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

Chapter Contents (Back)
Forest Changes. Forest. Bark Beetle.

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.,
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
BibRef

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
BibRef

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,
RS(6), No. 6, 2014, pp. 5696-5716.
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,
RS(7), No. 11, 2015, pp. 15467.
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,
IJGI(5), No. 2, 2016, pp. 9.
DOI Link 1603
BibRef

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
BibRef

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.[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
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

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

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
BibRef

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
BibRef

Hu, B.X., Li, J., Wang, J., Hall, G.B.,
The Early Detection of the Emerald Ash Borer (EAB) Using Advanced Geospacial Technologies,
Geospatial14(213-219).
DOI Link 1411
BibRef

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
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
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

Chapter on Remote Sensing, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
Deforestation, Degradation .


Last update:Mar 3, 2021 at 15:01:44