Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under Model-Based Inference
Due to climate change, treelines are moving to higher elevations and latitudes. The estimation of biomass of trees and shrubs advancing into alpine areas is necessary for carbon reporting. Remotely sensed (RS) data have previously been utilised extensively for the estimation of forest variables such...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/14/3508 |
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| author | Ritwika Mukhopadhyay Erik Næsset Terje Gobakken Ida Marielle Mienna Jaime Candelas Bielza Gunnar Austrheim Henrik Jan Persson Hans Ole Ørka Bjørn-Eirik Roald Ole Martin Bollandsås |
| author_facet | Ritwika Mukhopadhyay Erik Næsset Terje Gobakken Ida Marielle Mienna Jaime Candelas Bielza Gunnar Austrheim Henrik Jan Persson Hans Ole Ørka Bjørn-Eirik Roald Ole Martin Bollandsås |
| author_sort | Ritwika Mukhopadhyay |
| collection | DOAJ |
| description | Due to climate change, treelines are moving to higher elevations and latitudes. The estimation of biomass of trees and shrubs advancing into alpine areas is necessary for carbon reporting. Remotely sensed (RS) data have previously been utilised extensively for the estimation of forest variables such as tree height, volume, basal area, and aboveground biomass (AGB) in various forest types. Model-based inference is found to be efficient for the estimation of forest attributes using auxiliary RS data, and this study focused on testing model-based estimations of AGB in the treeline ecotone using an area-based approach. Shrubs (<i>Salix</i> spp., <i>Betula nana</i>) and trees (<i>Betula pubescens</i> ssp. <i>czerepanovii</i>, <i>Sorbus aucuparia</i>, <i>Populus tremula</i>, <i>Pinus sylvestris</i>, <i>Picea abies</i>) with heights up to about five meters constituted the AGB components. The study was carried out in a treeline ecotone in Hol, southern Norway, using field plots and point cloud data obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP). The field data were acquired for two different strata: tall and short vegetation. Two separate models for predicting the AGB were constructed for each stratum based on metrics calculated from ALS and DAP point clouds, respectively. From the stratified predictions, mean AGB was estimated for the entire study area. Despite the prediction models showing a weak fit, as indicated by their R<sup>2</sup>-values, the 95% CIs were relatively narrow, indicating adequate precision of the AGB estimates. No significant difference was found between the mean AGB estimates for the ALS and DAP models for either of the strata. Our results imply that RS data from ALS and DAP can be used for the estimation of AGB in treeline ecotones. |
| first_indexed | 2024-03-11T00:42:22Z |
| format | Article |
| id | doaj.art-c693e44190674130b673e05b29631927 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T00:42:22Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c693e44190674130b673e05b296319272023-11-18T21:12:01ZengMDPI AGRemote Sensing2072-42922023-07-011514350810.3390/rs15143508Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under Model-Based InferenceRitwika Mukhopadhyay0Erik Næsset1Terje Gobakken2Ida Marielle Mienna3Jaime Candelas Bielza4Gunnar Austrheim5Henrik Jan Persson6Hans Ole Ørka7Bjørn-Eirik Roald8Ole Martin Bollandsås9Department of Forest Resource Management, Swedish University of Agricultural Sciences, 90183 Umeå, SwedenFaculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, 1433 Ås, NorwayFaculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, 1433 Ås, NorwayFaculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, 1433 Ås, NorwayFaculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, 1433 Ås, NorwayDepartment of Natural History, Norwegian University of Science and Technology, 7491 Trondheim, NorwayDepartment of Forest Resource Management, Swedish University of Agricultural Sciences, 90183 Umeå, SwedenFaculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, 1433 Ås, NorwayFaculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, 1433 Ås, NorwayFaculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, 1433 Ås, NorwayDue to climate change, treelines are moving to higher elevations and latitudes. The estimation of biomass of trees and shrubs advancing into alpine areas is necessary for carbon reporting. Remotely sensed (RS) data have previously been utilised extensively for the estimation of forest variables such as tree height, volume, basal area, and aboveground biomass (AGB) in various forest types. Model-based inference is found to be efficient for the estimation of forest attributes using auxiliary RS data, and this study focused on testing model-based estimations of AGB in the treeline ecotone using an area-based approach. Shrubs (<i>Salix</i> spp., <i>Betula nana</i>) and trees (<i>Betula pubescens</i> ssp. <i>czerepanovii</i>, <i>Sorbus aucuparia</i>, <i>Populus tremula</i>, <i>Pinus sylvestris</i>, <i>Picea abies</i>) with heights up to about five meters constituted the AGB components. The study was carried out in a treeline ecotone in Hol, southern Norway, using field plots and point cloud data obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP). The field data were acquired for two different strata: tall and short vegetation. Two separate models for predicting the AGB were constructed for each stratum based on metrics calculated from ALS and DAP point clouds, respectively. From the stratified predictions, mean AGB was estimated for the entire study area. Despite the prediction models showing a weak fit, as indicated by their R<sup>2</sup>-values, the 95% CIs were relatively narrow, indicating adequate precision of the AGB estimates. No significant difference was found between the mean AGB estimates for the ALS and DAP models for either of the strata. Our results imply that RS data from ALS and DAP can be used for the estimation of AGB in treeline ecotones.https://www.mdpi.com/2072-4292/15/14/3508aboveground biomassairborne laser scanningimage matchingmodel-based inferencetreeline vegetationuncertainty estimation |
| spellingShingle | Ritwika Mukhopadhyay Erik Næsset Terje Gobakken Ida Marielle Mienna Jaime Candelas Bielza Gunnar Austrheim Henrik Jan Persson Hans Ole Ørka Bjørn-Eirik Roald Ole Martin Bollandsås Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under Model-Based Inference Remote Sensing aboveground biomass airborne laser scanning image matching model-based inference treeline vegetation uncertainty estimation |
| title | Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under Model-Based Inference |
| title_full | Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under Model-Based Inference |
| title_fullStr | Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under Model-Based Inference |
| title_full_unstemmed | Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under Model-Based Inference |
| title_short | Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under Model-Based Inference |
| title_sort | mapping and estimating aboveground biomass in an alpine treeline ecotone under model based inference |
| topic | aboveground biomass airborne laser scanning image matching model-based inference treeline vegetation uncertainty estimation |
| url | https://www.mdpi.com/2072-4292/15/14/3508 |
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