Abstract
Automatic recognition and segmentation methods have become an essential requirement in identifying large-scale earthquake-induced landslides. This used to be conducted through pixel-based or object-oriented methods. However, these methods fail to develop an accurate, rapid, and cross-scene solution for earthquake-induced landslide recognition because of the massive amount of remote sensing data and variations in different earthquake scenarios. To fill this research gap, this paper proposes a robust deep transfer learning scheme for high precision and fast recognition of regional landslides. First, a Multi-scale Feature Fusion regime with an Encoder-decoder Network (MFFENet) is proposed to extract and fuse the multi-scale features of objects in remote sensing images, in which a novel and practical Adaptive Triangle Fork (ATF) Module is designed to integrate the useful features across different scales effectively. Second, an Adversarial Domain Adaptation Network (ADANet) is developed to perform different seismic landslide recognition tasks, and a multi-level output space adaptation scheme is proposed to enhance the adaptability of the segmentation model. Experimental results on standard remote sensing datasets demonstrate the effectiveness of MFFENet and ADANet. Finally, a comprehensive and general scheme is proposed for earthquake-induced landslide recognition, which integrates image features extracted from MFFENet and ADANet with the side information including landslide geologic features, bi-temporal changing features, and spatial analysis. The proposed scheme is applied in two earthquake-induced landslides in Jiuzhaigou (China) and Hokkaido (Japan), using available pre- and post-earthquake remote sensing images. These experiments show that the proposed scheme presents a state-of-the-art performance in regional landslide identification and performs stably and robustly in different seismic landslide recognition tasks. Our proposed framework demonstrates a competitive performance for high-precision, high-efficiency, and cross-scene recognition of earthquake disasters, which may serve as a new starting point for the application of deep learning and transfer learning methods in earthquake-induced landslide recognition.
Similar content being viewed by others
References
Barlow J, Franklin SE, Martin YE (2006) High spatial resolution satellite imagery, DEM derivatives, and image segmentation for the detection of mass wasting processes. Photogramm Eng Remote Sens 72:687–692
Bashmal L, Bazi Y, AlHichri H, AlRahhal M, Ammour N, Alajlan N (2018) Siamese-GAN: learning invariant representations for aerial vehicle image categorization. Remote Sensing 10:351
Ben-David S, Blitzer J, Crammer K, Kulesza A, Pereira F, Vaughan JW (2010) A theory of learning from different domains. Mach Learn 79:151–175
Benjdira B, Bazi Y, Koubaa A, Ouni K (2019) Unsupervised domain adaptation using generative adversarial networks for semantic segmentation of aerial images. Remote Sensing 11:1369
Bilinski P, Prisacariu V (2018) Dense decoder shortcut connections for single-pass semantic segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 6596–6605
Borghuis AM, Chang K, Lee HY (2007) Comparison between automated and manual mapping of typhoon-triggered landslides from SPOT-5 imagery. Int J Remote Sens 28:1843–1856
Bui DT, Tsangaratos P, Nguyen VT, Van Liem N, Trinh PT (2020) Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. Catena 188:104426
Canny J (1986) A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence (6):679–698
Catani F (2021) Landslide detection by deep learning of non-nadiral and crowdsourced optical images. Landslides 18(3):1025–1044
Chang CY, Chang SE, Hsiao PY, Fu LC (2020) EPSNet: efficient panoptic segmentation network with cross-layer attention fusion, in Proceedings of the Asian Conference on Computer Vision
Chen G, Zhang X, Wang Q, Dai F, Gong Y, Zhu K (2018a) Symmetrical dense-shortcut deep fully convolutional networks for semantic segmentation of very-high-resolution remote sensing images. IEEE J. Sel Top Appl Earth Observations Remote Sensing 11:1633–1644
Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2018b) DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40:834–848
Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H (2018c) Encoder-decoder with atrous separable convolution for semantic image segmentation, in Proceedings of the European Conference on Computer Vision 801–818
Chen X, Xu C, Yang X, Tao D (2018d) Attention-GAN for object transfiguration in wild images, in Proceedings of the European Conference on Computer Vision 167–184
Chen Z, Zhang Y, Ouyang C, Zhang F, Ma J (2018e) Automated landslides detection for mountain cities using multi-temporal remote sensing imagery. Sensors 18:821
Cheng G, Wang Y, Xu S, Wang H, Xiang S, Pan C (2017) Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network. IEEE Transactions on Geoscience Remote Sensing 55:3322–3337
Cheng G, Xie X, Han J, Guo L, Xia GS (2020) Remote sensing image scene classification meets deep learning: challenges, methods, benchmarks, and opportunities. IEEE J. Sel Top Appl Earth Observations Remote Sensing 13:3735–3756
Cui P, Peng J, Shi P, Tang H, Ouyang C, Zou Q, Liu L, Li C, Lei Y (2021) Scientific challenges of research on natural hazards and disaster risk. Geography and Sustainability 2:216–223
Danneels G, Pirard E, Havenith H (2007) Automatic landslide detection from remote sensing images using supervised classification methods, in 2007 IEEE International Geoscience and Remote Sensing Symposium 3014–3017
Diakogiannis FI, Waldner F, Caccetta P, Wu C (2020) ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS J Photogramm Remote Sens 162:94–114
Ding A, Zhang Q, Zhou X, Dai B (2016) Automatic recognition of landslide based on CNN and texture change detection, in 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC) 444–448
Ding X, Shen C, Che Z, Zeng T, Peng Y (2021) Scarf: a semantic constrained attention refinement network for semantic segmentation, in Proceedings of the IEEE/CVF International Conference on Computer Vision 3002–3011
Eker AM, Dikmen M, Cambazoğlu S, Düzgün ŞH, Akgün H (2015) Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district, Bartın, northern Turkey. Int J Geogr Inf Sci 29(1):132–158
Elshamli A, Taylor GW, Berg A, Areibi S (2017) Domain adaptation using representation learning for the classification of remote sensing images. IEEE J. Sel Top Appl Earth Observations Remote Sensing 10:4198–4209
Fan X, Scaringi G, Xu Q, Zhan W, Dai L, Li Y, Pei X, Yang Q, Huang R (2018) Coseismic landslides triggered by the 8th August 2017 ms 7.0 Jiuzhaigou earthquake (Sichuan, China): factors controlling their spatial distribution and implications for the seismogenic blind fault identification. Landslides 15:967–983
Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J (2019) Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sensing 11(2):196
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets, in advances in neural information processing systems 2672–2680
Harp EL, Keefer DK, Sato HP, Yagi H (2011) Landslide inventories: the essential part of seismic landslide hazard analyses. Eng Geol 122:9–21
Hayder Z, He X, Salzmann M (2017) Boundary-aware instance segmentation, in 2017 IEEE Conference on Computer Vision and Pattern Recognition 587–595
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition, in 2016 IEEE Conference on Computer Vision and Pattern Recognition 770–778
Huang F, Zhang J, Zhou C, Wang Y, Huang J, Zhu L (2020) A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction. Landslides 17(1):217–229
Hoffman J, Wang D, Yu F, Darrell T (2016) FCNs in the wild: pixel-level adversarial and constraint-based adaptation. arXiv preprint arXiv: 1612.02649
Huang G, Liu Z, Maaten L van der, Weinberger KQ (2017) Densely connected convolutional networks, in 2017 IEEE Conference on Computer Vision and Pattern Recognition 2261–2269
Huang R, Zhao J, Ju N, Li G, Lee ML, Li Y (2013) Analysis of an anti-dip landslide triggered by the 2008 Wenchuan earthquake in China. Nat Hazards 68:1021–1039
Huang Z, Wang X, Huang L, Huang C, Wei Y, Liu W (2019) CCNet: Criss-cross attention for semantic segmentation, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) 603–612
Iqbal J, Ali M (2020) Weakly-supervised domain adaptation for built-up region segmentation in aerial and satellite imagery. ISPRS J Photogramm Remote Sens 167:263–275
Iwahashi J, Pike RJ (2007) Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology 86:409–440
Japan Meteorological Agency (JMA) (2018) URL http://www.data.jma.go.jp/svd/eqev/data/mech/ini/mc201809.html (accessed 8.11.19)
Ji S, Yu D, Shen C, Li W, Xu Q (2020) Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks. Landslides 17:1337–1352
Keefer DK (1984) Landslides caused by earthquakes. GSA. Bulletin 95:406–421
Kingma D, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980
Kjekstad O, Highland L (2009) Economic and social impacts of landslides, in Landslides - Disaster Risk Reduction 573–587
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems 1097–1105
Lee CY, Batra T, Baig MH, Ulbricht D (2019) Sliced wasserstein discrepancy for unsupervised domain adaptation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 10285–10295
Lei T, Zhang Q, Xue D, Chen T, Meng H, Nandi AK (2019a) End-to-end change detection using a symmetric fully convolutional network for landslide mapping, in IEEE International Conference on Acoustics, Speech and Signal Processing 3027–3031
Lei T, Zhang Y, Lv Z, Li S, Liu S, Nandi AK (2019b) Landslide inventory mapping from bitemporal images using deep convolutional neural networks. IEEE Geosci Remote Sens Lett 16:982–986
Li H, Xiong P, Fan H, Sun J (2019) DFANet: deep feature aggregation for real-time semantic segmentation, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 9514–9523
Li L, Zhou Z, Wang B, Miao L, Zong H (2020a) A novel CNN-based method for accurate ship detection in HR optical remotesensing images via rotated bounding box. IEEE T rans Geosci Remote Sens 59(1):686-699
Li X, Zhao H, Han L, Tong Y, Tan S, Yang K (2020b) Gated fully fusion for semantic segmentation, in Proceedings of the AAAI conference on artificial intelligence 11418–11425
Li Z, Shi W, Lu P, Yan L, Wang Q, Miao Z (2016) Landslide mapping from aerial photographs using change detection-based Markov random field. Remote Sens Environ 187:76–90
Lin D, Fu K, Wang Y, Xu G, Sun X (2017a) MARTA GANs: unsupervised representation learning for remote sensing image classification. IEEE Geosci Remote Sensing Lett 14:2092–2096
Lin D, Shen D, Shen S, Ji Y, Lischinski D, Cohen-Or D, Huang H (2019a) ZigZagNet: fusing top-down and bottom-up context for object segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 7490–7499
Lin G, Milan A, Shen C, Reid I (2017b) RefineNet: multi-path refinement networks for high-resolution semantic segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1925–1934
Lin J, Jing W, Song H (2019b) SAN: scale-aware network for semantic segmentation of high-resolution aerial images. arXiv preprint arXiv: 1907.03089
Lin TY, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017c) Feature pyramid networks for object detection, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2117-2125
Lin TY, Goyal P, Girshick R, He K, Dollar P (2018) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42(2):318–327
Liu M, Zhang P, Shi Q, Liu M (2021) An adversarial domain adaptation framework with KL-constraint for remote sensing land cover classification. IEEE Geosci Remote Sensing Letts
Liu Y, Fan B, Wang L, Bai J, Xiang S, Pan C (2018) Semantic labeling in very high resolution images via a self-cascaded convolutional neural network. ISPRS J Photogramm Remote Sens 145:78–95
Liu Y, Wu L (2016) Geological disaster recognition on optical remote sensing images using deep learning. Procedia Computer Science 91:566–575
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 3431–3440
Lu P, Qin Y, Li Z, Mondini AC, Casagli N (2019) Landslide mapping from multi-sensor data through improved change detection-based Markov random field. Remote Sensing of Environ 231:111235
Lv ZY, Shi W, Zhang X, Benediktsson JA (2018) Landslide inventory mapping from bitemporal high-resolution remote sensing images using change detection and multiscale segmentation. IEEE J. Sel Top Appl Earth Observations Remote Sensing 11:1520–1532
Ma HR, Cheng X, Chen L, Zhang H, Xiong H (2016) Automatic identification of shallow landslides based on Worldview2 remote sensing images. J Appl Remote Sensing 10:016008
Maas AL, Hannun AY, Ng AY (2013) Rectifier nonlinearities improve neural network acoustic models. Proc Icml 30(1):3
Martha TR, Kerle N, Jetten V, van Westen CJ, Kumar KV (2010) Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology 116:24–36
Martha TR, Kerle N, van Westen CJ, Jetten V, Vinod Kumar K (2012) Object-oriented analysis of multi-temporal panchromatic images for creation of historical landslide inventories. ISPRS J Photogramm Remote Sens 67:105–119
Mottaghi R, Chen X, Liu X, Cho NG, Lee SW, Fidler S, Urtasun R, Yuille A (2014) The role of context for object detection and semantic segmentation in the wild, in 2014 IEEE Conference on Computer Vision and Pattern Recognition 891–898
Mou L, Hua Y, Zhu XX (2019) A relation-augmented fully convolutional network for semantic segmentation in aerial scenes, in 2019 IEEE Conference on Computer Vision and Pattern Recognition 12416–12425
Nichol J, Wong MS (2005) Satellite remote sensing for detailed landslide inventories using change detection and image fusion. Int J Remote Sens 26:1913–1926
Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, Glocker B, Rueckert D (2018) Attention U-Net: learning where to look for the pancreas. arXiv preprint arXiv: 1804.03999
Orris GJ, Williams JW (1984) Landslide length-width ratios as an aid in landslide identification and verification. Bull Assoc Eng Geol 21:371–375
Ouyang CJ, Zhao W, He SM, Wang DP, Zhou S, An HC, Wang ZW, Cheng DX (2017) Numerical modeling and dynamic analysis of the 2017 Xinmo landslide in Maoxian county. China Journal of Mountain Science 14(9):1701–1711
Ouyang CJ, Zhao W, Xu Q, Peng D, Li W, Wang D, Zhou S, Hou S (2018) Failure mechanisms and characteristics of the 2016 catastrophic rockslide at Su village, Lishui, China. Landslides 15:1391–1400
Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional networks for biomedical image segmentation, in medical image computing and computer-assisted intervention - MICCAI 2015, Lecture Notes in Computer Science 234–241
Rudner TGJ, Rußwurm M, Fil J, Pelich R, Bischke B, Kopackova V, Bilinski P (2019) Multi3Net: segmenting flooded buildings via fusion of multiresolution, multisensor, and multitemporal satellite imagery, in Proceedings of the AAAI Conference on Artificial Intelligence 702–709
Rumelhart DE, Hinton GE, Williams RJ (1988) Learning representations by back-propagating errors. Nature 323:533–536
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vision 115(3):211–252
Saha S, Bovolo F, Bruzzone L (2019) Unsupervised deep change vector analysis for multiple-change detection in VHR images. IEEE Trans Geosci Remote Sens 57(6):3677–3693
Sun W, Wang R (2018) Fully convolutional networks for semantic segmentation of very high resolution remotely sensed images combined with DSM. IEEE Geosci Remote Sens Lett 15:474–478
Tasar O, Happy SL, Tarabalka Y, Alliez P (2020a) ColorMapGAN: unsupervised domain adaptation for semantic segmentation using color mapping generative adversarial networks. IEEE Trans on Geosci Remote Sensing 58(10):7178–7193
Tasar O, Tarabalka Y, Giros A, Alliez P, Clerc S (2020b) StandardGAN: multi-source domain adaptation for semantic segmentation of very high resolution satellite images by data standardization, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops 192–193
Teng W, Wang N, Shi H, Liu Y, Wang J (2020) Classifier-constrained deep adversarial domain adaptation for cross-domain semisupervised classification in remote sensing images. IEEE Geosci Remote Sensing Lett 17:789–793
The People’s Government of Sichuan Province (2017) The government report about Jiuzhaigou earthquake. http://www.sc.gov.cn/10462/12771/2017/8/14/10430678.shtml
Thonfeld F, Feilhauer H, Braun M, Menz G (2016). Robust Change Vector Analysis (RCVA) for multi-sensor very high resolution optical satellite data. Int J Appl Earth Obs Geoinf 50:131–140
Tsai YH, Hung W-C, Schulter S, Sohn K, Yang M-H, Chandraker M (2018) Learning to adapt structured output dpace for semantic segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 7472–7481
Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 7167–7176
Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(11)
Van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Eng Geol 102:112–131
Wang H, Wang Y, Zhang Q, Xiang S, Pan C (2017) Gated convolutional neural network for semantic segmentation in high-resolution images. Remote Sensing 9:446
Wang TC, Liu MY, Zhu JY, Tao A, Kautz J, Catanzaro B (2018a) High-resolution image synthesis and semantic manipulation with conditional GANs, in 2018a IEEE/CVF Conference on Computer Vision and Pattern Recognition 8798–8807
Wang X, Girshick R, Gupta A, He K (2018b) Non-local neural networks, in 2018b IEEE/CVF Conference on Computer Vision and Pattern Recognition 7794–7803
Wei K, Ouyang CJ, Duan H, Li Y, Chen M, Ma J, An HC, Zhou S (2020) Reflections on the catastrophic 2020 Yangtze River Basin flooding in southern China. The Innovation 1(2):100038
Wittich D, Rottensteiner F (2021) Appearance based deep domain adaptation for the classification of aerial images. ISPRS J Photogramm Remote Sens 180:82–102
Xu C (2015) Preparation of earthquake-triggered landslide inventory maps using remote sensing and GIS technologies: principles and case studies. Geosci Front 6:825–836
Xu Y, Liu X, Cao X et al (2021) Artificial intelligence: a powerful paradigm for scientific research. The Innov 2(4):100179
Yan L, Fan B, Liu H, Huo C, Xiang S, Pan C (2020) Triplet adversarial domain adaptation for pixel-level classification of VHR remote sensing images. IEEE Trans Geosci Remote Sensing 58:3558–3573
Yan L, Fan B, Xiang S, Pan C (2018) Adversarial domain adaptation with a domain similarity discriminator for semantic segmentation of urban areas, in 2018 25th IEEE International Conference on Image Processing 1583–1587
Yang M, Yu K, Zhang C, Li Z, Yang K (2018) DenseASPP for semantic segmentation in street scenes, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 3684–3692
Yang X, Li S, Chen Z, Chanussot J, Jia X, Zhang B, Li B, Chen P (2021) An attention-fused network for semantic segmentation of very-high-resolution remote sensing imagery. ISPRS J Photogramm Remote Sens 177:238–262
Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Comput Geosci 35(6):1125–1138
Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv: 1511.07122. 13
Yu F, Koltun V, Funkhouser T (2017) Dilated residual networks, in IEEE Conference on Computer Vision and Pattern Recognition 636–644
Yu Y, Li X, Liu F (2020) Attention GANs: unsupervised deep feature learning for aerial scene classification. IEEE Trans Geosci Remote Sensing 58:519–531
Yue K (2019) TreeUNet: adaptive tree convolutional neural networks for subdecimeter aerial image segmentation. ISPRS J Photogramm Remote Sens 156:1–13
Zhang F, Chen Y, Li Z, Hong Z, Liu J, Ma F, Han J, Ding E (2019) ACFNet: attentional class feature network for semantic segmentation, in Proceedings of the IEEE/CVF International Conference on Computer Vision 6798–6807
Zhang Z, Zhang X, Peng C, Xue X, Sun J (2018) ExFuse: enhancing feature fusion for semantic segmentation, In Proceedings of the European Conference on Computer Vision 273–288
Zhao H, Zhang Y, Liu S, Shi J, Loy CC, Lin D, Jia J (2018) PSANet: point-wise spatial attention network for scene parsing, in Computer Vision – ECCV 2018 270–286
Zhen M, Wang J, Zhou L, Fang T, Quan L (2019) Learning fully dense neural networks for image semantic segmentation, in Proceedings of the AAAI Conference on Artificial Intelligence 33:01
Zhu Q, Chen L, Hu H, Xu B, Zhang Y, Li H (2020) Deep fusion of local and non-local features for precision landslide recognition. arXiv preprint arXiv: 2002.08547
Funding
Financial support was provided by Key Research Program of Frontier Sciences of CAS (QYZDY-SSW-DQC006), NSFC (42022054), Strategic Priority Research Program of CAS (XDA23090303), and Youth Innovation Promotion Association.
Algorithms, MFFENet model, and ADANet, as well as recognition models of earthquake-induced landslides (including the MFFENet model trained with landslide database and the ADANet model transferred to the earthquake-induced Hokkaido Landslides), are available to the public on Github under a GNU General Public License (https://github.com/xupine/LandslideNet). The landslide database in “28” will be available in due course. In addition, utilizing our schemes to accomplish recognition tasks for other seismic landslides is highly welcomed.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Xu, Q., Ouyang, C., Jiang, T. et al. MFFENet and ADANet: a robust deep transfer learning method and its application in high precision and fast cross-scene recognition of earthquake-induced landslides. Landslides 19, 1617–1647 (2022). https://doi.org/10.1007/s10346-022-01847-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10346-022-01847-1