Characterizing the Accelerated Global Carbon Emissions from Forest Loss during 1985–2020 Using Fine-Resolution Remote Sensing Datasets
Previous studies on global carbon emissions from forest loss have been marked by great discrepancies due to uncertainties regarding the lost area and the densities of different carbon pools. In this study, we employed a new global 30 m land cover dynamic dataset (GLC_FCS30D) to improve the assessmen...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/6/978 |
| _version_ | 1797239469403799552 |
|---|---|
| author | Wendi Liu Xiao Zhang Hong Xu Tingting Zhao Jinqing Wang Zhehua Li Liangyun Liu |
| author_facet | Wendi Liu Xiao Zhang Hong Xu Tingting Zhao Jinqing Wang Zhehua Li Liangyun Liu |
| author_sort | Wendi Liu |
| collection | DOAJ |
| description | Previous studies on global carbon emissions from forest loss have been marked by great discrepancies due to uncertainties regarding the lost area and the densities of different carbon pools. In this study, we employed a new global 30 m land cover dynamic dataset (GLC_FCS30D) to improve the assessment of forest loss areas; then, we combined multi-sourced carbon stock products to enhance the information on carbon density. Afterwards, we estimated the global carbon emissions from forest loss over the period of 1985–2020 based on the method recommended by the Intergovernmental Panel on Climate Change Guidelines (IPCC). The results indicate that global forest loss continued to accelerate over the past 35 years, totaling about 582.17 Mha and leading to total committed carbon emissions of 35.22 ± 9.38 PgC. Tropical zones dominated global carbon emissions (~2/3) due to their higher carbon density and greater forest loss. Furthermore, global emissions more than doubled in the period of 2015–2020 (1.77 ± 0.44 PgC/yr) compared to those in 1985–2000 (0.69 ± 0.21 PgC/yr). Notably, the forest loss at high altitudes (i.e., above 1000 m) more than tripled in mountainous regions, resulting in more pronounced carbon emissions in these areas. Therefore, the accelerating trend of global carbon emissions from forest loss indicates that great challenges still remain for achieving the COP 26 Declaration to halt forest loss by 2030. |
| first_indexed | 2024-04-24T17:52:02Z |
| format | Article |
| id | doaj.art-b99a0951c46341889c83305cc31feb77 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-24T17:52:02Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b99a0951c46341889c83305cc31feb772024-03-27T14:02:32ZengMDPI AGRemote Sensing2072-42922024-03-0116697810.3390/rs16060978Characterizing the Accelerated Global Carbon Emissions from Forest Loss during 1985–2020 Using Fine-Resolution Remote Sensing DatasetsWendi Liu0Xiao Zhang1Hong Xu2Tingting Zhao3Jinqing Wang4Zhehua Li5Liangyun Liu6Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaThe High-Tech Research & Development Center (HTRDC) of the Ministry of Science & Technology, Beijing 100044, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaPrevious studies on global carbon emissions from forest loss have been marked by great discrepancies due to uncertainties regarding the lost area and the densities of different carbon pools. In this study, we employed a new global 30 m land cover dynamic dataset (GLC_FCS30D) to improve the assessment of forest loss areas; then, we combined multi-sourced carbon stock products to enhance the information on carbon density. Afterwards, we estimated the global carbon emissions from forest loss over the period of 1985–2020 based on the method recommended by the Intergovernmental Panel on Climate Change Guidelines (IPCC). The results indicate that global forest loss continued to accelerate over the past 35 years, totaling about 582.17 Mha and leading to total committed carbon emissions of 35.22 ± 9.38 PgC. Tropical zones dominated global carbon emissions (~2/3) due to their higher carbon density and greater forest loss. Furthermore, global emissions more than doubled in the period of 2015–2020 (1.77 ± 0.44 PgC/yr) compared to those in 1985–2000 (0.69 ± 0.21 PgC/yr). Notably, the forest loss at high altitudes (i.e., above 1000 m) more than tripled in mountainous regions, resulting in more pronounced carbon emissions in these areas. Therefore, the accelerating trend of global carbon emissions from forest loss indicates that great challenges still remain for achieving the COP 26 Declaration to halt forest loss by 2030.https://www.mdpi.com/2072-4292/16/6/978forest losscarbon emissionsland covertropical rainforestmountainous forest |
| spellingShingle | Wendi Liu Xiao Zhang Hong Xu Tingting Zhao Jinqing Wang Zhehua Li Liangyun Liu Characterizing the Accelerated Global Carbon Emissions from Forest Loss during 1985–2020 Using Fine-Resolution Remote Sensing Datasets Remote Sensing forest loss carbon emissions land cover tropical rainforest mountainous forest |
| title | Characterizing the Accelerated Global Carbon Emissions from Forest Loss during 1985–2020 Using Fine-Resolution Remote Sensing Datasets |
| title_full | Characterizing the Accelerated Global Carbon Emissions from Forest Loss during 1985–2020 Using Fine-Resolution Remote Sensing Datasets |
| title_fullStr | Characterizing the Accelerated Global Carbon Emissions from Forest Loss during 1985–2020 Using Fine-Resolution Remote Sensing Datasets |
| title_full_unstemmed | Characterizing the Accelerated Global Carbon Emissions from Forest Loss during 1985–2020 Using Fine-Resolution Remote Sensing Datasets |
| title_short | Characterizing the Accelerated Global Carbon Emissions from Forest Loss during 1985–2020 Using Fine-Resolution Remote Sensing Datasets |
| title_sort | characterizing the accelerated global carbon emissions from forest loss during 1985 2020 using fine resolution remote sensing datasets |
| topic | forest loss carbon emissions land cover tropical rainforest mountainous forest |
| url | https://www.mdpi.com/2072-4292/16/6/978 |
| work_keys_str_mv | AT wendiliu characterizingtheacceleratedglobalcarbonemissionsfromforestlossduring19852020usingfineresolutionremotesensingdatasets AT xiaozhang characterizingtheacceleratedglobalcarbonemissionsfromforestlossduring19852020usingfineresolutionremotesensingdatasets AT hongxu characterizingtheacceleratedglobalcarbonemissionsfromforestlossduring19852020usingfineresolutionremotesensingdatasets AT tingtingzhao characterizingtheacceleratedglobalcarbonemissionsfromforestlossduring19852020usingfineresolutionremotesensingdatasets AT jinqingwang characterizingtheacceleratedglobalcarbonemissionsfromforestlossduring19852020usingfineresolutionremotesensingdatasets AT zhehuali characterizingtheacceleratedglobalcarbonemissionsfromforestlossduring19852020usingfineresolutionremotesensingdatasets AT liangyunliu characterizingtheacceleratedglobalcarbonemissionsfromforestlossduring19852020usingfineresolutionremotesensingdatasets |