Improving LandTrendr Forest Disturbance Mapping in China Using Multi-Season Observations and Multispectral Indices

Forest disturbance detection is of great significance for understanding forest dynamics. The Landsat-based detection of the Trends in Disturbance and Recovery (LandTrendr) algorithm is widely used for forest disturbance mapping. However, there are still two limitations in LandTrendr: first, it only...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-05, Vol.15 (9), p.2381
Main Authors: Qiu, Dean, Liang, Yunjian, Shang, Rong, Chen, Jing M.
Format: Article
Language:English
Subjects:
Algorithms
Carbon
Disturbance
Earth resources technology satellites
forest disturbance mapping
Forest ecosystems
Forest management
Forestry research
Forests
Google Earth Engine
Insects
Landsat
Landsat satellites
Landsat time series
LandTrendr
Mapping
Methods
multi-season
multispectral indices
Performance evaluation
Remote sensing
Seasons
Sensitivity analysis
Summer
Time series
Winter
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Summary:Forest disturbance detection is of great significance for understanding forest dynamics. The Landsat-based detection of the Trends in Disturbance and Recovery (LandTrendr) algorithm is widely used for forest disturbance mapping. However, there are still two limitations in LandTrendr: first, it only used for summer-composited observations, which may delay the detection of forest disturbances that occurred in autumn and winter by one year, and second, it detected all disturbance types simultaneously using a single spectral index, which may reduce the mapping accuracy for certain forest disturbance types. Here, we modified LandTrendr (mLandTrendr) for forest disturbance mapping in China by using multi-season observations and multispectral indices. Validations using the randomly selected 1957 reference forest disturbance samples across China showed that the overall accuracy (F1 score) of forest disturbance detection in China was improved by 21% with these two modifications. The mLandTrendr can quickly and accurately detect forest disturbance and can be extended to national and global forest disturbance mapping for various forest types.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15092381