10.5061/DRYAD.9W0VT4BGR
Bae, Soyeon
0000-0003-1961-1226
University of Würzburg
Müller, Jörg
University of Würzburg
Förster, Bernhard
Technical University of Munich
Hilmers, Torben
0000-0002-4982-8867
Technical University of Munich
Hochrein, Sophia
University of Würzburg
Jacobs, Martin
0000-0002-2906-8661
Technical University of Munich
Leroy, Benjamin
0000-0001-6007-7948
Technical University of Munich
Pretzsch, Hans
Technical University of Munich
Weisser, Wolfgang
Technical University of Munich
Mitesser, Oliver
0000-0002-3607-877X
University of Würzburg
Data for: Tracking the temporal dynamics of insect defoliation by
high-resolution radar satellite data
Dryad
dataset
2021
FOS: Earth and related environmental sciences
Canopy herbivory
Defoliation severity
Lymantria dispar
Insect disturbance
Intra-annual time-series
Remote sensing
Sentinel-1
Radar remote sensing
Bayerisches Staatsministerium für Ernährung, Landwirtschaft und Forsten
https://ror.org/0251nbz82
ST357
Bayerisches Staatsministerium für Ernährung, Landwirtschaft und Forsten
https://ror.org/0251nbz82
Z073
National Research Foundation of Korea
https://ror.org/013aysd81
2020R1A6A3A03038391
2021-09-30T00:00:00Z
2021-09-30T00:00:00Z
en
https://doi.org/10.5061/dryad.1vhhmgqrv
https://doi.org/10.1111/2041-210X.13726
https://doi.org/10.1002/2688-8319.12045
https://doi.org/10.1038/s41467-019-12737-x
258874 bytes
8
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
1. Quantifying tree defoliation by insects over large areas is a major
challenge in forest management, but it is essential in ecosystem
assessments of disturbance and resistance against herbivory. However, the
trajectory from leaf-flush to insect defoliation to refoliation in
broadleaf trees is highly variable. Its tracking requires high temporal-
and spatial-resolution data, particularly in fragmented forests. 2. In a
unique replicated field experiment manipulating gypsy moth Lymantria
dispar densities in mixed-oak forests, we examined the utility of publicly
accessible satellite-borne radar (Sentinel-1) to track the fine-scale
temporal trajectory of defoliation. The ratio of backscatter intensity
between two polarizations from radar data of the growing season
constituted a canopy development index (CDI) and a normalized CDI (NCDI),
which were validated by optical (Sentinel-2) and terrestrial laser
scanning (TLS) data as well by intensive caterpillar sampling from canopy
fogging. 3. The CDI and NCDI strongly correlated with optical and TLS data
(Spearman’s ρ=0.79 and 0.84, respectively). The ∆NCDIDefoliation
(A-C) significantly explained caterpillar abundance (R2=0.52). The NCDI at
critical time-steps and ΔNCDI related to defoliation and refoliation well
discriminated between heavily and lightly defoliated forests. 4. We
demonstrate that the high spatial and temporal resolution and the cloud
independence of Sentinel-1 radar potentially enable spatially unrestricted
measurements of the highly dynamic canopy herbivory. This can help monitor
insect pests, improve the prediction of outbreaks, and facilitate the
monitoring of forest disturbance, one of the high priority Essential
Biodiversity Variables, in the near future.
1. Data acquisition Sentinel-1 C-band SAR data and Sentinel-2 optical data
were obtained from the ESA Scientific Hub (https://scihub.copernicus.eu/).
Terrestrial laser scanning was conducted to track the forest canopy
structure in three dimensions before and after insect defoliation. 2.
Data processing For the Sentinel-1 data, all available level-1
GRDH products by the IW mode were pre-processed using the Sentinel
Application Platforms (SNAP) Sentinel-1 Toolbox software and transformed
to γ0 (see the Supplementary Note S 3.2 in Bae et al. (2019) and the batch
processing graph at
https://github.com/So-YeonBae/Sentinel1-Biodiversity). The γ0 values of
the VV and VH polarizations were converted to dB as 10×log10γ0 and their
time-series values were smoothed using the Gaussian window function. The
difference between the smoothed values of VV and VH, defined herein as the
canopy development index (CDI, unit: dB), was computed. The normalized CDI
(NCDI) was calculated by dividing the time-series CDI values at each plot
by the minimum CDI value during the leaf-off season. Four critical
time-steps (A, B, C, and D) indicating prominent phenological transitions
were selected from the 18 time-steps of the NCDI of the 12-day composite
time-series data. These four critical time-steps were used to calculate
two indices related to defoliation and two indices related to
refoliation. Please find the details from Bae et al.
(2021) "Tracking the temporal dynamics of insect defoliation by
high-resolution radar satellite data"
Each tab contains the data for making each figure in "Tracking the
temporal dynamics of insect defoliation by high-resolution radar satellite
data" (Bae et al.). Original paper DOI: 10.1111/2041-210X.13726