Search Results (1,632)

In line with UNESCO’s Historic Urban Landscape approach, this study highlights the need for integrative tools that connect heritage conservation with broader urban development dynamics, balancing preservation and growth. While several machine-learning models have been applied to analyse the drivers of urban change, there remains a need for comparative analyses that assess their strengths, limitations, and potential for combined applications tailored to specific contexts. This study aims to compare the predictive accuracy of three land-use change models (Random Forest, Logistic Regression, and Recursive Partitioning Regression Trees) in estimating the probability of land-use transitions, as well as their interpretative capacity to identify the main factors driving these changes. Using data from the Bellavista neighborhood in Tomé, Chile, the models were assessed through prediction and performance metrics, probability maps, and an analysis of key driving factors. The results underscore the potential of integrating predictive (Random Forest) and interpretative (Logistic Regression and Recursive Partitioning Regression Trees) approaches to support heritage planning. Specifically, the research demonstrates how these models can be effectively combined by leveraging their respective strengths: employing Random Forest for spatial simulations, Logistic Regression for identifying associative factors, and Recursive Partitioning Regression Trees for generating intuitive decision rules. Overall, the study shows that land-use change models constitute valuable tools for managing urban transformation in heritage urban areas of intermediate cities. Full article

The sustainability of apiculture within Mediterranean forest ecosystems is contingent upon the extent and health of melliferous tree habitats. This study outlines a five-year initiative (2020–2024) aimed at mapping and monitoring four principal honey-producing tree species—pine (Pinus halepensis and Pinus nigra), Greek fir (Abies cephalonica), oak (Quercus ithaburensis subsp. macrolepis), and chestnut (Castanea sativa)—across Evia, Greece. This is achieved through the utilization of high-resolution Sentinel-2 satellite imagery in conjunction with a hierarchical deep learning framework. Distinct from prior vegetation mapping endeavors, this research introduces an innovative application of a hierarchical framework for species-level semantic segmentation of apicultural flora, employing a U-Net convolutional neural network to capture fine-scale spatial and temporal dynamics. The proposed framework first stratifies forests into broadleaf and coniferous types using Copernicus DLT data, and subsequently applies two specialized U-Net models trained on Sentinel-2 NDVI time series and DEM-derived topographic variables to (i) discriminate pine from fir within coniferous forests and (ii) distinguish oak from chestnut within broadleaf stands. This hierarchical decomposition reduces spectral confusion among structurally similar species and enables fine-scale semantic segmentation of apicultural flora. Our hierarchical framework achieves 92.1% overall accuracy, significantly outperforming traditional multiclass approaches (89.5%) and classical ML methods (76.9%). The results demonstrate the framework’s efficacy in accurately delineating species distributions, quantifying the ecological and economic impacts of the catastrophic 2021 forest fires, and projecting long-term habitat recovery trajectories. The integration of a novel hierarchical approach with Deep Learning-driven monitoring of climate- and disturbance-driven changes in honey-producing habitats marks a significant step towards more effective assessment and management of four major beekeeping tree species. These findings highlight the significance of such methodologies in guiding conservation, restoration, and adaptive management strategies, ultimately supporting resilient apiculture and safeguarding ecosystem services in fire-prone Mediterranean landscapes. Full article

Coastal landscapes are complex socio-ecological systems that undergo rapid transformations driven by both natural dynamics and human pressures. Their sustainable management depends on robust, cost-effective remote sensing methodologies for long-term monitoring and quantitative assessment of spatiotemporal change. In this study, we developed an integrated remote-sensing-based framework that combines historical aerial photograph interpretation, transition matrix analysis, and machine learning to assess coastal dune landscape dynamics over a seventy-year period. Georeferenced orthorectified and preprocessed aerial imagery freely available from the Italian Ministry of the Environment for the years 1954, 1986, and Google Satellite Images for 2022 were used to generate detailed land-cover maps, enabling the analysis of two temporal intervals (1954–1986 and 1986–2022). Transition matrices quantified land-cover conversions and identified sixteen dynamic processes, while a Random Forest (RF) classifier, optimized through parameter tuning and cross-validation, modeled and compared landscape dynamics within protected Long-Term Ecological Research (LTER) sites and adjacent unprotected areas. Model performance was evaluated using Balanced Accuracy (BA) to ensure robustness and to interpret the relative importance of change-driving variables. The RF model achieved high accuracy in distinguishing change processes inside and outside LTER sites, effectively capturing subtle yet ecologically relevant transitions. Results reveal non-random, contrasting landscape trajectories between management regimes: protected sites tend toward naturalization, whereas unprotected sites exhibit persistent urban influence. Overall, this research demonstrates the potential of integrating multi-temporal remote sensing, spatial statistics, and machine learning as a scalable and transferable framework for long-term coastal landscape monitoring and conservation planning. Full article

Land surface temperature (LST) is a key indicator of the urban heat island effect and is affected by multiple factors. However, existing research mainly focuses on the contributions of urban landscape and meteorology, and the impact of changes in atmospheric environment has not been fully considered. Based on multisource data and a random forest model, this study quantified the independent and interactive effects of aerosols, meteorological conditions, and urban features on LST in Beijing. The results revealed that the effects of the meteorological factors and aerosol optical depth (AOD) on LST were significantly greater than those of the urban landscape index. The response of LST to multiple factors is nonlinear, and the interactions of precipitation with wind speed and vegetation have the strongest cooling effects on LST. The aerosol impact shifts seasonally, with its direct radiative effect dominating in spring and inducing a cooling of up to about 2.0 °C. Notably, the land use type plays a background role in determining the LST, and the average LST decreases by approximately 1.5 °C for every 50% increase in tree coverage. As the building height increases by 10%, the summer LST increases by approximately 2 °C. In addition, the interactions of precipitation with wind speed and vegetation were identified as having the strongest cooling effects on LST. By elucidating the nonlinear interactions among aerosol, meteorological, and urban features, this work moves beyond isolated factor analysis and offers mechanism cognition for urban planning strategies. Full article

Soil organic carbon (SOC) is a key indicator used to evaluate cropping systems, as it reflects long-term productivity, sustainability, and environmental impacts like carbon sequestration. Diversifying crops within intensive farming systems is a possible strategy for enhancing the environmental sustainability of agriculture, resulting in higher rates of SOC accumulation compared to monocultures. This study seeks to evaluate the influence of diversified cropping systems on SOC content at both the field and territorial levels. In Northern Italy, two crop management approaches—one incorporating diversification and one without—were analyzed. The ECOSSE model was employed to simulate changes in SOC content over a 30-year period of diversification, compared with monocropping. The results of the model, first run in available sampling sites, were upscaled to the field to which they belong. Then, using a machine learning approach—namely Random Forest—they were interpolated at the landscape scale, extending the information to an area with similar soil, climate, and management conditions. The maps obtained with this procedure represent valuable tools to assess the long-term effects of crop diversification with legumes on soil C at different scales and can support agricultural policymakers and planners. Full article

The evolution of landscape ecological risk in ecologically fragile areas constitutes a critical foundation for optimizing territorial spatial planning and ensuring ecological security. This study takes Zichang City as the research object and integrates the dynamic analysis of land use, landscape ecological risk assessment, and spatial simulation into a single framework. By analyzing the laws of land use change in Zichang City from 1980 to 2020, the CLUE-S model was used to predict land use change and ecological risks under multiple scenarios in 2035. Statistical and spatial analysis methods were comprehensively applied to verify the robustness and spatial differentiation characteristics of the risk assessment. Key findings indicate the following: (1) From 1980 to 2020, forest land, water bodies, and construction land in Zichang City continued to increase, while cultivated land and grassland tended to decrease. Multi-scenario simulations showed that under the business-as-usual scenario, grassland and forest land expanded; under the economic development scenario, urban land increased significantly; under the ecological protection scenario, grassland grew substantially, while cultivated land contracted noticeably. (2) The overall LERI from 1980 to 2020 first declined and then slightly rebounded, reflecting an “initial improvement followed by fluctuation” in ecological security, with a spatial pattern of “high in the central area, low in the periphery.” By 2035, high-risk levels remain predominant across scenarios, although the proportion of high-risk areas is limited. Monte Carlo simulation confirmed the robustness of the assessment (mean CV = 0.038). (3) Spatially, from 2020 to 2035, the clustering characteristics of LERI varied among scenarios; however, high–high and low–low clustering patterns remained predominant, indicating that spatial aggregation of ecological risk is relatively stable across scenarios. This study demonstrates that integrating landscape ecological risk assessment with land use scenario modeling provides robust scientific support for optimizing spatial planning and ecological security in ecologically fragile regions. The proposed framework offers methodological guidance and practical reference for identifying key risk areas and designing differentiated land use and risk management strategies in similar hilly–gully landscapes. Full article

In recent years, European cities have experienced rapid changes in their functional and spatial organisation, which have affected, among others, the natural environment, the economy and society. The intensive and often uncontrolled growth of residential development associated with suburbanisation significantly impacts areas located around urban areas. Growing investment pressures usually lead to the transformation of rural and naturally valuable areas, altering their character and functions. Solving these problems requires developing a method to determine the main directions and intensity of land use changes in the context of urbanisation pressures and sustainable spatial development. This article presents the results of a spatiotemporal analysis of the dynamics of built-up area development in rural and suburban zones, utilising Geographic Information Systems (GIS) technology. The study focused on the expansion of single- and multi-family housing around the city of Białystok, Poland, between 1997 and 2022. The analysis was based on spatial data, including available orthomosaics and cadastral data from the Topographic Objects Database (BDOT10k). The GIS-based analysis covered an area of nearly 2000 km² and included methods for change detection, analysis, and land cover classification. The results indicated a marked intensification in landscape transformations, particularly in transition zones between rural and urban areas. At the same time, forests and protected zones significantly influenced the direction and pace of development, acting as natural barriers limiting spatial expansion. The results indicate the need to consider environmental factors (e.g., protected areas and forests) in spatial planning processes and sustainable development policies. The study confirms the high usefulness of GIS tools in monitoring and forecasting spatial change at both the local and regional scales. This research also contributes to the discussion on urbanisation, its characteristics, causes, and consequences, and highlights the role of green spaces in limiting sprawl. Full article

Woody encroachment (WE) refers to the expansion of woody vegetation, particularly scrubs, into grasslands, altering ecosystem structure, function, and vegetation phenology. WE is especially pronounced in arid and semi-arid regions, where climate variability, land use, and ecological resilience interact strongly. Even though long-term monitoring of these dynamics in protected areas is essential to understanding landscape change and guiding conservation strategies, a few studies address this. The Flora and Fauna Protection Area (FFPA) Bavispe, a sky island in northwestern Mexico, provides an ideal setting to examine WE. Using remote sensing, we analyzed 30 years of land cover change (Landsat 5 TM and Landsat 8 OLI) in two reserve zones, Los Ajos and La Madera, and their 5 km buffer areas. Additionally, NDVI-based regressions (MODIS MOD13Q1) were applied to assess phenological responses across vegetation types. Classifications showed high accuracy (Kappa > 0.75) and revealed notable woody expansion: 960 ha of oak forest and 1322 ha of scrubland gained in Los Ajos, and 1420 ha of scrubland in La Madera. Grasslands declined by 2234 ha in Los Ajos and 1486 ha in La Madera, with stronger trends in surrounding buffers. Phenologically, the onset of the growing season was delayed by ~2 days per year in Los Ajos and ~3 days in La Madera. A generalized increment of woody vegetation in the region and the observed change in phenophases in selected land cover types indicated a shift in regional drivers (human or other ecological state factor) related to land cover distribution. Full article

As a UNESCO World Heritage Site, the Dujiangyan Irrigation District is a key area for Chengdu’s rural revitalisation. However, as the plan progresses, issues have emerged, including loss of traditional features, cultural heritage, and landscape degradation. Within the framework of “landscape information collection—landscape information processing—landscape information output”, the study utilized literature review, field surveys, and remote sensing interpretation to collect data for the years 2000, 2010, and 2020 as time slices. A system of landscape characteristic elements was then built to identify the types of landscape characteristics. The types were determined, and a systematic analysis of the regional landscape’s evolution was conducted. The results indicated that the types of landscape characteristics were classified as follows: Urban Settlement Landscape (8.70–16.10%), Low-Hill Forest Landscape (1.82–3.47%), Village Woodland-Grove Landscape (15.89–44.23%), and Idyllic Agricultural Landscape (36.20–73.59%). Over the last two decades, there has been a steady increase in Urban Settlement Landscape, a slow overall growth trend in Low-Hill Forest Landscape, a rapid growth trend in Village Woodland-grove Landscape, and a rapid decline in Idyllic Agricultural Landscape. Among these, built-up land dominates Urban Settlement Landscape evolution; forest land shapes Low-Hill Forest Landscape; cultivated and built-up land influence Village Woodland-grove Landscape; and cultivated land drives Idyllic Agricultural Landscape changes. Based on the changes observed, the study explored the impact of relevant policies on the landscape characteristics of the study area. Policies for urban-rural integration have encouraged the networked growth of settlement landscapes, creating a system with several centres. Both ecological and economic gains have resulted from forestry practices. Policies that safeguard farmhouse forests have made multifunctional transformation easier. Large-scale farming and ecological agriculture are now linked in a zone established by agricultural modernisation strategies. The study offers scientific references for the protection of regional landscapes and the construction of rural living environments in the irrigation area. Full article

Landscape aesthetic quality (LAQ) is a vital cultural ecosystem service in global forests, particularly in the subalpine forests across the Tibetan Plateau, which are considered popular tourist destinations due to their unique cultural services. However, the explicit spatial localization and spatial–temporal dynamics of LAQ in subalpine forests in the Tibetan Plateau remain largely unexplored. Herein, we introduced a method for assessing LAQ that integrates the species’ biophysical attributes with spatial landscape characteristics, allowing for a spatially explicit quantification of LAQ. We further employ this approach to project changes in LAQ under forest landscape dynamics (2016–2096) in Jiuzhaigou, eastern Tibetan Plateau. Most regions exhibited moderate or low LAQ, with high values ible, while over half of low-LAQ regions were not. The high-value zone of LAQ is projected to rise slightly by 2056 but decline sharply by 2096. These results reveal strong spatial heterogeneity in LAQ and indicate that future landscape dynamics will substantially reshape its distribution in the subalpine forests of the eastern Tibetan Plateau. Our findings provide early evidence of declining cultural ecosystem quality in subalpine forests and offer guidance for adaptive management in similar mountain ecosystems worldwide. Full article

Amid climate change and land-use transformation, the scientific identification of high-quality arable land reserves is critical for safeguarding both cropland quantity and quality. Conventional approaches, largely based on spatial autocorrelation and heterogeneity theories, inadequately capture the multi-scale integration of ecological functions and carbon cycling, particularly in ecologically high-risk areas where systematic identification and mechanism analysis are lacking. To address these challenges, this study introduces a geographically similar “grain-carbon” synergistic framework, paired with a “bidirectional optimization” strategy (negative elimination + positive selection), to overcome the shortcomings of traditional methods and mitigate grain–carbon trade-offs in high-risk areas. Using land-use data from Yunnan’s mountainous areas (2000–2020), integrated with InVEST-PLUS model outputs, multi-source remote sensing, and carbon pool datasets, we developed a dynamic land-use–carbon storage simulation framework under four policy scenarios: natural development, urban expansion, arable land protection, and ecological conservation. High-quality arable lands were identified through a geographic similarity analysis with the Geo detector, incorporating ecological vulnerability and landscape risk indices to delineate priority high-risk zones. Carbon storage degradation trends and land-use pressures were further considered to identify optimal areas for cropland-to-forest conversion, facilitating the implementation of the bidirectional optimization strategy. Multi-scenario simulations revealed an increase of 454.33 km² in high-quality arable land, with the optimized scenario achieving a maximum carbon storage gain of 23.54 × 10⁶ t, reversing carbon loss trends and enhancing both farmland protection and carbon sequestration. These findings validate the framework’s effectiveness, overcoming limitations of traditional methods and providing a robust strategy for coordinated optimization of carbon storage and arable land conservation in ecologically high-risk regions, with implications for regional carbon neutrality and food security. Full article

From the perspective of environmental history, which examines the interplay between socio-economic development and the natural environment, this paper discusses the evolution of Japanese landscapes. These landscapes evolved in somewhat different ways, absorbing influences from China and the West. Following the country’s opening up in the late 19th century, various forest management techniques were introduced from Europe and America. This paper examines the environmental history of the changes to the landscape that accompanied rapid Westernisation and the guidance provided by “Forest aesthetics” in forest operations—a crucial element of the landscape. Proposed by H. von Salisch, forest aesthetics is a forest management philosophy that provided guidelines for sustainability before the concept of ecosystems emerged. Although Japan is a small nation comprising elongated islands, mountains cover 67% of its land area. Its north-south orientation means that each region has unique forests and ways of life. This overview examines historical information concerning the formation of gardens and artificial forests, landscape transformations, and perceptions of forests across different eras. Using primarily secondary sources dating from around the 11th century, it demonstrates that, even in Japan, which is subject to natural disturbances under a monsoon climate, the sustainability of gardens and forests could be achieved by emulating the nature advocated for by forest aesthetics as closely as possible. This approach also considered hunting. Full article

This study explores the mechanisms through which the street built environment (BE) influences mobile physical activity (MPA) using multi-source data and explainable machine learning methods. The research combines Geographically Weighted Regression (GWR) and Random Forest (RF) models to reveal the complex spatial heterogeneity between BE factors and MPA, and enhances the interpretability of results through the SHAP model, providing theoretical support for future targeted urban planning and MPA interventions. The study finds that the “density” dimension of BE plays a crucial role in MPA, particularly population density and building density. Additionally, accessibility and safety also significantly influence MPA, while design factors such as greening rates, water landscapes, and building façade design promote MPA. The study emphasizes that the influence of BE factors on MPA is nonlinear, with significant interaction effects between different variables, indicating that improving a single variable alone cannot fully explain changes in MPA. This research provides a new theoretical perspective for understanding the impact of BE factors on MPA and offers empirical evidence for precise interventions. In areas with low MPA participation, improving street design, enhancing traffic safety, and increasing green and water-friendly spaces can significantly promote residents’ MPA, thereby improving public health. Full article

Climate change is profoundly reshaping watershed hydrological regimes and threatening the sustainability of regional ecosystems, rendering traditional ecological restoration planning—primarily reliant on static baselines—insufficient to support long-term resilience under future environmental conditions. To enhance the sustainability of metropolitan ecological restoration, this study develops a climate-adaptive restoration framework for the Wuhan Metropolitan Area, structured around “climate scenario—hydrological simulation—zoning delineation—strategy formulation.” The framework aims to elucidate how projected hydrological shifts constrain the spatial configuration of ecological restoration. Under the RCP4.5 (Representative Concentration Pathway 4.5) scenario, the WEP-L (Water and Energy transfer Processes in Large river basins) distributed hydrological model was calibrated and validated using observed hydrological data from 2016–2020 and subsequently applied to simulate the spatiotemporal evolution of precipitation, evapotranspiration, runoff, and total water resources in 2035. Hydrological trend analyses were further conducted at the secondary watershed scale to assess the differentiated impacts of future hydrological changes across planning units. Based on these simulations, ecological sensitivity and ecosystem service assessments were integrated to identify priority restoration areas, forming a “five-zone × three-tier” sustainable restoration zoning system encompassing farmland restoration, forest ecological restoration, soil and water conservation restoration, river and lake wetland ecological restoration, and urban habitat improvement restoration, classified into general, important, and extremely important levels. A comprehensive “four-water” management scheme—addressing water security, water resources, water environment, and water landscape—was subsequently proposed to strengthen the sustainable supply capacity and overall resilience of metropolitan ecosystems. Results indicate that by 2035, hydrological processes in the Wuhan Metropolitan Area will exhibit pronounced spatial heterogeneity, with uneven changes in precipitation and runoff further intensifying disparities in regional water availability. These findings highlight the necessity of incorporating scenario-based hydrological constraints into sustainable ecological restoration planning. The proposed technical framework provides a transferable pathway for enhancing watershed ecosystem sustainability and resilience under climate change. Full article

Bioindication is a key tool for monitoring habitat quality and ecosystem dynamics under increasing anthropogenic pressure. Among model organisms, ground beetles (Coleoptera: Carabidae) play a particularly important role, and one of the widely applied functional indicators describing their assemblage structure is the Mean Individual Biomass (MIB). Introduced in the 1980s, this index reflects the average body mass of Carabidae and allows assessment of successional stages. Its computational simplicity and intuitive interpretation have led to its application in forests, agricultural landscapes, post-industrial areas, and glacier forelands. This paper synthesizes the development and applications of the MIB, highlighting both its advantages and methodological limitations (including variability of length–mass models, seasonal activity patterns, and dependence on sampling methods). Particular attention is given to the potential of the MIB in the context of global environmental change, including its role as an indicator of ecosystem responses to climate change and processes related to soil carbon sequestration. Based on a literature review, future research directions are identified, encompassing methodological standardization, integration of MIB with other ecological and molecular indicators, and expansion of analyses to regions beyond Europe. By linking classical bioindication with ecosystem functioning studies, the MIB may serve as a universal tool for environmental monitoring and the assessment of ecosystem services under accelerated global change. Full article