Assessing the influence of return density on estimation of lidar-based aboveground biomass in tropical peat swamp forests of Kalimantan,Indonesia |
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| Institution: | 1. Department of Forest Sciences, University of Helsinki, PO Box 27, FI-00014 Helsinki, Finland;2. Department of Forest Resource Management, Swedish University of Agricultural Sciences, SLU Skogsmarksgränd, SE-90183 Umeå, Sweden;3. Natural Resources Institute Finland (Luke), PO Box 18, FI-01301 Vantaa, Finland;4. National Land Survey of Finland, Finnish Geospatial Research Institute (FGI), PO Box 15, FI-02431 Masala, Finland;1. Department of Agricultural, Food and Forestry Systems, Università degli Studi di Firenze, Italy;2. Northern Research Station, U.S. Forest Service, Saint Paul, MN, USA;3. Department of Economics and Statistics, Università di Siena, Italy;4. Department of Biosciences and Territory, University of Molise, Pesche (IS), Italy;1. Department of Forestry and Environmental Resources, North Carolina State University, Box 7646, Raleigh, NC 27695, USA;2. Pacific Northwest Research Station, USDA Forest Service, University of Washington, PO Box 352100, Seattle, WA 98195-2100, USA;3. Southern Research Station, USDA Forest Service, Bent Creek Experimental Forest, 1577 Brevard Road, Asheville, NC 28803, USA;4. Southern Research Station, USDA Forest Service, Savannah River, P.O. Box 700, New Ellenton, SC 29809, USA;5. Fisheries, Wildlife, and Conservation Biology Program, North Carolina State University, Box 7646, Raleigh, NC 27695, USA |
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| Abstract: | The airborne lidar system (ALS) provides a means to efficiently monitor the status of remote tropical forests and continues to be the subject of intense evaluation. However, the cost of ALS acquisition can vary significantly depending on the acquisition parameters, particularly the return density (i.e., spatial resolution) of the lidar point cloud. This study assessed the effect of lidar return density on the accuracy of lidar metrics and regression models for estimating aboveground biomass (AGB) and basal area (BA) in tropical peat swamp forests (PSF) in Kalimantan, Indonesia. A large dataset of ALS covering an area of 123,000 ha was used in this study. This study found that cumulative return proportion (CRP) variables represent a better accumulation of AGB over tree heights than height-related variables. The CRP variables in power models explained 80.9% and 90.9% of the BA and AGB variations, respectively. Further, it was found that low-density (and low-cost) lidar should be considered as a feasible option for assessing AGB and BA in vast areas of flat, lowland PSF. The performance of the models generated using reduced return densities as low as 1/9 returns per m2 also yielded strong agreement with the original high-density data. The use model-based statistical inferences enabled relatively precise estimates of the mean AGB at the landscape scale to be obtained with a fairly low-density of 1/4 returns per m2, with less than 10% standard error (SE). Further, even when very low-density lidar data was used (i.e., 1/49 returns per m2) the bias of the mean AGB estimates were still less than 10% with a SE of approximately 15%. This study also investigated the influence of different DTM resolutions for normalizing the elevation during the generation of forest-related lidar metrics using various return densities point cloud. We found that the high-resolution digital terrain model (DTM) had little effect on the accuracy of lidar metrics calculation in PSF. The accuracy of low-density lidar metrics in PSF was more influenced by the density of aboveground returns, rather than the last return. This is due to the flat topography of the study area. The results of this study will be valuable for future economical and feasible assessments of forest metrics over large areas of tropical peat swamp ecosystems. |
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| Keywords: | Airborne lidar Aboveground biomass Return proportion metric Digital terrain model Model regression Model-based inference |
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