Automated glacial lake extraction using an Object-Based Image Analysis approach in Google Earth Engine

Abstract. The combination of glacial retreat and climate change is increasing the number and size of glacial lakes globally. Many of these glacial lakes are in dangerous glaciated environments, and satellite remote sensing provides a way to improve monitoring efforts, though automated methods are needed to accurately and rapidly detect changes in these lakes. We undertake a total of 40 classification experiments to investigate the impact of classifier parameters, input features and training data on classification accuracy. We run 18 additional experiments to identify the optimal combination of Simple Non-Iterative Clustering segmentation parameters (connectivity and neighborhoodSize), assess the impact of input features, determine the required number of training and testing images and compare water extraction indices for the OBIA classification. Our results show that the best-performing combination of parameters was 100–250 training points per class, and values of four and 128 for connectivity and neighborhoodSize, respectively. The inclusion of input features such as hillshade, slope, the NDVI and MNDWI in our OBIA classifier improves the overall delineation of glacial lakes and other land classes in our study, particularly in shadow bodies, which are commonly misclassified as water bodies. Finally, we demonstrate that it is possible to accurately classify a time series of images using a single training image, with superior results compared to training with multiple images. We hypothesise that this is due to the complexities of radiometric sensitivity, heterogenous values for bands and indices and temporal changes in land cover throughout the study. Our OBIA approach is a more efficient and accurate way in mapping glacial lakes using Landsat 4-9 satellite imagery over traditional pixel-based approaches, with an overall accuracy of 94.6 %, with a producer’s accuracy and user’s accuracy of 95.3 % and 95.5 % respectively, for water. This suggests that this method has the potential to map glacial lakes accurately and rapidly over larger regions.