Abstract
Spatially explicit data on heterogeneously distributed plant populations are difficult to quantify using either traditional field-based methods or remote sensing techniques alone. Unmanned Aerial Vehicles (UAVs) offer new means and tools for baseline monitoring of such populations. We tested the use of vegetation classification of UAV-acquired photographs as a method to capture heterogeneously distributed plant populations, using Jacobaea vulgaris as a model species. Five sites, each containing 1–4 pastures with varying J. vulgaris abundance, were selected across Schleswig–Holstein, Germany. Surveys were conducted in July 2017 when J. vulgaris was at its flowering peak. We took aerial photographs at a 50 m altitude using three digital cameras (RGB, red-edge and near-infrared). Orthomosaics were created before a pixel-based supervised classification. Classification results were evaluated for accuracy; reliability was assessed with field data collected for ground verification. An ANOVA tested the relationship between field-based abundance estimations and the supervised classifications. Overall accuracy of the classification was very high (90.6%, ± 1.76 s.e.). Kappa coefficients indicated substantial agreement between field data and image classification (≥ 0.65). Field-based estimations were a good predictor of the supervised classifications (F = 7.91, df = 4, P = 0.007), resulting in similar rankings of J. vulgaris abundance. UAV-acquired images demonstrated the potential as an objective method for data collection and species monitoring. However, our method was more time consuming than field-based estimations due to challenges in image processing. Nonetheless, the increasing availability of low-cost consumer-grade UAVs is likely to increase the use of UAVs in plant ecological studies.
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References
Asner G, Martin R (2011) Canopy phylogenetic, chemical and spectral assembly in a lowland Amazonian forest. New Phytol 189:999–1012. https://doi.org/10.1111/j.1469-8137.2010.03549.x
Bendig J, Bolten A, Bareth G (2012) Introducing a low-cost mini-UAV for thermal-and multispectral-imaging. Int Arch Photogramm Remote Sens Spat Inf Sci XXXIX-B1:345–349. https://doi.org/10.5194/isprsarchives-XXXIX-B1-345-2012
Berni JA, Zarco-Tejada PJ, Suárez L, González-Dugo V, Fereres E (2009) Remote sensing of vegetation from UAV platforms using lightweight multispectral and thermal imaging sensors. Int Arch Photogramm Remote Sens Spatial Inform Sci 38:6
Berra E, Gaulton R, Barr S (2017) Commercial off-the-shelf digital cameras on unmanned aerial vehicles for multitemporal monitoring of vegetation reflectance and NDVI. IEEE Trans Geosci Remote Sens 55:4878–4886. https://doi.org/10.1109/TGRS.2017.2655365
Bevan EL, Wibbels TH, Navarro ER, Rosas MA, Najera BM, Sarti LA, Illescas F, Montano J, Peña LJ, Burchfield P (2016) Using unmanned aerial vehicle (UAV) technology for locating, identifying, and monitoring courtship and mating behavior in the green turtle (Chelonia mydas). Herpetol Rev 47:27–32
Buddenbaum H, Schlerf M, Hill J (2005) Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods. Int J Remote Sens 26:5453–5465. https://doi.org/10.1080/01431160500285076
Candiago S, Remondino F, De Giglio M, Dubbini M, Gattelli M (2015) Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sens 7:4026–4047. https://doi.org/10.3390/rs70404026
Carvalho S, Schlerf M, van der Putten W, Skidmore A (2013) Hyperspectral reflectance of leaves and flowers of an outbreak species discriminates season and successional stage of vegetation. Intl J App Earth Observ Geoinform 24:32–41. https://doi.org/10.1016/j.jag.2013.01.005
Climate-data.org (2017) Climate: Schleswig-Holstein. https://en.climate-data.org/region/399/. Assessed on 12 October 2017
Curran P (1989) Remote sensing of foliar chemistry. Remote Sens Environ 30:271–278. https://doi.org/10.1016/0034-4257(89)90069-2
Daubenmire R (1959) A canopy–coverage method of vegetational anaysis. Northwest Sci 33:43–64
Diaz-Varela R, Zarco-Tejada P, Angileri V, Loudjani P (2014) Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle. J Environ Manage 134:117–126. https://doi.org/10.1016/j.jenvman.2014.01.006
Ezat MA, Fritsch CJ, Downs CT (2018) Use of an unmanned aerial vehicle (drone) to survey Nile crocodile populations: a case study at Lake Nyamithi, Ndumo game reserve, South Africa. Biol Conserv 223:76–81. https://doi.org/10.1016/j.biocon.2018.04.032
Gillespie TW, Foody GM, Rocchini D, Giorgi AP, Saatchi S (2008) Measuring and modelling biodiversity from space. Prog Phys Geogr 32:203–221. https://doi.org/10.1177/0309133308093606
Graça NL, Mitishita EA, Gonçalves JE (2017) Use of UAV platform as an autonomous tool for estimating expansion on invaded agricultural land. Boletim de Ciências Geodésicas 23:509–519. https://doi.org/10.1590/s1982-21702017000300034
Heydemann B (1997) Neuer Biologischer Atlas: Ökologie für Schleswig–Holstein und Hamburg. Neumünster, Wachholtz, p 591
Hodgson A, Peel D, Kelly N (2017) Unmanned aerial vehicles for surveying marine fauna: assessing detection probability. Ecol Appl 27:1253–1267. https://doi.org/10.1002/eap.1519
Honkavaara E, Saari H, Kaivosoja J, Pölönen I, Hakala T, Litkey P, Mäkynen J, Pesonen L (2013) Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture. Remote Sens 5:5006–5039. https://doi.org/10.3390/rs5105006
Horcher A, Visser RJM (2004) Unmanned aerial vehicles: applications for natural resource management and monitoring. In: Proceedings of the Council on Forest Engineering Proceedings 2004: Machines and People, The Interface. Hot Springs, AR, USA, 27–30 April 2004
Hou W, Walz U (2013) Enhanced analysis of landscape structure: inclusion of transition zones and small-scale landscape elements. Ecol Indic 31:15–24. https://doi.org/10.1016/j.ecolind.2012.11.014
Ivošević B, Han YG, Cho Y, Kwon O (2015) The use of conservation drones in ecology and wildlife research. J Ecol Environ 38:113–118. https://doi.org/10.5141/ecoenv.2015.012
James MR, Robson S, d’Oleire-Oltmanns S, Niethammer U (2017) Optimising UAV topographic surveys processed with structure-from-motion: ground control quality, quantity and bundle adjustment. Geomorphology 280:51–66. https://doi.org/10.1016/j.geomorph.2016.11.021
Jones GP, Pearlstine LG, Percival HF (2006) An assessment of small unmanned aerial vehicles for wildlife research. Wildl Soc Bull 34:750–758. https://doi.org/10.2193/0091-7648(2006)34%5b750:AAOSUA%5d2.0.CO;2
Kelcey J, Lucieer A (2012) Sensor correction of a 6-band multispectral imaging sensor for UAV remote sensing. Remote Sens 4:1462–1493. https://doi.org/10.3390/rs4051462
Kerr JT, Ostrovsky M (2003) From space to species: ecological applications for remote sensing. Trends Ecol Evol 18:299–305. https://doi.org/10.1016/S0169-5347(03)00071-5
Knox NM, Skidmore AK, Schlerf M, De Boer WF, Van Wieren SE, Van der Waal C, Prins HHT, Slotow R (2010) Nitrogen prediction in grasses: effect of bandwidth and plant material state on absorption feature selection. Int J Remote Sens 31:691–704. https://doi.org/10.1080/01431160902895480
Koh LP, Wich SA (2012) Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Trop Conserv Sci 5:121–132. https://doi.org/10.1177/194008291200500202
Laliberte AS, Herrick JE, Rango A, Winters C (2010) Acquisition, orthorectification, and object-based classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring. Photogramm Eng Remote Sensing 76:661–672. https://doi.org/10.14358/PERS.76.6.661
Landinfo (2018) Worldwide mapping LLC. http://www.landinfo.com/satellite-imagery-pricing.html Assessed on 22 May 2018
Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174
Liang K, Ma Y, Xie Y, Zhou B, Wang R (2012) A new adaptive contrast enhancement algorithm for infrared images based on double plateaus histogram equalization. Infrared Phys Technol 55:309–315. https://doi.org/10.1016/j.infrared.2012.03.004
Lin CL (2011) An approach to improve the quality of infrared images of vein-patterns. Sensors 11:11447–11463. https://doi.org/10.3390/s111211447
LLUR (2012) Die Böden Schleswig-Holsteins: Entstehung, Verbreitung, Nutzung, Eigenschaften und Gefährdung. Landesamt für Landwirtschaft, Umwelt und ländliche Räume des Landes Schleswig-Holstein, Flintbek, Germany
Lu B, He Y (2017) Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland. ISPRS J Photogramm Remote Sens 128:73–85. https://doi.org/10.1016/j.isprsjprs.2017.03.011
McHugh ML (2012) Interrater reliability: the kappa statistic. Biochem Med (Zagreb) 22:276–282. https://doi.org/10.11613/BM.2012.031
Mirik M, Ansley RJ, Steddom K, Jones DC, Rush CM, Michels GJ, Elliott NC (2013) Remote distinction of a noxious weed (Musk Thistle: Carduus nutans) using airborne hyperspectral imagery and the support vector machine classifier. Remote Sens 5:612–630. https://doi.org/10.3390/rs5020612
Mulla DJ (2013) Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosyst Eng 114:358–371. https://doi.org/10.1016/j.biosystemseng.2012.08.009
Nagendra H, Rocchini D (2008) High resolution satellite imagery for tropical biodiversity studies: the devil is in the detail. Biodivers Conserv 17:3431–3442. https://doi.org/10.1007/s10531-008-9479-0
Neumann H, Lütt S, Schleich-Saidfar C, Rabe I, Walter A, Böhling J, Böttner E, Ehlers B, Trede J, Werner M, Ramert D, Kruse M (2015) Umgang mit dem Jakobs-Kreuzkraut: Meiden - Dulden - Bekämpfen. Landesamt für Landwirtschaft, Umwelt und ländliche Räume des Landes Schleswig-Holstein, Flintbek
Ostrom E, Nagendra H (2006) Insights on linking forests, trees, and people from the air, on the ground, and in the laboratory. Proc Natl Acad Sci USA 103:19224–19231. https://doi.org/10.1073/pnas.0607962103
Peña JM, Torres-Sánchez J, de Castro AI, Kelly M, López-Granados F (2013) Weed mapping in early-season maize field using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS ONE 8:e77151. https://doi.org/10.1371/journal.pone.0077151
Phinn SR, Stow DA, Zedler JB (1996) Monitoring wetland habitat restoration in southern California using airborne multispectral video data. Restor Ecol 4:412–422. https://doi.org/10.1111/j.1526-100X.1996.tb00194.x
R Core Team (2017) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/
Shuman CS, Ambrose RF (2003) A comparison of remote sensing and ground-based methods for monitoring wetland restoration success. Restor Ecol 11:325–333. https://doi.org/10.1046/j.1526-100X.2003.00182.x
Story M, Congalton RG (1986) Accuracy assessment: a user’s perspective. Photogramm Eng Remote Sensing 52:397–399
Suter M, Siegrist-Maag S, Connolly J, Lüscher A (2007) Can the occurrence of Senecio jacobaea be influenced by management practices? Weed Res 47:262–269. https://doi.org/10.1111/j.1365-3180.2007.00566.x
Sutherland WJ (2006) Ecological census techniques: a handbook, 2nd edn. Cambridge University Press, Cambridge
Thenkabail PS, Lyon JG, Huete A (2011) Hyperspectral remote sensing of vegetation. CRC Press, Boca Raton, FL
Torres-Sánchez J, López-Granados F, De Castro AI, Peña-Barragán JM (2013) Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. PLoS ONE 8:e58210. https://doi.org/10.1371/journal.pone.0058210
Wang B, Liu S, Li Q, Zhou H (2006) A real-time contrast enhancement algorithm for infrared images based on plateau histogram. Infrared Phys Technol 48:77–82. https://doi.org/10.1016/j.infrared.2005.04.008
Wang K, Franklin SE, Guo X, Cattet M (2010) Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists. Sensors 10:9647–9667. https://doi.org/10.3390/s101109647
Wilson AM, Barr J, Zagorski M (2017) The feasibility of counting songbirds using unmanned aerial vehicles. Auk 134:350–362. https://doi.org/10.1642/AUK-16-216.1
Zahawi RA, Dandois JP, Holl KD, Nadwodny D, Reid JL, Ellis EC (2015) Using lightweight unmanned aerial vehicles to monitor tropical forest recovery. Biol Conserv 186:287–295. https://doi.org/10.1016/j.biocon.2015.03.031
Zarco-Tejada PJ, Diaz-Varela R, Angileri V, Loudjani P (2014) Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. Eur J Agron 55:89–99. https://doi.org/10.1016/j.eja.2014.01.004
Zhang C, Kovacs JM (2012) The application of small unmanned aerial systems for precision agriculture: a review. Precis Agric 13:693–712. https://doi.org/10.1007/s11119-012-9274-5
Acknowledgements
The research carried out in this study was financially supported by the European Commission through the program Erasmus Mundus master course—International Master in Applied Ecology (EMMC-IMAE) (FPA 532524-1-FR-2012 ERA MUNDUS-EMMC). We dedicate this article to Wilhelm Windhorst; the recently deceased coordinator of the Erasmus Mundus programme at Kiel University. We would also like to acknowledge Stiftung Naturschutz Schleswig–Holstein, especially Aiko Huckauf, for granting access to their database, Yannick Robert for his invaluable logistic support, and the site managers and farmers for allowing entry to the field sites.
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Communicated by Joseph Paul Messina.
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Tay, J.Y.L., Erfmeier, A. & Kalwij, J.M. Reaching new heights: can drones replace current methods to study plant population dynamics?. Plant Ecol 219, 1139–1150 (2018). https://doi.org/10.1007/s11258-018-0865-8
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DOI: https://doi.org/10.1007/s11258-018-0865-8