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Urban forests as nature-based solutions (UF-NBS) are important tools for climate change adaptation and sustainable development. However, achieving both effective and sustainable UF-NBS solutions requires diverse knowledge. This includes knowledge on UF-NBS implementation, on the assessment of their environmental impacts in diverse spatial contexts, and on their management for the long-term safeguarding of delivered benefits. A successful integration of such bodies of knowledge demands a systematic understanding of UF-NBS. To achieve such an understanding, this paper presents a conceptual UF-NBS model obtained through a semantic, trait-based modelling approach. This conceptual model is subsequently implemented as an extendible, re-usable and interoperable ontology. In so doing, a formal, trait-based vocabulary on UF-NBS is created, that allows expressing spatial, morphological, physical, functional, and institutional UF-NBS properties for their typification and a subsequent integration of further knowledge and data. Thereby, ways forward are opened for a more systematic UF-NBS impact assessment, management, and decision-making.
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http://dx.doi.org/10.1016/j.ufug.2022.127780 | DOI Listing |
Environ Monit Assess
September 2025
Institute of Earth Sciences, Southern Federal University, Rostov-On-Don, Russia.
Sustainable urban development requires actionable insights into the thermal consequences of land transformation. This study examines the impact of land use and land cover (LULC) changes on land surface temperature (LST) in Ho Chi Minh city, Vietnam, between 1998 and 2024. Using Google Earth Engine (GEE), three machine learning algorithms-random forest (RF), support vector machine (SVM), and classification and regression tree (CART)-were applied for LULC classification.
View Article and Find Full Text PDFEnviron Monit Assess
September 2025
Indira Gandhi Conservation Monitoring Centre, World Wide Fund-India, New Delhi, 110003, India.
Understanding the intricate relationship between land use/land cover (LULC) transformations and land surface temperature (LST) is critical for sustainable urban planning. This study investigates the spatiotemporal dynamics of LULC and LST across Delhi, India, using thermal data from Landsat 7 (2001), Landsat 5 (2011) and Landsat 8 (2021) resampled to 30-m spatial resolution, during the peak summer month of May. The study aims to target three significant aspects: (i) to analyse and present LULC-LST dynamics across Delhi, (ii) to evaluate the implications of LST effects at the district level and (iii) to predict seasonal LST trends in 2041 for North Delhi district using the seasonal auto-regressive integrated moving average (SARIMA) time series model.
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September 2025
University of California, Los Angeles, Los Angeles, California.
Objective: To examine the prevalence and correlates of child involuntary mental health detentions through evaluation of legal documentation embedded in medical records and children's electronic health information.
Method: Medical records were analyzed from 3,440 children ages 10 to 17 years with MH-related emergency department visits in a large academic health system over 2 years (2017-2019). Bivariate analyses and random forests were deployed to identify child-, neighborhood-, and systems-level correlates of involuntary MH detentions.
Nat Plants
September 2025
Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, School of Atmospheric Sciences, School of Ecology, Sun Yat-sen University, Zhuhai, China.
Increasing leaf area and extending vegetation growing seasons are two primary drivers of global greening, which has emerged as one of the most significant responses to climate change. However, it remains unclear how these two leaf acclimation strategies would vary across forests at a large spatial scale. Here, using multiple satellite-based datasets and field measurements, we analysed the temporal changes (Δ) in maximal leaf area index (LAI) and length of the growing season (LOS) from 2002 to 2021 across deciduous broadleaf forests (DBFs) in the middle to high latitudes.
View Article and Find Full Text PDFPLoS One
September 2025
Center for Studies of Education and Psychology of Ethnic Minorities in Southwest China, Southwest University, Chongqing, China.
Background: Educational hypogamy, where women marry men with lower educational attainment, reflects evolving gender roles and societal norms. In China, the rapid expansion of education, coupled with persistent traditional values, provides a unique context to study this phenomenon.
Methods: Using data from the 2013, 2015, 2017, 2018, and 2021 waves of the China General Social Survey (CGSS), this study applies logistic regression models and Random Forest machine learning techniques to analyze the impact of education on women's selection of hypogamy.