24.4.13.10.1 Forest Change Evaluation, Bark Beetle, Pine Shoot Beetle, Other Insects

Chapter Contents (Back)
Forest Changes. Forest. Bark Beetle. Pine Wilt. For insects themselves:
See also Insects, Other Pests, Detection, Identification. Not trees:
See also Plant Disease Analysis, General Plant Diseasses.

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Elsevier DOI 1408
Random forest BibRef

Immitzer, M.[Markus], Atzberger, C.[Clement],
Early Detection of Bark Beetle Infestation in Norway Spruce (Picea abies, L.) using WorldView-2 Data,
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Liang, L.[Lu], Chen, Y.L.[Yan-Lei], Hawbaker, T.J.[Todd J.], Zhu, Z.L.[Zhi-Liang], Gong, P.[Peng],
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Näsi, R.[Roope], Honkavaara, E.[Eija], Lyytikäinen-Saarenmaa, P.[Päivi], Blomqvist, M.[Minna], Litkey, P.[Paula], Hakala, T.[Teemu], Viljanen, N.[Niko], Kantola, T.[Tuula], Tanhuanpää, T.[Topi], Holopainen, M.[Markus],
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Anderson, T.[Taylor], Dragicevic, S.[Suzana],
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Murfitt, J.[Justin], He, Y.H.[Yu-Hong], Yang, J.[Jian], Mui, A.[Amy], de Mille, K.[Kevin],
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Hais, M.[Martin], Wild, J.[Jan], Berec, L.[Ludek], Bruna, J.[Josef], Kennedy, R.[Robert], Braaten, J.[Justin], Brož, Z.[Zdenek],
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Anees, A.[Asim], Aryal, J.[Jagannath], O'Reilly, M.M.[Malgorzata M.], Gale, T.J.[Timothy J.], Wardlaw, T.[Tim],
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PandRS(122), No. 1, 2016, pp. 167-178.
Elsevier DOI 1612
Change detection BibRef

Lin, Q.[Qinan], Huang, H.G.[Hua-Guo], Yu, L.F.[Lin-Feng], Wang, J.X.[Jing-Xu],
Detection of Shoot Beetle Stress on Yunnan Pine Forest Using a Coupled LIBERTY2-INFORM Simulation,
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DOI Link 1808
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Housman, I.W.[Ian W.], Chastain, R.A.[Robert A.], Finco, M.V.[Mark V.],
An Evaluation of Forest Health Insect and Disease Survey Data and Satellite-Based Remote Sensing Forest Change Detection Methods: Case Studies in the United States,
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Chávez, R.O.[Roberto O.], Rocco, R.[Ronald], Gutiérrez, Á.G.[Álvaro G.], Dörner, M.[Marcelo], Estay, S.A.[Sergio A.],
A Self-Calibrated Non-Parametric Time Series Analysis Approach for Assessing Insect Defoliation of Broad-Leaved Deciduous Nothofagus pumilio Forests,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
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Abdullah, H.[Haidi], Darvishzadeh, R.[Roshanak], Skidmore, A.K.[Andrew K.], Heurich, M.[Marco],
Sensitivity of Landsat-8 OLI and TIRS Data to Foliar Properties of Early Stage Bark Beetle (Ips typographus, L.) Infestation,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Safonova, A.[Anastasiia], Tabik, S.[Siham], Alcaraz-Segura, D.[Domingo], Rubtsov, A.[Alexey], Maglinets, Y.[Yuriy], Herrera, F.[Francisco],
Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
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Kloucek, T.[Tomáš], Komárek, J.[Jan], Surový, P.[Peter], Hrach, K.[Karel], Janata, P.[Premysl], Vašícek, B.[Bedrich],
The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Lin, Q.[Qinan], Huang, H.G.[Hua-Guo], Wang, J.X.[Jing-Xu], Huang, K.[Kan], Liu, Y.Y.[Yang-Yang],
Detection of Pine Shoot Beetle (PSB) Stress on Pine Forests at Individual Tree Level using UAV-Based Hyperspectral Imagery and Lidar,
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DOI Link 1911
BibRef

Iordache, M.D.[Marian-Daniel], Mantas, V.[Vasco], Baltazar, E.[Elsa], Pauly, K.[Klaas], Lewyckyj, N.[Nicolas],
A Machine Learning Approach to Detecting Pine Wilt Disease Using Airborne Spectral Imagery,
RS(12), No. 14, 2020, pp. xx-yy.
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Fernandez-Carrillo, A.[Angel], Patocka, Z.[Zdenek], Dobrovolný, L.[Lumír], Franco-Nieto, A.[Antonio], Revilla-Romero, B.[Beatriz],
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DOI Link 2011
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Minarík, R.[Robert], Langhammer, J.[Jakub], Lendzioch, T.[Theodora],
Automatic Tree Crown Extraction from UAS Multispectral Imagery for the Detection of Bark Beetle Disturbance in Mixed Forests,
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DOI Link 2012
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Zhang, B.Y.[Bi-Yao], Ye, H.C.[Hui-Chun], Lu, W.[Wei], Huang, W.J.[Wen-Jiang], Wu, B.[Bo], Hao, Z.Q.[Zhuo-Qing], Sun, H.[Hong],
A Spatiotemporal Change Detection Method for Monitoring Pine Wilt Disease in a Complex Landscape Using High-Resolution Remote Sensing Imagery,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Minarík, R.[Robert], Langhammer, J.[Jakub], Lendzioch, T.[Theodora],
Detection of Bark Beetle Disturbance at Tree Level Using UAS Multispectral Imagery and Deep Learning,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
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Boucher, P.B.[Peter Brehm], Hancock, S.[Steven], Orwig, D.A.[David A], Duncanson, L.[Laura], Armston, J.[John], Tang, H.[Hao], Krause, K.[Keith], Cook, B.[Bruce], Paynter, I.[Ian], Li, Z.[Zhan], Elmes, A.[Arthur], Schaaf, C.[Crystal],
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Zhong, Y.[Yuan], Hu, B.X.[Bao-Xin], Hall, G.B.[G. Brent], Hoque, F.[Farah], Xu, W.[Wei], Gao, X.[Xin],
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Hu, B.X., Li, J., Wang, J., Hall, G.B.,
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Qin, J.[Jun], Wang, B.[Biao], Wu, Y.[Yanlan], Lu, Q.[Qi], Zhu, H.C.[Hao-Chen],
Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
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Klimetzek, D.[Dietrich], Stancioiu, P.T.[Petru Tudor], Paraschiv, M.[Marius], Nita, M.D.[Mihai Daniel],
Ecological Monitoring with Spy Satellite Images: The Case of Red Wood Ants in Romania,
RS(13), No. 3, 2021, pp. xx-yy.
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Rodman, K.C.[Kyle C.], Andrus, R.A.[Robert A.], Butkiewicz, C.L.[Cori L.], Chapman, T.B.[Teresa B.], Gill, N.S.[Nathan S.], Harvey, B.J.[Brian J.], Kulakowski, D.[Dominik], Tutland, N.J.[Niko J.], Veblen, T.T.[Thomas T.], Hart, S.J.[Sarah J.],
Effects of Bark Beetle Outbreaks on Forest Landscape Pattern in the Southern Rocky Mountains, U.S.A.,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
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Gdulová, K.[Katerina], Marešová, J.[Jana], Barták, V.[Vojtech], Szostak, M.[Marta], Cervenka, J.[Jaroslav], Moudrý, V.[Vítezslav],
Use of TanDEM-X and SRTM-C Data for Detection of Deforestation Caused by Bark Beetle in Central European Mountains,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
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Migas-Mazur, R.[Robert], Kycko, M.[Marlena], Zwijacz-Kozica, T.[Tomasz], Zagajewski, B.[Bogdan],
Assessment of Sentinel-2 Images, Support Vector Machines and Change Detection Algorithms for Bark Beetle Outbreaks Mapping in the Tatra Mountains,
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DOI Link 2109
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Xia, L.[Lang], Zhang, R.R.[Rui-Rui], Chen, L.P.[Li-Ping], Li, L.L.[Long-Long], Yi, T.C.[Tong-Chuan], Wen, Y.[Yao], Ding, C.C.[Chen-Chen], Xie, C.C.[Chun-Chun],
Evaluation of Deep Learning Segmentation Models for Detection of Pine Wilt Disease in Unmanned Aerial Vehicle Images,
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Hyperspectral Imaging Combined with Machine Learning for the Detection of Fusiform Rust Disease Incidence in Loblolly Pine Seedlings,
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Mountain Landscape Dynamics after Large Wind and Bark Beetle Disasters and Subsequent Logging: Case Studies from the Carpathians,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
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Yu, R.[Run], Luo, Y.Q.[You-Qing], Li, H.N.[Hao-Nan], Yang, L.Y.[Li-Yuan], Huang, H.G.[Hua-Guo], Yu, L.F.[Lin-Feng], Ren, L.[Lili],
Three-Dimensional Convolutional Neural Network Model for Early Detection of Pine Wilt Disease Using UAV-Based Hyperspectral Images,
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Hellwig, F.M.[Florian M.], Stelmaszczuk-Górska, M.A.[Martyna A.], Dubois, C.[Clémence], Wolsza, M.[Marco], Truckenbrodt, S.C.[Sina C.], Sagichewski, H.[Herbert], Chmara, S.[Sergej], Bannehr, L.[Lutz], Lausch, A.[Angela], Schmullius, C.[Christiane],
Mapping European Spruce Bark Beetle Infestation at Its Early Phase Using Gyrocopter-Mounted Hyperspectral Data and Field Measurements,
RS(13), No. 22, 2021, pp. xx-yy.
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Elsevier DOI 2112
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Zhang, Y.[Yahao], Dian, Y.[Yuanyong], Zhou, J.J.[Jing-Jing], Peng, S.[Shoulian], Hu, Y.[Yue], Hu, L.[Lei], Han, Z.[Zemin], Fang, X.W.[Xin-Wei], Cui, H.X.[Hong-Xia],
Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Abdollahnejad, A.[Azadeh], Panagiotidis, D.[Dimitrios], Surový, P.[Peter], Modlinger, R.[Roman],
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RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
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Xi, G.L.[Gui-Lin], Huang, X.J.[Xiao-Jun], Xie, Y.W.[Yao-Wen], Gang, B.[Bao], Bao, Y.[Yuhai], Dashzebeg, G.[Ganbat], Nanzad, T.[Tsagaantsooj], Dorjsuren, A.[Altanchimeg], Enkhnasan, D.[Davaadorj], Ariunaa, M.[Mungunkhuyag],
Detection of Larch Forest Stress from Jas's Larch Inchworm (Erannis jacobsoni Djak) Attack Using Hyperspectral Remote Sensing,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
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You, J.[Jie], Zhang, R.[Ruirui], Lee, J.[Joonwhoan],
A Deep Learning-Based Generalized System for Detecting Pine Wilt Disease Using RGB-Based UAV Images,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
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Junttila, S.[Samuli], Näsi, R.[Roope], Koivumäki, N.[Niko], Imangholiloo, M.[Mohammad], Saarinen, N.[Ninni], Raisio, J.[Juha], Holopainen, M.[Markus], Hyyppä, H.[Hannu], Hyyppä, J.[Juha], Lyytikäinen-Saarenmaa, P.[Päivi], Vastaranta, M.[Mikko], Honkavaara, E.[Eija],
Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
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Huang, J.X.[Ji-Xia], Lu, X.[Xiao], Chen, L.Y.[Li-Yuan], Sun, H.[Hong], Wang, S.H.[Shao-Hua], Fang, G.F.[Guo-Fei],
Accurate Identification of Pine Wood Nematode Disease with a Deep Convolution Neural Network,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
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Gao, B.T.[Bing-Tao], Yu, L.F.[Lin-Feng], Ren, L.[Lili], Zhan, Z.Y.[Zhong-Yi], Luo, Y.Q.[You-Qing],
Early Detection of Dendroctonus valens Infestation with Machine Learning Algorithms Based on Hyperspectral Reflectance,
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Retrieving the Infected Area of Pine Wilt Disease-Disturbed Pine Forests from Medium-Resolution Satellite Images Using the Stochastic Radiative Transfer Theory,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
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Zhou, Q.[Quan], Yu, L.F.[Lin-Feng], Zhang, X.D.[Xu-Dong], Liu, Y.J.[Yu-Jie], Zhan, Z.Y.[Zhong-Yi], Ren, L.[Lili], Luo, Y.Q.[You-Qing],
Fusion of UAV Hyperspectral Imaging and LiDAR for the Early Detection of EAB Stress in Ash and a New EAB Detection Index: NDVI(776,678),
RS(14), No. 10, 2022, pp. xx-yy.
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Yu, L.F.[Lin-Feng], Zhan, Z.Y.[Zhong-Yi], Zhou, Q.[Quan], Gao, B.T.[Bing-Tao], Ren, L.[Lili], Huang, H.G.[Hua-Guo], Luo, Y.Q.[You-Qing],
Climate Drivers of Pine Shoot Beetle Outbreak Dynamics in Southwest China,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
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Dalponte, M.[Michele], Solano-Correa, Y.T.[Yady Tatiana], Frizzera, L.[Lorenzo], Gianelle, D.[Damiano],
Mapping a European Spruce Bark Beetle Outbreak Using Sentinel-2 Remote Sensing Data,
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Estimating Tree Health Decline Caused by Ips typographus L. from UAS RGB Images Using a Deep One-Stage Object Detection Neural Network,
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Remote Sensing Monitoring of Pine Wilt Disease Based on Time-Series Remote Sensing Index,
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Detection of the Monitoring Window for Pine Wilt Disease Using Multi-Temporal UAV-Based Multispectral Imagery and Machine Learning Algorithms,
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Hofinger, P.[Peter], Klemmt, H.J.[Hans-Joachim], Ecke, S.[Simon], Rogg, S.[Steffen], Dempewolf, J.[Jan],
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Detecting Individual Plants Infected with Pine Wilt Disease Using Drones and Satellite Imagery: A Case Study in Xianning, China,
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Xu, D.[Dong], Lu, Y.W.[Yu-Wei], Liang, H.[Heng], Lu, Z.[Zhen], Yu, L.[Lejun], Liu, Q.[Qian],
Areca Yellow Leaf Disease Severity Monitoring Using UAV-Based Multispectral and Thermal Infrared Imagery,
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Li, H.C.[Hao-Cheng], Chen, L.[Long], Yao, Z.Q.[Zong-Qi], Li, N.[Niwen], Long, L.[Lin], Zhang, X.L.[Xiao-Li],
Intelligent Identification of Pine Wilt Disease Infected Individual Trees Using UAV-Based Hyperspectral Imagery,
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Estay, S.A.[Sergio A.], Chávez, R.O.[Roberto O.], Lastra, J.A.[José A.], Rocco, R.[Ronald], Gutiérrez, Á.G.[Álvaro G.], Decuyper, M.[Mathieu],
MODIS Time Series Reveal New Maximum Records of Defoliated Area by Ormiscodes amphimone in Deciduous Nothofagus Forests, Southern Chile,
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Detection Method of Infected Wood on Digital Orthophoto Map-Digital Surface Model Fusion Network,
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Turkulainen, E.[Emma], Honkavaara, E.[Eija], Näsi, R.[Roope], Oliveira, R.A.[Raquel A.], Hakala, T.[Teemu], Junttila, S.[Samuli], Karila, K.[Kirsi], Koivumäki, N.[Niko], Pelto-Arvo, M.[Mikko], Tuviala, J.[Johanna], Östersund, M.[Madeleine], Pölönen, I.[Ilkka], Lyytikäinen-Saarenmaa, P.[Päivi],
Comparison of Deep Neural Networks in the Classification of Bark Beetle-Induced Spruce Damage Using UAS Images,
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Marvasti-Zadeh, S.M.[S. Mojtaba], Goodsman, D.[Devin], Ray, N.[Nilanjan], Erbilgin, N.[Nadir],
Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review,
Surveys(56), No. 4, November 2023, pp. xx-yy.
DOI Link 2312
Survey, Bark Beetle. deep learning, machine learning, remote sensing, early detection, Bark beetles BibRef

Gao, S.[Sheng], Chen, F.[Fulong], Wang, Q.[Qin], Shi, P.[Pilong], Zhou, W.[Wei], Zhu, M.[Meng],
Susceptibility Mapping of Unhealthy Trees in Jiuzhaigou Valley Biosphere Reserve,
RS(15), No. 23, 2023, pp. 5516.
DOI Link 2312
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Hrdina, M.[Marek], Surový, P.[Peter],
Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method,
RS(15), No. 24, 2023, pp. 5712.
DOI Link 2401
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Tan, C.[Cheng], Lin, Q.[Qinan], Du, H.Q.[Hua-Qiang], Chen, C.[Chao], Hu, M.[Mengchen], Chen, J.J.[Jin-Jin], Huang, Z.[Zihao], Xu, Y.X.[Yan-Xin],
Detection of the Infection Stage of Pine Wilt Disease and Spread Distance Using Monthly UAV-Based Imagery and a Deep Learning Approach,
RS(16), No. 2, 2024, pp. 364.
DOI Link 2402
BibRef

Camarretta, N.[Nicolò], Pearse, G.D.[Grant D.], Steer, B.S.C.[Benjamin S. C.], McLay, E.[Emily], Fraser, S.[Stuart], Watt, M.S.[Michael S.],
Automatic Detection of Phytophthora pluvialis Outbreaks in Radiata Pine Plantations Using Multi-Scene, Multi-Temporal Satellite Imagery,
RS(16), No. 2, 2024, pp. 338.
DOI Link 2402
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Crosby, M.K.[Michael K.], McConnell, T.E.[T. Eric], Holderieath, J.J.[Jason J.], Meeker, J.R.[James R.], Steiner, C.A.[Chris A.], Strom, B.L.[Brian L.], Johnson, C.W.[Crawford Wood],
The Use of High-Resolution Satellite Imagery to Determine the Status of a Large-Scale Outbreak of Southern Pine Beetle,
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DOI Link 2402
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Sun, L.K.[Le-Kang], Zhang, L.[Li], Dai, Q.[Qiang], Li, Y.F.[Yue-Feng],
FP60 and FSNet: A Benchmark Dataset and a Family-Species Network for Forestry Pest Recognition,
ICPR22(4850-4856)
IEEE DOI 2212
Training, Image recognition, Insects, Taxonomy, Forestry, Benchmark testing, Stability analysis BibRef

Honkavaara, E., Näsi, R., Oliveira, R., Viljanen, N., Suomalainen, J., Khoramshahi, E., Hakala, T., Nevalainen, O., Markelin, L., Vuorinen, M., Kankaanhuhta, V., Lyytikäinen-Saarenmaa, P., Haataja, L.,
Using Multitemporal Hyper- and Multispectral UAV Imaging for Detecting Bark Beetle Infestation on Norway Spruce,
ISPRS20(B3:429-434).
DOI Link 2012
BibRef

Zhou, X., Liao, L., Cheng, D., Chen, X., Huang, Q.,
Extraction of the Individual Tree Infected By Pine Wilt Disease Using Unmanned Aerial Vehicle Optical Imagery,
ISPRS20(B3:247-252).
DOI Link 2012
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Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Deforestation, Degradation .


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