New 1 km Resolution Datasets of Global and Regional Risks of Tree Cover Loss

Despite global recognition of the social, economic and ecological impacts of deforestation, the world is losing forests at an alarming rate. Global and regional efforts by policymakers and donors to reduce deforestation need science-driven information on where forest loss is happening, and where it...

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Main Authors: Jennifer Hewson, Stefano C. Crema, Mariano González-Roglich, Karyn Tabor, Celia A. Harvey
Format: Article
Language:English
Published: MDPI AG 2019-01-01
Series:Land
Subjects:
Online Access:http://www.mdpi.com/2073-445X/8/1/14
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spelling doaj-1a3cd19f84f24849b442b2acc5b62de72020-11-25T02:48:14ZengMDPI AGLand2073-445X2019-01-01811410.3390/land8010014land8010014New 1 km Resolution Datasets of Global and Regional Risks of Tree Cover LossJennifer Hewson0Stefano C. Crema1Mariano González-Roglich2Karyn Tabor3Celia A. Harvey4Betty and Gordon Moore Center for Science, Conservation International, 2011 Crystal Drive Suite 600, Arlington, VA 22202, USAClark Labs, Clark University, Worcester, MA 01610, USABetty and Gordon Moore Center for Science, Conservation International, 2011 Crystal Drive Suite 600, Arlington, VA 22202, USABetty and Gordon Moore Center for Science, Conservation International, 2011 Crystal Drive Suite 600, Arlington, VA 22202, USABetty and Gordon Moore Center for Science, Conservation International, 2011 Crystal Drive Suite 600, Arlington, VA 22202, USADespite global recognition of the social, economic and ecological impacts of deforestation, the world is losing forests at an alarming rate. Global and regional efforts by policymakers and donors to reduce deforestation need science-driven information on where forest loss is happening, and where it may happen in the future. We used spatially-explicit globally-consistent variables and global historical tree cover and loss to analyze how global- and regional-scale variables contributed to historical tree cover loss and to model future risks of tree cover loss, based on a business-as-usual scenario. Our results show that (1) some biomes have higher risk of tree cover loss than others; (2) variables related to tree cover loss at the global scale differ from those at the regional scale; and (3) variables related to tree cover loss vary by continent. By mapping both tree cover loss risk and potential future tree cover loss, we aim to provide decision makers and donors with multiple outputs to improve targeting of forest conservation investments. By making the outputs readily accessible, we anticipate they will be used in other modeling analyses, conservation planning exercises, and prioritization activities aimed at conserving forests to meet national and global climate mitigation targets and biodiversity goals.http://www.mdpi.com/2073-445X/8/1/14land change modelingtree cover lossREDD+Sustainable Development Goalstree cover loss projections
collection DOAJ
language English
format Article
sources DOAJ
author Jennifer Hewson
Stefano C. Crema
Mariano González-Roglich
Karyn Tabor
Celia A. Harvey
spellingShingle Jennifer Hewson
Stefano C. Crema
Mariano González-Roglich
Karyn Tabor
Celia A. Harvey
New 1 km Resolution Datasets of Global and Regional Risks of Tree Cover Loss
Land
land change modeling
tree cover loss
REDD+
Sustainable Development Goals
tree cover loss projections
author_facet Jennifer Hewson
Stefano C. Crema
Mariano González-Roglich
Karyn Tabor
Celia A. Harvey
author_sort Jennifer Hewson
title New 1 km Resolution Datasets of Global and Regional Risks of Tree Cover Loss
title_short New 1 km Resolution Datasets of Global and Regional Risks of Tree Cover Loss
title_full New 1 km Resolution Datasets of Global and Regional Risks of Tree Cover Loss
title_fullStr New 1 km Resolution Datasets of Global and Regional Risks of Tree Cover Loss
title_full_unstemmed New 1 km Resolution Datasets of Global and Regional Risks of Tree Cover Loss
title_sort new 1 km resolution datasets of global and regional risks of tree cover loss
publisher MDPI AG
series Land
issn 2073-445X
publishDate 2019-01-01
description Despite global recognition of the social, economic and ecological impacts of deforestation, the world is losing forests at an alarming rate. Global and regional efforts by policymakers and donors to reduce deforestation need science-driven information on where forest loss is happening, and where it may happen in the future. We used spatially-explicit globally-consistent variables and global historical tree cover and loss to analyze how global- and regional-scale variables contributed to historical tree cover loss and to model future risks of tree cover loss, based on a business-as-usual scenario. Our results show that (1) some biomes have higher risk of tree cover loss than others; (2) variables related to tree cover loss at the global scale differ from those at the regional scale; and (3) variables related to tree cover loss vary by continent. By mapping both tree cover loss risk and potential future tree cover loss, we aim to provide decision makers and donors with multiple outputs to improve targeting of forest conservation investments. By making the outputs readily accessible, we anticipate they will be used in other modeling analyses, conservation planning exercises, and prioritization activities aimed at conserving forests to meet national and global climate mitigation targets and biodiversity goals.
topic land change modeling
tree cover loss
REDD+
Sustainable Development Goals
tree cover loss projections
url http://www.mdpi.com/2073-445X/8/1/14
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AT marianogonzalezroglich new1kmresolutiondatasetsofglobalandregionalrisksoftreecoverloss
AT karyntabor new1kmresolutiondatasetsofglobalandregionalrisksoftreecoverloss
AT celiaaharvey new1kmresolutiondatasetsofglobalandregionalrisksoftreecoverloss
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