Mapping temperate old-growth forests in Central Europe using ALS and Sentinel-2A multispectral data.
Airborne LiDAR
Data fusion
Forest structure
Multispectral
Stand age
Structural complexity
Journal
Environmental monitoring and assessment
ISSN: 1573-2959
Titre abrégé: Environ Monit Assess
Pays: Netherlands
ID NLM: 8508350
Informations de publication
Date de publication:
26 Aug 2024
26 Aug 2024
Historique:
received:
05
02
2024
accepted:
08
08
2024
medline:
26
8
2024
pubmed:
26
8
2024
entrez:
25
8
2024
Statut:
epublish
Résumé
Old-growth forests are essential to preserve biodiversity and play an important role in sequestering carbon and mitigating climate change. However, their existence across Europe is vulnerable due to the scarcity of their distribution, logging, and environmental threats. Therefore, providing the current status of old-growth forests across Europe is essential to aiding informed conservation efforts and sustainable forest management. Remote sensing techniques have proven effective for mapping and monitoring forests over large areas. However, relying solely on remote sensing spectral or structural information cannot capture comprehensive horizontal and vertical structure complexity profiles associated with old-growth forest characteristics. To overcome this issue, we combined spectral information from Sentinel-2A multispectral imagery with 3D structural information from high-density point clouds of airborne laser scanning (ALS) imagery to map old-growth forests over an extended area. Four features from the ALS data and fifteen from Sentinel-2A comprising raw band (spectral reflectance), vegetation indices (VIs), and texture were selected to create three datasets used in the classification process using the random forest algorithm. The results demonstrated that combining ALS and Sentinel-2A features improved the classification performance and yielded the highest accuracy for old-growth class, with an F1-score of 92% and producer's and user's accuracies of 93% and 90%, respectively. The findings suggest that features from ALS and Sentinel-2A data sensitive to forest structure are essential for identifying old-growth forests. Integrating open-access satellite imageries, such as Sentinel-2A and ALS data, can benefit forest managers, stakeholders, and conservationists in monitoring old-growth forest preservation across a broader spatial extent.
Identifiants
pubmed: 39183185
doi: 10.1007/s10661-024-12993-5
pii: 10.1007/s10661-024-12993-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
841Subventions
Organisme : European Research Council,European Union
ID : 397.ID 834709, H2020-EU.1.1
Organisme : European Research Council,European Union
ID : 397.ID 834709, H2020-EU.1.1
Organisme : European Research Council,European Union
ID : 397.ID 834709, H2020-EU.1.1
Organisme : European Research Council,European Union
ID : 397.ID 834709, H2020-EU.1.1
Organisme : European Research Council,European Union
ID : 397.ID 834709, H2020-EU.1.1
Informations de copyright
© 2024. The Author(s).
Références
Atkins, J. W., Fahey, R. T., Hardiman, B. H., & Gough, C. M. (2018). Forest canopy structural complexity and light absorption relationships at the subcontinental scale. Journal of Geophysical Research: Biogeosciences, 123, 1387–1405. https://doi.org/10.1002/2017JG004256
doi: 10.1002/2017JG004256
Ayrey, E., Hayes, D. J., Fraver, S., Kershaw, J. A., & Weiskittel, A. R. (2019). Ecologically-based metrics for assessing structure in developing area-based, enhanced forest inventories from LiDAR. Canadian Journal of Remote Sensing, 45(1), 88–112. https://doi.org/10.1080/07038992.2019.1612738
doi: 10.1080/07038992.2019.1612738
Barredo, J. I., Brailescu, C., Teller, A., Sabatini, F. M., & Mauri, A. (2021). Mapping and assessment of primary and old-growth forests in Europe (Issue EUR 30661 EN). https://doi.org/10.2760/13239
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
doi: 10.1023/A:1010933404324
Brunet, J., Fritz, Ö., & Richnau, G. (2010). Biodiversity in European beech forests – A review with recommendations for sustainable forest management. Ecological Bulletins, 53, 77–94.
Cailleret, M., Heurich, M., & Bugmann, H. (2014). Reduction in browsing intensity may not compensate climate change effects on tree species composition in the Bavarian Forest National Park. Forest Ecology and Management, 328, 179–192. https://doi.org/10.1016/j.foreco.2014.05.030
doi: 10.1016/j.foreco.2014.05.030
Chamberlain, C. P., Kane, V. R., & Case, M. J. (2021). Accelerating the development of structural complexity: Lidar analysis supports restoration as a tool in coastal Pacific Northwest forests. Forest Ecology and Management, 500, 119641. https://doi.org/10.1016/j.foreco.2021.119641
doi: 10.1016/j.foreco.2021.119641
Chen, D., Stow, D. A., & Gong, P. (2004). Examining the effect of spatial resolution and texture window size on classification accuracy: An urban environment case. International Journal of Remote Sensing, 25(11), 2177–2192. https://doi.org/10.1080/01431160310001618464
doi: 10.1080/01431160310001618464
Coburn, C. A., & Roberts, A. C. B. (2004). A multiscale texture analysis procedure for improved forest stand classification. International Journal of Remote Sensing, 25(20), 4287–4308. https://doi.org/10.1080/0143116042000192367
doi: 10.1080/0143116042000192367
Cohen, W. B., & Spies, T. A. (1992). Estimating structural attributes of Douglas-fir/western hemlock forest stands from landsat and SPOT imagery. Remote Sensing of Environment, 41(1), 1–17. https://doi.org/10.1016/0034-4257(92)90056-P
doi: 10.1016/0034-4257(92)90056-P
Congalton, R. G., Green, K., & Teply, J. (1993). Mapping old growth forests on national forest and park lands in the Pacific Northwest from remotely sensed data. Photogrammetric Engineering and Remote Sensing, 59(4), 529–535.
de 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.2020.107175
doi: 10.1016/j.ecolind.2020.107175
Donato, D. C., Campbell, J. L., & Franklin, J. F. (2012). Multiple successional pathways and precocity in forest development: Can some forests be born complex? Journal of Vegetation Science, 23, 576–584. https://doi.org/10.1111/j.1654-1103.2011.01362.x
doi: 10.1111/j.1654-1103.2011.01362.x
Falkowski, 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.2009.01.003
doi: 10.1016/j.rse.2009.01.003
Fiorella, M., & Ripple, W. J. (1993). Determining successional stage of temperate coniferous forests with Landsat satellite data. Photogrammetric Engineering & Remote Sensing, 59(2), 239–246. http://scientistswarning.forestry.oregonstate.edu/ .
Forzieri, G., Girardello, M., Ceccherini, G., Spinoni, J., Feyen, L., Hartmann, H., Beck, P. S. A., Camps-Valls, G., Chirici, G., Mauri, A., & Cescatti, A. (2021). Emergent vulnerability to climate-driven disturbances in European forests. Nature Communications, 12, 1081. https://doi.org/10.1038/s41467-021-21399-7
doi: 10.1038/s41467-021-21399-7
Frampton, W. J., Dash, J., Watmough, G., & Milton, E. J. (2013). Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 82, 83–92. https://doi.org/10.1016/j.isprsjprs.2013.04.007
doi: 10.1016/j.isprsjprs.2013.04.007
Franklin, J. F., & Van Pelt, R. (2004). Spatial aspects of structural complexity in old-growth forests. Journal of Forestry, 102(3), 22–29. https://doi.org/10.1093/jof/102.3.22
doi: 10.1093/jof/102.3.22
Franklin, S. E., Hall, R. J., Moskal, L. M., Maudie, A. J., & Lavigne, M. B. (2000). Incorporating texture into classification of forest species composition from airborne multispectral images. International Journal of Remote Sensing, 21(1), 61–79. https://doi.org/10.1080/014311600210993
doi: 10.1080/014311600210993
Franklin, J. F., Cromack, K., Jr., Denison, W., Mckee, A., Maser, C., Sedell, J., Swanson, F., & Juday, G. (1981). Ecological characteristics of old growth.
doi: 10.2737/PNW-GTR-118
Frey, S. J. K., Hadley, A. S., Johnson, S. L., Schulze, M., Jones, J. A., & Betts, M. G. (2016). Spatial models reveal the microclimatic buffering capacity of old-growth forests. Science Advances, 2(4), e1501392. https://doi.org/10.1126/sciadv.1501392
doi: 10.1126/sciadv.1501392
Fuhr, M., Lalechère, E., Monnet, J. M., & Bergès, L. (2022). Detecting overmature forests with airborne laser scanning (ALS). Remote Sensing in Ecology and Conservation, 8(5), 731–743. https://doi.org/10.1002/rse2.274
doi: 10.1002/rse2.274
Gao, S., Zhong, R., Yan, K., Ma, X., Chen, X., Pu, J., Gao, S., Qi, J., Yin, G., & Myneni, R. B. (2023). Evaluating the saturation effect of vegetation indices in forests using 3D radiative transfer simulations and satellite observations. Remote Sensing of Environment, 295, 113665. https://doi.org/10.1016/j.rse.2023.113665
doi: 10.1016/j.rse.2023.113665
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., Black, W. C., & Anderson, R. E. (2018). Multivariate data analysis (8th ed.). Cengage Learning EMEA. https://doi.org/10.1002/9781119409137.ch4
doi: 10.1002/9781119409137.ch4
Hall-beyer, M. (2017). Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales. International Journal of Remote Sensing, 38(5), 1312–1338. https://doi.org/10.1080/01431161.2016.1278314
doi: 10.1080/01431161.2016.1278314
Hamraz, H., Contreras, M. A., & Zhang, J. (2017). Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds. Scientific Reports, 7(1), 1–9. https://doi.org/10.1038/s41598-017-07200-0
doi: 10.1038/s41598-017-07200-0
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 3(6), 610–621.
doi: 10.1109/TSMC.1973.4309314
Heurich, M., & Englmaier, K. H. (2010). The development of tree species composition in the Rachel – Lusen region of the Bavarian Forest National Park. Silva Gabreta, 16(3), 165–186.
Hilmers, T., Friess, N., Bässler, C., Heurich, M., Brandl, R., Pretzsch, H., Seidl, R., & Müller, J. (2018). Biodiversity along temperate forest succession. Journal of Applied Ecology, 55, 2756–2766. https://doi.org/10.1111/1365-2664.13238
doi: 10.1111/1365-2664.13238
Hirschmugl, M., Sobe, C., Di Filippo, A., Berger, V., Kirchmeir, H., & Vandekerkhove, K. (2023). Review on the possibilities of mapping old - growth temperate forests by remote sensing in Europe. Environmental Modeling & Assessment, 28, 761–785. https://doi.org/10.1007/s10666-023-09897-y
doi: 10.1007/s10666-023-09897-y
Huete, A. R., Didan, K., & Miura., T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213. https://doi.org/10.1016/S0020-1693(00)85959-9
doi: 10.1016/S0020-1693(00)85959-9
Immitzer, M., Vuolo, F., & Atzberger, C. (2016). First experience with Sentinel-2 data for crop and tree species classifications in Central Europe. Remote Sensing, 8(3), 166. https://doi.org/10.3390/rs8030166
doi: 10.3390/rs8030166
Kane, V. R., Gillespie, A. R., McGaughey, R., Lutz, J. A., Ceder, K., & Franklin, J. F. (2008). Interpretation and topographic compensation of conifer canopy self-shadowing. Remote Sensing of Environment, 112, 3820–3832. https://doi.org/10.1016/j.rse.2008.06.001
doi: 10.1016/j.rse.2008.06.001
Kiala, Z., Mutanga, O., Odindi, J., & Peerbhay, K. (2019). Feature selection on Sentinel-2 multispectral imagery for mapping a landscape infested by Parthenium weed. Remote Sensing, 11, 1892. https://doi.org/10.3390/rs11161892
doi: 10.3390/rs11161892
Krzystek, P., Serebryanyk, A., Schnörr, C., Červenka, J., & Heurich, M. (2020). Large-scale mapping of tree species and dead trees in Sumava National Park and Bavarian Forest National Park using lidar and multispectral imagery. Remote Sensing, 12, 661. https://doi.org/10.3390/rs12040661
doi: 10.3390/rs12040661
Lahssini, K., Teste, F., Dayal, K. R., Durrieu, S., Ienco, D., & Monnet, J. M. (2022). Combining LiDAR metrics and Sentinel-2 imagery to estimate basal area and wood volume in complex forest environment via neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 4337–4348. https://doi.org/10.1109/JSTARS.2022.3175609
doi: 10.1109/JSTARS.2022.3175609
LaRue, E., Hardiman, B. S., Elliott, J. M., & Fei, S. (2019). Structural diversity as a predictor of ecosystem function. Environmental Research Letters, 14, 114011. https://doi.org/10.1088/1748-9326/ab49bb
doi: 10.1088/1748-9326/ab49bb
Lefsky, M. A., Cohen, W. B., Acker, S. A., Parker, G. G., Spies, T. A., & Harding, D. (1999). Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests. Remote Sensing of Environment, 70, 339–361. https://doi.org/10.1016/S0034-4257(99)00052-8
doi: 10.1016/S0034-4257(99)00052-8
Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(December), 18–22. https://doi.org/10.1177/154405910408300516
doi: 10.1177/154405910408300516
Luyssaert, S., Schulze, E. D., Börner, A., Knohl, A., Hessenmöller, D., Law, B. E., Ciais, P., & Grace, J. (2008). Old-growth forests as global carbon sinks. Nature, 455, 213–215. https://doi.org/10.1038/nature07276
doi: 10.1038/nature07276
Mcnemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153–157. https://doi.org/10.1007/BF02295996
doi: 10.1007/BF02295996
Meng, J., Li, S., Wang, W., Liu, Q., Xie, S., & Ma, W. (2016). Estimation of forest structural diversity using the spectral and textural information derived from SPOT-5 satellite images. Remote Sensing, 8(2), 125. https://doi.org/10.3390/rs8020125
doi: 10.3390/rs8020125
Mikoláš, M., Piovesan, G., Ahlström, A., Donato, D. C., Gloor, R., Hofmeister, J., Keeton, W. S., Muys, B., Sabatini, F. M., Svoboda, M., & Kuemmerle, T. (2023). Protect old-growth forests in Europe now. Science, 380, 466–466. https://doi.org/10.1126/science.adh2303
doi: 10.1126/science.adh2303
Moning, C., & Müller, J. (2009). Critical forest age thresholds for the diversity of lichens, molluscs and birds in beech (Fagus sylvatica L.) dominated forests. Ecological Indicators, 9, 922–932. https://doi.org/10.1016/j.ecolind.2008.11.002
doi: 10.1016/j.ecolind.2008.11.002
O’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/10.36333/rs1
doi: 10.36333/rs1
Oliver, C., & Larson, B. (1996). Forest stand dynamics (update edition). John Wiley & Sons.
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D’Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., & Kassem, K. R. (2001). Terrestrial ecoregions of the world: A new map of life on Earth. BioScience, 51(11), 933–938. https://doi.org/10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2
doi: 10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2
Pearse, G. D., Watt, M. S., Dash, J. P., Stone, C., & Caccamo, G. (2019). Comparison of models describing forest inventory attributes using standard and voxel-based lidar predictors across a range of pulse densities. International Journal of Applied Earth Observation and Geoinformation, 78, 341–351. https://doi.org/10.1016/j.jag.2018.10.008
doi: 10.1016/j.jag.2018.10.008
Piovesan, G., & Biondi, F. (2021). On tree longevity. New Phytologist, 231, 1318–1337. https://doi.org/10.1111/nph.17148
doi: 10.1111/nph.17148
Power, D. M. W. (2011). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. ArXiv, 2(1), 37–63. https://doi.org/10.1016/j.eswa.2019.03.048
doi: 10.1016/j.eswa.2019.03.048
Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48, 119–126. https://doi.org/10.1016/0034-4257(94)90134-1
doi: 10.1016/0034-4257(94)90134-1
Rouse, J. W., Haas, R. H., Well, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. Goddard Space Flight Center 3d ERTS-1 Symp, 1, Sect. A. https://doi.org/10.1021/jf60203a024
Roussel, J., Auty, D., Coops, N. C., Tompalski, P., Goodbody, T. R. H., Sánchez, A., Bourdon, J., Boissieu, F. D., & Achim, A. (2020). lidR: An R package for analysis of airborne laser scanning (ALS) data. Remote Sensing of Environment, 251, 112061. https://doi.org/10.1016/j.rse.2020.112061
doi: 10.1016/j.rse.2020.112061
San-Miguel-Ayanz, J., Rigo, D., de Caudullo, G., Durrant, T. H., & Mauri, A. (2016). European Atlas of Forest Tree Species. Publication Office of the European Union.
Scarth, P., Phinn, S. R., & McAlpine, C. (2001). Integrating high and moderate spatial resolution image data to estimate forest age structure. Canadian Journal of Remote Sensing, 27(2), 129–142. https://doi.org/10.1080/07038992.2001.10854927
doi: 10.1080/07038992.2001.10854927
Shi, Y., Wang, T., Skidmore, A. K., & Heurich, M. (2020). Improving LiDAR-based tree species mapping in Central European mixed forests using multi-temporal digital aerial colour-infrared photographs. International Journal of Applied Earth Observation and Geoinformation, 84, 101970. https://doi.org/10.1016/j.jag.2019.101970
doi: 10.1016/j.jag.2019.101970
Silveyra Gonzalez, R., Latifi, H., Weinacker, H., Dees, M., Koch, B., & Heurich, M. (2018). Integrating LiDAR and high-resolution imagery for object-based mapping of forest habitats in a heterogeneous temperate forest landscape. International Journal of Remote Sensing, 39(23), 8859–8884. https://doi.org/10.1080/01431161.2018.1500071
doi: 10.1080/01431161.2018.1500071
Skidmore, A. K., Coops, N. C., Neinavaz, E., Ali, A., Schaepman, M. E., Paganini, M., Kissling, W. D., Vihervaara, P., Darvishzadeh, R., Feilhauer, H., Fernandez, M., Fernández, N., Gorelick, N., Geizendorffe, I., Heiden, U., Heurich, M., Hobern, D., Holzwarth, S., Muller-Karger, F. E., Kerchove, R. V. D., Lausch, A., Leitãu, P. J., Lock, M. C., Mücher, C. A., O'Connor, B., Rocchini, D., Turner, W., Vis, J. K., Wang, T., Wegmann, M. Wingate, V. (2021). Priority list of biodiversity metrics to observe from space. Nature Ecology & Evolution, 5, 896–906. https://doi.org/10.1038/s41559-021-01451-x
Spracklen, B. D., & Spracklen, D. V. (2019). Identifying European old-growth forests using remote sensing: A study in the Ukrainian Carpathians. Forests, 10(2), 1–19. https://doi.org/10.3390/f10020127
doi: 10.3390/f10020127
Tíscar, P. A., & Lucas-Borja, M. E. (2016). Structure of old-growth and managed stands and growth of old trees in a Mediterranean Pinus nigra forest in southern Spain. Forestry, 89, 201–207. https://doi.org/10.1093/forestry/cpw002
doi: 10.1093/forestry/cpw002
van der Knaap, W. O., van Leeuwen, J. F. N., Fahse, L., Szidat, S., Studer, T., Baumann, J., Heurich, M., & Tinner, W. (2020). Vegetation and disturbance history of the Bavarian Forest National Park. Germany. Vegetation History and Archaeobotany, 29(2), 277–295. https://doi.org/10.1007/s00334-019-00742-5
doi: 10.1007/s00334-019-00742-5
Vandekerkhove, K., Meyer, P., Kirchmeir, H., Piovesan, G., Hirschmugl, M., Larrieu, L., Kozàk, D., Mikolas, M., Nagel, T., Schmitt, C., & Blumröder, J. (2022). Old-growth criteria and indicators for beech forests (Fageta). https://lifeprognoses.eu/wp-content/uploads/2022/04/Criteria-oldgrowth-PROGNOSES-Finalversion.pdf#page=1&zoom=auto,-274,848
White, J. C., Coops, N. C., Wulder, M. A., Vastaranta, M., Hilker, T., & Tompalski, P. (2016). Remote sensing technologies for enhancing forest inventories: A review. Canadian Journal of Remote Sensing, 42(5), 619–641. https://doi.org/10.1080/07038992.2016.1207484
doi: 10.1080/07038992.2016.1207484
Wirth, C., Messier, C., Bergeron, Y., & Frank, D. (2009). In C. Wirth, G. Gleixner, & M. Heimann (Eds.), Old-growth forests: Function, fate and value (pp. 1–33). Springer‐Verlag. https://doi.org/10.1007/978
doi: 10.1007/978
Xu, N., Tian, J., Tian, Q., Xu, K., & Tang, S. (2019). Analysis of vegetation red edge with different illuminated/shaded canopy proportions and to construct normalized difference canopy shadow index. Remote Sensing, 11, 1192. https://doi.org/10.3390/rs11101192
doi: 10.3390/rs11101192
Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., & Yan, G. (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sensing, 8, 501. https://doi.org/10.3390/rs8060501
doi: 10.3390/rs8060501
Zhang, W., Hu, B., Woods, M., & Brown, G. (2017). Characterizing forest succession stages for wildlife habitat assessment using multispectral airborne imagery. Forests, 8(7), 234. https://doi.org/10.3390/f8070234
doi: 10.3390/f8070234