of socioeconomic factors on ESV through meteorological and LULC factors was 0.149. Structural equation modeling demonstrated that under the dominance of LULC, interactions between natural and socioeconomic factors affected ESV in a complex manner. These results implied that identifying the direct and indirect effects of economic development and human activities on ESV could guide and implement effective land management policies.

Land use and land cover (LULC) change analysis using TM, ETM+ and OLI Landsat images in district of Okara, Punjab, Pakistan

Land use and land cover (LULC) changes are one of the world-wide variations which have the most significant effects on the natural environment and ecosystem due to human activities. Pakistan is facing enormous changes in LULC patterns without any good arrangement from the last few years. The main objective of this study is to identify the LULC changes in district Okara inat 3 different years, (2000, 2010, and 2020) using various Landsat images and to detect the reasons for the variations. Supervised classification was used to identify the LULC changes over the district of Okara, as it explains the maximum likelihood algorithm using ERDAS imagine 15 software. The total population in district Okara was 2.2 Million in 1998 that increased by 3 Million in 2017. The vegetation area was 91.6% in 2000, which decreased by 89.3% in 2020 in the study area. The built-up area was increased 4.5% from 2000 to 2020 due to the increasing population in district Okara. Overall accuracy assessment was observed 90% for 2000, 87% for 2010, and 85% for 2020 in the study area. Our result showed that, land which was used for vegetation area is changed into the build-up area due to the increasing in infrastructural and commercial areas in district Okara. As a result, proper observation of LULC changes will help the related agencies, government and policymakers for developing the land management.

Dynamics and drivers of land use and land cover changes in Migori River Watershed, western Kenya region

Understanding land use and land cover (LULC) change dynamics and their contributing factors are critical for developing sustainable land management strategies. Therefore, this paper assesses the trends and patterns of LULC changes and their drivers in the Migori River watershed in Kenya from 1980 to 2020. The spatial analysis is based on remote sensing data based on the maximum likelihood classifier algorithm; whereas the analysis of the drivers is based on index-based ranking and logistic regression of 318 households’ survey data. The results show that between 1980 and 2020, the watershed experienced a considerable decline in shrub lands by 40.63% (-235.97 km2), grasslands by 84.86% (-59.14 km2), forests by 52.90% (-98.36 km2), water by 82.03% (-39.27 km2) and wetlands by 38.44% (-3.69 km2); whereas cultivated land, bare land and built-up areas expanded over the same period by 34.25% (+347.42 km2), 132.28% (+60.95 km2) and 461.20% (+25.32 km2), respectively. The results of the household survey revealed that the perceptions of the locals tended to corroborate these observed LULC patterns obtained from spatial analysis, with 60.50% (n = 192) of the respondents reporting a significant expansion in agricultural land use (at p < 0.05), and 75.80% (n = 241) observing a significant decline in forest areas in the watershed (at p < 0.05). Fuel wood collection, timber/poles production, agricultural expansion, population pressure, and high poverty are the major drivers of these LULC changes. The findings also revealed that educational level significantly influenced the survey participants’ perceptions concerning these drivers. The paper concludes that the watershed’s natural landscapes have been undergoing destruction at the expense of human settlement and infrastructural developments driven by anthropogenic activities. Therefore, there is a need, among others, for land use zoning to regulate conflicting land uses on the watershed between settlement, conservation, and agricultural lands.

Analysis of land use and land-cover pattern to monitor dynamics of Ngorongoro world heritage site (Tanzania) using hybrid cellular automata-Markov model

Assessment of land-use and land-cover (LULC) change of any region is one of the prominent features used in environmental resource management and its overall sustainable development. This study analyzed the LULC changes of Ngorongoro Conservation Area (NCA) and its surroundings using Remote Sensing and Geographical Information System integrated with Cellular Automata-Markov model. The LULC maps for the years 1995, 2005, and 2016 were classified using unsupervised and supervised classification procedure, and projected for 2025 and 2035 under business-as-usual scenario using the CA–Markov model. The results indicated maximum gains and losses in cultivated land and woodland in the study duration, respectively. The projected LULC for the period 2025 to 2035 showed a reduction in bushland, forest, water, and woodland, but an intensification in cultivated land, grassland, bare land, and the built-up area. The natural forests with high environmental values were found to be continuously declining under the current land management trend, causing the loss in the NCA's ecological values. For sustainable management, the authorities must reach conciliation between the existing LULC patterns change and ecosystem services monitoring. A rational land use plan must be made to control the increase of cultivated land and built-up area counting a rational land use plan and ecosystem services protection guidelines. Decision makers should involve stakeholder to support improved land use management practices for balanced and sustainable ecosystem services strategies.

Spatio-temporal assessment on land use and land cover (LULC) and forest fragmentation in shifting agroecosystem landscape in Ukhrul district of Manipur, Northeast India.

The information on land use and land cover (LULC) plays a critical role in understanding the interactions between human activities and the natural environment. The changes in LULC have a significant impact on the ecological integrity of forests, biodiversity, and natural resources, which in turn trigger global environmental change. Forest fragmentation is an important conservation challenge that includes interdependent forest loss components and spatial shift patterns. Over the years, Northeast India has experienced major changes in LULC and forest fragmentation. There are limited information and data regarding the change in LULC patterns and causes of forest fragmentation. The present study was carried out with an attempt to analyze the change in LULC and forest fragmentation using satellite data of three different time series: 1991, 2005, and 2020 for Ukhrul district, Manipur, Northeast India. Different LULC classes were classified using the supervised method, viz., maximum likelihood algorithm in ERDAS Imagine 2014 and generated thematic maps in ArcGIS 10.4 software. Considering the classified forest class, fragmentation in the forest area was grouped into different categories of fragmentation using the Landscape Fragmentation Tool (LFT v 2.0). The distribution of the perforated category has tremendously increased in 2020 from 1991. The outcome of the present study will help to understand the inherent forest vulnerability and to adopt sustainable management strategies for forest and agriculture ecosystems in the hill landscape.