Using airborne LiDAR to assess spatial heterogeneity in forest structure on Mount Kilimanjaro
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Field inventory plots which usually have small sizes of around 0.25–1 ha can only represent a sample of the much larger surrounding forest landscape. Based on airborne laser scanning (LiDAR) it has been shown for tropical forests that the bias in the selection of small field plots may hamper the extrapolation of structural forest attributes to larger spatial scales.
We conducted a LiDAR study on tropical montane forest and evaluated the representativeness of chosen inventory plots with respect to key structural attributes.
We used six forest inventory and their surrounding landscape plots on Mount Kilimanjaro in Tanzania and analyzed the similarities for mean top-of-canopy height (TCH), aboveground biomass (AGB), gap fraction, and leaf-area index (LAI). We also analyzed the similarity in gap-size frequencies for the landscape plots.
Mean biases between inventory and landscape plots were large reaching as much as 77% for gap fraction, 22% for LAI or 15% for AGB. Despite spatial heterogeneity of the landscape, gap-size frequency distributions were remarkably similar between the landscape plots.
The study indicates that biases in field studies of forest structure may be strong. Even when mean values were similar between inventory and landscape plots, the mostly non-normally distributed probability densities of the forest variable indicated a considerable sampling error of the small field plot to approximate the forest variable in the surrounding landscape. This poses difficulties for the spatial extrapolation of forest structural attributes and for assessing biomass or carbon fluxes at larger regional scales.
KeywordsBiomass Carbon Canopy-height model LiDAR Spatial heterogeneity Tanzania
We are grateful to T. Nauss from the University of Marburg for providing the LiDAR data. The study was funded by the German Research Foundation (DFG) and is part of the DFG research unit FOR1246 “Kilimanjaro ecosystems under global change: linking biodiversity, biotic interactions and biogeochemical ecosystem processes”. AH and RF were supported by the Helmholtz-Alliance Remote Sensing and Earth System Dynamics. NK was funded by the German Federal Ministry for Economic Affairs and Energy (BMWi) under the funding reference 50EE1416. We thank two reviewers for their constructive comments on our paper.
- Asner GP, Mascaro J, Anderson C, Knapp DE, Martin RE, Kennedy-Bowdoin T, van Breugel M, Davies S, Hall JS, Muller-Landau HC, Potvin C (2013b) High-fidelity national carbon mapping for resource management and REDD+. Carbon Balance Manag 8(7):1–14Google Scholar
- Brown C, Burslem DF, Illian JB, Bao L, Brockelman W, Cao M, Chang LW, Dattaraja HS, Davies S, Gunatilleke CV, Gunatilleke IA (2013) Multispecies coexistence of trees in tropical forests: spatial signals of topographic niche differentiation increase with environmental heterogeneity. Proc R Soc Lond B Biol Sci 280(1764):20130502CrossRefGoogle Scholar
- Bustamante M, Roitman I, Aide TM, Alencar A, Anderson LO, Aragão L, Asner GP, Barlow J, Berenguer E, Chambers J, Costa MH (2016) Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity. Glob Change Biol 22(1):92–109CrossRefGoogle Scholar
- Team RDC (2016) R: a language and environment for statistical computing. Austria, ViennaGoogle Scholar