Application of Remote Sensing Technologies for Assessing Planted Forests Damaged by Insect Pests and Fungal Pathogens: a Review

Current Forestry Reports

Volumn 3, Issue 2, 2017, Pages 75-92

  Stone, Christine ^a   Mohammed, Caroline ^b  

^a NSW Department of Industry Lands   (Australia)

^b UNIVERSITY OF TASMANIA   (Australia)

Author keywords

Damage; Diseases; Fungal pathogens; Insect pests; Plantation health; Remote sensing

Indexed keywords

EID: 85044311847     PISSN: None     EISSN: 21986436     Source Type: Journal    
DOI: 10.1007/s40725-017-0056-1     Document Type: Review

References (105)

1. Loehle C, Idso C, Wigley TB. Physiological and ecological factors influencing recent trends in United States forest health responses to climate change. For Ecol Manag. 2016;363:179–89
2. Ferretti M. Forest health assessment and monitoring—issues for consideration. Environ Monit Assess. 1997;48:45–72
3. Tuominen J, Lipping T, Kuosmanen V, Haapanen R. Remote sensing of forest health. Chapter 2 In: Geoscience and remote sensing. (Ed.) Ho P-GP 2009; doi:10.5772/8283
4. Ferretti M, Fischer R. Forest monitoring methods for terrestrial investigations in Europe with an overview of North America and Asia. Developments in Environmental Science. 2013;12:2–507. Chapter 7 presents statistical approaches dealing with national estimates of defoliation while Chapter 8 is on the ground assessment of tree condition. These chapters contribute to a very comprehensive commentary on forest monitoring methods
5. Stone C, Carnegie A, Melville G, et al. Aerial mapping canopy damage by the aphid Essigella californica in a Pinus radiata plantation in southern New South Wales: what are the challenges? Aust For. 2013;76:101–9
6. Franklin SE, Fan H, Guo X. Relationship between Landsat TM and SPOT vegetation indices and cumulative spruce budworm defoliation. Int J Remote Sens. 2008;29:1215–20
7. Wulder MA, Ortlepp SM, White JC, et al. Monitoring the impacts of mountain pine beetle mitigation. For Ecol Manag. 2009;258:1181–7
8. Goodwin NR, Magnussen S, Coops NC, Wulder MA. Curve fitting of time-series Landsat imagery for characterizing a mountain pine beetle infestation. Int J Remote Sens. 2010;31:3263–71
9. Dennison PE, Brunelle AR, Carter VA. Assessing canopy mortality during a Mountain pine beetle outbreak using GeoEye-1 high spatial resolution satellite data. Remote Sens Environ. 2010;114:2431–5
10. Townsend P, Singh A, Foster J, et al. A general Landsat model to predict canopy defoliation in broadleaf deciduous forests. Remote Sens Environ. 2012;119:255-65
11. Kantola T, Vastaranta M, Lyytikäinen-Saarenmaa P. Classification of needle loss of individual Scots pine trees by means of airborne laser scanning. Forests. 2013;4:386–403. A representative study that demonstrates the application of airborne LiDAR data for classifying tree-level defoliation of pine trees attacked by the common pine sawfly in Finland
12. Meddens AJH, Hicke J. Spatial and temporal patterns of Landsat-based detection of tree mortality caused by a mountain pine beetle outbreak in Colorado, USA. For Ecol Manag. 2014;322:78–88. Provides modelled examples of using time-series data (e.g. Landsat) for examining the spatial and temporal dynamics of pine tree mortality caused by Dendroctonus ponderosae
13. Rullán-Silva C, Olthoff AE, Pando V, et al. Remote monitoring of defoliation by the beech leaf-mining weevil Rhynchaenus fagi in northern Spain. For Ecol Manag. 2015;347:200–8
14. Meigs GW, Kennedy RE, Gray AN, Gregory MJ. Spatiotemporal dynamics of recent mountain pine beetle and western spruce budworm outbreaks across the Pacific Northwest region, USA. For Ecol Manag. 2015;339:71–86
15. Murfitt J, He Y, Yang J, et al. Ash decline assessment in emerald ash borer infested natural forests using high spatial resolution images. Remote Sens. 2016;8:18. doi:10.3390/rs8030256. This recent study uses WorldView-2 imagery to map several crown-level damage classes of ash trees attacked by the emerald ash borer
16. Rullán-Silva CD, Olthoff AE, de la Mata Delgado JA, Pajarea-Alonso JA. Remote monitoring of forest insect defoliation. A review. Forest Syst. 2013;22:377–91. This review provides a comprehensive list of remote monitoring studies of insect defoliation during the period 2007–2012
17. Hall RJ, Castilla G, White JC, et al. Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective. Can Entomol. 2016;148:S296–356. This is a recent, very comprehensive review of remote sensing applications of forest pest damage in Canada. While most of the examples apply to large area surveys, many of the methodologies and analysis presented could be applied to the remote health assessment of plantations
18. Pietrzykowski E, Sims N, Stone C, et al. Predicting Mycosphaerella leaf disease severity in a Eucalyptus globulus plantation using digital multi-spectral imagery. Southern Hemisphere Forestry Journal. 2007;69:175–82
19. Somers B, Verbesselt J, Ampe EM, et al. Spectral mixture analysis to monitor defoliation in mixed-aged Eucalyptus globulus Labill plantations in southern Australia using Landsat 5-TM and EO-1 Hyperion data. Int J Appl Earth Obs Geoinf. 2010;112:270–7
20. Oumar Z, Mutanga O. Integrating environmental variables and WorldView-2 image data to improve the prediction and mapping of Thaumastocoris peregrinus (bronze bug) damage in plantation forests. ISPRS J Photogramm Remote Sens. 2014;87:39–46. Presents an efficient workflow for mapping the severity of damage caused by a common insect pest of Eucalypt plantations through the integration of high spatial resolution multispectral satellite data and auxillary environmental variables
21. Lottering R, Mutanga O. Optimising the spatial resolution of WorldView-2 pan-sharpened imagery for predicting levels of Gonipterus scutellatus defoliation in KwaZulu-Natal, South Africa. ISPRS J Photogramm Remote Sens. 2016;112:13–22. A recent study highlighting the need to determine the optimal spatial resolution most suited to the forest canopy/tree crown characteristics and the presented damage symptoms for improved prediction of defoliation
22. Coops N, Standford M, Old K, et al. Assessment of Dothistroma needle blight of Pinus radiata using airborne hyperspectral imagery. Phytopathology. 2003;93:1524–32
23. Goodwin N, Coops NC, Stone C. Assessing plantation canopy condition from airborne imagery using spectral mixture analysis and fractional abundances. Int J Appl Earth Obs Geoinf. 2005;7:11–28
24. Coops NC, Goodwin N, Stone C, Sims N. Application of narrow-band digital camera imagery to plantation canopy condition assessment. Can J Remote Sens. 2006;32:19–32
25. Sims NC, Stone C, Coops NC, Ryan P. Assessing the health of Pinus radiata plantations using remote sensing data and decision tree analysis. N Z J For Sci. 2007;37:57–80. One of the original Australian studies that demonstrated the potential of airborne multispectral imagery to detect and classify a range of crown damage symptoms in P. radiata plantations
26. Poona NK, Ismail R. Discriminating the occurrence of pitch canker fungus in Pinus radiata trees using QuickBird imagery and artificial neural networks. Southern Forests. 2013;75:29–40. One of the few examples that demonstrate the potential of high spatial resolution, multispectral satellite imagery to detect fungal disease of individual trees in a Pinus radiata plantation
27. Abdel-Rahman EM, Mutanga O, Elhadi A, Ismail R. Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machine classifiers. ISPRS J Photogramm Remote Sens. 2014;88:48–59. A recent representative study that demonstrates the application of machine learning classifiers to airborne hyperspectral data for the detection and mapping of multiple crown damage classes of pine trees attacked by the woodwasp Sirex noctilio
28. Garrity SR, Allen CD, Brumby SP, et al. Quantifying tree mortality in a mixed species woodland using multitemporal high spatial resolution satellite imagery. Remote Sens Environ. 2013;129:54–65
29. Wulder MA, Dymond CC, White JC, et al. Surveying mountain pine beetle damage of forests: a review of remote sensing opportunities. For Ecol Manag. 2006;221:27–41. A significant early introduction to the application of remote sensing technologies for surveying bark beetle damage of forests. In this early paper, the authors highlight the need to relate tree damage symptoms to the remotely sensed image characteristics of individual satellite sensors
30. Carver M, Kent DS. Essigella californica (Essig.) and Eulachnus thunbergia Wilson (Hemiptera: Aphididae: Lachninae) on Pinus in south-eastern Australia. Aust J Entomol. 2000;39:62–9
31. Pause M, Schweitzer C, Rosenthal M, et al. In situ/remote sensing integration to assess forest health—a review. Remote Sens. 2016;8:21. doi10.3390/rs8060471. A review that lists recent satellite missions and the potential forest health features that could be assessed using data from these sensors. Also highlights the requirement for robust, repeatable data workflows that integrate the remotely sensed data with ground-based validation data
32. Portillo-Quintero C, Sanchez-Azofeifa A, Culvenor D. Using VEGNET in-situ monitoring LiDAR (IML) to capture dynamics of plant area index, structure and phenology in aspen parkland forests in Alberta, Canada. Forests. 2014;5:1053–68
33. Carter GA, Knapp AK. Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am J Bot. 2001;88:677–84
34. Ustin SL, Roberts DA, Gamon JA, et al. Using imaging spectroscopy to study ecosystem processes and properties. Bioscience. 2004;54:523–34
35. Stone C, Chisholm LA, McDonald S. Spectral reflectance characteristics of Pinus radiata needles affected by dothistroma needle blight. Can J Bot. 2003;81:560-9
36. Barry KM, Corking R, Thi HP, et al. Spectral characterization of necrosis from reflectance of Eucalyptus globulus leaves with Mycospaerella leaf disease or subjected to artificial lesions. Int J Remote Sens. 2011;32:9243–59
37. Barry KM, Stone C, Mohammed CL. Crown-scale evaluation of spectral indices for defoliated and discoloured eucalypts. Int J Remote Sens. 2008;29:47–69
38. Stone C, Coops NC. Assessment and monitoring of damage from insects in Australian eucalypt forests and commercial plantations. Aust J Entomol. 2004;43:283–92
39. Pietrzykowski E, Stone C, Pinkard E, Mohammed C. Effects of Mycosphaerella leaf disease on the spectral reflectance properties of juvenile Eucalyptus globulus foliage. For Pathol. 2006;36:334–48
40. Ismail R, Mutanga O, Ahmed F. Discriminating Sirex noctilio attack in pine forest plantations in South Africa using high spectral resolution data. In: Kalácska M, Sánchez-Azofeifa G-A, editors. Hyperspectral remote sensing of tropical and sub-tropical hyperspectral remote sensing of tropical and sub-tropical forests. Boca Raton: CRC Press; 2008
41. Ollinger SV. Sources of variability in canopy reflectance and the convergent properties of plants. New Phytol. 2011;189:375–94
42. Shendryk I, Broich M, Tulbure M, et al. Mapping individual tree health using full-waveform airborne laser scans and imaging spectroscopy: a case study for a floodplain eucalypt forest. Remote Sens Environ. 2016;187:202–17. A recent study that presents a data workflow that fuses airborne LiDAR data and hyperspectral data for the classification of crown health in a native eucalypt forest. The authors present a methodology for accurately aligning the two datasets
43. Havašová M, Bucha T, Ferenčík J, Jakuš R. Applicability of a vegetation indices-based method to map bark beetle outbreaks in the High Tatra mountains. Ann For Res. 2015;58:295–310
44. Fassnacht FE, Latifi H, Ghosh A, et al. Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality. Remote Sens Environ. 2014;140:533–48
45. Waser LT, Küchler M, Jütte K, Stampfer T. Evaluating the potential of WorldView-2 data to classify tree species and different levels of ash mortality. Remote Sens. 2014;6:4515–45
46. Lausch A, Heurich M, Gordalla D, et al. Forecasting potential bark beetle outbreaks based on spruce forest vitality using hyperspectral remote-sensing techniques at different scales. For Ecol Manag. 2013;308:76–89
47. Evans B, Lyons T, Barber P, et al. Enhancing a eucalypt crown condition indicator driven by high spatial and spectral resolution remote sensing imagery. J Appl Remote Sens. 2012;6:15. A representative study that demonstrates the benefits of combining spectral and textural metrics extracted from high spatial resolution airborne multispectral imagery for classifying and mapping the individual crown condition of eucalypts
48. Havašová M, Bucha T, Ferenčík J, Jakuš R. Applicability of a vegetation indices-based method to map bark beetle outbreaks in the High Tatra mountains. J Appl Remote Sens. 2012;6:10
49. Wulder MA, White JC, Coops NC, Buston CR. Multitemporal analysis of high spatial resolution imagery for disturbance monitoring. Remote Sens Environ. 2008;112:2729–40
50. Pu R, Kelly M, Anderson GL, Gong P. Using CASI hyperspectral imagery to detect mortality and vegetation stress associated with a new hardwood forest disease. Photogramm Eng Remote Sens. 2008;74:65–75
51. Jensen JR. Introductory digital image processing—a remote sensing perspective. 3rd ed. Upper Saddle River: Pearson Prentice Hall; 2005
52. Meng J, Li S, Wang W, et al. Mapping forest health using spectral and textural information extracted from SPOT-5 satellite images. Remote Sens. 2016;8:20. doi:10.3390/rs8090719
53. Peñuelas J, Filella I, Gamon JA. Assessment of photosynthetic radiation use efficiency with spectral reflectance. New Phytol. 1995;131:291–6
54. Hernández-Clemente R, Navarro-Cerrillo RM, Suárez L, et al. Assessing structural effects on PRI for stress detection in conifer forests. Remote Sens Environ. 2011;115:2360–75
55. López-López M, Calderón R, González-Dugo V, et al. Early detection and quantification of almond red leaf blotch using high-resolution hyperspectral and thermal imagery. Remote Sens. 2016;8:23. doi:10.3390/rs8040276
56. Wang H, Zhao YZ, Pu R, Zhang Z. Mapping Robinia pseudoacacia forest health conditions by using combined spectral, spatial, and textural information extracted from IKONOS imagery and random forest classifier. Remote Sens. 2015;7:9020–44. doi:10.3390/rs70709020
57. Wang H, Pu R, Zhu Q, et al. Mapping health levels of Robinia pseudoacacia forests in the Yellow River delta, China, using IKONOS and Landsat 8 OLI imagery. Int J Remote Sens. 2015;36:1114–35
58. Kayitakire F, Hamel C, Defourny P. Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sens Environ. 2006;102:390–401
59. Haralick RM. Statistical rand structural approaches to texture. Proceedings of the IEEE 1979; 67:786–804
60. Verbesselt J, Robinson A, Stone C, Culvenor D. Forecasting tree mortality using change metrics derived from MODIS satellite data. For Ecol Manag. 2009;258:1166–73
61. Verbesselt J, Hyndman R, Newnham G, Culvenor D. Detecting trend and seasonal changes in satellite image time series. Remote Sens Environ. 2010;114:106–15
62. De Castro AI, Ehsani R, Ploetz R, et al. Optimum spectral and geometric parameters for early detection of laurel wilt disease in avocado. Remote Sens Environ. 2015;171:33–44
63. Eitel JUH, Vierling LA, Litvak ME, et al. Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland. Remote Sens Environ. 2011;115:3640–6
64. Mišurec J, Kopačková V, Lhotáková Z. Utilization of hyperspectral image optical indices to assess the Norway spruce forest health status. J Appl Remote Sens. 2012;6:25
65. Hill SL, Clemens P M. Minaturization of high spatial resolution hyperspectral imagers on unmanned aerial systems. Proc. SPIE9482, Next-Generation Spectroscopic Technologies VIII, 94821E (June 3, 2015). doi:10.1117/12.2193706
66. Michez A, Piégay H, Lisein J, et al. Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system. Environ Monit Assess. 2016;156:435–50
67. Näsi R, Honkavaara E, Lyytikäinen P, et al. Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level. Remote Sens. 2016;7:15467–93. A recent study that demonstrates the potential of a UAV platform carrying a hyperspectral sensor for classifying bark beetle damage at the tree level. The data workflow incorporated a photogrammetrically derived digital surface model which was then used for individual tree detection
68. Stone C, Webster M, Osborn J, Iqbal I. Alternatives to LiDAR-derived canopy height models for softwood plantations: a review and example using photogrammetry. Aust For. 2016;79:271–82. doi:10.1080/00049158.2016.1241134
69. White JC, Coops NC, Wulder MA, et al. Remote sensing technologies for enhancing forest inventories: a review. Can J Remote Sens. 2016;5:1–23
70. Puliti S, Ørka HO, Gobakken T, Næsset E. Inventory of small forest areas using an unmanned aerial system. Remote Sens. 2015;7:9632–54
71. Yang Y, Lin Z, Liu F. Stable imaging and accuracy issues of low-altitude unmanned aerial vehicle photogrammetry systems. Remote Sens. 2016;8:18. doi:10.3390/rs8040316
72. Olsson P-O, Kantola T, Lyytikäinen-Saarenmaa P, et al. Development of a method for monitoring of insect induced forest defoliation—limitations of MODIS data in Fennoscandian forest landscapes. Silva Fenn. 2016; 50 : 22
73. Meddens AJH, Hicke JA, Vierling LA. Evaluating the potential of multispectral imagery to map multiple stages of tree mortality. Remote Sens Environ. 2011;115:1632–42
74. Ismail R, Mutanga O, Kumar L, Urmilla B. Determining the optimal spatial resolution of remotely sensed data for the detection of Sirex noctilio infestations in pine plantations in Kwazulu-Natal, South Africa. S Afr Geogr J. 2008;90:20–30
75. Johnson B. Effects of pansharpening on vegetation indices. ISPRS Int J Geo-Inf. 2014;3:507–22
76. Solberg S, Næsset E, Hanssen KH, Christiansen E. Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning. Remote Sens Environ. 2006;102:364–76
77. Vastaranta M, Kantola T, Lyytikäinen-Saarenmaa P. Area-based mapping of defoliation of Scots pine stands using airborne scanning LiDAR. Remote Sens. 2013;5:1220–34
78. Kantola T, Vastaranta M, Yu X. Classification of defoliated trees using tree-level airborne laser scanning data combined with aerial images. Remote Sens. 2010;2:2665–79
79. Hakala T, Nevalainen O, Kaasalainen S, Mäkipää R. Technical note: multispectral lidar time series of pine canopy chlorophyll content. Biogeosciences. 2015;12:1629–34
80. Ortiz SM, Breidenbach J, Kändler G. Early detection of bark beetle green attack using TerraSAR-X and RapidEye data. Remote Sens. 2013;5:1912–31
81. Zarco-Tejada PJ, Miller JR, Harron J. Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies. Remote Sens Environ. 2004;89:189–99
82. Ligot G, Balandier P, Courbaub B, Claessens H. Forest radiative transfer models: which approach for which application? Can J For Res. 2014;44:391–403
83. White JC, Wulder MA, Brooks D, et al. Detection of red attack stage mountain pine beetle infestation with high spatial resolution satellite imagery. Remote Sens Environ. 2005;96:340–51
84. Lazaridis DC, Verbesselt J, Robinson AP. Penalized regression techniques for prediction: a case study for predicting tree mortality using remotely sensed vegetation indices. Can J For Res. 2011;41:24–34
85. Brieman L. Random forest. Mach Learn. 2001;45:5–32
86. Falkowski MJ, Hudak AT, Crookston NL, et al. Landscape-scale parameterization of a tree-level forest growth model: a k-nearest neighbour imputation approach incorporating LiDAR data. Can J For Res. 2010;40:184–99
87. Hsu P-H. Feature extraction of hyperspectral images using wavelet and matching pursuit. ISPRS Journal of Photogrammetry & Remote Sensing. 2007;62:78–92
88. Leckie DG, Walsworth N, Gougeon FA. Identifying tree crown delineation shapes and need for remediation on high resolution imagery using an evidence based approach. ISPRS J Photogramm Remote Sens. 2016;114:206–27
89. Vauhkonen J, Ene L, Gupta S, Heinzel J, et al. Comparative testing of single-tree detection algorithms under different types of forest. Forestry. 2012;85:27–40
90. Kathuria A, Turner R, Stone C, et al. Development of an automated individual tree detection model using point cloud LiDAR data for accurate tree counts in a Pinus radiata plantation. Aust For. 2016;79:126–36
91. Stone C, Penman T, Turner R. Managing drought-induced mortality in Pinus radiata plantations under climate change conditions: a local approach using digital camera data. For Ecol Manag. 2012;265:94–101
92. Culvenor DS. TIDA: an algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery. Comput Geosci. 2002;28:33–44
93. Zhen Z, Quackenbush LJ, Zhang L. Trends in automatic individual tree crown detection and delineation—evolution of LiDAR data. Remote Sens. 2016;8:26. doi:10.3390/rs8040333. Semi- and fully automated algorithms for detecting and delineating individual tree crowns from remotely sensed data are becoming common in data workflows processing high spatial resolution remotely sensed data for tree health assessment. This review presents and discusses recent approaches for tree crown segmentation
94. Blaschke T. Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens. 2010;65:2–16
95. Lehmann JRK, Nieberding F, Prinz T, Knoth C. Analysis of unmanned aerial system-based CIR images in forestry—a new perspective to monitor pest infestation levels. Forests. 2015;6:594–612. A study that demonstrates the use of a small commercial UAV to acquire colour infrared imagery for classifying crown damage of oak trees attacked by the oak splendour beetle
96. Bater CW, Wulder MA, White JC, Coops NC. Integration of LiDAR and digital aerial imagery for detailed estimates of lodgepole pine (Pinus contorta) volume killed by Mountain pine beetle (Dendroctonus pondersae). J Forest. 2010;April/May:111–119
97. McConnell T, Johnson E, Burns B. A guide to conducting aerial sketchmap surveys. Fort Collins: US Department of Agricultural Forest Service, Forest Health Technology Enterprise Team; 2000. FHTET 00-01 88pp
98. Wardlaw T, Bashford R, Wotherspoon K, et al. Effectiveness of routine forest health surveillance in detecting pest and disease damage in eucalypt plantations. N Z J For Sci. 2008;38:253–69
99. Congalton RG, Green K. Assessing the accuracy of remotely sensed data: principles and practices. 2nd ed. Boca Raton: CRC Press; 2002
100. Maltamo M, Bollandsas OM, Næsset E, et al. Different plot selection strategies for field training data in ALS-assisted forest inventory. Forestry. 2011;84:23–31
101. Melville G, Stone C. Optimising nearest neighbour information—a simple, efficient sampling strategy for forestry plot imputation using remotely sensed data. Aust For. 2016;79:217–28
102. Roberge C, Wulff S, Reese H, Stähl G. Improving the precision of sample-based forest damage inventories through two-phase sampling and post-stratification using remotely sensed auxiliary information. Environ Monit Assess. 2016;188:21. doi:10.1007/s10661-016-5208-4
103. Calderón R, Navas-Cortés JA, Zarco-Tejada PJ. Early detection and quantification of Verticillium wilt in olive using hyperspectral and thermal imagery over large areas. Remote Sens. 2015;7:5584–610
104. Clevers JGPW, Gitelson AA. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3. Int J Appl Earth Obs Geoinf. 2013;23:344–51
105. Richter R, Döllner J. Concepts and techniques for integration, analysis and visualization of massive 3D point clouds. Comput Environ Urban Syst. 2014;45:114–24