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Using Landsat-derived disturbance history (1972–2010) to predict current forest structure

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dc.creator Pflugmacher, Dirk
dc.creator Cohen, Warren B.
dc.creator Kennedy, Robert E.
dc.date.accessioned 2012-11-12T23:56:51Z
dc.date.available 2012-11-12T23:56:51Z
dc.date.issued 2012-07
dc.identifier.citation Pflugmacher, D., Cohen, W., & Kennedy, R. (2012). Using landsat-derived disturbance history (1972-2010) to predict current forest structure. REMOTE SENSING OF ENVIRONMENT, 122, 146-165. doi: 10.1016/j.rse.2011.09.025 en_US
dc.identifier.uri http://hdl.handle.net/1957/35053
dc.description This is the publisher’s final pdf. The published article is copyrighted by Elesvier and can be found at: http://www.journals.elsevier.com/remote-sensing-of-environment/. To the best of our knowledge, one or more authors of this paper were federal employees when contributing to this work. en_US
dc.description.abstract Lidar is currently the most accurate method for remote estimation of forest structure, but it has limited spatial and temporal coverage. Conversely, Landsat data are more widely available, but exhibit a weaker relationship with structure under medium to high leaf area conditions. One potentially valuable means of enhancing the relationship between Landsat reflectance and forest structure is to incorporate Landsat spectral trends prior to a date of interest. Because the condition of a forest stand at any point in time is linked to the stand's disturbance history, an approach that directly leverages the temporal information of Landsat time series should improve estimates of forest structure. The main objective of this study was to test and demonstrate the utility of disturbance and recovery metrics derived from spectral profiles of annual Landsat time series (LTS) to predict current forest structure attributes (as compared to more traditional approaches, including airborne, discrete return lidar and single-date Landsat). We estimated aboveground live biomass (AGB[subscript live]), dead woody biomass (AGB[subscript dead]), basal area (live and dead), and Lorey's mean stand height for a mixed-conifer forest in eastern Oregon, USA, and compared the results with estimates from lidar and single, current-date Landsat imagery. Annual time-series stacks for the entire Landsat record (1972–2010) were obtained to characterize all long-term (insect, growth) and short-term (fire, harvest) vegetation changes that occurred during that period. This required the additional objective of integrating Landsat data from MSS and TM/ETM + sensors, and we describe here our approach. To extract spectral trajectories and change metrics associated with forest disturbances and recovery we applied a temporal segmentation to the calibrated time series. Lidar predicted forest structure of live trees most accurately (e.g. AGB[subscript live]: R² = 0.88, RMSE = 35.3 Mg ha⁻¹). However, LTS metrics significantly improved model predictions (e.g. AGB[subscript live]: R² = 0.80, RMSE = 46.9 Mg ha⁻¹) compared to single-date Landsat data (AGB[subscript live], R² = 0.58, RMSE = 65.1 Mg ha⁻¹). Conversely, distributions of AGB[subscript dead] were more strongly associated with disturbance history than current structure of live trees. As a result, LTS models performed significantly better in estimating AGB[subscript dead] (R² = 0.73, RMSE = 31.0 Mg ha⁻¹), than lidar models (R² = 0.21, RMSE = 43.8 Mg ha⁻¹); and single-date Landsat data failed completely (R² = 0, RMSE = 47.8 Mg ha⁻¹). Further, LTS metrics that integrated disturbance and recovery history over the entire time series generally predicted AGB[subscript dead] better than metrics describing single events only (e.g. the greatest disturbance). This study demonstrates the unique value of the long, historic Landsat record, and suggests new potentials for mapping current forest structure with Landsat. en_US
dc.description.sponsorship This research was supported by NASA Headquarters under the NASA Earth and Space Science Fellowship Program – Grant "NNX10AN49H" and by the Oregon Watershed Enhancement Board. en_US
dc.language.iso en_US en_US
dc.publisher Elsevier en_US
dc.relation.ispartofseries Remote Sensing of Environment en_US
dc.relation.ispartofseries Vol. 122 (2012) en_US
dc.subject Landsat en_US
dc.subject Time series en_US
dc.subject Forest disturbance en_US
dc.subject Biomass en_US
dc.subject Carbon en_US
dc.subject Lidar en_US
dc.subject MSS en_US
dc.subject Tasseled cap en_US
dc.subject LandTrendr en_US
dc.subject TimeSync en_US
dc.title Using Landsat-derived disturbance history (1972–2010) to predict current forest structure en_US
dc.type Article en_US
dc.description.peerreview yes en_US
dc.identifier.doi 10.1016/j.rse.2011.09.025

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