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Article: Landsat-observed changes in forest cover and attribution analysis over Northern China from 1996‒2020

TitleLandsat-observed changes in forest cover and attribution analysis over Northern China from 1996‒2020
Authors
Keywordsclimate variables
cover change analysis
driver contribution rates
feedback effects
Forests
Northern China
Issue Date15-Jan-2024
PublisherTaylor and Francis Group
Citation
GIScience & Remote Sensing, 2024, v. 61, n. 1 How to Cite?
Abstract

Forest dynamics provide important information on the ecological environment. The Three-North Shelter Forest Program (TNSFP) is one of the world’s largest reforestation/afforestation programs, however the actual changes in forest cover in the Three-North Regions (TNR) of China resulting from this program are highly uncertain. This study quantified changes in fractional forest cover (FFC) at 30 m using Landsat data from 1996 to 2020. Using the Google Earth Engine platform, more than 40,000 images from Landsat-5, Landsat 7 and Landsat-8 were integrated, and the annual surface reflectance was normalized based on the multi-band least squares regression and maximum normalized difference vegetation index composite method. An ensemble learning model trained using high-resolution Gao-Fen 2 satellite imagery was used to generate the FFC long time-series product. FFC showed an increasing trend with average rates of 0.022/10a in the last 25 years, and 0.03/10a after 2010 largely corresponding to the fourth and fifth phases of the TNSFP. There are significant regional differences in the relationship between FFC and air temperature ((Formula presented.) = 0.37) and precipitation ((Formula presented.) = 0.49). The increased air temperature in arid and less rainy areas inhibit the FFC increase, whereas the increase in precipitation had a promoting effect. FFC appeared more sensitive to changes in solar radiation and heat conditions in humid and rainy areas. The attribution analysis revealed that 34% of FFC changes were caused by climatic variables and 66% were caused by non-climatic factors. Among them, afforestation associated with the TNSFP significantly increased FFC, and forest fire is a key factor of forest change in the Greater Khingan Ranges and Lesser Khingan Ranges regions. Planting single tree species caused biological disasters in forests of Xinjiang and Inner Mongolia. Further analysis of the increased FFC using high-level satellite products demonstrated an improvement in environmental conditions with cooler land surface temperature and higher vegetation gross primary production over the TNR.


Persistent Identifierhttp://hdl.handle.net/10722/348836
ISSN
2023 Impact Factor: 6.0
2023 SCImago Journal Rankings: 1.756

 

DC FieldValueLanguage
dc.contributor.authorLiu, Xiaobang-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorMa, Han-
dc.contributor.authorLi, Bing-
dc.contributor.authorZhang, Yufang-
dc.contributor.authorLi, Yingying-
dc.contributor.authorHe, Tao-
dc.contributor.authorZhang, Guodong-
dc.contributor.authorXu, Jianglei-
dc.contributor.authorXiong, Changhao-
dc.contributor.authorMa, Rui-
dc.contributor.authorWu, Wenfu-
dc.contributor.authorTeng, Jiahua-
dc.date.accessioned2024-10-17T00:30:21Z-
dc.date.available2024-10-17T00:30:21Z-
dc.date.issued2024-01-15-
dc.identifier.citationGIScience & Remote Sensing, 2024, v. 61, n. 1-
dc.identifier.issn1548-1603-
dc.identifier.urihttp://hdl.handle.net/10722/348836-
dc.description.abstract<p>Forest dynamics provide important information on the ecological environment. The Three-North Shelter Forest Program (TNSFP) is one of the world’s largest reforestation/afforestation programs, however the actual changes in forest cover in the Three-North Regions (TNR) of China resulting from this program are highly uncertain. This study quantified changes in fractional forest cover (FFC) at 30 m using Landsat data from 1996 to 2020. Using the Google Earth Engine platform, more than 40,000 images from Landsat-5, Landsat 7 and Landsat-8 were integrated, and the annual surface reflectance was normalized based on the multi-band least squares regression and maximum normalized difference vegetation index composite method. An ensemble learning model trained using high-resolution Gao-Fen 2 satellite imagery was used to generate the FFC long time-series product. FFC showed an increasing trend with average rates of 0.022/10a in the last 25 years, and 0.03/10a after 2010 largely corresponding to the fourth and fifth phases of the TNSFP. There are significant regional differences in the relationship between FFC and air temperature ((Formula presented.) = 0.37) and precipitation ((Formula presented.) = 0.49). The increased air temperature in arid and less rainy areas inhibit the FFC increase, whereas the increase in precipitation had a promoting effect. FFC appeared more sensitive to changes in solar radiation and heat conditions in humid and rainy areas. The attribution analysis revealed that 34% of FFC changes were caused by climatic variables and 66% were caused by non-climatic factors. Among them, afforestation associated with the TNSFP significantly increased FFC, and forest fire is a key factor of forest change in the Greater Khingan Ranges and Lesser Khingan Ranges regions. Planting single tree species caused biological disasters in forests of Xinjiang and Inner Mongolia. Further analysis of the increased FFC using high-level satellite products demonstrated an improvement in environmental conditions with cooler land surface temperature and higher vegetation gross primary production over the TNR.</p>-
dc.languageeng-
dc.publisherTaylor and Francis Group-
dc.relation.ispartofGIScience & Remote Sensing-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectclimate variables-
dc.subjectcover change analysis-
dc.subjectdriver contribution rates-
dc.subjectfeedback effects-
dc.subjectForests-
dc.subjectNorthern China-
dc.titleLandsat-observed changes in forest cover and attribution analysis over Northern China from 1996‒2020-
dc.typeArticle-
dc.identifier.doi10.1080/15481603.2023.2300214-
dc.identifier.scopuseid_2-s2.0-85182482677-
dc.identifier.volume61-
dc.identifier.issue1-
dc.identifier.eissn1943-7226-
dc.identifier.issnl1548-1603-

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