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

Chapter Contents (Back)
Forest Changes. Forest. Tree. Disease. 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.

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Lin, Q.[Qinan], Huang, H.G.[Hua-Guo], Yu, L.F.[Lin-Feng], Wang, J.X.[Jing-Xu],
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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,
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DOI Link 1903
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Safonova, A.[Anastasiia], Tabik, S.[Siham], Alcaraz-Segura, D.[Domingo], Rubtsov, A.[Alexey], Maglinets, Y.[Yuriy], Herrera, F.[Francisco],
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DOI Link 1907
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Lin, Q.[Qinan], Huang, H.G.[Hua-Guo], Wang, J.X.[Jing-Xu], Huang, K.[Kan], Liu, Y.Y.[Yang-Yang],
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Iordache, M.D.[Marian-Daniel], Mantas, V.[Vasco], Baltazar, E.[Elsa], Pauly, K.[Klaas], Lewyckyj, N.[Nicolas],
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A Spatiotemporal Change Detection Method for Monitoring Pine Wilt Disease in a Complex Landscape Using High-Resolution Remote Sensing Imagery,
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DOI Link 2106
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Minarík, R.[Robert], Langhammer, J.[Jakub], Lendzioch, T.[Theodora],
Detection of Bark Beetle Disturbance at Tree Level Using UAS Multispectral Imagery and Deep Learning,
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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.,
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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,
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Migas-Mazur, R.[Robert], Kycko, M.[Marlena], Zwijacz-Kozica, T.[Tomasz], Zagajewski, B.[Bogdan],
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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],
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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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RS(14), No. 1, 2022, pp. xx-yy.
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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.
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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],
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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],
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Climate Drivers of Pine Shoot Beetle Outbreak Dynamics in Southwest China,
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Mapping a European Spruce Bark Beetle Outbreak Using Sentinel-2 Remote Sensing Data,
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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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MODIS Time Series Reveal New Maximum Records of Defoliated Area by Ormiscodes amphimone in Deciduous Nothofagus Forests, Southern Chile,
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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.C.[Meng-Chen], 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

Zhao, Y.X.[Yu-Xin], Cui, Z.[Zeyu], Liu, X.N.[Xiang-Nan], Liu, M.L.[Mei-Ling], Yang, B.[Ben], Feng, L.[Lei], Zhou, B.[Botian], Zhang, T.W.[Ting-Wei], Tan, Z.[Zheng], Wu, L.[Ling],
EWMACD Algorithm in Early Detection of Defoliation Caused by Dendrolimus tabulaeformis Tsai et Liu,
RS(16), No. 13, 2024, pp. 2299.
DOI Link 2407
BibRef

Veling, S.S.[Shripad S.], Mohite-Patil, T.B.,
Multi-Disease Classification of Mango Tree Using Meta-Heuristic-Based Weighted Feature Selection and LSTM Model,
IJIG(24), No. 4, July 2024, pp. 2450039.
DOI Link 2408
BibRef

Joll, E.G.[Elisabeth G.], Ginzel, M.D.[Matthew D.], Hoover, K.[Kelli], Couture, J.J.[John J.],
Influence of Spotted Lanternfly (Lycorma delicatula) on Multiple Maple (Acer spp.) Species Canopy Foliar Spectral and Chemical Profiles,
RS(16), No. 15, 2024, pp. 2706.
DOI Link 2408
BibRef

Kanaskie, C.R.[Caroline R.], Routhier, M.R.[Michael R.], Fraser, B.T.[Benjamin T.], Congalton, R.G.[Russell G.], Ayres, M.P.[Matthew P.], Garnas, J.R.[Jeff R.],
Early Detection of Southern Pine Beetle Attack by UAV-Collected Multispectral Imagery,
RS(16), No. 14, 2024, pp. 2608.
DOI Link 2408
BibRef

Huo, L.[Langning], Koivumäki, N.[Niko], Oliveira, R.A.[Raquel A.], Hakala, T.[Teemu], Markelin, L.[Lauri], Näsi, R.[Roope], Suomalainen, J.[Juha], Polvivaara, A.[Antti], Junttila, S.[Samuli], Honkavaara, E.[Eija],
Bark beetle pre-emergence detection using multi-temporal hyperspectral drone images: Green shoulder indices can indicate subtle tree vitality decline,
PandRS(216), 2024, pp. 200-216.
Elsevier DOI 2408
European spruce bark beetle, Green attack, Early detection, Remote sensing, Hyperspectral imagery, Drone imagery 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:Nov 26, 2024 at 16:40:19