Environmental Planning Research Articles

Impact of landscape pattern on habitat quality in the Yangtze River Economic Belt from 2000 to 2030

Recently, rapid economic development of the Yangtze River Economic Belt region in China and the growing problem of regional habitat quality have seriously threatened the process of biodiversity conservation in the Yangtze River Basin. Studying the impact of changes in landscape pattern indexes on habitat quality in the Yangtze River Economic Belt can provide policy insights for biodiversity conservation in the Yangtze River Basin. However, existing studies are still lacking in exploring the effects of changing landscape pattern indices on habitat quality under future scenarios. Based on the land use data of the Yangtze River Economic Belt from 2000 to 2020, this study simulates the land use situation in 2030, and analyzes the landscape pattern indexes and habitat quality during the 30 years, to investigate the impact of landscape pattern indexes on habitat quality at global and local scales through various methods. The results show that landscape patterns were significantly affected by human activities and land use types, with the Yangtze River Economic Belt experiencing increased levels of land use and structural stabilization during the three decades (Shannon Diversity Index increased from 0.90 in 2000 to 0.94 in 2030), fragmentation of the landscape (Landscape fragmentation index increase from 0.094 in 2000 to 0.102 in 2030), and weakened connectivity (contagion index decrease from 48.213 in 2000 to 45.437 in 2030). The Habitat Quality has a large variation in spatial distribution at the county scale, with an average change in habitat quality of 1.04 % from 2000 to 2030, and an overall downward trend. There is a strong localized spatial autocorrelation between landscape pattern indices and habitat quality, with significant spatial heterogeneity and some form of variability at time scales. The results of this study help to further understand the eco-environmental problems of the Yangtze River Economic Belt, and provide theoretical references for the formulation of ecological environmental protection policies and ecological functional area planning.

Improving estimation for asymptotically independent bivariate extremes via global estimators for the angular dependence function

Modelling the extremal dependence of bivariate variables is important in a wide variety of practical applications, including environmental planning, catastrophe modelling and hydrology. The majority of these approaches are based on the framework of bivariate regular variation, and a wide range of literature is available for estimating the dependence structure in this setting. However, such procedures are only applicable to variables exhibiting asymptotic dependence, even though asymptotic independence is often observed in practice. In this paper, we consider the so-called ‘angular dependence function’; this quantity summarises the extremal dependence structure for asymptotically independent variables. Until recently, only pointwise estimators of the angular dependence function have been available. We introduce a range of global estimators and compare them to another recently introduced technique for global estimation through a systematic simulation study, and a case study on river flow data from the north of England, UK.

Innovative trend analysis technique with fuzzy logic and K-means clustering approach for identification of homogenous rainfall region: A long-term rainfall data analysis over Bangladesh

Understanding regional climatic trends is crucial for taking appropriate actions to mitigate the impacts of climate change and managing water resources effectively. This study aims to investigate the dissimilarities and similarities among various climate stations in Bangladesh from 1981 to 2021. Fuzzy C-means (FCM) and K-means clustering techniques were employed to identify regions with comparable rainfall patterns. Moreover, the innovative trend analysis (ITA) and the Mann-Kendall (MK) test family were utilized to analyze rainfall trends. The results indicate that both K-means and FCM methods successfully detected two rainfall regions in Bangladesh with distinct patterns. The ITA curve analysis revealed that out of the 29 stations, 13 had a non-monotonic increasing trend having no monotonic increasing trend, 8 had a non-monotonic decreasing trend, and 8 exhibited a monotonic decreasing trend. Additionally, the MK tests employed in the study showed predominantly negative trends across Bangladesh. The majority of stations (65.51%) fell into Cluster 1, while the remaining 34.48% were in Cluster 2. In terms of ITA analysis, 17.24% of stations exhibited a monotonic decrease, while there were no stations with a monotonic increase. However, 37.93% of stations showed a non-monotonic increase, and 44.83% displayed a non-monotonic decrease. These identified regions can provide valuable insights for water resource management, disaster risk reduction, and agricultural planning. Moreover, detailed rainfall analysis can help policymakers and scientists develop sustainable and effective regional-scale policies for managing the country's flood and drought situations, ultimately supporting agricultural development and environmental planning.

Multi-AUVs cooperative path planning in 3D underwater terrain and vortex environments based on improved multi-objective particle swarm optimization algorithm

This paper presents an improved multi-objective particle swarm optimization (IMOPSO) algorithm for cooperative path planning of multiple autonomous underwater vehicles (multi-AUVs) in 3D underwater terrain and vortex environment. Traditional PSO algorithms often struggle with local optima in high-dimensional, complex search spaces. To address this, we propose enhancements including Sine double-sequence perturbation interpolation for initial particle population generation, adaptive differential evolution strategies, iterative local search, and traction operations to enhance global search capability and avoid local optima. Additionally, cubic spline interpolation is employed to smooth the planned paths. The objective function is tailored to the ocean currents environment, considering path length, smoothness, deflection angle, and current impact, with a penalty function method to handle multi-AUVs cooperation and environmental constraints optimization for optimal energy consumption. Simulation results demonstrate that the proposed algorithm effectively plans short-range, high-security global paths for multi-AUVs, verifying its effectiveness and practicality in meeting the navigation requirements in ocean currents environments.

Deep learning model for optimizing control and planning in stochastic manufacturing environments

Within the context of Industry 4.0, manufacturing plants implement smart technologies, which adopt machine learning and deep learning, to identify manufacturing problems and provide sensible solutions to the experts. In literature, such applications have demonstrated high-performance in terms of feature extraction and big data analysis compared to typical machine learning methods. However, in the context of control and planning, the research efforts are still focused on integrating ad-hoc solutions, e.g., heuristic algorithms and deep reinforcement learning, thus making it difficult to generate general scheduling rules for manufacturing processes that could be easily applied to a variety of stochastic manufacturing systems. To this end, this paper considers a deep learning application for addressing the scheduling optimization problem in multi-stage stochastic manufacturing environments. The aim is the formulation of a predictive model that could derive suitable authorizations for activities under uncertainty. Findings demonstrate that the proposed predictive model generates policies that improve the performance of the integrated operations within manufacturing systems, constituting to efficient and sustainable manufacturing with effective material management and near-to-zero waste.

Using a comparative of DRASTIC and Bayesian weights of evidence approach to assess transboundary aquifer vulnerability in a data scarcity region: Tuli-Karoo aquifer

Study regionThe Tuli-Karoo Transboundary Aquifer system is shared among Botswana, South Africa and Zimbabwe. It hosts a complex aquifer predominately of sand, heavily tapped in South Africa and sandstone substantially used in Botswana threatening it’s quality. Study focusTwo vulnerability models, the classic DRASTIC (Depth to water table [D], Recharge to the aquifer [R], Aquifer media [A], Soil media [S], Topography [T], Impact of vadose zone [I], Conductivity [C]) and the bayesian based, Weights of Evidence (WofE), were used to delineate zones vulnerable to groundwater contamination under limited nitrate observations. New hydrological insights for the regionThe study revealed that the generic DRASTIC vulnerability results were comparable to the WofE, for the highly vulnerable zones, as both had an area under the curve (AUC) below 0.6. The high vulnerability was attributed to the shallow depth of water level, fractured geological condition (based on transmissivity) and high net recharge rate in and around the Tuli-Karoo Aquifer. However, there are differences in spatial variability in the low/medium vulnerability zones. WofE was superior in capturing spatial variability of groundwater vulnerability. Considerations must therefore be provided, under data scarcity, to incorporate proxies that include landuse-landcover and population density as evidential layers. Overall, WofE performs better due to better prediction power under data-scarcity conditions. This study recommends that under data scarcity, high predictive models like WofE should be used for environmental planning and groundwater protection strategies.

Quantifying marginal utilities of ecosystem services for sustainable management

Understanding the marginal characteristics of ecosystem services (ESs) is crucial for sustainable management. However, there is limited research on quantifying thresholds for ESs, making it challenging for policymakers to plan effectively. This study established a framework to assess the influencing factors and marginal characteristics of cultural and regulatory ecosystem services (RES) in the Taihu Lake Basin of China and its wetland parks. The framework uses a Structural Equation Model to analyze the impact of visitors' attributes on the perception of different types of cultural ecosystem services (CES). It also employed the InVEST model to quantify the spatial patterns of RES in the basin. Multiple regression analysis and marginal utility theory were used to examine the influencing factors and marginal utilities for different types of cultural and regulatory services. The findings reveal that 1) Education level, income, gender, and age significantly influence the perception of five types of CES provision, with different factors affecting each type. 2) Most park characteristics influence the CES provision score, with their maximum and marginal utility peaks. For instance, as patch density (PD) increases, the score for recreational services initially decreases, then increases, and finally decreases again, reaching a peak of 0.50 at a PD of 325.60. The marginal utility of PD follows a similar pattern, initially increasing and then decreasing, with an increase in utility observed in the range of 0 < PD < 187.80. When PD reaches 187.80, it attains its maximum utility, indicating the point of highest efficiency for PD's impact on recreational services. 3) Heterogeneity exists in the spatial distribution of RES within the basin, with different landscape features providing varying marginal benefits for services like water supply, water purification, carbon storage, and soil erosion prevention. Carbon storage demonstrates an 'increase-then-decrease’ pattern in response to rising FP (Percentage of forest area), reaching a maximum value of 13129.3 t/km2 at FP of 66.4. The marginal utility of FP also exhibits a 'rise-then-fall’ trend, increasing up to FP of 30.4, where it peaks at 230.5. This peak represents the point of maximum efficiency in FP's influence on carbon storage services. 4) The effects of park and landscape characteristics on cultural and regulatory services exhibit marginal utilities that can be used to determine the optimal scale and location of these features to maximize ecological benefits. This research aims to expand the scope of ESs assessment and provide insights from wetland parks for broader environmental planning and optimization.

Predicting Particulate Matter (PM 10) Levels in Morocco: A 5-Day Forecast Using the Analog Ensemble Method.

Accurate prediction of Particulate Matter (PM 10) levels, an indicator of natural pollutants such as those resulting from dust storms, is crucial for public health and environmental planning. This study aims to provide accurate forecasts of PM 10 over Morocco for five days. The Analog Ensemble (AnEn) and the Bias Correction (AnEnBc) techniques were employed to post-process PM 10 forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS) global atmospheric composition forecasts, using CAMS reanalysis data as a reference. The results show substantial prediction improvements: the Root Mean Square Error (RMSE) decreased from 63.83 μg/m 3 in the original forecasts to 44.73 μg/m 3 with AnEn and AnEnBc, while the Mean Absolute Error (MAE) reduced from 36.70 μg/m 3 to 24.30 μg/m 3. Additionally, the coefficient of determination (R 2) increased more than twofold from 29.11% to 65.18%, and the Pearson correlation coefficient increased from 0.61 to 0.82. This is the first use of this approach for Morocco and the Middle East and North Africa and has the potential for translation into early and more accurate warnings of PM 10 pollution events. The application of such approaches in environmental policies and public health decision making can minimize air pollution health impacts.

TOWARDS HARMONY IN THE CAPITAL OF THE ARCHIPELAGO: VISION OF FUTURE SUSTAINABLE CITY DEVELOPMENT FUTURISTIC-ORIENTED

This research aims to explore and formulate a sustainable and futuristic-oriented development strategy for the capital of the archipelago that can integrate the principles of environmental, economic, and social sustainability in addition to utilizing sustainable advanced technology. This study employs an exploratory qualitative research methodology. and descriptive study design. This approach is carried out by collecting and analyzing data in depth through a document study. This study uses data analysis techniques by making a SWOT analysis to assess the advantages, disadvantages, and opportunities faced during the development of the Indonesian capital. Thus, it can be said that the overall plan for development for the Indonesian capital is sustainable and futuristic using an integrated approach by applying ESG sustainability principles and the use of environmentally friendly technology. Urban development close to the 20th century was accompanied by the growth of urbanization processes without planning so cities experienced gradual inefficiency, which resulted in the erosion of old buildings that were unable to fulfill the requirements of society. Through the above policies, it’s hoped that Indonesian capital will develop into a futuristic-oriented sustainable city by integrating the principles of ESG (environmental, social, and economic) sustainability using advanced, environmentally friendly technology. Recommendation formulation regarding the sustainable and futuristic-oriented development strategy for the Indonesian capital consists of Environmental Policy, Economic Policy, Social Policy, Planning and Supervision Policy, Technology Policy, and Collaboration and Participation for Sustainable City Futuristic.

Assessment of genetically modified maize DP910521 (application GMFF-2021-2473).

Genetically modified (GM) maize DP910521 was developed to confer resistance against certain lepidopteran insect pests as well as tolerance to glufosinate herbicide; these properties were achieved by introducing the mo-pat, pmi and cry1B.34 expression cassettes. The molecular characterisation data and bioinformatic analyses did not identify issues requiring food/feed safety assessment. None of the identified differences in the agronomic/phenotypic and compositional characteristics tested between maize DP910521 and its conventional counterpart needs further assessment except for the levels of iron in grain, which do not raise safety and nutritional concerns. The GMO Panel does not identify safety concerns regarding the toxicity and allergenicity of the Cry1B.34, PAT and PMI proteins as expressed in maize DP910521. The GMO panel finds no evidence that the genetic modification impacts the overall safety of maize DP910521. In the context of this application, the consumption of food and feed from maize DP910521 does not represent a nutritional concern in humans and animals. The GMO Panel concludes that maize DP910521 is as safe as its conventional counterpart and non-GM maize varieties tested, and no post-market monitoring of food/feed is considered necessary. In the case of accidental release of maize DP910521 material into the environment, this would not raise environmental safety concerns. The post-market environmental monitoring plan and reporting intervals are in line with the intended uses of maize DP910521. The GMO Panel concludes that maize DP910521 is as safe as its conventional counterpart and the tested non-GM maize varieties with respect to potential effects on human and animal health and the environment.

Assessment of genetically modified maize MON 95275 (application GMFF-2022-5890).

Genetically modified maize MON 95275 was developed to confer protection to certain coleopteran species. These properties were achieved by introducing the mpp75Aa1.1, vpb4Da2 and DvSnf7 expression cassettes. The molecular characterisation data and bioinformatic analyses reveal similarity to known toxins, which was further assessed. None of the identified differences in the agronomic/phenotypic and compositional characteristics tested between maize MON 95275 and its conventional counterpart needs further assessment. The GMO Panel does not identify safety concerns regarding the toxicity and allergenicity of the Mpp75Aa1.1 and Vpb4Da2 proteins and the DvSnf7 dsRNA and derived siRNAs as expressed in maize MON 95275 and finds no evidence that the genetic modification would change the overall allergenicity of maize MON 95275. In the context of this application, the consumption of food and feed from maize MON 95275 does not represent a nutritional concern in humans and animals. The GMO Panel concludes that maize MON 95275 is as safe as the conventional counterpart and non-GM maize varieties tested, and no post-market monitoring of food/feed is considered necessary. In the case of accidental release of maize MON 95275 material into the environment, this would not raise environmental safety concerns. The post-market environmental monitoring plan and reporting intervals are in line with the intended uses of maize MON 95275. The GMO Panel concludes that maize MON 95275 is as safe as its conventional counterpart and the tested non-GM maize varieties with respect to potential effects on human and animal health and the environment.

Complex network analysis of climate and landscape satellite data to explore spatio-temporal patterns in urban environment: the case of Athens

Motivated by the significance and complexity of exploring spatiotemporal patterns - regions within an urban environment, particularly in the context of extreme heat events- this research analyzes meteorological time series through complex network analysis. The data collected for the examination area is focused on Athens, Greece, and covers sections of the city’s urban landscape. The data was obtained from the Copernicus observation component of the European Union. Initially, the time series are transformed into networks using correlation network methodology, followed by examination of the discriminative capability of the topological measures of networks degree and modularity as community - region detection methods. Of particular interest is that our findings suggest that the proposed complex network analysis can lead to the extraction of spatial urban regions closely linked to land use and building heights in corresponding areas. These results may help investigate the spatial variability of heat in the urban environment and inform urban planning and management strategies in policy decision-making regarding the intensity of urban heat throughout the city and the planning of climate change adaptation strategies.

Penerapan Pembangunan Berkelanjutan Dalam Upaya Peningkatan Pengelolaan Lingkungan

The use of natural resources for the implementation of development is closely related to the environment and spatial planning. These two things have so far tended to be unplanned and unsustainable, the result of which is a decline in the quality and function of the environment, including the natural resources within it. Environmental problems are increasing day by day, this indicates that environmental policy has not been successful. The community service activity was carried out on Friday 26 July 2024 at the Subak Sibang Kaja Abiansemal Hall. The problem formulation in this report is firstly, What is the form of community synergy to realize sustainable development in environmental management? and secondly, what are the inhibiting factors in realizing sustainable development? The aim of this report is to find out the synergy of society in realizing sustainable development in environmental management and what factors are obstacles in realizing sustainable development. This implementation method uses socialization where implementation is carried out through lectures given by resource persons with the aim of making the community understand the importance of management. environment . The results of the first community service activities are in the form of synergy which occurs in the form of cooperation between the community and the government in environmental management such as establishing a waste bank and the first inhibiting factors in sustainable development, lack of public awareness, the absence of legal regulations that provide appropriate sanctions. firm and finally the existence of climate change in an area Keywords: environment; improvement; sustainable developmen.

Kajian Tekno “Waste to Electric” dengan Bahan Baku Pelet Woodchip Memalalui Gasifikasi Multi Stage pada Mesin Diesel Dual Fuel

Global warming and climate change have significant impacts on various sectors, including water scarcity, climate changes, social vulnerabilities, ecological damage, infrastructure damage, economic damage, health risks, air quality issues, biodiversity loss, and air quality. As of the end of 2023, Indonesia has power plants with a total capacity of 83.8 GW, comprising 79.8 GW of on-grid plants and 3.95 GW of off-grid plants. This figure indicates an increase in power plants of nearly 1.7 times over the past 10 years. Power generation is still dominated by coal, which constitutes up to half of the total national capacity, followed by gas energy at about 25%. Meanwhile, renewable energy-based power plants make up only 15%, having increased by just around 6 GW in the last 10 years. The utilization of renewable energy in power generation is dominated by hydro power (58%), geothermal (20%), and biomass (18%). In line with the government’s plan to decarbonize and achieve the Net Zero Emissions target by 2060, the 2021-2030 RUPTL (Long-Term Electricity Supply Plan) is designed to be the greenest RUPTL, with the Environmental Friendly Power Generation Plan or New Renewable Energy (EBT) reaching 52% of the total additional power plant capacity. One source of EBT power generation is biomass, derived from wood waste or the use of HTI (Industrial Timber Estate) wood processed into syngas through a gasification process for further conversion into environmentally friendly energy. Gasification is the process of converting woodchips as the raw material in a three-stage gasification process to produce syngas as fuel for diesel engines. During the gasification process, variations in the air ratio will be managed in the pyrolysis, oxidation, and reduction zones with air ratio settings of 0:10:0, 1:8:1, 2:7:1, and 1:7:2. The syngas produced from this process will be used as fuel for diesel engines with a dual fuel system. The characterization of the performance of the dual fuel diesel engine will be conducted by testing the engine at 5000 rpm with load variations from 500 watts to 5000 watts in 500-watt increments and varying the syngas mass flow rate by adjusting the syngas valve opening to the diesel engine. The objective of this research is to determine that gasification of woodchips into syngas can be a promising potential source of renewable, environmentally friendly electrical energy and an alternative to support government programs through the decarbonization of diesel engines.

3D dynamic landscape simulation of artificial intelligence in environmental landscape design

Three-dimensional (3D) simulations and precise landscape visualizations are crucial for various applications, like landscape management and planning, computer and connection of the landscape, evaluation, and tracking of land use. The consequences of several plans and a large scene cannot be communicated using older methods of comprehensive environmental planning and development in a timely, rational, and coordinated manner. Architects have trouble incorporating ideas into other comprehensive planning implementation processes. Architects did not thoroughly investigate the neighbourhood's demographics and matching behavioural needs and lacked critical thinking. The 3D dynamic landscape simulation is a detailed computerized three-dimensional simulation of the environment that can be dynamically presented. With the aid of Artificial Intelligence (AI) technology, the system possesses a strong sense of reality, a user-friendly interface, and interactive features that can be tailored to the requirements of the contemporary urban environmental landscape. Regarding exterior publicity, domestic assistance, environmental land use planning, and information systems. The novelty of the proposed Interactive Design System based on AI (IDS-AI) is to create a 3D dynamic landscape model based on a real-life environmental scene, utilizing a Geographic Information System (GIS) to optimize landscape vision. Secondly, 3D environmental landscape design simulation was implemented using GIS spatial analysis in conjunction with the Fuzzy Analytical Hierarchical Process (FAHP) to reduce the data overlap rate and help make an accurate decision. Finally, the design incorporates the development of the interactive interface system application of landscape design and environmental resources for viewing the landscape, the factors that affect them, and the area coverage ratio of various land cover types. The experimental outcomes show that the suggested IDS model increases the gradient sensitivity level of 98.3 % and area coverage ratio of 93.4 % compared to other existing models.

Climatic features in the Serra do Sincorá geopark, in Chapada Diamantina, in the state of Bahia, Brazil

Climate studies and their elements are important, because in addition to research and weather forecasting, other sectors of the community need information about weather and climate, such as agriculture, aviation, civil defense, water resources management, navigation and the energy sector. In this sense, the present study aimed to analyze the variability of some elements of the climate of the Bahian municipalities of Andaraí, Lençóis, Mucugê and Palmeiras, the area of proposition for the creation of the Serra do Sincorá Geopark, in Chapada Diamantina, based on air temperature, rainfall and evapotranspiration in the perspective of understanding the behavior of the local climate and its possible effects in the region. For this analysis, specific time intervals were selected for each municipality, available at the National Institute of Meteorology (INMET) and Climatempo. The data were tabulated for statistical analysis of the behavior of the climate elements over the months and years, using as parameters the sum, minimum, maximum, average and the correlation between the meteorological variables, with the generation of climograms and comparative graphs. The relevance of the study lies in understanding the climatic variations of the municipalities in the proposed area of the Serra do Sincorá Geopark in relation to its climatic dimension, since the results may offer subsidies regarding environmental planning, in addition to a contribution to the local population, since there are few studies in the field of climatology in the region. With the analysis of meteorological data, it is proven that the municipalities of Andaraí, Lençóis, Mucugê and Palmeiras, due to climate factors, such as altitude, latitude and longitude, on a regional scale, influence the behavior of climate elements.

Challenges for the steel industry in terms of environmental control in the production of pig iron in the state of Minas Gerais - Brazil

This text highlights the importance of implementing an Environmental Management System (EMS) in the steel industry sector focused on pig iron production in the state of Minas Gerais, Brazil. The text refers to the general scenario of pig iron production in Minas Gerais, addresses the main environmental impacts observed in the area of influence of the enterprises and the legislation directed at the sector. It discusses the behavior of the steel industry in the face of the need for environmental management plans and programs, and points out the possible competitive advantages of industrial companies that meet acceptable environmental and production control standards.

The need for proactive environmental planning in local government: Examining challenges and pathways forward in Indiana

ABSTRACT Many factors, including regulations, community values, and economic and environmental considerations, influence environmental planning decisions. Across the nation, local elected and appointed boards and commissions are responsible for planning and land use regulations. However, few studies have focused on environmental planning through comprehensive and parks and recreation plans. Using Indiana communities as a case study, this research highlights the theory and practice of environmental planning and community development, emphasizing the importance of collaboration and the presence of social capital. Statewide focus groups found that communities integrate varying environmental planning processes into comprehensive and parks and recreation plans. As a result, these plans represent underutilized potential to guide conservation and collaboration. Integrating environmental planning into local government activities is limited by time, political will, and community awareness. As such, communities need continued support and resources to build social capital that leads to engaging with the public, decision-makers, and collaborators in meaningful ways to foster effective environmental planning.

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In the field of autonomous mobile robots, sampling-based motion planning methods have demonstrated their efficiency in complex environments. Although the Rapidly-exploring Random Tree (RRT) algorithm and its variants have achieved significant success in known static environment, it is still challenging in achieving optimal motion planning in unknown dynamic environments. To address this issue, this paper proposes a novel motion planning algorithm Bi-HS-RRTX\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\ ext {X}$$\\end{document}, which facilitates asymptotically optimal real-time planning in continuously changing unknown environments. The algorithm swiftly determines an initial feasible path by employing the bidirectional search. When dynamic obstacles render the planned path infeasible, the bidirectional search is reactivated promptly to reconstruct the search tree in a local area, thereby significantly reducing the search planning time. Additionally, this paper adopts a hybrid heuristic sampling strategy to optimize the planned path quality and search efficiency. The convergence of the proposed algorithm is accelerated by merging local biased sampling with nominal path and global heuristic sampling in hyper-ellipsoid region. To verify the effectiveness and efficiency of the proposed algorithm in unknown dynamic environments, numerous comparative experiments with existing algorithms were conducted. The experimental results indicate that the proposed planning algorithm has significant advantages in planned path length and planning time.

Predicting land use and land cover change dynamics in the eThekwini Municipality: a machine learning approach with Landsat imagery

ABSTRACT Monitoring and providing accurate land use and land cover (LULC) change information is vital for sustainable environmental planning. This study used Landsat imagery from 2002 to 2022 to create updated LULC change maps for the eThekwini Municipality. Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) were used to conduct these LULC classifications, with XGBoost achieving the highest accuracy (80.57%). The generated maps revealed a significant decrease in cropland and an increase in impervious surfaces. As such, this research established a framework for continuous LULC mapping and highlighted Landsat 9’s potential in LULC classifications.