The co-effect of image resolution and crown size on deep learning for individual tree detection and delineation

References

• Bolya, D., C. Zhou, F. Xiao, and Y. J. Lee. 2022. “Yolact++ Better Real-Time Instance Segmentation.” IEEE Transactions On Pattern Analysis and Machine Intelligence 44 (2): 1108–1121. https://doi.org/10.1109/TPAMI.2020.3014297.
   PubMed Web of Science ®Google Scholar
• Braga, J. R., V. Peripato, R. Dalagnol, M. P. Ferreira, Y. Tarabalka, L. E. O. C. Aragão, H. F. De Campos Velho, E. H. Shiguemori, and F. H. Wagner. 2020. “Tree Crown Delineation Algorithm Based on a Convolutional Neural Network.” Remote Sensing 12 (8): 1288. https://doi.org/10.3390/rs12081288.
   Web of Science ®Google Scholar
• Brandt, M., C. J. Tucker, A. Kariryaa, K. Rasmussen, C. Abel, J. Small, J. Chave, et al. 2020. “An Unexpectedly Large Count of Trees in the West African Sahara and Sahel.” Nature 587 (7832): 78–82. https://doi.org/10.1038/s41586-020-2824-5.
   PubMed Web of Science ®Google Scholar
• Chadwick, A. J., T. R. H. Goodbody, N. C. Coops, A. Hervieux, C. W. Bater, L. A. Martens, B. White, and D. Röeser. 2020. “Automatic Delineation and Height Measurement of Regenerating Conifer Crowns Under Leaf-off Conditions Using UAV Imagery.” Remote Sensing 12 (24): 4104. https://doi.org/10.3390/rs12244104.
   Web of Science ®Google Scholar
• Chen, L., Y. Zhu, G. Papandreou, F. Schroff, and H. Adam. 2018. “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.” In Proceedings of the European Conference on Computer Vision (ECCV), 801-818. Munich, Germany.
   Google Scholar
• Chiang, C., C. Barnes, P. Angelov, and R. Jiang. 2020. “Deep Learning-Based Automated Forest Health Diagnosis from Aerial Images.” IEEE Access, 144064–144076. https://doi.org/10.1109/ACCESS.2020.3012417.
   Google Scholar
• Dash, J. P., M. S. Watt, G. D. Pearse, M. Heaphy, and H. S. Dungey. 2017. “Assessing Very High Resolution UAV Imagery for Monitoring Forest Health During a Simulated Disease Outbreak.” ISPRS Journal of Photogrammetry and Remote Sensing, 1–14. https://doi.org/10.1016/j.isprsjprs.2017.07.007.
   Web of Science ®Google Scholar
• Egli, S., and M. Höpke. 2020. “CNN-based Tree Species Classification Using High Resolution RGB Image Data from Automated UAV Observations.” Remote Sensing 12 (23): 3892. https://doi.org/10.3390/rs12233892.
   Web of Science ®Google Scholar
• Ferreira, M. P., D. R. A. D. Almeida, D. D. A. Papa, J. B. S. Minervino, H. F. P. Veras, A. Formighieri, C. A. N. Santos, M. A. D. Ferreira, E. O. Figueiredo, and E. J. L. Ferreira. 2020. “Individual Tree Detection and Species Classification of Amazonian Palms Using UAV Images and Deep Learning.” Forest Ecology and Management 475118397, https://doi.org/10.1016/j.foreco.2020.118397.
   Google Scholar
• Ferreira, M. P., R. G. Lotte, F. V. D'Elia, C. Stamatopoulos, D. Kim, and A. R. Benjamin. 2021. “Accurate Mapping of Brazil nut Trees (Bertholletia Excelsa) in Amazonian Forests Using Worldview-3 Satellite Images and Convolutional Neural Networks.” Ecological Informatics 63101302, https://doi.org/10.1016/j.ecoinf.2021.101302.
   Google Scholar
• Fromm, M., M. Schubert, G. Castilla, J. Linke, and G. Mcdermid. 2019. “Automated Detection of Conifer Seedlings in Drone Imagery Using Convolutional Neural Networks.” Remote Sensing 11 (21): 2585. https://doi.org/10.3390/rs11212585.
   Web of Science ®Google Scholar
• Gallardo-Salazar, J. L., and M. Pompa-García. 2020. “Detecting Individual Tree Attributes and Multi-Spectral Indices Using Unmanned Aerial Vehicles: Applications in a Pine Clonal Orchard.” Remote Sensing 12 (24): 4144. https://doi.org/10.3390/rs12244144.
   Web of Science ®Google Scholar
• Gallmann, J., B. Schüpbach, K. Jacot, M. Albrecht, J. Winizki, N. Kirchgessner, and H. Aasen. 2022. “Flower Mapping in Grasslands with Drones and Deep Learning.” Frontiers in Plant Science 12774965, https://doi.org/10.3389/fpls.2021.774965.
   Google Scholar
• Gan, Y., Q. Wang, and A. Iio. 2023. “Tree Crown Detection and Delineation in a Temperate Deciduous Forest from UAV RGB Imagery Using Deep Learning Approaches: Effects of Spatial Resolution and Species Characteristics.” Remote Sensing 15 (3): 778. https://doi.org/10.3390/rs15030778.
   Web of Science ®Google Scholar
• Goodbody, T. R. H., N. C. Coops, and J. C. White. 2019. “Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions.” Current Forestry Reports 5 (2): 55–75. https://doi.org/10.1007/s40725-019-00087-2.
   Web of Science ®Google Scholar
• Goutte, C., and E. Gaussier. 2005. “A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation.” In Proceedings of the 27th European Conference on IR Research, 345–359. Santiago de Compostela, Spain.
   Google Scholar
• Guimarães, N., L. Pádua, P. Marques, N. Silva, E. Peres, and J. J. Sousa. 2020. “Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities.” Remote Sensing 12 (6): 1046. https://doi.org/10.3390/rs12061046.
   Web of Science ®Google Scholar
• Guirado, E., J. Blanco-Sacristán, E. Rodríguez-Caballero, S. Tabik, D. Alcaraz-Segura, J. Martínez-Valderrama, and J. Cabello. 2021. “Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors.” Sensors 21 (1): 320. https://doi.org/10.3390/s21010320.
   PubMed Web of Science ®Google Scholar
• Hao, Z., L. Lin, C. J. Post, Y. Jiang, M. Li, N. Wei, K. Yu, and J. Liu. 2021a. “Assessing Tree Height and Density of a Young Forest Using a Consumer Unmanned Aerial Vehicle (UAV).” New Forests 52 (5): 843–862. https://doi.org/10.1007/s11056-020-09827-w.
   Web of Science ®Google Scholar
• Hao, Z., L. Lin, C. J. Post, E. A. Mikhailova, M. Li, Y. Chen, K. Yu, and J. Liu. 2021b. “Automated Tree-Crown and Height Detection in a Young Forest Plantation Using Mask Region-Based Convolutional Neural Network (Mask R-CNN).” ISPRS Journal of Photogrammetry and Remote Sensing 178: 112–123. https://doi.org/10.1016/j.isprsjprs.2021.06.003.
   Web of Science ®Google Scholar
• Hao, Z., C. J. Post, E. A. Mikhailova, L. Lin, J. Liu, and K. Yu. 2022. “How Does Sample Labeling and Distribution Affect the Accuracy and Efficiency of a Deep Learning Model for Individual Tree-Crown Detection and Delineation.” Remote Sensing 14 (7): 1561. https://doi.org/10.3390/rs14071561.
   Web of Science ®Google Scholar
• He, K., G. Gkioxari, P. Dollár, and R. Girshick. 2017. “Mask R-CNN.” In Proceedings of the IEEE International Conference on Computer Vision, 2961-2969. Venice, Italy.
   Google Scholar
• Heenkenda, M. K., K. E. Joyce, and S. W. Maier. 2015. “Mangrove Tree Crown Delineation from High-Resolution Imagery.” Photogrammetric Engineering & Remote Sensing 81 (6): 471–479. https://doi.org/10.14358/PERS.81.6.471.
   Web of Science ®Google Scholar
• Iqbal, M. S., H. Ali, S. N. Tran, and T. Iqbal. 2021. “Coconut Trees Detection and Segmentation in Aerial Imagery Using Mask Region-Based Convolution Neural Network.” IET Computer Vision 15 (6): 428–439. https://doi.org/10.1049/cvi2.12028.
   Web of Science ®Google Scholar
• Kameyama, S., and K. Sugiura. 2021. “Effects of Differences in Structure from Motion Software on Image Processing of Unmanned Aerial Vehicle Photography and Estimation of Crown Area and Tree Height in Forests.” Remote Sensing 13 (4): 626. https://doi.org/10.3390/rs13040626.
   Web of Science ®Google Scholar
• Kattenborn, T., J. Eichel, and F. E. Fassnacht. 2019. “Convolutional Neural Networks Enable Efficient, Accurate and Fine-Grained Segmentation of Plant Species and Communities from High-Resolution UAV Imagery.” Scientific Reports 9 (1): 17656. https://doi.org/10.1038/s41598-019-53797-9.
   PubMedGoogle Scholar
• Kattenborn, T., J. Eichel, S. Wiser, L. Burrows, F. E. Fassnacht, and S. Schmidtlein. 2020. “Convolutional Neural Networks Accurately Predict Cover Fractions of Plant Species and Communities in Unmanned Aerial Vehicle Imagery.” Remote Sensing in Ecology and Conservation 6 (4): 472–486. https://doi.org/10.1002/rse2.146.
   Web of Science ®Google Scholar
• Kattenborn, T., J. Leitloff, F. Schiefer, and S. Hinz. 2021. “Review on Convolutional Neural Networks (CNN) in Vegetation Remote Sensing.” ISPRS Journal of Photogrammetry and Remote Sensing, 173, 24–49. https://doi.org/10.1016/j.isprsjprs.2020.12.010.
   Web of Science ®Google Scholar
• Kwong, I. H. Y., and T. Fung. 2020. “Tree Height Mapping and Crown Delineation Using LIDAR, Large Format Aerial Photographs, and Unmanned Aerial Vehicle Photogrammetry in Subtropical Urban Forest.” International Journal of Remote Sensing 41 (14): 5228–5256. https://doi.org/10.1080/01431161.2020.1731002.
   Web of Science ®Google Scholar
• La Rosa, L. E. C., C. Sothe, R. Q. Feitosa, C. M. de Almeida, M. B. Schimalski, and D. A. B. Oliveira. 2021. “Multi-task Fully Convolutional Network for Tree Species Mapping in Dense Forests Using Small Training Hyperspectral Data.” ISPRS Journal of Photogrammetry and Remote Sensing 179: 35–49. https://doi.org/10.1016/j.isprsjprs.2021.07.001.
   Web of Science ®Google Scholar
• Lassalle, G., M. P. Ferreira, L. E. C. La Rosa, and C. R. de Souza Filho. 2022. “Deep Learning-Based Individual Tree Crown Delineation in Mangrove Forests Using Very-High-Resolution Satellite Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 189: 220–235. https://doi.org/10.1016/j.isprsjprs.2022.05.002.
   Web of Science ®Google Scholar
• Lee, C. K. F., G. Song, H. C. Muller-Landau, S. Wu, S. J. Wright, K. C. Cushman, R. F. Araujo, et al. 2023. “Cost-effective and Accurate Monitoring of Flowering Across Multiple Tropical Tree Species Over two Years with a Time Series of High-Resolution Drone Imagery and Deep Learning.” ISPRS Journal of Photogrammetry and Remote Sensing 201: 92–103. https://doi.org/10.1016/j.isprsjprs.2023.05.022.
   Web of Science ®Google Scholar
• Li, Y., G. Chai, Y. Wang, L. Lei, and X. Zhang. 2022a. “Ace R-CNN: An Attention Complementary and Edge Detection-Based Instance Segmentation Algorithm for Individual Tree Species Identification Using UAV RGB Images and Lidar Data.” Remote Sensing 14 (13): 3035. https://doi.org/10.3390/rs14133035.
   Web of Science ®Google Scholar
• Li, X., J. Tian, X. Li, L. Wang, H. Gong, C. Shi, S. Nie, et al. 2022b. “Developing a sub-Meter Phenological Spectral Feature for Mapping Poplars and Willows in Urban Environment.” ISPRS Journal of Photogrammetry and Remote Sensing 193: 77–89. https://doi.org/10.1016/j.isprsjprs.2022.09.002.
   Web of Science ®Google Scholar
• Madec, S., X. Jin, H. Lu, B. De Solan, S. Liu, F. Duyme, E. Heritier, and F. Baret. 2019. “Ear Density Estimation from High Resolution RGB Imagery Using Deep Learning Technique.” Agricultural and Forest Meteorology 264: 225–234. https://doi.org/10.1016/j.agrformet.2018.10.013.
   Web of Science ®Google Scholar
• Miraki, M., H. Sohrabi, P. Fatehi, and M. Kneubuehler. 2021. “Individual Tree Crown Delineation from High-Resolution UAV Images in Broadleaf Forest.” Ecological Informatics 61: 101207. https://doi.org/10.1016/j.ecoinf.2020.101207.
   Web of Science ®Google Scholar
• Mo, J., Y. Lan, D. Yang, F. Wen, H. Qiu, X. Chen, and X. Deng. 2021. “Deep Learning-Based Instance Segmentation Method of Litchi Canopy from UAV-Acquired Images.” Remote Sensing 13 (19): 3919. https://doi.org/10.3390/rs13193919.
   Web of Science ®Google Scholar
• Mohan, M., C. Silva, C. Klauberg, P. Jat, G. Catts, A. Cardil, A. Hudak, and M. Dia. 2017. “Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest.” Forests 8 (9): 340. https://doi.org/10.3390/f8090340.
   Web of Science ®Google Scholar
• Ocer, N. E., G. Kaplan, F. Erdem, D. Kucuk Matci, and U. Avdan. 2020. “Tree Extraction from Multi-Scale UAV Images Using Mask R-CNN with FPN.” Remote Sensing Letters 11 (9): 847–856. https://doi.org/10.1080/2150704X.2020.1784491.
   Web of Science ®Google Scholar
• Panagiotidis, D., A. Abdollahnejad, P. Surový, and V. Chiteculo. 2017. “Determining Tree Height and Crown Diameter from High-Resolution UAV Imagery.” International Journal of Remote Sensing 38 (8-10): 2392–2410. https://doi.org/10.1080/01431161.2016.1264028.
   Web of Science ®Google Scholar
• Pearse, G. D., A. Y. S. Tan, M. S. Watt, M. O. Franz, and J. P. Dash. 2020. “Detecting and Mapping Tree Seedlings in UAV Imagery Using Convolutional Neural Networks and Field-Verified Data.” ISPRS Journal of Photogrammetry and Remote Sensing 168: 156–169. https://doi.org/10.1016/j.isprsjprs.2020.08.005.
   Web of Science ®Google Scholar
• Pleșoianu, A., M. Stupariu, I. andric, I. Pătru-Stupariu, and L. Drăguț. 2020. “Individual Tree-Crown Detection and Species Classification in Very High-Resolution Remote Sensing Imagery Using a Deep Learning Ensemble Model.” Remote Sensing 12 (15): 2426. https://doi.org/10.3390/rs12152426.
   Web of Science ®Google Scholar
• Pouliot, D. A., D. J. King, F. W. Bell, and D. G. Pitt. 2002. “Automated Tree Crown Detection and Delineation in High-Resolution Digital Camera Imagery of Coniferous Forest Regeneration.” Remote Sensing of Environment 82 (2): 322–334. https://doi.org/10.1016/S0034-4257(02)00050-0.
   Web of Science ®Google Scholar
• Qin, H., W. Zhou, Y. Yao, and W. Wang. 2022. “Individual Tree Segmentation and Tree Species Classification in Subtropical Broadleaf Forests Using UAV-Based LIDAR, Hyperspectral, and Ultrahigh-Resolution RGB Data.” Remote Sensing of Environment 280: 113143. https://doi.org/10.1016/j.rse.2022.113143.
   Web of Science ®Google Scholar
• Rezatofighi, H., N. Tsoi, J. Gwak, A. Sadeghian, I. Reid, and S. Savarese. 2019. “Generalized Intersection Over Union: A Metric and a Loss for Bounding box Regression.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 658-666. Long Beach, CA, USA.
   Google Scholar
• Safonova, A., E. Guirado, Y. Maglinets, D. Alcaraz-Segura, and S. Tabik. 2021. “Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN.” Sensors 21 (5): 1617. https://doi.org/10.3390/s21051617.
   PubMed Web of Science ®Google Scholar
• Saltiel, T. M., P. E. Dennison, M. J. Campbell, T. R. Thompson, and K. R. Hambrecht. 2022. “Tradeoffs Between UAS Spatial Resolution and Accuracy for Deep Learning Semantic Segmentation Applied to Wetland Vegetation Species Mapping.” Remote Sensing 14 (11): 2703. https://doi.org/10.3390/rs14112703.
   Web of Science ®Google Scholar
• Schiefer, F., T. Kattenborn, A. Frick, J. Frey, P. Schall, B. Koch, and S. Schmidtlein. 2020. “Mapping Forest Tree Species in High Resolution UAV-Based RGB-Imagery by Means of Convolutional Neural Networks.” ISPRS Journal of Photogrammetry and Remote Sensing 170: 205–215. https://doi.org/10.1016/j.isprsjprs.2020.10.015.
   Web of Science ®Google Scholar
• Sokolova, M., N. Japkowicz, and S. Szpakowicz. 2006. “Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation.” In Proceedings of the 19th Australian Joint Conference on Artificial Intelligence, 1015-1021. Hobart, Australia: Springer Berlin Heidelberg. doi:10.1007/11941439_114.
   Google Scholar
• Sun, Y., Z. Li, H. He, L. Guo, X. Zhang, and Q. Xin. 2022. “Counting Trees in a Subtropical Mega City Using the Instance Segmentation Method.” International Journal of Applied Earth Observation and Geoinformation 106: 102662. https://doi.org/10.1016/j.jag.2021.102662.
   Web of Science ®Google Scholar
• Swayze, N. C., W. T. Tinkham, M. B. Creasy, J. C. Vogeler, C. M. Hoffman, and A. T. Hudak. 2022. “Influence of UAS Flight Altitude and Speed on Aboveground Biomass Prediction.” Remote Sensing 14 (9): 1989. https://doi.org/10.3390/rs14091989.
   Web of Science ®Google Scholar
• Swayze, N. C., W. T. Tinkham, J. C. Vogeler, and A. T. Hudak. 2021. “Influence of Flight Parameters on UAS-Based Monitoring of Tree Height, Diameter, and Density.” Remote Sensing of Environment 263: 112540. https://doi.org/10.1016/j.rse.2021.112540.
   Web of Science ®Google Scholar
• Tang, Z., M. Li, and X. Wang. 2020. “Mapping tea Plantations from VHR Images Using OBIA and Convolutional Neural Networks.” Remote Sensing 12 (18): 2935. https://doi.org/10.3390/rs12182935.
   Web of Science ®Google Scholar
• Tu, Y., K. Johansen, S. Phinn, and A. Robson. 2019. “Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment.” Remote Sensing 11 (3): 269. https://doi.org/10.3390/rs11030269.
   Web of Science ®Google Scholar
• Tu, Y., S. Phinn, K. Johansen, A. Robson, and D. Wu. 2020. “Optimising Drone Flight Planning for Measuring Horticultural Tree Crop Structure.” ISPRS Journal of Photogrammetry and Remote Sensing 160: 83–96. https://doi.org/10.1016/j.isprsjprs.2019.12.006.
   Web of Science ®Google Scholar
• Veras, H. F. P., M. P. Ferreira, E. M. Da Cunha Neto, E. O. Figueiredo, A. P. D. Corte, and C. R. Sanquetta. 2022. “Fusing Multi-Season UAS Images with Convolutional Neural Networks to map Tree Species in Amazonian Forests.” Ecological Informatics 71: 101815. https://doi.org/10.1016/j.ecoinf.2022.101815.
   Web of Science ®Google Scholar
• Weinstein, B. G., S. Marconi, S. Bohlman, A. Zare, and E. White. 2019. “Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks.” Remote Sensing 11 (11): 1309. https://doi.org/10.3390/rs11111309.
   Web of Science ®Google Scholar
• Weinstein, B. G., S. Marconi, S. A. Bohlman, A. Zare, and E. P. White. 2020. “Cross-site Learning in Deep Learning RGB Tree Crown Detection.” Ecological Informatics 56: 101061. https://doi.org/10.1016/j.ecoinf.2020.101061.
   Web of Science ®Google Scholar
• Wu, D., K. Johansen, S. Phinn, A. Robson, and Y. Tu. 2020a. “Inter-comparison of Remote Sensing Platforms for Height Estimation of Mango and Avocado Tree Crowns.” International Journal of Applied Earth Observation and Geoinformation 89: 102091. https://doi.org/10.1016/j.jag.2020.102091.
   Web of Science ®Google Scholar
• Wu, G., and Y. Li. 2021. “Non-maximum Suppression for Object Detection Based on the Chaotic Whale Optimization Algorithm.” Journal of Visual Communication and Image Representation 74: 102985. https://doi.org/10.1016/j.jvcir.2020.102985.
   Web of Science ®Google Scholar
• Wu, X., D. Sahoo, and S. C. H. Hoi. 2020b. “Recent Advances in Deep Learning for Object Detection.” Neurocomputing 396: 39–64. https://doi.org/10.1016/j.neucom.2020.01.085.
   Web of Science ®Google Scholar
• Xi, Z., C. Hopkinson, S. B. Rood, and D. R. Peddle. 2020. “See the Forest and the Trees: Effective Machine and Deep Learning Algorithms for Wood Filtering and Tree Species Classification from Terrestrial Laser Scanning.” ISPRS Journal of Photogrammetry and Remote Sensing 168: 1–16. https://doi.org/10.1016/j.isprsjprs.2020.08.001.
   Web of Science ®Google Scholar
• Yang, M., Y. Mou, S. Liu, Y. Meng, Z. Liu, P. Li, W. Xiang, X. Zhou, and C. Peng. 2022. “Detecting and Mapping Tree Crowns Based on Convolutional Neural Network and Google Earth Images.” International Journal of Applied Earth Observation and Geoinformation 108: 102764. https://doi.org/10.1016/j.jag.2022.102764.
   Web of Science ®Google Scholar
• Yin, D., and L. Wang. 2019. “Individual Mangrove Tree Measurement Using UAV-Based LIDAR Data: Possibilities and Challenges.” Remote Sensing of Environment 223: 34–49. https://doi.org/10.1016/j.rse.2018.12.034.
   Web of Science ®Google Scholar
• Zhang, L., H. Lin, and F. Wang. 2022. “Individual Tree Detection Based on High-Resolution RGB Images for Urban Forestry Applications.” IEEE Access 10: 46589–46598. https://doi.org/10.1109/ACCESS.2022.3171585.
   Web of Science ®Google Scholar
• Zhao, H., F. Liu, H. Zhang, and Z. Liang. 2019. “Convolutional Neural Network Based Heterogeneous Transfer Learning for Remote-Sensing Scene Classification.” International Journal of Remote Sensing 40 (22): 8506–8527. https://doi.org/10.1080/01431161.2019.1615652.
   Web of Science ®Google Scholar