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Community Organizations MDPI Online, Open Access Journals
MDPI Online, Open Access Journals
MDPI Online, Open Access Journals
Acronym
MDPI
Publishing Company
Phone number
+41 61 683 77 34

Location

St. Alban-Anlage 66
Basel
Basel-Stadt
Switzerland
Working languages
English

MDPI AG, a publisher of open-access scientific journals, was spun off from the Molecular Diversity Preservation International organization. It was formally registered by Shu-Kun Lin and Dietrich Rordorf in May 2010 in Basel, Switzerland, and maintains editorial offices in China, Spain and Serbia. MDPI relies primarily on article processing charges to cover the costs of editorial quality control and production of articles. Over 280 universities and institutes have joined the MDPI Institutional Open Access Program; authors from these organizations pay reduced article processing charges. MDPI is a member of the Committee on Publication Ethics, the International Association of Scientific, Technical, and Medical Publishers, and the Open Access Scholarly Publishers Association (OASPA).

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Resources

Displaying 456 - 460 of 1524

Exploring the Spatial and Temporal Changes of Carbon Storage in Different Development Scenarios in Foshan, China

Peer-reviewed publication
december, 2021
China

Carbon storage (CS) is strongly associated with climate change and ecosystem services. Herein, taking Foshan City, Guangdong Province, China as the study object, analysis was performed upon the potential impacts of the urban–rural relationship of CS by combining the Integrated Assessment of Ecosystem Services and Trade-offs (InVEST) and the Patch Generation Land-use Simulation (PLUS) models. Based on three different development plans under regional policies, land-use/ land-cover (LULC) changes in Foshan City in 2035 were simulated.

Forests and Forestry in Support of Sustainable Development Goals (SDGs): A Bibliometric Analysis

Peer-reviewed publication
december, 2021
Global

To address the world’s ongoing environmental challenges, 193 countries have committed to 17 sustainable development goals (SDGs) concerning the economy, society, and the environment. However, there are gaps in our understanding of forests and forestry support SDGs. Through a systematized review, we identified which SDGs are relevant to forests and forestry at the target level, along with their interactions (synergies or tradeoffs).

Remote Sensing and Phytoecological Methods for Mapping and Assessing Potential Ecosystem Services of the Ouled Hannèche Forest in the Hodna Mountains, Algeria

Peer-reviewed publication
december, 2021
Global

Regardless of their biogeographic origins or degree of artificialization, the world’s forests are a source of a wide range of ecosystem services (ES). However, the quality and quantity of these services depend on the type of forest studied and its phytogeographic context. Our objective is to transpose the concept of ES, in particular, the assessment of forest ES, to the specific Mediterranean context of the North African mountains, where this issue is still in its infancy and where access to the data needed for assessment remains difficult.

Land Use Preference for Ecosystem Services and Well-Being in Chittagong Hill Tracts of Bangladesh

Peer-reviewed publication
december, 2021
Bangladesh

Researchers increasingly investigate ecosystem services to assess their role in supporting livelihoods, well-being and economic value in order to inform decision-making. Many studies have explored links between ecosystem services and community-based livelihoods, with a very narrow focus on the importance of land use to well-being. We evaluated the value of ecosystem services from various land uses supporting livelihoods and the overall well-being of local communities in the Chittagong Hill Tracts (CHT) of Bangladesh.

A Comparative Study of Shallow Machine Learning Models and Deep Learning Models for Landslide Susceptibility Assessment Based on Imbalanced Data

Peer-reviewed publication
december, 2021
China

A landslide is a type of geological disaster that poses a threat to human lives and property. Landslide susceptibility assessment (LSA) is a crucial tool for landslide prevention. This paper’s primary objective is to compare the performances of conventional shallow machine learning methods and deep learning methods in LSA based on imbalanced data to evaluate the applicability of the two types of LSA models when class-weighted strategies are applied.