Detection and characterization of forest disturbances in California
type: CreativeWork
description: Natural and anthropogenic forest disturbances are a major influence on the global carbon cycle, with the local and global impacts of a disturbance event depending in large part on the timing, intensity, and cause of the disturbance. With some disturbance agents expected to increase in frequency and severity under the influence of climate change, knowledge of disturbance events and trends has become particularly crucial for a variety of scientific, political and management-related needs. Many studies have made use of time series analyses of Landsat imagery to study forest disturbance events at a variety of scales, but few have endeavored to attribute specific causal information to the disturbances detected, particularly at a subannual temporal scale. The purpose of this research is to investigate the suitability of a Landsat time series approach for detecting and describing the causes of disturbance events across the heterogeneous, forested landscapes of California. We used a random forest machine learning algorithm to relate normalized Landsat time series from 29,909 Landsat scenes extracted at the locations of known disturbance events with the cause of each event, including classes of logging, fire, pest damage, land use conversion, and no change. Validation of the model’s thematic accuracy performed using an independent dataset demonstrated an overall accuracy of 89.2% and a kappa value of 0.77, with by-class user’s accuracies ranging from 65% to 92% and by-class producer's accuracies ranging from 37% to 98%. We then applied this model to a sample footprint in California using all Landsat imagery available, generating a pixel-by-pixel disturbance record for every pixel from 1990 to 2010. While the model demonstrates some limitations in efficiency and temporal accuracy, it also highlights the capacity for disturbance detection at a subannual interval to offer insight into forest disturbance dynamics at a finer temporal scale.
abstract: Natural and anthropogenic forest disturbances are a major influence on the global carbon cycle, with the local and global impacts of a disturbance event depending in large part on the timing, intensity, and cause of the disturbance. With some disturbance agents expected to increase in frequency and severity under the influence of climate change, knowledge of disturbance events and trends has become particularly crucial for a variety of scientific, political and management-related needs. Many studies have made use of time series analyses of Landsat imagery to study forest disturbance events at a variety of scales, but few have endeavored to attribute specific causal information to the disturbances detected, particularly at a subannual temporal scale. The purpose of this research is to investigate the suitability of a Landsat time series approach for detecting and describing the causes of disturbance events across the heterogeneous, forested landscapes of California. We used a random forest machine learning algorithm to relate normalized Landsat time series from 29,909 Landsat scenes extracted at the locations of known disturbance events with the cause of each event, including classes of logging, fire, pest damage, land use conversion, and no change. Validation of the model’s thematic accuracy performed using an independent dataset demonstrated an overall accuracy of 89.2% and a kappa value of 0.77, with by-class user’s accuracies ranging from 65% to 92% and by-class producer's accuracies ranging from 37% to 98%. We then applied this model to a sample footprint in California using all Landsat imagery available, generating a pixel-by-pixel disturbance record for every pixel from 1990 to 2010. While the model demonstrates some limitations in efficiency and temporal accuracy, it also highlights the capacity for disturbance detection at a subannual interval to offer insight into forest disturbance dynamics at a finer temporal scale.
author: Reents, Courtney E
language: en
keywords: forest disturbance, change detection, multitemporal, Landsat, time series, California
date published: 2016-07-07T19:54:31Z
date modified: 2026-07-22T22:26:34Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2142/90603