Land Use Analysis Research Articles

Challenge of political integration in the territorial management of a protected area based on the analysis of land use and land cover change

Challenge of political integration in the territorial management of a protected area based on the analysis of land use and land cover change

A bottom-up nationwide analysis of sectoral land use reveals spatial heterogeneity across the United States

The high degree of urbanization and economic development in the United States has intensified competition for land resources, leading to land use conflicts and environmental challenges. Addressing these issues first requires an exploration of high-resolution, sector-specific land use patterns. However, existing land classification studies rarely provide spatially explicit data on urban land use across all sectors at a national level. This study aims to fill this gap by analyzing land use areas across various economic sectors in the United States, particularly focusing on urban impervious surfaces. Using a bottom-up approach, we created a full-sector land use inventory, integrating Points of Interest (POI) data, road networks, building characteristics, and geographic information processing techniques. Our primary innovation lies in the development of a spatially explicit database that details sector-specific land use areas nationwide. On this robust foundation, we further explored the heterogeneity of sectoral land use eco-efficiency (SLUEE) among different sectors and states, providing supplementary insights into spatial disparities in urban land use. Our findings reveal significant spatial heterogeneity, with service sectors dominating land use and exhibiting higher SLUEE compared to goods-producing industries. Key sectors such as Professional, Scientific, and Technical Services (441064.58 ha), and Administrative and Support Services (420148.34 ha) are prominent across states according to land use area, reflecting the country's service-oriented economy. Additionally, regional disparities in SLUEE are evident, with jurisdictions like Washington D.C., New York, and California demonstrating more efficient land use. Compared with traditional land use evaluations, our model effectively offers higher spatial resolution, down to the census block level, to sectoral land use evaluation. Building on this robust database, our SLUEE analysis has uncovered notable heterogeneity between sectors and states, offering new insights into cross-sectoral and inter-state land use dynamics that can inform and promote sustainable urban planning.

The application of remote sensing and geographic information system techniques for the analysis of land use and land cover changes in Wayanad district of Kerala

Mapping Land Use and Land cover Changes (LULC) and detecting changes using remote sensing and Geographic Information System (GIS) techniques is a cost-effective way of gaining a good understanding of the land cover alteration processes caused by land use change and their effects. This study assessed the transformation of the Wayanad district landscape over a period of 23 years. LANDSAT satellite images (of 30 m resolution) encompassing the area at three epochs were classified into nine classes (coffee dominated mixed crop, built-up, evergreen forest, deciduous forest, grassland, mixed crop with built-up, paddy, tea plantation, and waterbody) using the maximum likelihood algorithm, resulting in classes for each land use. The results showed that over the past 23 years, coffee-dominated mixed crops have increased by 0.84% in 2014 and 11.84% in 2022 compared to 1999; deciduous forest area decreased 3.6% and increased 0.6% in 2014 and 2022, respectively. Tea plantations increased by 0.9% in 2014 and by 0.49% in 2022, which decreased by 0.41% compared to 2014. Built-up has increased by 6.42% and 6.02% in 2014 and 2022 respectively, and slightly decreased by 0.4% compared to 2014. Evergreen forest areas increased in 2014 by 6.68% and 2.28% in 2022 and decreased by 4.44% compared to the previous time period. Grass land areas have decreased by 6.25% and 5.33%, respectively, and mixed crops with built-up areas has decreased by 0.74% and remained the same in the last two epochs, while paddy and waterbodies have decreased by 3.4%, 15.79%, and 0.07 and 0.19%, respectively, in 2014 and 2022 of the total geographical area Keywords: Remote sensing, GIS techniques, LULC, forest cover

Spatio-Temporal Analysis and Prediction of Land Use and Land Cover in Jagdalpur Sub-Division of Bastar District in State of Chhattisgarh, India from 2012 to 2037

Spatio-Temporal Analysis and Prediction of Land Use and Land Cover in Jagdalpur Sub-Division of Bastar District in State of Chhattisgarh, India from 2012 to 2037

Andean Landscape Legacies: Comprehensive Remote Sensing Mapping and GIS Analysis of Long-Term Settlement and Land Use for Sustainable Futures (NW Argentina)

The Andes region has an exceptional record of high-altitude settlements integrated within widespread regional chains of mobility and exchange. The Sierra de Aconquija (NW Argentina, south-central Andes) is an effective climatic barrier that has afforded an enduring indigenous approach to land use, mobility, and exchange over millennia. Despite this rich history, the Sierra has been largely considered marginal in pre-Columbian regional cultural developments. Today, the expansion of extractive industries threatens the region’s heritage and the sustainable futures of local communities. Innovative, integrative methodologies are needed for landscape characterisation, heritage assessment, and sustainable policy development. Building on earlier work, we undertook the first comprehensive mapping of archaeological features over 3800 sq. km of the Sierra using interpreter-led assessment of commercial and open-access satellite imagery and DSM data, to verify earlier assumptions and to identify previously unnoticed trends in the aggregation, distribution, and connectivity of archaeological features. The mapping identified 6794 features distributed unevenly but with clear tendencies towards maximising topographic, ecologic, and connectivity advantages expressed consistently across the study area. The outcomes confirm the important role the Sierra had in pre-Hispanic times, highlighting the significance of ancient indigenous practices for the sustainability of vulnerable upland landscapes both in the Andes and worldwide.

Historic street layout and its impact on shaping residents’ daily lives: insights from Sanxue Street, Xi’an

ABSTRACT Historic street layouts are integral to historic districts’ tangible cultural heritage and are intricately linked to the residents’ daily lives; however, existing studies have only examined their spatial aspects, primarily using quantitative methods. Conversely, studies on tangible cultural heritage’s impact on residents’ lives have principally employed qualitative methods. Therefore, this study bridges qualitative and quantitative approaches to propose a layout – daily life model, which explains the relationship between the layout of Xi’an, China’s Sanxue Street historic and cultural district, and its residents’ daily lives. A combination of space syntax and geographic information system-based land-use analyses revealed that eating, shopping, calligraphy, and transportation were the main activities of residents. Questionnaire responses from 335 participants validated the proposed model and confirmed its feasibility. When certain types of facilities are widely distributed in streets with high choice and integration, residents’ daily lives tend to involve the functions represented by them. Furthermore, calligraphy is an important part of most residents’ daily lives, regardless of the transportation mode used. This study contributes to the understanding of the effect of tangible cultural heritage, particularly street layouts, on residents’ daily lives in historic districts.

Land Transformations in Irpinia (Southern Italy): A Tale on the Socio-Economic Dynamics Acting in a Marginal Area of the Mediterranean Europe

Marginal areas in economically advanced countries are a critical issue that European and national policies have been addressing for some time. These areas are affected by depopulation, infrastructural gaps and labor systems that do not reach the corresponding national levels and where often agriculture still plays a critical role. In Italy, despite the fact that the National Strategy for Inner Areas (SNAI) has been active for about a decade with the aim of increasing the territorial cohesion of these fragile areas, rather limited results have been achieved in terms of halting economic marginalization and demographic decline. In this specific context, our work is aimed at analyzing land use changes, the loss of ecosystem services and demographic trends in a Mediterranean region (Irpinia—Southern Italy) on district and municipal scales in the last 30 years (1990–2018) to capture current, subtle socio-economic dynamics. The analysis carried out has indicated a substantial increase in urban areas due to the development of new industrial areas and discontinuous urban fabric (urban sprawl) at the expense of natural areas (mainly meadows and shrublands). The agricultural areas have remained substantially unchanged in terms of extension, with a slight increase in heterogeneous agricultural areas and an expansion of high-value crops (vineyards), that are the most suitable for multifunctional agriculture activities (experiential and rural tourism). The analysis of the demographic trend has highlighted a widespread phenomenon of depopulation, with the exception of those municipalities who economically orbit around the provincial capital of Avellino. The municipalities in depopulation are mostly located in the inner areas characterized by a more rugged morphology and infrastructural gaps. Unexpectedly, most of municipalities show a significant anticorrelation among the population and agricultural areas which is an indicator of social and economic phenomena as complex as they are underestimated. As a final step, this analysis highlights also a loss of carbon storage mainly attributable to the soil sealing of large areas. This study can help to comprehensively understand the conditions of marginal areas in Mediterranean Europe over recent decades in the light of the main socio-economic dynamics to better direct efforts towards the containment of the human capital hemorrhage, consisting of persistently negative natural and migratory rates, and the sustainable empowerment of these geo-economic peripheries.

Analisis Perubahan Penggunaan Lahan Kawasan Kumuh Kecamatan Kandis Kabupaten Siak Tahun 2014 dan 2023

This research aims to identify the causes of land use change in the slum areas of Kandis Sub-district, Siak Regency, and analyze land use change from 2014 to 2023 using information maps. The method used is descriptive quantitative, with data analysis techniques based on weighting the quality of slum areas through geographic information systems (GIS) using GIS applications to identify location, distance, and settlement patterns. Sampling was done through proportional stratified random sampling. Data collection used primary data in the form of interviews and secondary data in the form of documentation studies. The results showed that: (1) The causes of land use change in Kandis Sub-district slum areas include an increase in population density every year, narrow spacing between buildings (1.5 to 3 meters), lack of clean water infrastructure, inadequate drainage, inadequate toilet conditions, and lack of waste disposal facilities. Community income is also low, below Rp. 1,000,000 per month. (2) The area of the slum area has increased significantly, from 0.27 Ha in 2014 to 9.6 Ha in 2023.

Spatiotemporal Analysis of Land Use and Land Cover Dynamics of Dinderesso and Peni Forests in Burkina Faso

Spatiotemporal Analysis of Land Use and Land Cover Dynamics of Dinderesso and Peni Forests in Burkina Faso

Devastation of the cerrado of mato grosso do sul and the advance of arenization in the pardo river watershed

Despite the significant biodiversity in the Cerrado, the process of occupation, subsequent economic cycles, and intensification of agriculture in a predatory manner have caused profound alterations in this biome. This study analyzed changes in land use and land cover in the Cerrado of Mato Grosso do Sul between 1985 and 2022 by mapping and also investigating the formation and dynamics of sand dunes in the Bauru sedimentary basin, focusing on the Pardo River watershed. MapBiomas Network data, collection 8, were used for land use and land cover analysis. Arenization foci cartography was conducted using brightness index 2 based on Sentinel-2 satellite images in Sentinel Application Platform software. The historical analysis of land use and cover in the Cerrado of Mato Grosso do Sul revealed a rapid transformation of the landscape, with a drastic reduction of native vegetation and an intensification of large-scale monocultures. Between 1985 and 2022, the Cerrado of Mato Grosso Sul lost 4.6 million hectares of native vegetation (forest formations, savannahs, grasslands, and wetlands), representing 24.54% of this territory. Recent changes in land use include pasture-soybean and pasture-eucalyptus. In the Pardo River watershed, arenization foci cover an area of 17,834.34 hectares, with varying dimensions ranging from less than 1 hectare to 376.41 hectares. The arenization process occurs in different slope compartments, such as the base, slope, and interfluves. The physical and chemical characteristics of Quartzipsamments, such as low organic carbon content in soil, are associated with high rates of surface exposure and weakened load-dependent structures, leading to degradation. Arenization occurs where natural vegetation has been suppressed and soil has been depleted by poorly managed production processes.

Machine Learning Approaches for Mapping and Predicting Landslide-Prone Areas in São Sebastião (Southeast Brazil)

This study employs machine learning techniques to map and predict landslide-prone areas in São Sebastião, Brazil, a region susceptible to landslides due to its steep terrain and intense rainfall. We compared five algorithms: Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and k-Nearest Neighbors, using various environmental factors as inputs. The Gradient Boosting model performed best, achieving an AUC-ROC of 0.963 and an accuracy of 99.6%. Slope degree, soil moisture index, and relief dissection emerged as the most influential factors in predicting landslide susceptibility. Analysis of land use and land cover changes between 1985 and 2021 revealed significant increases in forest cover and urban areas, with implications for landslide risk distribution. The resulting susceptibility map shows predominantly low-risk areas with scattered high-risk zones, providing crucial information for targeted risk management. This research demonstrates the effectiveness of machine learning in landslide susceptibility mapping and offers valuable insights for disaster risk reduction and urban planning in coastal mountainous regions.

Cloud Computing Application for the Analysis of Land Use and Land Cover Changes in Dry Forests of Peru

Cloud Computing Application for the Analysis of Land Use and Land Cover Changes in Dry Forests of Peru

Spatiotemporal analysis of land use and land cover dynamics using multi-temporal remote sensed imagery and quantifying landscape fragmentation: A case study of Chandannagar Municipal Corporation (CMC), West Bengal, India

Spatiotemporal analysis of land use and land cover dynamics using multi-temporal remote sensed imagery and quantifying landscape fragmentation: A case study of Chandannagar Municipal Corporation (CMC), West Bengal, India

Evaluation of Coastal Management in the Municipality of Paulista/PE in the Light of the Decalogue

Brazil adopts Integrated Coastal Management as a way of protecting coastal environments through sustainable use and through the National Coastal Management Plan – PNGC structures actions directed at coastal areas, guaranteeing municipalities a prominent position in the integrative process. The structuring of the legal and institutional framework does not remove the picture of degradation borne by coastal cities, which have problems with sanitation, erosion and reduction of biodiversity. The general objective of this article is to analyze the Coastal Management process adopted by the municipality of Paulista, Pernambuco, in order to provide subsidies for institutionalization and sustainable governance. The methodology was structured in two parts. The first part used the Land Use and Land Cover (LULC) dataset available free of charge MapBiomas collection 6. The second part used the methodology proposed by Barragán in the “Decalogue for the Management of Coastal Areas”, where the 10 indicators are analyzed fundamental to evaluate actions related to coastal management. The methodology was adapted to include the mapping of the use and occupation of the municipality's coastal area over a period of 36 (thirty-six) years. The results of the analysis of land use and occupancy indicate a steady growth in anthropized areas from 1986 until the end of the 1990s, stabilizing with few changes. After an increase, there was a new stabilization between 2006 and 2012, followed by a notable growth in 2013–2014 and a subsequent period with less pronounced increments until the year 2021. Regarding the matrix analysis, there is a greater advancement in aspects related to Knowledge and Information, Normative and Competencies, whereas items such as Training and Capacity Building, Resources, and Public Policies require better strategic definitions by the municipality. As for the results of the matrix analysis, they reveal the need for the implementation of impact mitigation measures, the strengthening of oversight and monitoring, the development of strategies for climate change adaptation, the promotion of environmental education, and the fostering of scientific research in coastal areas. Finally, there is a suggestion for the continuous evaluation and correction of actions related to coastal and marine areas of the municipality, as well as the use of scientific evidence in decision-making.

Temporal dynamics of land use land cover (LULC) changes and assessment of environmental and climatic parameters using remote sensing and GIS: A case study in Limboti Reservoir, Loha Taluka, Maharashtra, India

In this study, multispectral satellite data from Landsat 4-5 TM and Landsat 8 OLI/TIRS was used to analyse Land Use Land Cover (LULC) change detection and to retrieve environmental parameters. The LULC change detection was done between 2007 and 2021. This study used the supervised classification-maximum likelihood approach in ArcGIS 10.3 software to detect LULC in Loha Taluka, Maharashtra. The taluka was classified into four major LULC classes, viz. water, vegetation, settlement and barren-land. Changes in LULC directly or indirectly will also induce a change in the environmental parameters. Environmental parameters (water surface temperature, chlorophyll-a concentration and total suspended solids) were analysed for Limboti Reservoir. Data were predicted for next two years (2022 and 2023) using multiplicative seasonal decomposition and Holt-Winter’s multiplicative method and trends of each parameter were analysed using Mann-Kendall trend method. For both years, vegetative land was the most extensive LC in Loha Taluka, accounting for more than 60% of the total land area. However, throughout these years, urbanisation was rampant and vegetative land was converted to settlement. In these years, open water resources such as reservoirs, lakes and rivers covered a very small percentage of the total area, which is a serious threat to the ecosystem. As a result, proper water body management is essential; otherwise, these resources will be destroyed and will no longer be able to contribute to the area’s socioeconomic growth. Keywords: Change detection, Chlorophyll content, Land use land cover, Water quality, Water surface temperature

Enhanced Monitoring of Sub-Seasonal Land Use Dynamics in Vietnam’s Mekong Delta through Quantile Mapping and Harmonic Regression

In response to economic and environmental challenges like sea-level rise, salinity intrusion, groundwater extraction, sand mining, and sinking delta phenomena, the demand for solutions to adapt to changing conditions in riverine environments has increased significantly. High-quality analyses of land use and land cover (LULC) dynamics play a critical role in addressing these challenges. This study introduces a novel high-spatial resolution satellite-based approach to identify sub-seasonal LULC dynamics in the Mekong River Delta (MRD), employing a three-year (2021–2023) Sentinel-1 and Sentinel-2 satellite data time series. The primary obstacle is discerning detailed vegetation dynamics, particularly the seasonality of rice crops, answered through quantile mapping, harmonic regression with Fourier transform, and phenological metrics as inputs to a random forest machine learning classifier. Due to the substantial data volume, Google’s cloud computing platform Earth Engine was utilized for the analysis. Furthermore, the study evaluated the relative significance of various input features. The overall accuracy of the classification is 82.6% with a kappa statistic of 0.81, determined using comprehensive reference data collected in Vietnam. While the purely pixel-based approach has limitations, it proves to be a viable method for high-spatial resolution satellite image time series classification of the MRD.

Spatiotemporal analysis of land use and land cover changes, LST and NDVI in Thatta district, Sindh, Pakistan

The purpose of this work is to determine land-use and land-cover (LULC) patterns, land surface temperature (LST), and normalized difference vegetation index (NDVI) changes in Thatta district using Landsat data from 1991 to 2021 and evaluate the relationship between LST and NDVI. The research process employed the selection of the study area, data acquisition, preprocessing, and classification of remotely sensed images for the estimation of the land use land cover change (LULC), vegetation index (NDVI), and evaluation of LST using thermal bands in the Landsat dataset. The study revealed the area under built-up structures has increased from 1991 to 2021. Although the vegetation cover showed an increase, the bare soil showed a decreasing pattern, indicating a constant change in the LULC patterns in the region. The confusion matrix method for accuracy valuation of LULC data of 2021 revealed an overall accuracy of 88.24%, with a Kappa coefficient of 84.22%, while the Artificial Neural Network Multilayer Perceptron (ANN-MLP) model had a Kappa validation of 0.95 for 2021. The highest maximum temperature is observed for 2021, indicating a positive relationship between LST and built-up structures, while regression analysis found a negative correlation between LST and NDVI. This study provides a valuable monitoring framework to help resource managers develop strategies to manage land resources.

Research Progress in Spatiotemporal Dynamic Simulation of LUCC

Land Use and Land Cover Change (LUCC) represents the interaction between human societies and the natural environment. Studies of LUCC simulation allow for the analysis of Land Use and Land Cover (LULC) patterns in a given region. Moreover, these studies enable the simulation of complex future LUCC scenarios by integrating multiple factors. Such studies can provide effective means for optimizing and making decisions about the future patterns of a region. This review conducted a literature search on geographic models and simulations in the Web of Science database. From the literature, we summarized the basic steps of spatiotemporal dynamic simulation of LUCC. The focus was on the current major models, analyzing their characteristics and limitations, and discussing their expanded applications in land use. This review reveals that current research still faces challenges such as data uncertainty, necessitating the advancement of more diverse data and new technologies. Future research can enhance the precision and applicability of studies by improving models and methods, integrating big data and multi-scale data, and employing multi-model coupling and various algorithmic experiments for comparison. This would support the advancement of land use spatiotemporal dynamic simulation research to higher levels.

Spatio-temporal analysis of land use and land cover dynamics in a part of Southern Nigeria

Agriculture, industrialization, transportation, urbanization and other anthropogenic activities have impacted on the natural cover of the earth surface, hence the dual concepts of land use and land cover (LULC). This study analyses spatio-temporal dynamics in LULC in part of southern Nigeria (path 189 and row 056) between 2002 and 2023using satellite imageries: namely, Landsat Enhanced Thematic Mapper Plus ETM+ of 2023, 2014 and 2002. The classification of land coverwas done with the aid of ArcGIS.The study revealed spatial dynamics in LULC over the study period. The percentage of the study area under cultivation increased from 7.96% in 2002, 17.79% in 2014 to 30.67% in 2023. Also, built-up area increased from 4.36% in 2002, 8.29% in 2014 but decreased to 7.06% in 2023. While areas of dense forest were 34.59% in 2002, 21.42% in 2014 and 11.57% in 2023, bare surface was 4.94% in 2014, 1.19% in 2002 and 25.34% in 2023.Oil spill was 6.13% in 2002, 6.42% in 2014 and 2.07% in 2023. Secondary regrowth/Plantation was 0.19%, 4.22% and 5.53% in 2002, 2014 and 2023 respectively. Water bodieswere 3.37% (2002), 7.06% (2002) and 11.27% (2023). The study recommends enactment of an enabling law or enforcement of existing laws governing forest protection and raising awareness and education of inhabitants on the need to preserve the natural land cover.

A sacrifice for the greater good? On the main drivers of excessive land take and land use change in Hungary

Despite continuous population decline or stagnation, many Central and Eastern European cities experience urban sprawl and expansion, accompanied by excessive land take and land use change. This paper investigates recent trends in land take and land use change in Hungarian cities, aiming to identify the drivers of these processes. The study utilizes the Urban Atlas (UA) databases from 2006 and 2018 to analyze land use change in selected Hungarian cities, employing QGIS software to compile the database and MS Excel to conduct the statistical analysis. A linear trajectory model is employed to project land use change by 2026, based on 2006–2018 UA data. Additionally, via the in-depth analysis of Debrecen, the study highlights the recent acceleration of land use change to compare the projected trend with the forecasted one. Local climate strategies focus on addressing environmental concerns, including changes in land use at the city level. Therefore, this study reviews the relevant documents associated with each city to assess its commitment to reducing land take. The results indicate that despite its declining population, Hungary has seen a significant expansion in artificial surfaces, primarily due to industrialization and population policies introduced by the central government, which is likely to continue in the coming years. Unfortunately, local climate strategies have been insufficient in preventing cities from experiencing urban sprawl and expansion and preserving local ecosystem services. This underscores the inability of municipalities to defend local interests against central government directives. The findings of this study indicate that the current central policies are the main drivers of land take and urban sprawl and expansion. Prioritizing the compact city model and brownfield developments could effectively mitigate these issues. Finally, the paper advocates that complete jurisdiction over land use decisions be restored to local governments, enabling them to reject investments that may jeopardize local environmental goals.