Global Forest Types Based on Climatic and Vegetation Data

Chen Xu, Xianliang Zhang, Rocío Hernandez-Clemente, Wei Lu and Rubén D. Manzanedo
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Chen Xu: College of Landscape Architecture and Tourism, Hebei Agricultural University, Baoding 071001, China
Xianliang Zhang: College of Forestry, Hebei Agricultural University, Baoding 071001, China
Rocío Hernandez-Clemente: Department of Geography, Swansea University, Singleton Park, Swansea SA2 8PP, UK
Wei Lu: College of Forestry, Hebei Agricultural University, Baoding 071001, China
Rubén D. Manzanedo: Department of Environmental Systems Science, Institute of Integrative Biology, D-USYS, ETH-Zürich, 8006 Zurich, Switzerland

Sustainability, 2022, vol. 14, issue 2, 1-14

Abstract: Forest types are generally identified using vegetation or land-use types. However, vegetation classifications less frequently consider the actual forest attributes within each type. To address this in an objective way across different regions and to link forest attributes with their climate, we aimed to improve the distribution of forest types to be more realistic and useful for biodiversity preservation, forest management, and ecological and forestry research. The forest types were classified using an unsupervised cluster analysis method by combining climate variables with normalized difference vegetation index (NDVI) data. Unforested regions were masked out to constrict our study to forest type distributions, using a 20% tree cover threshold. Descriptive names were given to the defined forest types based on annual temperature, precipitation, and NDVI values. Forest types had distinct climate and vegetation characteristics. Regions with similar NDVI values, but with different climate characteristics, which would be merged in previous classifications, could be clearly distinguished. However, small-range forest types, such as montane forests, were challenging to differentiate. At macroscale, the resulting forest types are largely consistent with land-cover types or vegetation types defined in previous studies. However, considering both potential and current vegetation data allowed us to create a more realistic type distribution that differentiates actual vegetation types and thus can be more informative for forest managers, conservationists, and forest ecologists. The newly generated forest type distribution is freely available to download and use for non-commercial purposes as a GeoTIFF file via doi: 10.13140/RG.2.2.19197.90082).

Keywords: forest types; NDVI; AVHRR GIMMS; temperature range; precipitation range (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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