Utility of unmanned aerial vehicles for mapping invasive plant species: a case study on yellow flag iris (Iris pseudacorus L.)

ABSTRACT This study investigates the utility of an off-the-shelf, consumer-grade unmanned aerial vehicle (UAV) for invasive species mapping in a lacustrine fringe environment. Specifically, this work sought to determine whether such a UAV would be capable of creating accurate maps of the extent of patches of an invasive plant, yellow flag iris (Iris pseudacorus L.), more efficiently than could be accomplished by a traditional field survey, which is often considered in the literature to provide the most accurate maps. The study was conducted at two lakes in the central interior of British Columbia. The UAV used in this study was a DJI Phantom 3 Professional that can acquire images using the built-in 12.4 MP digital camera. This UAV was selected because it is representative of the type of aerial platform that is easily accessible to invasive plant managers in terms of cost, ease of use, and lack of legal restrictions. Three methods of mapping the yellow flag iris were compared: (1) field survey, (2) manual interpretation of the raw UAV-acquired imagery and the orthoimage created from these data, and (3) pixel-based classification of the orthoimage created from the UAV imagery using a random forest classifier. The results revealed that, at both lakes considered, manual interpretation of the UAV-acquired imagery produced the most accurate maps of yellow flag iris infestation, with a false-positive and false-negative classification rate of less than 1%.

[1]  Gregory Asner,et al.  Determining Subcanopy Psidium cattleianum Invasion in Hawaiian Forests Using Imaging Spectroscopy , 2016, Remote. Sens..

[2]  D. A. Hill,et al.  Mapping of Scotch Broom (Cytisus scoparius) with Landsat Imagery , 2016, Weed Technology.

[3]  P. Raven,et al.  IRIS PSEUDACORUS IN WESTERN NORTH AMERICA , 1970 .

[4]  R. Valentini,et al.  Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data , 2016 .

[5]  Liang Liang,et al.  Identification of understory invasive exotic plants with remote sensing in urban forests , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[6]  Austin M. Jensen,et al.  Use of high-resolution multispectral imagery acquired with an autonomous unmanned aerial vehicle to quantify the spread of an invasive wetlands species , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[7]  Karim Solaimani,et al.  Fuzzy Classification for Mapping Invasive Species from Multispectral Imagery , 2013, Journal of the Indian Society of Remote Sensing.

[8]  Stephen J. Walsh,et al.  A Geographical Approach to Optimization of Response to Invasive Species , 2013 .

[9]  O. Mutanga,et al.  Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review , 2010, Wetlands Ecology and Management.

[10]  Clement Atzberger,et al.  Mapping areas invaded by Prosopis juliflora in Somaliland on Landsat 8 imagery , 2015, SPIE Remote Sensing.

[11]  M. Neteler,et al.  Potential of remote sensing to predict species invasions , 2015 .

[12]  Le Wang,et al.  Develop an Ensemble Support Vector Data Description method for improving invasive tamarisk mapping at regional scale , 2014 .

[13]  D. Pimentel,et al.  Update on the environmental and economic costs associated with alien-invasive species in the United States , 2005 .

[14]  S. Ustin,et al.  Mapping nonnative plants using hyperspectral imagery , 2003 .

[15]  Wei Gao,et al.  Errata: Integrating pan-sharpening and classifier ensemble techniques to map an invasive plant (Spartina alterniflora) in an estuarine wetland using Landsat 8 imagery , 2016 .

[16]  J. Greenberg,et al.  Measuring landscape-scale spread and persistence of an invaded submerged plant community from airborne remote sensing. , 2016, Ecological applications : a publication of the Ecological Society of America.

[17]  T. Kleinebecker,et al.  Unmanned aerial vehicles as innovative remote sensing platforms for high‐resolution infrared imagery to support restoration monitoring in cut‐over bogs , 2013 .

[18]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[19]  David C. Jones,et al.  Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier , 2013, Remote. Sens..

[20]  A. S. Ackleh,et al.  Invasion, Disturbance, and Competition: Modeling the Fate of Coastal Plant Populations , 2009, Conservation biology : the journal of the Society for Conservation Biology.

[21]  Arko Lucieer,et al.  apping invasive Fallopia japonica by combined spectral , spatial , and temporal nalysis of digital orthophotos , 2012 .

[22]  Jocelyn Chanussot,et al.  On the use of binary partition trees for the tree crown segmentation of tropical rainforest hyperspectral images , 2015 .

[23]  Y. Shimabukuro,et al.  Mapping tree species in tropical seasonal semi-deciduous forests with hyperspectral and multispectral data , 2016 .

[24]  Tomaž Podobnikar,et al.  Recognition of the invasive species Robinia pseudacacia from combined remote sensing and GIS sources , 2012 .

[25]  José M. C. Pereira,et al.  Optimal attributes for the object based detection of giant reed in riparian habitats: A comparative study between Airborne High Spatial Resolution and WorldView-2 imagery , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[26]  Adrien Michez,et al.  Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[27]  Riyad Ismail,et al.  Mapping Bugweed (Solanum mauritianum) Infestations in Pinus patula Plantations Using Hyperspectral Imagery and Support Vector Machines , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[28]  Srinivasulu Ale,et al.  Detection of two intermixed invasive woody species using color infrared aerial imagery and the support vector machine classifier , 2013 .

[29]  G. Velde,et al.  Seed Dispersal, Germination and Seedling Growth of six Helophyte Species in Relation to Water-Level Zonation , 1995 .

[30]  Jianhua Gong,et al.  UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis , 2015, Remote. Sens..

[31]  Jana Müllerová,et al.  UNMANNED AERIAL VEHICLES FOR ALIEN PLANT SPECIES DETECTION AND MONITORING , 2015 .

[32]  R. Lewin,et al.  Biological flora of the British Isles , 1948 .

[33]  Zhe Xu,et al.  Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV , 2014, Remote. Sens..

[34]  F. López-Granados,et al.  Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images , 2013, PloS one.

[35]  Susan L. Ustin,et al.  Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem , 2008 .

[36]  H. Birks,et al.  The Impact of Three Exotic Plant Species on a Potomac Island. , 1982 .

[37]  Brenner Silva,et al.  Mapping Two Competing Grassland Species from a Low-Altitude Helium Balloon , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[38]  R. Bobbink,et al.  Variation in seed buoyancy of species in wetland ecosystems with different flooding dynamics , 2005 .

[39]  Runhe Shi,et al.  Integrating pan-sharpening and classifier ensemble techniques to map an invasive plant (Spartina alterniflora) in an estuarine wetland using Landsat 8 imagery , 2016 .

[40]  M. Vilà,et al.  An Assessment of Stakeholder Perceptions and Management of Noxious Alien Plants in Spain , 2009, Environmental management.

[41]  Huawei Wan,et al.  Monitoring the Invasion of Spartina alterniflora Using Very High Resolution Unmanned Aerial Vehicle Imagery in Beihai, Guangxi (China) , 2014, TheScientificWorldJournal.

[42]  William J. Sutherland.  Iris Pseudacorus L. , 1990 .

[43]  R. Ricklefs,et al.  The role of exotic species in homogenizing the North American flora. , 2006, Ecology letters.

[44]  A. Brenning,et al.  Assessing fruit-tree crop classification from Landsat-8 time series for the Maipo Valley, Chile , 2015 .

[45]  J. Havel,et al.  Aquatic invasive species: challenges for the future , 2015, Hydrobiologia.

[46]  Stephen Pike,et al.  Object-Based Image Analysis for Detection of Japanese Knotweed s.l. taxa (Polygonaceae) in Wales (UK) , 2011, Remote. Sens..

[47]  O. Lakela.  A Floristic Study of a Developing Plant Community on Minnesota Point, Minnesota , 1939 .

[48]  S. Saura.  Effects of minimum mapping unit on land cover data spatial configuration and composition , 2002 .

[49]  Petr Pyšek,et al.  Invasive Species, Environmental Change and Management, and Health , 2010 .

[50]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[51]  B. Ostendorf,et al.  Detecting new Buffel grass infestations in Australian arid lands: evaluation of methods using high-resolution multispectral imagery and aerial photography , 2013, Environmental Monitoring and Assessment.

[52]  Jana Müllerová,et al.  Remote sensing as a tool for monitoring plant invasions: Testing the effects of data resolution and image classification approach on the detection of a model plant species Heracleum mantegazzianum (giant hogweed) , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[53]  V. Robinson,et al.  Multispectral detection of European frog-bit in the South Nation River using Quickbird imagery , 2012 .

[54]  Onisimo Mutanga,et al.  Mapping Solanum mauritianum plant invasions using WorldView-2 imagery and unsupervised random forests , 2016 .

[55]  Timothy L. Hawthorne,et al.  Mapping non-native invasive species and accessibility in an urban forest: A case study of participatory mapping and citizen science in Atlanta, Georgia , 2015 .