Classification of ‘potential' forests based on remote sensing data
Klasyfikacja gruntów „potencjalnie” leśnych na podstawie danych teledetekcyjnych
Sylwan 166 (3):194-210, 2022
DOI:
https://doi.org/10.26202/sylwan.2022011Available online: 2022-06-17
Open Access (CC-BY)
species • classification • hyperspectral data • ALS data • potential forest area • reporting • FAO/UN forest definition
The aim of this study is to estimate the area with forest vegetation that does not yet meet the criteria formulated in the FAO/UN definition (minimum height 5 m, minimum canopy cover 10%, minimum area 0.5 ha), but will potentially meet them in the future (5 years or more, depending on the individual site conditions), which means that (according to the definition) they also represent forest areas. The study was conducted in the Białowieża Glade. Tree species were classified individually and then divided into two groups: those that will reach a height of 5 m in the future and those that will not (grey willow, hawthorn). Hyperspectral (reduced with MNF transformation) and ALS−based features were used for classification with the SVM algorithm. Classification accuracy based on ALS data was better than that of hyperspectral data for indi− vidual species but similar for the two species groups – 95.5% (Kappa 87.5%). Information about species and height was used to perform the classification of a fishnet layer into ‘forests’, ‘potential forests’ and ‘non−forests’, with an accuracy of 96% (Kappa 87.7%). A map of forests and potential forest vegetation was created in the form of a thematic map, taking into account height, canopy cover, area of the complex and land use. This study provides new solutions in the context of cli− mate change, deforestation and the need for reporting the forest area by individual countries (including Poland) to the FAO/UN.
Castillo-Núńez, M., Sánchez-Azofeifa, A., Croitoru, A., Rivard, B., Calvo-Alvarado, J., Dubayah, R.O., 2011. Delineation of secondary succession mechanisms for tropical dry forests using LiDAR. Remote Sensing of Environment, 115: 2217-2231. DOI: https://doi.org/10.1016/j.rse.2011.04.020.
Dalponte, M., Bruzzone, L., Gianelle, D., 2008. Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas. IEEE Transactions on Geoscience and Remote Sensing, 46: 1416-1427. DOI: https://doi.org/10.1109/TGRS.2008.916480.
Dalponte, M., Bruzzone, L., Gianelle, D., 2012. Tree species classification in the southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LIDAR data. Remote Sensing of Environment, 123: 258-270. DOI: https://doi.org/10.1016/j.rse.2012.03.013.
Dalponte, M., Bruzzone, L., Vescovo, L., Gianelle, D., 2009. The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas. Remote Sensing of Environment, 133 (11): 2345-2355. DOI: http://dx.doi.org/10.1016/j.rse.2009.06.013.
Dalponte, M., Řrka, H.O., Ene, L.T., Gobakken, T., Nćsset, E., 2013. Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote Sensing of Environment, 140: 306-317 DOI: https://doi.org/10.1016/j.rse.2013.09.006.
Eysn, L., Hollaus, M., Schadauer, K., Pfeifer, N., 2012. Forest Delineation Based on Airborne LIDAR Data. Remote Sensing, 4 (3): 762-783. DOI: https://doi.org/10.3390/rs4030762.
Eysn, L., Hollaus, M., Vetter, M., Mücke, W., Pfeifer, N., Regner, B., 2010. Adapting alpha-shapes for forest delineation using ALS Data. 10th International Conference on LiDAR Applications for Assessing Forest Ecosystems – Silvilaser 2010, 14-17 September, Freiburg, Germany, 10 pp. DOI: https://doi.org/10.13140/2.1.2460.2887
Farreira, M.P., Zanotta, D.C., Zortea, M., de Souza Filho, C.R., 2016. Mapping tree species in tropical seasonal semi-deciduous forests with hyperspectral and multispectral data. Remote Sensing of Environment, 179: 66-78. DOI: https://doi.org/10.1016/j.rse.2016.03.021.
Fassnacht, F.E., Latifi, H., Stereńczak, K., Modzelewska, A., Lefsky, M., Waser, L.T., Straub, C., Ghosh, A., 2016. Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment, 186: 64-87. DOI: https://doi.org/10.1016/j.rse.2016.08.013.
Fassnacht, F.E., Neumann, C., Förster, M., Buddenbaum, H., Ghosh, A., Clasen, A., Joshi, P.K., Koch, B., 2014. Comparison of feature reduction algorithms for classifying tree species with hyperspectral data on three central European test sites. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 7 (6): 2547-2561. DOI: https://doi.org/10.1109/JSTARS.2014.2329390.
Forest Resources Assessment, 2004. Working Paper 83. Global Forest Resources Assessment Update 2005, Terms and Definitions.
Forest Resources Assessment, 2007. Working Paper 135. Specification of National Reporting Tables for FRA 2010.
Forest Resources Assessment, 2012. Working Paper 180. Forest Resources Assessment Update 2015, Terms and Definitions.
Forzieri, G., Moser, G., Catani, F., 2012. Assessment of hyperspectral MIVIS sensor capability for heterogeneous landscape classification. ISPRS Journal of Photogrammetry and Remote Sensing, 74: 175-184. DOI: https://doi.org/10.1016/j.isprsjprs.2012.09.011.
Ghiyamat, A., Shafri, H.Z.M., Mahdiraji, G.A., Shariff, A.R.M., Mansor, S., 2013. Hyperspectral discrimination of tree species with different classifications using single- and multiple-endmember. International Journal of Applied Earth Observation and Geoinformation, 23: 177-191. DOI: https://doi.org/10.1016/j.jag.2013.01.004.
Ghosh, A., Fassnacht, F.E., Joshia, P.K., Koch, B., 2014. A framework for mapping tree species combining hyper-spectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales. International Journal of Applied Earth Observation and Geoinformation, 26: 49-63. DOI: https://doi.org/10.1016/j.jag.2013.05.017.
Green, A., Berman, M., Switzer, P., Craig, M.D., 1988. A transformation for ordering multispectral data in terms of image quality with implications for noise removal, IEEE Transactions on Geoscience and Remote Sensing, 26 (1): 65-74. DOI: https://doi.org/10.1109/36.3001.
Haapanen, R., Ek, A.R., Bauer, M.E., Finley, E.O., 2004. Delineation of forest/non forest land use classes using nearest neighbor methods. Remote Sensing of Environment, 89 (3): 265-271, DOI: https://doi.org/10.1016/j.rse.2003.10.002.
Hościło, A., Mirończuk, A., Lewandowska, A., Gąsiorowski, J., 2015. Inventory of the actual forest cover of the country using the existing photogrammetric data. Final Report. Warsaw: Institute of Geodesy and Cartography, 24 pp. Available from https://bip2.lasy.gov.pl/pl/bip/px_dg~rdlp_poznan~nadl_turek~lesistosc_polski___wyniki_badan_2015_rok.pdf [accessed: 01.03.2022].
Hovi, A., Korhonen, L., Vauhkonen, J., Korpela, I., 2016. LiDAR waveform features for tree species classification and their sensitivity to tree- and acquisition related parameters. Remote Sensing of Environment, 173: 224-237. DOI: https://doi.org/10.1016/j.rse.2015.08.019.
Hughes, G.F., 1968. On the mean accuracy of statistical pattern recognizers. Transactions on Information Theory, 14: 55-63. DOI: https://doi.org/10.1109/TIT.1968.1054102.
Hycza, T., Stereńczak, K., Kamińska, A., 2021. The use of remote sensing data to estimate land area with forest vegetation cover in the context of selected forest definitions. Forests, 12: 1489. DOI: https://doi.org/10.3390/f12111489.
James, G., Witten, D., Hastie, T., Tibshirani, R., 2013. An introduction to statistical learning. New York: Springer, 426 pp. DOI: https://doi.org/10.1007/978-1-4614-7138-7.
Jabłoński, M., 2015. Definicja lasu w ujęciu krajowym i międzynarodowym oraz jej znaczenie dla wielkości i zmian powierzchni lasów w Polsce. Sylwan, 159 (6): 469-482. DOI: https://doi.org/10.26202/sylwan.2014264.
Jabłoński, M., Korhonen, K.T., Budniak, P., Mionskowski, M., Zajączkowski, G., Sućko, K., 2017. Comparing land use registry and sample based inventory to estimate forest area in Podlaskie, Poland. iForest, 10: 315-321. DOI: https://doi.org/10.3832/ifor2078-009.
Kamińska, A., Lisiewicz, M., Stereńczak, K., Kraszewski, B., Sadkowski, R., 2018. Species-related single dead tree detection using multi-temporal ALS data and CIR imagery. Remote Sensing of Environment, 219: 31-43. DOI: https://doi.org/10.1016/j.rse.2018.10.005.
Kamińska A., Lisiewicz M., Stereńczak K., 2021. Single tree classification using multi-temporal ALS data and CIR imagery in the mixed old-growth forest in Poland. Remote Sensing, 13: 5101. DOI: https://doi.org/10.3390/rs13245101.
Kolecka, N., Kozak, J., Kaim, D., Dobosz, M., Ginzler, Ch., Psomas, A., 2015. Mapping Secondary Forest Succession on Abandoned Agricultural Land with LiDAR Point Clouds and Terrestrial Photography. Remote Sensing, 7: 8300-8322. DOI: https://doi.org/10.3390/rs70708300.
Kunz, M., Nienartowicz, A., Deptuła, M., 2000. Teledetekcja satelitarna wtórnych lasów na gruntach porolnych na przykładzie Zaborskiego Parku Krajobrazowego. Fotointerpretacja w Geografii, 31: 122-128.
Li, Q., Wong, F., Fung, T., 2019. Classification of Mangrove Species Using Combined WordView-3 and LiDAR Data in Mai Po Nature Reserve, Hong Kong. Remote Sensing, 11: 2114. DOI: https://doi.org/10.3390/rs11182114.
Liao, W., Van Coillie, F., Gao, L., Li, L., Chanussot, J., 2018. Deep Learning for Fusion of APEX Hyperspectral and Full-waveform LiDAR Remote Sensing Data for Tree Species Mapping. IEEE Access, 6: 68716-68729. DOI: https://doi.org/10.1109/ACCESS.2018.2880083.
Melgani, F., Bruzzone, L., 2004. Classification of Hyperspectral Remote Sensing Images with Support Vector Machines. IEEE Transactions on Geoscience and Remote Sensing, 42 (8): 1778-1790. DOI: https://doi.org/10.1109/TGRS.2004.831865.
McRoberts, R.E., 2011. Satellite image-based maps: Scientific inference or pretty pictures. Remote Sensing of Environment, 115: 715-724. DOI: https://doi.org/10.1016/j.rse.2010.10.013.
McRoberts, R.E., Gobakken, T., Naesset, E., 2012. Post-stratified estimation of forest area and growing stock volume using lidar-based stratifications. Remote Sensing of Environment, 125: 157-166. DOI: https://doi.org/10.1016/j.rse.2012.07.002.
Michałowska, M., Rapiński, J., 2021. A review of tree species classification based on airborne LiDAR data and applied classifiers. Remote Sensing, 13: 353. DOI: https://doi.org/10.3390/rs13030353.
Modzelewska, A., Kamińska, A., Fassnacht, F.E., Stereńczak, K., 2020. Multitemporal hyperspectral tree species classification in the Białowieża Forest World Heritage site. An International Journal of Forest Research, 94 (3): 464-476. DOI: https://doi.org/10.1093/forestry/cpaa048.
Mountrakis, G., Im, J., Ogole, C., 2011. Support vector machines in remote sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing, 66: 247-259. DOI: https://doi.org/10.1016/j.isprsjprs.2010.11.001.
Naesset, E., Orka, H.O., Solberg, S., Bollandsas, O.M., Hansen, E.H., Mauya, E., Zahabu, E., Malimbwi, R., Chamuya, N., Olsson, H., Gobakken, T., 2016. Mapping and estimating forest area and aboveground biomass in miombo woodlands in Tanzania using data from airborne. Remote Sensing of Environment, 175: 282-300. DOI: https://doi.org/10.1016/j.rse.2016.01.006.
Pabjanek, P., 2003. Kształtowanie zapustów leśnych w warunkach puszczańskich polany osadniczej. Doctoral dissertation, University of Warsaw (msc.).
Pekkarinen, A., Reithmaier, L., Strobl, P., 2009. Pan-European forest/non-forest mapping with Landsat ETM+ and CORINE Land Cover 2000 data. Journal of Photogrammetry and Remote Sensing, 64: 171-183. DOI: https://doi.org/10.1016/j.isprsjprs.2008.09.004.
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. Ecology and Society, 13 (2): 51. DOI: https://doi.org/10.5751/ES-02670-130251.
Próchnicki, P., 2006. Wykorzystanie GIS i teledetekcji jako narzędzi do analizy sukcesji zakrzewień w Narwiańskim Parku Narodowym. Roczniki Geomatyki, 4 (2): 127-134.
Pujar, G.S., Reddy, P.M., Reddy, C.S., Jha, C.S., Dadhwal, V.K., 2014. Estimation of trees outside forests using IRS high resolution data by Object Based Image Analysis. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 9-12 December 2014, Hyderabad, India, pp. 623-629.
Putz, F.E., Redford, K., 2009. The Importance of defining ‘forest’: tropical forest degradation, deforestation. Long-termp shifts, and further transitions. Biotropica, 42 (1): 10-20. DOI: https://doi.org/10.1111/j.1744-7429.2009.00567.x.
R Core Team, 2021. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org.
Sasaki, N., Putz, F.E., 2009. Critical need for new definitions of ‘forest’ and ‘forest degradation’ in global climate change agreements. A Journal of Society and Conservative Biology, 2 (5): 226-232. DOI: https://doi.org/ 10.1111/j.1755-263X.2009.00067.x.
Sims, D., Gamon, J., 2002. Relationship between leaf pigment con- tent and spectral reflectance across a wide range species, leaf structures and development stages. Remote Sensing of Environment, 81: 337-354. DOI: https://doi.org/10.1016/S0034-4257(02)00010-X.
Shi, Y., Skidmore, A., Heurich, M., 2018. Important LiDAR metrics for discriminating forest tree species in Central Europe. ISPRS Journal of Photogrammetry and Remote Sensing, 137: 163-174. DOI: https://doi.org/10.1016/j.isprsjprs.2018.02.002.
Shi, Y., Skidmore, A., Holzwarth, S., Heiden, U., Pinnel, N., Zhu, X., Heurich, M., 2018. Tree species classification using plant functional traits from LiDAR and hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 73: 207-219. DOI: https://doi.org/10.1016/j.jag.2018.06.018.
Shi, Y., Skidmore, A., Heurich, M., 2019. 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. DOI: https://doi.org/10.1016/j.jag.2019.101970.
Stereńczak, K., Kraszewski, B., Mielcarek, M., Piasecka, Ż., Lisiewicz, M., Heurich, M., 2020. Mapping individual trees with airborne laser scanning data in an European lowland forest using a self-calibration algorithm. International Journal of Applied Earth Observations and Geoinformation, 93: 102191. DOI: https://doi.org/10.1016/j.jag.2020.102191.
Straub, C., Weinacker, H., Koch, B., 2008. A fully automated procedure for delineation and classification of forest and non-forest vegetation based on full waveform laser scanner data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37: 1013-1019.
Szostak, M., Hawryło, P., Piela, P., 2017. Using of Sentinel-2 images for automation of the forest succession detection. European Journal of Remote Sensing, 51 (1): 142-149. DOI: https://doi.org/10.1080/22797254.2017.1412272.
Thompson, S.D., Nelson, T.A., Giesbrecht, I., Frazer, G., Saunders, S.C., 2016. Data-driven regionalization of forested and non-forested ecosystems in coastal British Columbia with LiDAR and RapidEye imagery. Applied Geography, 69: 35-50, DOI: https://doi.org/10.1016/j.apgeog.2016.02.002.
Vapnik, V.N., 1999. An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10 (5): 988-999. DOI: https://doi.org/10.1109/72.788640.
Vincheh, Z.H., Arfania, R., 2017. Lithological mapping from OLI and ASTER multispectral data using matched filtering and spectral analogues techniques in the Pasab-e-Bala Area, Central Iran. Open Journal of Geology, 7 (10): 1494-508. DOI: https://doi.org/10.4236/ojg.2017.710100.
Wang, Z., Boesch, R., Ginzler, C., 2008. Integration of high resolution aerial images and airborne Lidar data for forest delineation. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37: 1203-1207.
Wężyk, P., de Kok, R., 2005. Automatic mapping of the dynamics of forest succession on abandoned parcels in south Poland. In: J. Strobl, ed. Angewandte Geoinformatik. Heidelberg: Herbert Wichman Verlag, pp. 774-779.
Wietecha, M., Modzelewska, A., Stereńczak, K., 2017. Airborne hyperspectral data for the classification of treespecies a temperate forests [Wykorzystanie lotniczej teledetekcji hiperspektralnej w klasyfikacji gatunkowej lasów strefy umiarkowanej]. Sylwan, 161 (1): 3-17. DOI: https://doi.org/10.26202/sylwan.2016101.
Yao, W., Krzystek, P., Heurich, M., 2012. Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data. Remote Sensing of Environment, 123: 368-380. DOI: https://doi.org/10.1016/j.rse.2012.03.027.
Yang, G., Zhao, Y., Li, B., Ma, Y., Li, R., Jing, J., Dian, Y., 2019. Tree species classification by employing multiple features acquired from integrated sensors. Journal of Sensors, 2019: 3247946. DOI: https://doi.org/10.1155/2019/3247946.
You, H., Lei, P., Li, M., Ruan, F., 2020. Forest species classification based on three-dimensional coordinate and intensity information of airborne LiDAR data with random forest method. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W10: 117-123. DOI: https://doi.org/10.5194/isprs-archives-XLII-3-W10-117-2020.
Zhang, C., Xie, Z., 2012. Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery. Remote Sensing of Environment, 124: 310-320. DOI: https://doi.org/10.1016/j.rse.2012.05.015.
Zhang, Z., Liu, X., 2012. Support vector machines for tree species identification using LiDAR-derived structure and intensity variables. Geocarto International, 28: 1-15. DOI: https://doi.org/10.1080/10106049.2012.710653.