Use of TanDEM-X and Sentinel Products to Derive Gully Activity Maps in Kunene Region (Namibia) Based on Automatic Iterative Random Forest Approach
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Date
2021
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Abstract
Gullies are landforms with specific patterns of shape,
topography, hydrology, vegetation, and soil characteristics. Remote
sensing products (TanDEM-X, Sentinel-1, and Sentinel-2) serve
as inputs into an iterative algorithm, initialized using a micromapping
simulation as training data, to map gullies in the northwestern
of Namibia. A Random Forest Classifier examines pixels
with similar characteristics in a pool of unlabeled data, and gully
objects are detected where high densities of gully pixels are enclosed
by an alpha shape. Gully objects are used in subsequent iterations
following a mechanism where the algorithm uses the most reliable
pixels as gully training samples. The gully class continuously grows
until an optimal scenario in terms of accuracy is achieved. Results
are benchmarked with manually tagged gullies (initial gully labeled
area <0.3% of the total study area) in two different watersheds
(408 and 302 km2, respectively) yielding total accuracies of>98%,
with 60% in the gully class, Cohen Kappa >0.5, Matthews Correlation
Coefficient >0.5, and receiver operating characteristic
Area Under the Curve >0.89. Hence, our method outlines gullies
keeping low false-positive rates while the classification quality has
a good balance for the two classes (gully/no gully). Results show
the most significant gully descriptors as the high temporal radar
signal coherence (22.4%) and the low temporal variability in Normalized
Difference Vegetation Index (21.8%). This research builds
on previous studies to face the challenge of identifying and outlining
gully-affected areas with a shortage of training data using global
datasets, which are then transferable to other large (semi-) arid
regions
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Keywords
Arid regions, soil erosion mapping, Namibia, random forest (RF), iterative learning, land degradation, automatic classification
Citation
Orti, M. V., Winiwarter, L., Corral-Pazos-de-Provens, E., Williams, J. G., Bubenzer, O., & Höfle, B. (2020). Use of TanDEM-X and Sentinel products to derive gully activity maps in Kunene Region (Namibia) based on automatic iterative Random Forest approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.