Land Use Planning Research Articles

Evaluating Trends of Temperature Index in Gia Lai Province Based on IITA and IPTA Approaches under Climate Change

This study performs historical trend analysis for average, maximum, and minimum temperatures using the integration of the Improved visualization for trend analysis (IITA) and Innovative Polygon Trend Analysis (IPTA) of Şen to evaluate the tendency of extreme temperatures on different time scales (annual, monthly and seasonal) in Gia Lai province from 1980 to 2019 at three stations, including Pleiku, AyunPa, and An Khe. Results recorded over the past four decades, the temperature trend in Gia Lai has been gradually warming. The average temperature in each month of the year ranges from 19 to 28°C, December and January usually have the lowest temperature of the year with a variation lower than 20°C. On the contrary, the months of April, May, and June often have the highest temperatures of the year, with a variation greater than 26°C. The outcomes of this research have provided a general picture of temperature characteristics, useful information for the management and planning of land use, irrigation and water supply in Gia Lai province under unpredictable climate change.

Impact of Industrial Estate Development on Infrastructure Sustainability in the Wijayakusuma Industrial Area, Semarang

The trend of urbanization forms a sustainable urban urgency through the planning of land use and infrastructure. In fulfilling urban productivity, these plans are often fragmented. This phenomenon occurs surrounding the Wijayakusuma Industrial Area, Semarang City. As a generator/vibrant of the economy and surrounding area, industry coexists with uncontrolled and non-standard infrastructure. The research aims to analyze the impact of industrial development on the infrastructure sustainability within and surrounding the Wijayakusuma Industrial Area. The study used a qualitative approach with mapping techniques, document review, and in-depth interviews that were analyzed descriptively and spatially. The results show that the attractiveness and economic potential of the industry have a strong impact on increasing infrastructure needs. However, the authority for independent infrastructure planning has resulted in the industry only focusing on internal but has not considered the impact/system unity of the surrounding infrastructure, like slums, not-interconnected, and non-standard infrastructure so it is not sustainable. The novelty is an infrastructure impact solution considering vibrant concepts in industrial strategic areas, through the concept of handling existing infrastructure, alternative infrastructure provision, especially livable and sustainable housing for workers, increasing access through TOD/neighborhood units, also physical planning and control policies.

Application of land-use change model for supporting land-use planning in An Bien district, Kien Giang province, Vietnam

Forecasting land-use change is a critical step in assisting land-use planning, especially for coastal areas that are influenced by many economic and natural environmental factors. This study aims to estimate land use changes based for An Bien district, Mekong Delta based on multi-criteria assessment of factors affecting land use change. The model has been calibrated with the 2015 land-use map and verified with the 2020 map. The results of land-use change analysis in An Bien district were carried out according two scenarios: (i) forecasting the area under present conditions and (ii) forecasting land use area in the case of economic benefits and suitable environmental conditions for aquaculture production. Scenario 1 showed that the results will not change much compared to the current situation, in which rice-shrimp plays a leading role locality, however Scenario 2 showed that shrimp area will rapidly rise. In general, the simulation results allow users to explore future planning possibilities and serve as a basis for selecting appropriate land-use types.

The Climate Moratorium

Climate change is our new reality. The impacts of climatic changes, including massive forest fires, floods, drought, severe storms, saltwater intrusion, and the resulting migration of people displaced by such impacts, will continue to ravage communities across the nation into the foreseeable future. In the meantime, communities continue to expand and growth continues unabated in many of the most climate-impacted areas. Given that most communities are unprepared for the onslaught of climate disasters and many continue to increase existing community vulnerabilities through unsustainable growth and development practices, we need legal tools that will provide space to engage in effective adaptation planning. The climate moratorium is one such tool. Moratoria, which have been used to temporarily halt development and associated impacts to facilitate effective land-use planning, have long been used by communities to address community and infrastructure vulnerabilities. This Article proposes a climate moratorium.

Remote Sensing and GIS-Based Accuracy Assessment of LULC Map and Landslide Susceptibility Prediction for Meghalaya, India

ABSTRACT Through this study, a Landslide Susceptibility Map (LSM) has been developed for the Meghalaya state, India using an Analytical Hierarchy Process (AHP). According to a 2012 Geological Survey of India report, the annual average number of landslides in Meghalaya is nearly 30, which is due to a combination of mountains, steep slopes, and excessive rainfall, leading the state to suffer a huge loss of life and property from landslides. For effective management of the current landslide situation, information about prior landslides is needed. Therefore, the landslide inventory map is prepared with 380 previously occurred events. The Landslide inventory records were separated into training samples (70%) and testing samples (30%) for the purpose of validation. In this regard, the present study has 15 conditioning factors, i.e., slope, rainfall, elevation, relative relief, aspect, distance from the road, curvature, distance from the stream, LULC, lineament density, geomorphology, geology, NDVI, MSAVI, NDWI, which are used to develop susceptibility map. Classification and accuracy assessment of LULC is carried out with segregation as 77% vegetation, 16.4% range land, 3.1% built area, 2.8% crops, 0.4% waterbodies, and 0.3% others (bare land, flooded vegetation, etc.). The Kappa for LULC categorization is 0.92, which is quite satisfactory and suggests that the LULC categorization is reliable. The developed susceptibility map is classified into four different classes, low susceptibility (35%), moderate susceptibility (41%), high susceptibility (20%), and very high susceptibility (4%), and has been verified using physical and Receiver Operating Characteristics (ROC) techniques. Results show that anticipated susceptibility classes are in good match with previous landslide events. The prepared map is reliable and can be used for land-use planning of the state in the future.

BLEI: Research on a Novel Remote Sensing Bare Land Extraction Index

Bare land, as a significant land cover type on the Earth’s surface, plays a crucial role in supporting land-use planning, urban management, and ecological environmental research through the investigation of its spatial distribution. However, due to the diversity of land-cover types on the Earth’s surface and the spectral complexity exhibited by bare land under the influence of environmental factors, it is prone to confusion with urban and other land features. In order to extract bare land rapidly and efficiently, this study introduces a novel bare land extraction index called the Bare Land Extraction Index (BLEI). Then, considering both Ganzi Tibetan Autonomous Prefecture and Urumqi, China as the study areas, we compared BLEI with three presented indices: the Bare-soil Index (BI), Dry Bare Soil Index (DBSI), and Bare Soil Index (BSI). The results show that BLEI exhibits excellent efficacy in distinguishing bare land and urban areas. It gets the most outstanding accuracy in bare land identification and mapping, with overall accuracy (OA), kappa coefficient, and F1-score of 98.91%, 0.97, and 97.89%, respectively. Furthermore, BLEI is also effective in distinguishing bare land from sandy soil, which can not only improve the mapping accuracy of bare land in soil-deserted areas but also provide technological support for soil research and land-use planning.

Meaningful Action: Evaluating Local Government Plans to Affirmatively Further Fair Housing in California

A 2018 California law requires local governments to affirmatively further fair housing (AFFH) in their General Plan’s housing element. This paper examines how eight municipalities reacted to this requirement in three areas of their 2021 plans: the analysis of fair housing issues, proposed fair housing programs, and the location of sites identified for low-income housing development. We consider whether these cities’ over 200 fair housing programs are meaningful actions by evaluating their potential impact, and measure the distribution of proposed sites for new low-income housing across neighborhoods. The cities created many new programs in response to the mandate; however, most programs do not meaningfully advance fair housing goals. Moreover, most cities do not modify land-use plans to allow affordable housing in affluent neighborhoods. Additionally, we find a mismatch in the plans. The affluent, majority-white cities that developed more meaningful fair housing programs continued to concentrate sites for affordable housing in their least affluent neighborhoods. Our analysis of potential program impact allows us to identify AFFH oversight challenges and make recommendations for AFFH guidelines. We focus on California, but the research is relevant to federal AFFH implementation and raises questions about how to best advance fair housing goals.

Discrepancy and estimates of groundwater recharge under different land use types on the Loess Plateau

Study regionThe Dongzhi tableland on the loess plateau, China. Study focusThis study investigates how various land use types on the Loess Plateau affect groundwater recharge using the chloride mass balance method (CMB). By integrating data from Cl–, NO3/Cl–, and stable isotopes (δ18O and δ2H), the research develops a new approach to estimate the average ion concentration rates in soil profiles. This methodology provides a nuanced understanding of how precipitation infiltration recharges groundwater under different land use scenarios and the impact of land use changes on these processes. New hydrological insights for the regionThe research findings highlight significant variations in groundwater recharge rates among different land uses. Grasslands show the highest recharge rate at 26 mm/yr, followed by forests at 23 mm/yr, while shrublands have the lowest at 5 mm/yr. Agricultural lands have moderate recharge rates ranging from 14 to 16 mm/yr. Transitioning from agricultural land to other land uses like shrublands and forests results in dramatic changes in recharge rates, with decreases and increases in recharge of 72% and 61%, respectively. These insights are crucial for developing strategic land-use planning to optimize groundwater recharge and enhance water security on the Loess Plateau. The study emphasizes the need for policies that balance hydrological science and land management to sustain both human livelihoods and ecological continuity.

Studying Urbanization Pattern in Sambalpur City during 1992-2042 using CA-ANN, and Markov-Chain Model

Models of land use/land cover (LULC) are crucial for assessing changes in LULC, forecasting land use needs, and providing guidance for sensible land use planning and management especially in urban areas. Urban sprawl is one of the main causes of the erratic variation in LULC around the globe. In this study, we used the CA-ANN and Markov-Chain models to analyse the LULC simulation based on LULC patterns of previous decades as well as the directional changes of urban expansion in Sambalpur city. We used the random forest (RF) model and Landsat imageries to prepare LULC maps for the years 1992, 2002, 2012 and 2022, for better classification accuracy. The result showed that the overall accuracy and kappa value were 94.24%, 89%, 94%, and 90% and 0.92, 0.80, 0.90 and 0.85, respectively for the selective years. Based on the transition matrix model (1992-2012), the LULC map of the year 2022 was obtained, and validated, and subsequently, LULC maps for the years 2032 and 2042 were predicted with a Kappa value of 94.97% and 93.24% respectively. The results revealed that the maximum amount of bare land converted in the settlement area, and the maximum sprawl occur in NW and 4km buffer zones. These findings define current and future patterns in LULC and offer vital information for planning and sustainable land use management in Sambalpur city.

Spatio-temporal classification of land use and land cover and its changes in Kerala using remote sensing and machine learning approach.

Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform. RF and CART are versatile machine learning algorithms frequently employed for classification and regression, offering effective tools for predictive modelling across diverse domains due to their flexibility and data-handling capabilities. Normalised Difference Vegetation Index (NDVI), Normalised Differences Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), and Bare soil index (BSI) are integral indices utilised to enhance the precision of land use and land cover classification in satellite imagery, playing a crucial role by providing valuable insights into specific landscape attributes that may be challenging to identify using individual spectral bands alone. The results showed that the performance of RF is better than that of CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values point out the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren land. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted the area of the barren class, as well as the water class, decreased steadily from 1990 to 2020. The results of the current study will provide insight into the land-use planners, government, and non-governmental organizations (NGOs) for the necessary sustainable land-use practices.

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.

Landslide susceptibility assessment along the Karakoram highway, Gilgit Baltistan, Pakistan: A comparative study between ensemble and neighbor-based machine learning algorithms

This study addressed the complex challenges associated with landslide detection along the Karakoram Highway (KKH), where tectonic events and data availability limitations posed significant obstacles. To overcome these hurdles, the research framework encompassed several critical components. First, it tackled the issue of multicollinearity through the application of statistical measures such as Variable Inflation Factor (VIF) and Information Gain (IG). Secondly, the study emphasized the importance of selecting a study area that would comprehensively represent the multivariate landscape, with KKH serving as an illustrative example. In striving for an equilibrium between implementation ease and algorithmic performance, the research favored the adoption of Random Forest (RF) and Extremely Randomized Trees (EXT) over XGBoost. Lastly, to fine-tune the algorithms and optimize their parameters, the study employed Particle Swarm Optimization (PSO) and evaluated their performance using metrics like the Area Under the Curve (AUC). Remarkably, this comprehensive approach yielded accuracy rates exceeding 90% for all algorithms tested (RF, EXT, and K-Nearest Neighbor (KNN)), with specific AUC values of 0.967, 0.968, and 0.914, respectively. These findings offer invaluable insights into enhancing disaster prevention strategies and informing land-use planning efforts along the KKH highway.

Optimizing agricultural management in China for soil greenhouse gas emissions and yield balance: A regional heterogeneity perspective

Balancing soil greenhouse gas emissions (SGE) with crop production is crucial for sustainable agriculture in China. Optimizing field management practices regionally can effectively promote carbon reduction and food security, given the varying SGE responses across agroecological regions. This study employed meta-analysis and machine learning techniques to assess SGE responses to nine different farming practices in seven distinct agroecological regions. It aimed to identify optimal management strategies for balancing SGE and crop production within each region. Findings underscore substantial spatial heterogeneity in the effects of management practices on SGE, with the combined use of organic and inorganic fertilizers (MOF-MF) demonstrating the most pronounced variability (standard deviation, SD = 1.80), where MOF resulted in SGE 610% of that with MF in the Southern China (SC) region, yet only 54% in Northwestern China (NWC). Conversely, straw incorporation exhibited the most minor variability (SD = 0.27), influencing SGE by 35%–62%. Additionally, the study reveals that soil and climatic conditions significantly modulate SGE responses. In scenarios characterized by humid and warm climates with soil pH ranging from 5.5 to 8.5 or initial SOC ≤10, the environmental impact on SGE was marginal. However, in highly acidic or alkaline soils or with initial SOC >10, SGE responses varied markedly with management practices in arid and cold conditions. The study further proposes agricultural management strategies to achieve the lowest SGE while maximizing crop yield. It recommends specific methods for fertilizer applications, straw management, irrigation, and tillage practices meticulously tailored to the unique conditions of each agroecological region. This research offers essential insights for agricultural stakeholders regarding cultivation strategies and delivers theoretical guidance for enhancing agricultural management and land-use planning throughout China's heterogeneous landscapes.

Applying analytic hierarchy process for site selection of a recreational-educational children complex in Shiraz City, Iran

The development of educational-recreational complexes for children has a significant impact on their cognitive growth and behaviour. Proper site selection is essential to ensure the success of such projects. This paper aims to identify the best location for a children’s educational-recreational complex in Shiraz, Iran. The study used a descriptive-comparative and survey method to determine effective criteria for site selection. A questionnaire was administered to experts in architecture, urban design, and planning to identify appropriate sites. The Analytic Hierarchy Process (AHP) method was then employed to select the best location. The hierarchical model included three levels: goal (educational-recreational site selection), criteria (natural potential, accessibility, land size, neighbourhood and social context, expansibility, appropriate land use (the neighbourhood between proposed and master plan land use)), and three alternative sites. The results showed that a site located near residential areas, green spaces, and natural elements is suitable for the development of an educational-recreational complex for children. The study recommends the use of the SWOT (strength, weaknesses, opportunities and threats) - AHP method for site selection in children’s complexes. The paper highlights the need for a reliability test or table/figure to support the research findings.

The Evolution of Open Space Planning within a Developing, Biodiverse City (Durban, South Africa)

Conserving and restoring biodiversity is central to the achievement of the Sustainable Development Goals. The need to curb biodiversity loss through the mainstreaming of biodiversity considerations within land-use planning is consistently highlighted in global biodiversity assessments intended for policymakers and practitioners. We present a Global South local government-led examination of the mainstreaming of biodiversity issues within a biodiversity hotspot area. Here, we evaluated the four-decade-long evolution in open space planning in Durban, South Africa, in response to shifting urbanisation, governance and policy/legislative contexts. We assessed the role of science in responding to contextual changes, the need for champions, and key institutional interventions undertaken to embed a biodiversity function within local government. In addition, we investigated how biodiversity concerns have been incorporated into land-use planning applications via the city’s environmental planning function. We provide evidence of the advancement of mainstreaming biodiversity concerns within local government processes, institutional functions, and land-use decision-making. This has been achieved through effective and sustained leadership; the use of science and scientific information in advancing the policy and legislative environment and building political support by responding to shifting governance contexts; investment in institutional scientific capacity and generating scale-appropriate biodiversity information. Learnings from this paper may be useful for other local governments addressing biodiversity loss through land-use planning processes, by identifying critical investment areas that may shorten the time required for effective mainstreaming.

Application of Artificial Neural Networks in Soil Science Research

Artificial Neural Networks utilize high-performance computation and large data technology, allowing research to generate new prospects in agriculture. ANN is currently a preferred technique for crop yield prediction, forecasting, and classification in biological science domains. Among different agriculture fields, soil science research plays a vital role in understanding and managing the complex processes occurring within the soil environment as oil is a complex system with dynamic surface layers that differ from the other parts of the matrix. Due to the increasing accessibility of innovative computing techniques, Artificial Neural Networks (ANNs) have developed into useful tools for modeling and forecasting soil-related activities. The numerous applications of ANNs in soil science research, with a focus on how well they can classify soils, assess soil fertility, forecast soil erosion, and estimate soil moisture. They are vital tools for identifying soil types, evaluating fertility levels, predicting erosion, and soil moisture estimation. ANN models were effective at predicting soil characteristics like pH, organic carbon concentration, and clay content. By training on vast datasets that contain the chemical, biological, and physical properties of soil, ANNs are able to accurately predict different soil types and enable land-use planning, precision farming, and environmental management. This mini-review focuses on ANN approaches that possess the potential to increase our understanding of soil science and encourage informed decisions for soil management and conservation.

Precision agriculture: exploration of deep learning models for farmland mapping

Precision is required for agricultural advancements to be sustainable. Traditional farming lacks effective monitoring, resulting in resource waste and environmental problems. Farmland mapping is important for agricultural management and land-use planning. The use of deep learning techniques in farmland mapping is increasing rapidly. Excellent results have been generated from deep learning approaches in a number of applications, such as image processing and prediction. Agricultural agencies are now considering different applications of deep learning including land mapping, crop classification, and monitoring of paddy fields. This paper shall explore different deep learning models that are commonly used for image processing specifically in land mapping. The three deep learning models convolutional neural network (CNN), long short-term memory (LSTM), and recurrent neural network (RNN) were evaluated to find out which among the deep learning models is best for land mapping. It compares the classification accuracy of the models on image processing and it can be concluded that CNN algorithm normally makes better results when compared to other deep learning models. This study offers guideline and suggestions to researchers who are interested in contributing to the field of precision agriculture with the used of deep learning techniques.

Monitoring spatiotemporal changes in land use/land cover and its impacts on ecosystem services in southern Zambia

Ecosystems play a vital role in human well-being, yet the widespread loss of ecosystem services due to human activities, including agricultural expansion and deforestation, remains a significant concern. Despite the wealth of research highlighting the importance of ecosystem conservation in Zambia, a critical gap exists in understanding the interplay between the conservation of ecosystem services and the socio-economic needs of local communities. This study presents a comparative analysis of ecosystem services in two distinct landscapes within southern Zambia: the protected area of Kafue National Park (KNP) and the agricultural landscape of Kalomo district between 2000–2020. Employing a combination of quantitative and qualitative methods, we evaluate the impacts of land/use and land cover (LULC) changes on selected ecosystem services, with a particular focus on carbon storage and the habitat quality of the trumpeter hornbill. The results of the comparison indicate that: (1) the Kalomo district has suffered from extensive land conversion, with forest changing to cropland, while KNP was well protected from encroachment, with forest area increasing over time; (2) carbon stocks and the habitat quality of trumpeter hornbills continually decreased in the Kalomo district but improved in KNP; (3) Kalomo district has suffered rapid environmental degradation due to an imbalance between economic development and environmental conservation, while strict enforcement in KNP has preserved ecosystems. The findings underscore the importance of integrated and inclusive land-use planning and natural resource governance for maintaining and enhancing ecosystem services in Zambia. To progress towards landscape management that is both sustainable and equitable, aligning with the objectives of the Global Biodiversity Framework, it is proposed that a comprehensive approach be adopted in the region. This approach should encompass a more thorough consideration of local livelihood requirements, as well as the wider political-economic and social factors at play.

Environmental Protection Through Sustainable Land Management

Sustainable land planning and successful land use change methods are essential as the world faces rising environmental challenges. This study examines the transformative potential of biomimicry and biophilia in addressing these challenges and the potential of bio-collaboration, bio-utilization, bio-inspiration, biophilic design, and biomimicry in creating a sustainable environment. The argument is that such integration can potentially create creative ways to lessen the effects of human activity on the environment by utilising the knowledge of nature and combining biological system concepts. This study argues that by incorporating the above environmental factors into land planning practises, a holistic and sustainable approach can be achieved, fostering peaceful coexistence between human activities and the natural environment. This will also improve the resilience of urban and rural environments while offering practical solutions for a climate change-conscious world. As the main theoretical contribution, this study synthesizes a theoretical framework in the form of a conceptual structure for understanding, analyzing, and interpreting sustainability by adopting an ecosystem theoretical framework for achieving sustainable land management.
 Keywords: Biophilic Design, Biomimicry, Sustainable Design Strategy, Bio-Collaboration, Biodiversity, Land use Management, Sustainable Land Management, Environmental Protection, Global Climate.

Assessing flood susceptibility in the Godavari River Basin, Bhadrachalam Region, Sothern India: A GIS-AHP Multi-Criteria Approach

Floods, as recurrent natural disasters, exert significant impacts on both the environment and human settlements. This study focuses on evaluating the flood susceptibility of the Bhadradri Kothagudem district in Telangana, situated within the Godavari River Basin. The research integrates Geographic Information System (GIS) and the Analytic Hierarchy Process (AHP) in a multi-criteria approach to analyze and map flood susceptibility. Landsat-8 data, digital elevation model data and rainfall serve as inputs for assessing the flood susceptibility. The study considers various topographical features such as elevation, slope, roughness, contours and aspect, along with factors like land use land cover, flow accumulation, stream direction, stream network, drainage density, flow length, distance from the river, soil, normalized difference vegetation index and topographic wetness index. These variables are rescaled on a scale of one to five and combined to generate a comprehensive flood susceptibility map of the Kothagudem district using GIS. The AHP is implemented through GIS, assigning weightages on a scale of one to five based on the priority of spatial classes within thematic maps. The flood susceptibility map is produced on a scale of five, designating scale class five as of very high susceptibility and scale class one as low. Scale classes two, three and four represent intermediate levels of susceptibility. The resulting flood susceptibility maps offer valuable insights for disaster preparedness, risk mitigation and land-use planning in the Kothagudem district. The integration of GIS and AHP provides a robust methodology for assessing and visualizing flood susceptibility, enabling informed decision-making for resilient and sustainable development in flood-prone regions.