A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests

article Insects are important forest disturbance agents, and mapping their effects on tree mortality and surface fuels represents a critical research challenge. Although various remote sensing approaches have been developed to monitor insect impacts, most studies have focused on single insect agents or single locations and have not related observed changes to ground-based measurements. This study presents a remote sensing framework to (1) characterize spectral trajectories associated with insect activity of varying duration and severity and (2) relate those trajectories to ground-based measurements of tree mortality and surface fuels in the Cascade Range, Oregon, USA. We leverage a Landsat time series change detection algorithm (LandTrendr), annual forest health aerial detection surveys (ADS), and field measurements to investigate two study landscapes broadly applicable to conifer forests and dominant insect agents of western North America. We distributed 38 plots across multiple forest types (ranging from mesic mixed-conifer to xeric lodgepole pine) and insect agents (defoliator (western spruce budworm) and bark beetle (mountain pine beetle)). Insect effects were evident in the Landsat time series as combinations of both short- and long-duration changes in the Normal- ized Burn Ratio spectral index. Western spruce budworm trajectories appeared to show a consistent tempo- ral evolution of long-duration spectral decline (loss of vegetation) followed by recovery, whereas mountain pine beetle plots exhibited both short- and long-duration spectral declines and variable recovery rates. Although temporally variable, insect-affected stands generally conformed to four spectral trajectories: short- duration decline then recovery, short- then long-duration decline, long-duration decline, long-duration decline then recovery. When comparing remote sensing data with field measurements of insect impacts, we found that spectral changes were related to cover-based estimates (tree basal area mortality (R 2

[1]  C. Daly,et al.  A knowledge-based approach to the statistical mapping of climate , 2002 .

[2]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[3]  J. Régnière,et al.  Assessing the Impacts of Global Warming on Forest Pest Dynamics , 2022 .

[4]  J. Rotella,et al.  Snag longevity in relation to wildfire and postfire salvage logging , 2006 .

[5]  J. Régnière,et al.  Climate Change and Bark Beetles of the Western United States and Canada: Direct and Indirect Effects , 2010 .

[6]  Heather J. Lynch,et al.  A spatiotemporal Ripley's K-function to analyze interactions between spruce budworm and fire in British Columbia, Canada , 2008 .

[7]  J. Hicke,et al.  Cross-scale Drivers of Natural Disturbances Prone to Anthropogenic Amplification: The Dynamics of Bark Beetle Eruptions , 2008 .

[8]  A. McGuire,et al.  Assessing the response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach , 2009 .

[9]  N. Benson,et al.  Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio , 2006 .

[10]  R. Martin,et al.  Fire, fungi, and beetle influences on a lodgepole pine ecosystem of south-central Oregon , 2004, Oecologia.

[11]  Nicholas C. Coops,et al.  Curve fitting of time-series Landsat imagery for characterizing a mountain pine beetle infestation , 2010 .

[12]  P. Moorcroft,et al.  The Influence of Previous Mountain Pine Beetle (Dendroctonus ponderosae) Activity on the 1988 Yellowstone Fires , 2006, Ecosystems.

[13]  B. Law,et al.  Carbon dynamics of Oregon and Northern California forests and potential land-based carbon storage. , 2009, Ecological applications : a publication of the Ecological Society of America.

[14]  W. Kurz,et al.  Mountain pine beetle and forest carbon feedback to climate change , 2008, Nature.

[15]  Nicholas C. Coops,et al.  Integrating remotely sensed and ancillary data sources to characterize a mountain pine beetle infestation , 2006 .

[16]  Robert A. Norheim,et al.  Forest ecosystems, disturbance, and climatic change in Washington State, USA , 2010 .

[17]  Richard A. Fleming,et al.  Landscape-Scale Analysis of Interactions between Insect Defoliation and Forest Fire in Central Canada , 2002 .

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

[19]  P. Teillet Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions , 1997 .

[20]  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 .

[21]  M. Canty,et al.  Automatic radiometric normalization of multitemporal satellite imagery , 2004 .

[22]  P. Chavez Image-Based Atmospheric Corrections - Revisited and Improved , 1996 .

[23]  T. Swetnam,et al.  Multicentury, regional-scale patterns of western spruce budworm outbreaks. , 1993 .

[24]  N. Coops,et al.  Estimation of insect infestation dynamics using a temporal sequence of Landsat data , 2008 .

[25]  R. Colwell Remote sensing of the environment , 1980, Nature.

[26]  J. Negrón,et al.  US Forest Service bark beetle research in the western United States: Looking toward the future , 2008 .

[27]  P. Dennison,et al.  Assessing canopy mortality during a mountain pine beetle outbreak using GeoEye-1 high spatial resolution satellite data. , 2010 .

[28]  W. Cohen,et al.  Aerial and satellite sensor detection and classification of western spruce budworm defoliation in a subalpine forest , 1995 .

[29]  Limin Yang,et al.  COMPLETION OF THE 1990S NATIONAL LAND COVER DATA SET FOR THE CONTERMINOUS UNITED STATES FROM LANDSAT THEMATIC MAPPER DATA AND ANCILLARY DATA SOURCES , 2001 .

[30]  W. Cohen,et al.  Landsat's Role in Ecological Applications of Remote Sensing , 2004 .

[31]  James K. Brown,et al.  Handbook for inventorying surface fuels and biomass in the interior West. General technical report , 1982 .

[32]  Wesley G. Page,et al.  Mountain Pine Beetle-Induced Changes to Selected Lodgepole Pine Fuel Complexes within the Intermountain Region , 2007, Forest Science.

[33]  S. Hummel,et al.  Western spruce budworm defoliation effects on forest structure and potential fire behavior , 2003 .

[34]  T. Swetnam,et al.  Warming and Earlier Spring Increase Western U.S. Forest Wildfire Activity , 2006, Science.

[35]  Wesley G. Page,et al.  Predicted Fire Behavior in Selected Mountain Pine Beetle-Infected Lodgepole Pine , 2007 .

[36]  Jan Verbesselt,et al.  Forecasting tree mortality using change metrics derived from MODIS satellite data , 2009 .

[37]  J. Vogelmann,et al.  Monitoring forest changes in the southwestern United States using multitemporal Landsat data , 2009 .

[38]  Daniel C. Donato,et al.  Forest Fire Impacts on Carbon Uptake, Storage, and Emission: The Role of Burn Severity in the Eastern Cascades, Oregon , 2009, Ecosystems.

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

[40]  Charles C. Rhoades,et al.  Stand characteristics and downed woody debris accumulations associated with a mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in Colorado , 2009 .

[41]  Jay,et al.  GUIDELINES for MEASUREMENTS of WOODY DETRITUS • zn FOREST ECOSYSTEMS , 2013 .

[42]  B. Quayle,et al.  A Project for Monitoring Trends in Burn Severity , 2007 .

[43]  M. Turner,et al.  Do mountain pine beetle outbreaks change the probability of active crown fire in lodgepole pine forests , 2011 .

[44]  James K. Brown Handbook for inventorying downed woody material , 1974 .

[45]  Trisalyn A. Nelson,et al.  Large-area mountain pine beetle infestations: Spatial data representation and accuracy , 2006 .