Measuring forest loss with remote-sense data
We quantified changes in forest cover for each river catchment between
the before and after by comparing year-matched (i.e., from
1 January to 31 December in the year fish specimens were collected)
Landsat images on Google Earth Engine (GEE) (codes in Supplementary
Material 1).
First, we downloaded all raw images available of our sites taken in the
sampling year by USGS Landsat 5 (before time point) and USGS
Landsat 8 (after time point). These were used to create
cloud-free mosaics with the simple composite function on GEE. We
delineated target river catchments using HydroSHEDS polygons (Lehner &
Grill, 2013). In order to classify land-cover in the images with GEE’s
Random Forest machine learning algorithm, we plotted ‘ground-truth’
polygons comprising pixels associated with forest and non-forest
land-cover types. Our ‘ground-truth’ polygons were plotted manually over
areas which were clearly associated with any of the above land-uses
(e.g., residence complexes with urban land-use). We allocated 60% of
our ‘ground-truth’ points to the training of the GEE Random Forest
algorithm (or classifier) while the remaining data points were allocated
for classifier testing and validation. We included the following
spectral bands for land-cover classification: 1) RGB (red, green, and
blue) bands; 2) near infrared; 3) shortwave infrared 1 & 2; and 4)
thermal infrared 1 & 2. Generally, trained classifiers comprise models
which have been parameterised with ‘ground-truth’ training data which
can then be used to categorise all remaining pixels in relevant
satellite images into one of the three land-cover types of interest. We
tested the trained classifiers against our testing data (i.e., 40% of
‘ground-truth’ points) in a validation error matrix. Classification
accuracy for all eight classifiers (two time points across four river
catchments) were greater than the 95% threshold we set a priori .
Finally, we used the trained Random Forest classifiers to produce
land-cover raster layers of the catchments at both time points.
We quantified the following by comparing land-cover maps in thebefore and after time points:
- Proportion of total catchment area associated with forest loss
(ΔFtotal ).
- Net proportion of catchment area associated with forest loss
(ΔFnet ).
- Ratio of catchment area associated with forest loss versus forest gain
(ΔFratio ).
In addition to catchment-wide changes, we were also interested in
quantifying change in land-use cover in a sub-section of the overall
catchment area, specifically, in the area upstream of our sampling
points. To this end, we delineated upstream areas by processing
void-filled NASA Shuttle Radar Topographic (SRTM) Digital Elevation
Models (DEMs) (Jarvis et al., 2008) using watershed hydrological tools
on Whitebox GAT (Lindsay, 2016). We then quantified the following:
- Proportion of catchment area associated with forest loss in the
immediate upstream area of sampling points
(ΔFsub.total ).
- Net proportion of catchment area associated with forest loss in the
immediate upstream are of sampling points
(ΔFsub.net ).
- Ratio of catchment area associated with forest loss versus forest gain
in the immediate upstream area of sampling points
(ΔFsub.ratio ).