1932

Abstract

Wood-boring pests (WBPs) pose an enormous threat to global forest ecosystems because their early stage infestations show no visible symptoms and can result in rapid and widespread infestations at later stages, leading to large-scale tree death. Therefore, early-stage WBP detection is crucial for prompt management response. Early detection of WBPs requires advanced and effective methods like remote sensing. This review summarizes the applications of various remote sensing sensors, platforms, and detection methods for monitoring WBP infestations. The current capabilities, gaps in capabilities, and future potential for the accurate and rapid detection of WBPs are highlighted.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-ento-120220-125410
2023-01-23
2024-05-08
Loading full text...

Full text loading...

/deliver/fulltext/ento/68/1/annurev-ento-120220-125410.html?itemId=/content/journals/10.1146/annurev-ento-120220-125410&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Abdel-Rahman EM, Mutanga O, Adam E, Ismail R 2014. Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers. ISPRS J. Photogramm. Remote Sens. 88:48–59
    [Google Scholar]
  2. 2.
    Abdullah H, Darvishzadeh R, Skidmore AK, Groen TA, Heurich M. 2018. European spruce bark beetle (Ips typographus, L.) green attack affects foliar reflectance and biochemical properties. Int. J. Appl. Earth Obs. Geoinf. 64:199–209
    [Google Scholar]
  3. 3.
    Abdullah H, Darvishzadeh R, Skidmore AK, Heurich M 2019. Sensitivity of Landsat-8 OLI and TIRS data to foliar properties of early stage bark beetle (Ips typographus, L.) infestation. Remote Sens. 11:4398
    [Google Scholar]
  4. 4.
    Abdullah H, Skidmore AK, Darvishzadeh R, Heurich M. 2019. Sentinel-2 accurately maps green-attack stage of European spruce bark beetle (Ips typographus, L.) compared with Landsat-8. Remote Sens. Ecol. Conserv. 5:187–106
    [Google Scholar]
  5. 5.
    Abdullah H, Skidmore AK, Darvishzadeh R, Heurich M. 2019. Timing of red-edge and shortwave infrared reflectance critical for early stress detection induced by bark beetle (Ips typographus, L.) attack. Int. J. Appl. Earth Obs. Geoinf. 82:101900
    [Google Scholar]
  6. 6.
    Ahern FJ. 1988. The effects of bark beetle stress on the foliar spectral reflectance of lodgepole pine. Int. J. Remote Sens. 9:91451–68
    [Google Scholar]
  7. 7.
    Ba J, Caruana R. 2014. Do deep nets really need to be deep?. Advances in Neural Information Processing Systems 27 (NIPS 2014)2654–62 N.p: NeurIPS
    [Google Scholar]
  8. 8.
    Barta V, Lukes P, Homolova L. 2021. Early detection of bark beetle infestation in Norway spruce forests of Central Europe using Sentinel-2. Int. J. Appl. Earth Obs. Geoinform. 100:102335
    [Google Scholar]
  9. 9.
    Bhattarai R, Rahimzadeh-Bajgiran P, Weiskittel A, Meneghini A, MacLean DA. 2021. Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery. ISPRS J. Photogramm. Remote Sens. 172:28–40
    [Google Scholar]
  10. 10.
    Buddenbaum H, Rock G, Hill J, Werner W. 2015. Measuring stress reactions of beech seedlings with PRI, fluorescence, temperatures and emissivity from VNIR and thermal field imaging spectroscopy. Eur. J. Remote Sens. 48:1263–82
    [Google Scholar]
  11. 11.
    Cao JJ, Leng WC, Liu K, Liu L, He Z, Zhu YH. 2018. Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens 10:189
    [Google Scholar]
  12. 12.
    Chenari A, Erfanifard Y, Dehghani M, Pourghasemi HR. 2017. Woodland mapping at single-tree levels using object-oriented classification of unmanned aerial vehicle (UAV) images. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci XLII-4/W4 43–49
    [Google Scholar]
  13. 13.
    Cheng T, Rivard B, Sánchez-Azofeifa GA, Feng J, Calvo-Polanco M. 2010. Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation. Remote Sens. Environ. 114:899–910
    [Google Scholar]
  14. 14.
    Cole B, McMorrow J, Evans M. 2014. Spectral monitoring of moorland plant phenology to identify a temporal window for hyperspectral remote sensing of peatland. ISPRS J. Photogramm. Remote Sens. 90:49–58
    [Google Scholar]
  15. 15.
    Coops NC, Johnson M, Wulder MA, White JC. 2006. Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation. Remote Sens. Environ. 103:67–80
    [Google Scholar]
  16. 16.
    Coops NC, Varhola A, Bater CW, Teti P, Boon S et al. 2009. Assessing differences in tree and stand structure following beetle infestation using lidar data. Can. J. Remote Sens. 35:6497–508
    [Google Scholar]
  17. 17.
    Coops NC, Wulder M, Iwanicka D. 2009. Large area monitoring with a MODIS-based Disturbance Index (DI) sensitive to annual and seasonal variations. Remote Sens. Environ. 113:61250–61
    [Google Scholar]
  18. 18.
    Croft H, Chen JM, Zhang Y, Simic A. 2013. Modelling leaf chlorophyll content in broadleaf and needle leaf canopies from ground, CASI, Landsat TM 5 and MERIS reflectance data. Remote Sens. Environ. 133:128–40
    [Google Scholar]
  19. 19.
    Dawson TP, Curran PJ, Plummer SE. 1998. LIBERTY: modeling the effects of leaf biochemical concentration on reflectance spectra. Remote Sens. Environ. 65:150–60
    [Google Scholar]
  20. 20.
    Dennison PE, Brunelle AR, Carter VA. 2010. Assessing canopy mortality during a mountain pine beetle outbreak using GeoEye-1 high spatial resolution satellite data. Remote Sens. Environ. 114:2431–35
    [Google Scholar]
  21. 21.
    Derose R, Long J, Ramsey R. 2011. Combining dendrochronological data and the disturbance index to assess Engelmann spruce mortality caused by a spruce beetle outbreak in southern Utah, USA. . Remote Sens. Environ. 115:92342–49
    [Google Scholar]
  22. 22.
    Dickinson C, Siqueira P, Clewley D, Lucas R. 2013. Classification of forest composition using polarimetric decomposition in multiple landscapes. Remote Sens. Environ. 131:206–14
    [Google Scholar]
  23. 23.
    Edwards D, Tobias J, Sheil D, Meijaard E, Laurance W 2014. Maintaining ecosystem function and services in logged tropical forests. Trends Ecol. Evol. 29:9511–20
    [Google Scholar]
  24. 24.
    Einzmann K, Atzberger C, Pinnel N, Glas C, Bock S et al. 2021. Early detection of spruce vitality loss with hyperspectral data: results of an experimental study in Bavaria, Germany. . Remote Sens. Environ. 266:112676
    [Google Scholar]
  25. 25.
    Eriksson H, Eklundh L, Kuusk A, Nilson T. 2006. Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates. Remote Sens. Environ. 103:408–18
    [Google Scholar]
  26. 26.
    FAO 2009. Global review of forest pests and diseases FAO For. Pap. 156, Food Agric. Org. U. N., Rome
  27. 27.
    Fassnacht FE, Latifi H, Ghosh A, Joshi PK, Koch B. 2014. Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality. Remote Sens. Environ. 140:533–48
    [Google Scholar]
  28. 28.
    Feret JB, François C, Asner GP, Gitelson AA, Martin RE et al. 2011. PROSPECT-4 and 5: advances in the leaf optical properties model separating photosynthetic pigments. Remote Sens. Environ. 112:63030–43
    [Google Scholar]
  29. 29.
    Fernandez-Carrillo A, Patočka Z, Dobrovolný L, Franco-Nieto A, Revilla-Romero B. 2020. Monitoring bark beetle forest damage in central Europe: a remote sensing approach validated with field data. Remote Sens 12:213634
    [Google Scholar]
  30. 30.
    Ferreira MP, Wagner FH, Aragão LEOC, Shimabukuro YE, de Souza Filho CR. 2019. Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis. ISPRS J. Photogramm. Remote Sens. 149:119–31
    [Google Scholar]
  31. 31.
    Finley K, Chhin S, Nzokou P, O'Brien J. 2016. Use of near-infrared spectroscopy as an indicator of emerald ash borer infestation in white ash stem tissue. For. Ecol. Manag. 366:41–52
    [Google Scholar]
  32. 32.
    Fisher JB, Lee B, Purdy AJ, Halverson GH, Dohlen MB et al. 2020. ECOSTRESS: NASA's next generation mission to measure evapotranspiration from the international space station. Water Resour. Res. 56:4e2019WR026058
    [Google Scholar]
  33. 33.
    Foster AC, Walter JA, Shugart HH, Sibold J, Negron J. 2017. Spectral evidence of early-stage spruce beetle infestation in Engelmann spruce. For. Ecol. Manag. 384:347–57
    [Google Scholar]
  34. 34.
    Gitelson AA, Kaufman YJ, Merzlyak MN. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58:289–98
    [Google Scholar]
  35. 35.
    Gitelson AA, Viña A, Ciganda V, Rundquist DC, Arkebauer TJ. 2005. Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett. 32:L08403
    [Google Scholar]
  36. 36.
    Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D et al. 2014. Generative adversarial networks. arXiv:1406.2661 [stat.ML]
  37. 37.
    Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D et al. 2020. Generative adversarial networks. Commun. ACM 63:139–44
    [Google Scholar]
  38. 38.
    Goodwin NR, Coops NC, Wulder MA, Gillanders S, Schroeder TA, Nelson T 2008. Estimation of insect infestation dynamics using a temporal sequence of Landsat data. Remote Sens. Environ. 112:3680–89
    [Google Scholar]
  39. 39.
    Goodwin NR, Magnussen S, Coops NC, Wulder MA. 2010. Curve fitting of time-series Landsat imagery for characterizing a mountain pine beetle infestation. Int. J. Remote Sens. 31:3263–71
    [Google Scholar]
  40. 40.
    Hais M, Wild J, Berec L, Brůna J, Kennedy R et al. 2016. Landsat imagery spectral trajectories—important variables for spatially predicting the risks of bark beetle disturbance. Remote Sens 8:8687
    [Google Scholar]
  41. 41.
    Hansen WD, Chapin FS, Naughton HT, Rupp TS, Verbyla D. 2016. Forest-landscape structure mediates effects of a spruce bark beetle (Dendroctonus rufipennis) outbreak on subsequent likelihood of burning in Alaskan boreal forest. For. Ecol. Manag. 369:38–46
    [Google Scholar]
  42. 42.
    Hatala JA, Crabtree RL, Halligan KQ, Moorcroft PR. 2010. Landscape-scale patterns of forest pest and pathogen damage in the Greater Yellowstone Ecosystem. Remote Sens. Environ. 114:2375–84
    [Google Scholar]
  43. 43.
    Havašová M, Ferenčík J, Jakuš R. 2017. Interactions between windthrow, bark beetles and forest management in the Tatra national parks. For. Ecol. Manag. 391:349–61
    [Google Scholar]
  44. 44.
    Hemmerling J, Pflugmacher D, Hostert P. 2021. Mapping temperate forest tree species using dense Sentinel-2 time series. Remote Sens. Environ. 267:112743
    [Google Scholar]
  45. 45.
    Hilker T, Coops NC, Coggins SB, Wulder MA, Brown M et al. 2009. Detection of foliage conditions and disturbance from multi-angular high spectral resolution remote sensing. Remote Sens. Environ. 113:421–34
    [Google Scholar]
  46. 46.
    Host TK, Russell MB, Windmuller-Campione MA, Slesak RA, Knight JF. 2020. Ash presence and abundance derived from composite Landsat and Sentinel-2 time series and lidar surface models in Minnesota, USA. . Remote Sens. 12:81341
    [Google Scholar]
  47. 47.
    Hu B, Li J, Wang J, Hall B 2014. The early detection of the emerald ash borer (EAB) using advanced geospatial technologies. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci XL-2:213–19
    [Google Scholar]
  48. 48.
    Huang HG, Qin WH, Liu QH. 2013. RAPID: a radiosity applicable to porous individual objects for directional reflectance over complex vegetated scenes. Remote Sens. Environ. 132:10221–37
    [Google Scholar]
  49. 49.
    Huang HG, Zhang ZY, Ni WJ, Chai L, Qin WH et al. 2018. Extending RAPID model to simulate forest microwave backscattering. Remote Sens. Environ. 217:10272–91
    [Google Scholar]
  50. 50.
    Huang K, Huang HG. 2019. Using ground-based LiDAR to detect shoot dieback: a case study on Yunnan pine shoots. Remote Sens. Lett. 10:7–9903–12
    [Google Scholar]
  51. 51.
    Hunt ER, Rock BN. 1989. Detection of changes in leaf water content using near- and middle-infrared reflectance. Remote Sens. Environ. 30:143–54
    [Google Scholar]
  52. 52.
    Huo LN, Persson H J, Lindberg E. 2021. Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: normalized distance red & SWIR (NDRS). Remote Sens. Environ. 255:112240
    [Google Scholar]
  53. 53.
    Huo LN, Zhang N, Zhang XL, Wu YS. 2019. Tree defoliation classification based on point distribution features derived from single-scan terrestrial laser scanning data. Ecol. Indic. 103:782–90
    [Google Scholar]
  54. 54.
    Huo LN, Zhang XL. 2019. A new method of equiangular sectorial voxelization of single-scan terrestrial laser scanning data and its applications in forest defoliation estimation. ISPRS J. Photogramm. Remote Sens. 151:302–12
    [Google Scholar]
  55. 55.
    Immitzer M, Atzberger C. 2014. Early detection of bark beetle infestation in Norway spruce (Picea abies, L.) using WorldView-2. Photogramm. Fernerkund. 5:351–67
    [Google Scholar]
  56. 56.
    Iordache M-D, Mantas V, Baltazar E, Pauly K, Lewyckyj N 2020. A machine learning approach to detecting pine wilt disease using airborne spectral imagery. Remote Sens 12:142280
    [Google Scholar]
  57. 57.
    Ismail R, Mutanga O. 2011. Discriminating the early stages of Sirex noctilio infestation using classification tree ensembles and shortwave infrared bands. Int. J. Remote Sens. 32:154249–66
    [Google Scholar]
  58. 58.
    Jin RH, Zhang HM, Lin P. 1991. Discussion on remote sensing monitoring of biological disasters. Remote Sens. Inf. 3:35–37
    [Google Scholar]
  59. 59.
    Junttila S, Holopainen M, Vastaranta M, Lyytikäinen-Saarenmaa P, Kaartinen H et al. 2019. The potential of dual-wavelength terrestrial Lidar in early detection of Ips typographus (L.) infestation—leaf water content as a proxy. Remote Sens. Environ. 231:111264
    [Google Scholar]
  60. 60.
    Kislov D, Korznikov K, Altman J, Vozmishcheva A, Krestov P. 2021. Extending deep learning approaches for forest disturbance segmentation on very high-resolution satellite images. Remote Sens. Ecol. Conserv. 7:355–68
    [Google Scholar]
  61. 61.
    Klouček T, Komárek J, Surový P, Hrach K, Janata P, Vašíček B. 2019. The use of UAV mounted sensors for precise detection of bark beetle infestation. Remote Sens 11:131561
    [Google Scholar]
  62. 62.
    Krzystek P, Serebryanyk A, Schnörr C, Červenka J, Heurich M. 2021. Large-scale mapping of tree species and dead trees in Šumava National Park and Bavarian Forest National Park using lidar and multispectral imagery. Remote Sens 12:4661
    [Google Scholar]
  63. 63.
    Latifi H, Dahms T, Beudert B, Heurich M, Kübert C, Dech S. 2018. Synthetic RapidEye data used for the detection of area-based spruce tree mortality induced by bark beetles. GISci. Remote Sens. 55:6839–59
    [Google Scholar]
  64. 64.
    Latifi H, Fassnacht FE, Schumann B, Dech S. 2014. Object-based extraction of bark beetle (Ips typographus L.) infestations using multi-date Landsat and SPOT satellite imagery. Prog. Phys. Geogr. 38:755–85
    [Google Scholar]
  65. 65.
    Lausch A, Heurich M, Fahse L. 2013. Spatio-temporal infestation patterns of Ips typographus (L.) in the Bavarian Forest National Park, Germany. . Ecol. Indic 31:73–81
    [Google Scholar]
  66. 66.
    Lausch A, Heurich M, Gordalla D, Dobner H-J, Gwillym-Margianto S, Salbach C. 2013. Forecasting potential bark beetle outbreaks based on spruce forest vitality using hyperspectral remote-sensing techniques at different scales. For. Ecol. Manag. 308:76–89
    [Google Scholar]
  67. 67.
    Li XY, Huang HG, Nikolay V, Chen L, Yan K, Shi J 2020. Extending the stochastic radiative transfer theory to simulate BRF over forests with heterogeneous distribution of damaged foliage inside of tree crowns. Remote Sens. Environ. 250:112040
    [Google Scholar]
  68. 68.
    Liang L, Chen YL, Hawbaker TJ, Zhu ZL, Gong P. 2014. Mapping mountain pine beetle mortality through growth trend analysis of time-series Landsat data. Remote Sens 6:65696–716
    [Google Scholar]
  69. 69.
    Lin QN, Huang HG, Chen L, Wang J, Huang K, Liu Y. 2021. Using the 3D model RAPID to invert the shoot dieback ratio of vertically heterogeneous Yunnan pine forests to detect beetle damage. Remote Sens. Environ. 260:6112475
    [Google Scholar]
  70. 70.
    Lin QN, Huang HG, Wang JX, Huang K, Liu YY. 2019. Detection of pine shoot beetle (PSB) stress on pine forests at individual tree level using UAV-based hyperspectral imagery and lidar. Remote Sens 11:212540
    [Google Scholar]
  71. 71.
    Lin QN, Huang HG, Yu LF, Wang JX. 2018. Detection of shoot beetle stress on Yunnan pine forest using a coupled LIBERTY2-INFORM simulation. Remote Sens 10:71133
    [Google Scholar]
  72. 72.
    Liu YJ, Zhan ZY, Ren LL, Ze SZ, Yu LF et al. 2021. Hyperspectral evidence of early-stage pine shoot beetle attack in Yunnan pine. For. Ecol. Manag. 497:119505
    [Google Scholar]
  73. 73.
    Luo YQ, Liu YJ, Huang HG, Yu LF, Ren LL. 2021. Pathway and method of forest health assessment using remote sensing technology. J. Beijing For. Univ. 43:91–13
    [Google Scholar]
  74. 74.
    Luz B, Crowley JK 2010. Identification of plant species by using high spatial and spectral resolution thermal infrared (8.0–13.5 μm) imagery. Remote Sens. Environ. 114:2404–13
    [Google Scholar]
  75. 75.
    Majdák A, Jakuš R, Blaženec M. 2021. Determination of differences in temperature regimes on healthy and bark-beetle colonized spruce trees using a handheld thermal camera. iForest 14:3203–11
    [Google Scholar]
  76. 76.
    Maltamo M, Bollandsas OM, Næsset E, Gobakken T, Packalén P. 2011. Different plot selection strategies for field training data in ALS-assisted forest inventory. Forestry 84:23–31
    [Google Scholar]
  77. 77.
    Meddens AJH, Hicke JA, Vierling LA. 2011. Evaluating the potential of multispectral imagery to map multiple stages of tree mortality. Remote Sens. Environ. 115:1632–42
    [Google Scholar]
  78. 78.
    Meddens AJH, Hicke JA, Vierling LA, Hudak AT. 2013. Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery. Remote Sens. Environ. 132:49–58
    [Google Scholar]
  79. 79.
    Meigs GW, Kennedy RE, Gray AN, Gregory MJ. 2015. Spatiotemporal dynamics of recent mountain pine beetle and western spruce budworm outbreaks across the Pacific Northwest Region, U.S.A. . For. Ecol. Manag. 339:71–86
    [Google Scholar]
  80. 80.
    Meng R, Dennison PE, Zhao F, Shendryk L, Rickert A et al. 2018. Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and LiDAR measurements. Remote Sens. Environ. 215:170–83
    [Google Scholar]
  81. 81.
    Meroni M, Busetto L, Colombo R, Guanter L, Moreno J, Verhoef W. 2010. Performance of spectral fitting methods for vegetation fluorescence quantification. Remote Sens. Environ. 114:2363–74
    [Google Scholar]
  82. 82.
    Meroni M, Colombo R. 2006. Leaf level detection of solar induced chlorophyll fluorescence by means of a subnanometer resolution spectroradiometer. Remote Sens. Environ. 103:4438–48
    [Google Scholar]
  83. 83.
    Mildrexler D, Zhao M, Heinsch F, Running S. 2007. A new satellite-based methodology for continental-scale disturbance detection. Ecol. Appl. 17:1235–50
    [Google Scholar]
  84. 84.
    Mohammed GH, Colombo R, Middleton EM, Rascher U, van der Tol C et al. 2019. Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress. Remote Sens. Environ. 231:111177
    [Google Scholar]
  85. 85.
    Moisen GG, Meyer MC, Schroeder TA, Liao X, Schleeweis KG et al. 2016. Shape selection in Landsat time series: a tool for monitoring forest dynamics. Glob. Change Biol. 22:3518–28
    [Google Scholar]
  86. 86.
    Mullen KE. 2016. Early detection of mountain pine beetle damage in Ponderosa pine forests of the Black Hills using hyperspectral and WorldView-2 data MA Thesis, Minn. State Univ. Mankato:
  87. 87.
    Mullen KE, Yuan F, Mitchell M. 2018. The mountain pine beetle epidemic in the Black Hills, South Dakota: the consequences of long-term fire policy, climate change and the use of remote sensing to enhance mitigation. J. Geogr. Geol. 10:169
    [Google Scholar]
  88. 88.
    Murfitt J, He Y, Yang J, Mui A, De Mille K. 2016. Ash decline assessment in emerald ash borer infested natural forests using high spatial resolution images. Remote Sens 8:3256
    [Google Scholar]
  89. 89.
    Netherer S, Panassiti B, Pennerstorfer J, Matthews B. 2019. Acute drought is an important driver of bark beetle infestation in Austrian Norway spruce stands. Front. For. Glob. Chang. 2:39
    [Google Scholar]
  90. 90.
    Nguyen HM, Begüm D, Dalponte M. 2019. A weighted SVM-based approach to tree species classification at individual tree crown level using LiDAR data. Remote Sens 11:242948
    [Google Scholar]
  91. 91.
    Nowak DJ, Pasek JE, Sequeira RA, Crane DE, Mastro VC. 2001. Potential effect of Anoplophora glabripennis (Coleoptera: Cerambycidae) on urban trees in the United States. J. Econ. Entomol. 94:1116–22
    [Google Scholar]
  92. 92.
    Öhrn P, Långström B, Lindelöw Å, Björklund N. 2014. Seasonal flight patterns of Ips typographus in southern Sweden and thermal sums required for emergence. Agric. For. Entomol. 16:147–57
    [Google Scholar]
  93. 93.
    Ortiz SM, Breidenbach J, Kändler G. 2013. Early detection of bark beetle green attack using TerraSAR-X and RapidEye data. Remote Sens 5:41912–31
    [Google Scholar]
  94. 94.
    Pflugmachera D, Cohenb W, Kennedy R. 2012. Using Landsat-derived disturbance history (1972–2010) to predict current forest structure. Remote Sens. Environ. 122:146–65
    [Google Scholar]
  95. 95.
    Pontius J, Hanavan RP, Hallett RA, Cook BD, Corp LA. 2017. High spatial resolution spectral unmixing for mapping ash species across a complex urban environment. Remote Sens. Environ. 199:360–69
    [Google Scholar]
  96. 96.
    Pontius J, Martin M, Plourde L, Hallett R. 2008. Ash decline assessment in emerald ash borer-infested regions: a test of tree-level, hyperspectral technologies. Remote Sens. Environ. 112:52665–76
    [Google Scholar]
  97. 97.
    Pu R, Landry S 2020. Mapping urban tree species by integrating multi-seasonal high resolution Pléiades satellite imagery with airborne LiDAR data. Urban For. Urban Green 53:126675
    [Google Scholar]
  98. 98.
    Qin J, Wang B, Wu YL, Lu Q, Zhu HC 2021. Identifying pine wood nematode disease using UAV images and deep learning algorithms. Remote Sens 13:2162
    [Google Scholar]
  99. 99.
    Rautiainen M, Lukeš P. 2015. Spectral contribution of understory to forest reflectance in a boreal site: an analysis of EO-1 Hyperion data. Remote Sens. Environ. 171:98–104
    [Google Scholar]
  100. 100.
    Rencz AN, Nemeth J. 1985. Detection of mountain pine beetle infestation using Landsat MSS and simulated thematic mapper data. Can. J. Remote Sens. 11:150–58
    [Google Scholar]
  101. 101.
    Roberge C, Wulff S, Reese H, Stähl G. 2016. Improving the precision of sample-based forest damage inventories through two-phase sampling and post-stratification using remotely sensed auxiliary information. Environ. Monit. Assess. 188:213
    [Google Scholar]
  102. 102.
    Rodman KC, Andrus RA, Butkiewicz CL, Chapman TB, Gill NS et al. 2021. Effects of bark beetle outbreaks on forest landscape pattern in the southern Rocky Mountains, U.S.A. Remote Sens 13:61089
    [Google Scholar]
  103. 103.
    Scherrer D, Bader KF, Körnera C. 2011. Drought-sensitivity ranking of deciduous tree species based on thermal imaging of forest canopies. Agric. For. Meteorol. 151:121632–40
    [Google Scholar]
  104. 104.
    Schroeder T, Wulder M, Healey S, Moisen GG. 2011. Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data. Remote Sens. Environ. 115:61421–33
    [Google Scholar]
  105. 105.
    Sebald J, Senf C, Seidl R. 2021. Human or natural? Landscape context improves the attribution of forest disturbances mapped from Landsat in Central Europe. Remote Sens. Environ. 262:2112502
    [Google Scholar]
  106. 106.
    Senf C, Pflugmacher D, Wulder MA, Hostert P. 2015. Characterizing spectral–temporal patterns of defoliator and bark beetle disturbances using Landsat time series. Remote Sens. Environ. 170:166–77
    [Google Scholar]
  107. 107.
    Senf C, Seidl R, Hostert P. 2017. Remote sensing of forest insect disturbances: current state and future directions. Int. J. Appl. Earth Obs. Geoinform. 60:49–60
    [Google Scholar]
  108. 108.
    Shen Q, Deng J, Liu XS, Huang HG. 2018. Prediction of bark beetles pests based on temperature vegetation dryness index. Trans. Chin. Soc. Agric. Eng. 34:9167–74
    [Google Scholar]
  109. 109.
    Smigaj M, Gaulton R, Suárez JC, Barr SL. 2019. Canopy temperature from an Unmanned Aerial Vehicle as an indicator of tree stress associated with red band needle blight severity. For. Ecol. Manag. 433:699–708
    [Google Scholar]
  110. 110.
    Solberg S, Næsset E, Hanssen KH, Christiansen E. 2006. Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning. Remote Sens. Environ. 102:364–76
    [Google Scholar]
  111. 111.
    Song L, Guanter L, Guan KY, You LZ, Huete A et al. 2018. Satellite sun-induced chlorophyll fluorescence detects early response of winter wheat to heat stress in the Indian Indo-Gangetic Plains. Glob. Change Biol. 24:94023–37
    [Google Scholar]
  112. 112.
    Stereńczak K, Kraszewski B, Mielcarek M, Piasecka Ż. 2017. Inventory of standing dead trees in the surroundings of communication routes—the contribution of remote sensing to potential risk assessments. For. Ecol. Manag. 402:76–91
    [Google Scholar]
  113. 113.
    Stereńczak K, Mielcarek M, Modzelewska A, Kraszewski B, Fassnacht FE, Hilszczański J. 2019. Intra-annual Ips typographus outbreak monitoring using a multi-temporal GIS analysis based on hyperspectral and ALS data in the Białowieża Forests. For. Ecol. Manag. 442:105–16
    [Google Scholar]
  114. 114.
    Stone C, Mohammed C. 2017. Application of remote sensing technologies for assessing planted forests damaged by insect pests and fungal pathogens: a review. Curr. For. Rep. 3:275–92
    [Google Scholar]
  115. 115.
    Syifa M, Park SJ, Lee CW. 2020. Detection of the pine wilt disease tree candidates for drone remote sensing using artificial intelligence techniques. . Engineering 6:8919–26
    [Google Scholar]
  116. 116.
    Tanase MA, Aponte C, Mermoz S, Bouvet A, Le Toan T, Heurich M 2018. Detection of windthrows and insect outbreaks by L-band SAR: a case study in the Bavarian Forest National Park. Remote Sens. Environ. 209:700–11
    [Google Scholar]
  117. 117.
    Ullah S, Schlerf M, Sikdmore A, Hecker C. 2012. Identifying plant species using mid-wave infrared (2.5–6 μm) and thermal infrared (8–14 μm) emissivity spectra. Remote Sens. Environ. 118:95–102
    [Google Scholar]
  118. 118.
    Valderrama-Landeros L, Flores-De-Santiago F, Kovacs JM, Flores-Verdugo F. 2018. An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme. Environ. Monit. Assess. 190:2323
    [Google Scholar]
  119. 119.
    Vorster AG, Evangelista PH, Stohlgren TJ, Kumar S, Rhoades CC et al. 2017. Severity of a mountain pine beetle outbreak across a range of stand conditions in Fraser Experimental Forest, Colorado, United States. For. Ecol. Manag. 389:116–26
    [Google Scholar]
  120. 120.
    Wang JX, Huang HG, Lin QN, Wang B, Huang K 2019. Shoot beetle damage to Pinus yunnanensis monitored by infrared thermal imaging at needle scale. Chin. J. Plant Ecol. 43:959–68
    [Google Scholar]
  121. 121.
    White JC, Wulder MA, Brooks D, Reich R, Wheate R. 2005. Detection of red attack stage mountain pine beetle infestation with high spatial resolution satellite imagery. Remote Sens. Environ. 96:340–51
    [Google Scholar]
  122. 122.
    Wu BZ, Liang AJ, Zhang HF, Zhu T, Zou Z et al. 2021. Application of conventional UAV-based high-throughput object detection to the early diagnosis of pine wilt disease by deep learning. For. Ecol. Manag. 486:2118986
    [Google Scholar]
  123. 123.
    Wulder MA, White JC, Coggins SB, Ortlepp SM, Coops NC et al. 2012. Digital high spatial resolution aerial imagery to support forest health monitoring: the mountain pine beetle context. J. Appl. Remote Sens. 6:06257
    [Google Scholar]
  124. 124.
    Wulder MA, White JC, Coops NC, Butson CR. 2008. Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring. Remote Sens. Environ. 112:2729–40
    [Google Scholar]
  125. 125.
    Xia L, Zhang RR, Chen LP, Li LL, Yi TC et al. 2021. Evaluation of deep learning segmentation models for detection of pine wilt disease in unmanned aerial vehicle images. Remote Sens 13:183594
    [Google Scholar]
  126. 126.
    Ye S, Rogan J, Zhu Z, Hawbaker TJ, Hart SJ et al. 2021. Detecting subtle change from dense Landsat time series: case studies of mountain pine beetle and spruce beetle disturbance. Remote Sens. Environ. 263:5112560
    [Google Scholar]
  127. 127.
    Yu LF, Huang JX, Zong SX, Huang HG, Luo YQ. 2018. Detecting shoot beetle damage on Yunnan pine using Landsat time-series data. Forests 9:139
    [Google Scholar]
  128. 128.
    Yu R, Luo YQ, Li HN, Yang LF, Huang HG et al. 2021. Three-dimensional convolutional neural network model for early detection of pine wilt disease using UAV-based hyperspectral images. Remote Sens 13:204065
    [Google Scholar]
  129. 129.
    Yu R, Luo YQ, Zhou Q, Zhang XD, Ren LL. 2021. Early detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery. For. Ecol. Manag. 497:4119493
    [Google Scholar]
  130. 130.
    Yu R, Luo YQ, Zhou Q, Zhang XD, Wu DW, Ren LL. 2021. A machine learning algorithm to detect pine wilt disease using UAV-based hyperspectral imagery and LiDAR data at the tree level. Int. J. Appl. Earth Obs. Geoinf. 101:1102363
    [Google Scholar]
  131. 131.
    Yu R, Ren LL, Luo YQ. 2021. Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery. For. Ecosyst. 8:3583–601
    [Google Scholar]
  132. 132.
    Zarco-Tejada PJ, Camino C, Beck PSA, Calderon R, Hornero A et al. 2018. Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nat. Plants 4:7432–39
    [Google Scholar]
  133. 133.
    Zhan ZY, Yu LF, Li Z, Ren LL, Gao BT et al. 2020. Combining GF-2 and Sentinel-2 images to detect tree mortality caused by red turpentine beetle during the early outbreak stage in North China. Forests 11:2172
    [Google Scholar]
  134. 134.
    Zhang B, Ye HC, Lu W, Huang WJ, Wu B et al. 2021. A spatiotemporal change detection method for monitoring pine wilt disease in a complex landscape using high-resolution remote sensing imagery. Remote Sens 13:112083
    [Google Scholar]
  135. 135.
    Zhang B, Zhao L, Zhang XL. 2020. Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images. Remote Sens. Environ. 247:111938
    [Google Scholar]
  136. 136.
    Zhang JG, Han HQ, Hu CH, Luo YQ. 2018. Identification method of Pinus yunnanensis pest area based on UAV multispectral images. Trans. Chin. Soc. Agric. Mach. 49:5249–55
    [Google Scholar]
  137. 137.
    Zhang KW, Hu BX, Robinson J 2014. Early detection of emerald ash borer infestation using multisourced data: a case study in the town of Oakville, Ontario, Canada. J. Appl. Remote Sens. 8:1083602
    [Google Scholar]
  138. 138.
    Zhang LY, Chen QC, Zhang XB. 2002. Studies on the morphological characters and bionomics of Dendroctonus valens Leconte. Sci. Silv. Sin. 38:495–99
    [Google Scholar]
  139. 139.
    Zhang M, Gong M, He H, Zhu S. 2020. Symmetric all convolutional neural-network-based unsupervised feature extraction for hyperspectral images classification. IEEE Trans. Cybern. 52:52981–93
    [Google Scholar]
  140. 140.
    Zhang N, Zhang XL, Yang GJ, Zhu CH, Huo LN, Feng HK. 2018. Assessment of defoliation during the Dendrolimus tabulaeformis Tsai et Liu disaster outbreak using UAV-based hyperspectral images. Remote Sens. Environ. 217:323–39
    [Google Scholar]
  141. 141.
    Zhao YX, Dong Y, Xu ZH. 2004. Bionomics and geographical distribution of Monochamus alternatus Hope (Coleoptera: Cerambycidae) in Yunnan Province. For. Pest Dis. 23:513–16
    [Google Scholar]
  142. 142.
    Zhou K, Cao L. 2021. The status and prospects of remote sensing applications in precision silviculture. Nat. Remote Sens. Bull. 25:1423–38
    [Google Scholar]
  143. 143.
    Zhou Q, Zhang XD, Yu LF, Ren LL, Luo YQ. 2021. Combining WV-2 images and tree physiological factors to detect damage stages of Populus gansuensis by Asian longhorned beetle (Anoplophora glabripennis) at the tree level. For. Ecosyst. 8:35
    [Google Scholar]
/content/journals/10.1146/annurev-ento-120220-125410
Loading
/content/journals/10.1146/annurev-ento-120220-125410
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error