Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality

[1]  Max Kuhn,et al.  caret: Classification and Regression Training , 2015 .

[2]  Marco Heurich,et al.  Spatio-temporal infestation patterns of Ips typographus (L.) in the Bavarian Forest National Park, Germany , 2013 .

[3]  J. Hicke,et al.  Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery , 2013 .

[4]  Fabian Ewald Fassnacht,et al.  An angular vegetation index for imaging spectroscopy data - Preliminary results on forest damage detection in the Bavarian National Park, Germany , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[5]  Alfonso Fernández-Manso,et al.  Spectral unmixing , 2012 .

[6]  Jonathan Cheung-Wai Chan,et al.  An evaluation of ensemble classifiers for mapping Natura 2000 heathland in Belgium using spaceborne angular hyperspectral (CHRIS/Proba) imagery , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[7]  Barbara Koch,et al.  Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[8]  Steven E. Franklin,et al.  A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery , 2012 .

[9]  Warren B. Cohen,et al.  A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests , 2011 .

[10]  M. Kautz,et al.  Quantifying spatio-temporal dispersion of bark beetle infestations in epidemic and non-epidemic conditions , 2011 .

[11]  J. Hicke,et al.  Evaluating the potential of multispectral imagery to map multiple stages of tree mortality , 2011 .

[12]  Jungho Im,et al.  ISPRS Journal of Photogrammetry and Remote Sensing , 2022 .

[13]  M. Heurich,et al.  Factors affecting the spatio-temporal dispersion of Ips typographus (L.) in Bavarian Forest National Park: A long-term quantitative landscape-level analysis , 2011 .

[14]  B. Koch,et al.  Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors , 2010 .

[15]  Benoit Rivard,et al.  Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation , 2010 .

[16]  Jon Atli Benediktsson,et al.  Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[17]  John F. Weishampel,et al.  Using Lidar-Derived Vegetation Profiles to Predict Time since Fire in an Oak Scrub Landscape in East-Central Florida , 2010, Remote. Sens..

[18]  Leif Martin Schroeder,et al.  Colonization of storm gaps by the spruce bark beetle: influence of gap and landscape characteristics , 2010 .

[19]  Maja Jurc,et al.  Sanitary felling of Norway spruce due to spruce bark beetles in Slovenia: A model and projections for various climate change scenarios , 2010 .

[20]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[21]  Chi Hau Chen,et al.  Statistical pattern recognition in remote sensing , 2008, Pattern Recognit..

[22]  K. Itten,et al.  Estimating foliar biochemistry from hyperspectral data in mixed forest canopy , 2008 .

[23]  Lorenzo Bruzzone,et al.  The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas. , 2007 .

[24]  A. Skidmore,et al.  A hyperspectral band selector for plant species discrimination , 2007 .

[25]  Joanne C. White,et al.  Detecting mountain pine beetle red attack damage with EO‐1 Hyperion moisture indices , 2007 .

[26]  Michael A. Wulder,et al.  Detecting and mapping mountain pine beetle red-attack damage with SPOT-5 10-m multispectral imagery , 2006, Journal of Ecosystems and Management.

[27]  Francesco Falciani,et al.  GALGO: an R package for multivariate variable selection using genetic algorithms , 2006, Bioinform..

[28]  Nicholas C. Coops,et al.  Estimating the probability of mountain pine beetle red-attack damage , 2006 .

[29]  Michael A. Wulder,et al.  Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities , 2006 .

[30]  D. Roberts,et al.  Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales , 2005 .

[31]  Roger Wheate,et al.  Detection of red attack stage mountain pine beetle infestation with high spatial resolution satellite imagery , 2005 .

[32]  Beat Wermelinger,et al.  Ecology and management of the spruce bark beetle Ips typographus—a review of recent research , 2004 .

[33]  Kurt Hornik,et al.  kernlab - An S4 Package for Kernel Methods in R , 2004 .

[34]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[35]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[36]  N. Coops,et al.  Assessment of Dothistroma Needle Blight of Pinus radiata Using Airborne Hyperspectral Imagery. , 2003, Phytopathology.

[37]  Michael A. Wulder,et al.  Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage , 2003 .

[38]  R. Lawrence,et al.  Early Detection of Douglas-Fir Beetle Infestation with Subcanopy Resolution Hyperspectral Imagery , 2003 .

[39]  D. Sims,et al.  Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features , 2003 .

[40]  Michael A. Wulder,et al.  Mountain Pine Beetle Red-Attack Forest Damage Classification Using Stratified Landsat TM Data in British Columbia, Canada , 2003 .

[41]  John R. Miller,et al.  Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on hyperspectral imagery. , 2002, Journal of environmental quality.

[42]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[43]  Nicholas C. Coops,et al.  Spectral reflectance characteristics of eucalypt foliage damaged by insects , 2001 .

[44]  G. Carter,et al.  Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. , 2001, American journal of botany.

[45]  H. Peltola,et al.  Mechanical stability of Scots pine, Norway spruce and birch: an analysis of tree-pulling experiments in Finland , 2000 .

[46]  F. E. LaMastus,et al.  The analysis of hyperspectral data using Savitzky-Golay filtering-practical issues. 2 , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[47]  R. Clark,et al.  Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression , 1999 .

[48]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[49]  Marian Kazda,et al.  Priority assessment for conversion of Norway spruce forests through introduction of broadleaf species , 1998 .

[50]  R. Tibshirani,et al.  Improvements on Cross-Validation: The 632+ Bootstrap Method , 1997 .

[51]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[52]  G. Carter Ratios of leaf reflectances in narrow wavebands as indicators of plant stress , 1994 .

[53]  Gregory A. Carter,et al.  Responses of leaf spectral reflectance to plant stress. , 1993 .

[54]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[55]  J. Dickens Behavioural and electrophysiological responses of the bark beetle, Ips typographus, to potential pheromone components , 1981 .

[56]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[57]  N. Coops,et al.  Integration of LIDAR and digital aerial imagery for detailed estimates of lodgepole pine (Pinus contorta) volume killed by mountain pine beetle (Dendroctonus ponderosae). , 2010 .

[58]  M. Heurich Progress of forest regeneration after a large-scale Ips typographus outbreak in the subalpine Picea abies forests of the Bavarian Forest National Park , 2009 .

[59]  Nicholas C. Coops,et al.  Challenges for the operational detection of mountain pine beetle green attack with remote sensing , 2009 .

[60]  M. Wulder,et al.  Detection and mapping of mountain pine beetle red attack: Matching information needs with appropriate remotely sensed data , 2005 .

[61]  J. Régnière,et al.  Effect of climate change on range expansion by the mountain pine beetle in British Columbia , 2003 .

[62]  J. Heath,et al.  The detection of mountain pine beetle green attacked lodgepole pine using compact airborne spectrographic imager (CASI) data , 2001 .

[63]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.