Global forest management data for 2015 at a 100 m resolution

Author:

Lesiv MyroslavaORCID,Schepaschenko DmitryORCID,Buchhorn MarcelORCID,See LindaORCID,Dürauer Martina,Georgieva IvelinaORCID,Jung MartinORCID,Hofhansl FlorianORCID,Schulze KatharinaORCID,Bilous AndriiORCID,Blyshchyk VolodymyrORCID,Mukhortova LiudmilaORCID,Brenes Carlos Luis MuñozORCID,Krivobokov LeonidORCID,Ntie StephanORCID,Tsogt KhongorORCID,Pietsch Stephan Alexander,Tikhonova ElenaORCID,Kim MoonilORCID,Di Fulvio Fulvio,Su Yuan-Fong,Zadorozhniuk RomaORCID,Sirbu Flavius SorinORCID,Panging KripalORCID,Bilous Svitlana,Kovalevskii Sergii B.,Kraxner FlorianORCID,Rabia Ahmed HarbORCID,Vasylyshyn RomanORCID,Ahmed RekibORCID,Diachuk PetroORCID,Kovalevskyi Serhii S.,Bungnamei KhangsembouORCID,Bordoloi KusumborORCID,Churilov Andrii,Vasylyshyn Olesia,Sahariah Dhrubajyoti,Tertyshnyi Anatolii P.,Saikia AnupORCID,Malek ŽigaORCID,Singha Kuleswar,Feshchenko Roman,Prestele ReinhardORCID,Akhtar Ibrar ul Hassan,Sharma KiranORCID,Domashovets Galyna,Spawn-Lee Seth A.ORCID,Blyshchyk Oleksii,Slyva Oleksandr,Ilkiv Mariia,Melnyk Oleksandr,Sliusarchuk Vitalii,Karpuk Anatolii,Terentiev AndriiORCID,Bilous Valentin,Blyshchyk KaterynaORCID,Bilous Maxim,Bogovyk Nataliia,Blyshchyk IvanORCID,Bartalev SergeyORCID,Yatskov Mikhail,Smets BrunoORCID,Visconti PieroORCID,Mccallum IanORCID,Obersteiner Michael,Fritz Steffen

Abstract

AbstractSpatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki (https://www.geo-wiki.org/). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services.

Funder

NatureMap project funded by NICFI

Russian Science Foundation

NatureMap funded by NICFI

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability

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