Journal article 794 views 412 downloads
Global Forest Types Based on Climatic and Vegetation Data
Sustainability, Volume: 14, Issue: 2, Start page: 634
Swansea University Author: Rocio Hernandez-Clemente
-
PDF | Version of Record
Copyrightr© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
Download (3.14MB)
DOI (Published version): 10.3390/su14020634
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 aime...
Published in: | Sustainability |
---|---|
ISSN: | 2071-1050 |
Published: |
MDPI AG
2022
|
Online Access: |
Check full text
|
URI: | https://cronfa.swan.ac.uk/Record/cronfa59121 |
first_indexed |
2022-01-10T14:38:10Z |
---|---|
last_indexed |
2022-01-22T04:28:10Z |
id |
cronfa59121 |
recordtype |
SURis |
fullrecord |
<?xml version="1.0"?><rfc1807><datestamp>2022-01-21T09:32:22.6632654</datestamp><bib-version>v2</bib-version><id>59121</id><entry>2022-01-10</entry><title>Global Forest Types Based on Climatic and Vegetation Data</title><swanseaauthors><author><sid>0b007e63ef097cd47d6bc60b58379103</sid><ORCID>0000-0002-4434-8346</ORCID><firstname>Rocio</firstname><surname>Hernandez-Clemente</surname><name>Rocio Hernandez-Clemente</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-01-10</date><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).</abstract><type>Journal Article</type><journal>Sustainability</journal><volume>14</volume><journalNumber>2</journalNumber><paginationStart>634</paginationStart><paginationEnd/><publisher>MDPI AG</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2071-1050</issnElectronic><keywords>forest types, NDVI, AVHRR GIMMS, temperature range, precipitation range</keywords><publishedDay>7</publishedDay><publishedMonth>1</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-01-07</publishedDate><doi>10.3390/su14020634</doi><url/><notes/><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><apcterm/><funders>Education Department of Hebei Province Grant: BJ2020025</funders><lastEdited>2022-01-21T09:32:22.6632654</lastEdited><Created>2022-01-10T14:35:34.3169261</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Biosciences, Geography and Physics - Geography</level></path><authors><author><firstname>Chen</firstname><surname>Xu</surname><order>1</order></author><author><firstname>Xianliang</firstname><surname>Zhang</surname><order>2</order></author><author><firstname>Rocio</firstname><surname>Hernandez-Clemente</surname><orcid>0000-0002-4434-8346</orcid><order>3</order></author><author><firstname>Wei</firstname><surname>Lu</surname><order>4</order></author><author><firstname>Rubén D.</firstname><surname>Manzanedo</surname><order>5</order></author></authors><documents><document><filename>59121__22090__7919b41368264a92bc67cda4a1949032.pdf</filename><originalFilename>sustainability-14-00634.pdf</originalFilename><uploaded>2022-01-10T14:35:34.3167541</uploaded><type>Output</type><contentLength>3287340</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>Copyrightr© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807> |
spelling |
2022-01-21T09:32:22.6632654 v2 59121 2022-01-10 Global Forest Types Based on Climatic and Vegetation Data 0b007e63ef097cd47d6bc60b58379103 0000-0002-4434-8346 Rocio Hernandez-Clemente Rocio Hernandez-Clemente true false 2022-01-10 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). Journal Article Sustainability 14 2 634 MDPI AG 2071-1050 forest types, NDVI, AVHRR GIMMS, temperature range, precipitation range 7 1 2022 2022-01-07 10.3390/su14020634 COLLEGE NANME COLLEGE CODE Swansea University Education Department of Hebei Province Grant: BJ2020025 2022-01-21T09:32:22.6632654 2022-01-10T14:35:34.3169261 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography Chen Xu 1 Xianliang Zhang 2 Rocio Hernandez-Clemente 0000-0002-4434-8346 3 Wei Lu 4 Rubén D. Manzanedo 5 59121__22090__7919b41368264a92bc67cda4a1949032.pdf sustainability-14-00634.pdf 2022-01-10T14:35:34.3167541 Output 3287340 application/pdf Version of Record true Copyrightr© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/licenses/by/4.0/ |
title |
Global Forest Types Based on Climatic and Vegetation Data |
spellingShingle |
Global Forest Types Based on Climatic and Vegetation Data Rocio Hernandez-Clemente |
title_short |
Global Forest Types Based on Climatic and Vegetation Data |
title_full |
Global Forest Types Based on Climatic and Vegetation Data |
title_fullStr |
Global Forest Types Based on Climatic and Vegetation Data |
title_full_unstemmed |
Global Forest Types Based on Climatic and Vegetation Data |
title_sort |
Global Forest Types Based on Climatic and Vegetation Data |
author_id_str_mv |
0b007e63ef097cd47d6bc60b58379103 |
author_id_fullname_str_mv |
0b007e63ef097cd47d6bc60b58379103_***_Rocio Hernandez-Clemente |
author |
Rocio Hernandez-Clemente |
author2 |
Chen Xu Xianliang Zhang Rocio Hernandez-Clemente Wei Lu Rubén D. Manzanedo |
format |
Journal article |
container_title |
Sustainability |
container_volume |
14 |
container_issue |
2 |
container_start_page |
634 |
publishDate |
2022 |
institution |
Swansea University |
issn |
2071-1050 |
doi_str_mv |
10.3390/su14020634 |
publisher |
MDPI AG |
college_str |
Faculty of Science and Engineering |
hierarchytype |
|
hierarchy_top_id |
facultyofscienceandengineering |
hierarchy_top_title |
Faculty of Science and Engineering |
hierarchy_parent_id |
facultyofscienceandengineering |
hierarchy_parent_title |
Faculty of Science and Engineering |
department_str |
School of Biosciences, Geography and Physics - Geography{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Geography |
document_store_str |
1 |
active_str |
0 |
description |
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). |
published_date |
2022-01-07T14:09:38Z |
_version_ |
1822049045785346048 |
score |
11.048453 |