Remote Monitoring of Forest Insect Defoliation -A Review-

Aim of study: This paper reviews the global research during the last 6 years (2007-2012) on the state, trends and potential of remote sensing for detecting, mapping and monitoring forest defoliation caused by insects. Area of study: The review covers research carried out within different countries in Europe and America. Main results: A nation or region wide monitoring system should be scaled in two levels, one using time-series with moderate to coarse resolutions, and the other with fine or high resolution. Thus, MODIS data is increasingly used for early warning detection, whereas Landsat data is predominant in defoliation damage research. Furthermore, ALS data currently stands as the more promising option for operative detection of defoliation. Vegetation indices based on infrared-medium/near-infrared ratios and on moisture content indicators are of great potential for mapping insect pest defoliation, although NDVI is the most widely used and tested. Research highlights: Among most promising methods for insect defoliation monitoring are Spectral Mixture Analysis, best suited for detection due to its sub-pixel recognition enhancing multispectral data, and use of logistic models as function of vegetation index change between two dates, recommended for predicting defoliation. Key words: vegetation damage; pest outbreak; spectral change detection.

[1]  Christopher Justice,et al.  The impact of misregistration on change detection , 1992, IEEE Trans. Geosci. Remote. Sens..

[2]  Warren B. Cohen,et al.  Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — Tools for calibration and validation , 2010 .

[3]  Effects of climate change on insect defoliator population processes in Canada's boreal forest: Some plausible scenarios , 1995 .

[4]  G. Hunt SPECTRAL SIGNATURES OF PARTICULATE MINERALS IN THE VISIBLE AND NEAR INFRARED , 1977 .

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

[6]  Juha Hyyppä,et al.  SAR Satellite Images and Terrestrial Laser Scanning in Forest Damages Mapping in Finland , 2010 .

[7]  Erik Næsset,et al.  Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning , 2006 .

[8]  T. Soomere,et al.  Variations in extreme wave heights and wave directions in the north-eastern Baltic Sea , 2010 .

[9]  David W. Williams,et al.  Herbivorous insects and climate change: potential changes in the spatial distribution of forest defoliator outbreaks , 1995 .

[10]  T. Soomere,et al.  Spatial patterns of the wave climate in the Baltic Proper and the Gulf of Finland , 2011 .

[11]  Kenton W. Ross,et al.  Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks , 2011 .

[12]  R. Baker,et al.  An analysis of pest risk from an Asian longhorn beetle (Anoplophora glabripennis) to hardwood trees in the European community , 2002 .

[13]  Lars Eklundh,et al.  Mapping insect defoliation in Scots pine with MODIS time-series data , 2009 .

[14]  G. Carter,et al.  Narrow-band Reflectance Imagery Compared with ThermalImagery for Early Detection of Plant Stress , 1996 .

[15]  Alice Deschamps,et al.  MAPPING INSECT DEFOLIATION USING MULTI-TEMPORAL LANDSAT DATA , 2007 .

[16]  G. Hay,et al.  Remote Sensing Contributions to the Scale Issue , 1999 .

[17]  Thomas T. Veblen,et al.  Spatial prediction of caterpillar (Ormiscodes) defoliation in Patagonian Nothofagus forests , 2011, Landscape Ecology.

[18]  Tarmo Soomere,et al.  Spatial variations of wave loads and closure depths along the coast of the eastern Baltic Sea , 2013 .

[19]  Stein Rune Karlsen,et al.  Monitoring the spatio-temporal dynamics of geometrid moth outbreaks in birch forest using MODIS-NDVI data , 2009 .

[20]  T. Ebata,et al.  Augmenting the existing survey hierarchy for mountain pine beetle red-attack damage with satellite remotely sensed data1 , 2006 .

[21]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[22]  R. Fleming,et al.  Climate change and impacts of boreal forest insects , 2000 .

[23]  V. Zhamoida,et al.  Recent sedimentation processes in the coastal zone of the Curonian Spit (Kaliningrad region, Baltic Sea) , 2009 .

[24]  J. García-López,et al.  Effects of climate change on the distribution of Pinus sylvestris L. stands in Spain. A phytoclimatic approach to defining management alternatives , 2010 .

[25]  J. Santos,et al.  Climate change and forest plagues: the case of the pine processionary moth in Northeastern Portugal , 2011 .

[26]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[27]  Svein Solberg,et al.  Mapping gap fraction, LAI and defoliation using various ALS penetration variables , 2010 .

[28]  T. Lipping,et al.  Remote Sensing of Forest Health , 2009 .

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

[30]  Susan L. Ustin,et al.  Using hyperspectral remote sensing to detect and quantify southeastern pine senescence effects in red-cockaded woodpecker (Picoides borealis) habitat , 2010 .

[31]  N. Coops,et al.  Assessing plantation canopy condition from airborne imagery using spectral mixture analysis and fractional abundances , 2005 .

[32]  Ronald J. Hall,et al.  4 Remotely Sensed Data in the Mapping of Insect Defoliation , 2006 .

[33]  S. Goward,et al.  An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks , 2010 .

[34]  Keith N. Eshleman,et al.  Relationship of a Landsat cumulative disturbance index to canopy nitrogen and forest structure , 2012 .

[35]  S. Kellomäki,et al.  Climate Change and Range Shifts in Two Insect Defoliators: Gypsy Moth and Nun Moth - a Model Study , 2007 .

[36]  Juha Hyyppä,et al.  Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images , 2010, Remote. Sens..

[37]  W. Cohen,et al.  Characterizing 23 Years (1972–95) of Stand Replacement Disturbance in Western Oregon Forests with Landsat Imagery , 2002, Ecosystems.

[38]  James E. Vogelmann,et al.  Comparison between two vegetation indices for measuring different types of forest damage in the north-eastern United States , 1990 .

[39]  Clayton C. Kingdon,et al.  A general Landsat model to predict canopy defoliation in broadleaf deciduous forests , 2012 .

[40]  Steven E. Franklin,et al.  Understanding Forest Disturbance and Spatial Pattern : Remote Sensing and GIS Approaches , 2006 .

[41]  S. Gulbinskas Main patterns of coastal zone development of the Curonian Spit, Lithuania , 2010 .

[42]  R. Nelson Depth limited design wave heights in very flat regions , 1994 .

[43]  T. Soomere,et al.  Coastal erosion processes in the eastern Gulf of Finland and their links with geological and hydrometeorological factors , 2011 .

[44]  Nicholas C. Coops,et al.  Prediction and assessment of bark beetle-induced mortality of lodgepole pine using estimates of stand vigor derived from remotely sensed data , 2009 .

[45]  S. T. Im,et al.  Siberian silkmoth outbreak pattern analysis based on SPOT VEGETATION data , 2009 .

[46]  U. Ratas,et al.  Increasing Activity of Coastal Processes Associated with Climate Change in Estonia , 2003 .

[47]  Qihao Weng,et al.  Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling , 2007, Sensors.

[48]  P. Fröhle,et al.  Near-shore evolution model for Palanga area: feasibility study of beach erosion management , 2007 .

[49]  Juha Hyyppä,et al.  Using high density ALS data in plot level estimation of the defoliation by the common pine sawfly. , 2011 .

[50]  Mary E. Martin,et al.  Using AVIRIS to assess hemlock abundance and early decline in the Catskills, New York , 2005 .

[51]  N. Yoccoz,et al.  Climate change and outbreaks of the geometrids Operophtera brumata and Epirrita autumnata in subarctic birch forest: evidence of a recent outbreak range expansion. , 2008, The Journal of animal ecology.

[52]  Philip A. Townsend,et al.  Estimating the effect of gypsy moth defoliation using MODIS , 2008 .

[53]  D. Lu,et al.  Change detection techniques , 2004 .

[54]  Zhiqiang Yang,et al.  Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms , 2010 .

[55]  Pamela L. Nagler,et al.  Remote monitoring of tamarisk defoliation and evapotranspiration following saltcedar leaf beetle attack , 2009 .

[56]  Michael A. Wulder,et al.  Detection and monitoring of the mountain pine beetle , 2004 .

[57]  Holger Lange,et al.  Leaf Area Index Estimation using Lidar and Forest Reflectance Modelling of Airborne Hyperspectral Data , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[58]  J. R. Jensen Remote Sensing of the Environment: An Earth Resource Perspective , 2000 .

[59]  Eric F. Lambin,et al.  Time series of remote sensing data for land change science , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[60]  Daniel E. Irwin,et al.  Estimating proportional change in forest cover as a continuous variable from multi-year MODIS data , 2008 .

[61]  Lei Wang,et al.  Remote sensing of insect pests in larch forest based on physical model , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[62]  D. M. Moss,et al.  Red edge spectral measurements from sugar maple leaves , 1993 .

[63]  W. M. Ciesla,et al.  Interpretation of SPOT-1 color composites for mapping defoliation of hardwood forests by gypsy moth , 1989 .

[64]  Robert H. Fraser,et al.  Mapping insect‐induced tree defoliation and mortality using coarse spatial resolution satellite imagery , 2005 .

[65]  Eberhard Parlow,et al.  Landsat TM/ETM+ and tree-ring based assessment of spatiotemporal patterns of the autumnal moth (Epirrita autumnata) in northernmost Fennoscandia , 2010 .

[66]  O. Mutanga,et al.  Imaging spectroscopy (hyperspectral remote sensing) in southern Africa: an overview , 2010 .

[67]  Andrea Battisti,et al.  EXPANSION OF GEOGRAPHIC RANGE IN THE PINE PROCESSIONARY MOTH CAUSED BY INCREASED WINTER TEMPERATURES , 2005 .

[68]  J. Innes Forest Health: Its Assessment and Status , 1993 .

[69]  Richard A. Hallett,et al.  Ash decline assessment in emerald ash borer-infested regions: A test of tree-level, hyperspectral technologies , 2008 .

[70]  R. E. Harrison,et al.  Review of Satellite Remote Sensing Use in Forest Health Studies~!2010-01-27~!2010-04-05~!2010-06-29~! , 2010 .