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

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

Delalieux, S., Auwerkerken, A., Verstraeten, W., Somers, B., Valcke, R., Lhermitte, S., Keulemans, J., Coppin, P.,
Hyperspectral Reflectance and Fluorescence Imaging to Detect Scab Induced Stress in Apple Leaves,
RS(1), No. 4, December 2009, pp. 858-874.
DOI Link 1203

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

Ortiz, S., Breidenbach, J., Kändler, G.,
Early Detection of Bark Beetle Green Attack Using TerraSAR-X and RapidEye Data,
RS(5), No. 4, April 2013, pp. 1912-1931.
DOI Link 1305

Neigh, C.S.R.[Christopher S.R.], Bolton, D.K.[Douglas K.], Diabate, M.[Mouhamad], Williams, J.J.[Jennifer J.], Carvalhais, N.[Nuno],
An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data,
RS(6), No. 4, 2014, pp. 2782-2808.
DOI Link 1405

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

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,
RS(6), No. 6, 2014, pp. 5696-5716.
DOI Link 1407

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,
RS(7), No. 11, 2015, pp. 15467.
DOI Link 1512

Anderson, T.[Taylor], Dragicevic, S.[Suzana],
A Geosimulation Approach for Data Scarce Environments: Modeling Dynamics of Forest Insect Infestation across Different Landscapes,
IJGI(5), No. 2, 2016, pp. 9.
DOI Link 1603

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,
RS(8), No. 3, 2016, pp. 256.
DOI Link 1604

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

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.[Huaguo], 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

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

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

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

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

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

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

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

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

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

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

Hu, B.X., Li, J., Wang, J., Hall, G.B.,
The Early Detection of the Emerald Ash Borer (EAB) Using Advanced Geospacial Technologies,
DOI Link 1411

Qin, J.[Jun], Wang, B.[Biao], Wu, Y.[Yanlan], Lu, Q.[Qi], Zhu, H.[Haochen],
Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101

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

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

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

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

Xia, L.[Lang], Zhang, R.[Ruirui], Chen, L.P.[Li-Ping], Li, L.L.[Long-Long], Yi, T.[Tongchuan], Wen, Y.[Yao], Ding, C.[Chenchen], Xie, C.[Chunchun],
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

Pandey, P.[Piyush], Payn, K.G.[Kitt G.], Lu, Y.[Yuzhen], 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

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

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

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,
DOI Link 2012

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

Last update:Nov 1, 2021 at 09:26:50