Sustainable Land Use Planning Research Articles

The use of EUNIS habitat classification to assess ecosystem services capacity: the case of Mamak district (Ankara, Türkiye)

Objective: The ecosystem services (ES) term is defined as all of the products, services, and benefits provided by ecosystems on earth to human beings and other living entities. In order to determine the ES capacity and integrating such data into management plans is a key element for nature conservation and sustainable land-use planning. Hence, a study was conducted, and the objective of this study was to determine the ES capacity of Mamak district (Ankara, Turkey) with an evaluation approach based on expert opinions using EUNIS habitat data. Material and Methods: Besides raw scores obtained from experts with the evaluation approach based on expert opinions, the areal ES capacity (AESC) indices for each EUNIS habitat type were calculated, and maps were developed accordingly. Results: The results obtained from this study showed that; (1) with raw scores, ES capacities of habitat types are very diverse for each ES type, (2) with AESC indices, almost all ES capacity class are same for each habitat type. Conclusion: As a conclusion, it can be stated that the use of EUNIS habitat maps is applicable to assess ES capacities in such studies if and when the presence of such habitat data.

Monitoring Land Surface Temperature Relationship to Land Use and Land Cover in Hai Duong Province, Vietnam

This study utilised remote sensing data and ArcGIS 10.8 software to evaluate changes in land use and land cover (LULC) and their effects on land surface temperature (LST) in Hai Duong Province, Vietnam, from 1992 to 2022. Landsat satellite data were pre-processed and classified using supervised methods for the years 1992, 2010, and 2022. In 1992, vegetation cover accounted for 57.89% of land cover, increasing to 84.49% in 2010, but then decreasing again to 66.67% in 2022. In contrast, the built-up area consistently increased, from 2.88% in 1992 to 29.35% in 2022, as most of the barren land present in 1992 became built-up area in 2022. The LST values were calculated from the thermal bands for the years 1992, 2010, and 2022 and ranged from 16.09°C to 34.27°C, 17.04°C to 36.74°C, and 11.03°C to 28.44°C, respectively. In addition, the Normalized Difference Vegetation Index (NDVI) values were calculated using the near-infrared band and the red band, with values ranging from -0.40 to 0.70 over the study period. A linear regression analysis indicated a shift in the correlation between NDVI and LST from positive to negative. This study highlights the significant transformation that occurred in Hai Duong Province due to rapid population density increases, urban growth and infrastructure development, leading to a decline in greenery. These LULC changes can cause severe environmental damage. These research findings will assist policymakers in formulating management strategies and sustainable land-use plans to minimize potential harm and promote sustainable development in the area.

Assessing Coastal Vulnerability and Evaluating the Effectiveness of Natural Habitats in Enhancing Coastal Resilience: A Case Study in Shanghai, China

This research evaluates the coastal vulnerability of Shanghai, evaluates the effectiveness of existing natural habitats in reducing that vulnerability, and, finally, provides recommendations to improve the resilience of the coastal areas. Shanghai is an important economic center that is home to a large population. However, the combination of ground subsidence, rising sea levels, and more frequent coastal flooding due to tropical cyclones poses escalating climate risks for Shanghai, demanding urgent mitigation measures. The InVEST Coastal Vulnerability Model was used in this study to assess Shanghai’s coastal vulnerability under the current situation and various scenarios that simulated the absence of natural habitats. The assessment results were analyzed through a comparison between different scenarios and spatial aggregation analysis. This study pinpointed highly vulnerable areas, primarily located on the east coast of Chongming Island, the east and northeast coasts of Hengsha Island, and the east coast of the mainland of Shanghai. These areas need to be prioritized for intervention. Also, it demonstrated the effectiveness of existing natural habitats in reducing coastal vulnerability, with large green spaces and salt marshes playing a greater role compared to small green spaces. This is the first study applying the InVEST Coastal Vulnerability Model to Shanghai, demonstrating the model’s potential in providing valuable information regarding coastal protection against the impacts of climate change in Shanghai. Insights from the findings of this study are useful in crafting sustainable land-use policies and plans for Shanghai.

Characterization of landuse and landcover dynamics and their impact on runoff generation patterns in dam catchments of Northern Ghana

Landuse alteration is a critical factor that exerts influence over hydrological mechanisms and the oversight of water resources across the globe. Northern Ghana is characterized by erratic rainfall, leading to the intense construction of small dams and dugouts. This study investigated historical landuse/cover (LULC) changes and physical factors associated with surface runoff generation within eight (8) sub-catchments namely; Guno, Gbahali, Sambu, Duago, Denugo, Kwisini, Sandu, and Nyeko, with areas of 29.5, 110, 38.5, 106.04, 67.81, 2.59, 10.56 and 120.65 km2, respectively, located in four (4) regions of northern Ghana. For LULC change detection, historical satellite imagery and geospatial data were used with the aid of remote sensing and applying supervised maximum likelihood classification to Landsat images of 1995, 2005, 2015, and 2023. Runoff factors and depths were identified based on LULC classes, hydrologic soil groups, and precipitation conditions prevailing in the areas, based on the curve number (CN) method. With regards to LULC, the results from the overall analysis gave an insight into the rampant conversion, where grassland/mix vegetation witnessed significant declines, with decreases ranging from 150 to 395.25% in different catchments. Agricultural and arable lands emerged as the dominant landuse category, indicating a remarkable expansion of 11.61–51.17 km2, from 1995 to 2023 respectively, followed by built-up areas. While grassland decreased in all catchments over the same period, in some places, open tree (savannah woodland) increased, which confirmed the implementation of the afforestation policy. The CN values offered essential insights into the hydrological characteristics of each catchment. In addition to the impact of LULC and watershed characteristics on runoff, the observed variations in runoff patterns can be attributed to interannual variability in total annual precipitation which is critical driver of runoff. The findings hold significant importance for developing effective strategies and integrated watershed management policies to contribute to better water resource management and inform sustainable landuse planning in the face of changing environmental conditions.

Spillover Effect of Surface Mining on Cocoa Farming in The Wassa Amenfi East and West Districts, Ghana.

Purpose: Based on the resource curse paradox, we argued that farmers are crowded out due to their lower marginal propensity to bargain, making them vulnerable to livelihood shocks. Consequently, we examined the spillover effect of surface mining on cocoa farmers in the Wassa Amenfi East and West districts. Design/Methodology/Approach: Using a mixed research design, a total sample of 758 farmers were proportionately sampled from 14 purposively selected communities from Wassa Amenfi East and West districts. In addition, community extension agents, chief farmers and opinion leaders were formed into a focus group for discussion. The quantitative data were presented using descriptive statistics and the Wilcoxon Sign Ranked test and triangulated with emergent issues from the focus group discussions. Research Implications/Limitations: The study largely focused on cocoa farmers who were affected by surface mining. As a result, the findings of this study were interpreted within that scope unless they are confirmed by studies that involve a cross-section of cocoa farmers. Findings: We found that the livelihoods of cocoa farmers have deteriorated due to significant reductions in cocoa farm sizes and yields attributed to surface mining activities in the districts. Generally, cocoa farmers willingly gave out farmlands to miners to receive a bulk sum of money. The processes involved in the computation and payment of compensation were not in line with protocols enshrined in the Minerals and Mining Act 703 of Ghana and the tenets of the entitlement theory of justice. Practical Implications: Sustainable land-use planning is a crucial element in mitigating the negative impacts and fostering resilient cocoa-farming communities in the affected districts. Based on this the study highlights the need for a comprehensive approach that addresses environmental and economic challenges. Social Implications: The social implication of the study lies in the ability of cocoa farmer groups to advise their members against giving out their cocoa farms to surface miners and to make them aware that cocoa trees are heritage cash crops that are for use by current and future generations. Originality/Value: The paper contributes to the debate on the weakness of mining laws in protecting vulnerable groups with a focus on surface mining that crowds out farmers from the land market and dislocates them from their livelihood activities.

Landslide Susceptibility Assessment in Nepal’s Chure Region: A Geospatial Analysis

The Chure Hills, already vulnerable due to their fragile nature, face increased landslide risk, prompting the need for reliable susceptibility assessment. This study uses Poisson regression modeling to assess landslide susceptibility in two highly susceptible districts of the Chure region. Variance inflation factor (VIF) tests were conducted to ensure robustness, indicating no multicollinearity among the variables. Subsequently, Poisson regression analysis identified eight significant variables, among which geology, lineament density, elevation, relief, slope, rainfall, solar radiance, and land cover types emerged as important factors associated with landslide count. The analysis revealed that higher lineament density and slope were associated with lower landslide counts, indicating potential stabilizing geological and topographical influences. The categorical variable, namely geology, revealed that middle Siwalik, upper Siwalik, and quaternary geological formations were associated with lower landslide counts than lower Siwalik. Land cover types, including areas under forest, shrubland, grassland, agricultural land, water bodies, and bare ground, had a substantial significant positive association with landslide count. The generated susceptibility map that exhibited a substantial portion (23.32% in Dang and 5.22% in Surkhet) of the study area fell within the very-high-susceptibility categories, indicating pronounced landslide susceptibility in the Dang and Surkhet districts of the Chure hills. This study offers valuable insights into landslide vulnerability in the Chure region, serving as a foundation for informed decision-making, disaster risk reduction strategies, and sustainable land-use and developmental policy planning.

Detecting, Analyzing, and Predicting Land Use/Land Cover (LULC) Changes in Arid Regions Using Landsat Images, CA-Markov Hybrid Model, and GIS Techniques

Understanding the change dynamics of land use and land cover (LULC) is critical for efficient ecological management modification and sustainable land-use planning. This work aimed to identify, simulate, and predict historical and future LULC changes in the Sohag Governorate, Egypt, as an arid region. In the present study, the detection of historical LULC change dynamics for time series 1984–2002, 2002–2013, and 2013–2022 was performed, as well as CA-Markov hybrid model was employed to project the future LULC trends for 2030, 2040, and 2050. Four Landsat images acquired by different sensors were used as spatial–temporal data sources for the study region, including TM for 1984, ETM+ for 2002, and OLI for 2013 and 2022. Furthermore, a supervised classification technique was implemented in the image classification process. All remote sensing data was processed and modeled using IDRISI 7.02 software. Four main LULC categories were recognized in the study region: urban areas, cultivated lands, desert lands, and water bodies. The precision of LULC categorization analysis was high, with Kappa coefficients above 0.7 and overall accuracy above 87.5% for all classifications. The results obtained from estimating LULC change in the period from 1984 to 2022 indicated that built-up areas expanded to cover 12.5% of the study area in 2022 instead of 5.5% in 1984. This urban sprawl occurred at the cost of reducing old farmlands in old towns and villages and building new settlements on bare lands. Furthermore, cultivated lands increased from 45.5% of the total area in 1984 to 60.7% in 2022 due to ongoing soil reclamation projects in desert areas outside the Nile Valley. Moreover, between 1984 and 2022, desert lands lost around half of their area, while water bodies gained a very slight increase. According to the simulation and projection of the future LULC trends for 2030, 2040, and 2050, similar trends to historical LULC changes were detected. These trends are represented by decreasing desert lands and increasing urban and cultivated newly reclaimed areas. Concerning CA-Markov model validation, Kappa indices ranged across actual and simulated maps from 0.84 to 0.93, suggesting that this model was reasonably excellent at projecting future LULC trends. Therefore, using the CA-Markov hybrid model as a prediction and modeling approach for future LULC trends provides a good vision for monitoring and reducing the negative impacts of LULC changes, supporting land use policy-makers, and developing land management.

Infiltration Efficiency Index for GIS Analysis Using Very-High-Spatial-Resolution Data

Infiltration models and impervious surface models have gained significant attention in recent years as crucial tools in urban and environmental planning, to assess the extent of land-surface changes and their impacts on hydrological processes. These models are important for understanding the hydrological dynamics and ecological impacts of urbanization and for the improvement of sustainable land-use planning and stormwater-management strategies. Due to the fact that many authors partially or entirely overlook the significance of the infiltration process in geographic information system (GIS) analyses, there is currently no universally accepted method for creating an infiltration model that is suitable for GIS multicriteria decision analysis (GIS-MCDA). This research paper presents an innovative approach to modeling the infiltration-efficiency index (IEI) for GIS analysis, with a focus on achieving high-quality results. The proposed methodology integrates very-high-resolution (VHR) remote-sensing data, GIS-MCDA, and statistical methods. The methodology was tested and demonstrated on a small sub-catchment in Metković, Croatia. The study developed a VHR IEI model from six specific criteria that produced values between 0 and 0.71. The model revealed that 14.89% of the research area is covered by impervious surfaces. This percentage is relatively favorable when compared to urban areas globally. The majority of the research area (62.79%) has good infiltration efficiency. These areas are predominantly characterized by agricultural land use, encompassing orchards, tangerines, olive groves, vineyards, and a diverse range of low-lying and high vegetation on flat terrain. The IEI model can provide input spatial data for high-resolution GIS analysis of hydrological processes. This model will aid decision-makers in stormwater-management, flood-risk assessment, land-use planning, and the design of green infrastructure. By utilizing the information derived from this study, policymakers can make informed decisions to mitigate flooding risks and promote sustainable urban development.

Multitemporal Analysis of Land Use/Cover Changes and Landscape Fragmentation in Murshidabad District of West Bengal, India

Rapid population growth, human migration, and commercial activities are changing land use and Land cover at a faster rate. The human being's need and greed to sustain themselves alter the earth's natural environment, and that change affected us. However, poor and unmanageable land conversion led to severe environmental effects. For planning and management purposes, precise information regarding land use and its characteristics is required to ensure the sustainability of the area. The current study uses multi-temporal satellite images to analyze the decadal change from 1991 to 2021. Supervised image classification is performed using the Maximum likelihood classifier. The main goal of this study is to compare post-classification results using change vector analysis and analyze human impact on the environment using FRAGSTAT. Fragstat is a widely used software program designed for analyzing spatial patterns in categorical maps. It is commonly employed in landscape ecology, conservation biology, and land management studies. The primary purpose of Fragstats is to quantify and assess the composition and configuration of patches or landscape elements within a given area. The built-up area increased from 2.57% to 8.41% over the past 30 years, while the agricultural land decreased from 83.51% to 70.05%. It was observed that the density of patches and percentage of landscape reduction over time, the rise in the number of patches for agricultural class from 3570 in 1991 to 10173 in 2021 indicates that spatial diversity is increasing in the class with higher levels of anthropogenic disturbances. Moreover, in landscape-level indices, the number of patch and landscape shape index increases, and a fall in the largest patch index indicate that the landscape is becoming more complicated and fragmented. To achieve the sustainable land-use planning and safeguard natural ecosystems and biodiversity from anthropogenic activities, land-use change maps are utilized as an early warning system.

“Whose carbon is it?” Understanding municipalities role in blue carbon ecosystems management in Canada

Blue carbon ecosystems are marine vegetated ecosystems, such as mangroves, salt marshes, kelp forests and seagrass meadows, that naturally sequester and store atmospheric carbon in their deep sediments and biomass. Their ecosystem services extend from carbon capture and storage to coastline protection and flood mitigation, making their management and protection a form of nature-based solution (NbS) for climate change adaptation. However, these ecosystems are subject to degradation and destruction from coastal development and land use change, releasing sequestered carbon into the atmosphere. In Canada, although home to the largest coastline of any country in the world, neither federal nor provincial policy explicitly address the management or protection of blue carbon ecosystems, leaving them susceptible to these threats. Coastal municipalities, however, may play an important role in the current management and protection of blue carbon. Land-use planning decisions on the local level offer an opportunity to protect and conserve coastal ecosystems and the co-benefits these ecosystems provide offer a NbS to address many climate threats faced by coastal communities. This study examines the current and potential role of municipalities in the management, protection and/or restoration of blue carbon ecosystems for sustainable land-use planning practices and carbon pollution mitigation by interviewing municipal staff from coastal municipalities across Canada. Study results demonstrated municipal interest in blue carbon ecosystems for their tangible co-benefits, not carbon sequestration and storage capacity, and that restoring blue carbon ecosystems for living shoreline projects was the most common active management strategy at the local level. However, institutional capacity, a lack of jurisdictional clarity and uncertainty surrounding blue carbon were identified as barriers that hindered municipal engagement in active management strategies, largely restricting them to play a supportive role to blue carbon projects led by local environmental non-governmental organizations.

Large-scale stresses, decay of River Jamuna and resilience strategies in the Anthropocene

In the recent decades, many worlds' large rivers have been exceptionally modified by human interference. The principal objective of this study is to investigate the causes and consequences of decay of river Jamuna (distributary of river Bhagirathi-Hooghly). The entire investigation was conducted by the data of historical maps like Rennel's map of Bengal, Hunter's map, digital version of US army map, landsat satellite imageries (1980-MSS, 1990-TM, 2000-TM, 2010-ETM+ and 2020-OLI) and fluvio-hydrological study. Maximum likelihood classification algorithm was used to delineate the land use and land cover classes and Kappa based accuracy assessment indices were also used for the validation. There is no scientific work on this historically important river and afterward, millions of people are also underprivileged from this river ecosystem services due to severe degeneration. In this milieu, it is a genuine research gap. Novelty of the work is exposed the authentic causes of river decay and anthropogenic stressors. Result showed that the average width of river Jamuna has been significantly decreased whereas maximum average width of the channel was recorded in the year 1956 (319.81 m) and the present channel is flowing comparatively within narrow path (69.27 m). Rate of siltation (average rate of siltation 0.23 m/year) on channel bed has been increased due to impacts of channelization. Channel bed, natural levee and proximity floodplain have been encroached by large scale human intervention. LULC patterns showed that the urban built-up area is rapidly amplified from 2000 (22.26%) to 2020 (31.50%) whereas, agricultural and swampy land turned into built up area. Off-take of Jamuna is completely detached from the parent river Bhagirathi-Hooghly in the early 1956 and gradual anthropogenic interferences within river corridor accelerate river decaying processes. River restoration is highly required for sustainable river ecosystem health management and environmental development. Some management strategies have been recommended such as reconnecting of river Jamuna with Bhagirathi-Hooghly, dredging, decommissioning of culvert and sluice gate, cleaning of fishing pool and water hyacinth and sustainable land-use planning.

A New Approach Based on TensorFlow Deep Neural Networks with ADAM Optimizer and GIS for Spatial Prediction of Forest Fire Danger in Tropical Areas

Frequent forest fires are causing severe harm to the natural environment, such as decreasing air quality and threatening different species; therefore, developing accurate prediction models for forest fire danger is vital to mitigate these impacts. This research proposes and evaluates a new modeling approach based on TensorFlow deep neural networks (TFDeepNN) and geographic information systems (GIS) for forest fire danger modeling. Herein, TFDeepNN was used to create a forest fire danger model, whereas the adaptive moment estimation (ADAM) optimization algorithm was used to optimize the model, and GIS with Python programming was used to process, classify, and code the input and output. The modeling focused on the tropical forests of the Phu Yen Province (Vietnam), which incorporates 306 historical forest fire locations from 2019 to 2023 and ten forest-fire-driving factors. Random forests (RF), support vector machines (SVM), and logistic regression (LR) were used as a baseline for the model comparison. Different statistical metrics, such as F-score, accuracy, and area under the ROC curve (AUC), were employed to evaluate the models’ predictive performance. According to the results, the TFDeepNN model (with F-score of 0.806, accuracy of 79.3%, and AUC of 0.873) exhibits high predictive performance and surpasses the performance of the three baseline models: RF, SVM, and LR; therefore, TFDeepNN represents a novel tool for spatially predicting forest fire danger. The forest fire danger map from this study can be helpful for policymakers and authorities in Phu Yen Province, aiding sustainable land-use planning and management.

Hydrological and Precipitation Extremes and Trends over the Paraiba do Sul River Basin, Brazil

The Paraiba do Sul River Basin (PSRB) is a vital source of water resources in Brazil, providing water for human consumption, industry, agriculture, and hydroelectric energy generation. As part of one of the most developed areas of the country, in the Southeast of Brazil, the region is vulnerable to the impacts of climate change, with evidence of extreme events such as droughts and floods affecting the availability and quality of water. Hence, this study analyzes precipitation and streamflow rates data from the PSRB between 1939–2020 to investigate the spatial variability of average patterns and extreme events, trends, and their relationship with urban growth and socioeconomic development. The analysis reveals significant spatial variations in precipitation and runoff rates, with higher altitude areas, such as the Serra da Mantiqueira, exhibiting higher average values. Moreover, the Mann–Kendall trend results showed in most of the sites no significant trend regarding precipitation data; however, about 50% of the sites in the PSRB presented a decreasing trend of runoff rates. Since the precipitation does not explain identified changes in the hydrological patterns, the evaluation of the area’s urban growth and socioeconomic development throughout the decades suggested that human activities, such as those associated with urbanization, have played a significant role in altering the runoff patterns in the basin. These findings highlight the importance of sustainable land-use planning and water resource-management practices in the PSRB to mitigate the negative impacts of urbanization on the hydrological cycle and to enhance the resilience of the region’s water resources.

Dynamics between Population Growth and Construction Land Expansion: Evidence from the Yangtze River Economic Belt of China

Population growth and construction land expansion’s link to sustainable development has gained attention. This study investigated the urban–rural divergence in the population–construction land relationship in China’s Yangtze River Economic Belt (YREB) from 2000 to 2020 using census and land-use data. This study utilized an integrated urban–rural framework to discuss reasons for the disparity. The findings suggested the following: (1) A spatial mismatch formed between population distribution and construction land allocation in the YREB from 2000 to 2020. The mismatch gap in rural areas was larger than in urban areas. (2) The urban areas maintained double growth rates in the population and construction land, while rural areas experienced constant population loss accompanied by construction land expansion. (3) An expansive negative decoupling relationship dominated the urban population–land system, while a strong negative decoupling relationship dominated the rural population–land system. (4) Institutional factors, such as land financialization and urban–rural dualism, were major triggers for the mismatch between population and construction land. Policy responses such as a new type of urbanization and rural revitalization strategies can shape the population–land relationship’s evolution. Our comparative analysis of urban and rural areas highlights the population–land relationship’s complexity, promoting sustainable land-use planning in urban–rural spaces.

Understanding land-use sustainability with a systematical framework: An evaluation case of China

Appraisals of land-use sustainability (LUS) are indispensable for the attainment of sustainable land-use planning and management. At present, China is experiencing a pivotal phase of comprehensive reform in land-use policy, engendering a practical necessity for system-oriented land evaluations. Despite this, studies focused on national-scale systematic LUS evaluation remain scarce. Within this context, we developed a hierarchical structural framework for land-use systems based on the assumption that sustainable land-use systems adapted to development needs have mutually adaptable internal subsystems and fewer conflicting system elements. Employing this framework, we assessed the LUS across Chinese counties from 2000 to 2015 by using a newly introduced static equilibrium equation；we also analyzed the natural and socioeconomic influences and driving effect of land-use change to LUS. Findings reveal a significant spatial clustering and urban-rural differentiation within the LUS spatial pattern. Five LUS hot and cold spots and eight metropolitan areas with poor LUS were identified. Furthermore, conflicting land capability elements and the non-adaptability of land function subsystems with capability subsystems are the primary constraints on LUS; gross domestic product (GDP), population growth, and the GDP growth per unit land area had the most significant impacts on LUS; The transformation of construction and cropland into ecological land and the re-purposing of grassland into building land are primary drivers of LUS. Our findings highlight the prospective utility of the proposed LUS framework in integrated natural resource management and territorial spatial planning. Meanwhile, Insights from this evaluation case could be harnessed to optimize land-use in critical regions (including northern arid and semiarid regions, the Qinghai–Tibet Plateau region, the poverty belt around the capital, Yangtze River Delta and Pearl River Delta metropolitan areas, as well as Fuzhou and Xiamen metropolitan areas), thereby enhancing land management decision-making processes.

Simulating future land-use within the uThukela and uMngeni catchments in KwaZulu-Natal

Due to anthropogenic activities, the earth's surface is consistently being altered. These alterations take the form of Land-use/cover change (LULCC), which is a fundamental driver of global, regional and local environmental change. LULCC studies have become pivotal in supplementing our understanding and observations of environmental change. However, understanding the past and present spatial-temporal variability of LULCC characteristics and their link to future land-use/cover trajectories at a catchment scale is limited, particularly in Southern Africa. To address this limitation, this study simulated future land-use change utilising a spatially distributed, empirical land-use modelling approach for the uThukela and uMngeni catchments in the KwaZulu-Natal province of South Africa. The CA-Markov model, a popular and frequently utilised model employed in land-use and land-cover (LULC) predictive modelling, was selected to simulate LULCC conjointly with Geographic Information Systems (GIS) techniques. The obtained kappa values (Kstandard, Klocation and Kno) achieved during the validation were all above 80%, thus indicating the model's reliability and capability to successfully predict future LULC in the study sites. Future projections indicated that both study areas are anticipated to experience anthropogenic induced LULCC, which further fragments the landscape configuration, functionality and ecological stability. Historical analysis of LULCC between 1990 and 2018, in the study catchments revealed considerable declines in the areas under grassland and indigenous forest, while the areas under cultivated land, commercial forestry and urban LULC classes increased. Future LULC projections showed that urban and agriculture land-uses increased significantly, with natural land-use classes such as grassland, other vegetation and indigenous forest declining in spatial extent across both. With an understanding of the extent of projected LULCC by 2030 within both catchments, proactive planning and management within the framework of sustainable land-use planning and water resource management in the respective catchments can be undertaken. Moreover, the results of the land-use modelling study can be used to infill historical data gaps, support effective land-use planning and provide a means to evaluate the impacts of different future development pathways.

Landform classification and geomorphological mapping of the Chota Nagpur Plateau, India

Geomorphological map plays a key role to illustrate landscape evolutionary history along with the guidelines of sustainable landuse planning. The Chota Nagpur Plateau is situated in the eastern part of the Indian subcontinent and it is a storehouse of valuable rocks and minerals of Precambrian origin. Classification of geomorphological units is highly required for sustainable landuse planning and natural hazards management. So, the principal objective of this scientific study is to classify the Chota Nagpur Plateau region into micro geomorphological units by applying automated and semi-automated methods. Digital elevation model (DEM) data and satellite imageries (from United States Geological Survey) have been used to improve the precision of this map. Geomorphological mapping techniques such as terrain attributes classification, Topographical Position Index (TPI) and Slope Position Index (SPI) have been applied to extract the major geomorphological units or features. TPI values show that the maximum area comes under the valley bottom with stream (35.26%) followed by the high ridge (24.55%) whereas minimum area coverage is found in the open slope zone (0.02%). Local ridges and mid-slope ridges lie in 8.77% and 4.94% area respectively. The result has been verified through field verification techniques with the help of GPS data. This high-accuracy map should be applied for regional planning and natural hazards management. These techniques also give very good results for terrain classification as well as landforms evolution.

Landslide susceptibility mapping using O-CURE and PAM clustering algorithms

Landslide susceptibility mapping (LSM) is a crucial step during landslide assessment and environmental management. Clustering algorithms can construct effective models for LSM. However, a random selection of important parameters, inconsideration of uncertain data, noise data, and large datasets can limit the implementation of clustering in LSM, resulting in low and unreliable performance results. Thus, to address these problems, this study proposed an optimized clustering algorithm named O-CURE, which combines: the traditional Clustering Using REpresentatives algorithm (CURE), that is, efficient for large datasets and noise data, the partition influence weight (PIW)-based method to enhance the selection of sample sets and the city block distance (CIBD) for processing of the uncertain data in CURE clustering during LSM modeling. A database containing 293 landslide location samples, 213 non-landslide samples, and 7 landslide conditioning factors was prepared for the implementation and evaluation of the method. Also, a Multicollinearity analysis was conducted to select the most appropriate factors, and all the factors were acceptable for modeling. Based on O-CURE, landslide density, and the partitioning around medoids (PAM) algorithm a susceptibility map was constructed and classified into very high (33%), high (18%), moderate (24%), low (13%), and very low (12%) landslide susceptible levels. To evaluate the performance of the O-CURE model, five statistic metrics including accuracy, sensitivity, specificity, kappa, and AUC were applied. The analysis shows that O-CURE obtained accuracy = .9368, sensitivity = .9215, specificity = .9577, kappa = .8496, and AUC = .896 is an indication of high-performance capability. Also, the proposed method was compared with the CURE algorithm, three existing clustering methods, and popular supervised learning methods. From this assessment, O-CURE outperformed the other clustering methods while showing significant and more consistent performance than the supervised learning methods. Therefore, we recommend that the O-CURE model and the constructed map can be useful in assessing landslides and contribute to sustainable land-use planning and environmental management in light of future disasters.

Identification and prediction of land use changes based on artificial neural network and CA-Markov for sustainable land-use planning

Identification and prediction of land use changes based on artificial neural network and CA-Markov for sustainable land-use planning

A Framework of Systematic Land Use Vulnerability Modeling Based on Seismic Microzonation: A Case Study of Miri District of Sarawak, Malaysia

The objective of this research is to develop the framework for sustainable land-use planning on the basis of seismic microzonation to reduce the devastating effects of future earthquakes by utilizing the software geographical information system (ArcGIS). Miri district of Sarawak in Malaysia has been chosen as the study area because of having the highest peak ground acceleration which is 0.09g in terms of the 10% probability of exceedance in 50 years. In addition, the frequency of an earthquake with a magnitude up to 5.3 is approximately every 5-7 years. Therefore, it is vital to introduce land use planning in order to diminish the adverse effects of earthquakes in the future. For this purpose, Google Earth Pro was used for the collection of satellite image data for land use planning purposes. From the results, it was found that the seismic hazard in the Miri district varies from low to high corresponding to 2475 years of return period with low to moderate as predominant over the Miri district. Only a few areas are under high hazard. Also, the land use planning map was compared with the current land use map acquired from satellite imagery and it was found that all built-up is in the low hazard area. It is envisaged that the findings from this research will contribute immensely to the literature that will serve as background information and a guide for analysts, disaster management, engineering designers and seismologists in Malaysia and the world as a whole.