{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:49:26Z","timestamp":1760147366146,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T00:00:00Z","timestamp":1674777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005769","name":"Wisconsin Space Grant Consortium, Faculty Research and Infrastructure","doi-asserted-by":"publisher","award":["RIP21_1.1"],"award-info":[{"award-number":["RIP21_1.1"]}],"id":[{"id":"10.13039\/100005769","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Aquatic invasive plants (AIPs) are a global threat to local biodiversity due to their rapid adaptation to the new environments. Lythrum salicaria, commonly known as purple loosestrife, is a predominant AIP in the upper Midwestern region of the United States and has been designated as a deadly threat to the wetlands of this region. Accurate estimation of its current extent is a top priority, but regular monitoring is limited due to cost-, labor-, and time-intensive field surveys. Therefore, the goal of the present study is to accurately detect purple loosestrife from very high-resolution UAV imagery using deep neural network-based models. As a case study, this study implemented U-Net and LinkNet models with ResNet-152 encoder in the wetlands of the upper Mississippi River situated in La Crosse County, Wisconsin. The results showed that both models produced 88\u201394% training accuracy and performed better in landscapes that were occupied by smaller, disaggregated, and more equitably distributed purple loosestrife. Furthermore, the study adopted a transfer learning approach to implement a trained purple loosestrife model of the first study site and implemented it for the second study site. The results showed that the pre-trained model implementation generated better accuracy in less than half the time of the original model. Therefore, the transfer learning approach, if adapted efficiently, can be highly beneficial for continuous monitoring of purple loosestrife and strategic planning for application of direct biocontrol measures.<\/jats:p>","DOI":"10.3390\/rs15030734","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T10:19:28Z","timestamp":1675073968000},"page":"734","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Detection of Aquatic Invasive Plants in Wetlands of the Upper Mississippi River from UAV Imagery Using Transfer Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5534-0692","authenticated-orcid":false,"given":"Gargi","family":"Chaudhuri","sequence":"first","affiliation":[{"name":"Department of Geography and Earth Science, University of Wisconsin, La Crosse, WI 54601, USA"},{"name":"Center of River Studies, University of Wisconsin, La Crosse, WI 54601, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5544-7596","authenticated-orcid":false,"given":"Niti B.","family":"Mishra","sequence":"additional","affiliation":[{"name":"Department of Geography and Earth Science, University of Wisconsin, La Crosse, WI 54601, USA"},{"name":"Center of River Studies, University of Wisconsin, La Crosse, WI 54601, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.tplants.2008.03.004","article-title":"Adaptive Evolution in Invasive Species","volume":"13","author":"Prentis","year":"2008","journal-title":"Trends Plant Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1038\/ncomms12485","article-title":"Global Threats from Invasive Alien Species in the Twenty-First Century and National Response Capacities","volume":"7","author":"Early","year":"2016","journal-title":"Nat. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s10750-014-2166-0","article-title":"Aquatic Invasive Species: Challenges for the Future","volume":"750","author":"Havel","year":"2015","journal-title":"Hydrobiologia"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1146\/annurev-environ-033009-095548","article-title":"Invasive Species, Environmental Change and Management, and Health","volume":"35","author":"Richardson","year":"2010","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4081","DOI":"10.1073\/pnas.1600366113","article-title":"Invasive Species Triggers a Massive Loss of Ecosystem Services through a Trophic Cascade","volume":"113","author":"Walsh","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Olden, J.D., and Tamayo, M. (2014). Incentivizing the Public to Support Invasive Species Management: Eurasian Milfoil Reduces Lakefront Property Values. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0110458"},{"key":"ref_7","unstructured":"Johnson, M., and Meder, M.E. (2022, November 29). Effects of Aquatic Invasive Species on Home Prices. Available online: https:\/\/ssrn.com\/abstract=2316911."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1016\/j.jglr.2016.03.016","article-title":"The Role of Anglers in Preventing the Spread of Aquatic Invasive Species in the Great Lakes Region","volume":"42","author":"Connelly","year":"2016","journal-title":"J. Great Lakes Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"151318","DOI":"10.1016\/j.scitotenv.2021.151318","article-title":"Economic Costs of Biological Invasions in the United States","volume":"806","author":"Haubrock","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"12986","DOI":"10.1038\/ncomms12986","article-title":"Massive yet Grossly Underestimated Global Costs of Invasive Insects","volume":"7","author":"Bradshaw","year":"2016","journal-title":"Nat. Commun."},{"key":"ref_11","unstructured":"Bisbee, G., Blumer, D., Burbach, D., Iverson, B., Kemp, D., Richter, L., Sklavos, S., Strohl, D., Thompson, B., and Welch, R.J. (2016). Purple Loosestrife Biological Control Activities for Educators, Wisconsin Department of Natural Resources, Wisconsin Wetland Association. PUBL-SS-981 REV2016."},{"key":"ref_12","unstructured":"University of Wisconsin Sea Grant and Water resource Institute (2018). Wisconsin Aquatic Invasive Species Management Plan, Wisconsin Department of Natural Resources."},{"key":"ref_13","unstructured":"Wisconsin DNR (2015). Wisconsin Invasive Species Program Report, Wisconsin Department of Natural Resources."},{"key":"ref_14","unstructured":"US Dept of the Interior (2016). Safeguarding America\u2019s Lands and Waters from Invasive Species: A National Framework for Early Detection and Rapid Response Contents."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"17656","DOI":"10.1038\/s41598-019-53797-9","article-title":"Convolutional Neural Networks Enable Efficient, Accurate and Fine-Grained Segmentation of Plant Species and Communities from High-Resolution UAV Imagery","volume":"9","author":"Kattenborn","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_16","first-page":"1207","article-title":"The Photogrammetric Potential of Low-Cost Uavs in Forestry and Agriculture","volume":"31","author":"Engel","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.-ISPRS Arch."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9070","DOI":"10.1080\/01431161.2019.1569793","article-title":"A Bibliometric Analysis on the Use of Unmanned Aerial Vehicles in Agricultural and Forestry Studies","volume":"40","author":"Raparelli","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s11676-015-0088-y","article-title":"Drone Remote Sensing for Forestry Research and Practices","volume":"26","author":"Tang","year":"2015","journal-title":"J. Res."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kentsch, S., Caceres, M.L.L., Serrano, D., Roure, F., and Diez, Y. (2020). Computer Vision and Deep Learning Techniques for the Analysis of Drone-Acquired Forest Images, a Transfer Learning Study. Remote Sens., 12.","DOI":"10.3390\/rs12081287"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.12988\/ces.2016.68130","article-title":"Forest and UAV: A Bibliometric Review","volume":"9","author":"Gambella","year":"2016","journal-title":"Contemp. Eng. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"475","DOI":"10.5194\/isprs-archives-XLII-2-W13-475-2019","article-title":"Resnet-Based Tree Species Classification Using Uav Images","volume":"XLII-2\/W13","author":"Natesan","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.-ISPRS Arch."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cabezas, M., Kentsch, S., Tomhave, L., Gross, J., Larry, M., Caceres, L., and Diez, Y. (2020). Remote Sensing Detection of Invasive Species in Wetlands: Practical DL with Heavily Imbalanced Data. Remote Sens., 12.","DOI":"10.3390\/rs12203431"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1109\/LRA.2017.2774979","article-title":"WeedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming","volume":"3","author":"Sa","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1002\/rse2.111","article-title":"Using the U-Net Convolutional Network to Map Forest Types and Disturbance in the Atlantic Rainforest with Very High Resolution Images","volume":"5","author":"Wagner","year":"2019","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2562","DOI":"10.1002\/ece3.4919","article-title":"Performances of Machine Learning Algorithms for Mapping Fractional Cover of an Invasive Plant Species in a Dryland Ecosystem","volume":"9","author":"Shiferaw","year":"2019","journal-title":"Ecol. Evol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bhatnagar, S., Gill, L., and Ghosh, B. (2020). Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities. Remote Sens., 12.","DOI":"10.3390\/rs12162602"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"9351","author":"Ronneberger","year":"2015","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1016\/j.tree.2019.03.006","article-title":"Uncovering Ecological Patterns with Convolutional Neural Networks","volume":"34","author":"Brodrick","year":"2019","journal-title":"Trends Ecol. Evol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., and Culurciello, E. (2018, January 10\u201313). LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation. Proceedings of the 2017 IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA.","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learnin","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_32","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015\u2014Conference Track Proceedings, San Diego, CA, USA."},{"key":"ref_33","unstructured":"Chollet, F. (2021). Deep Learning with Python, Manning. [2nd ed.]."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.knosys.2015.01.010","article-title":"Transfer Learning Using Computational Intelligence: A Survey","volume":"80","author":"Lu","year":"2015","journal-title":"Knowl. Based Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"R977","DOI":"10.1016\/j.cub.2019.08.016","article-title":"Deep Learning for Environmental Conservation","volume":"29","author":"Lamba","year":"2019","journal-title":"Curr. Biol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kimura, N., Yoshinaga, I., Sekijima, K., Azechi, I., and Baba, D. (2019). Convolutional Neural Network Coupled with a Transfer-Learning Approach for Time-Series Flood Predictions. Water, 12.","DOI":"10.3390\/w12010096"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1967","DOI":"10.1007\/s10530-009-9600-7","article-title":"Should We Care about Purple Loosestrife? The History of an Invasive Plant in North America","volume":"12","author":"Lavoie","year":"2010","journal-title":"Biol. Invasions"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Huang, B., Lu, K., Audebert, N., Khalel, A., Tarabalka, Y., Malof, J., Boulch, A., Le Saux, B., Collins, L., and Bradbury, K. (2018, January 22\u201327). Large-Scale Semantic Classification: Outcome of the First Year of Inria Aerial Image Labeling Benchmark. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518525"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2019.08.006","article-title":"An Enhanced Bloom Index for Quantifying Floral Phenology Using Multi-Scale Remote Sensing Observations","volume":"156","author":"Chen","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","unstructured":"Chollet, F., and TensorFlower Gardener (2020, May 20). Keras. Available online: https:\/\/github.com\/fchollet\/keras."},{"key":"ref_42","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2015). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. arXiv."},{"key":"ref_43","unstructured":"Yakubovskiy, P., and Segmentation Models (2020, June 12). GitHub Repos. Available online: https:\/\/github.com\/qubvel\/segmentation_models."},{"key":"ref_44","unstructured":"McGarigal, K., Cushman, S.A., and Ene, E. (2012). FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps, Computer Software Program Produced by the Authors at the University of Massachusetts. Available online: http:\/\/www.umass.edu\/landeco\/research\/fragstats\/fragstats.html."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/BF00125347","article-title":"A New Contagion Index to Quantify Spatial Patterns of Landscapes","volume":"8","author":"Li","year":"1993","journal-title":"Landsc. Ecol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans Med. Imaging"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep Learning in Remote Sensing Applications: A Meta-Analysis and Review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/734\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:16:51Z","timestamp":1760120211000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/734"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,27]]},"references-count":47,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15030734"],"URL":"https:\/\/doi.org\/10.3390\/rs15030734","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,1,27]]}}}