Image of UAV-based reference data for the prediction of fractional cover of standing deadwood from Sentinel time series

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UAV-based reference data for the prediction of fractional cover of standing deadwood from Sentinel time series

Increasing tree mortality due to climate change has been observed globally. Remote sensing is a suitable means for detecting tree mortality and has been proven effective for the assessment of abrupt and large-scale stand-replacing disturbances, such as those caused by windthrow, clear-cut harvesting, or wildfire. Non-stand replacing tree mortality events (e.g., due to drought) are more difficult to detect with satellite data – especially across regions and forest types. A common limitation for this is the availability of spatially explicit reference data. To address this issue, we propose an automated generation of reference data using uncrewed aerial vehicles (UAV) and deep learning-based pattern recognition. In this study, we used convolutional neural networks (CNN) to semantically segment crowns of standing dead trees from 176 UAV-based very high-resolution (

Availability
35621.3678Perpustakaan BIG (Eksternal Harddisk)Available
Detail Information
Series Title
ISPRS Open Journal of Photogrammetry and Remote Sensing
Call Number
621.3678
Publisher
Amsterdam : Elsevier.,
Collation
12 hlm PDF, 14.697 KB
Language
Inggris
ISBN/ISSN
1872-8235
Classification
621.3678
Content Type
text
Media Type
-
Carrier Type
-
Edition
Vol.8, April 2023
Subject(s)
Deep learning
Reference data
Standing deadwood
Tree mortality
Upscaling
Specific Detail Info
-
Statement of Responsibility
Other version/related

No other version available

File Attachment
  • UAV-based reference data for the prediction of fractional cover of standing deadwood from Sentinel time series
    Increasing tree mortality due to climate change has been observed globally. Remote sensing is a suitable means for detecting tree mortality and has been proven effective for the assessment of abrupt and large-scale stand-replacing disturbances, such as those caused by windthrow, clear-cut harvesting, or wildfire. Non-stand replacing tree mortality events (e.g., due to drought) are more difficult to detect with satellite data – especially across regions and forest types. A common limitation for this is the availability of spatially explicit reference data. To address this issue, we propose an automated generation of reference data using uncrewed aerial vehicles (UAV) and deep learning-based pattern recognition. In this study, we used convolutional neural networks (CNN) to semantically segment crowns of standing dead trees from 176 UAV-based very high-resolution (
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