Search Results (3,863)

Recognizing the evolution of landscape patterns in the Chishui River source region is essential for protecting ecosystems and sustainable growth in the Yangtze River Basin and other similar areas. However, knowledge of landscape pattern evolution within the primary channel zone remains insufficient. To address this gap, the current study used 2000–2020 land-use, geography, and socio-economic data, integrating landscape pattern indices, land-use transfer matrices, dynamic degree, the GeoDetector model, and the PLUS model. Results revealed that forest and cropland remained the prevailing land-use types throughout 2000–2020, comprising over 85% of the landscape. Grassland had the highest dynamic degree (1.58%), and landscape evolution during the study period was characterized by increased fragmentation, enhanced diversity, and stable dominance of major forms of land use. Anthropogenic influence on different landscape types followed the order: construction land > cropland > grassland > forest > water bodies. Land-use change in this region is a complex process governed by the interrelationships among various factors. Scenario-based predictions demonstrate pronounced variability in various land types. These findings provided a more comprehensive understanding of landscape patterns in karst river source regions, provided evidence-based support for regional planning, and offered guidance for ecological management of similar global river sources. Full article

Climate change and land-use intensification are speeding up the loss of ecosystem services that support human health, food security, and environmental stability. Vegetative interventions—such as urban forests in cities and cover crops in farming systems—are increasingly seen as nature-based solutions for climate adaptation. However, their benefits are often viewed separately. This review combines 20 years of research to explore how these strategies, together, improve provisioning, regulating, supporting, and cultural ecosystem services across various landscapes. Urban forests help reduce urban heat islands, improve air quality, manage stormwater, and offer cultural and health benefits. Cover crops increase soil fertility, regulate water, support nutrient cycling, and enhance crop yields, with potential for carbon sequestration and biofuel production. We identify opportunities and challenges, highlight barriers to adopting these strategies, and suggest integrated frameworks—including spatial decision-support tools, incentive programs, and education—to encourage broader use. By connecting urban and rural systems, this review underscores vegetation as a versatile tool for resilience, essential for reaching global sustainability goals. Full article

Technological innovation plays a vital role in enhancing the economic growth and sustainability of the forestry sector. However, research on the nature, dynamics, and impact of such innovations, particularly within small and medium-sized enterprises (SMEs), remains limited. The forestry sector is often characterised by low levels of technological advancement and a traditionally conservative attitude toward change. Limited expertise, financial constraints, and ownership structures further influence the potential for innovation. This study examines the development of innovation among SMEs in Lithuania’s forestry sector and its contribution to sustainable forest management. Forestry innovations are understood as new processes, products, or services introduced by forest owners and managers to improve management efficiency and sustainability. The study employed the method of a structured questionnaire survey to evaluate technological, organisational, and financial aspects of innovation adoption among small and medium-sized enterprises in the forestry sector. Drawing on comparative survey data from 2005 and 2024, the study analyses the types of innovations implemented by forestry enterprises, the factors driving or hindering their adoption, and the evolving trends in innovation application. The results reveal a significant shift toward digitalisation and technology-based management practices, suggesting that Lithuanian forestry enterprises are gradually transitioning toward a more innovation-driven model. These developments appear to be influenced by the EU Green Deal policy framework, evolving innovation support mechanisms, and broader socio-economic changes. Nonetheless, technological transformation introduces new challenges, including the need for workforce upskilling and enhanced adaptability to rapidly changing market conditions. Full article

Forest certification in developing countries faces significant challenges due to weak institutions, limited market incentives, and complex trade conditions. This study investigates the status and key constraints of the Myanmar forest certification sector through a survey of 180 stakeholders from government organizations, NGOs, INGOs, third-party certification bodies, and private plantation owners, complemented by quantitative analysis and qualitative interviews. The results indicate a moderate level of familiarity with the Myanmar forest certification standard and high awareness of the Myanmar Forest Certification Committee; however, progress remains slow due to limited transparency, poor institutional coordination, financial and technical constraints, and insufficient stakeholder involvement. Non-compliances issues identified during pilot audits were primarily related to incomplete documentation, unclear land tenure, and weaknesses in environmental assessment. Geopolitical factors continue to limit Myanmar’s participation in certified timber markets and weaken efforts to improve traceability. Experiences from Indonesia, Malaysia, and Vietnam highlight that developing credible national certification systems requires time, clear legal frameworks, and strong cooperation among stakeholders. Strengthening institutional capacity, improving transparency, and aligning national standards with international forest governance frameworks are essential for Myanmar to build trust, achieve sustainable forest management, and regain market access. Full article

Mountain Excavation and Land Creation Projects (MELCPs) have emerged as a critical strategy for expanding urban development space in mountainous regions facing land scarcity. Dynamic monitoring and risk management of these projects are essential for promoting sustainable urban development. This study develops an integrated monitoring framework for MELCPs by combining ascending and descending Sentinel-1 SAR data, Sentinel-2 optical imagery, SRTM digital elevation models (DEM), and field survey data. The framework incorporates multi-temporal change detection, random forest classification, and time-series InSAR analysis to systematically capture the spatiotemporal evolution and subsidence mechanisms associated with MELCPs. Key findings include: (1) The use of dual-orbit SAR data significantly improves the detection accuracy of excavation areas, achieving an overall accuracy of 87.1% (Kappa = 0.85) and effectively overcoming observation limitations imposed by complex terrain. (2) By optimizing the combination of spectral, texture, topographic, and polarimetric features using a random forest algorithm, the classification accuracy of MELCPs is enhanced to 91.2% (Kappa = 0.889). This enables precise annual identification of MELCP progression from 2017 to 2022, revealing a three-stage evolution pattern: concentrated expansion, peak activity, and restricted slowdown. Specifically, the reclaimed area increased from 2.66 km² (pre-2018) to a peak of 12.61 km² in 2021, accounting for 34.56% of the total area of the study region, before decreasing to 2.69 km² in 2022. (3) InSAR monitoring from 2017 to 2023 indicates that areas with only filling experience minor shallow subsidence (<50 mm), whereas subsequent building loads and underground engineering activities lead to continuous deep soil consolidation, with maximum cumulative subsidence reaching 333.8 mm. This study demonstrates that subsidence in MELCPs follows distinct spatiotemporal patterns and is predictable, offering important theoretical insights and practical tools for engineering safety management and territorial spatial optimization in mountainous cities. Full article

Water is vital for producing summer maize (SM) and winter wheat (WW); therefore, its proper management is crucial for sustainable farming. This study aimed to develop new tri-band spectral vegetation indices that enhance the accuracy of monitoring plant moisture content (PMC) in SM and WW. We conducted irrigation treatments, including W0, W1, W2, W3, and W4, in SM–WW rotations to address this issue. Canopy reflectance was measured with a field spectroradiometer. Tri-band hyperspectral vegetation indices were constructed: Normalised Water Stress Index (NWSI), Normalised Difference Index (NDI), and Exponential Water Stress Index (EWSI), for assessing the PMC of SM and WW. Results indicate that NWSI outperformed other indices. In the maize trials, the correlation reached R = −0.8369, while in wheat, it reached R = −0.9313, surpassing traditional indices. Four mainstream machine learning models (Random Forest, Partial Least Squares Regression, Support Vector Machine, and Artificial Neural Network) were employed for modelling. NWSI-PLSR exhibited the best index-type performance with an R² of 0.7878. When the new indices were combined with traditional indices as input data, the NWSI-Published indices-SVM model achieved superior performance with an R² of 0.8203, outperforming other models. The RF model produced the most consistent performance and achieved the highest average R² across all input types. The NDI-Published indices models also outperformed those of the published indices alone. This indicates that these new indices improve the accuracy of moisture content monitoring in SM and WW fields. It provides a technical basis and support for precision irrigation, holding significant potential for application. Full article

The escalating threat of wildfires under global climate change necessitates rigorous monitoring to mitigate environmental and socio-economic risks. Burned area (BA) mapping is crucial for understanding fire dynamics, assessing ecosystem impacts, and supporting sustainable land management under increasing fire frequency. This study aims to develop a high-resolution detection framework specifically calibrated for Mediterranean environmental conditions, ensuring the production of consistent and accurate annual BA maps. Using Sentinel-2 MSI time series over Sardinia (Italy), the research objectives were to: (i) integrate field surveys with high-resolution photointerpretation to build a robust, locally tuned training dataset; (ii) evaluate the discriminative power of multi-temporal spectral indices; and (iii) implement a Random Forest classifier capable of providing higher spatial precision than current operational products. Validation results show a Dice Coefficient (DC) of 91.8%, significantly outperforming the EFFIS Burnt Area product (DC = 79.9%). The approach proved particularly effective in detecting small and rapidly recovering fires, often underrepresented in existing datasets. While inaccuracies persist due to cloud cover and landscape heterogeneity, this study demonstrates the effectiveness of a machine learning approach for long-term monitoring, for generating multi-year wildfire inventories, offering a vital tool for data-driven forest policy, vegetation recovery assessment and land-use change analysis in fire-prone regions. Full article

Photovoltaic (PV) power generation is essential for achieving carbon neutrality and advancing renewable energy development. In Northwest China, the rapid expansion of PV installations requires accurate and timely spatial data to support effective monitoring and planning. Addressing the limitations of existing datasets in spatiotemporal resolution and driver analysis, this study develops a scalable solar facility inventory framework on the Google Earth Engine (GEE) platform. The framework integrates Sentinel-1 SAR, Sentinel-2 multispectral imagery, and interpretable machine learning. Feature redundancy is first assessed using correlation-based metrics, after which a Random Forest classifier is applied to generate a 10 m resolution distribution map of utility-scale photovoltaic power plants as of December 2023. To elucidate model behavior, SHAP (SHapley Additive exPlanations) is used to identify key predictors, and MaxEnt is incorporated to provide a preliminary quantitative assessment of spatial drivers of PV deployment. The RFECV-optimized model, retaining 44 key features, achieves an overall accuracy of 98.4% and a Kappa coefficient of 0.96. The study region contains approximately 2560 km² of PV installations, with pronounced clusters in northern Ningxia, central Shaanxi, and parts of Xinjiang and Gansu. SHAP analysis highlights the Enhanced Photovoltaic Index (EPVI), the Normalized Difference Built-up Index (NDBI), Sentinel-2 Band 8A, and related texture metrics as primary contributors to model predictions. High EPVI, NDBI, and Sentinel-2 Band 8A values contribute positively to PV classification, whereas vegetation-related indices (e.g., NDVI) exhibit predominantly negative contributions; these results indicate that PV mapping relies on the integrated discrimination of multiple spectral and texture features rather than on a single dominant variable. MaxEnt results indicate that grid accessibility and land-use constraints (e.g., nighttime light intensity reflecting human activity) are dominant drivers of PV clustering, often exerting more influence than solar irradiance alone. This framework provides robust technical support for PV monitoring and offers high-resolution spatial distribution data and driver insights to inform sustainable energy management and regional renewable-energy planning. Full article

As a key component of urban green spaces, which provide sustainability-relevant ecosystem services such as carbon sequestration, soils support plant growth and represents an important carbon pool in urban ecosystems. However, surface soil organic carbon (SSOC) in urban green spaces can be highly heterogeneous due to the combined influences of natural conditions and human activities. To quantify national-scale patterns and major correlates of SSOC in China’s urban green spaces, we compiled published surface (0–20 cm) SSOC observations from 154 field studies and synthesized SSOC density and stocks across 224 Chinese cities, providing a nationally comparable assessment at the city scale. Measurements were harmonized to a consistent depth, and a random forest gap-filling approach was used to extend estimates for data-poor cities. The mean SSOC density and total SSOC stock of urban green spaces were 3.22 kg C m⁻² and 57.87 × 10⁹ kg C, respectively, and SSOC density showed no obvious latitudinal gradient across the 224 cities. Variable importance from the random forest analysis indicated that soil physicochemical properties (e.g., bulk density, total nitrogen, and texture) were the strongest predictors of SSOC density, whereas climatic and topographic variables showed comparatively lower importance. This pattern may suggest that anthropogenic modification and management dampen macro climatic signals such as temperature and precipitation at the national scale. Full article

Maple syrup production has the potential to promote sustainable rural economic development in regions with suitable forest and climate conditions. Kentucky emerges as a promising candidate due to its extensive maple tree inventory and favorable seasonal patterns. However, the broader economy-wide implications of developing a maple syrup industry in the state remain underexplored. To fill this knowledge gap, this study employs a customized static single-region computable general equilibrium (CGE) modeling approach for Kentucky under nine scenarios based on production capacities and potential levels. The results consistently show positive impacts on net household income, social welfare (measured by equivalent variation), government revenues, and state GDP across all scenarios. Medium production capacities generate the most balanced and efficient outcomes, while high-potential scenarios, especially under small and large scales produce the largest absolute gains. These results underscore the viability of maple syrup production as an economic development strategy and highlight the role of production scale in maximizing benefits. Furthermore, expanding maple syrup production can enhance rural livelihoods by diversifying forest-based income and promoting long-term stewardship. As a non-timber forest product, maple syrup tapping provides economic incentives to maintain healthy forests, strengthening rural sustainability and resilience. Our findings indicate that developing this industry beyond traditional regions can generate meaningful economic benefits while encouraging sustainable resource use when appropriately scaled and managed. Full article

Forests are important biodiversity reservoirs and require sustainable management to prevent deforestation and forest degradation. Forest landscape restoration (FLR) has been proposed as a sustainable initiative aimed at restoring ecosystem functions and improving the well-being of surrounding populations. In 2005, the World Wildlife Fund (WWF) initiated a project to protect 200 ha of savanna in Manzonzi landscape, Democratic Republic of Congo, on the outskirts of the Luki Biosphere Reserve. The biodiversity changes related to this ecological restoration project remain unpublished. To address this knowledge gap, floristic inventories of the protected Manzonzi landscape were carried out over a 12-year period and we assessed how changes in the floral composition of this landscape evolved and affected the provision of ecosystem services (ES). We found that protection of the savanna by banning recurring bush fires and fencing off the area promoted the richness and abundance of forest species, such as Xylopia aethiopica (Dunal) A. Rich, Albizia adianthifolia (Schumach.) W. Wight. These forest taxa replaced grassland species, such as Hymenocardia acida Tul. and Maprounea africana Müll. Arg., and served to benefit the local population, who use these forest taxa as food, fuelwood, and medicines. This study revealed that protected savanna improved woody biomass, plant diversity (richness/abundance), and carbon storage, significantly boosting essential ES for communities; yet these positive trends reversed when active monitoring ceased. Protecting savannas improves the environment and benefits communities, but stopping protection efforts can undo these gains, emphasizing the need for ongoing conservation. Full article

Rice yield prediction at high spatial resolution is essential to support precision management and sustainable intensification in irrigated systems. While many remote sensing studies provide yield estimates at the field scale, pixel-level predictions are required to characterize within-field variability. This study assesses the potential of multitemporal Sentinel-2 imagery and machine learning to estimate rice yield at pixel level in the Albufera rice area (Valencia, Spain). Yield data from combine harvester maps were collected for ‘JSendra’ and ‘Bomba’ Japonica varieties over five growing seasons (2020–2024) and linked to 10 m Sentinel-2 bands in the visible, near-infrared (NIR) and short-wave infrared (SWIR) regions. Random Forest (RF) and XGBoost (XGB) models were trained with 2020–2023 data and independently validated in 2024. XGB systematically outperformed RF, achieving at 110 and 130 DAS (days after showing), R² values of 0.74 and 0.85 and RMSE values of 0.63 and 0.28 t·ha⁻¹ for ‘JSendra’ and ‘Bomba’. Prediction accuracy increased as the season progressed, and models using all spectral bands clearly outperformed configurations based only on spectral indices, confirming the dominant contribution of NIR reflectance. Spatial error analysis revealed errors at field edges and headlands, while central pixels were more accurately predicted. Overall, the proposed approach provides accurate, spatially explicit rice yield maps that capture within-field variability and support both end-of-season yield estimation and early season forecasting, enabling the identification of potentially low-yield zones to support targeted management decisions. Full article

Assessing post-fire disturbance in Mediterranean ecosystems is essential for quantifying ecological impacts and guiding restoration strategies. This study evaluates fire severity following an extreme wildfire event (~28,000 ha) in northwestern Spain using vegetation indices (VIs) derived from PRISMA hyperspectral imagery, validated against field-based Composite Burn Index (CBI) measurements at the vegetation, soil, and site levels across three vegetation formations (coniferous forests, broadleaf forests, and shrublands). Hyperspectral VIs were benchmarked against multispectral VIs derived from Sentinel-2. Hyperspectral VIs yielded stronger correlations with CBI values than multispectral VIs. Vegetation-level CBI showed the highest correlations, reflecting the sensitivity of most VIs to canopy structural and compositional changes. Indices incorporating red-edge, near-infrared (NIR), and shortwave infrared (SWIR) bands demonstrated the greatest explanatory power. Among hyperspectral indices, DVIRED, EVI, and especially CAI performed best. For multispectral data, NDRE, CIREDGE, ENDVI, and GNDVI were the most effective. These findings highlight the strong potential of hyperspectral remote sensing for accurate, scalable post-fire severity assessment in heterogeneous Mediterranean ecosystems. Full article

The contemporary agricultural market is profoundly volatile, where agricultural prices are based on a complex supply chain, climatic irregularity or unscheduled market demand. Prices of crops need to be predicted in a reliable and timely manner for farmers, policy-makers and other stakeholders to take evidence-based decisions ultimately for the benefit towards sustainable agriculture and economic sustainability. Objective: The objective of this study is to develop and evaluate a comprehensive machine learning model for predicting agricultural prices incorporating logistic, economic and environmental considerations. It is the desire to make agriculture more profitable by building simple and accurate forecasting models. Methods: An assorted dataset was collected, which covers major factors to constitute the dataset of temperature, rainfall, fertiliser use, pest and disease attack level, cost of transportation, market demand-supply ratio and regional competitiveness. The data was subjected to pre-processing and feature extraction for quality control/quality assurance. Several machine learning models (Linear Regression, Support Vector Machines, AdaBoost, Random Forest, and XGBoost) were trained and evaluated using performance metrics such as R2 score, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Results: Out of the model approaches that were analysed, predictive performance was superior for XGBoost (with an R2 Score of 0.94, RMSE of 12.8 and MAE of 8.6). To generate accurate predictions, the ability to account for complex non-linear relationships between market and environmental information was necessary. Conclusions: The forecast model of the XGBoost-based prediction system is reliable, of low complexity and widely applicable to large-scale real-time forecasting of agricultural monitoring. The model substantially reduces the uncertainty of price forecasting, and does so by including multivariate environmental and economic aspects that permit more profitable management practices in a schedule for future sustainable agriculture. Full article

Accurate modeling of stem taper is essential for forest management decisions, including the definition of cutting cycles, the feasibility of annual harvesting, assortment classification, size and volume estimation, and ensuring sustainable production continuity. This study modeled the stem taper of Araucaria angustifolia (Bertol.) Kuntze stands in southern Brazil using Kozak’s variable-exponent model fitted with nonlinear mixed-effects techniques. Both fixed- and mixed-effects models showed high predictive performance, regardless of calibration. An unstructured (UN) covariance structure was required to reduce autocorrelation. The mixed-effects model improved predictive accuracy by up to 22%, achieved R² values above 0.99 with RMSE < 0.74 cm, and significantly reduced residual autocorrelation in diameter estimates. The most effective calibration of random effects was achieved using diameter measurements taken at heights between 0.3 and 6.3 m above ground (approximately between 1.3% and 28.3% of the total height, considering the tallest tree as a reference). This research improves the accuracy of volume estimation and the definition of timber assortments for A. angustifolia, thereby supporting forest management decision-making in southern Brazil. Full article