Abstract
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.
Highlights
Forest biomass is defined by [1] as the above-ground portion of live trees per unit area
Landsat TM data were selected for above-ground biomass (AGB) estimation due to its suitability in terms of resolution and practical considerations associated with its use [40]; the spatial resolution of approximately 30 m by 30 m is adequate to assess information at the forest stand level, and this imagery has a minimum mapping area of about 0.5 ha, which is compatible with standard forestry mapping practices [86, 113]
In order to make comparisons between the field data and the inventory data, including timber volumes from lookup tables provided by the Saskatchewan government, two issues needed to be resolved: (i) the total stem volumes reported in the field data included merchantable and nonmerchantable portions of all trees, whereas the volume figures provided in the forest inventory lookup tables included only the merchantable portion of trees with a DBH above the merchantable limit; and (ii), there was an age difference of up to 10 years between the field plot and forest inventory data, an amount of time in which trees can grow enough to markedly change the stand volume
Summary
Forest biomass is defined by [1] as the above-ground portion of live trees per unit area. Biomass estimates may range from local to global scales, and for some regions, tropical forest regions, there are large variations in the estimates reported in the literature [5, 26, 27, 28, 29]. Global and national estimates of forest above-ground biomass (AGB) are often aspatial estimates, compiled through the tabular generalization of national level forest inventory data [1, 30, 31, 32, 33, 34, 35, 6]. Methods and data sources for generating spatially explicit large-area AGB estimates have been the subject of extensive research [8, 25, 28, 36, 37, 38, 39]
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