Assessing the utility of aerial imagery to quantify the density, age structure and spatial pattern of alien conifer invasions

  • Rowan SpragueEmail author
  • William Godsoe
  • Philip E. Hulme
Original Paper


Effective detection and monitoring tools are essential to manage the major ecological and economic problems posed by alien conifer invasions. Low-cost aerial imagery has been promoted as a promising tool for the detection of alien trees over large landscapes, but as yet there have been few attempts to assess its reliability for monitoring invasions. In particular, studies have not yet examined how well aerial imagery can detect densities of trees across invasions. To evaluate this, we used freely available, high-resolution aerial imagery to examine how age structure, spatial patterns and density of alien conifers varied across an invasion front. Overall, we were able to detect both the spatial pattern and distribution of trees with canopy diameters greater than 2.5 m, but we could only detect smaller trees with certainty where they were found at low density. These results point to aerial imagery being suitable for detecting trees at the edge of the invasion front, where they are often small and at low density. While assessments of the overall age-structure will underestimate the number of small trees, the number and spatial pattern of larger reproductive individuals can still be adequately captured. Thus low-cost aerial imagery can inform managers of where best to target control efforts at the invasion edge and also the location of large reproductive trees that are likely to contribute to future population expansion.


Exotic Non-native Orthophotography Size threshold Spatial patterns Wilding pine 



The authors would like to thank Land Information New Zealand (LINZ) for providing the aerial imagery. We also are grateful to Nick Ledgard and Gordon Baker for their permission to access the Mt Barker Forest. We are indebted to Johnathon and Brendon Ridden for their many hours of help with fieldwork. Finally, we are grateful to the Bio-Protection Research Centre at Lincoln University for its research support.


This work was supported by the Miss EL Hellaby Indigenous Grasslands Research Trust, New Zealand.

Compliance with ethical standards

Conflict of interest

The authors declare they have no conflicts of interest.

Supplementary material

10530_2019_1960_MOESM1_ESM.docx (13 kb)
Supplementary material 1 (DOCX 12 kb)


  1. Andrew ME, Ustin SL (2009) Habitat suitability modelling of an invasive plant with advanced remote sensing data. Divers Distrib 15:627–640. CrossRefGoogle Scholar
  2. April Sahara E, Sarr DA, Van Kirk RW, Jules ES (2015) Quantifying habitat loss: assessing tree encroachment into a serpentine savanna using dendroecology and remote sensing. For Ecol Manage 340:9–21. CrossRefGoogle Scholar
  3. Asner GP, Jones MO, Martin RE et al (2008) Remote sensing of native and invasive species in Hawaiian forests. Remote Sens Environ 112:1912–1926. CrossRefGoogle Scholar
  4. Bivand R, Keitt T, Rowlingson B (2017) rgdal: Bindings for the geospatial data abstraction library. R package version 1.2-8Google Scholar
  5. Bolker B, Pacala SW (1997) Using moment equations to understand stochastically driven spatial pattern formation in ecological systems. Theor Popul Biol 52:179–197. CrossRefGoogle Scholar
  6. Bradley BA (2014) Remote detection of invasive plants: a review of spectral, textural and phenological approaches. Biol Invasions 16:1411–1425. CrossRefGoogle Scholar
  7. Bradley BA, Mustard JF (2006) Characterizing the landscape dynamics of an invasive plant and risk of invasion using remote sensing. Ecol Appl 16:1132–1147.[1132:CTLDOA]2.0.CO;2 CrossRefGoogle Scholar
  8. Buckley YM, Brockerhoff E, Langer L et al (2005) Slowing down a pine invasion despite uncertainty in demography and dispersal. J Appl Ecol 42:1020–1030. CrossRefGoogle Scholar
  9. Caplat P, Coutts S, Buckley YM (2012) Modeling population dynamics, landscape structure, and management decisions for controlling the spread of invasive plants. Ann N Y Acad Sci 1249:72–83. CrossRefGoogle Scholar
  10. Carreiras JMB, Pereira JMC, Pereira JS (2006) Estimation of tree canopy cover in evergreen oak woodlands using remote sensing. For Ecol Manag 223:45–53. CrossRefGoogle Scholar
  11. Clark PJ, Evans FC (1954) Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35:445–453. CrossRefGoogle Scholar
  12. Clark JS, Lewis M, Horvath L (2001) Invasion by extremes: population spread with variation in dispersal and reproduction. Am Nat 157:537–554. CrossRefGoogle Scholar
  13. Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37:35–46. CrossRefGoogle Scholar
  14. Dalponte M, Ørka HO, Ene LT et al (2014) Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote Sens Environ 140:306–317. CrossRefGoogle Scholar
  15. Dash JP, Pearse GD, Watt MS, Paul T (2017) Combining airborne laser scanning and aerial imagery enhances echo classification for invasive conifer detection. Remote Sens 9:156. CrossRefGoogle Scholar
  16. Delmas C, Delzon S, Lortie C (2011) A meta-analysis of the ecological significance of density in tree invasions. Commun Ecol 12:171–178. CrossRefGoogle Scholar
  17. Deng S, Katoh M, Yu X et al (2016) Comparison of tree species classifications at the individual tree level by combining ALS data and RGB images using different algorithms. Remote Sens 8:1034. CrossRefGoogle Scholar
  18. Department of Conservation (2018) Methods of control for wilding conifers. Accessed 23 July 2018
  19. Department of Finance, Service and Innovation: Spatial Services NSW (2018) Mapping and Spatial Services: Information Sheet. Accessed 23 July 2018
  20. Department of Rural Development and Land Reform (2018) National aerial photography and imagery programme. Accessed 23 July 2018
  21. Dieckmann U, Law R, Metz JAJ (2000) The geometry of ecological interactions. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  22. Dovčiak M, Hrivnák R, Ujházy K, Gömöry D (2014) Patterns of grassland invasions by trees: insights from demographic and genetic spatial analyses. J Plant Ecol 8:468–479. CrossRefGoogle Scholar
  23. Falkowski MJ, Smith AMS, Gessler PE et al (2008) The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data. Can J Remote Sens 34:338–350. CrossRefGoogle Scholar
  24. Froude VA (2011) Wilding conifers in New Zealand: status report. Pacific Eco-Logic Ltd, Report prepared for the Ministry of Agriculture and Forestry, New Zealand. Bay of Islands, New ZealandGoogle Scholar
  25. Gougeon FA (1995) A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images. Can J Remote Sens 21:274–284. CrossRefGoogle Scholar
  26. Haby N, Tunn Y, Cameron J (2010) Application of QuickBird and aerial imagery to detect Pinus radiata in remnant vegetation. Austral Ecol 35:624–635. CrossRefGoogle Scholar
  27. He KS, Rocchini D, Neteler M, Nagendra H (2011) Benefits of hyperspectral remote sensing for tracking plant invasions. Divers Distrib 17:381–392. CrossRefGoogle Scholar
  28. Higgins SI, Richardson DM (1998) Pine invasions in the Southern Hemisphere: modelling interactions between organism, environment and disturbance. Plant Ecol 135:79–93. CrossRefGoogle Scholar
  29. Hijmans RJ (2016) raster: Geographic data analysis and modeling. R package version 2.5-8Google Scholar
  30. Huang C-Y, Asner GP (2009) Applications of remote sensing to alien invasive plant studies. Sensors 9:4869–4889. CrossRefGoogle Scholar
  31. Hulet A, Roundy BA, Petersen SL et al (2013) Assessing the relationship between ground measurements and object-based image analysis of land cover classes in pinyon and juniper woodlands. Photogramm Eng Remote Sens 79:799–808. CrossRefGoogle Scholar
  32. Illian J, Penttinen A, Stoyan H, Stoyan D (2008) Statistical analysis and modelling of spatial point Patterns. Wiley, ChichesterGoogle Scholar
  33. Ke Y, Quackenbush LJ (2011) A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing. Int J Remote Sens 32:4725–4747. CrossRefGoogle Scholar
  34. Kelcey J, Lucieer A (2012) Sensor correction of a 6-band multispectral imaging sensor for UAV remote sensing. Remote Sens 4:1462–1493. CrossRefGoogle Scholar
  35. Komura R, Kubo M, Muramoto K (2004) Delineation of tree crown in high resolution satellite image using circle expression and watershed algorithm. In: Geoscience and remote sensing symposium, 2004. IGARSS’04. IEEE, pp 1577–1580Google Scholar
  36. Lamar WR, McGraw JB, Warner TA (2005) Multitemporal censusing of a population of eastern hemlock (Tsuga canadensis L.) from remotely sensed imagery using an automated segmentation and reconciliation procedure. Remote Sens Environ 94:133–143. CrossRefGoogle Scholar
  37. Landgate Government of Western Australia (2018) Online Aerial Photography. Accessed 23 July 2018
  38. Law R, Dieckmann U (2000) A dynamical system for neighborhoods in plant communities. Ecology 81:2137–2148.[2137:ADSFNI]2.0.CO;2 Google Scholar
  39. Lawrence RL, Wood SD, Sheley RL (2006) Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (Random Forest). Remote Sens Environ 100:356–362. CrossRefGoogle Scholar
  40. Ledgard N (2001) The spread of lodgepole pine (Pinus contorta, Dougl.) in New Zealand. For Ecol Manag 141:43–57. CrossRefGoogle Scholar
  41. Madsen MD, Zvirzdin DL, Davis BD et al (2011) Feature extraction techniques for measuring piñon and juniper tree cover and density, and comparison with field-based management surveys. Environ Manag 47:766–776. CrossRefGoogle Scholar
  42. Mast JN, Veblen TT, Hodgson ME (1997) Tree invasion within a pine/grassland ecotone: an approach with historic aerial photography and GIS modeling. For Ecol Manag 93:181–194. CrossRefGoogle Scholar
  43. Mauck J, Brown K, Carswell Jr WJ (2016) The National Map—Orthoimagery. In: United States Geol. Surv. Fact Sheet 2009-3055. Accessed 23 July 2018
  44. Ministry of Primary Industries (2014) The right tree in the right place: New Zealand wilding conifer management strategy 2015–2030. Report produced for the Ministry of Primary Industries, New Zealand. Accessed 2 July 2018
  45. Mirik M, Chaudhuri S, Surber B et al (2013) Evaluating biomass of juniper trees (Juniperus pinchotii) from imagery-derived canopy area using the Support Vector Machine classifier. Adv Remote Sens 2:181–192. CrossRefGoogle Scholar
  46. Müllerová J, Pyšek P, Jarošík V, Pergl J (2005) Aerial photographs as a tool for assessing the regional dynamics of the invasive plant species Heracleum mantegazzianum. J Appl Ecol 42:1042–1053. CrossRefGoogle Scholar
  47. Natural Resources Canada (2016) National Air Photo Library. Accessed 23 July 2018
  48. Nuñez MA, Chiuffo MC, Torres A et al (2017) Ecology and management of invasive Pinaceae around the world: progress and challenges. Biol Invasions 19:3099–3120. CrossRefGoogle Scholar
  49. OpenAerialMap (2018) The Open Collection of Aerial Imagery. Accessed 23 July 2018
  50. Pau G, Fuchs F, Sklyar O et al (2010) EBImage—an R package for image processing with applications to cellular phenotypes. Bioinformatics 26:979–981CrossRefGoogle Scholar
  51. Pebesma EJ, Bivand RS (2005) Classes and methods for spatial data in R. R N 5:1–21Google Scholar
  52. Peters HA (2003) Neighbour-regulated mortality: the influence of positive and negative density dependence on tree populations in species-rich tropical forests. Ecol Lett 6:757–765. CrossRefGoogle Scholar
  53. Poznanovic AJ, Falkowski MJ, Maclean AL et al (2014) An accuracy assessment of tree detection algorithms in juniper woodlands. Photogramm Eng Remote Sens 80:627–637. CrossRefGoogle Scholar
  54. R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Accessed 25 July 2018
  55. Richardson DM, Williams PA, Hobbs RJ (1994) Pine invasions in the Southern Hemisphere: determinants of spread and invadability. J Biogeogr 21:511–527. CrossRefGoogle Scholar
  56. Richardson DM, Van Wilgen BW, Nuñez MA (2008) Alien conifer invasions in South America: short fuse burning? Biol Invasions 10:573–577. CrossRefGoogle Scholar
  57. Shigesada N, Kawasaki K, Takeda Y (1995) Modeling stratified diffusion in biological invasions. Am Nat 146:229–251. CrossRefGoogle Scholar
  58. Simberloff D, Nuñez MA, Ledgard NJ et al (2010) Spread and impact of introduced conifers in South America: lessons from other Southern Hemisphere regions. Austral Ecol 35:489–504. CrossRefGoogle Scholar
  59. Strand EK, Robinson AP, Bunting SC (2007) Spatial patterns on the sagebrush steppe/Western juniper ecotone. Plant Ecol 190:159–173. CrossRefGoogle Scholar
  60. Sykes MT (2001) Modelling the potential distribution and community dynamics of lodgepole pine (Pinus contorta Dougl. ex. Loud.) in Scandinavia. For Ecol Manage 141:69–84. CrossRefGoogle Scholar
  61. Taylor KT, Maxwell BD, Pauchard A et al (2016) Drivers of plant invasion vary globally: evidence from pine invasions within six ecoregions. Glob Ecol Biogeogr 25:96–106. CrossRefGoogle Scholar
  62. Tomiolo S, Harsch MA, Duncan RP, Hulme PE (2016) Influence of climate and regeneration microsites on Pinus contorta invasion into an alpine ecosystem in New Zealand. AIMS Environ Sci 3:525–540. CrossRefGoogle Scholar
  63. Tomljenovic I, Tiede D, Blaschke T (2016) A building extraction approach for airborne laser scanner data utilizing the object based image analysis paradigm. Int J Appl Earth Obs Geoinf 52:137–148. CrossRefGoogle Scholar
  64. Visser V, Langdon B, Pauchard A, Richardson DM (2014) Unlocking the potential of Google Earth as a tool in invasion science. Biol Invasions 16:513–534. CrossRefGoogle Scholar
  65. Wang L, Gong P, Biging GS (2004) Individual tree-crown delineation and treetop detection in high-spatial-resolution aerial imagery. Photogramm Eng Remote Sens 70:351–357CrossRefGoogle Scholar
  66. Yokomizo H, Possingham HP, Thomas MB, Buckley YM (2009) Managing the impact of invasive species: the value of knowing the density-impact curve. Ecol Appl 19:376–386. CrossRefGoogle Scholar
  67. Yu X, Hyyppä J, Vastaranta M et al (2011) Predicting individual tree attributes from airborne laser point clouds based on the random forests technique. ISPRS J Photogramm Remote Sens 66:28–37. CrossRefGoogle Scholar
  68. Zhu K, Woodall CW, Monteiro JVD, Clark JS (2015) Prevalence and strength of density-dependent tree recruitment. Ecology 96:2319–2327. CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Bio-Protection Research Centre, Lincoln UniversityCanterburyNew Zealand

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