Global forest management data for 2015 at a 100 m resolution

Myroslava Lesiv; Dmitry Schepaschenko; Marcel Buchhorn; Linda See; Martina Dürauer; Ivelina Georgieva; Martin Jung; Florian Hofhansl; Katharina Schulze; Andrii Bilous; Volodymyr Blyshchyk; Liudmila Mukhortova; Carlos Luis Muñoz Brenes; Leonid Krivobokov; Stephan Ntie; Khongor Tsogt; Stephan Alexander Pietsch; Elena Tikhonova; Moonil Kim; Fulvio Di Fulvio; Yuan Fong Su; Roma Zadorozhniuk; Flavius Sorin Sirbu; Kripal Panging; Svitlana Bilous; Sergii B. Kovalevskii; Florian Kraxner; Ahmed Harb Rabia; Roman Vasylyshyn; Rekib Ahmed; Petro Diachuk; Serhii S. Kovalevskyi; Khangsembou Bungnamei; Kusumbor Bordoloi; Andrii Churilov; Olesia Vasylyshyn; Dhrubajyoti Sahariah; Anatolii P. Tertyshnyi; Anup Saikia; Žiga Malek; Kuleswar Singha; Roman Feshchenko; Reinhard Prestele; Ibrar ul Hassan Akhtar; Kiran Sharma; Galyna Domashovets; Seth A. Spawn-Lee; Oleksii Blyshchyk; Oleksandr Slyva; Mariia Ilkiv; Oleksandr Melnyk; Vitalii Sliusarchuk; Anatolii Karpuk; Andrii Terentiev; Valentin Bilous; Kateryna Blyshchyk; Maxim Bilous; Nataliia Bogovyk; Ivan Blyshchyk; Sergey Bartalev; Mikhail Yatskov; Bruno Smets; Piero Visconti; Ian Mccallum; Michael Obersteiner; Steffen Fritz

doi:10.1038/s41597-022-01332-3

Global forest management data for 2015 at a 100 m resolution

Myroslava Lesiv^*, Dmitry Schepaschenko, Marcel Buchhorn, Linda See, Martina Dürauer, Ivelina Georgieva, Martin Jung, Florian Hofhansl, Katharina Schulze, Andrii Bilous, Volodymyr Blyshchyk, Liudmila Mukhortova, Carlos Luis Muñoz Brenes, Leonid Krivobokov, Stephan Ntie, Khongor Tsogt, Stephan Alexander Pietsch, Elena Tikhonova, Moonil Kim, Fulvio Di FulvioYuan Fong Su, Roma Zadorozhniuk, Flavius Sorin Sirbu, Kripal Panging, Svitlana Bilous, Sergii B. Kovalevskii, Florian Kraxner, Ahmed Harb Rabia, Roman Vasylyshyn, Rekib Ahmed, Petro Diachuk, Serhii S. Kovalevskyi, Khangsembou Bungnamei, Kusumbor Bordoloi, Andrii Churilov, Olesia Vasylyshyn, Dhrubajyoti Sahariah, Anatolii P. Tertyshnyi, Anup Saikia, Žiga Malek, Kuleswar Singha, Roman Feshchenko, Reinhard Prestele, Ibrar ul Hassan Akhtar, Kiran Sharma, Galyna Domashovets, Seth A. Spawn-Lee, Oleksii Blyshchyk, Oleksandr Slyva, Mariia Ilkiv, Oleksandr Melnyk, Vitalii Sliusarchuk, Anatolii Karpuk, Andrii Terentiev, Valentin Bilous, Kateryna Blyshchyk, Maxim Bilous, Nataliia Bogovyk, Ivan Blyshchyk, Sergey Bartalev, Mikhail Yatskov, Bruno Smets, Piero Visconti, Ian Mccallum, Michael Obersteiner, Steffen Fritz

^*Corresponding author for this work

Environmental Geography

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

Spatially 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.

Original language	English
Article number	199
Pages (from-to)	1-14
Number of pages	14
Journal	Scientific Data
Volume	9
DOIs	https://doi.org/10.1038/s41597-022-01332-3
Publication status	Published - 10 May 2022

Bibliographical note

Funding Information:
The work was conducted by the NatureMap consortium, funded by Norway’s International Climate and Forest Initiative (NICFI). Reference data collection for the Russian Federation was supported by the Russian Science Foundation through projects No 19-77-30015 (European part of the country) and RSF-MAFF/AFFRCS No 21-46-07002 (Siberia).

Publisher Copyright:
© 2022, The Author(s).

Funding

The work was conducted by the NatureMap consortium, funded by Norway’s International Climate and Forest Initiative (NICFI). Reference data collection for the Russian Federation was supported by the Russian Science Foundation through projects No 19-77-30015 (European part of the country) and RSF-MAFF/AFFRCS No 21-46-07002 (Siberia).

Funders	Funder number
Norway’s International Climate and Forest Initiative
Russian Science Foundation	19-77-30015, 21-46-07002
Russian Science Foundation

Keywords

biodiversity
environemtal impact
forestry
land use
big data
remote sensing
land cover
land system

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1038/s41597-022-01332-3Licence: CC BY-ND

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Cite this

Lesiv, M., Schepaschenko, D., Buchhorn, M., See, L., Dürauer, M., Georgieva, I., Jung, M., Hofhansl, F., Schulze, K., Bilous, A., Blyshchyk, V., Mukhortova, L., Brenes, C. L. M., Krivobokov, L., Ntie, S., Tsogt, K., Pietsch, S. A., Tikhonova, E., Kim, M., ... Fritz, S. (2022). Global forest management data for 2015 at a 100 m resolution. Scientific Data, 9, 1-14. Article 199. https://doi.org/10.1038/s41597-022-01332-3

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abstract = "Spatially 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.",

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note = "Funding Information: The work was conducted by the NatureMap consortium, funded by Norway{\textquoteright}s International Climate and Forest Initiative (NICFI). Reference data collection for the Russian Federation was supported by the Russian Science Foundation through projects No 19-77-30015 (European part of the country) and RSF-MAFF/AFFRCS No 21-46-07002 (Siberia). Publisher Copyright: {\textcopyright} 2022, The Author(s).",

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Lesiv, M, Schepaschenko, D, Buchhorn, M, See, L, Dürauer, M, Georgieva, I, Jung, M, Hofhansl, F, Schulze, K, Bilous, A, Blyshchyk, V, Mukhortova, L, Brenes, CLM, Krivobokov, L, Ntie, S, Tsogt, K, Pietsch, SA, Tikhonova, E, Kim, M, Di Fulvio, F, Su, YF, Zadorozhniuk, R, Sirbu, FS, Panging, K, Bilous, S, Kovalevskii, SB, Kraxner, F, Rabia, AH, Vasylyshyn, R, Ahmed, R, Diachuk, P, Kovalevskyi, SS, Bungnamei, K, Bordoloi, K, Churilov, A, Vasylyshyn, O, Sahariah, D, Tertyshnyi, AP, Saikia, A, Malek, Ž, Singha, K, Feshchenko, R, Prestele, R, Akhtar, IUH, Sharma, K, Domashovets, G, Spawn-Lee, SA, Blyshchyk, O, Slyva, O, Ilkiv, M, Melnyk, O, Sliusarchuk, V, Karpuk, A, Terentiev, A, Bilous, V, Blyshchyk, K, Bilous, M, Bogovyk, N, Blyshchyk, I, Bartalev, S, Yatskov, M, Smets, B, Visconti, P, Mccallum, I, Obersteiner, M & Fritz, S 2022, 'Global forest management data for 2015 at a 100 m resolution', Scientific Data, vol. 9, 199, pp. 1-14. https://doi.org/10.1038/s41597-022-01332-3

TY - JOUR

T1 - Global forest management data for 2015 at a 100 m resolution

AU - Lesiv, Myroslava

AU - Schepaschenko, Dmitry

AU - Buchhorn, Marcel

AU - See, Linda

AU - Dürauer, Martina

AU - Georgieva, Ivelina

AU - Jung, Martin

AU - Hofhansl, Florian

AU - Schulze, Katharina

AU - Bilous, Andrii

AU - Blyshchyk, Volodymyr

AU - Mukhortova, Liudmila

AU - Brenes, Carlos Luis Muñoz

AU - Krivobokov, Leonid

AU - Ntie, Stephan

AU - Tsogt, Khongor

AU - Pietsch, Stephan Alexander

AU - Tikhonova, Elena

AU - Kim, Moonil

AU - Di Fulvio, Fulvio

AU - Su, Yuan Fong

AU - Zadorozhniuk, Roma

AU - Sirbu, Flavius Sorin

AU - Panging, Kripal

AU - Bilous, Svitlana

AU - Kovalevskii, Sergii B.

AU - Kraxner, Florian

AU - Rabia, Ahmed Harb

AU - Vasylyshyn, Roman

AU - Ahmed, Rekib

AU - Diachuk, Petro

AU - Kovalevskyi, Serhii S.

AU - Bungnamei, Khangsembou

AU - Bordoloi, Kusumbor

AU - Churilov, Andrii

AU - Vasylyshyn, Olesia

AU - Sahariah, Dhrubajyoti

AU - Tertyshnyi, Anatolii P.

AU - Saikia, Anup

AU - Malek, Žiga

AU - Singha, Kuleswar

AU - Feshchenko, Roman

AU - Prestele, Reinhard

AU - Akhtar, Ibrar ul Hassan

AU - Sharma, Kiran

AU - Domashovets, Galyna

AU - Spawn-Lee, Seth A.

AU - Blyshchyk, Oleksii

AU - Slyva, Oleksandr

AU - Ilkiv, Mariia

AU - Melnyk, Oleksandr

AU - Sliusarchuk, Vitalii

AU - Karpuk, Anatolii

AU - Terentiev, Andrii

AU - Bilous, Valentin

AU - Blyshchyk, Kateryna

AU - Bilous, Maxim

AU - Bogovyk, Nataliia

AU - Blyshchyk, Ivan

AU - Bartalev, Sergey

AU - Yatskov, Mikhail

AU - Smets, Bruno

AU - Visconti, Piero

AU - Mccallum, Ian

AU - Obersteiner, Michael

AU - Fritz, Steffen

N1 - Funding Information: The work was conducted by the NatureMap consortium, funded by Norway’s International Climate and Forest Initiative (NICFI). Reference data collection for the Russian Federation was supported by the Russian Science Foundation through projects No 19-77-30015 (European part of the country) and RSF-MAFF/AFFRCS No 21-46-07002 (Siberia). Publisher Copyright: © 2022, The Author(s).

PY - 2022/5/10

Y1 - 2022/5/10

N2 - Spatially 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.

AB - Spatially 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.

KW - biodiversity

KW - environemtal impact

KW - forestry

KW - land use

KW - big data

KW - remote sensing

KW - land cover

KW - land system

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DO - 10.1038/s41597-022-01332-3

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AN - SCOPUS:85129821081

SN - 2052-4463

VL - 9

SP - 1

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JO - Scientific Data

JF - Scientific Data

M1 - 199

ER -

Global forest management data for 2015 at a 100 m resolution

Abstract

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

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