Spatial prediction of permafrost occurrence in Sikkim Himalayas using logistic regression, random forests, support vector machines and neural networks

Abstract

We have generated permafrost probability distribution maps (10 m resolution) for the north-eastern Himalayan region in Sikkim using remote sensing measurements and machine learning algorithms. Four machine learning algorithms, logistic regression, random forests, support vector machines and neural networks, and two different sets of input data set, were used to generate a total of 8 machine learning models and hence 8 permafrost probability distribution maps. The first set of input data set included surface reflectance from atmospherically corrected Sentinel-2A spectral bands, elevation and slope parameters while the second set of input data set included mean annul air temperature (MAAT) and potential incoming solar radiation (PISR). Permafrost probability distribution maps obtained from the 8 models show reasonable agreement in the total spatial extent of permafrost occurrence but dissimilarities in the pattern of probability distribution. Accuracy assessment results are more optimistic towards models developed using spectral reflectance, elevation and slope parameters as input data set. Nevertheless, 5 out of 8 models agree that around 60% of total area under observation is highly likely to contain permafrost. This congruence in outputs, despite the use of different machine learning algorithms and separate sets of input data set, establishes reliability in the application of machine learning models for the preliminary estimation of permafrost distribution for remote and data-scarce Himalayan region.