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Canadian Journal of Remote Sensing
Journal canadien de télédétection
Volume 48, 2022 - Issue 6
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Research Articles

Toward Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping

Vers une détection ciblée de changements à l’aide d’images de télédétection hétérogènes pour la cartographie de la mortalité sylvestre

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 826-848 | Received 25 Mar 2022, Accepted 15 Sep 2022, Published online: 20 Oct 2022

References

  • Agersborg, J.A., Anfinsen, S.N., and Jepsen, J.U. 2021. “Guided nonlocal means estimation of polarimetric covariance for canopy state classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 60(No. 5208417): pp. 1–17. doi:10.1109/TGRS.2021.3090831.
  • Bae, S., Müller, J., Förster, B., Hilmers, T., Hochrein, S., Jacobs, M., Leroy, B.M., Pretzsch, H., Weisser, W.W., and Mitesser, O. 2022. “Tracking the temporal dynamics of insect defoliation by high-resolution radar satellite data.” Methods in Ecology and Evolution, Vol. 13(No. 1): pp. 121–132. doi:10.1111/2041-210X.13726.
  • Bekker, J., and Davis, J. 2020. “Learning from positive and unlabeled data: A survey.” Machine Learning, Vol. 109(No. 4): pp. 719–760. doi:10.1007/s10994-020-05877-5.
  • Bishop, C. 2006. Pattern Recognition and Machine Learning. New York, NY: Springer. ISBN: 0387310738.
  • Biuw, M., Jepsen, J.U., Cohen, J., Ahonen, S.H., Tejesvi, M., Aikio, S., Wäli, P.R., et al. 2014. “Long-term impacts of contrasting management of large ungulates in the Arctic tundra-forest ecotone: Ecosystem structure and climate feedback.” Ecosystems, Vol. 17(No. 5): pp. 890–905. doi:10.1007/s10021-014-9767-3.
  • CAFF. 2013. Arctic Biodiversity Assessment 2013. Akureyri, Iceland: Conservation of Arctic Flora/Fauna.
  • Camps-Valls, G., Gómez-Chova, L., Munõz-Marí, J., Luis Rojo-Álvarez, J., and Martínez-Ramón, M. 2008. “Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 46(No. 6): pp. 1822–1835. doi:10.1109/TGRS.2008.916201.
  • Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., and Lambin, E. 2004. “Digital change detection methods in ecosystem monitoring: A review.” International Journal of Remote Sensing, Vol. 25(No. 9): pp. 1565–1596. doi:10.1080/0143116031000101675.
  • Dempster, A.P., Laird, N.M., and Rubin, D.B. 1977. “Maximum likelihood from incomplete data via the EM algorithm.” Journal of the Royal Statistical Society: Series B (Methodological), Vol. 39(No. 1): pp. 1–22. doi:10.1111/j.2517-6161.1977.tb01600.x.
  • Elkan, C., and Noto, K. 2008. “Learning classifiers from only positive and unlabelled data.” Proceedings of 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 213–220.
  • Gao, Y., Skutsch, M., Paneque-G’Alvez, J., and Ghilardi, A. 2020. “Remote sensing of forest degradation: A review.” Environmental Research Letters, Vol. 15(No. 10): pp. 103001. doi:10.1088/1748-9326/abaad7.
  • Hall, R.J., Castilla, G., White, J.C., Cooke, B.J., and Skakun, R.S. 2016. “Remote sensing of forest pest damage: A review and lessons learned from a Canadian perspective.” The Canadian Entomologist, Vol. 148(No. S1): pp. S296–S356. doi:10.4039/tce.2016.11.
  • Henden, J.-A., Ims, R.A., Yoccoz, N.G., Asbjornsen, E.J., Stien, A., Mellard, J.P., Tveraa, T., Marolla, F., and Jepsen, J.U. 2020. “End-user involvement to improve predictions and management of populations with complex dynamics and multiple drivers.” Ecological Applications, Vol. 30(No. 6): pp. e02120. doi:10.1002/eap.2120.
  • Hsu, C.-W., Chang, C.-C., and Lin, C.-J. 2003. A Practical Guide to Support Vector Classification.
  • Ims, R.A., Uhd Jepsen, J., Stien, A., and Yoccoz, N.G. 2013. Science Plan for COAT: Climate-Ecological Observatory for Arctic Tundra. Fram Centre Report Series 1. Tromso: Fram Centre.
  • Jepsen, J.U., Kapari, L., Hagen, S.B., Schott, T., Vindstad, O.P.L., Nilssen, A.C., and Ims, R.A. 2011. “Rapid northwards expansion of a forest insect pest attributed to spring phenology matching with sub-Arctic birch.” Global Change Biology, Vol. 17(No. 6): pp. 2071–2083. doi:10.1111/j.1365-2486.2010.02370.x.
  • Jepsen, J.U., Biuw, M., Ims, R.A., Kapari, L., Schott, T., Vindstad, O.P.L., and Hagen, S.B. 2013. “Ecosystem impacts of a range expanding forest defoliator at the forest-tundra ecotone.” Ecosystems, Vol. 16(No. 4): pp. 561–575. doi:10.1007/s10021-012-9629-9.
  • Jepsen, J.U., Hagen, S.B., Høgda, K.A., Ims, R.A., Karlsen, S.R., Tømmervik, H., and Yoccoz, N.G. 2009. “Monitoring the spatio-temporal dynamics of geometrid moth outbreaks in birch forest using MODIS-NDVI data.” Remote Sensing of Environment., Vol. 113(No. 9): pp. 1939–1947. doi:10.1016/j.rse.2009.05.006.
  • Jepsen, J.U., Hagen, S.B., Ims, R.A., and Yoccoz, N.G. 2008. “Climate change and outbreaks of the geometrids Operophtera brumata and Epirrita autumnata in subarctic birch forest: evidence of a recent outbreak range expansion.” The Journal of Animal Ecology, Vol. 77(No. 2): pp. 257–264. doi:10.1111/j.1365-2656.2007.01339.x.
  • Jian, P., Chen, K., and Cheng, W. 2022. “GAN-based one-class classification for remote-sensing image change detection.” IEEE Geoscience and Remote Sensing Letters, Vol. 19: pp. 1–5. doi:10.1109/LGRS.2021.3066435.
  • Khan, S.S., and Madden, M.G. 2014. “One-class classification: Taxonomy of study and review of techniques.” The Knowledge Engineering Review, Vol. 29(No. 3): pp. 345–374. doi:10.1017/S026988891300043X.
  • Kingma, D.P., and Ba, J. 2014. “Adam: A method for stochastic optimization.” arXiv preprint arXiv:1412.6980.
  • Li, W., Guo, Q., and Elkan, C. 2011. “A positive and unlabeled learning algorithm for one-class classification of remote-sensing data.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 49(No. 2): pp. 717–725. doi:10.1109/TGRS.2010.2058578.
  • Li, W., Guo, Q., and Elkan, C. 2020. “One-class remote sensing classification from positive and unlabeled background data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 14: pp. 730–746.
  • Li, X., and Liu, B. 2003. “Learning to classify texts using positive and unlabeled data.” IJCAI, Vol. 3, pp. 587–592. Citeseer.
  • Liu, B., Sun Lee, W., Yu, P.S., and Li, X. 2002. “Partially supervised classification of text documents.” ICML, Vol. 2, pp. 387–394.
  • Li, P., and Xu, H. 2010. “Land-cover change detection using one-class support vector machine.” Photogrammetric Engineering & Remote Sensing, Vol. 76(No. 3): pp. 255–263. doi:10.14358/PERS.76.3.255.
  • Liu, Bing., Dai, Yang., Li, Xiaoli., Sun Lee, Wee., and Yu, PhilipS. 2003. “Building text classifiers using positive and unlabeled examples.” Third IEEE International Conference on Data Mining, 179–186. IEEE.
  • Luppino, L.T. 2020. Unsupervised change detection in heterogeneous remote sensing imagery. PhD diss. UiT The Arctic University of Norway, Department of Physics and Technology.
  • Luppino, L.T., Hansen, M.A., Kampffmeyer, M., Bianchi, F.M., Moser, G., Jenssen, R., and Anfinsen, S.N. 2022. “Code-aligned autoencoders for unsupervised change detection in multimodal remote sensing images.” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–13. doi:10.1109/TNNLS.2022.3172183.
  • Mitchell, A.L., Rosenqvist, A., and Mora, B. 2017. “Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verification (MRV) systems for REDD+.” Carbon Balance and Management, Vol. 12(No. 1): pp. 1–22. doi:10.1186/s13021-017-0078-9.
  • Mũnoz-Marí, J., Bovolo, F., Gómez-Chova, L., Bruzzone, L., and Camp-Valls, G. 2010. “Semisupervised one-class support vector machines for classification of remote sensing data.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 48(No. 8): pp. 3188–3197. doi:10.1109/TGRS.2010.2045764.
  • Olsson, P.-O., Kantola, T., Lyytikäinen-Saarenmaa, P., Jönsson, A., and Eklundh, L. 2016a. “Development of a method for monitoring of insect induced forest defoliation-limitation of MODIS data in Fennoscandian forest landscapes.” Silva Fennica, Vol. 50(No. 2): pp. 1–22. doi:10.14214/sf.1495.
  • Olsson, P.-O., Lindström, J., and Eklundh, L. 2016b. “Near real-time monitoring of insect induced defoliation in subalpine birch forests with MODIS derived NDVI.” Remote Sensing of Environment, Vol. 181: pp. 42–53. doi:10.1016/j.rse.2016.03.040.
  • Pedersen, Å.Ø., Jepsen, J.U., Paulsen, I.M.G., Fuglei, E., Mosbacher, J.B., Ravolainen, V., and Yoccoz, N.G. 2021. Norwegian Arctic tundra: a panel-based assessment of ecosystem condition. Technical report. Norsk Polarinstitutt.
  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., et al. 2011. “Scikit-learn: Machine Learning in Python.” Journal of Machine Learning Research, Vol. 12: pp. 2825–2830.
  • Perbet, P., Fortin, M., Ville, A., and B’Eland, M. 2019. “Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors.” International Journal of Remote Sensing, Vol. 40(No. 19): pp. 7439–7458. doi:10.1080/01431161.2019.1579390.
  • Ran, Q., Zhang, M., Li, W., and Du, Q. 2016. “Change detection with one-class sparse representation classifier.” Journal of Applied Remote Sensing, Vol. 10(No. 4): pp. 042006. doi:10.1117/1.JRS.10.042006.
  • Ran, Q., Li, W., and Du, Q. 2018. “Kernel one-class weighted sparse representation classification for change detection.” Remote Sensing Letters, Vol. 9(No. 6): pp. 597–606. doi:10.1080/2150704X.2018.1452063.
  • Senf, C., Seidl, R., and Hostert, P. 2017. “Remote sensing of forest insect disturbances: Current state and future directions.” International Journal of Applied Earth Observation and Geoinformation, Vol. 60: pp. 49–60. doi:10.1016/j.jag.2017.04.004.
  • Sun, Y., Lei, L., Li, X., Sun, H., and Kuang, G. 2021. “Nonlocal patch similarity based heterogeneous remote sensing change detection.” Pattern Recognition, Vol. 109: pp. 107598. doi:10.1016/j.patcog.2020.107598.
  • Touati, R., Mignotte, M., and Dahmane, M. 2020. “Multimodal change detection in remote sensing images using an unsupervised pixel pairwise-based Markov random field model.” IEEE Transactions on Image Processing, Vol. 29: pp. 757–767. doi:10.1109/TIP.2019.2933747.
  • Volpi, M., Camps-Valls, G., and Tuia, D. 2015. “Spectral alignment of multitemporal cross-sensor images with automated kernel canonical correlation analysis.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 107: pp. 50–63. doi:10.1016/j.isprsjprs.2015.02.005.
  • Ye, S., Chen, D., and Yu, J. 2016. “A targeted change-detection procedure by combining change vector analysis and post-classification approach.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 114: pp. 115–124. doi:10.1016/j.isprsjprs.2016.01.018.
  • Yu, H. 2005. “Single-class classification with mapping convergence.” Machine Learning, Vol. 61(No. 1–3): pp. 49–69. doi:10.1007/s10994-005-1122-7.