Gini coefficient predictions from airborne lidar remote sensing display the effect of management intensity on forest structure |
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| Institution: | 1. European Forest Institute HQ, Yliopistokatu 6, FI-80100 Joensuu, Finland;2. Natural Resources Institute of Finland (Luke), Joensuu Unit, Yliopistokatu 6, FI-80100 Joensuu, Finland;3. University of Eastern Finland, School of Forest Sciences, Yliopistokatu 7, FI-80100 Joensuu, Finland;1. Consiglio Nazionale delle Ricerche (CNR) – Istituto di Scienze Marine (ISMAR), Sede di Ancona [National Research Council (CNR) – Institute of Marine Sciences (ISMAR), Fisheries Section], Largo Fiera della Pesca, 60125 Ancona, Italy;2. Fondazione Cetacea onlus, Viale Torino 7/A, 47838 Riccione, (RN), Italy;3. Università degli Studi di Roma, Tor Vergata, Via Orazio Raimondo, 18, 00173 Roma, Italy;1. Northwest A & F University, College of Forestry, 712100 Yangling, China;2. Swedish University of Agricultural Sciences, Alnarp, Sweden;3. University of Copenhagen, Copenhagen, Danmark;1. Department of Forest Sciences, “Luiz de Queiroz” College of Agriculture, University of São Paulo (USP/ESALQ), Piracicaba, SP, Brazil;2. School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA;3. Social-Environmental Studies Institute/Image Processing and GIS Laboratory, Federal University of Goias, Brazil;4. Department of Ecology & Evolutionary Biology, University of Connecticut, Storrs, CT, USA;5. Department of Forestry, Federal University of Vales do Jequitinhonha e Mucuri (UFVJM), Diamantina, MG, Brazil;6. Biosciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, 20707, USA;7. Department of Forestry, Michigan State University, East Lansing, MI, USA;8. School of Natural Sciences, Bangor University, Bangor, UK;9. Embrapa Acre, Rodovia BR-364, km 14, CEP 69900-056, Rio Branco, Acre, Brazil;1. Department of Biology, University of Waterloo, 200 University Ave. W., Waterloo, ON N2L 3G1, Canada;2. Ecosystem Health Assessment, Environment Canada, 867 Lakeshore Road, Burlington, ON L7R 4A6, Canada |
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| Abstract: | In this study, two forest sites located in Finland were compared by means of predictions of Gini coefficient (GC) obtained from airborne laser scanning (ALS). We discuss the potential of the proposed method for identifying differences in structural complexity in relation with the management history of forests. The first study site (2200 ha), the Koli National Park (NP), includes areas where human intervention was restricted after 1907, in addition to forests which were protected only after the 1990s. The second study site in the municipality of Kiihtelysvaara (800 ha) has been under intensive management. These are commercial forests which include areas with different types of ownership: a large estate owned by an industrial company together with smaller private properties. We observed that GC predictions may be used to evaluate the effects of management practice on forest structure. Conservation and commercial forests showed significant differences, with the old-protected area of Koli having the highest, and the most intensively managed area in Kiihtelysvaara the lowest GC values. The effect of management history was revealed, as the 1990s’ extensions of Koli NP were more similar to unprotected areas than to forests contained within the original borders of the 1907s’ state property. Yet, their conservation status for almost two decades has been sufficient for developing significant differences against the outside of the NP. In Kiihtelysvaara, we found significant differences in GC according to the type of ownership. Moreover, the ALS predictions of GC also detected differences near lakeshores, which are driven by limitations on logging governed by Finnish law. Estimating this indicator with ALS remote sensing allowed to observe its spatial distribution and to detect peculiarities which would otherwise be unavailable from field plot sampling. Consequently, the method presented appears to be well suited for monitoring the effects of management practice, as well as verifying its compliance with legal restrictions. |
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| Keywords: | Lidar Remote sensing Forest structure Tree size inequality Management history Forest ownership Forest law Environmental services Airborne laser scanning |
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