Spatially Explicit Large Area Biomass Estimation: Three Approaches Using Forest Inventory and Remotely Sensed Imagery in a GIS
Forest inventory data often provide the required base data to enable the largearea mapping of biomass over a range of scales. However, spatially explicit estimates ofabove-ground biomass (AGB) over large areas may be limited by the spatial extent of theforest inventory relative to the area of intere...
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MDPI AG
2008-01-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/8/1/529/ |
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| author | Steen Magnussen Joan E. Luther Richard A. Fournier Joanne C. White Michael A. Wulder |
| author_facet | Steen Magnussen Joan E. Luther Richard A. Fournier Joanne C. White Michael A. Wulder |
| author_sort | Steen Magnussen |
| collection | DOAJ |
| description | Forest inventory data often provide the required base data to enable the largearea mapping of biomass over a range of scales. However, spatially explicit estimates ofabove-ground biomass (AGB) over large areas may be limited by the spatial extent of theforest inventory relative to the area of interest (i.e., inventories not spatially exhaustive), orby the omission of inventory attributes required for biomass estimation. These spatial andattributional gaps in the forest inventory may result in an underestimation of large areaAGB. The continuous nature and synoptic coverage of remotely sensed data have led totheir increased application for AGB estimation over large areas, although the use of thesedata remains challenging in complex forest environments. In this paper, we present anapproach to generating spatially explicit estimates of large area AGB by integrating AGBestimates from multiple data sources; 1. using a lookup table of conversion factors appliedto a non-spatially exhaustive forest inventory dataset (R2 = 0.64; RMSE = 16.95 t/ha), 2.applying a lookup table to unique combinations of land cover and vegetation densityoutputs derived from remotely sensed data (R2 = 0.52; RMSE = 19.97 t/ha), and 3. hybridmapping by augmenting forest inventory AGB estimates with remotely sensed AGB estimates where there are spatial or attributional gaps in the forest inventory data. Over our714,852 ha study area in central Saskatchewan, Canada, the AGB estimate generated fromthe forest inventory was approximately 40 Mega tonnes (Mt); however, the inventoryestimate represents only 51% of the total study area. The AGB estimate generated from theremotely sensed outputs that overlap those made from the forest inventory based approachdiffer by only 2 %; however in total, the remotely sensed estimate is 30 % greater (58 Mt)than the estimate generated from the forest inventory when the entire study area isaccounted for. Finally, using the hybrid approach, whereby the remotely sensed inputswere used to fill spatial gaps in the forest inventory, the total AGB for the study area wasestimated at 62 Mt. In the example presented, data integration facilitates comprehensiveand spatially explicit estimation of AGB for the entire study area. |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
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| publishDate | 2008-01-01 |
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| series | Sensors |
| spelling | doaj.art-b10fa45ced38421388fba14205bfb8592022-12-22T02:10:28ZengMDPI AGSensors1424-82202008-01-0181529560Spatially Explicit Large Area Biomass Estimation: Three Approaches Using Forest Inventory and Remotely Sensed Imagery in a GISSteen MagnussenJoan E. LutherRichard A. FournierJoanne C. WhiteMichael A. WulderForest inventory data often provide the required base data to enable the largearea mapping of biomass over a range of scales. However, spatially explicit estimates ofabove-ground biomass (AGB) over large areas may be limited by the spatial extent of theforest inventory relative to the area of interest (i.e., inventories not spatially exhaustive), orby the omission of inventory attributes required for biomass estimation. These spatial andattributional gaps in the forest inventory may result in an underestimation of large areaAGB. The continuous nature and synoptic coverage of remotely sensed data have led totheir increased application for AGB estimation over large areas, although the use of thesedata remains challenging in complex forest environments. In this paper, we present anapproach to generating spatially explicit estimates of large area AGB by integrating AGBestimates from multiple data sources; 1. using a lookup table of conversion factors appliedto a non-spatially exhaustive forest inventory dataset (R2 = 0.64; RMSE = 16.95 t/ha), 2.applying a lookup table to unique combinations of land cover and vegetation densityoutputs derived from remotely sensed data (R2 = 0.52; RMSE = 19.97 t/ha), and 3. hybridmapping by augmenting forest inventory AGB estimates with remotely sensed AGB estimates where there are spatial or attributional gaps in the forest inventory data. Over our714,852 ha study area in central Saskatchewan, Canada, the AGB estimate generated fromthe forest inventory was approximately 40 Mega tonnes (Mt); however, the inventoryestimate represents only 51% of the total study area. The AGB estimate generated from theremotely sensed outputs that overlap those made from the forest inventory based approachdiffer by only 2 %; however in total, the remotely sensed estimate is 30 % greater (58 Mt)than the estimate generated from the forest inventory when the entire study area isaccounted for. Finally, using the hybrid approach, whereby the remotely sensed inputswere used to fill spatial gaps in the forest inventory, the total AGB for the study area wasestimated at 62 Mt. In the example presented, data integration facilitates comprehensiveand spatially explicit estimation of AGB for the entire study area.http://www.mdpi.com/1424-8220/8/1/529/above-ground biomassforestremote sensingGISLandsat |
| spellingShingle | Steen Magnussen Joan E. Luther Richard A. Fournier Joanne C. White Michael A. Wulder Spatially Explicit Large Area Biomass Estimation: Three Approaches Using Forest Inventory and Remotely Sensed Imagery in a GIS Sensors above-ground biomass forest remote sensing GIS Landsat |
| title | Spatially Explicit Large Area Biomass Estimation: Three Approaches Using Forest Inventory and Remotely Sensed Imagery in a GIS |
| title_full | Spatially Explicit Large Area Biomass Estimation: Three Approaches Using Forest Inventory and Remotely Sensed Imagery in a GIS |
| title_fullStr | Spatially Explicit Large Area Biomass Estimation: Three Approaches Using Forest Inventory and Remotely Sensed Imagery in a GIS |
| title_full_unstemmed | Spatially Explicit Large Area Biomass Estimation: Three Approaches Using Forest Inventory and Remotely Sensed Imagery in a GIS |
| title_short | Spatially Explicit Large Area Biomass Estimation: Three Approaches Using Forest Inventory and Remotely Sensed Imagery in a GIS |
| title_sort | spatially explicit large area biomass estimation three approaches using forest inventory and remotely sensed imagery in a gis |
| topic | above-ground biomass forest remote sensing GIS Landsat |
| url | http://www.mdpi.com/1424-8220/8/1/529/ |
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