Adiningrat, D.P.

Schlund, M.

Skidmore, A.K.

Wang, Tiejun

English

Old-growth forest is the ultimate forest stand development stage with the most complex structure. The complexity allows different ecological niches to be assembled, which benefits biodiversity. Some attributes are well known for defining old-growth forests' complexity, such as multi-age story, tree size (height and DBH), basal area, stand density, and deadwood presence. Their appearance and diversity characterize the degree of old-growth forests' structural complexity. However, only a few studies incorporated remote sensing data to identify those attributes for measuring the structural complexity of the old-growth forest stage. Through this study, we aimed to use the integration of multisource remote sensing data -mainly LiDAR and hyperspectral-, and ancillary GIS data to derive information the complexity of old-growth forests as well as to incorporate ground measured structural attributes as a proxy for enhancing the characterization. LiDAR has been proven to derive 3D forest structural information, which is suitable for delivering the complexity information of structural attributes both in horizontal and vertical dimensions. The hyperspectral imagery is useful for further old-growth forests complexity characterization, especially to define how the complex ecosystem structure will affect the dynamics of the ecosystem function such as productivity and disturbances in old-growth forest ecosystems. The expected results are a correlation model of the impact of the structural complexity on the ecosystem function of old-growth and earlier stages and a further explanation of the impact on the biodiversity in the old-growth forest ecosystem

University of Twente Research Information

Characterizing old-growth forests from multisource remote sensingGeneral Proposed FrameworkReferencesde Assis Barros, L., & Elkin, C. (2021). An index for tracking old growth value in disturbance-prone forest landscapes. Ecological Indicators, 121, 107175. https://doi.org/10.1016/j.ecolind.2020.107175Falkowski, M. J., Evans, J. S., Martinuzzi, S., Gessler, P. E., & Hudak, A. T. (2009). Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA. Remote Sensing of Environment, 113, 946–956. https://doi.org/10.1016/j.rse.2009.01.003Franklin, J. F., & Pelt, R. Van. (2004). Spatial Aspects of Structural Complexity in Old growth Forests. Journal of Forestry, 102(3), 22–28. https://doi.org/10.1093/jof/102.3.22Martin, M., Cerrejón, C., & Valeria, O. (2021). Complementary airborne LiDAR and satellite indices are reliable predictors of disturbance-induced structural diversity in mixed old growth forest landscapes. Remote Sensing of Environment, 267, 112746. https://doi.org/10.1016/j.rse.2021.112746O’Brien, L., Schuck, A., Fraccaroli, C., Pötzelsberger, E., Winkel, G., & Lindner, M. (2021). Protecting old growth forests in Europe: A review of scientific evidence to inform policy implementation. https://doi.org/https://doi.org/10.36333/rs1Spies, T. A. (2004). Ecological Concepts and Diversity of Old growth Forests. Journal of Forestry, 102(3), 14–20. https://doi.org/10.2307/2260958AcknowledgmentThe work is part of “BIOSPACE-MonitoringBiodiversity from Space” project with funding fromEuropean Research Council (grant 397 agreementID 834709, H2020-EU.1.1) and the “Data PoolInitiative” for allowing the BFNP datasets to be used.Devara P. Adiningrat 1*, Andrew K. Skidmore 1,2, Michael Schlund 1, Tiejun Wang 11 Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands2 Department of Environmental Science, Macquarie University, Sydney, Australia.Field measurementsSpectral IndicesAncillary GIS datasetsExpert Rule-basedOld-growth IndexIndex generation LidarHyperspectralStructural Diversity/ComplexitySecond-growthDeadwood Distribution Modelold-growthAttributes/Features derivationTime Series AnalysisModel ExtrapolationModel ComparisonEvaluation• Disturbance• Species Composition• ProductivityModel upscaling and evaluationIntroduction• Old-growth forest is the ultimate standdevelopment stage with the most complexstructure, which benefits the biodiversity as thedifferent ecological niches are allowed to beassembled.• Commonly, old-growth forests are characterizedby the complexity and diversity of structuralattributes generated from field measurement, andfunctional traits are often missing in old-growthforest characterization.• Functional traits can be valuable to define the old-growth forests by explaining the changes in theecosystem function over the time of standdevelopments.• Only a few studies have used the integration ofstructural and functional traits generated fromremote sensing to characterize old-growth forests.• We integrated multisource remote sensing data -mainly LiDAR and hyperspectral- to deriveinformation on the complexity of structure andfunction of old-growth forests over space and time.Objectives• To identify RS-enabled structural and functionaltraits of old-growth forests.• To upscale the characterization of old-growthforests to higher spatial extents• To extrapolate the method to other old-growthforest ecosystems in different biomes andlatitudes.Study SitesStudy Sites of this research: a) Veluwe, The Netherlands, b) Bavarian ForestNational Park (BFNP), Germany, c) Evo, Finland. BFNP is the site for benchmarkingthe procedure, methods, and tests for old-growth forests indices developments.a bcFuture Works• Develop “old-growthness” indices by combiningthe RS-derived structural and functionalattributes using expert rule-based approach.• Upscale the old-growth forest indices-basedmodel from plot to landscape level.• Apply model extrapolation and evaluate themethod and the used approach.Further contact:*Email : d.p.adiningrat@utwente.nlWebsite : https://www.bio-space.nl/Structural TraitsOld-growth forests have more complex and diverse structure than second-growth forests both in horizontal (canopy heterogeneity) and vertical(multilayer canopy) dimensions.The median of LiDAR structural diversity metrics of old-growth and second-growth forests show a significantdifference based on the Wilcoxon test (p < 0.05)Functional Traits Old-growth forests distribution mapTime Series AnalysisLeaf Area Index DisturbanceVegetation Lignin ContentNitrogen ContentVegetation Cellulose ContentExample of different vegetation indices to observe the dynamics of ecosystemfunction of old-growth forests. Later will be combined with structural traits and atime-series approach to developing old-growth indices, mapping and upscaling thedistribution of the old-growth forest.

Characterizing old-growth forests from multisource remote sensing

https://ris.utwente.nl/ws/files/313520683/ForestSat_Poster_DA.pdf

Characterizing old-growth forests from multisource remote sensing

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