{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T17:46:00Z","timestamp":1773423960985,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T00:00:00Z","timestamp":1681776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Institute of Civil Military Technology Cooperation funded by the Defense Acquisition Program Administration and Ministry of Trade, Industry and Energy of Korean government","award":["22-CM-EO-02"],"award-info":[{"award-number":["22-CM-EO-02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As the importance of forests has increased, continuously monitoring and managing information on forest ecology has become essential. The composition and distribution of tree species in forests are essential indicators of forest ecosystems. Several studies have been conducted to classify tree species using remote sensing data and machine learning algorithms because of the constraints of the traditional approach for classifying tree species in forests. In the machine learning approach, classification accuracy varies based on the characteristics and quantity of the study area data used. Thus, applying various classification models to achieve the most accurate classification results is necessary. In the literature, patch-based deep learning (DL) algorithms that use feature maps have shown superior classification results than point-based techniques. DL techniques substantially affect the performance of input data but gathering highly explanatory data is difficult in the study area. In this study, we analyzed (1) the accuracy of tree classification by convolutional neural networks (CNNs)-based DL models with various structures of CNN feature extraction areas using a high-resolution LiDAR-derived digital surface model (DSM) acquired from a drone platform and (2) the impact of tree classification by creating input data via various geometric augmentation methods. For performance comparison, the drone optic and LiDAR data were separated into two groups according to the application of data augmentation, and the classification performance was compared using three CNN-based models for each group. The results demonstrated that Groups 1 and CNN-1, CNN-2, and CNN-3 were 0.74, 0.79, and 0.82 and 0.79, 0.80, and 0.84, respectively, and the best mode was CNN-3 in Group 2. The results imply that (1) when classifying tree species in the forest using high-resolution bi-seasonal drone optical images and LiDAR data, a model in which the number of filters of various sizes and filters gradually decreased demonstrated a superior classification performance of 0.95 for a single tree and 0.75 for two or more mixed species; (2) classification performance is enhanced during model learning by augmenting training data, especially for two or more mixed tree species.<\/jats:p>","DOI":"10.3390\/rs15082140","type":"journal-article","created":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T01:09:22Z","timestamp":1681866562000},"page":"2140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Mapping Tree Species Using CNN from Bi-Seasonal High-Resolution Drone Optic and LiDAR Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3026-0543","authenticated-orcid":false,"given":"Eu-Ru","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Geoinformatics, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea"},{"name":"Department of Smart Cities, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9779-0752","authenticated-orcid":false,"given":"Won-Kyung","family":"Baek","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea"},{"name":"Korea Ocean Satellite Center, Korea Institute of Ocean Science & Technology, Haeyang-ro, Yeongdo-gu, Busan 49111, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2335-8438","authenticated-orcid":false,"given":"Hyung-Sup","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea"},{"name":"Department of Smart Cities, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1111\/j.1468-2346.2006.00575.x","article-title":"The role of forests in global climate change: Whence we come and where we go","volume":"82","author":"Streck","year":"2006","journal-title":"Int. Aff."},{"key":"ref_2","first-page":"3870","article-title":"Artificial forest management for global change mitigation","volume":"26","author":"Feng","year":"2006","journal-title":"Acta Ecol. Sin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1023\/A:1025772928833","article-title":"Institutions, forest management, and sustainable human development\u2013experiences from India","volume":"5","author":"Prasad","year":"2003","journal-title":"Environ. Dev. Sustain."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lee, S.H., Han, K.J., Lee, K., Lee, K.J., Oh, K.Y., and Lee, M.J. (2020). Classification of Landscape affected by Deforestation using High-Resolution Remote Sensing Data and Deep-Learning Techniques. Remote Sens., 12.","DOI":"10.3390\/rs12203372"},{"key":"ref_5","first-page":"1037","article-title":"The development of major tree species classification model using different satellite images and machine learning in Gwangneung area","volume":"35","author":"Lim","year":"2019","journal-title":"Korean J. Remote Sens."},{"key":"ref_6","unstructured":"Kim, K.M., and Lee, S.H. (2013). Distribution of Major Species in Korea (Based on 1:5000 Forest Type Map), National Institute of Forest Science."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Persson, M., Lindberg, E., and Reese, H. (2018). Tree species classification with multi-temporal Sentinel-2 data. Remote Sens., 10.","DOI":"10.3390\/rs10111794"},{"key":"ref_8","unstructured":"Sobhan, I. (2007). Species Discrimination from a Hyperspectral Perspective, Wageningen University."},{"key":"ref_9","unstructured":"Kent, M., and Coker, P. (1996). Vegetation Description and Analysis: A Practical Approach, John Willey & Sons, Inc."},{"key":"ref_10","first-page":"447","article-title":"Forest vertical Structure classification in Gongju city, Korea from optic and RADAR satellite images using artificial neural network","volume":"35","author":"Lee","year":"2019","journal-title":"Korean J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.rse.2014.03.018","article-title":"Urban tree species mapping using hyperspectral and lidar data fusion","volume":"148","author":"Alonzo","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Grybas, H., and Congalton, R.G. (2021). A comparison of multi-temporal RGB and multispectral UAS imagery for tree species classification in heterogeneous New Hampshire Forests. Remote Sens., 13.","DOI":"10.3390\/rs13132631"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Guo, Y., Chen, S., Wu, Z., Wang, S., Robin Bryant, C., Senthilnath, J., Cunha, M., and Fu, Y.H. (2021). Integrating Spectral and Textural Information for Monitoring the Growth of Pear Trees Using Optical Images from the UAV Platform. Remote Sens., 13.","DOI":"10.3390\/rs13091795"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2012.03.013","article-title":"Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral\/hyperspectral images and LiDAR data","volume":"123","author":"Dalponte","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Luo, C., Li, X., Wang, L., He, J., Li, D., and Zhou, J. (2018, January 10\u201312). How Does the Data set Affect CNN-based Image Classification Performance?. Proceedings of the 2018 5th International Conference on Systems and Informatics (ICSAI), Nanjing, China.","DOI":"10.1109\/ICSAI.2018.8599448"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"193","DOI":"10.5194\/isprs-annals-V-3-2020-193-2020","article-title":"Evaluating a Convolutional Neural Network for Feature Extraction and Tree-species classification using UAV-hyperspectral images","volume":"5","author":"Sothe","year":"2020","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pawara, P., Okafor, E., Schomaker, L., and Wiering, M. (2017, January 18\u201321). Data augmentation for plant classification. Proceedings of the International Conference on Advanced Concepts for Intelligent Vision Systems, Antwerp, Belgium.","DOI":"10.1007\/978-3-319-70353-4_52"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Nezami, S., Khoramshahi, E., Nevalainen, O., P\u00f6l\u00f6nen, I., and Honkavaara, E. (2020). Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks. Remote Sens., 12.","DOI":"10.20944\/preprints202002.0334.v1"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9952","DOI":"10.1038\/s41598-020-67024-3","article-title":"Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms","volume":"10","author":"Li","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_20","unstructured":"Kim, J.H. (2019). Seasonal Changes in Plants in Temperate Forests in Korea. [Ph.D. Thesis, The Seoul National University]."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ngadze, F., Mpakairi, K.S., Kavhu, B., Ndaimani, H., and Maremba, M.S. (2020). Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0232962"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yu, J.W., Yoon, Y.W., Baek, W.K., and Jung, H.S. (2021). Forest vertical structure mapping using two-seasonal optic images and LIDAR DSM acquired from UAV platform through Random Forest, XGBoost, and support vector machine approaches. Remote Sens., 13.","DOI":"10.3390\/rs13214282"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compag.2017.05.027","article-title":"Application of UAV imaging platform for vegetation analysis based on spectral-spatial methods","volume":"140","author":"Senthilnath","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1080\/15481603.2021.1974275","article-title":"Urban tree species classification using UAV-based multi-sensor data fusion and machine learning","volume":"58","author":"Hartling","year":"2021","journal-title":"GIScience Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"420","DOI":"10.7745\/KJSSF.2016.49.5.420","article-title":"Estimation of highland kimchi cabbage growth using UAV NDVI and agro-meteorological factors","volume":"49","author":"Na","year":"2016","journal-title":"Korean J. Soil Sci. Fertil."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Khan, R.S., and Bhuiyan, M.A.E. (2021). Artificial intelligence-based techniques for rainfall estimation integrating multisource precipitation datasets. Atmosphere, 12.","DOI":"10.3390\/atmos12101239"},{"key":"ref_27","unstructured":"Perez, L., and Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","article-title":"Learning deep architectures for AI","volume":"2","author":"Bengio","year":"2009","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_29","first-page":"1691","article-title":"Observation of Ice Gradient in Cheonji, Baekdu Mountain Using Modified U-Net from Landsat -5\/-7\/-8 Images","volume":"38","author":"Lee","year":"2022","journal-title":"Korean J. Remote Sens."},{"key":"ref_30","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_31","unstructured":"Tache, N. (2017). Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems, O\u2019Reilly Media."},{"key":"ref_32","first-page":"599","article-title":"Classification model evaluation metrics","volume":"12","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"J. Big Data"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"101675","DOI":"10.1016\/j.bspc.2019.101675","article-title":"A novel data augmentation method to enhance deep neural networks for detection of atrial fibrillation","volume":"56","author":"Cao","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1002\/bimj.201800148","article-title":"Tuning model parameters in class-imbalanced learning with precision-recall curve","volume":"61","author":"Fu","year":"2019","journal-title":"Biom. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.3390\/rs70201736","article-title":"Time series analysis of landslide dynamics using an unmanned aerial vehicle (UAV)","volume":"7","author":"Turner","year":"2015","journal-title":"Remote Sens."},{"key":"ref_37","first-page":"229","article-title":"Classification of Forest Vertical Structure Using Machine Learning Analysis","volume":"35","author":"Kwon","year":"2019","journal-title":"Korean J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2140\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:18:33Z","timestamp":1760123913000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,18]]},"references-count":37,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15082140"],"URL":"https:\/\/doi.org\/10.3390\/rs15082140","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,18]]}}}