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This paper investigates differences between morphometric parameters (slope and aspect) derived from a resampled DEM and resampled morphometric data derived from a medium resolution DEM, with examples for three study areas in South America selected to represent flatlands, hilly terrain, and mountain ranges. Using a low resolution DEM for regional scale morphometric analysis is not an optimal choice, since attenuation of elevation will strongly affect the distribution of calculated parameters. Unless bounded by computational constraints, one should choose to derive basic morphometric parameters from higher resolution data, and resample it to a coarser resolution as needed.

Event-based landslide inventories are essential sources to broaden our understanding of the causal relationship between triggering events and the occurring landslides. Moreover, detailed inventories are crucial for the succeeding phases of landslide risk studies like susceptibility and hazard assessment. The openly available inventories differ in the quality and completeness levels. Event-based landslide inventories are created based on manual interpretation, and there can be significant differences in the mapping preferences among interpreters. To address this issue, we used two different datasets to analyze the potential of U-Net and machine learning approaches for automated landslide detection in the Himalayas. Dataset-1 is composed of five optical bands from the RapidEye satellite imagery. Dataset-2 is composed of the RapidEye optical data, and ALOS-PALSAR derived topographical data. We used a small dataset consisting of 239 samples acquired from several training zones and one testing zone to evaluate our models’ performance using the fully convolutional U-Net model, Support Vector Machines (SVM), K-Nearest Neighbor, and the Random Forest (RF). We created thirty-two different maps to evaluate and understand the implications of different sample patch sizes and their effect on the accuracy of landslide detection in the study area. The results were then compared against the manually interpreted inventory compiled using fieldwork and visual interpretation of the RapidEye satellite image. We used accuracy assessment metrics such as F1-score, Precision, Recall, and Mathews Correlation Coefficient (MCC). In the context of the Nepali Himalayas, employing RapidEye images and machine learning models, a viable patch size was investigated. The U-Net model trained with 128 × 128 pixel patch size yields the best MCC results (76.59%) with the dataset-1. The added information from the digital elevation model benefited the overall detection of landslides. However, it does not improve the model’s overall accuracy but helps differentiate human settlement areas and river sand bars. In this study, the U-Net achieved slightly better results than other machine learning approaches. Although it can depend on architecture of the U-Net model and the complexity of the geographical features in the imagery, the U-Net model is still preliminary in the domain of landslide detection. There is very little literature available related to the use of U-Net for landslide detection. This study is one of the first efforts of using U-Net for landslide detection in the Himalayas. Nevertheless, U-Net has the potential to improve further automated landslide detection in the future for varied topographical and geomorphological scenes.

Aim: There is little consensus on which environmental variables are best at predicting multiple dimensions of diversity. We ask whether there are common environmental correlates of diversity, despite ecological differences, across nine clades of plants and animals distributed along a single rainforest domain. For that, we compare the environmental correlates of species richness, phylogenetic diversity, and phylogenetic endemism.Location: Brazilian Atlantic Forest.