Validation of a high-resolution, remotely operated aerial remote-sensing system for the identification of herbaceous plant species
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Validation of a high-resolution, remotely operated aerial remote-sensing system for the identification of herbaceous plant species

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Validation of a high-resolution, remotely operated aerial remote-sensing system for the identification of herbaceous plant species Validation of a high-resolution, remotely operated aerial remote-sensing system for the identification of herbaceous plant species Document Transcript

  • Applied Vegetation Science 15 (2012) 383–389 Validation of a high-resolution, remotely operated aerial remote-sensing system for the identification of herbaceous plant species Fumiko Ishihama, Yasuyuki Watabe & Hiroyuki OgumaKeywords AbstractHigh positioning accuracy; Non-destructivesurvey; Portable remote-sensing system; Question: Is a high-resolution remote-sensing system based on a radio-Radio-controlled helicopter; Wetland controlled helicopter (the ‘Falcon-PARS system’) an effective tool to obtain images that can be used to identify herbaceous species?AbbreviationsIMU = Inertial measurement unit; GCP = Location: Watarase wetland, Japan.Ground control point Methods: We applied the remote-sensing system to a wetland composedNomenclature mainly of Phragmites australis and Miscanthus sacchariflorus. The aerial observationBG Plants Japanese-name-scientific-name Index was performed in a 100 9 200 m area at a flying height of 30 m. From the(YList), http://bean.bio.chiba-u.jp/bgplants/ obtained images, we tried to identify P. australis and M. sacchariflorus throughylist_main.html (accessed 30 November 2011) visual interpretation.Received 15 July 2010 Results: We obtained images with a high spatial resolution (1 cm) and a posi-Revised 30 November 2011 tioning accuracy of finer than 1 m using this small and lightweight system, andAccepted 20 December 2011 confirmed that we could identify the above two species from the obtainedCo-ordinating Editor: Aaron Moody images. Conclusion: Such a high-resolution system can be used to directly identify her-Ishihama, F. (corresponding author, baceous species, and as a non-destructive alternative to ground surveys. Thisishihama@nies.go.jp) & Oguma, H. lightweight system can be carried to sites such as a high-altitude bog that cannot(oguma@nies.go.jp): National Institute forEnvironmental Studies, Onogawa, Tsukuba, be reached by a motor vehicle. Because of the low flying height (below cloudIbaraki 305-8506, Japan level), aerial observation is possible even on cloudy days, thereby permittingWatabe, Y. (y_watabe@ists.co.jp): observations in all seasons.Information & Science Techno-System Co.,Ltd., Takezono, Tsukuba, Ibaraki 305-0032,Japan type in detail, fine-scale remote sensing with a resolutionIntroduction of  1 cm would be required.Remote sensing is a convenient tool for efficient, non- Although there is an inevitable trade-off between reso-destructive mapping of vegetation over wide spatial scales. lution and observation speed, a high-resolution remote-Satellite and aircraft remote sensing is widely used to sensing system capable of distinguishing among detailedobtain distribution maps of vegetation classification (De- vegetation types or identifying small plant species hasFries 2008; Xie et al. 2008; Hill et al. 2010) and habitat advantages that outweigh its reduced speed. The first ismaps of species (Kerr & Ostrovsky 2003), and to estimate that it permits non-destructive observation. Ground sur-biomass (e.g. Boudreau et al. 2008) and plant phenology veys sometimes cause substantial damage to the vegeta-(Verbesselt et al. 2010; Reed et al. 2009). Although these tion, particularly at fragile sites such as bogs. Althoughremote-sensing systems are effective for such observations, long-term monitoring is required to examine changes inthey are only useful for relatively large targets, such as tall biodiversity and to plan effective conservation measurestrees, or for rough classification of vegetation types. This is (Marsh & Trenham 2008), damage to vegetation duringbecause the resolution of these systems is relatively low monitoring on foot can be especially serious when(5 cm at best for aircraft remote sensing). To identify her- repeated surveys are required. Remote sensing with suffi-baceous or small woody species or to classify vegetation ciently high resolution would be a valuable alternative toApplied Vegetation ScienceDoi: 10.1111/j.1654-109X.2012.01184.x © 2012 International Association for Vegetation Science 383
  • High resolution remote-sensing system F. Ishihama et al.ground surveys because it would reduce or eliminate dam- veys that require resolutions ranging from several metersage to vegetation. A second advantage is the ability to to several tens of centimeters has been reported (Davis &obtain detailed observations of sites that are difficult for Johnson 1991; Gerard et al. 1997; Johnson et al. 2004;humans to approach, such as cliff faces and the canopies of Miyamoto et al. 2004; Sugiura et al. 2005; Berni et al.tall trees. Third, even if the speed is relatively limited, 2009; Artigas & Pechmann 2010). However, some of thesehigh-resolution remote sensing still provides a faster tool systems are not suitable to capture georeferenced high-for mapping individual plants than is possible in surveys resolution images at resolutions of 1 cm or finer in a non-conducted on foot. destructive way. A balloon system is very vulnerable to The criteria for a remote-sensing system suitable for wind, and it is difficult to control its position, especially inhigh-resolution observation include high positioning accu- high-resolution surveys, which require delicate position-racy, a robust ability to work under a range of weather ing control with accuracy finer than a few meters. Becauseconditions, and portability (light weight). High positioning tethered balloon systems need to be towed by a human foraccuracy is essential to allow comparison of images from positioning control, they can cause damage to vegetationdifferent times so that researchers can monitor temporal in study sites susceptible to trampling. In addition, balloonchanges in vegetation and can overlay images with other systems require containers of pressurized, lighter-than-airgeographical information, such as elevation. Robustness gas, which cannot be carried by humans over long dis-under a range of weather conditions is required to permit tances to reach remote sites. Although fixed-wing aircraftsurveys in all seasons. Phenological changes represent have superior positioning control and robustness againstinformation that can be used to distinguish plant species, wind, their high flight speed can cause serious problems;and multi-seasonal observations capable of detecting phe- obtaining high-resolution images with sufficiently highnological changes are an effective way to distinguish plant positioning accuracy faces many specific problems (e.g.species or vegetation types (Gilmore et al. 2008). Remote motion blur in the images due to a combination of insuffi-sensing from piloted aircraft is possible only under a lim- cient light and an insufficiently high camera shutterited range of weather conditions (i.e. clear days) because speed). These problems can be solved by flying morethe piloted aircraft fly as high as 2000 m, and their sensors slowly or by hovering, if the aircraft has a low level ofmay be blocked by low cloud. Obtaining a cloud-free vibration (Appendix S1). In addition, a fixed-wing aircraftimage is also an important problem for satellite remote often requires flight strips for takeoff and landing, andsensing (Xie et al. 2008; Wang et al. 2009). Such limita- these are rarely available in survey areas.tions often make it difficult to perform surveys in certain To solve these problems, we chose a lightweightseasons. Portable systems would be required at study sites remote-sensing system capable of hovering and withsuch as those at high altitudes, wetlands and oceanic low vibration. To meet these criteria, we chose a heli-islands, which are usually inaccessible to ground vehicles. copter (AscTec Falcon 8; Ascending Technologies GmbH, Remote sensing using a radio-controlled helicopter, Krailling, Germany; Fig. 1a) that can hover at thefixed-wing aircraft and balloon is a potential candidate for assigned coordinates (using an autopilot function) andhigh-resolution remote sensing because such vehicles can obtain photographs by automatically activating the cam-fly at much lower altitudes than piloted aircraft. The effec- era shutter. It is only in the last few years that light-tiveness of these systems for ecological or agricultural sur- weight radio-controlled remote-sensing systems with an (a) (b)Fig. 1. (a) The helicopter (AscTec Falcon 8; Ascending Technologies GmbH, Krailling, Germany) and (b) camera used in the high-resolution remote sensingsystem. Applied Vegetation Science384 Doi: 10.1111/j.1654-109X.2012.01184.x © 2012 International Association for Vegetation Science
  • F. Ishihama et al. High resolution remote-sensing systemautopilot function became available. The autopilot func- We have named this system (helicopter, digital cameration allows the aircraft to fly along a predefined course and Cartomaton software) the ‘Falcon- photogrammetryand obtain photographs automatically at preset coordi- and remote-sensing (PARS)’ system.nates, and it is therefore an essential function for easyand speedy image acquisition. Such systems have been Study site for the aerial observation of vegetationdeveloped mainly for military (Newcome 2004) or geo-graphical use (e.g. Delacourt et al. 2009), so their appli- We tested the Falcon-PARS system in the Watarase wet-cability to plant surveys has rarely been evaluated (but land of central Japan (139°41′ E, 36°14′ N, 14 m a.s.l.;see Rango et al. 2009). Fig. 2a). The Watarase wetland is a floodplain wetland that In this study, we aimed to validate the use of a remote- covers about 1500 ha, and its vegetation is mainly com-sensing system based on a radio-controlled helicopter to posed of Phragmites australis (Cav.) Trin. ex Steud. andexamine whether it could satisfy our criteria (high resolu- Miscanthus sacchariflorus (Maxim.) Benth. Because thesetion, positioning accuracy, robustness across a range of species form dense vegetation that reaches a maximumweather conditions, and portability) for monitoring of her- height of 4 m in July, ground surveys are impractical, andbaceous plants. We tested whether we could use images remote sensing is therefore an essential monitoring tool.obtained by this system to distinguish among herbaceous Although a previous study reported successful detection ofplants species in the Watarase wetland, Japan. expansion of pure stands of P. australis using a balloon sys- tem with 12-cm spatial resolution (Artigas & PechmannMethods 2010), the species forms extremely mixed stands with M. sacchariflorus in the Watarase wetland, and finer spatialThe radio-controlled helicopter system resolution is required for distinguishing these two speciesThe helicopter usedinthisstudyissmall (85 9 80 9 15 cm) in this wetland.and light (1.6 kg, including its battery). Because the helicop-ter has a small payload capacity (500 g), we used a Conditions during aerial observations of the vegetationlightweight compact digital camera (GX200; Ricoh, Tokyo,Japan; Fig. 1b) as the image sensor. The continuous flight We performed the aerial observations on 10 July 2009. Thetime is <20 min.Thehorizontal flight rangeiswithin 1 km of weather was cloudy. We set the digital camera’s focalthe operator due to radio control limitation, and maximum length at 24 mm, shutter speed at 1/500 s, diaphragm atflight height is 300 m. The radio frequency of the control F5.1 and ISO setting at 200. The camera has an effective res-system is 2.4 GHz. The helicopter includes an onboard olution of 12.1 megapixels. Our preliminary surveyGPS(LEA; u-blox,Thalwil,Switzerland). revealed that a maximum flying height of 30 m was needed Although this small helicopter is suitable for high- to distinguish between P. australis and M. sacchariflorusresolution photography, it is difficult to obtain high posi- (F. Ishihama et al., unpublished data) using these camerational accuracy using only the onboard GPS. To obtain settings, so we performed the survey at this height. Imagehighly accurate georeferencing capability and to allow us resolution is a function of the flying height, effective pixelto combine multiple digital pictures into one mosaic image, resolution and focal length. Given the above-mentionedwe used the Cartomaton software (Information & Science settings, the spatial resolution of our images was 1 cm andTechno-System Co., Ltd., Tsukuba, Japan). Cartomaton the areal footprint of each image was 30 9 40 m.generates simple ortho-images (i.e. images corrected for We performed aerial observations in a 100 9 200-mdistortion caused by changes in flight attitude of an aircraft area. To cover this area, we took 99 photographs to allowand by chromatic and spherical aberration resulting from for overlap between adjacent images within a given flightthe camera’s lens). This software estimates the external line and side-lap between adjacent flight lines. The Car-orientation (three-dimensional position and angle) of the tomaton software requires 60% overlap and 30% side-lapcamera when the photos are taken. After performing geo- to combine the photographs into a single mosaic image; inmetric corrections based on those angles, the software pro- our study, we used 75% overlap and 30% side-lap.jects the photographs onto a plane that is assumed torepresent the ground surface, and then combines all the Study site for testing positional accuracyphotographs into a single georeferenced mosaic image.During this processing sequence, it uses side-by-side pairs To test the positional accuracy of the ortho-mosaic imageof photos to calculate an external orientation; thus, it does and digital surface map (DSM) generated by the Falcon-not require an inertial measurement unit (IMU) or ground PARS system, we conducted aerial observations in acontrol points (GCP) to achieve precise corrections of research field at the National Institute for Environmentaldistortion. Studies (140°4′41″ E, 36°3′3″ N). We chose this field as aApplied Vegetation ScienceDoi: 10.1111/j.1654-109X.2012.01184.x © 2012 International Association for Vegetation Science 385
  • High resolution remote-sensing system F. Ishihama et al. (a) (b) (c) (d)Fig. 2. (a) Location of the study site, the Watarase wetland, in Japan. (b) A simple ortho-image obtained by the radio-controlled helicopter remote-sensingsystem. (c) A sample magnified image [location marked by light blue box in (b)]. (d) The resulting map of the species distribution identified from visualinterpretation of the magnified images.study site because it was difficult to establish a sufficient GPS (Geodetic IV; Ashtech, Carquefou, France). The stan-number of GCPs throughout the survey area in the dard deviations of the positioning accuracies of all GCPsWatarase wetland due to the extremely dense and tall obtained with the GPS were <1 cm. The flight covered avegetation; this vegetation made it nearly impossible to walk 70 9 50-m area and we obtained 20 photographs (fiveat the study site, which is why high-resolution remote- photographs per course).sensing observations are required for monitoring of this site. After the photography, we performed baseline analysis using the raw data from the onboard GPS. By using these photographs and the analysed GPS data, we generated aConditions for testing positional accuracy true ortho-mosaic image and DSM; we did not use anyWe performed the aerial observations on 23 February GCP data to create these mosaic images.2011. The camera settings, flying height and overlap andside-lap settings were the same as those used in our obser- Test of repeatability of the classification of plant speciesvations of the wetland vegetation. from the aerial image We established ten 10 9 10-cm plates as GCPs, andused these GCPs to evaluate the position accuracy of To test the repeatability of species classification based onmosaic images. The coordinates of the ground control the mosaic image obtained from the aerial observation inpoints were obtained using a two-carrier-wave-frequency Watarase wetland, we performed classification of plant Applied Vegetation Science386 Doi: 10.1111/j.1654-109X.2012.01184.x © 2012 International Association for Vegetation Science
  • F. Ishihama et al. High resolution remote-sensing systemspecies by means of visual interpretation using three photo Table 1. Repeatability of the classification of plant species from aerialinterpreters. The three photo interpreters had different images. The numbers of image areas in which the three different interpret- ers agreed or disagreed on species are shown.experience in vegetation research: one was an experiencedplant ecologist, the second was a remote-sensing Pattern of classification by three interpreters Number ofresearcher with little experience in vegetation surveys, image areasand the third was a non-researcher who had experience Three interpreters classified as 39assisting in vegetation surveys. Before the test, we taught Phragmites australisthe photo interpreters the criteria they should use to distin- Two interpreters classified as 6guish among the three categories. Appendix S2 shows the P. australis, one as Miscanthus sacchariflorustutorial materials that were used. One interpreter classified as 10 P. australis, two as M. sacchariflorus As samples of a classification test, we first selected 200 Three interpreters classified as M. sacchariflorus 45random 30 9 30-cm test image areas within the image.Then we omitted test image areas that meet at least one of of image areas in which the three different interpretersthe following four criteria: (1) the image did not include agreed or disagreed on species are shown in Table 1. Wheneither P. australis or M. sacchariflorus; (2) the image we compared the classification by the two non-expertincluded both P. australis and M. sacchariflorus; (3) the photo interpreters to that of the experienced plant ecolo-image was too dark because the area is composed of low gist, the rates of correct answers were 90.0% and 93.0%,plants shaded by surrounding tall plants; and (4) the image respectively.was blurred due to movement of leaves by wind. We omit-ted the image that matched criteria 1 and 2 because suchareas require classification categories such as ‘other plants’ Discussionand ‘both P. australis and M. sacchariflorus’ in the test. Set- Because we used a helicopter that can hover above ating such categories can inflate repeatability of classifica- desired position, we did not experience any of the prob-tion, because it is expected that photo interpreters tend to lems described in the Appendix S1: we obtained clearchoose these categories when they are not sure. images with sufficient overlap to create a mosaic image. Finally, we used 100 image areas for classification tests. From the high-resolution mosaic image generated fromWe asked each photo interpreter to classify the species of the simple ortho-images (Fig. 2b,c) we could distinguishthe plant at the test areas using two categories: P. australis both P. australis and M. sacchariflorus (also see Appendix S2and M. sacchariflorus. for ground images of these species) through visual inter- pretation, with high repeatability among photo interpret-Results ers of different experience in vegetation research. AnAerial observations of vegetation in the Watarase example of classification by the experienced photo inter-wetland preter is shown in Fig. 2d. Because the resolution was much higher than could be obtained using conventionalTo obtain an image of the whole 100 9 200-m study area aerial photographs (Table 2), the photo interpreters couldfrom a flying height of 30 m, it took only 11 min and 10 s. use both colour differences and differences in form of theWe obtained clear images with sufficient overlap and side- leaves and structure of the plant bodies as clues to assist inlap, and were able to create a high-resolution mosaic the identification of the two species. Because the colourimage from the simple ortho-images (Fig. 2 b,c). depends on weather conditions (e.g. light intensity and quality) and season (e.g. summer vs autumn leaves)Test of positional accuracy Table 2. Comparison of the characteristics of a remote-sensing systemWe calculated the root-mean-square errors (RMSEs) for with a piloted aircraft and the radio-controlled helicopter system validatedthe positions measured in the field at the National Institute in this study (the Falcon-PARS system).for Environmental Studies. The RMSEs were 0.974 and Ordinary aerial Falcon-PARS0.360 m for the horizontal and ellipsoidal body height photographs system (30-mpositioning errors, respectively. from a piloted aircraft flying height) Highest resolution 5 cm 1 cm* Area photographed in 1 h Several km2 ca. 0.06 km2Repeatability of the classification of plant species from Minimum weather Clear day Bright cloudy daythe aerial images conditionsThe rate of agreement of the species classification among *Finer resolution is possible at a lower flying height, but this decreases thethe three photo interpreters was 84.0%, and the numbers area that can be photographed per hour.Applied Vegetation ScienceDoi: 10.1111/j.1654-109X.2012.01184.x © 2012 International Association for Vegetation Science 387
  • High resolution remote-sensing system F. Ishihama et al.during the aerial observations, the form of the plants is a record the status of vegetation. This is important formore reliable clue to identify species. researchers because some vegetation changes its state so We confirmed the ability of our system to provide a res- quickly that ground surveys cannot be performed suffi-olution of ca. 1 cm while imaging natural herbaceous veg- ciently rapidly to cover the whole study area before itetation. Although many studies (e.g. Lelong et al. 2008; changes (e.g. the state of flushing of spring ephemeralsBerni et al. 2009) have used unmanned aerial vehicles changes within a few days). In addition, this approach per-(UAVs), few of these systems have attained a spatial reso- mits non-destructive surveys, which is very useful at frag-lution finer than 5 cm. The only system we are aware of ile sites such as bogs. The approach also makes it possiblethat provides 1-cm resolution is a helicopter-based UAV to monitor sites such as tree canopies or cliff faces thatsystem used for observation of coastal areas (Delacourt would be difficult or impossible to study in any other way.et al. 2009). The other system attained resolutions of ca. The system’s portability (small size and light weight) is an5 cm and was used to observe rangeland (Rango et al. additional advantage for use in such places.2009). Previous UAV systems that attained a high spatial The studied species at the study site were relatively largeresolution (ranging from 1 to 5 cm) were large (1.0– grasses, making the plant characteristics easy to distin-1.8 m) and heavy (10–11 kg, excluding image sensors) guish, but application to smaller herbs should be possibleand were therefore difficult to transport without ground using a lower flying height. Although observation speedvehicles. Although such systems have some merits (larger decreases at a lower height, better resolution will be attain-battery capacity and pay-load than the Falcon-PARS sys- able in the near future using a camera with a larger num-tem), it would be difficult to take them to many study sites, ber of pixels without reducing flying height. We used asuch as alpine sites. Our system is only 1.6 kg including common compact digital camera with a relatively smallthe battery (1.8 kg including the camera) and can there- number of effective pixels, but the remarkable speed offore be transported by a single person to almost all possible development of digital cameras suggests that higher imagestudy sites. Moreover, our system does not need any exter- quality will soon provide the same image resolution from anal orientation to obtain georeferenced images. This char- greater flying height (i.e. will allow observation of a largeracteristic further reduces difficulties in field surveys; this area per unit time). In addition to high-resolution cameras,system does not require setting GCPs in tall and dense veg- researchers can also use other sensors such as near-etation where it is difficult to walk or in fragile bogs, or car- infrared cameras, which can be used to measure plant pho-rying a heavy two-way GPS to sites that are difficult for tosynthetic activity. The only limitation of the system ishumans to approach with heavy baggage, such as alpine that the sensor must be light enough to mount on to thesites. The main drawbacks of our system are small battery radio-controlled helicopter.capacity (ca. 20 min of continuous flight time) and small The Falcon-PARS system is a promising tool for efficient,payload (ca. 500 g), but its portability outweighs these non-destructive surveys of herbaceous vegetation.drawbacks for sites such as bogs that are difficult to reach Although we identified plant species by eye in the presentwith a vehicle and too fragile to survey on foot. It should study, the development of image analysis techniques toalso be noted that although the imagery has a spatial reso- automatically identify species will further improve thelution of 1 cm, which allows for fine-scale image interpre- applicability of this system in the near future.tation, the positional accuracy of ca. 1 m limits theresolution of vegetation classification to larger areas inwhich the positional error is negligible. References It took only 11 min and 10 s to obtain an image of the Artigas, F. & Pechmann, I.C. 2010. Balloon imagery verificationentire 100 9 200-m study area from a flying height of of remotely sensed Phragmites australis expansion in an urban30 m. A survey performed at this speed covers a much estuary of New Jersey, USA. Landscape and Urban Planningsmaller area than would be covered by conventional aerial 95: 105–112.remote sensing with a piloted aircraft (Table 2) because of Berni, J.A.J., Zarco-Tejada, P.J.Z., Suarez, L. & Fereres, E. 2009.the inevitable trade-off between image resolution and fly- Thermal and narrowband multispectral remote sensing foring speed. However, this was still remarkably faster than vegetation monitoring from an unmanned aerial vehicle.would have been possible by means of a ground survey. IEEE Transactions of Geoscience and Remote Sensing 47:Even though additional time is required to process the data 722–738.to produce the true ortho-image (5–6 h per 100 photo- Boudreau, J., Nelson, R.F., Margolis, H.A., Beaudoin, A., Guin-graphs, although this time varies depending on perfor- don, L. & Kimes, D.S. 2008. Regional aboveground forestmance of a personal computer) and classify the vegetation biomass using airborne and spaceborne LiDAR in Quebec.types from the image, we were nonetheless able to rapidly Remote Sensing of Environment 112: 3876–3890. Applied Vegetation Science388 Doi: 10.1111/j.1654-109X.2012.01184.x © 2012 International Association for Vegetation Science
  • F. Ishihama et al. High resolution remote-sensing systemDavis, M.A. & Johnson, G.W. 1991. A simple and inexpensive Rango, A., Laliberte, A., Herrick, J.E., Winters, C., Havstad, K., method of obtaining low-altitude photographs of vegetation Steele, C. & Browing, D. 2009. Unmanned aerial vehi- using a tethered balloon. Prairie Naturalist 23: 153–164. cle-based remote sensing for rangeland assessment, monitor-DeFries, R. 2008. Terrestrial vegetation in the coupled human- ing, and management. Journal of Applied Remote Sensing 3: earth system: contributions of remote sensing. Annual Review 1–15. of Environment and Resources 33: 369–390. Reed, B.C., Schwartz, M.D. & Xiao, X. 2009. Remote sensingDelacourt, C., Allemand, P., Jaud, M., Grandjean, P., Des- phenology: status and the way forward. In: Noormets, A. champs, A., Ammann, J. & Coq, V. 2009. DRELIO: An (ed.) Phenology of ecosystem processes: applications in global unmanned helicopter for imaging coastal areas. Journal of change research. pp. 231–246. Springer, New York, NY, US. Costal Research 56: 1498–1493. Sugiura, R., Noguchi, N. & Ishii, K. 2005. Remote-sensingGerard, B., Buerkert, A., Hiernaux, P. & Marschner, H. 1997. technology for vegetation monitoring using an unmanned Non-destructive measurement of plant growth and nitrogen helicopter. Biosystems Engineering 90: 369–379. status of pearl millet with low-altitude aerial photography. Verbesselt, J., Hyndman, R., Newnham, G. & Culvenor, D. 2010. Soil Science and Plant Nutrition 43: 993–998. Detecting trend and seasonal changes in satellite image timeGilmore, M.S., Wilson, E.H., Barrett, N., Civco, D.L., Prisloe, S., series. Remote Sensing of Environment 114: 106–115. Hurd, J.D. & Chadwick, C. 2008. Integrating multi-temporal Wang, K., Franklin, S.E., Guo, X., He, Y. & McDermid, G.J. spectral and structural information to map wetland vegeta- 2009. Problems in remote sensing of landscapes and habitats. tion in a lower Connecticut River tidal marsh. Remote Sensing Progress in Physical Geography 33: 747–768. of Environment 112: 4048–4060. Xie, Y., Sha, Z. & Yu, M. 2008. Remote sensing imagery in vege-Hill, R.A., Wilson, A.K., George, M. & Hinsley, S.A. 2010. Map- tation mapping: a review. Journal of Plant Ecology 1: 9–23. ping tree species in temperate deciduous woodland using time-series multi-spectral data. Applied Vegetation Science 13: 86–99. Supporting InformationJohnson, L.F., Herwitz, S.R., Lobitz, B.M. & Dunagan, S.E. 2004. Additional supporting information may be found in the Feasibility of monitoring coffee field ripeness with airborne online version of this article: multispectral imagery. Applied Engineering in Agriculture 20: 845–849. Appendix S1. Possible problems in high-resolutionKerr, J.T. & Ostrovsky, M. 2003. From space to species: ecological remote sensing (with resolution finer than 1 cm) and applications for remote sensing. Trends in Ecology and solutions. Evolution 18: 299–305. Appendix S2. Ground-level photographs of theLelong, C.C.D., Burger, P., Jubelin, G., Roux, B., Labbe, S. & leaves and plant bodies of (a,c) Phragmites australis and Baret, F. 2008. Assessment of unmanned aerial vehicles (b,d) Miscanthus sacchariflorus. The remote-sensing imagery for quantitative monitoring of wheat crops in small images magnified from Fig. 1(c,d): (e) P. australis and (f) plots. Sensors 8: 3557–3585. M. sacchariflorus.Marsh, D.M. & Trenham, P.C. 2008. Current trends in plant and animal population monitoring. Conservation Biology 22: 647–655. Please note: Wiley-Blackwell are not responsible forMiyamoto, M., Yoshino, K., Nagano, T., Ishida, T. & Sato, Y. the content or functionality of any supporting materials 2004. Use of balloon aerial photography for classification of supplied by the authors. Any queries (other than missing Kushiro wetland vegetation, Northeastern Japan. Wetlands material) should be directed to the corresponding author 24: 701–710. for the article.Newcome, L.R. 2004. Unmanned aviation: A brief history of unmanned aerial vehicles. American Institute of Aeronautics and Astronautics Inc., Reston, VA, USA.Applied Vegetation ScienceDoi: 10.1111/j.1654-109X.2012.01184.x © 2012 International Association for Vegetation Science 389
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