An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

Forests contain a majority of the aboveground carbon (C) found in ecosystems, and understanding biomass lost from disturbance is essential to improve our C-cycle knowledge. Our study region in the Wisconsin and Minnesota Laurentian Forest had a strong decline in Normalized Difference Vegetation Inde...

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Main Authors: Christopher S.R. Neigh, Douglas K. Bolton, Mouhamad Diabate, Jennifer J. Williams, Nuno Carvalhais
Format: Article
Language:English
Published: MDPI AG 2014-03-01
Series:Remote Sensing
Subjects:
US
Online Access:http://www.mdpi.com/2072-4292/6/4/2782
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spelling doaj-36775fd74c064b2e9350fa7cb4ad7d192020-11-24T23:45:20ZengMDPI AGRemote Sensing2072-42922014-03-01642782280810.3390/rs6042782rs6042782An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series DataChristopher S.R. Neigh0Douglas K. Bolton1Mouhamad Diabate2Jennifer J. Williams3Nuno Carvalhais4Biospheric Sciences Laboratory, Code 618, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USADepartment of Forest Resources Management, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaDepartment of Geographical Sciences, University of Maryland, College Park, MD 20742, USARoyal Botanic Gardens, Kew, Richmond, Surrey TW9 3AE, UKMax Planck Institute for Biogeochemistry, P.O. Box 10 01 64, D-07701 Jena, GermanyForests contain a majority of the aboveground carbon (C) found in ecosystems, and understanding biomass lost from disturbance is essential to improve our C-cycle knowledge. Our study region in the Wisconsin and Minnesota Laurentian Forest had a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 2007, observed with the National Ocean and Atmospheric Administration’s (NOAA) series of Advanced Very High Resolution Radiometer (AVHRR). To understand the potential role of disturbances in the terrestrial C-cycle, we developed an algorithm to map forest disturbances from either harvest or insect outbreak for Landsat time-series stacks. We merged two image analysis approaches into one algorithm to monitor forest change that included: (1) multiple disturbance index thresholds to capture clear-cut harvest; and (2) a spectral trajectory-based image analysis with multiple confidence interval thresholds to map insect outbreak. We produced 20 maps and evaluated classification accuracy with air-photos and insect air-survey data to understand the performance of our algorithm. We achieved overall accuracies ranging from 65% to 75%, with an average accuracy of 72%. The producer’s and user’s accuracy ranged from a maximum of 32% to 70% for insect disturbance, 60% to 76% for insect mortality and 82% to 88% for harvested forest, which was the dominant disturbance agent. Forest disturbances accounted for 22% of total forested area (7349 km2). Our algorithm provides a basic approach to map disturbance history where large impacts to forest stands have occurred and highlights the limited spectral sensitivity of Landsat time-series to outbreaks of defoliating insects. We found that only harvest and insect mortality events can be mapped with adequate accuracy with a non-annual Landsat time-series. This limited our land cover understanding of NDVI decline drivers. We demonstrate that to capture more subtle disturbances with spectral trajectories, future observations must be temporally dense to distinguish between type and frequency in heterogeneous landscapes.http://www.mdpi.com/2072-4292/6/4/2782LandsatAVHRRforestdisturbancemortalityinsectharvestUSclassificationdecision tree
collection DOAJ
language English
format Article
sources DOAJ
author Christopher S.R. Neigh
Douglas K. Bolton
Mouhamad Diabate
Jennifer J. Williams
Nuno Carvalhais
spellingShingle Christopher S.R. Neigh
Douglas K. Bolton
Mouhamad Diabate
Jennifer J. Williams
Nuno Carvalhais
An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data
Remote Sensing
Landsat
AVHRR
forest
disturbance
mortality
insect
harvest
US
classification
decision tree
author_facet Christopher S.R. Neigh
Douglas K. Bolton
Mouhamad Diabate
Jennifer J. Williams
Nuno Carvalhais
author_sort Christopher S.R. Neigh
title An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data
title_short An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data
title_full An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data
title_fullStr An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data
title_full_unstemmed An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data
title_sort automated approach to map the history of forest disturbance from insect mortality and harvest with landsat time-series data
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2014-03-01
description Forests contain a majority of the aboveground carbon (C) found in ecosystems, and understanding biomass lost from disturbance is essential to improve our C-cycle knowledge. Our study region in the Wisconsin and Minnesota Laurentian Forest had a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 2007, observed with the National Ocean and Atmospheric Administration’s (NOAA) series of Advanced Very High Resolution Radiometer (AVHRR). To understand the potential role of disturbances in the terrestrial C-cycle, we developed an algorithm to map forest disturbances from either harvest or insect outbreak for Landsat time-series stacks. We merged two image analysis approaches into one algorithm to monitor forest change that included: (1) multiple disturbance index thresholds to capture clear-cut harvest; and (2) a spectral trajectory-based image analysis with multiple confidence interval thresholds to map insect outbreak. We produced 20 maps and evaluated classification accuracy with air-photos and insect air-survey data to understand the performance of our algorithm. We achieved overall accuracies ranging from 65% to 75%, with an average accuracy of 72%. The producer’s and user’s accuracy ranged from a maximum of 32% to 70% for insect disturbance, 60% to 76% for insect mortality and 82% to 88% for harvested forest, which was the dominant disturbance agent. Forest disturbances accounted for 22% of total forested area (7349 km2). Our algorithm provides a basic approach to map disturbance history where large impacts to forest stands have occurred and highlights the limited spectral sensitivity of Landsat time-series to outbreaks of defoliating insects. We found that only harvest and insect mortality events can be mapped with adequate accuracy with a non-annual Landsat time-series. This limited our land cover understanding of NDVI decline drivers. We demonstrate that to capture more subtle disturbances with spectral trajectories, future observations must be temporally dense to distinguish between type and frequency in heterogeneous landscapes.
topic Landsat
AVHRR
forest
disturbance
mortality
insect
harvest
US
classification
decision tree
url http://www.mdpi.com/2072-4292/6/4/2782
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