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

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

Marx, A.[Alexander],
Detection and Classification of Bark Beetle Infestation in Pure Norway Spruce Stands with Multi-temporal RapidEye Imagery and Data Mining Techniques,
PFG(2010), No. 4, 2010, pp. 243-252.
WWW Link. 1211
BibRef

Ortiz, S., Breidenbach, J., Kändler, G.,
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RS(5), No. 4, April 2013, pp. 1912-1931.
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BibRef

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Adelabu, S.[Samuel], Mutanga, O.[Onisimo], Adam, E.[Elhadi],
Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels,
PandRS(95), No. 1, 2014, pp. 34-41.
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,
PFG(2014), No. 5, 2014, pp. 351-367.
DOI Link 1411
BibRef

Liang, L.[Lu], Chen, Y.L.[Yan-Lei], Hawbaker, T.J.[Todd J.], Zhu, Z.L.[Zhi-Liang], Gong, P.[Peng],
Mapping Mountain Pine Beetle Mortality through Growth Trend Analysis of Time-Series Landsat Data,
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DOI Link 1407
BibRef

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],
Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level,
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DOI Link 1512
BibRef

Anderson, T.[Taylor], Dragicevic, S.[Suzana],
A Geosimulation Approach for Data Scarce Environments: Modeling Dynamics of Forest Insect Infestation across Different Landscapes,
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DOI Link 1603
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Murfitt, J.[Justin], He, Y.H.[Yu-Hong], Yang, J.[Jian], Mui, A.[Amy], de Mille, K.[Kevin],
Ash Decline Assessment in Emerald Ash Borer Infested Natural Forests Using High Spatial Resolution Images,
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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],
Landsat Imagery Spectral Trajectories: Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance,
RS(8), No. 8, 2016, pp. 687.
DOI Link 1609
BibRef

Anees, A.[Asim], Aryal, J.[Jagannath], O'Reilly, M.M.[Malgorzata M.], Gale, T.J.[Timothy J.], Wardlaw, T.[Tim],
A robust multi-kernel change detection framework for detecting leaf beetle defoliation using Landsat 7 ETM+ data,
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,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
BibRef

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,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
BibRef

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
BibRef

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
BibRef

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,
RS(11), No. 21, 2019, pp. xx-yy.
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.
DOI Link 2007
BibRef

Fernandez-Carrillo, A.[Angel], Patocka, Z.[Zdenek], Dobrovolný, L.[Lumír], Franco-Nieto, A.[Antonio], Revilla-Romero, B.[Beatriz],
Monitoring Bark Beetle Forest Damage in Central Europe. A Remote Sensing Approach Validated with Field Data,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

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,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
BibRef

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
BibRef

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],
Detecting Change in Forest Structure with Simulated GEDI Lidar Waveforms: A Case Study of the Hemlock Woolly Adelgid (HWA; Adelges tsugae) Infestation,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004
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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],
A Generalized Linear Mixed Model Approach to Assess Emerald Ash Borer Diffusion,
IJGI(9), No. 7, 2020, pp. xx-yy.
DOI Link 2007
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Hu, B.X., Li, J., Wang, J., Hall, G.B.,
The Early Detection of the Emerald Ash Borer (EAB) Using Advanced Geospacial Technologies,
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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
BibRef

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.
DOI Link 2102
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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
BibRef

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,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

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,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
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Pandey, P.[Piyush], Payn, K.G.[Kitt G.], Lu, Y.Z.[Yu-Zhen], Heine, A.J.[Austin J.], Walker, T.D.[Trevor D.], Acosta, J.J.[Juan J.], Young, S.[Sierra],
Hyperspectral Imaging Combined with Machine Learning for the Detection of Fusiform Rust Disease Incidence in Loblolly Pine Seedlings,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
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Faltan, V.[Vladimír], Petrovic, F.[František], Gábor, M.[Marián], Šagát, V.[Vladimír], Hruška, M.[Matej],
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,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
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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.
DOI Link 2112
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Kagan, D.[Dima], Fuhrmann Alpert, G.[Galit], Fire, M.[Michael],
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PandRS(182), 2021, pp. 122-133.
Elsevier DOI 2112
Data science, Data fusion BibRef

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],
Investigating the Correlation between Multisource Remote Sensing Data for Predicting Potential Spread of IPS typographus L. Spots in Healthy Trees,
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
BibRef

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
BibRef

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
BibRef

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,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
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Li, X.Y.[Xiao-Yao], Tong, T.[Tong], Luo, T.[Tao], Wang, J.X.[Jing-Xu], Rao, Y.M.[Yue-Ming], Li, L.Y.[Lin-Yuan], Jin, D.[Decai], Wu, D.[Dewei], Huang, H.G.[Hua-Guo],
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
BibRef

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.
DOI Link 2206
Emerald Ash Borer. BibRef

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,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
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Han, Z.M.[Ze-Min], Hu, W.J.[Wen-Jie], Peng, S.L.[Shou-Lian], Lin, H.R.[Hao-Ran], Zhang, J.[Jian], Zhou, J.J.[Jing-Jing], Wang, P.C.[Peng-Cheng], Dian, Y.Y.[Yuan-Yong],
Detection of Standing Dead Trees after Pine Wilt Disease Outbreak with Airborne Remote Sensing Imagery by Multi-Scale Spatial Attention Deep Learning and Gaussian Kernel Approach,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
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Allen, B.[Benjamin], Dalponte, M.[Michele], Ørka, H.O.[Hans Ole], Næsset, E.[Erik], Puliti, S.[Stefano], Astrup, R.[Rasmus], Gobakken, T.[Terje],
UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce,
RS(14), No. 15, 2022, pp. xx-yy.
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A Comparison of Analytical Approaches for the Spectral Discrimination and Characterisation of Mite Infestations on Banana Plants,
RS(14), No. 21, 2022, pp. xx-yy.
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Satellite Remote Sensing Identification of Discolored Standing Trees for Pine Wilt Disease Based on Semi-Supervised Deep Learning,
RS(14), No. 23, 2022, pp. xx-yy.
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Simovic, I.[Isidora], Šikoparija, B.[Branko], Panic, M.[Marko], Radulovic, M.[Mirjana], Lugonja, P.[Predrag],
Remote Sensing of Poplar Phenophase and Leaf Miner Attack in Urban Forests,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
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Kanerva, H.[Heini], Honkavaara, E.[Eija], Näsi, R.[Roope], Hakala, T.[Teemu], Junttila, S.[Samuli], Karila, K.[Kirsi], Koivumäki, N.[Niko], Oliveira, R.A.[Raquel Alves], Pelto-Arvo, M.[Mikko], Pölönen, I.[Ilkka], Tuviala, J.[Johanna], Östersund, M.[Madeleine], Lyytikäinen-Saarenmaa, P.[Päivi],
Estimating Tree Health Decline Caused by Ips typographus L. from UAS RGB Images Using a Deep One-Stage Object Detection Neural Network,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
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Long, L.[Lin], Chen, Y.Y.[Yuan-Yuan], Song, S.J.[Shao-Jun], Zhang, X.L.[Xiao-Li], Jia, X.[Xiang], Lu, Y.G.[Ya-Gang], Liu, G.[Gao],
Remote Sensing Monitoring of Pine Wilt Disease Based on Time-Series Remote Sensing Index,
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Wu, D.[Dewei], Yu, L.F.[Lin-Feng], Yu, R.[Run], Zhou, Q.[Quan], Li, J.X.[Jia-Xing], Zhang, X.D.[Xu-Dong], Ren, L.[Lili], Luo, Y.Q.[You-Qing],
Detection of the Monitoring Window for Pine Wilt Disease Using Multi-Temporal UAV-Based Multispectral Imagery and Machine Learning Algorithms,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
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Hofinger, P.[Peter], Klemmt, H.J.[Hans-Joachim], Ecke, S.[Simon], Rogg, S.[Steffen], Dempewolf, J.[Jan],
Application of YOLOv5 for Point Label Based Object Detection of Black Pine Trees with Vitality Losses in UAV Data,
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Cai, P.H.[Pei-Hua], Chen, G.Z.[Guan-Zhou], Yang, H.[Haobo], Li, X.W.[Xian-Wei], Zhu, K.[Kun], Wang, T.[Tong], Liao, P.[Puyun], Han, M.[Mengdi], Gong, Y.F.[Yuan-Fu], Wang, Q.[Qing], Zhang, X.D.[Xiao-Dong],
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,
RS(15), No. 12, 2023, pp. xx-yy.
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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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Kosarevych, R.[Rostyslav], Jonek-Kowalska, I.[Izabela], Rusyn, B.[Bohdan], Sachenko, A.[Anatoliy], Lutsyk, O.[Oleksiy],
Analysing Pine Disease Spread Using Random Point Process by Remote Sensing of a Forest Stand,
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DOI Link 2309
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Wang, G.B.[Guang-Biao], Zhao, H.B.[Hong-Bo], Chang, Q.[Qing], Lyu, S.C.[Shu-Chang], Liu, B.[Binghao], Wang, C.L.[Chun-Lei], Feng, W.[Wenquan],
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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DOI Link 2310
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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
BibRef

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
BibRef

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
BibRef

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,
RS(16), No. 3, 2024, pp. 582.
DOI Link 2402
BibRef

Watt, M.S.[Michael S.], Estarija, H.J.C.[Honey Jane C.], Bartlett, M.[Michael], Main, R.[Russell], Pasquini, D.[Dalila], Yorston, W.[Warren], McLay, E.[Emily], Zhulanov, M.[Maria], Dobbie, K.[Kiryn], Wardhaugh, K.[Katherine], Hossain, Z.[Zulfikar], Fraser, S.[Stuart], Buddenbaum, H.[Henning],
Early Detection of Myrtle Rust on Pohutukawa Using Indices Derived from Hyperspectral and Thermal Imagery,
RS(16), No. 6, 2024, pp. 1050.
DOI Link 2403
BibRef

Shrestha, A.[Abhinav], Hicke, J.A.[Jeffrey A.], Meddens, A.J.H.[Arjan J. H.], Karl, J.W.[Jason W.], Stahl, A.T.[Amanda T.],
Evaluating a Novel Approach to Detect the Vertical Structure of Insect Damage in Trees Using Multispectral and Three-Dimensional Data from Drone Imagery in the Northern Rocky Mountains, USA,
RS(16), No. 8, 2024, pp. 1365.
DOI Link 2405
BibRef

Watt, M.S.[Michael S.], Holdaway, A.[Andrew], Watt, P.[Pete], Pearse, G.D.[Grant D.], Palmer, M.E.[Melanie E.], Steer, B.S.C.[Benjamin S. C.], Camarretta, N.[Nicolò], McLay, E.[Emily], Fraser, S.[Stuart],
Early Prediction of Regional Red Needle Cast Outbreaks Using Climatic Data Trends and Satellite-Derived Observations,
RS(16), No. 8, 2024, pp. 1401.
DOI Link 2405
BibRef

Zwieback, S., Young-Robertson, J., Robertson, M., Tian, Y., Chang, Q., Morris, M., White, J., Moan, J.,
Low-severity spruce beetle infestation mapped from high-resolution satellite imagery with a convolutional network,
PandRS(212), 2024, pp. 412-421.
Elsevier DOI 2406
Forestry, Insect outbreak, Deep learning, Satellite image BibRef


Jemaa, H.[Hela], Bouachir, W.[Wassim], Leblon, B.[Brigitte], LaRocque, A.[Armand], Haddadi, A.[Ata], Bouguila, N.[Nizar],
Tree Health Assessment from UAV Images: Improving Object Detection and Classification Using Hard Negative Mining and Semi-Supervised Autoencoder,
CRV23(312-319)
IEEE DOI 2406
Surveys, Vegetation mapping, Vegetation, Object detection, Autonomous aerial vehicles, Feature extraction, Robustness, DeepForest BibRef

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
BibRef

Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Deforestation, Degradation .


Last update:Jun 5, 2024 at 10:22:22