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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 386 - 390 of 1524

RePlant Alfa: Integrating Google Earth Engine and R Coding to Support the Identification of Priority Areas for Ecological Restoration

Peer-reviewed publication
декабря, 2022
Chile

Land degradation and climate change are among the main threats to the sustainability of ecosystems worldwide. As a result, the restoration of degraded landscapes is essential to maintaining the functionality of ecosystems, especially those with greater social, economic, and environmental vulnerability. Nevertheless, policymakers are frequently challenged by deciding where to prioritize restoration actions, which usually includes dealing with multiple and complex needs under an always limited budget.

Land Use and Landscape Characteristics Are Associated with Core Forest Patches in Ghana

Peer-reviewed publication
декабря, 2022
Ghana

Land uses and terrain characteristics would likely influence the types and spatial arrangements of forest patches, and generally, forest fragmentation. Whereas prior research has focused mainly on direct land use-induced forest fragmentation, this study models the relationship between the spatial distribution of core forest patches, land uses, and terrain variables.

Drivers of Degradation of Croplands and Abandoned Lands: A Case Study of Macubeni Communal Land in the Eastern Cape, South Africa

Peer-reviewed publication
декабря, 2022
South Africa

Soil erosion is a global environmental problem and a pervasive form of land degradation that threatens land productivity and food and water security. Some of the biggest sources of sediment in catchments are cultivated and abandoned lands. However, the abandonment of cultivated fields is not well-researched. Our study assesses the level of degradation in cultivated and abandoned lands using a case study in South Africa. We answer three main questions: (1) What is the extent of crop field degradation on used, partly used, and abandoned fields?

Spatio-Temporal Variation of the Ecosystem Service Value in Qilian Mountain National Park (Gansu Area) Based on Land Use

Peer-reviewed publication
декабря, 2022
Global

The value of ecosystem services and service capabilities continue to improve, and the way to form a path of resource industrialization development has become one of the important directions of sustainable development. This paper mainly takes the construction of national parks as a major opportunity and explores the temporal and spatial changes in the value of ecosystem services in Qilian Mountain National Park (Gansu area) and the construction path of the industrial system of national park construction.

Scrutinizing Urbanization in Kathmandu Using Google Earth Engine Together with Proximity-Based Scenario Modelling

Peer-reviewed publication
декабря, 2022
Nepal

‘Urbanization’ refers to the expansion of built-up areas caused by several factors. This study focuses on the urbanization process in Kathmandu, the capital of Nepal. Supervised classification was conducted in Google Earth Engine by using Landsat data for years 2001, 2011 and 2021. The random forest classifier with 250 trees was used for classification to generate land-cover map. A land-cover map of 2021 was used as base map in the InVEST tool for scenario modelling.