Support Land Use Research Articles

The limitations and risks of land use change tools in decision-making: Lessons from Galloway and Southern Ayrshire UNESCO Biosphere, Scotland

Land use change is needed to mitigate climate change and biodiversity loss and is increasingly incentivised through natural capital approaches that reward delivery of ecosystem services including carbon sequestration and biodiversity. A proliferation of land use change decision-support tools are developed –often by private companies– to support land use change decision-making and measure success of government policy and private investment targets. However, understanding of land manager uptake of these tools is often limited, and the limitations and risks of tools used to support land use change decision-making is understudied. We explore these knowledge gaps in a UNESCO Biosphere in Galloway and Southern Ayrshire, Scotland through nineteen interviews with a wide range of land managers. We found that the promotion of tools as a mechanism to deliver rapid land use change is unlikely to be successful for three reasons: 1) the dominant focus on instrumental values, neglecting land managers’ broader values; 2) the technocratic tools-based approach to decision-making is at odds with land manager behaviour; and 3) the importance of peer-led networks is neglected. Framing the promotion of tools as another form of environmental rescaling, we argue that they are a red herring in addressing issues of land use change. The emphasis should instead be on appropriately supporting and resourcing local peer-led networks to enable and incentivise land-use change. These findings have global relevance given increasing promotion of these approaches within international policy and country-level policy in many countries worldwide.

Artificial intelligence in land use prediction modeling: a review

<p><span lang="EN-US">This study aims to review methods of artificial intelligence (AI) in land use modelling. Data were extracted from journals in the Scopus and Google Scholar databases using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. The review demonstrates that modelling land use predictions is a complex matter that involves land use maps and driving forces. AI technology can support land use forecasting by interpreting land use data, analyzing drivers, and modeling. However, AI has limitations in terms of broad contextual understanding and algorithmic errors. To anticipate this, it is necessary to select the appropriate image resolution and interpretation method in accordance with digital data segmentation. It is also recommended to use spatial regression methods to determine the driving forces that affect land use. Hybrid models such as multilayer perceptron neural network Markov chain (MLPNN-MC), random forest algorithm (RFA), and cellular automata (CA)-Markov chain (MC) are recommended for modelling. The selection of a model should be based on the data's characteristics and tested for accuracy. The use of AI for land use prediction modelling is expected to provide accurate predictions that can be used as a basis for land use policy.</span></p>

Shallow soils in dryland ecosystems: Drivers of C accumulation and land management implications

Shallow soils (i.e., Lithic Entisols) cover about 20% of Brazilian drylands. Inherent soil characteristics (i.e., shallow depths) and water scarcity restrict plant growth and carbon (C) inputs in these soils. In such a sensitive ecosystem, sustainable land management options are key to promoting socio-economic development and ensuring food security. Here, we use a dataset of 50 Lithic Entisol profiles distributed within the Brazilian drylands (northeastern region) to: (i) investigate the effects of land-use (cropland, grassland, and native forest), climate (semi-arid and dry sub-humid), and slope classes (0–3%, 3–8%, 8–20% and 20–45%) on soil C accumulation; and (ii) evaluate how understanding multiple drivers C accumulation can support the identification of sustainable land management options. The results suggested that land use does not affect C stock and C/N ratio, nevertheless, they were affected by climate. Under dry sub-humid climate conditions, C stocks were 41.7 Mg ha−1, 44% higher than under semi-arid climate (28.9 Mg ha−1), a result that reflects the effect of higher biomass production in wetter environments. Under slopes of 20–45%, C stocks were 54.3 Mg ha−1, 172% higher than under slopes of 0–3% (19.9 Mg ha−1), because of the higher altitudes and wetter conditions under steeper slopes. Our results showed that areas under lower slopes have lower C stocks and lower aridity index. These drier conditions reduce the productive potential of annual crops and grasslands but enable the cultivation of high-yielding Crassulacean Acid Metabolism (CAM) crops, such as Agave spp, which have a high potential for biofuel production. Furthermore, the possibility of reintroducing organic residues from ethanol production can promote an increase in C stocks, contributing to climate change mitigation. Ultimately, our study provides insights from a holistic view of SOC accumulation drivers, supporting land use planning of highly sensitive environments in tropical drylands around the world.

AI4LUC: deep learning and automated mask labelling to support land use and land cover mapping in the Cerrado biome

ABSTRACT Land use and land cover (LULC) mapping in the Brazilian Cerrado is challenging due to its vastness, seasonal vegetation changes, and spectral class confusion. Artificial intelligence, particularly deep learning (DL), offers promising solutions through semantic segmentation of Cerrado images, though it traditionally requires manual image annotation. This paper introduces the AI4LUC (Artificial Intelligence for Land Use and Land Cover Classification) method, leveraging DL and automated mask labeling to address LULC in the Brazilian Cerrado. AI4LUC includes pre-processing modules and the Smart Mask©Labelling (SML) module, which combines morphological operations with the CerraNetv3 deep convolutional neural network to automatically generate training masks for DL models like U-Net and DeepLabv3+. Additionally, we present the Cerrado Dataset (CerraData-v3), a high-resolution image dataset with 80,000 labeled samples. Our experiments show the SML module achieved an F1-score of 0.6647 and mIoU of 0.5068, demonstrating significant potential for automated semantic segmentation in this large biome. Furthermore, DeepLabv3+ outperformed U-Net by 69.86%. Source code and dataset: https://github.com/ai4luc/AI4LUC.

Place-based diversification: Pathways to diversified shared landscapes in the Wairau Marlborough region of Aotearoa New Zealand

This study aims to explore the potential of place-based theory to contribute to land use diversification, particularly against the backdrop of prevailing climate change challenges. The study delves into local Māori and wider community perspectives in Wairau Marlborough, New Zealand, through two sets of semi-structured interviews. Questions asked aimed to (1) develop an understanding of the community’s vision of land use in the Wairau Marlborough region, (2) identify and understand the elements necessary to develop a pathway to achieving a shared vision for land use in the region, and (3) assess the potential for place-based approaches to support land use and diversification where there is high potential for conflicting interests between different groups, or in contexts with a history of colonisation. Overall, the study highlights the importance of understanding the community’s connection to the place and land, empowering them to be leaders in the development of pathways to solutions for the challenges that impact them. Interviewees emphasise the need for diversification, protection of natural resources, and inclusion of diverse perspectives. The studies provide useful insights for achieving a shared vision for land use diversification in Wairau, Marlborough, that can also be applied to other regions.

A Multicriteria Model for the Assessment of Source Water Contamination by Anthropogenic Activities to Support Land Use Management

A Multicriteria Model for the Assessment of Source Water Contamination by Anthropogenic Activities to Support Land Use Management

Potential for Agricultural Expansion in Degraded Pasture Lands in Brazil Based on Geospatial Databases

Important public and private initiatives to map agricultural lands and natural resources have been carried out in Brazil to support land use planning. Some studies indicate that Brazil still has up to 109.7 million hectares of cultivated pastures with some level of degradation, representing around 60% of the total pasturelands, estimated at 177 million hectares. This study aimed to gather, process, and analyze publicly available databases to generate quantitative and spatial information about the potential of Brazilian degraded pastures for agricultural expansion. We considered data related to the natural agricultural potential, restrictions imposed by special areas (indigenous lands and Afro-Brazilian “quilombola” settlements), areas with high biodiversity conservation priorities, infrastructure such as distance between major highways and availability of warehouses, current agricultural areas, and the information made available by Agricultural Climate Risk Zoning. The results indicated the existence of approximately 28 million hectares of planted pastures with intermediate and severe levels of degradation that show high potential for agricultural crops. These areas could increase the planted areas with grains in Brazil by approximately 35% in relation to the total area used in the 2022/23 crop season.

Optimization of information support for land use in transboundary territories

The article examines and defines the provisions for the use of land in the adjacent territories of the border zone of the Russian Federation and the Republic of Kazakhstan based on the use of remote sensing materials and their processing using GIS technologies. The need to solve the problem raised and the specifics of its solution are shown. The article discusses the main problems of economic use of land in the border zone of the Russian Federation and the Russian Federation. and ways to solve them. The authors proposed approaches that solve the problems of information support for monitoring the condition and use of lands in the border zone of the Russian Federation and the Republic of Kazakhstan. A model is proposed for creating an interstate database containing the main qualitative and quantitative characteristics of lands, with the aim of involving them in economic and economic turnover.

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.

Quantitative and qualitative approaches in recognizing viable centres to support transit system implementation in developing countries. Case study: Makassar, Indonesia

Implementing transit systems has been seen as one approach to better support land use development and the transport system to overcome the spatial mismatch and subsequently, the traffic congestion problems in many cities. One of the key components of transit system implementation is recognizing the viable center. However, the application of this system in practice is still limited due inadequate spatial form and mobility information, especially in cities in the developing world. This study aims to propose a spatial clustering approach to identify urban center(s) based on openly available data sources combined with expert interviews. The proposed method is intended to support local transport planning processes. Following the case study approach, the method was developed and tested in Makassar, Indonesia. Urban land use and the built environment data were used alongside data about the streets network and the distribution of facilities derived from the local authority’s spatial database and OpenStreetMap (OSM). The results show that urban centers are developed outside the central business district mainly in linear form. Additional findings based on the morphological perspective show that Makassar has three types of urban fabrics, i.e., walking, car city, and transit city fabrics. Based on the urban center distribution and urban fabrics’ typology, recommendations for the adjustment of transport nodes and the city network can be made, which could become a basis for transport planning in the future.

Geomorphology, Land-Use, and Hemeroby of Foothills in Colombian Orinoquia: Classification and Correlation at a Regional Scale

In the management of ecosystem services, it is significant to relate land use with the physical characteristics of the terrain, which allows establishing the conditioning factors of human activities and planning their distribution. These analyzes are based on thematic cartography, usually generated with visual classifications of satellite images. Traditional mapping techniques involve limiting the timely availability of information by taking extended periods for interpretation and integration of multiple data sets. This article presents a methodology to overcome these difficulties, implements machine learning and cloud computing to generate timely thematic cartography and spatial analysis to support land use planning. The study area was delimited according to altitudinal levels that define braided and anastomosed river systems. Acquisition, processing, and classification of input data for modeling were performed on the Google Earth Engine platform. The spatial correlation between hemeroby and geomorphology was calculated with the odds ratio and its respective confidence interval. Maps of 27 geomorphological units, 11 types of land use, and six hemeroby levels are presented at a scale of 1:50,000. Confusion matrices of implemented classification models were also reported, allowed evaluating global, user’s, and producer’s accuracy. Correlations between relict of natural areas with the structural environment and urban infrastructure with alluvial fans stand out. The information generated by these procedures is essential for planning land use and prioritizing the maintenance of ecosystem services.

A methodology for risk assessment of LNG carriers accessing vulnerable port areas

The navigation course of Liquefied Natural Gas (LNG) carriers and fueled ships may cross vulnerable areas where commercial, civil, and industrial assets are present. Despite the effort made by scientific community and authorities, there are no consolidated methodologies addressing the risks arising from the interaction between LNG carriers and fueled ships accessing ports and onshore facilities. This work focuses on the development of a simplified methodology for risk assessment of LNG carriers and fueled ships in port areas able to identify accident scenarios involving the ship course and vulnerable targets. The analysis starts verifying the compliance with respect to the port regulations and the feasibility of the course with respect to the channels. Then, based on standard features of LNG carriers and fueled ships, a set of reference accidental scenarios to be considered during the risk assessment is identified. A specific guidance for the identification of hazards, estimation of frequencies and consequences is provided, finally adopting a customized risk matrix to support decision-making on prevention and mitigation measures. The application of the methodology to a real case study dealing with an important Italian harbor, highlights the capability of the method to support land use planning.

Urban agrobiodiversity, health and city climate adaptation plans.

To identify the scope and nature of agricultural biodiversity actions within the climate adaptation plans of a sample of large world cities. I evaluated data from the 2021 Cities Climate Adaptation Actions database curated by the Carbon Disclosure Project. Cities with a population over 1million and reporting at least one adaptation action were included. I identified actions involving agriculture and biodiversity using a framework consisting of five agrobiodiversity categories: urban and peri-urban land use and water management, and urban food supply chains, food availability and food environments. I also identified reported health co-benefits and health sector involvement. Of 141 cities reviewed, 61 cities reported actions on agricultural biodiversity, mostly supporting land use or water management. Key health outcomes addressed were illnesses linked to air pollution and excessive heat and vector-borne diseases, corresponding with cities' major health concerns. Greenhouse gas mitigation was also addressed by many cities. Fewer cities reported actions in food categories or concern for noncommunicable diseases or poor nutrition. Nearly two thirds of cities (40/61) reported health co-benefits or health-sector involvement for at least one intervention. A higher proportion of the 43 cities in low- and middle-income countries reported agrobiodiversity actions and health co-benefits than the 18 cities in high-income countries. Cities are key partners in achieving sustainable global agriculture that promotes health and supports climate and biodiversity goals. Cities can enhance this role through climate adaptation plans with strong health engagement, a focus on nature-based solutions and greater emphasis on food and nutrition.

FERA-Net: A Building Change Detection Method for High-Resolution Remote Sensing Imagery Based on Residual Attention and High-Frequency Features

Buildings can represent the process of urban development, and building change detection can support land use management and urban planning. However, existing building change detection models are unable to extract multi-scale building features effectively or fully utilize the local and global information of the feature maps, such as building edges. These defections affect the detection accuracy and may restrict further applications of the models. In this paper, we propose the feature-enhanced residual attention network (FERA-Net) to improve the performance of the ultrahigh-resolution remote sensing image change detection task. The FERA-Net is an end-to-end network with a U-shaped encoder–decoder structure. The Siamese network is used as the encoder with an attention-guided high-frequency feature extraction module (AGFM) extracting building features and enriching detail information, and the decoder applies a feature-enhanced skip connection module (FESCM) to aggregate the enhanced multi-level differential feature maps and gradually recover the change feature maps in this structure. The FERA-Net can generate predicted building change maps by the joint supervision of building change information and building edge information. The performance of the proposed model is tested on the WHU-CD dataset and the LEVIR-CD dataset. The experimental results show that our model outperforms the state-of-the-art models, with 93.51% precision and a 92.48% F1 score on the WHU-CD dataset, and 91.57% precision and an 89.58% F1 score on the LEVIR-CD dataset.

TOOLKIT FOR THE FORMATION OF GEOSPATIAL PROVISION OF REGIONAL LAND USE

It is proved that in modern conditions, which are determined by ambiguous conditions, characterized by a decrease in the main indicators of the functioning of the regions. At the regional level, there is a deepening of structural disparities in the field of land relations and a decrease in the efficiency of land use. In such conditions, it is necessary to rethink the approach to ensuring positive changes in land use based on the use of modern tools for the formation of geospatial support in the context of the formation and use of monitoring, making effective decisions. It was determined that theoretical approaches to the formation and application of geospatial support for regional land use are insufficiently developed, which is a set of spatial information created through the use of modern geo-informational tools, which made it possible to build geospatial maps for making informed decisions at the regional level. The goal of the research on the formation of geospatial support for the use of land in the regions has been achieved. To achieve the goal, the following tasks have been solved: creating a quantitative basis for the formation of geospatial support for land use at the regional level; construction of geoinformation maps of regional land use. In order to increase the effectiveness of the formation and use of the lands of the objects of the nature reserve fund and the creation of a monitoring system, it is proposed to ensure an increase in the level of information and analytical support and the rational use and protection of natural resources by 10%.This difficult task is carried out on the basis of the development of scientifically based recommendations on the formation and use of land of nature reserve fund objects at the regional level and the creation of a monitoring geo-information system. According to the results of the geo-informational analysis of the level of land use of the objects of the nature reserve fund, it was concluded that most regions of the country have an insignificant level of the integral factor. At the same time, other regions have a moderately insignificant level of the integral factor. These results indicate the absence of actions regarding the formation and use of land of nature reserve objects by region, the imperfection of the system of monitoring the use of land of nature reserve objects, which requires the development of scientifically based recommendations for the development of monitoring the use of land of nature objects - the reserve fund of the regions.

Soil Carbon and Nitrogen Stocks under Agrosilvopastoral Systems with Different Arrangements in a Transition Area between Cerrado and Caatinga Biomes in Brazil

Production systems that promote the accumulation of soil organic matter (SOM) must be implemented to maintain the sustainability of agriculture, livestock, and forestry. Since increases in MOS content contribute to improving the chemical, physical, and biological quality of the soil, as well as helping to reduce carbon emissions to mitigate climate change. Therefore, the objective of this study was to evaluate soil organic carbon (SOC) and nitrogen (N) stocks after the implementation of agrosilvopastoral (ASP) systems in a Cerrado-Caatinga transition zone in Brazil. Native vegetation of Cerrado-Caatinga (NV), regenerating stratum of Cerrado-Caatinga (RS), two arrangements of ASP systems cultivating Cenchrus ciliaris L. intercropped with Eucalyptus camaldulensis Dehnh. × Eucalyptus tereticornis Sm. hybrid (ASP1 and ASP2), and intercropped with Eucalyptus urophylla S.T. Blake × Eucalyptus grandis W. Mill ex Maiden hybrid (ASP3 and ASP4) were evaluated. Soil C and N stocks and the C content in the humic fractions of SOM were evaluated at 0–10, 10–20, and 20–30 cm soil depths. The introduction of ASP2, ASP3, and ASP4 systems in an area previously occupied by low productivity pasture increased and restored SOC stocks to levels found in NV, at a depth of 0–30 cm. N stocks were higher in ASP systems, regardless of the arrangement studied. As a result, the ASP systems provided accumulations that ranged from 1.0 to 4.31 Mg SOC ha−1 yr−1 and from 0.33 to 0.36 Mg N ha−1 yr−1. The carbon contents in humic fractions remained higher in NV. The hierarchical grouping and principal component analysis showed that the implementation of the ASP systems was efficient in increasing soil C and N stocks over time. In conclusion, the present study identified that integrated production systems can support land use intensification strategies based on sustainable and low-carbon agriculture in a transition area between the Cerrado and Caatinga biomes in Brazil.

Transit Ridership Modeling at the Bus Stop Level: Comparison of Approaches Focusing on Count and Spatially Dependent Data

Boarding and alighting modeling at the bus stop level is an important tool for operational planning of public transport systems, in addition to contributing to transit-oriented development. The interest variables, in this case, present two particularities that strongly influence the performance of proposed estimates: they demonstrate spatial dependence and are count data. Moreover, in most cases, these data are not easy to collect. Thus, the present study proposes a comparison of approaches for transit ridership modeling at the bus stop level, applying linear, Poisson, Geographically Weighted and Geographically Weighted Poisson (GWPR) regressions, as well as Universal Kriging (UK), to the boarding and alighting data along a bus line in the city of São Paulo, Brazil. The results from goodness-of-fit measures confirmed the assumption that adding asymmetry and spatial autocorrelation, isolated and together, to the transportation demand modeling, contributes to a gradual improvement in the estimates, highlighting the GWPR and UK spatial estimation techniques. Moreover, the spatially varying relationships between the variables of interest (boardings and alightings) and their predictors (land use and transport system features around the bus stops), shown in the present study, may support land use policies toward transit-oriented development. In addition, by using an approach with little information, the good results achieved proved that satisfactory boarding and alighting modeling can be done in regions where there is a lack of travel demand data, as in the case of emerging countries.

Potencial natural de erosão no Estado de Mato Grosso, Brasil

ABSTRACT Understanding the susceptibility of soils to erosion is crucial for planning land use towards sustainable agriculture. This study aimed to determine the spatial variability of natural erosion potential for the state of Mato Grosso, an important agricultural center of Brazil. Natural erosion potential was calculated using the Universal Soil Loss Equation, which accounts for erosivity, erodibility, and the topographic factor. For each of these three factors, a map was generated in raster format that was combined into a Geographic Information System and used to create a map of natural erosion potential. This map was then used to separate classes of natural erosion potential for the state of Mato Grosso. The state predominantly has medium levels of natural erosion potential (58.38% in area), followed by high (21.67%) and low (19.57%) levels. Areas of low natural erosion potential are predominantly located in the flatter sections of the state. The topographic factor was strongly correlated with natural erosion potential. It is an important component to support land use planning and soil conservation practices. Regions considered to have high natural erosion potential are most commonly in the northwest (46.69% in area), north (32.7%), and west (30.05%) macro-regions.

Development of a planning support system to evaluate transit-oriented development masterplan concepts for optimal health outcomes

There is growing consensus that planning professionals need clearer guidance on features of the built environment that promote health benefits. Concomitantly, the smart city movement has created renewed opportunity and interest in data-driven urban modelling to support land use planning. Planning Support Systems (PSS) are spatially enabled computer-based analytical tools incorporating health-related metrics that apply empirical evidence on built environment relationships with health-related outcomes to inform real-world urban design, urban planning and transportation planning decisions. This paper presents the development of the Urban Health Check PSS to use local empirical data to explore and predict relative health impacts associated with proposed urban design planning changes from alternative new station precinct masterplan concepts. We present a case study where we compare a baseline scenario with alternative design concepts for a new train station precinct in Perth, Western Australia, that incorporated possible built environment interventions. Subsequently, we discuss potential future applications of health impact PSS for the translation and application of health evidence into practice.

Urban Land Cover/Use Mapping and Change Detection Analysis Using Multi-Temporal Landsat OLI with Lidar-DEM and Derived TPI

The mapping and change detection of land cover and land use are essential for urban management. The aim of this study was to map and monitor the spatial and temporal change in urban land cover and land use in Davidson County, Tennessee in the periods of 2013, 2016, and 2020. The urban land cover and land use categories were classified and mapped using Random Forest algorithm. A combination of Landsat Operational Land Imager (OLI) satellite data with Light Detection and Ranging (lidar)-Digital Elevation Model (DEM) and derived Topographic Position Index (TPI) were used in the classification and monitoring of urban land cover and land use change. The urban land cover and land use types were mapped with average overall accuracies of about 87% in 2020, 85% in 2016 and 2013. The overall accuracy increased by around 8%, 9%, and 6% in 2020, 2016, and 2013 classifications respectively when lidarDEMand derived TPIwere added to Landsat OLIsatellite data in the classification relative to standalone Landsat OLI. Total change occurred in about 63% of Davidson County between 2016 and 2020 with significant net gains and losses among land cover and land use types. This information could support land use planning.