Remote Sensing and Geomorphological Covariates and Labels of main Peruvian Glaciers

Abstract: The Peruvian Glacier Classification Dataset is a comprehensive collection designed to facilitate research and analysis in the field of glaciology and remote sensing. Comprising a total of 9 rasterStacks, each representing distinct glacier regions within Peru, the dataset offers a rich set of 25 morphological and Landsat 8 derived covariates for in-depth study. These covariates have been meticulously selected to capture a wide range of features and characteristics associated with glaciers and their surrounding environments. Moreover, the dataset includes true labels indicating the classification of each pixel into Glacier and Non-Glacier classes, as determined by the National Inventory of 2017.

Description: The Peruvian Glacier Classification Dataset presents a valuable resource for researchers, scientists, and practitioners interested in glacial dynamics, environmental monitoring, and geospatial analysis. Comprising 9 main glacier regions within Peru (Blanca, Central, Huallanca, Huayhuasha, Huaytapallana, Raura,Urubamba, Vilcabamba,Vilcanota), the dataset provides an extensive collection of covariates derived from both morphological features and Landsat 8 satellite imagery. These covariates have been processed and curated to offer comprehensive insights into the intricate nature of glaciers and their surroundings.

Key Features:

1. Morphological Covariates: The dataset encompasses a diverse array of morphological features extracted from high-resolution elevation data calculated with SAGA GIS. 

2. Landsat 8 Derived Covariates: Landsat Covariates corresponding to the period 2017–2018 obtained from Landsat Collection 2 Level 2 and Tier 1 surface reflectance (SR) products (Vermote et al., 2016) available online: https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance (accessed on 1 July 2023). procesing with GoogleEarth Engine study. https://code.earthengine.google.com/fea31b7f2a3fdbc1065644616819134a. This includes the following covariates: BLUE, GREEN, RED, NIR, SWIR1, SWIR2, NDFI, ndsi, ndvi, NDWI , NDWIns, NDSInw, nbr2, VNSIR, NDMI, TCG .

3. True Labels: Ground truth information is provided through accurate classification labels for each pixel, classifying it as either belonging to the Glacier or Non-Glacier class. These labels are based on the authoritative National Inventory of 2017, enhancing the reliability and usability of the dataset.