Forest disturbance mapping using dense synthetic Landsat/MODIS time-series and permutation-based disturbance index detection

Frantz, David, Roeder, Achim, Udelhoven, Thomas and Schmidt, Michael (2016) Forest disturbance mapping using dense synthetic Landsat/MODIS time-series and permutation-based disturbance index detection. Remote Sensing, 8 4: . doi:10.3390/rs8040277


Author Frantz, David
Roeder, Achim
Udelhoven, Thomas
Schmidt, Michael
Title Forest disturbance mapping using dense synthetic Landsat/MODIS time-series and permutation-based disturbance index detection
Journal name Remote Sensing   Check publisher's open access policy
ISSN 2072-4292
Publication date 2016-04-01
Year available 2016
Sub-type Article (original research)
DOI 10.3390/rs8040277
Open Access Status DOI
Volume 8
Issue 4
Total pages 22
Place of publication Basel, Switzerland
Publisher MDPI AG
Language eng
Abstract Spatio-temporal information on process-based forest loss is essential for a wide range of applications. Despite remote sensing being the only feasible means of monitoring forest change at regional or greater scales, there is no retrospectively available remote sensor that meets the demand of monitoring forests with the required spatial detail and guaranteed high temporal frequency. As an alternative, we employed the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to produce a dense synthetic time series by fusing Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) nadir Bidirectional Reflectance Distribution Function (BRDF) adjusted reflectance. Forest loss was detected by applying a multi-temporal disturbance detection approach implementing a Disturbance Index-based detection strategy. The detection thresholds were permutated with random numbers for the normal distribution in order to generate a multi-dimensional threshold confidence area. As a result, a more robust parameterization and a spatially more coherent detection could be achieved. (i) The original Landsat time series; (ii) synthetic time series; and a (iii) combined hybrid approach were used to identify the timing and extent of disturbances. The identified clearings in the Landsat detection were verified using an annual woodland clearing dataset from Queensland's Statewide Landcover and Trees Study. Disturbances caused by stand-replacing events were successfully identified. The increased temporal resolution of the synthetic time series indicated promising additional information on disturbance timing. The results of the hybrid detection unified the benefits of both approaches, i.e., the spatial quality and general accuracy of the Landsat detection and the increased temporal information of synthetic time series. Results indicated that a temporal improvement in the detection of the disturbance date could be achieved relative to the irregularly spaced Landsat data for sufficiently large patches.
Keyword Landsat
Modis
Forest disturbance detection
Starfm
Data fusion
Australia
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Geography, Planning and Environmental Management Publications
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