Testing a Landsat-based approach for mapping disturbance causality in U.S. forests

  • Authors: Schroeder, Todd A.; Schleeweis, Karen G.; Moisen, Gretchen G.; Toney, Chris; Cohen, Warren B.; Freeman, Elizabeth A.; Yang, Zhiqiang; Huang, Chengquan
  • Publication Year: 2017
  • Publication Series: Scientific Journal (JRNL)
  • Source: Remote Sensing of Environment
  • DOI: 10.1016/j.rse.2017.03.033


In light of Earth's changing climate and growing human population, there is an urgent need to improve monitoring of natural and anthropogenic disturbanceswhich effect forests' ability to sequester carbon and provide other ecosystem services. In this study, a two-step modeling approach was used to map the type and timing of forest disturbances occurring between 1984 and 2010 in ten Landsat scenes located in diverse forest systems of the conterminous U.S. In step one, RandomForest (RF) models were developed to predict the presence of five forest disturbance agents (conversion, fire, harvest, stress and wind) and stable (i.e. undisturbed) forest. Models were developed using a suite of predictors including spectral change metrics derived from a nonparametric shape-restricted spline fitting algorithm, as well as several topographic and biophysical variables which potentially influence the initiation and/or spread of forest disturbance agents. Step two involved applying a rule-based model to the spectrally-based shape parameters (e.g. shape type, year and duration) to assign a year to the disturbance types and locations predicted in step one. Out of bag (OOB) predictions from RF showed that across the ten scenes, overall agreement was highest when only causal agent was considered (avg = 80%, min = 69%, max =86%), andwas lowest when both agent and year (within±1 of the reference date)were required to be correct (avg=71%, min=56%,max=80%). Across sceneomission and commission errors for fire and stable forest classeswere mostly around 10% to 20%, respectively. Harvestswere also modeled well, as five of nine test scenes had error rates b26%. Accuracy of the wind and stress classes were much more variable with model errors ranging from24% to 88%. The years assigned by the rule-basedmodel were reasonably accurate, as 88% of all disturbances were assigned a year that fell within±2 years of the reference date. Fire disturbances were assigned the correct year 78% of the time, followed by harvest (69%) and conversion (54%). Although 17% and 63% of wind and stress disturbances were under-estimated by 5 or more years, the impact on overall accuracy was nominal given these two classes only accounted for roughly 5% of all disturbances. Our results also revealed that causal agent models summarized to broader disturbed/not disturbed classes were as accurate as models specifically constructed to predict binary disturbance, thus there appears to be no advantage tomodeling disturbance prior to assigning causality. A relative evaluation of mean decrease in accuracy fromRF showed that although awide range of predictor variables contributed to the successful modeling of causal agents and stable forest (e.g. patch metrics, forest occurrence, and topography), disturbance variables (e.g. MTBS) and spectral change metrics (e.g. absolute and relative magnitude) were by far the most important. Modeled causality maps and annual disturbance rates were examined and found to be in good agreement with existing literature and other published data sets. Lastly, results are used to make recommendations for mapping forest disturbance agents nationally across the U.S.

  • Citation: Schroeder, Todd A.; Schleeweis, Karen G.; Moisen, Gretchen G.; Toney, Chris; Cohen, Warren B.; Freeman, Elizabeth A.; Yang, Zhiqiang; Huang, Chengquan. 2017.Testing a Landsat-based approach for mapping disturbance causality in U.S. forests. Remote Sensing of Environment. 195: 230-243. https://doi.org/10.1016/j.rse.2017.03.033.
  • Keywords: Forest disturbance mapping, Causal agents, Landsat time series, Empirical modeling, Random Forests Remote sensing
  • Posted Date: August 21, 2017
  • Modified Date: January 22, 2021
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