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Search Results (128)

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Keywords = national resource inventory

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17 pages, 1142 KiB  
Article
Logistical Challenges in Home Health Care: A Comparative Analysis Between Portugal and Brazil
by William Machado Emiliano, Thalyta Cristina Mansano Schlosser, Vitor Eduardo Molina Júnior, José Telhada and Yuri Alexandre Meyer
Logistics 2025, 9(3), 101; https://doi.org/10.3390/logistics9030101 - 31 Jul 2025
Viewed by 214
Abstract
Background: This study aims to compare the logistical challenges of Home Health Care (HHC) services in Portugal and Brazil, highlighting the structural and operational differences between both systems. Methods: Guided by an abductive research approach, data were collected using a semi-structured [...] Read more.
Background: This study aims to compare the logistical challenges of Home Health Care (HHC) services in Portugal and Brazil, highlighting the structural and operational differences between both systems. Methods: Guided by an abductive research approach, data were collected using a semi-structured survey with open-ended questions, applied to 13 HHC teams in Portugal and 18 in Brazil, selected based on national coordination recommendations. The data collection process was conducted in person, and responses were analyzed using descriptive statistics and qualitative content analysis. Results: The results reveal that Portugal demonstrates higher productivity, stronger territorial coverage, and a more integrated inventory management system, while Brazil presents greater multidisciplinary team integration, more flexible fleet logistics, and more advanced digital health records. Despite these strengths, both countries continue to address key logistical aspects, such as scheduling, supply distribution, and data management, largely through empirical strategies. Conclusions: This research contributes to the theoretical understanding of international HHC logistics by emphasizing strategic and systemic aspects often overlooked in operational studies. In practical terms, it offers insights for public health managers to improve resource allocation, fleet coordination, and digital integration in aging societies. Full article
(This article belongs to the Section Humanitarian and Healthcare Logistics)
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16 pages, 1913 KiB  
Article
Stem Volume Prediction of Chamaecyparis obtusa in South Korea Using Machine Learning and Field-Measured Tree Variables
by Chiung Ko, Jintaek Kang and Donggeun Kim
Forests 2025, 16(8), 1228; https://doi.org/10.3390/f16081228 - 25 Jul 2025
Viewed by 249
Abstract
Accurate estimation of individual tree stem volume is essential for forest resource assessment and the implementation of sustainable forest management. In South Korea, traditional regression models based on non-destructive and easily measurable field variables such as diameter at breast height (DBH) and total [...] Read more.
Accurate estimation of individual tree stem volume is essential for forest resource assessment and the implementation of sustainable forest management. In South Korea, traditional regression models based on non-destructive and easily measurable field variables such as diameter at breast height (DBH) and total height (TH) have been widely used to construct stem volume tables. However, these models often fail to adequately capture the nonlinear taper of tree stems. In this study, we evaluated and compared the predictive performance of traditional regression models and two machine learning algorithms—Random Forest (RF) and Extreme Gradient Boosting (XGBoost)—using stem profile data from 1000 destructively sampled Chamaecyparis obtusa trees collected across 318 sites nationwide. To ensure compatibility with existing national stem volume tables, all models used only DBH and TH as input variables. The results showed that all three models achieved high predictive accuracy (R2 > 0.997), with XGBoost yielding the lowest RMSE (0.0164 m3) and MAE (0.0126 m3). Although differences in performance among the models were marginal, the machine learning approaches demonstrated flexible and generalizable alternatives to conventional models, providing a practical foundation for large-scale forest inventory and the advancement of digital forest management systems. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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24 pages, 2162 KiB  
Article
African Small Mammals (Macroscelidea and Rodentia) Housed at the National Museum of Natural History and Science (University of Lisbon, Portugal)
by Maria da Luz Mathias and Rita I. Monarca
Diversity 2025, 17(7), 485; https://doi.org/10.3390/d17070485 - 15 Jul 2025
Viewed by 212
Abstract
The National Museum of Natural History and Science holds a historical collection of 279 small African mammal specimens (Macroscelidea and Rodentia), representing 32 species, gathered during the Portuguese colonial period in Mozambique, Angola, and Guinea-Bissau. This study examines the collection, updates the small [...] Read more.
The National Museum of Natural History and Science holds a historical collection of 279 small African mammal specimens (Macroscelidea and Rodentia), representing 32 species, gathered during the Portuguese colonial period in Mozambique, Angola, and Guinea-Bissau. This study examines the collection, updates the small mammal species lists for each country, and highlights its importance as a historical baseline for biodiversity research. Rodents dominate the collection, reflecting their natural abundance and diversity, while Macroscelidea are less represented. The Angolan subset of the collection has the highest number of both specimens and species represented. Mozambique is underrepresented, and the Guinea-Bissau subset offers an extensive rodent representation of the country’s inventory. The most well-represented species are Gerbilliscus leucogaster, Lemniscomys striatus, Lemniscomys griselda (from Angola), and Heliosciurus gambianus (from Guinea-Bissau). Notably, the collection includes the neo-paratype of Dasymys nudipes (from Angola). Most species are common and not currently threatened, with geographic origin corresponding to savanna and forest habitats. These findings underscore the importance of integrating historical data and current biodiversity assessments to support multidisciplinary studies on target species, regions, or countries. In this context, the collection remains a valuable key resource for advanced research on African small mammals. Full article
(This article belongs to the Section Animal Diversity)
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18 pages, 3951 KiB  
Article
Spatiotemporal Dynamics and Driving Factors of Arbor Forest Carbon Stocks in Yunnan Province, China (2016–2020)
by Jinxia Wu, Yue Chen, Wei Yang, Hongtian Leng, Qingzhong Wen, Minmin Li, Yunrong Huang and Jingfei Wan
Forests 2025, 16(7), 1076; https://doi.org/10.3390/f16071076 - 27 Jun 2025
Viewed by 439
Abstract
In the context of accelerating global climate change, the accurate quantification of forest carbon sequestration at the regional scale is of critical importance to estimate carbon budgets and formulate targeted ecological policies. This study systematically investigated the spatiotemporal dynamics and driving mechanisms of [...] Read more.
In the context of accelerating global climate change, the accurate quantification of forest carbon sequestration at the regional scale is of critical importance to estimate carbon budgets and formulate targeted ecological policies. This study systematically investigated the spatiotemporal dynamics and driving mechanisms of arbor forest carbon stocks between 2016 and 2020 in Yunnan Province, China. Based on the “One Map” forest resource inventory, the continuous biomass expansion factor (CBEF) method, standard deviational ellipse (SDE) analysis, and multiple linear regression (MLR) modeling, the results showed the following. (1) Arbor forest carbon stocks steadily increased from 832.13 Mt to 938.84 Mt, and carbon density increased from 41.92 to 42.32 t C·hm−2. Carbon stocks displayed a dual high pattern in the northwest and southwest, with lower values in the central and eastern regions. (2) The spatial centroid of carbon stocks shifted 4.8 km eastward, driven primarily by afforestation efforts in central and eastern Yunnan. (3) The MLR results revealed that precipitation and economic development were significant positive drivers, whereas temperature, elevation, and anthropogenic disturbances were major limiting factors. A negative correlation to afforestation area indicated a diminished need for new plantations as forest quality and quantity improved. These results provided a theoretical foundation for spatially differentiated carbon sequestration strategies in Yunnan, providing key insights for reinforcing ecological security in Southwest China and enhancing national carbon neutrality objectives. Full article
(This article belongs to the Special Issue Forest Inventory: The Monitoring of Biomass and Carbon Stocks)
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18 pages, 11621 KiB  
Article
Accuracy of Vegetation Height and Terrain Elevation Derived from Terrestrial Ecosystem Carbon Inventory Satellite in Forested Areas
by Zhao Chen, Sijie He and Anmin Fu
Appl. Sci. 2025, 15(12), 6824; https://doi.org/10.3390/app15126824 - 17 Jun 2025
Viewed by 323
Abstract
Forest ecosystems serve as pivotal components of the global carbon cycle, with canopy height representing a critical biophysical parameter for quantifying ecosystem functionality, thereby holding substantial implications for forest resource management and carbon sequestration assessments. The precise extraction of ground elevation and vegetation [...] Read more.
Forest ecosystems serve as pivotal components of the global carbon cycle, with canopy height representing a critical biophysical parameter for quantifying ecosystem functionality, thereby holding substantial implications for forest resource management and carbon sequestration assessments. The precise extraction of ground elevation and vegetation canopy height is essential for advancing topographic and ecological research. The Terrestrial Ecosystem Carbon Inventory Satellite (referred to as TECIS hereafter) offers unprecedented capabilities for the large-scale, high-precision extraction of ground elevation and vegetation canopy height. Using the Northeast China Tiger and Leopard National Park as our study area, we first processed TECIS data to derive topographic and canopy height profiles. Subsequently, the accuracy of TECIS-derived ground and canopy height estimates was validated using onboard light detection and ranging (LiDAR) measurements. Finally, we systematically evaluated the influence of multiple factors on estimation accuracy. Our analysis revealed that TECIS-derived ground and canopy height estimates exhibited mean errors of 0.7 m and −0.35 m, respectively, with corresponding root mean square error (RMSE) values of 3.83 m and 2.70 m. Furthermore, slope gradient, vegetation coverage, and forest composition emerged as the dominant factors influencing canopy height estimation accuracy. These findings provide a scientific basis for optimizing the screening and application of TECIS data in global forest carbon monitoring. Full article
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21 pages, 10337 KiB  
Article
Study on Forest Growing Stock Volume in Kunming City Considering the Relationship Between Stand Density and Allometry
by Jing Zhang, Cheng Wang, Jinliang Wang, Xiang Huang, Zilin Zhou, Zetong Zhou and Feng Cheng
Forests 2025, 16(6), 891; https://doi.org/10.3390/f16060891 - 25 May 2025
Viewed by 510
Abstract
Forest growing stock volume (GSV) is a fundamental indicator for assessing the status of forest resources. It reflects forest carbon storage levels and serves as a key metric for evaluating the carbon sequestration capacity of forest ecosystems, thereby playing a crucial role in [...] Read more.
Forest growing stock volume (GSV) is a fundamental indicator for assessing the status of forest resources. It reflects forest carbon storage levels and serves as a key metric for evaluating the carbon sequestration capacity of forest ecosystems, thereby playing a crucial role in supporting national “dual-carbon” objectives. Traditional allometric models typically estimate GSV using tree species, diameter at breast height (DBH), and canopy height. However, at larger spatial scales, these models often neglect stand density, resulting in substantial estimation errors in regions characterized by significant density variability. To enhance the accuracy of large-scale GSV estimation, this study incorporates high-resolution, spatially continuous forest structural parameters—including dominant tree species, stand density, canopy height, and DBH—extracted through the synergistic utilization of active (e.g., Sentinel-1 SAR, ICESat-2 photon data) and passive (e.g., Landsat-8 OLI, Sentinel-2 MSI) multi-source remote sensing data. Within an allometric modeling framework, stand density is introduced as an additional explanatory variable. Subsequently, GSV is modeled in a stratified manner according to tree species across distinct ecological zones within Kunming City. The results indicate that: (1) the total estimated GSV of Kunming City in 2020, based on remote sensing imagery and second-class forest inventory data collected in the same year, was 1.01 × 108 m3, which closely aligns with contemporaneous statistical records. The model yielded an R2 of 0.727, an RMSE of 537.566 m3, and a MAE of 239.767 m3, indicating a high level of overall accuracy when validated against official ground-based inventory plots organized by provincial and municipal forestry authorities; (2) the incorporation of the dynamic stand density parameter significantly improved model performance, which elevated R2 from 0.565 to 0.727 and significantly reduced RMSE. This result confirms that stand density is a critical explanatory factor; and (3) GSV exhibited pronounced spatial heterogeneity across both tree species and administrative regions, underscoring the spatial structural variability of forests within the study area. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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30 pages, 19867 KiB  
Article
Geomorphological Analysis and Heritage Value of Dobreștilor–Brusturet Cave: A Significant Geomorphosite in the Bran–Dragoslavele Corridor, Romania
by Septimius Trif, Ștefan Bilașco, Roșca Sanda, Fodorean Ioan, Iuliu Vescan, András-István Barta and Raboșapca Irina
Heritage 2025, 8(5), 183; https://doi.org/10.3390/heritage8050183 - 21 May 2025
Viewed by 686
Abstract
This study examines the morphology and development of Dobreștilor–Brusturet Cave, located in the Brusturet gorge at the western edge of the Bran–Dragoslavele Corridor, an important tourist route in the Romanian Carpathians. The research aims to analyze the geomorphological characteristics and establish the heritage [...] Read more.
This study examines the morphology and development of Dobreștilor–Brusturet Cave, located in the Brusturet gorge at the western edge of the Bran–Dragoslavele Corridor, an important tourist route in the Romanian Carpathians. The research aims to analyze the geomorphological characteristics and establish the heritage value of the Dobreştilor Cave geomorphosite, supporting protection efforts for invertebrate species that led to the cave’s designation as a natural monument. The inventory of physical features prompted the Piatra Craiului National Park Scientific Council to consider including this speleological site in a thematic geotourism circuit called “The Road of Gorges and Caves in the Upper Basin of the Dâmbovițean”, integrated within protected areas. This represents the first geomorphological study of the cave. Given its ecological significance within the national park’s strict protection zone, recreational tourism is prohibited. The cave should only be used as a geotourism resource for scientific research and education. Morphogenetic analysis reveals that the cave has evolved in a vadose hydrological regime since the Pleistocene, with cavity expansion influenced by free-flowing water alternating with that under pressure during torrential episodes, concomitant with the precipitation of calcium carbonate that formed various speleothems. This research supports documentation for promotional materials and could assist local authorities in the Dâmbovicioara commune with geotourism development decisions, potentially integrating the site into a proposed “Moieciu–Fundata–Dâmbovicioara–Rucăr Geological and Geomorphological Complex”. Full article
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15 pages, 887 KiB  
Article
Decarbonizing the Construction Sector: Strategies and Pathways for Greenhouse Gas Emissions Reduction
by Charikleia Karakosta and Jason Papathanasiou
Energies 2025, 18(5), 1285; https://doi.org/10.3390/en18051285 - 6 Mar 2025
Cited by 2 | Viewed by 1610
Abstract
The construction sector is a significant contributor to global greenhouse gas (GHG) emissions, necessitating urgent decarbonization efforts to align with international climate goals such as the Paris Agreement and the European Green Deal. This study explores a comprehensive framework for construction companies to [...] Read more.
The construction sector is a significant contributor to global greenhouse gas (GHG) emissions, necessitating urgent decarbonization efforts to align with international climate goals such as the Paris Agreement and the European Green Deal. This study explores a comprehensive framework for construction companies to map and reduce their GHG emissions through a structured four-step approach: defining emission scopes, conducting GHG inventories, setting reduction targets, and planning actionable reductions. Four key pathways are proposed: electricity decarbonization through renewable energy adoption and energy efficiency measures; direct emissions reduction via fleet electrification and infrastructure optimization; recycling and resource efficiency improvements through waste diversion and material reuse; and supply chain emissions reduction by enforcing sustainability standards and responsible sourcing practices. The analysis highlights the importance of integrating technological, organizational, and policy-driven solutions, such as rooftop photovoltaic systems, virtual power purchase agreements, waste management strategies, and supplier codes of conduct aligned with global sustainability benchmarks. The study concludes that construction companies can achieve significant emission reductions by adopting a structured, multi-pathway approach; emphasizing progress over perfection; and aligning their strategies with national and international climate targets. This research provides actionable insights for the construction sector to transition toward a net-zero future by 2050. Full article
(This article belongs to the Section G: Energy and Buildings)
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16 pages, 14757 KiB  
Article
Effects of Choosing Different Parameterization Data in Two-Phase Forest Inventories for Standing Stock Estimation
by Ambros Berger and Thomas Gschwantner
Forests 2025, 16(2), 259; https://doi.org/10.3390/f16020259 - 30 Jan 2025
Viewed by 785
Abstract
The demands on national forest inventories to provide detailed information for small geographical regions are rising. Two-phase estimators are often employed to obtain forest resource estimates, yet there is little information on optimal training data selection. This study evaluates the impact of different [...] Read more.
The demands on national forest inventories to provide detailed information for small geographical regions are rising. Two-phase estimators are often employed to obtain forest resource estimates, yet there is little information on optimal training data selection. This study evaluates the impact of different training data on two-phase estimators, with a focus on small area estimators for standing stock and aims to develop guidelines on selecting appropriate training datasets. Linear regression models were parameterized using multiple datasets and subsets based on ecological and administrative boundaries. The models were then applied on varying scales, and their estimates and their confidence intervals were compared to each other as well as to the single-phase, purely terrestrial forest inventory. Results suggest that the different two-phase models generally yield comparable estimates but differ notably from single-phase estimates. Specifically, differences increase in smaller areas and with correspondingly smaller training datasets, suggesting a minimum of 100 data points. To ensure robust estimates, we recommend adapting training sets to local conditions and exercising caution with small training datasets and areas because implausible results may occur. Pooling appropriate datasets is the preferable solution. Full article
(This article belongs to the Special Issue Modeling of Biomass Estimation and Stand Parameters in Forests)
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12 pages, 4957 KiB  
Technical Note
National Exposed Sediment Search and Inventory (NESSI): Utilizing Satellite Imagery and Machine Learning to Identify Dredged Sediment Placement Site Recovery
by Thomas P. Huff, Emily R. Russ and Todd M. Swannack
Remote Sens. 2025, 17(2), 186; https://doi.org/10.3390/rs17020186 - 7 Jan 2025
Viewed by 758
Abstract
Anthropogenic activity leads to changes in sediment dynamics, creating imbalances in sediment distributions across the landscape. These imbalances can be variable within a littoral system, with adjacent areas experiencing sediment starvation and excess sediment. Historically, sediments were viewed as an inconvenient biproduct destined [...] Read more.
Anthropogenic activity leads to changes in sediment dynamics, creating imbalances in sediment distributions across the landscape. These imbalances can be variable within a littoral system, with adjacent areas experiencing sediment starvation and excess sediment. Historically, sediments were viewed as an inconvenient biproduct destined for disposal; however, beneficial use of dredge material (BUDM) is a practice that has grown as a preferred methodology for utilizing sediment as a resource to help alleviate the sediment imbalances within a system. BUDM enables organizations to adopt a more innovative and sustainable sediment management approach that also provides ecological, economic, and social co-benefits. Although location data are available on BUDM sites, especially in the US, there is limited understanding on how these sites evolve within the larger landscape, which is necessary for quantifying the co-benefits. To move towards BUDM more broadly, new tools need to be developed to allow researchers and managers to understand the effects and benefits of this practice. The National Exposed Sediment Search and Inventory (NESSI) was built to show the capability of using machine learning techniques to identify dredged sediments. A combination of satellite imagery data obtained and processed using Google Earth Engine and machine learning algorithms were applied at known dredged material placement sites to develop a time series of dredged material placement events and subsequent site recovery. These disturbance-to-recovery time series are then used in a landscape analysis application to better understand site evolution within the context of the surrounding areas. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Vegetation Monitoring)
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28 pages, 6086 KiB  
Article
“Where the Moose Were”: Fort William First Nation’s Ancestral Land, Two–Eyed Seeing, and Industrial Impacts
by Keshab Thapa, Melanie Laforest, Catherine Banning and Shirley Thompson
Land 2024, 13(12), 2029; https://doi.org/10.3390/land13122029 - 27 Nov 2024
Viewed by 1804
Abstract
A two-eyed seeing approach considered Indigenous knowledge and Western science towards eco–health, reconciliation and land back with Fort William First Nation (FWFN) in Ontario, Canada. To map traditional land use, occupancy, and ecological knowledge, we interviewed 49 FWFN members about their hunting, fishing, [...] Read more.
A two-eyed seeing approach considered Indigenous knowledge and Western science towards eco–health, reconciliation and land back with Fort William First Nation (FWFN) in Ontario, Canada. To map traditional land use, occupancy, and ecological knowledge, we interviewed 49 FWFN members about their hunting, fishing, trapping, plant harvesting, cultural sites, and sacred gatherings on their ancestral land. Their traditional land use and occupancy includes more than 7.5 million ha of their ancestral land. The FWFN members reported many industrial impacts on their reserve and ancestral land. We analyzed the normalized difference vegetation index (NDVI) change over time on FWFN’s ancestral land and the Thunder Bay Pulp and Paper Mill (TBPP)’s National Pollutant Release Inventory data to investigate the FWFN members’ ecohealth concerns. The NDVI analysis revealed large tracts of degraded FWFN’s ancestral land due to logging areas, mining claims, settlements, and paper mills. Mining claims and greenstone belts occupy a quarter of the FWFN members’ ancestral land. The TBPP mill dumped pollution into the Kaministiquia River upstream and upwind of the FWFN community, exposing FWFN members to kilotons of cancerous and other toxic chemicals each year for over a century. Resource extraction and pollution in Northwestern Ontario negatively impacted the human health and ecosystem integrity of FWFN, requiring reconciliation by restoring damaged land and preventing pollution as the starting point for land back. The first step to land back is ending the environmental racism of the TBPP’s pollution directed downstream and downwind of FWFN and protecting ancestral land against logging, mining, and other extractive industries. Full article
(This article belongs to the Special Issue Ecological Restoration and Reusing Brownfield Sites)
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17 pages, 2835 KiB  
Article
A Study on the Growth Model of Natural Forests in Southern China Under Climate Change: Application of Transition Matrix Model
by Xiangjiang Meng, Zhengrui Ma, Ying Xia, Jinghui Meng, Yuhan Bai and Yuan Gao
Forests 2024, 15(11), 1947; https://doi.org/10.3390/f15111947 - 5 Nov 2024
Cited by 2 | Viewed by 1119
Abstract
This study establishes a climate-sensitive transition matrix growth model and predicts forest growth under different carbon emission scenarios (representative concentration pathways RCP2.6, RCP4.5, and RCP8.5) over the next 40 years. Data from the Eighth (2013) and Ninth (2019) National Forest Resource Inventories in [...] Read more.
This study establishes a climate-sensitive transition matrix growth model and predicts forest growth under different carbon emission scenarios (representative concentration pathways RCP2.6, RCP4.5, and RCP8.5) over the next 40 years. Data from the Eighth (2013) and Ninth (2019) National Forest Resource Inventories in Chongqing and climate data from Climate AP are utilized. The model is used to predict forest growth and compare the number of trees, basal area, and stock volume under different climate scenarios. The results show that the climate-sensitive transition matrix growth model has high accuracy. The relationships between the variables and forest growth, mortality, and recruitment correspond to natural succession and growth. Although the number of trees, basal area, and stock volume do not differ significantly for different climate scenarios, the forest has sufficient seedling regeneration and large-diameter trees. The growth process aligns with succession, with pioneer species being replaced by climax species. The proposed climate-sensitive transition matrix growth model fills the gap in growth models for natural secondary forests in Chongqing and is an accurate method for predicting forest growth. The model can be used for long-term prediction of forest stands to understand future forest growth trends and provide reliable references for forest management. Forest growth can be predicted for different harvesting intensities to determine the optimal intensity to guide natural forest management in Chongqing City. The results of this study can help formulate targeted forest management policies to deal more effectively with climate change and promote sustainable forest health. Full article
(This article belongs to the Special Issue Estimation and Monitoring of Forest Biomass and Fuel Load Components)
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25 pages, 71521 KiB  
Article
Contributions to the Morphogenesis, Inventory, and Valorization of a Unique Speleological Geomorphosite from Miresii Cave—The Large Key of Dâmbovița, the Corridor Bran—Dragoslave (Romania)
by Ștefan Bilașco, Septimius Trif, Dănuț Petrea, Pompei Cocean, Fodorean Ioan, Roșca Sanda and Iuliu Vescan
Heritage 2024, 7(10), 5814-5838; https://doi.org/10.3390/heritage7100274 - 17 Oct 2024
Cited by 2 | Viewed by 1712
Abstract
The present study contributes to the morphogenesis of the Miresii Cave, located in Cheia Mare of Dâmbovița in the area of the Bran—Dragoslavele Corridor, an important tourist axis in Romania. The main aim of the research is the proposal to the Scientific Council [...] Read more.
The present study contributes to the morphogenesis of the Miresii Cave, located in Cheia Mare of Dâmbovița in the area of the Bran—Dragoslavele Corridor, an important tourist axis in Romania. The main aim of the research is the proposal to the Scientific Council of Piatra Craiului National Park to bring to the attention of the national decision-making commissions that the cave be declared a natural monument. The inventory of this speleological geomorphosite suggested its inclusion in a thematic geotouristic circuit integrated into national and EU-protected natural areas. The novelty of the present research lies in the fact that the cave has never been studied before, being difficult to access. This cave, spatially found in the strict protection zone of the national park, is not included in its management plan. When being integrated into other nature protection areas, it is necessary to exclude any form of recreational tourism so that the cave can be exploited as a geotouristic resource strictly for research and educational purposes. The morphogenetic analysis of the cave, based on the information synthesized from geomorphological and geological literature, allowed us to decipher the morphological individualization of Miresii Cave in the local and regional geocronomorphological context, in accordance with the chronological separation of the karstification phenomenon manifested first in phreatic and later in vadose karst. The diversified typology of speleothems has been rendered according to the geomorphologic generating processes. The identification of the Rhinolophus ferrumequinum chiropteran colony and observations of its biotope highlighted the ecological significance of the cave. The inventory of the individuals of the colony led to the conclusion that the cave harbors one of the first two largest bat communities of this species in the national park and the adjacent depressional corridor. The present study may allow the documentation of the photographs and description of the geomorphosite integrated into the proposed thematic circuit to be included on billboards and in promotional brochures. Thus, it could also be useful for the decision-making authorities of Rucar and Podu Dâmboviței villages, which are interested in making decisions related to the promotion of geotourism, especially due to the existence of numerous geological and geomorphological tourist resources in the administrative territories. Full article
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19 pages, 5207 KiB  
Article
Enhancing the Precision of Forest Growing Stock Volume in the Estonian National Forest Inventory with Different Predictive Techniques and Remote Sensing Data
by Temitope Olaoluwa Omoniyi and Allan Sims
Remote Sens. 2024, 16(20), 3794; https://doi.org/10.3390/rs16203794 - 12 Oct 2024
Cited by 2 | Viewed by 1667
Abstract
Estimating forest growing stock volume (GSV) is crucial for forest growth and resource management, as it reflects forest productivity. National measurements are laborious and costly; however, integrating satellite data such as optical, Synthetic Aperture Radar (SAR), and airborne laser scanning (ALS) with National [...] Read more.
Estimating forest growing stock volume (GSV) is crucial for forest growth and resource management, as it reflects forest productivity. National measurements are laborious and costly; however, integrating satellite data such as optical, Synthetic Aperture Radar (SAR), and airborne laser scanning (ALS) with National Forest Inventory (NFI) data and machine learning (ML) methods has transformed forest management. In this study, random forest (RF), support vector regression (SVR), and Extreme Gradient Boosting (XGBoost) were used to predict GSV using Estonian NFI data, Sentinel-2 imagery, and ALS point cloud data. Four variable combinations were tested: CO1 (vegetation indices and LiDAR), CO2 (vegetation indices and individual band reflectance), CO3 (LiDAR and individual band reflectance), and CO4 (a combination of vegetation indices, individual band reflectance, and LiDAR). Across Estonia’s geographical regions, RF consistently delivered the best performance. In the northwest (NW), the RF model achieved the best performance with the CO3 combination, having an R2 of 0.63 and an RMSE of 125.39 m3/plot. In the southwest (SW), the RF model also performed exceptionally well, achieving an R2 of 0.73 and an RMSE of 128.86 m3/plot with the CO4 variable combination. In the northeast (NE), the RF model outperformed other ML models, achieving an R2 of 0.64 and an RMSE of 133.77 m3/plot under the CO4 combination. Finally, in the southeast (SE) region, the best performance was achieved with the CO4 combination, yielding an R2 of 0.70 and an RMSE of 21,120.72 m3/plot. These results underscore RF’s precision in predicting GSV across diverse environments, though refining variable selection and improving tree species data could further enhance accuracy. Full article
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19 pages, 13020 KiB  
Article
Time-Varying Evolution and Impact Analysis of Forest Tourism Carbon Emissions and Forest Park Carbon Sinks in China
by Liguo Wang, Haoxiang Zhao, Wenna Wu, Wei Song, Qishan Zhou and Yanting Ye
Forests 2024, 15(9), 1517; https://doi.org/10.3390/f15091517 - 29 Aug 2024
Cited by 4 | Viewed by 1034
Abstract
Forests are an important part of natural resources and play an important role in carbon sinks. We measured carbon sinks in provincial forest parks using data from four forest inventory surveys in China and the forest stock expansion method. Carbon emissions from forest [...] Read more.
Forests are an important part of natural resources and play an important role in carbon sinks. We measured carbon sinks in provincial forest parks using data from four forest inventory surveys in China and the forest stock expansion method. Carbon emissions from forest tourism were also estimated using energy statistics and forest park tourism data. On this basis, spatial analysis was used to summarize the spatial and temporal evolution of the carbon balance and the analysis of influencing factors. The results show the following: (1) With the passage of time, the carbon emissions from forest tourism in all provinces have increased to different degrees, and the national forest tourism carbon emissions have increased from 1,071,390.231 (million tons) in 2003 to 286,255,829.7 (million tons) in 2018; spatially, the distribution of carbon emissions from forest tourism is uneven, with an overall high in the south and low in the north, and a high in the east and a low in the west. (2) The carbon sink of forest parks showed a trend of gradual growth and spatially formed a spatial pattern of high in the northeast and low in the southwest, which is consistent with the distribution of forest resources in China. (3) For forest tourism carbon emissions, the total number of tourists, tourism income, and playing roads are significant influencing factors, and the baseline regression coefficients are 0.595, 0.433, and 0.799, respectively, while for forest park carbon sinks, the number of forest park employees can play a certain positive role in carbon sinks, with the regression coefficient being 1.533. Full article
(This article belongs to the Special Issue Forest Recreation and Ecotourism)
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