ORIGINAL ARTICLE
Application of Mobile GIS and the Flora Incognita App for Documenting Vegetation Along the Green Ecological Trail in the Barlinek Landscape Park
 
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Research Center of Post-Mining, Technische Hochschule Georg Agricola, Bochum, Germany
 
 
Submission date: 2025-10-07
 
 
Final revision date: 2026-05-11
 
 
Acceptance date: 2026-05-15
 
 
Online publication date: 2026-06-08
 
 
Publication date: 2026-06-08
 
 
Corresponding author
Marcin Piotr Pawlik   

Research Center of Post-Mining, Technische Hochschule Georg Agricola, Herner Str. 45, 44787, Bochum, Germany
 
 
Civil and Environmental Engineering Reports 2026;36(2):37-50
 
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ABSTRACT
This article examines the use of mobile Geographic Information Systems (GIS), particularly the Flora Incognita application, for documenting vegetation along the green ecological trail in the Barlinek Landscape Park. The study evaluates the usability of the application, the accuracy of plant identification, and its potential to generate high-quality georeferenced data. The results indicate that Flora Incognita is an efficient and cost-effective tool for rapid floristic surveys, achieving high identification accuracy which, when complemented by expert verification, can significantly enhance monitoring efforts. The analysis demonstrates how citizen-generated data can be utilized to develop dynamic spatial databases, supporting park management in tasks such as mapping invasive species and tracking phenological changes. The article concludes that, although mobile GIS applications cannot replace traditional scientific methods, they represent a complementary approach that promotes public engagement and contributes to a more comprehensive understanding of local and macroecological patterns.
REFERENCES (33)
1.
Balmford, A et al. 2003. Measuring the changing state of nature. Trends in Ecology & Evolution 18, 326–330.
 
2.
Balmford, A et al. 2005.The 2010 challenge: data availability, information needs and ex-traterrestrial insights. Philosophical Transactions of the Royal Society of London Series B Biological Sciences 360, 221–228.
 
3.
Schmeller, DS et al. 2009. Advantages of Volunteer-Based Biodiversity Monitoring in Europe. Conservation Biology 23, 2, 307-316.
 
4.
Dickinson, JL et al. 2010. Citizen science as an ecological research tool: Challenges and benefits. Annual Review of Ecology, Evolution, and Systematics 41, 149–172.
 
5.
Isaac, NJB et al. 2014. Statistics for citizen science: extracting signals of change from noisy ecological data. Methods in Ecology and Evolution 5, 10, 1052–1060.
 
6.
Pocock, MJO et al. 2018. A vision for global biodiversity monitoring with citizen science. Advances in Ecological Research 59, 169–223.
 
7.
Sutherland, WJ et al. 2015. An agenda for the future of biological recording for ecological monitoring and citizen science, Biological Journal of the Linnean Society 115, 3,779–784.
 
8.
Chandler, M et al. 2017. Contribution of citizen science towards international biodiversity monitoring. Biological Conservation 213, 280–294.
 
9.
Pawlik, M et al. 2023. The use of Mobile GIS in scientific research – Post-Mining Case Studies. IOP Conference Series Earth and Environment Science 1189 012023, 1-19.
 
10.
Pawlik, MP et al. 2025. Mobile GIS in Mapping Vegetation on Mine Heaps: A modern Approach to Reclamation of Post-mining Areas. Civil and Environmental Engineering Reports 35(3), 181-197.
 
11.
Teacher, AGF et al. 2013. Smartphones in ecology and evolution: a guide for the app-rehensive. Ecology and Evolution 3(16), 5268–5278.
 
12.
Bonnet, P, Goëau, H, Hang, ST, Lasseck, M, Milan Šulc, M, Malécot, V, Jauzein, P, Melet, J-C, You, C and Joly, A 2018. Plant identification: Experts vs. machines in the era of deep learning. In: Joly, A, Vrochidis, S, Karatzas, K, Karppinen, A, Bonnet, P (ed) Multimedia Tools and Applications for Environmental & Biodiversity Informatics. Cham: Springer, 131-149.
 
13.
Seltzer, C 2019. Making biodiversity data social, shareable, and scalable: Reflections on iNaturalist and citizen science. Biodiversity Information Science and Standards 3 e46670, 1-2.
 
14.
Van Horn, G et al. 2018 The iNaturalist species classification and detection dataset. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Salt Lake City, USA, 8769-8778.
 
15.
Nugent, J 2020 iNaturalist: Citizen science for the digital age. The Science Teacher 87(8), 58-63.
 
16.
Mason, BM 2025 iNaturalist accelerates biodiversity research. BioScience 75, 11, 953–965.
 
17.
Carranza-Rojas, J et al. 2017 Going deeper in the automated identification of herbarium specimens. BMC Evolutionary Biology 17, 181, 1-14.
 
18.
Devi, A and Rav, G 2018. Reviews on Augmented Reality: Google Lens. International Journal of Computer Trends and Technology 58, 1, 94–97.
 
19.
du Plessis, LK 2015. Through the Google Lens: Development of lecturing practice in Photography. Master of Technology in Photography. Durban: Durban University of Technology.
 
20.
Ahmad Syawaldi, F and Apandi, Y 2019. Augmented Reality (Studi Kasus: GoogleLens). Informan’s – Jurnal Ilmu-ilmu Informatika dan Manajemen 2(1).
 
21.
Shapovalov, VB, Shapovalov, YB, Bilyk, ZI, Megalinska, AP and Muzyka, IO 2019. The Google Lens analyzing quality: an analysis of the possibility to use in the educational process. Educational Dimension 1 (53), 219-234.
 
22.
Bilyk, ZI, Shapovalov, YB, Shapovalov, VB, Megalinska, AP, Zhadan, SO, Andruszkiewicz, F, Dołhańczuk-Śródka, A and Antonenko, PD 2022. Comparing Google Lens Recognition Accuracy with Other Plant Recognition Apps. In: Semerikov, S, Osadchyi, V and Kuzminska, O (ed) AET 2020 Proceedings of the 1st Symposium on Advances in Educational Technology Volume 2, SciTePress, 20–33.
 
23.
Mäder, P et al. 2021. The Flora Incognita app – Interactive plant species identification. Methods in Ecology and Evolution 12 (7), 1335–1342.
 
24.
Wäldchen, J and Mäder, P 2018. Machine learning for image-based species identification. Methods in Ecology and Evolution 9 (11), 2216–2225.
 
25.
TU Ilmenau (2023). TU Ilmenau: Neue KI für Flora Incognita. [TU Ilmenau: New AI for Flora Incognita.] https://www.tu-ilmenau.de/aktu... (avaible on 07.10.2025).
 
26.
Uchwała Sejmiku Województwa Zachodniopomorskiego. (2020). Uchwała nr XV/182/20 z dnia 27 maja 2020 r. w sprawie utworzenia Barlineckiego Parku Krajobrazowego. [Resolution No. XV/182/20 of 27 May 2020 on the establishment of the Barlinek Landscape Park] https://e-dziennik.szczecin.uw... (accessed on 20.05.2026).
 
27.
Mapa Barlineckiego Parku Krajobrazowego [Map of the Barlinek Landscape Park] https://www.zpkwz.pl/images/pd... (accessed on 20.05.2026).
 
28.
Mapa turystyczna [Tourist Map] https://mapserver.bdl.lasy.gov... (accessed on 20.05.2026).
 
29.
Openstreetmap https://www.openstreetmap.org (accessed on 20.05.2026).
 
30.
Rzanny, M et al. 2024. More than rapid identification – Free plant identification apps can also be highly accurate. People and Nature 6(5), 1031–1043.
 
31.
Araujo, VM et al. 2020. Two-view fine-grained classification of plant species. Pattern Recognition Letters 135, 180–187.
 
32.
Pärtel, J et al. 2021. Plant image identification application demonstrates high accuracy in Northern Europe. AoB Plants 13(4), plab050, 1-10.
 
33.
Tokarska-Guzik, B, Dajdok, Z, Zając, M, Zając, A, Urbisz, A, Danielewicz, W and Hołdyński, C 2012. Rośliny obcego pochodzenia w Polsce: ze szczególnym uwzględnieniem gatunków inwazyjnych. Katowice: Generalna Dyrekcja Ochrony Środowiska [Plants of foreign origin in Poland: with particular emphasis on invasive species. Katowice: General Directorate for Environmental Protection].
 
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ISSN:2080-5187
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