Estimation de la densite des individus avec des images drones dans les ecosystemes Saheliens du Senegal
Keywords:
Drone, densité, écosystèmes, Sahel, Sénégal, Ferlo
Abstract
Les nouveaux progrès de la technologie des drones et les capacités de traitement des données ont permis d'obtenir des images de haute résolution et des données tridimensionnelles (3D) qui peuvent être utilisées pour évaluer les attributs des arbres. Cette étude a été menée à Widou thiengoly dans la localité du Ferlo, Nord du Sénégal, avec comme objectif général d’appliquer une approche photogrammétrique pour la mesure de la densité des tiges (tiges/ha) avec des images drones. Une méthode de l’approche arbre basée sur un modèle numérique de hauteur d'une zone d'étude de 10 hectares a été mise en œuvre, ce modèle a été construit à partir d'images obtenues par des drones. De là, la performance de l'algorithme de détection de l’arbre individuel a été évaluée. Pour chaque parcelle, le nombre d'arbres a été compté manuellement en utilisant l'ortho mosaïque dérivée du drone pour référence. Au total, 92 arbres de référence ont été comptés dans le cadre de cette étude et l'algorithme a détecté 75 arbres, ce qui donne une précision supérieure à 90 % (score F de 0,93). Dans l'ensemble, l'algorithme a manqué 10 arbres (erreurs d'omission) et a faussement détecté 3 arbres (erreurs de commission), ce qui donne un compte total de 88 arbres. Cette étude suggère que l'algorithme de filtrage des maxima locaux combiné avec des tailles de fenêtre optimale, appliqués sur un Modèle Numérique de Hauteur construit par photogrammétrie est capable d’effectuer des comptages d'arbres avec une précision acceptable (F > 0,90) dans la zone sahélienne.
New advances in UAV technology and data processing capabilities have made it possible to obtain high resolution images and three-dimensional (3D) data that can be used to assess tree attributes. This study was conducted at Widou thiengoly in the Ferlo locality, northern Senegal, with the overall objective of applying a photogrammetric approach for measuring stem density (stems/ha) with UAV images. A tree approach method based on a digital height model of a 10-hectare study area was implemented, which was constructed from UAV images. From this, the performance of the individual tree detection algorithm was evaluated. For each plot, the number of trees was counted manually using the drone-derived ortho-mosaic as a reference. A total of 92 reference trees were counted in this study and the algorithm detected 75 trees, giving an accuracy of over 90% (F-score of 0.93). Overall, the algorithm missed 10 trees (omission errors) and falsely detected 3 trees (commission errors), giving a total count of 88 trees. This study suggests that the local maxima filtering algorithm combined with optimal window sizes, applied on a photogrammetrically constructed Digital Height Model is capable of performing tree counts with acceptable accuracy (F > 0.90) in the Sahelian zone.
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References
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2. Agisoft LLC., 2018. Agisoft PhotoScan User Manual. Edition: Agisoft LLC. 121p
3. Akpo, L. E., Grouzis, M., & Ba, A. T., 1995. L’arbre et l’herbe au Sahel : effets de l’arbre sur la composition chimique des pâturages naturels du Nord-Sénégal Afrique de l’Ouest. Revue Med. Vet, 14610, 663-670.
4. Bergseng, E., Ørka, H. O., Næsset, E., & Gobakken, T., 2015. Assessing forest inventory information obtained from different inventory approaches and remote sensing data sources. Annals of forest science, 721, 33-45.
5. Berhaut, J., 1967. Flora of Senegal. Flora of Senegal. 2nd ed Clairafrique : Dakar, 485 p
6. Birdal, A.C.; Avdan, U.; Türk, T., 2017. Estimating tree heights with images from an unmanned aerial vehicle. Geomat. Nat. Hazards Risk . 8:2, 1144-1156, DOI: 10.1080/19475705.2017.130060
7. Blanchard E., Birnbaum P., Ibanez T., Boutreux T., Antin C., Ploton P., 2016. Contrasted allometries between stem diameter, crown area, and tree height in five tropical biogeographic areas. Trees - Structure and Function, 30 6, 1953-1968. https://doi.org/10.1007/s00468-016- 1424-3
8. Bourgoin C., Betbeder J., Couteron P., Blanc L., Le Roux R., Cornu G., 2020. UAV-based canopy textures assess degraded structures in human-modified Amazonian forest. Ecological Indicators. 1-29. in press
9. Bradbury, R. B., Hill, R. A., Mason, D. C., Hinsley, S. A., Wilson, J. D., Balzter, H., Bellamy, P. E., 2005. Modelling relationships between birds and vegetation structure using airborne LiDAR data: A review 4 with case studies from agricultural and woodland environments. IBIS, 147, 443–452. https://doi.org/10.1111/j.1474-919x.2005. 00438.x
10. Brundtland, G. H., 1987. Our common future—Call for action. Environmental Conservation, 144, 291-294.
11. Campbell, G., Phinn, S. R., Dekker, A. G., And Brando, V. E., 2011. Remote sensing of water quality in an Australian tropical freshwater impoundment using matrix inversion and MERIS images, Remote Sensing of Environment, 115 9, 2402-2414.
12. Clark D. B., Castro C. S., Alvarado L. D. A., Read J. M., 2004a. Quantifying mortality of tropical rain forest trees using high-spatial resolution satellite data. Ecology Letters, 7 1, 52-59. https://doi.org/10.1046/j.1461-0248.2003.00547.x
13. Dandois J.P. & Ellis E.C., 2013. High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sens, Environ.,136, 259–276.
14. Dandois, J. P., Olano, M., & Ellis, E. C., 2015. Optimal altitude, overlap, and weather conditions for computer vision UAV estimates of forest structure. Remote Sensing, 710, 13895-13920.
15. Diallo A., Faye M. N. et Guissé., 2011. Structure des peuplements ligneux dans les plantations d’Acacia senegal L. Wild dans la zone de Dahra Ferlo, Sénégal. Rev. Ecol., 66, 415-427.
16. Foli E. G., Alder D., Miller H. G., Swaine M. D., 2003. Modelling growing space requirements for some tropical forest tree species. Forest Ecology and Management, 173, 79-88. https://doi.org/10.1016/s0378-11270100815-5
17. Fraser, B. T. and Congalton, R. G., 2018. Issues in Unmanned Aerial Systems UAS data collection of complex forest environments. Remote Sensing, 106, 908.
18. Gonçalves, J. A., & Henriques, R., 2015. UAV photogrammetry for topographic monitoring of coastal areas. ISPRS Journal of Photogrammetry and Remote Sensing, 104, 101-111.
19. Goodman R. C., Phillips O. L., Baker T. R., 2014. The importance of crown dimensions to improve tropical tree biomass estimates. Ecological Applications, 24 4, 680-698. https://doi.org/10.1890/13-0070.1
20. Huang, X., Ziniti, B., Torbick, N., and Ducey, M. J., 2018b. Assessment of forest above ground biomass estimation using multi-temporal C-band Sentinel-1 and Polarimetric L-band PALSAR-2 data. Remote Sensing, 10, 9, 1424, 1-15.
21. Iizuka, K., Yonehara, T., Itoh, M., and Kosugi, Y., 2018. Estimating Tree Height and Diameter at Breast Height DBH from Digital surface models and orthophotos obtained with an unmanned aerial system for a Japanese Cypress Chamaecyparis obtusa Forest. Remote Sensing, 10, n°1, 13, 14p.
22. Johansen, K., Raharjo, T., McCabe, M.F., 2018. Using multispectral UAV imagery to extract tree crop structural properties and assess pruning effects. Remote Sens, 8, n°1, 50. doi.org/10.3390/rs8010050
23. Jucker T., Caspersen J., Chave J., Antin C., Barbier N., Bongers F., 2017. Allometric equations for integrating remote sensing imagery into forest monitoring programmes. Global Change Biology, 23 1, 177-190. https://doi.org/10.1111/gcb.13388
24. Ke, Y. and Quackenbush, L. J., 2011. A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing. International Journal of Remote Sensing, 3217, 4725–4747.
25. La, H.P., Eo, Y.D., Chang, A., Kim, C., 2015 Extraction of individual tree crown using hyperspectral image and LiDAR data. KSCE J. Civ. Eng, 19, 1078–1087. [CrossRef]
26. Leclère, L., & Tech, G. A. B., 2018. Apport de la télédétection aérienne à très haute résolution pour la caractérisation qualitative et quantitative de la ressource forestière : Application à un massif forestier feuillus en Ardenne belge. Université Liéges, Gembloux Agrobiotech, Master, 96 p.
27. Lei, X., Peng, C., Wang, H., & Zhou, X., 2009. Individual height–diameter models for young black spruce Picea mariana and jack pine Pinus banksiana plantations in New Brunswick, Canada. The Forestry Chronicle, 851, 43-56.
28. Lim, Y.S.; La, P.H.; Park, J.S.; Lee, M.H.; Pyeon, M.W.; Kim, J.I., 2015. Calculation of Tree Height and Canopy Crown from Drone Images Using Segmentation. J. Korean Soc. Surv. Geod. Photogramm. Cartogr. 33, 605–614. [CrossRef]
29. Lintz, J., and Simonett, D. S., 1976, Sensors for spacecraft. rse, Remote Sensing of Environment, Addison-Wesley, Reading, MA, 323-343
30. Lisein J, Pierrot-Deseilligny M, Bonnet S, Lejeune P., 2013. A photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery. Forests 4, 922-944. http://dx.doi.org/10.3390/ f4040922.
31. Mercklé Pierre, « Corrélation », in Paugam Serge dir., Les 100 mots de la sociologie, Paris, Presses universitaires de France, coll. « Que Sais-Je ? », pp. 30-31.
32. Messinger, M., Asner, G. P., & Silman, M., 2016. Rapid assessments of Amazon forest structure and biomass using small unmanned aerial systems. Remote Sensing, 88, 615.
33. Michez A., Henrotay F. & Claessens H., 2014. Exploitation des données aéroportées LiDAR et drone dans le cadre des programmes d'actions sur les rivières par une approche intégrée et sectorisée Rapport final IMAGEAU. 42
34. Mohan, M., Silva, C. A., Klauberg, C., Jat, P., Catts, G., Cardil, A., Hudak, A. T., and Dia, M., 2017. Individual tree detection from unmanned aerial vehicle UAV derived canopy height model in an open canopy mixed conifer forest. Forests, 89, 1–17.
35. Navarro, J. A., Algeet, N., Fernández-Landa, A., Esteban, J., Rodríguez- Noriega, P., and Guillén-Climent, M. L., 2019. Integration of UAV, Sentinel-1, and Sentinel-2 data for mangrove plantation aboveground biomass monitoring in Senegal. Remote Sensing, 111, 1–23.
36. Ndiaye, O., 2013. Caractéristiques des sols, de la flore et de la végétation du Ferlo, Sénégal Doctoral dissertation, Thèse de doctorat unique en biologie végétale, option écologie, FST, UCAD, 114.
37. Ota T., Ogawa M., Shimizu K., Kajisa T., Mizoue N., Yoshida S., 2015. Aboveground biomass estimation using structure from motion approach with aerial photographs in a seasonal tropical forest. Forests, 6, 3882-3898. https://doi.org/10.3390/f6113882
38. Panagiotidis, D., Abdollahnejad, A., Surový, P., and Chiteculo, V., 2017. Determining tree height and crown diameter from high-resolution UAV imagery. International Journal of Remote Sensing, 388-10, 2392–2410.
39. Patrick, A. and Li, C., 2017. High throughput phenotyping of blueberry bush morphological traits using unmanned aerial systems. Remote Sens., 9, n°12, 1250, 1-18. doi.org/10.3390/rs9121250
40. Payer, D. C., & Harrison, D. J., 2003. Influence of forest structure on habitat use by American marten in an industrial forest. Forest ecology and Management, 1791-3, 145-156.
41. Ploton P., Barbier N., Momo S. T., Réjou-Méchain M., Boyemba B. F., Chuyong G., 2016. Closing a gap in tropical forest biomass estimation: Taking crown mass variation into account in pantropical allometries. Biogeosciences, 13 5, 1571-1585. https://doi. org/10.5194/bg-13-1571-2016
42. Pouliot, D. A., King, D. J., & Pitt, D. G., 2005. Development and evaluation of an automated tree detection delineation algorithm for monitoring regenerating coniferous forests. Canadian Journal of Forest Research, 351, 2332-2345.
43. Puttock, A. K., Cunliffe, A. M., Anderson, K., & Brazier, R. E., 2015. Aerial photography collected with a multirotor drone reveals impact of Eurasian beaver reintroduction on ecosystem structure. Journal of Unmanned Vehicle Systems, 33, 123-130.
44. Read J. M., Clark D. B., Venticinque E. M., Moreira M. P., 2003. Application of merged 1-m and 4-m resolution satellite data to research and management in tropical forests. Journal of Applied Ecology, 40, 592-600. https://doi.org/10.1046/j.1365-2664.2003.00814.x
45. Sadhasivam, N., Dineshkumar, C., Rahaman, S. A., & Bhardwaj, A., 2019., Estimation of Forest Tree Heights and Crown Diameter Using High Resolution Images from UAV: A Case Study of Kalesar, Haryana. In International Conference on Unmanned Aerial System in Geomatics. Springer, Cham, 253-263
46. Shin, P., Sankey, T., Moore, M., Thode, A., 2018. Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand. Remote Sens., 10, n°8, 1266. doi.org/10.3390/rs10081266
47. Silva, C.A.; Crookston, N.L.; Hudak, A.T.; Vierling, L.A., 2015 An R Package for Reading, Processing and Visualizing LiDAR Light Detection and Ranging Data, Version 0.1, 24p. Available online: http://cran.rproject.org/web/packages/rLiDAR/index.html
48. Sprinz, P. T., & Burkhart, H. E., 1987. Relationships between tree crown, stem, and stand characteristics in unthinned loblolly pine plantations. Canadian Journal of Forest Research, 176, 534-538.
49. Tang, L., & Shao, G., 2015. Drone remote sensing for forestry research and practices. Journal of Forestry Research, 264, 791-797.
50. Thibaut, Q., 2019. Structure d'une forêt claire de type miombo par imageries drone et satellitaire. Travail de fin d’études, Université Gembloux Agro-Bio Tech (GxABT), 70p.
51. Tu, Y. H., Johansen, K., Phinn, S., and Robson, A., 2019. Measuring canopy structure and condition using multi-spectral UAS imagery in a horticultural environment. Remote Sensing, 113, 15–17.
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Published
2025-09-07
How to Cite
Talla, R., Sylla, D., Faye, N., Sagna, M. B., Diallo, A., Sarr, O., & Guisse, A. (2025). Estimation de la densite des individus avec des images drones dans les ecosystemes Saheliens du Senegal. European Scientific Journal, ESJ, 45, 274. Retrieved from https://eujournal.org/index.php/esj/article/view/20008
Section
ESI Preprints
Copyright (c) 2025 Ramata Talla, Diara Sylla, Ndiabou Faye, Moustapha Bassimbé Sagna, Aly Diallo, Oumar Sarr, Aliou Guisse
This work is licensed under a Creative Commons Attribution 4.0 International License.
