The utility of dynamic forest structure from GEDI lidar fusion in tropical mammal species distribution models.
Remote sensing is an important tool for monitoring species habitat spatially and temporally. Species distribution models (SDM) often rely on remotely-sensed geospatial datasets to predict probability of occurrence and infer habitat preferences. Lidar measurements from the Global Ecosystem Dynamic...
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
Frontiers Media S.A.
2025
|
| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/48211/1/dynamic%20forest.pdf http://ir.unimas.my/id/eprint/48211/ https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2025.1563430/full https://doi.org/10.3389/frsen.2025.1563430 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Remote sensing is an important tool for monitoring species habitat spatially and
temporally. Species distribution models (SDM) often rely on remotely-sensed
geospatial datasets to predict probability of occurrence and infer habitat
preferences. Lidar measurements from the Global Ecosystem Dynamics
Investigation (GEDI) are shedding light on three dimensional forest structure in
regions of the world where this aspect of species habitat has previously been
poorly quantified. Here we combine a large camera trap dataset of mammal
species in Borneo and Sumatra with a diverse set of geospatial data to predict the
probability of occurrence of 47 species. Multi-temporal GEDI predictors were
created through fusion with Landsat time series, extending back to the year 2001.
The availability of these GEDI-based forest structure predictors and other
temporally-resolved predictor variables enabled temporal matching of species
occurrences and hindcast predictions of species probability of occurrence at
years 2001 and 2021. Our GEDI-Landsat fusion approach worked well for forest
structure metrics related to canopy height (relative height of the 95th percentile
of returned energy R2 = 0.62 and relative RMSE = 41%) but, not surprisingly, was
less accurate for metrics related to interior canopy vegetation structure (e.g.,
plant area volume density from 0 to 5 m above the ground R2 = 0.05 and relative
RMSE = 85%). For the SDM analyses, we tested several combinations of predictor
sets and found that when considering a large pool of multiscale predictors, the
exact composition, and whether GEDI Fusion predictors were included, didn’t
have a large impact on generalized linear modeling (GLM) and Random Forest (RF) model performance. Adding GEDI Fusion predictors to a baseline set only meaningfully improved performance for some species (n = 4 for RF and n = 3 for
GLM). However, when GEDI Fusion predictors were used in a smaller predictor set
that is more suitable for hindcasting species probability of occurrence, more SDMs
showed meaningful performance improvements relative to the baseline model (n =
9 for RF and n = 4 for GLM) and the relative importance of GEDI-based canopy
structure predictors increased relative to when they were combined with the
baseline predictor set. Moreover, as we examined predictor importance and
partial dependence, the utility of GEDI Fusion predictors in hindcast models was
evident in regards to ecological interpretability. We produced a catalog of
probability of occurrence maps for all 47 mammals species at 90 m spatial
resolution for years 2001 and 2021, enabling subsequent ecological
interpretation and conservation analyses. |
|---|