Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating
Monitoring and understanding the spatio-temporal variations of forest aboveground biomass (AGB) is a key basis to quantitatively assess the carbon sequestration capacity of a forest ecosystem. To map and update forest AGB in the Greater Khingan Mountains (GKM) of China, this work proposes a physical...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-09-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/17/9/2062 |
| _version_ | 1818038339049095168 |
|---|---|
| author | Xiaoman Lu Guang Zheng Colton Miller Ernesto Alvarado |
| author_facet | Xiaoman Lu Guang Zheng Colton Miller Ernesto Alvarado |
| author_sort | Xiaoman Lu |
| collection | DOAJ |
| description | Monitoring and understanding the spatio-temporal variations of forest aboveground biomass (AGB) is a key basis to quantitatively assess the carbon sequestration capacity of a forest ecosystem. To map and update forest AGB in the Greater Khingan Mountains (GKM) of China, this work proposes a physical-based approach. Based on the baseline forest AGB from Landsat Enhanced Thematic Mapper Plus (ETM+) images in 2008, we dynamically updated the annual forest AGB from 2009 to 2012 by adding the annual AGB increment (ABI) obtained from the simulated daily and annual net primary productivity (NPP) using the Boreal Ecosystem Productivity Simulator (BEPS) model. The 2012 result was validated by both field- and aerial laser scanning (ALS)-based AGBs. The predicted forest AGB for 2012 estimated from the process-based model can explain 31% (n = 35, p < 0.05, RMSE = 2.20 kg/m2) and 85% (n = 100, p < 0.01, RMSE = 1.71 kg/m2) of variation in field- and ALS-based forest AGBs, respectively. However, due to the saturation of optical remote sensing-based spectral signals and contribution of understory vegetation, the BEPS-based AGB tended to underestimate/overestimate the AGB for dense/sparse forests. Generally, our results showed that the remotely sensed forest AGB estimates could serve as the initial carbon pool to parameterize the process-based model for NPP simulation, and the combination of the baseline forest AGB and BEPS model could effectively update the spatiotemporal distribution of forest AGB. |
| first_indexed | 2024-12-10T07:41:10Z |
| format | Article |
| id | doaj.art-3937711ff2d740a0b4d20b66b0a73d03 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T07:41:10Z |
| publishDate | 2017-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3937711ff2d740a0b4d20b66b0a73d032022-12-22T01:57:18ZengMDPI AGSensors1424-82202017-09-01179206210.3390/s17092062s17092062Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass UpdatingXiaoman Lu0Guang Zheng1Colton Miller2Ernesto Alvarado3Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, ChinaJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023, ChinaSchool of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USASchool of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USAMonitoring and understanding the spatio-temporal variations of forest aboveground biomass (AGB) is a key basis to quantitatively assess the carbon sequestration capacity of a forest ecosystem. To map and update forest AGB in the Greater Khingan Mountains (GKM) of China, this work proposes a physical-based approach. Based on the baseline forest AGB from Landsat Enhanced Thematic Mapper Plus (ETM+) images in 2008, we dynamically updated the annual forest AGB from 2009 to 2012 by adding the annual AGB increment (ABI) obtained from the simulated daily and annual net primary productivity (NPP) using the Boreal Ecosystem Productivity Simulator (BEPS) model. The 2012 result was validated by both field- and aerial laser scanning (ALS)-based AGBs. The predicted forest AGB for 2012 estimated from the process-based model can explain 31% (n = 35, p < 0.05, RMSE = 2.20 kg/m2) and 85% (n = 100, p < 0.01, RMSE = 1.71 kg/m2) of variation in field- and ALS-based forest AGBs, respectively. However, due to the saturation of optical remote sensing-based spectral signals and contribution of understory vegetation, the BEPS-based AGB tended to underestimate/overestimate the AGB for dense/sparse forests. Generally, our results showed that the remotely sensed forest AGB estimates could serve as the initial carbon pool to parameterize the process-based model for NPP simulation, and the combination of the baseline forest AGB and BEPS model could effectively update the spatiotemporal distribution of forest AGB.https://www.mdpi.com/1424-8220/17/9/2062process-based modelNPPAGBBEPSALS |
| spellingShingle | Xiaoman Lu Guang Zheng Colton Miller Ernesto Alvarado Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating Sensors process-based model NPP AGB BEPS ALS |
| title | Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating |
| title_full | Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating |
| title_fullStr | Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating |
| title_full_unstemmed | Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating |
| title_short | Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating |
| title_sort | combining multi source remotely sensed data and a process based model for forest aboveground biomass updating |
| topic | process-based model NPP AGB BEPS ALS |
| url | https://www.mdpi.com/1424-8220/17/9/2062 |
| work_keys_str_mv | AT xiaomanlu combiningmultisourceremotelysenseddataandaprocessbasedmodelforforestabovegroundbiomassupdating AT guangzheng combiningmultisourceremotelysenseddataandaprocessbasedmodelforforestabovegroundbiomassupdating AT coltonmiller combiningmultisourceremotelysenseddataandaprocessbasedmodelforforestabovegroundbiomassupdating AT ernestoalvarado combiningmultisourceremotelysenseddataandaprocessbasedmodelforforestabovegroundbiomassupdating |