AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Forest Ecosystems Article
PDF (4.9 MB)
Cite
EndNote(RIS) BibTeX
Collect
AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Detecting the presence of natural forests using airborne laser scanning data

Marie-Claude Jutras-Perreault( ), Terje Gobakken, Erik Næsset, Hans Ole Ørka
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box, NO-1432 Ås, Norway
Show Author Information

Abstract

Centuries of forest exploitation have caused significant loss of natural forests in Europe, leading to a decline in populations for many species. To prevent further loss in biodiversity, the Norwegian government has set a target of protecting 10% of the forested area. However, recent data from the National Forest Inventory (NFI) reveals that less than 2% of Norway's forested area consists of natural forests. To identify forests with high conservation value, we used vertical and horizontal variables derived from airborne laser scanning (ALS) data, along with NFI plot measurements. Our study aimed to predict the presence of natural forests across three counties in southeastern Norway, using three different definitions: pristine, near-natural, and semi-natural forests. Natural forests are scarce, and their underrepresentation in field reference data can compromise the accuracy of the predictions. To address this, we assessed the potential gain of including additional field data specifically targeting natural forests to achieve a better balance in the dataset. Additionally, we examined the impact of stratifying the data by dominant tree species on the performance of the models. Our results revealed that semi-natural forests were the most accurately predicted, followed by near-natural and pristine forests, with Matthews correlation coefficient values of 0.32, 0.24, and 0.17, respectively. Including additional field data did not improve the predictions. However, stratification by species improved the accuracy of predictions for near-natural and semi-natural forests, while reducing accuracy for pristine forests. The use of horizontal variables did not improve the predictions. Our study demonstrates the potential of ALS data in identifying forests with high conservation value. It provides a basis for further research on the use of ALS data for the detection and conservation of natural forests, offering valuable insights to guide future forest preservation efforts.

Keywords

BiodiversityEcosystem servicesNatural forestALSNaturalnessVertical variablesHorizontal variablesForest condition

References

 
Ågren, G.I., Chertov, O., Kahle, H.-P., Kellomäki, S., Komarov, A., Mellert, K.-H., Van Oijen, M., Prietzel, J., Spiecker, H., Straussberger, R., Rehfuess, K.-E., 2008. Analysis of the long-term consequences for sustainability of observed growth changes of the European forests. In: Kahle, H.-P. (Ed.), Causes and Consequences of Forest Growth Trends in Europe: Results of the Recognition Project. European Forest Institute Research, p. 235.
 

Baldi, P., Brunak, S., Chauvin, Y., Andersen, C.A., Nielsen, H., 2000. Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16 (5), 412-424. https://doi.org/10.1093/bioinformatics/16.5.412.
Crossref Google Scholar
 

Bater, C.W., Coops, N.C., Gergel, S.E., LeMay, V., Collins, D., 2009. Estimation of standing dead tree class distributions in northwest coastal forests using lidar remote sensing. Can. J. For. Res. 39 (6), 1080-1091. https://doi.org/10.1139/X09-030.
Crossref Google Scholar
 

Blaschke, T., Tiede, D., Heurich, M., 2004. 3D landscape metrics to modelling forest structure and diversity based on laser scanning data. Int. Arch. Photogrammetry. 36 (8/W2), 129-132.
Google Scholar
 

Bonnet, S., Gaulton, R., Lehaire, F., Lejeune, P., 2015. Canopy gap mapping from airborne laser scanning: an assessment of the positional and geometrical accuracy. Rem. Sens. 7 (9), 11267-11294. https://doi.org/10.3390/rs70911267.
Crossref Google Scholar
 

Bontemps, J.-D., Hervé, J.-C., Dhôte, J.-F., 2009. Long-term changes in forest productivity: a consistent assessment in even-aged stands. For. Sci. 55 (6), 549-564. https://doi.org/10.1093/forestscience/55.6.549.
Crossref Google Scholar
 

Braastad, H., 1966. Volume tables for birch. Medd. Nor. Skogforsoksves. 21 (1), 23–78.
Google Scholar
 
Cao, C., Chicco, D., Hoffman, M.M., 2020. The MCC-F1 Curve: a Performance Evaluation Technique for Binary Classification. arXiv preprint arXiv:2006.11278. https://arxiv.org/abs/2006.11278. (Accessed 15 August 2023).
 

Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P., 2002. SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321-357.
Crossref Google Scholar
 

Chicco, D., Jurman, G., 2020. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom. 21 (1), 6. https://doi.org/10.1186/s12864-019-6413-7.
Crossref Google Scholar
 

Dalponte, M., Coomes, D.A., 2016. Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data. Methods Ecol. Evol. 7 (10), 1236-1245. https://doi.org/10.1111/2041-210X.12575.
Crossref Google Scholar
 

de Assis Barros, L., Elkin, C., 2021. An index for tracking old-growth value in disturbance-prone forest landscapes. Ecol. Indicat. 121, 107175. https://doi.org/10.1016/j.ecolind.2020.107175.
Crossref Google Scholar
 
Direktoratet for naturforvaltning (engl.: Norwegian Directorat for Nature Management), 2007. Kartlegging Av Naturtyper-Verdisetting Av Biologisk Mangfold (engl.: Mapping Nature Types-Valuing Biological Diversity). Trondheim, Norway.
 

Edman, M., Gustafsson, M., Stenlid, J., Ericson, L., 2004. Abundance and viability of fungal spores along a forestry gradient – responses to habitat loss and isolation? Oikos 104 (1), 35-42. https://doi.org/10.1111/j.0030-1299.2004.12454.x.
Crossref Google Scholar
 

Esseen, P.-A., Ehnström, B., Ericson, L., Sjöberg, K., 1997. Boreal forests. Ecol. Bull. 46, 16-47.
Google Scholar
 
Framstad, E., Blindheim, T., Granhus, A., Nowell, M., Sverdrup-Thygeson, A., 2016. Evaluering Av Norsk Skogvern I 2016. Dekning Av Mål for Skogvernet Og Behov for Supplerende Vern (engl.: Evaluation of Norwegian Forest Protection in 2016. Coverage of Targets for Forest Protection and Need for Supplementary Protection). Norsk Institutt for Natursforsking (engl.: Norwegian Institute for Natural Research), Oslo, Norway.
 

Fuhr, M., Lalechère, E., Monnet, J.M., Bergès, L., 2022. Detecting overmature forests with airborne laser scanning (ALS). Remote Sens. Ecol. Conserv. 8 (5), 731-743. https://doi.org/10.1002/rse2.274.
Crossref Google Scholar
 

Gaulton, R., Malthus, T.J., 2010. LiDAR mapping of canopy gaps in continuous cover forests: a comparison of canopy height model and point cloud based techniques. Int. J. Rem. Sens. 31 (5), 1193-1211. https://doi.org/10.1080/01431160903380565.
Crossref Google Scholar
 

Gjerde, I., Sætersdal, M., Blom, H.H., 2007. Complementary Hotspot Inventory – a method for identification of important areas for biodiversity at the forest stand level. Biol. Conserv. 137 (4), 549-557.
Crossref Google Scholar
 

Huo, L., Strengbom, J., Lundmark, T., Westerfelt, P., Lindberg, E., 2023. Estimating the conservation value of boreal forests using airborne laser scanning. Ecol. Indicat. 147, 109946. https://doi.org/10.1016/j.ecolind.2023.109946.
Crossref Google Scholar
 

Jutras-Perreault, M.-C., Gobakken, T., Ørka, H.O., 2021. Comparison of two algorithms for estimating stand-level changes and change indicators in a boreal forest in Norway. Int. J. Appl. Earth Obs. 98, 102316. https://doi.org/10.1016/j.jag.2021.102316.
Crossref Google Scholar
 

Kane, V.R., Gersonde, R.F., Lutz, J.A., McGaughey, R.J., Bakker, J.D., Franklin, J.F., 2011. Patch dynamics and the development of structural and spatial heterogeneity in Pacific Northwest forests. Can. J. For. Res. 41 (12), 2276-2291. doi: 10.1139/X11-128.
Crossref Google Scholar
 

Kennedy, R.E., Yang, Z., Cohen, W.B., 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - temporal segmentation algorithms. Remote Sens. Environ. 114, 2897-2910. https://doi.org/10.1016/j.rse.2010.07.008.
Crossref Google Scholar
 

Kennedy, R. E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W. B., Healey, S., 2018. Implementation of the LandTrendr Algorithm on Google Earth Engine. Rem. Sens. 10 (691), 1–10. https://doi.org/10.3390/rs10050691.
Crossref Google Scholar
 

King, G., Zeng, L., 2001. Logistic regression in rare events data. Polit. Anal. 9 (2), 137-163. https://doi.org/10.1093/oxfordjournals.pan.a004868.
Crossref Google Scholar
 

Koukoulas, S., Blackburn, G.A., 2004. Quantifying the spatial properties of forest canopy gaps using LiDAR imagery and GIS. Int. J. Rem. Sens. 25 (15), 3049-3072. https://doi.org/10.1080/01431160310001657786.
Crossref Google Scholar
 

Linder, P., Elfving, B., Zackrisson, O., 1997. Stand structure and successional trends in virgin boreal forest reserves in Sweden. For. Ecol. Manag. 8 (1), 17-33. https://doi.org/10.1016/S0378-1127(97)00076-5.
Crossref Google Scholar
 

Magnusson, M., Olsson, J., Hedenås, H., 2014. Red-listed wood-inhabiting fungi in natural and managed forest landscapes adjacent to the timberline in central Sweden. Scand. J. For. Res. 29 (5), 455-465.
Crossref Google Scholar
 

Maltamo, M., Packalén, P., Yu, X., Eerikäinen, K., Hyyppä, J., Pitkänen, J., 2005. Identifying and quantifying structural characteristics of heterogeneous boreal forests using laser scanner data. For. Ecol. Manag. 216 (1-3), 41-50. https://doi.org/10.1016/j.foreco.2005.05.034.
Crossref Google Scholar
 

Martin, M., Valeria, O., 2022. "Old" is not precise enough: airborne laser scanning reveals age-related structural diversity within old-growth forests. Remote Sens. Environ. 278, 113098. https://doi.org/10.1016/j.rse.2022.113098.
Crossref Google Scholar
 

Martinuzzi, S., Vierling, L.A., Gould, W.A., Falkowski, M.J., Evans, J.S., Hudak, A.T., Vierling, K.T., 2009. Mapping snags and understory shrubs for a LiDAR-based assessment of wildlife habitat suitability. Remote Sens. Environ. 113 (12), 2533-2546. https://doi.org/10.1016/j.rse.2009.07.002.
Crossref Google Scholar
 

Næsset, E., 2004. Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scand. J. For. Res. 19 (2), 164-179. https://doi.org/10.1080/02827580310019257.
Crossref Google Scholar
 
PEFC Norway, 2022. Norwegian PEFC forest standard. https://cdn.pefc.org/pefc.no/media/2023-08/97ce7286-674d-4ff3-9735-b4fccf2b5e99/6458d675-2be4-568c-b7ef-70c54fbfb7a3.pdf. (Accessed 15 August 2023).
 

Ørka, H.O., Jutras-Perreault, M.-C., Næsset, E., Gobakken, T., 2022. A framework for a forest ecological base map–an example from Norway. Ecol. Indicat. 136, 108636. https://doi.org/10.1016/j.ecolind.2022.108636.
Crossref Google Scholar
 

Paillet, Y., Bergès, L., Hjältén, J., Ódor, P., Avon, C., Bernhardt-Römermann, M., Bijlsma, R.J., De Bruyn, L., Fuhr, M., Grandin, U., Kanka, R., Lundin, L., Luque, S., Magura, T., Matesanz, S., Mészáros, I., Sebastià, M.T., Schmidt, W., Standovár, T., Tóthmérész, B., Uotila, A., Valladares, F., Vellak, K., Virtanen, R., 2010. Biodiversity differences between managed and unmanaged forests: meta-analysis of species richness in Europe. Conserv. Biol. 24 (1), 101-112.
Crossref Google Scholar
 

Penttilä, R., Lindgren, M., Miettinen, O., Rita, H., Hanski, I., 2006. Consequences of forest fragmentation for polyporous fungi at two spatial scales. Oikos 114 (2), 225-240. https://doi.org/10.1111/j.2006.0030-1299.14349.x.
Crossref Google Scholar
 

Pesonen, A., Maltamo, M., Eerikäinen, K., Packalèn, P., 2008. Airborne laser scanning-based prediction of coarse woody debris volumes in a conservation area. For. Ecol. Manag. 255 (8), 3288-3296. https://doi.org/10.1016/j.foreco.2008.02.017.
Crossref Google Scholar
 

Potapov, P., Yaroshenko, A., Turubanova, S., Dubinin, M., Laestadius, L., Thies, C., Aksenov, D., Egorov, A., Yesipova, Y., Glushkov, I., Karpachevskiy, M., Kostikova, A., Manisha, A., Tsybikova, E., Zhuravleva, I., 2008. Mapping the World's intact forest landscapes by remote sensing. Ecol. Soc. 13 (2), 51.
Crossref Google Scholar
 

Rolstad, J., Framstad, E., Gundersen, V., Storaunet, K.O., 2002. Naturskog i Norge. Definisjoner, økologi og bruk i norsk skog-og miljøforvaltning (engl.: natural forest in Norway. Definitions, ecology and use in Norwegian forest and environmental management). Aktuelt fra skogforskningen (engl.: News from Forest Research) 1, 1-53.
Google Scholar
 

Schumacher, J., Hauglin, M., Astrup, R., Breidenbach, J., 2020. Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data. For. Ecosyst. 7 (1), 1-14. https://doi.org/10.1186/s40663-020-00274-9.
Crossref Google Scholar
 

Siitonen, J., 2001. Forest management, coarse woody debris and saproxylic organisms: fennoscandian boreal forests as an example. Ecol. Bull. 49(49), 11-41.
Google Scholar
 

Socha, J., Solberg, S., Tymińska-Czabańska, L., Tompalski, P., Vallet, P., 2021. Height growth rate of Scots pine in Central Europe increased by 29% between 1900 and 2000 due to changes in site productivity. For. Ecol. Manag. 490, 119102. https://doi.org/10.1016/j.foreco.2021.119102.
Crossref Google Scholar
 
Søgaard, G., Eriksen, R., Astrup, R.A., Øyen, B.-H., 2012. Effekter Av Ulike Miljøhensyn På Tilgjengelig Skogareal Og Volum I Norske Skoger (engl.: Effects of Various Environmental Considerations on Available Forest Area and Volume in Norwegian Forests). Norsk Institutt for Skog Landskap (engl.: Norwegian Institute of Forests and Lanscape), Norway.
 
Steinset, T.A., 1999. Norsk Skoghåndbok 2000 (engl.: Norwegian Forest Handbook 2000). Landbruksforlaget (engl.: The Agriculture Publishing House), Norway.
 

Stokland, J., Kauserud, H., 2004. Phellinus nigrolimitatus—a wood-decomposing fungus highly influenced by forestry. For. Ecol. Manag. 187 (2-3), 333-343. https://doi.org/10.1016/j.foreco.2003.07.004.
Crossref Google Scholar
 

Stokland, J.N., Larsson, K.-H., 2011. Legacies from natural forest dynamics: different effects of forest management on wood-inhabiting fungi in pine and spruce forests. For. Ecol. Manag. 261 (11), 1707-1721. https://doi.org/10.1016/j.foreco.2011.01.003.
Crossref Google Scholar
 
Stokland, J.N., Tomter, S.M., Söderberg, G.U., 2004. Development of dead wood indicators for biodiversity monitoring: experiences from Scandinavia. In: Marchetti, M. (Ed.), Monitoring and Indicators of Forest Biodiversity in Europe–From Ideas to Operationality. European Forest Institute, Finland, pp. 207–226.
 
Storaunet, K.O., Rolstad, J., 2015. Mengde og utvikling av død ved i produktiv skog i Norge. Med basis i data fra Landsskogtakseringens 7. (1994-1998) og 10. takst (2010-2013) (engl.: Amount and development of dead wood in productive forests in Norway. Based on data from the 7th (1994-1998) and 10th (2010-2013) National Forest Inventory assessment). Oppdragsrapport fra Skog og landskap (engl.: Assignment report from Forest and Landscape), Norway.
 
Storaunet, K.O., Rolstad, J., 2020. Naturskog i Norge. En areal beregning basert på bestandsalder i Landsskogtakseringens takstimdrev fra 1990 til 2016 (engl.: Natural forest in Norway. An area calculation based on stand age in National Forest Inventory from 1990 to 2016). NIBIO, Norway.
 

Storaunet, K.O., Rolstad, J., Gjerde, I., Gundersen, V.S., 2005. Historical logging, productivity, and structural characteristics of boreal coniferous forests in Norway. Silva Fenn. 39 (3), 429.
Crossref Google Scholar
 
Stortinget (engl.: Norwegian Parliament), 2016. Vedtak 667 (engl.: Resolution 667). https://www.stortinget.no/no/Saker-og-publikasjoner/Vedtak/Vedtak/Sak/?p=64248. (Accessed 15 August 2023).
 

Svensson, J., Bubnicki, J.W., Jonsson, B.G., Andersson, J., Mikusiński, G., 2020. Conservation significance of intact forest landscapes in the scandinavian mountains green belt. Landsc. Ecol. 35, 2113-2131. https://doi.org/10.1007/s10980-020-01088-4.
Crossref Google Scholar
 

Sverdrup-Thygeson, A., Ørka, H.O., Gobakken, T., Næsset, E., 2016. Can airborne laser scanning assist in mapping and monitoring natural forests? For. Ecol. Manag. 369, 116-125. https://doi.org/10.1016/j.foreco.2016.03.035.
Crossref Google Scholar
 

Tikkanen, O.-P., Martikainen, P., Hyvärinen, E., Junninen, K., Kouki, J., 2006. Red-listed boreal forest species of Finland: associations with forest structure, tree species, and decaying wood. Ann. Zool. Fenn. 43 (4), 373-383.
Google Scholar
 
Tomter, S.M., Dalen, L.S., 2014. Bærekraftig Skogbruk I Norge (engl.: Sustainable Forestry in Norway). Norsk Institutt for Skog Og Landskap (engl.: Norwegian Institute for Forests and Landscape), Norway.
 
Tveite, B., 1977. Bonitetskurver for Gran: Site-Index Curves for Norway Spruce (Picea Abies (L.) Karst). Norsk Institutt for Skogforskning (engl.: Norwegian Institute for Forest Research), Norway.
 

Vehmas, M., Packalén, P., Maltamo, M., Eerikäinen, K., 2011. Using airborne laser scanning data for detecting canopy gaps and their understory type in mature boreal forest. Ann. For. Sci. 68 (4), 825-835. https://doi.org/10.1007/s13595-011-0079-x.
Crossref Google Scholar
 

Vepakomma, U., St-Onge, B., Kneeshaw, D., 2008. Spatially explicit characterization of boreal forest gap dynamics using multi-temporal lidar data. Remote Sens. Environ. 112 (5), 2326-2340. https://doi.org/10.1016/j.rse.2007.10.001.
Crossref Google Scholar
 
Viken, K.O., 2018. Landsskogtakseringens Feltinstruks 2018 (engl.: National Forest Inventory Field Instruction 2018). NIBIO, Norway.
 

Zimble, D.A., Evans, D.L., Carlson, G.C., Parker, R.C., Grado, S.C., Gerard, P.D., 2003. Characterizing vertical forest structure using small-footprint airborn LiDAR. Remote Sens. Environ. 87 (2-3), 171-182.
Crossref Google Scholar
Forest Ecosystems
Volume 10 Issue 6,
December 2023
Article number: 100146
DOI: 10.1016/j.fecs.2023.100146
Cite this article:
Jutras-Perreault M-C, Gobakken T, Næsset E, et al. Detecting the presence of natural forests using airborne laser scanning data. Forest Ecosystems, 2023, 10(6): 100146. https://doi.org/10.1016/j.fecs.2023.100146

236

Views

1

Downloads

1

Crossref

1

Web of Science

1

Scopus

0

CSCD

Altmetrics
Received: 13 June 2023
Revised: 28 September 2023
Accepted: 19 October 2023
Published: 24 October 2023
© 2023 The Authors.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Return