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15 pages, 7118 KiB  
Technical Note
Reconstruction of Sea Surface Chlorophyll-a Concentration in the Bohai and Yellow Seas Using LSTM Neural Network
by Qing Xu, Guiying Yang, Xiaobin Yin and Tong Sun
Remote Sens. 2025, 17(1), 174; https://doi.org/10.3390/rs17010174 - 6 Jan 2025
Viewed by 209
Abstract
In order to improve the spatiotemporal coverage of satellite Chlorophyll-a (Chl-a) concentration products in marginal seas, a physically constrained deep learning model was established in this work to reconstruct sea surface Chl-a concentration in the Bohai and Yellow Seas using a Long Short-Term [...] Read more.
In order to improve the spatiotemporal coverage of satellite Chlorophyll-a (Chl-a) concentration products in marginal seas, a physically constrained deep learning model was established in this work to reconstruct sea surface Chl-a concentration in the Bohai and Yellow Seas using a Long Short-Term Memory (LSTM) neural network. Adopting the permutation feature importance method, time sequences of several geographical and physical variables, including longitude, latitude, time, sea surface temperature, salinity, sea level anomaly, wind field, etc., were selected and integrated to the reconstruction model as input parameters. Performance inter-comparisons between LSTM and other machine learning or deep learning models was conducted based on OC-CCI (Ocean Color Climate Change Initiative) Chl-a product. Compared with Gated Recurrent Unit, Random Forest, XGBoost, and Extra Trees models, the LSTM model exhibits the highest accuracy. The average unbiased percentage difference (UPD) of reconstructed Chl-a concentration is 11.7%, which is 2.9%, 7.6%, 10.6%, and 10.5% smaller than that of the other four models, respectively. Over the majority of the study area, the root mean square error is less than 0.05 mg/m3 and the UPD is below 10%, indicating that the LSTM model has considerable potential in accurately reconstructing sea surface Chl-a concentrations in shallow waters. Full article
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28 pages, 3246 KiB  
Article
Comparative Analysis of Carbon Density Simulation Methods in Grassland Ecosystems: A Case Study from Gansu Province, China
by Luyao Wu, Jiaqiang Du, Xinying Liu, Lijuan Li, Xiaoqian Zhu, Xiya Chen, Yue Gong and Yushuo Li
Remote Sens. 2025, 17(1), 172; https://doi.org/10.3390/rs17010172 - 6 Jan 2025
Viewed by 195
Abstract
An accurate assessment of grassland carbon stocks is essential for understanding their role in China’s terrestrial carbon cycle. At regional scales, combining remote sensing technology with carbon density has become a common approach. However, substantial variability among remote sensing inversion models, particularly in [...] Read more.
An accurate assessment of grassland carbon stocks is essential for understanding their role in China’s terrestrial carbon cycle. At regional scales, combining remote sensing technology with carbon density has become a common approach. However, substantial variability among remote sensing inversion models, particularly in theoretical foundations, variable selection, and algorithmic implementation, introduces significant uncertainty into estimating grassland carbon density. This study focuses on the grassland ecosystems in Gansu Province, China, employing both an overall approach (without distinguishing between grassland types) and a stratified approach, classifying the grassland into seven distinct types: alpine meadow steppe, temperate steppe, lowland meadow, alpine meadow, mountain meadow, shrubby grassland, and temperate desert. Using remote sensing, topography, climate, and 490 measured sample data points, this study employs five representative inversion models from three model categories: parametric (single-factor model and stepwise multivariate linear regression), spatial (geographically weighted regression (GWR) and multi-scale geographically weighted regression (MGWR)), and non-parametric (random forest (RF)). Inversion models were constructed for four components of the grassland ecosystem: aboveground (AGBC) and belowground biomass carbon density (BGBC), dead organic matter carbon density (DOMC), and soil organic carbon density (SOC). The applicability of each model was then systematically compared and analyzed. The main conclusions are as follows: (1) The overall estimation results demonstrate that the GWR model is the optimal choice for inverting AGBC, DOMC, and SOC, with coefficients of determination (R2) of 0.67, 0.60, and 0.92, respectively. In contrast, the MGWR model is best suited for BGBC, with an R2 value of 0.73. (2) The stratified estimation results suggest that the optimal inversion models for AGBC and BGBC are predominantly the MGWR and RF models selected through the recursive feature elimination algorithm. For DOMC, the optimal model is a spatial model, while SOC is most accurately estimated using the GWR and RF models selected via the Boruta algorithm. (3) When comparing the inversion results of the optimal overall and stratified approaches, the stratified estimations of AGBC, BGBC, and DOMC (R2 = 0.80, 0.78, and 0.73, respectively) outperformed those of the overall approach. In contrast, the SOC estimates followed an opposite trend, with the overall approach yielding a higher R2 value of 0.92. (4) Generally, variable selection significantly enhanced model accuracy, with spatial and non-parametric models demonstrating superior precision and stability in estimating the four carbon density components of grassland. These findings provide methodological guidance for converting sample point carbon density data into regional-scale estimates of grassland carbon storage. Full article
30 pages, 9893 KiB  
Article
Impacts of Land Use on Soil Erosion: RUSLE Analysis in a Sub-Basin of the Peruvian Amazon (2016–2022)
by Moises Ascencio-Sanchez, Cesar Padilla-Castro, Christian Riveros-Lizana, Rosa María Hermoza-Espezúa, Dayan Atalluz-Ganoza and Richard Solórzano-Acosta
Geosciences 2025, 15(1), 15; https://doi.org/10.3390/geosciences15010015 - 6 Jan 2025
Viewed by 275
Abstract
The Peruvian Amazon faces an increasing threat of soil erosion, driven by unsustainable agricultural practices and accelerated deforestation. In Neshuya (Ucayali region), agricultural activity has intensified since 2014, but the effect on soil erosion is unknown. The present study aimed to evaluate the [...] Read more.
The Peruvian Amazon faces an increasing threat of soil erosion, driven by unsustainable agricultural practices and accelerated deforestation. In Neshuya (Ucayali region), agricultural activity has intensified since 2014, but the effect on soil erosion is unknown. The present study aimed to evaluate the increase in erosion levels, at a sub-basin of the central–eastern Amazon of Peru, in a Geographic Information System (GIS) environment. The revised universal soil loss equation (RUSLE) model was used for assessing the effect of vegetation cover change from 2016 to 2022. In the Neshuya sub-basin (973.4 km2), the average erosion increased from 3.87 to 4.55 t ha−1 year−1, on average. In addition, there is great spatial variability in the values. In addition, 7.65% of the study area (74.52 km2) exceeds the soil loss tolerance limit (15 t ha−1 year−1). The deforestation rate was 17.99 km2 year−1 and by 2022 the forested area reached 237.65 km2. In conclusion, the transition from forest to farmland was related to the most critical erosion values. Unsustainable soil management practices can be the underlying explanation of changes in soil chemical and physical properties. Also, social dynamic changes and differences in landscape patterns play a role. Full article
(This article belongs to the Topic Basin Analysis and Modelling)
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14 pages, 2492 KiB  
Article
Molecular Detection of Acetobacter aceti and Acetobacter pasteurianus at Different Stages of Wine Production
by Irina Mitina, Cristina Grajdieru, Rodica Sturza, Valentin Mitin, Silvia Rubtov, Anatol Balanuta, Emilia Behta, Angela Deaghileva, Fatih Inci, Nedim Hacıosmanoğlu and Dan Zgardan
Foods 2025, 14(1), 132; https://doi.org/10.3390/foods14010132 - 5 Jan 2025
Viewed by 425
Abstract
Acetobacter aceti and Acetobacter pasteurianus belong to acetic acid bacteria (AAB), associated with wine spoilage. The timely detection of AAB, thought essential for their control, is however challenging due to the difficulties of their isolation. Thus, it would be advantageous to detect them [...] Read more.
Acetobacter aceti and Acetobacter pasteurianus belong to acetic acid bacteria (AAB), associated with wine spoilage. The timely detection of AAB, thought essential for their control, is however challenging due to the difficulties of their isolation. Thus, it would be advantageous to detect them using molecular methods at all stages of winemaking and storage. In this paper, we analyzed wines, musts and grapes of 13 varieties grown in different regions with Protected Geographical Indication of the Republic of Moldova for the presence of AAB, Acetobacter aceti and Acetobacter pasteurianus by real-time PCR and measured wine volatile acidity. Overall, the AAB content in the mature wine explained 33.7% of the variance in the volatile acidity of the mature wine, while the A. pasteurianus content in the mature wine alone explained 59.6% of the variability in the volatile acidity in the wine, and its content in the grapes, must and wine explained about 70% of the variance in the the volatile acidity. This makes A. pasteurianus a good candidate to be a potential predictor of wine volatile acidity. Full article
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21 pages, 8679 KiB  
Article
First Results of a Geometric Morphometric Analysis of the Leaf Size and Shape Variation in Quercus petraea Across a Wide European Area
by Paola Fortini, Elisa Proietti, Srdjan Stojnic, Piera Di Marzio, Filippos A. Aravanopoulos, Raquel Benavides, Anna Loy and Romeo Di Pietro
Forests 2025, 16(1), 70; https://doi.org/10.3390/f16010070 - 4 Jan 2025
Viewed by 358
Abstract
The high leaf morphological variability of European white oaks is largely documented in the botanical literature, and several papers have been published in the last two decades focusing on inter- and intraspecific leaf phenotypic plasticity. Studies involving landmark-based geometric morphometrics proved to be [...] Read more.
The high leaf morphological variability of European white oaks is largely documented in the botanical literature, and several papers have been published in the last two decades focusing on inter- and intraspecific leaf phenotypic plasticity. Studies involving landmark-based geometric morphometrics proved to be useful in highlighting relationships between leaf size and shape variation and environmental factors, phylogenetic patterns, or hybridization events. In this paper, the leaf size and shape variations of 18 populations of Quercus petraea distributed throughout a wide geographical area were analyzed by means of geometric morphometric methods (GMMs). This study involved 10 European countries and investigated the intraspecific leaf variability of Q. petraea within a wide latitudinal and longitudinal gradient. Analyses of variance for shape and centroid size were performed through Procrustes ANOVA. Multivariate analysis procedures, partial least squares method, and regression analyses were used to highlight possible patterns of covariation between leaf shape and size and geographical/environmental variables. The results revealed that the Q. petraea populations analyzed mainly differed in their leaf size, where a decrease was observed according to a north to south geographical gradient. Both leaf size and shape were found to be significantly related to latitude, and, to a lesser extent, to mean annual temperature and the leaf isotopic signature of 15N. All the other variables considered did not provide significant results. Unexpected differences observed comparing the leaf traits of geographically strictly adjacent populations suggest the involvement of local hybridization/introgression events. However, with a few exceptions, Q. petraea turned out to be quite conservative in its leaf shape and size at both the local and continental scale. Full article
(This article belongs to the Section Forest Biodiversity)
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22 pages, 12560 KiB  
Article
Resilient Waterfront Futures: Mapping Vulnerabilities and Designing Floating Urban Models for Flood Adaptation on the Tiber Delta
by Livia Calcagni, Adriano Ruggiero and Alessandra Battisti
Land 2025, 14(1), 87; https://doi.org/10.3390/land14010087 - 4 Jan 2025
Viewed by 323
Abstract
This paper explores the feasibility of floating urban development in Italy, given its extensive coastline and inland hydrographic network. The key drivers for floating urban development, as an adaptive approach in low-lying waterfront areas, include the increasing threats posed by rising sea levels [...] Read more.
This paper explores the feasibility of floating urban development in Italy, given its extensive coastline and inland hydrographic network. The key drivers for floating urban development, as an adaptive approach in low-lying waterfront areas, include the increasing threats posed by rising sea levels and flooding and the shortage of land for urban expansion. However, as not all waterfront areas are suitable for floating urban development, a geographical analysis based on a thorough evaluation of multiple factors, including urban–economic parameters and climate-related variables, led to the identification of a specific area of the Lazio coast, the river Tiber Delta. A comprehensive urban mapping process provided a multifaceted geo-referenced information layer, including several climatic, urban, anthropic, and environmental parameters. Within the GIS environment, it is possible to extract and perform statistical analyses crucial for assessing the impact of flood and sea-level rise hazards, particularly regarding buildings and land cover. This process provides a robust framework for understanding the spatial dimensions of flood and sea-level rise impacts and supporting informed design-making. A research-by-design phase follows the simulation research and mapping process. Several design scenarios are developed aimed at regenerating this vulnerable area. These scenarios seek to transform its susceptibility to flooding into a resilient, adaptive, urban identity, offering climate-resilient housing solutions for a population currently residing in unauthorized, substandard housing within high flood-risk zones. This paper proposes a comprehensive analytical methodology for supporting the design process of floating urban development, given the highly determinant role of site-specificity in such a challenging and new urban development approach. Full article
(This article belongs to the Section Urban Contexts and Urban-Rural Interactions)
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26 pages, 4452 KiB  
Article
Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine Learning
by Jiming Tang, Yao Huang, Dingli Liu, Liuyuan Xiong and Rongwei Bu
Systems 2025, 13(1), 31; https://doi.org/10.3390/systems13010031 - 4 Jan 2025
Viewed by 362
Abstract
Traffic accidents occur frequently, causing significant losses to people’s lives and property safety. Accurately predicting the severity level of traffic accidents is of great significance. Based on traffic accident data, this study comprehensively considers various influencing factors such as the geographical location, road [...] Read more.
Traffic accidents occur frequently, causing significant losses to people’s lives and property safety. Accurately predicting the severity level of traffic accidents is of great significance. Based on traffic accident data, this study comprehensively considers various influencing factors such as the geographical location, road conditions, and environment. The data are divided into accident-related categories, weather-related categories, and road- and environment-related categories. The machine learning method is improved through integration for the accident level prediction. In the experiment, effective preprocessing measures were taken for problems such as data imbalance, missing values, the encoding of categorical variables, and the standardization of numerical features. The unbalanced distribution of “Severity” was improved through under-sampling and over-sampling techniques. Firstly, we adopted a multi-stage fusion strategy. A multi-layer perceptron (MLP) was used for the preliminary prediction, and then its result was combined with the original features to form a new feature. Decision tree, XGBoost, and random forest algorithms, respectively, were applied for the secondary prediction. The analysis results show that the improved machine learning model is significantly superior to a single model in the overall performance. The “MLP + random forest” model performs well in evaluation indicators such as the accuracy, recall rate, and F1 value. The accuracy rate is as high as 94%. In the prediction of different traffic accident severity levels (minor, moderate, and severe), the improved machine learning model also generally shows better performance and stability. The research results of this study have broad prospects in the field of intelligent driving. It can realize real-time accident prediction and early warnings, and provide decision support for drivers and autonomous driving systems. The research also provides a scientific basis for traffic planning and management departments to improve driving conditions and reduce the probability and losses of traffic accidents. Full article
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22 pages, 992 KiB  
Article
Advancing Self-Social Engineering in Tourism-Related Environmental Management: Integrating Environmental Psychology, Planned Behavior, and Norm Activation Theories
by Laila Refiana Said, Fifi Swandari, Sufi Jikrillah, Sausan Sausan and Fathia Azizah
Tour. Hosp. 2025, 6(1), 6; https://doi.org/10.3390/tourhosp6010006 - 4 Jan 2025
Viewed by 348
Abstract
This study aims to develop the concept of self-social engineering in the context of tourism, focusing on tourists’ pro-environmental behavior. By integrating psychological theories such as Environmental Psychology Theory, the Theory of Planned Behavior, and Norm Activation Theory, the purpose of the investigation [...] Read more.
This study aims to develop the concept of self-social engineering in the context of tourism, focusing on tourists’ pro-environmental behavior. By integrating psychological theories such as Environmental Psychology Theory, the Theory of Planned Behavior, and Norm Activation Theory, the purpose of the investigation was to determine the extent of the direct influence of independent variables of perceived environmental quality (PEQ), attitude, subjective norm (SN), and perceived behavioral control (PBC) on self-social engineering (SSE) and their indirect influence through intention to engage in environmentally responsible behavior (ERB). The structural analysis results from a sample of 191 visitors indicated that the unified model demonstrates a satisfactory predictive capability for SSE. This study’s findings highlight significant and insignificant relationships among the research variables, providing insights into the dynamics of pro-environmental behavior. Significant positive relationships were observed between attitude and SSE and between SN and SSE, demonstrating the influence of individual attitudes and social pressures on fostering self-initiated environmental actions. Similarly, PBC was found to significantly impact both SSE and ERB, indicating that individuals who feel capable of taking environmental actions are more likely to do so. Conversely, some relationships were found to be insignificant. The relationship between PEQ and SSE was insignificant, suggesting that positive perceptions of environmental quality alone may not motivate individuals to engage in self-directed environmental behaviors. Additionally, PEQ showed a negative relationship with ERB, indicating that high environmental quality perceptions might reduce the urgency to act, potentially leading to complacency. These findings highlight pro-environmental behavior’s complex and context-dependent characteristics, underscoring the importance of adopting integrated approaches considering individual and situational factors. The limitations of this study include its cross-sectional design, which restricts the ability to analyze behavioral changes over time. Additionally, its relatively localized sample does not fully capture broader tourist populations’ diverse demographic and geographical contexts. Full article
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27 pages, 4307 KiB  
Article
Socioeconomic Attributes in the Topology of the Intercity Road Network in Greece
by Dimitrios Tsiotas
Future Transp. 2025, 5(1), 3; https://doi.org/10.3390/futuretransp5010003 - 3 Jan 2025
Viewed by 289
Abstract
This paper studies the Greek interregional road network (GRN) using network, statistical, and empirical analysis. The research aims to extract the socioeconomic information embedded in the topology of the GRN and to interpret to what extent this network serves and promotes regional development. [...] Read more.
This paper studies the Greek interregional road network (GRN) using network, statistical, and empirical analysis. The research aims to extract the socioeconomic information embedded in the topology of the GRN and to interpret to what extent this network serves and promotes regional development. The analysis reveals that the topology of the GRN is subject to spatial constraints, relevant to the theoretical model of the lattice network but with some geographically dispersed hub-and-spoke modules. It also reveals that the network structure is described by an adjusted gravitational pattern, with priority given to serving regions according to their population and, secondarily, geographical remoteness, and that its association with regional variables outlines an elementary pattern of “axial development through road connectivity”. Interesting contrasts between metropolitan and non-metropolitan (excluding Attica and Thessaloniki) cases emerge from the study. Overall, this paper highlights the effectiveness of complex network analysis in modeling spatial-economic and, in particular, transportation networks and promotes the network paradigm in transportation research. Full article
14 pages, 4285 KiB  
Article
Cropland Suitability Prediction Method Based on Biophysical Variables from Copernicus Data and Machine Learning
by Dorijan Radočaj, Mateo Gašparović and Mladen Jurišić
Appl. Sci. 2025, 15(1), 372; https://doi.org/10.3390/app15010372 - 2 Jan 2025
Viewed by 367
Abstract
The goal of this study was to propose and validate a method for predicting cropland suitability based on biophysical variables and machine learning according to an FAO land suitability standard using soybean (Glycine max L.) as a representative crop, aiming to provide [...] Read more.
The goal of this study was to propose and validate a method for predicting cropland suitability based on biophysical variables and machine learning according to an FAO land suitability standard using soybean (Glycine max L.) as a representative crop, aiming to provide an alternative to geographic information system (GIS)-based multicriteria analysis. The peak leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FAPAR) from PROBA-V/Sentinel-3 data were calculated according to ground-truth soybean agricultural parcels in continental Croatia during 2015–2021. Four machine learning regression algorithms, including random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), as well as their combination, were evaluated for predicting the peak LAI and FAPAR on the entire agricultural land in the study area, with RF producing the highest prediction accuracy with an R2 in the range of 0.250–0.590. The translation from K-means classes to the FAO land suitability standard was performed using a relative-based approach, ranking five resulting classes based on their relative mean sums of LAI and FAPAR values. The results of the proposed approach indicate that it is viable for major crops, while cropland suitability prediction for minor crops would require higher spatial resolution, such as vegetation indices from Sentinel-2 imagery. Full article
(This article belongs to the Special Issue Recent Advances in Precision Farming and Digital Agriculture)
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14 pages, 2971 KiB  
Article
Influence of Geological and Soil Factors on Pine, Birch, and Alder Stability During the Holocene Climate Change in Central Latvia, Northeastern Europe
by Normunds Stivrins and Marianna Jarmakovica
Quaternary 2025, 8(1), 2; https://doi.org/10.3390/quat8010002 - 2 Jan 2025
Viewed by 414
Abstract
Understanding the past dynamics of vegetation in response to climate change is crucial for predicting future ecological outcomes. This study has two primary objectives: (1) to reconstruct the vegetation history of the coastal region around Lake Lilaste in Central Latvia during the Holocene [...] Read more.
Understanding the past dynamics of vegetation in response to climate change is crucial for predicting future ecological outcomes. This study has two primary objectives: (1) to reconstruct the vegetation history of the coastal region around Lake Lilaste in Central Latvia during the Holocene and (2) to assess the impacts of climate change on forest composition through the analysis of pollen data and radiocarbon dating. The results indicate that dominant tree species, particularly pine (Pinus), have shown remarkable resilience despite significant climate fluctuations. Pine’s adaptation to the sandy, mineral-poor soils surrounding the lake likely underpins its sustained dominance, while the influence of climate change on overall tree biomass is more notable. Our results suggest that vegetation may be more susceptible to future climate variability, yet the region’s geological and soil conditions continue to favor pine, birch (Betula), and alder (Alnus) populations. While human activities have influenced the region during the last millennia, their impact has been more pronounced in areas further from the lake. This study underlines the importance of long-term forest dynamics and emphasizes that the soil and geological and geographical setting must be considered for climate change assessments. Full article
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27 pages, 18443 KiB  
Article
Revealing Land-Use Dynamics on Thermal Environment of Riverine Cities Under Climate Variability Using Remote Sensing and Geospatial Techniques
by Nazia Iftakhar, Fakhrul Islam, Mohammad Izhar Hussain, Muhammad Nasar Ahmad, Jinwook Lee, Nazir Ur Rehman, Saleh Qaysi, Nassir Alarifi and Youssef M. Youssef
ISPRS Int. J. Geo-Inf. 2025, 14(1), 13; https://doi.org/10.3390/ijgi14010013 - 31 Dec 2024
Viewed by 491
Abstract
Urbanized riverine cities in southern Asian developing countries face significant challenges in understanding the spatiotemporal thermal impacts of land use/land cover (LULC) changes driven by rapid urbanization and climatic variability. While previous studies have investigated factors influencing land surface temperature (LST) variations, gaps [...] Read more.
Urbanized riverine cities in southern Asian developing countries face significant challenges in understanding the spatiotemporal thermal impacts of land use/land cover (LULC) changes driven by rapid urbanization and climatic variability. While previous studies have investigated factors influencing land surface temperature (LST) variations, gaps persist in integrating Landsat imagery (7 and 8), meteorological data, and Geographic Information System (GIS) tools to evaluate the thermal effects of specific LULC types, including cooling and warming transitions, and their influence on air temperature under variable precipitation patterns. This study investigates LST variations in Islamabad, Pakistan, from 2000 to 2020 using quantile classification at three intervals (2000, 2010, 2020). The thermal contributions of each LULC type across the LST-based temperature classes were analyzed using the Land Contribution Index (LCI). Finally, Warming and Cooling Transition (WCT) maps were generated by intersecting LST classes with 2000 as the baseline. Results indicated a rise in LST from 32.39 °C in 2000 to 45.63 °C in 2020. The negative LCI values revealed that vegetation and water bodies in lower temperature zones (Ltc_1 to Ltc_3) contributed to cooling effects, while positive LCI values in built-up and bare land areas in higher temperature zones (Ltc_5–Ltc_7) exhibited warming effects. The WCT map showed a general warming trend (cold-to-hot type) from 2000 to 2020, particularly in newly urbanized areas due to a 49.63% population increase, while cooling effects (hot-to-cold type) emerged in the newly developed agricultural lands with a 46.46% rise in vegetation. The mean annual air temperature gap with LST narrowed from 11.55 °C in 2000 to 2.28 °C in 2020, reflecting increased precipitation due to increasing yearly rainfall from 982.88 mm in 2000 to 1365.47 mm in 2020. This change also coincided with an expansion of water bodies from 2.82 km2 in 2000 to 6.35 km2 in 2020, impacting the local climate and hydrology. These findings highlight the importance of green spaces and water management to mitigate urban heat and improve ecological health. Full article
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25 pages, 7482 KiB  
Article
How Do Temporal and Geographical Kernels Differ in Reflecting Regional Disparities? Insights from a Case Study in China
by Chunzhu Wei, Xufeng Liu, Wei Chen, Lupan Zhang, Ruixia Chao and Wei Wei
Land 2025, 14(1), 59; https://doi.org/10.3390/land14010059 - 31 Dec 2024
Viewed by 363
Abstract
Rapid economic growth in China has brought about a significant challenge: the widening gap in regional development. Addressing this disparity is crucial for ensuring sustainable development. However, existing studies have largely overlooked the intrinsic spatial and temporal dynamics of regional disparities on various [...] Read more.
Rapid economic growth in China has brought about a significant challenge: the widening gap in regional development. Addressing this disparity is crucial for ensuring sustainable development. However, existing studies have largely overlooked the intrinsic spatial and temporal dynamics of regional disparities on various levels. This study thus employed five advanced multiscale geographically and temporally weighted regression models—GWR, MGWR, GTWR, MGTWR, and STWR—to analyze the spatio-temporal relationships between ten key conventional socio-economic indicators and per capita GDP across different administrative levels in China from 2000 to 2019. The findings highlight a consistent increase in regional disparities, with secondary industry emerging as a dominant driver of long-term economic inequality among the indicators analyzed. While a clear inland-to-coastal gradient underscores the persistence of regional disparity determinants, areas with greater economic disparities exhibit pronounced spatio-temporal heterogeneity. Among the models, STWR outperforms others in capturing and interpreting local variations in spatio-temporal disparities, demonstrating its utility in understanding complex regional dynamics. This study provides novel insights into the spatio-temporal determinants of regional economic disparities, offering a robust analytical framework for policymakers to address region-specific variables driving inequality over time and space. These insights contribute to the development of targeted and dynamic policies for promoting balanced and sustainable regional growth. Full article
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19 pages, 2363 KiB  
Article
Risk Mitigation in Durian Cultivation in Thailand Using the House of Risk (HOR) Method: A Case Study of Pak Chong GI Durian
by Phongchai Jittamai, Sovann Toek, Phumrapee Sathaporn, Kingkan Kongkanjana and Natdanai Chanlawong
Sustainability 2025, 17(1), 222; https://doi.org/10.3390/su17010222 - 31 Dec 2024
Viewed by 496
Abstract
Durian, often regarded as the “king of fruits”, plays a significant role in Thailand’s economy, with durian production expanding rapidly due to its profitability and high demand in both domestic and international markets. This growth has introduced challenges, particularly for geographic indication (GI)-certified [...] Read more.
Durian, often regarded as the “king of fruits”, plays a significant role in Thailand’s economy, with durian production expanding rapidly due to its profitability and high demand in both domestic and international markets. This growth has introduced challenges, particularly for geographic indication (GI)-certified durians like those from Pak Chong, where the unique soil, climate, and cultivation practices contribute to the fruit’s distinctive quality. Maintaining these standards is crucial to preserving GI certification, but farmers face increasing risks related to pests, diseases, climate variability, and cultivation practices. Effective risk management is essential to ensure the quality and sustainability of GI-certified durian production. This study analyzes risks in Pak Chong GI durian cultivation and proposes strategies to mitigate these risks. The House of Risk (HOR) method was used to identify potential risks at various stages of durian cultivation, including planting, maintenance, pre-harvest, harvest, and postharvest, and to recommend proactive mitigation strategies. This case study focuses on Pak Chong GI durian farmers. Thirty-one risk events driven by 17 risk agents were identified throughout the durian cultivation process. Key risk agents included observation of durian tree behavior, physical characteristics of the planting area, irrigation quantity, understanding of nutrient management, soil nutrients, and soil pH. The three most significant mitigation strategies identified were the implementation of targeted training and learning programs, improved data collection and plating progress tracking ability, and investment in advanced cultivation technology. This study analyzes the critical risks in Pak Chong GI-certified durian cultivation and proposes targeted mitigation strategies using the House of Risk (HOR) method. By identifying risks (HOR1) and developing proactive solutions (HOR2) across key cultivation stages, this research offers practical insights to enhance the quality and sustainability of GI-certified durian production. The findings aim to support farmers, policymakers, and stakeholders in preserving the economic and cultural value of Pak Chong durians. Full article
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15 pages, 1366 KiB  
Article
Disentangling the Roles of Climate Variables in Forest Fire Occurrences in China
by Chenqin Lian, Zhiming Feng, Hui Gu and Beilei Gao
Remote Sens. 2025, 17(1), 88; https://doi.org/10.3390/rs17010088 - 29 Dec 2024
Viewed by 360
Abstract
In the context of global warming, climate strongly affects forest fires. With long-term and strict fire prevention policies, China has become a unique test arena for comprehending the role of climatic variables in affecting forest fires. Here, using GIS spatial analysis, Pearson correlation, [...] Read more.
In the context of global warming, climate strongly affects forest fires. With long-term and strict fire prevention policies, China has become a unique test arena for comprehending the role of climatic variables in affecting forest fires. Here, using GIS spatial analysis, Pearson correlation, and geographical detector, the climate drivers of forest fires in China are revealed using the 2003–2022 active fire data from the MODIS C6 and climate products from CHELSA (Climatologies at high resolution for the Earth’s land surface areas). The main conclusions are as follows: (1) In total, 82% of forest fires were prevalent in the southern and southwestern forest regions (SR and SWR) in China, especially in winter and spring. (2) Forest fires were mainly distributed in areas with a mean annual temperature and annual precipitation of 14~22 °C (subtropical) and 800~2000 mm (humid zone), respectively. (3) Incidences of forest fires were positively correlated with temperature, potential evapotranspiration, surface downwelling shortwave flux, and near-surface wind speed but negatively correlated with precipitation and near-surface relative humidity. (4) Temperature and potential evapotranspiration dominated the roles in determining spatial variations of China’s forest fires, while the combination of climate variables complicated the spatial variation. This paper not only provides new insights on the impact of climate drives on forest fires, but also offers helpful guidance for fire management, prevention, and forecasting. Full article
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