Mapping Ecosystems in the Peruvian Andes Using Hyperspectral Imagery and Machine Learning

Mapping the Peruvian Andes has high ecological value because its ecosystems are immensely diverse. These mountains shelter numerous endemic species that could be protected if informed decisions are made when delineating conservation zones. Rigorous analysis of high-altitude regions traditionally requires multiple field visits, which place a financial burden on research teams. Such visits can pose safety risks, as several remote areas are difficult to access on foot due to the steep gradients, cloud cover, and logistical limitations.

Recent advances in satellite missions and machine learning (ML) allow land-cover features to be characterised with fewer ground-truthing expeditions, by utilising patterns present in large imagery datasets. However, the Andes remain challenging to map, because of the spectral similarity among some land-use and land-cover (LULC) classes and because steep gradients can lead to geometric distortions in the recorded images. 

This study highlights an easy-to-use method for generating LULC map prototypes for high-altitude Andean regions using EnMAP and EMIT hyperspectral imagery (HSI). Machine learning algorithms (e.g., K-means clustering, principal component analysis) were applied to the HSI to generate clusters and extract features with high discriminant power among LULC types. Expert interpretation allowed pairing the obtained clusters with suitable ecosystem labels, producing prototype LULC maps.