Characterizing aboveground biomass and tree cover of regrowing forests in Brazil using multi‐source remote sensing data
Abstract Characterization of regrowing forests is vital for understanding forest dynamics to assess the impacts on carbon stocks and to support sustainable forest management. Although remote sensing is a key tool for understanding and monitoring forest dynamics, the use of exclusively remotely sense...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-08-01
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| Series: | Remote Sensing in Ecology and Conservation |
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| Online Access: | https://doi.org/10.1002/rse2.328 |
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| author | Na Chen Nandin‐Erdene Tsendbazar Daniela Requena Suarez Jan Verbesselt Martin Herold |
| author_facet | Na Chen Nandin‐Erdene Tsendbazar Daniela Requena Suarez Jan Verbesselt Martin Herold |
| author_sort | Na Chen |
| collection | DOAJ |
| description | Abstract Characterization of regrowing forests is vital for understanding forest dynamics to assess the impacts on carbon stocks and to support sustainable forest management. Although remote sensing is a key tool for understanding and monitoring forest dynamics, the use of exclusively remotely sensed data to explore the effects of different variables on regrowing forests across all biomes in Brazil has rarely been investigated. Here, we analyzed how environmental and human factors affect regrowing forests. Based on Brazil's secondary forest age map, 3060 locations disturbed between 1984 and 2018 were sampled, interpreted and analyzed in different biomes. We interpreted the time since disturbance for the sampled pixels in Google Earth Engine. Elevation, slope, climatic water deficit (CWD), the total Nitrogen of soil, cation exchange capacity (CEC) of soil, surrounding tree cover, distance to roads, distance to settlements and fire frequency were analyzed in their importance for predicting aboveground biomass (AGB) and tree cover derived from global forest aboveground biomass map and tree cover map, respectively. Results show that time since disturbance interpreted from satellite time series is the most important predictor for characterizing AGB and tree cover of regrowing forests. AGB increased with increasing time since disturbance, surrounding tree cover, soil total N, slope, distance to roads, distance to settlements and decreased with larger fire frequency, CWD and CEC of soil. Tree cover increased with larger time since disturbance, soil total N, surrounding tree cover, distance to roads, distance to settlements, slope and decreased with increasing elevation and CWD. These results emphasize the importance of remotely sensing products as key opportunities to improve the characterization of forest regrowth and to reduce data gaps and uncertainties related to forest carbon sink estimation. Our results provide a better understanding of regional forest dynamics, toward developing and assessing effective forest‐related restoration and climatic mitigation strategies. |
| first_indexed | 2024-03-12T13:17:13Z |
| format | Article |
| id | doaj.art-293b7d1d9ec84c38b6dc920634132b08 |
| institution | Directory Open Access Journal |
| issn | 2056-3485 |
| language | English |
| last_indexed | 2024-03-12T13:17:13Z |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Remote Sensing in Ecology and Conservation |
| spelling | doaj.art-293b7d1d9ec84c38b6dc920634132b082023-08-26T15:26:48ZengWileyRemote Sensing in Ecology and Conservation2056-34852023-08-019455356710.1002/rse2.328Characterizing aboveground biomass and tree cover of regrowing forests in Brazil using multi‐source remote sensing dataNa Chen0Nandin‐Erdene Tsendbazar1Daniela Requena Suarez2Jan Verbesselt3Martin Herold4Laboratory of Geo‐information Science and Remote Sensing Wageningen University & Research 6708 PBWageningen Droevendaalsesteeg 3The NetherlandsLaboratory of Geo‐information Science and Remote Sensing Wageningen University & Research 6708 PBWageningen Droevendaalsesteeg 3The NetherlandsLaboratory of Geo‐information Science and Remote Sensing Wageningen University & Research 6708 PBWageningen Droevendaalsesteeg 3The NetherlandsLaboratory of Geo‐information Science and Remote Sensing Wageningen University & Research 6708 PBWageningen Droevendaalsesteeg 3The NetherlandsLaboratory of Geo‐information Science and Remote Sensing Wageningen University & Research 6708 PBWageningen Droevendaalsesteeg 3The NetherlandsAbstract Characterization of regrowing forests is vital for understanding forest dynamics to assess the impacts on carbon stocks and to support sustainable forest management. Although remote sensing is a key tool for understanding and monitoring forest dynamics, the use of exclusively remotely sensed data to explore the effects of different variables on regrowing forests across all biomes in Brazil has rarely been investigated. Here, we analyzed how environmental and human factors affect regrowing forests. Based on Brazil's secondary forest age map, 3060 locations disturbed between 1984 and 2018 were sampled, interpreted and analyzed in different biomes. We interpreted the time since disturbance for the sampled pixels in Google Earth Engine. Elevation, slope, climatic water deficit (CWD), the total Nitrogen of soil, cation exchange capacity (CEC) of soil, surrounding tree cover, distance to roads, distance to settlements and fire frequency were analyzed in their importance for predicting aboveground biomass (AGB) and tree cover derived from global forest aboveground biomass map and tree cover map, respectively. Results show that time since disturbance interpreted from satellite time series is the most important predictor for characterizing AGB and tree cover of regrowing forests. AGB increased with increasing time since disturbance, surrounding tree cover, soil total N, slope, distance to roads, distance to settlements and decreased with larger fire frequency, CWD and CEC of soil. Tree cover increased with larger time since disturbance, soil total N, surrounding tree cover, distance to roads, distance to settlements, slope and decreased with increasing elevation and CWD. These results emphasize the importance of remotely sensing products as key opportunities to improve the characterization of forest regrowth and to reduce data gaps and uncertainties related to forest carbon sink estimation. Our results provide a better understanding of regional forest dynamics, toward developing and assessing effective forest‐related restoration and climatic mitigation strategies.https://doi.org/10.1002/rse2.328AGBmixed‐effects modelregrowing forestssatellite datatime seriestree cover |
| spellingShingle | Na Chen Nandin‐Erdene Tsendbazar Daniela Requena Suarez Jan Verbesselt Martin Herold Characterizing aboveground biomass and tree cover of regrowing forests in Brazil using multi‐source remote sensing data Remote Sensing in Ecology and Conservation AGB mixed‐effects model regrowing forests satellite data time series tree cover |
| title | Characterizing aboveground biomass and tree cover of regrowing forests in Brazil using multi‐source remote sensing data |
| title_full | Characterizing aboveground biomass and tree cover of regrowing forests in Brazil using multi‐source remote sensing data |
| title_fullStr | Characterizing aboveground biomass and tree cover of regrowing forests in Brazil using multi‐source remote sensing data |
| title_full_unstemmed | Characterizing aboveground biomass and tree cover of regrowing forests in Brazil using multi‐source remote sensing data |
| title_short | Characterizing aboveground biomass and tree cover of regrowing forests in Brazil using multi‐source remote sensing data |
| title_sort | characterizing aboveground biomass and tree cover of regrowing forests in brazil using multi source remote sensing data |
| topic | AGB mixed‐effects model regrowing forests satellite data time series tree cover |
| url | https://doi.org/10.1002/rse2.328 |
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