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キーワード:
Paleo-NDVI, Hindcasting, Artificial neural network, Ecosystem modeling
要旨:
Vegetation dynamics play a crucial role in understanding the complex interactions between climate variability, ecosystem changes and human land use. In this study, we use the Hindcasting Ecosystems Model (HEMO) to reconstruct the paleo Normalized Difference Vegetation Index (paleo-NDVI), which provides valuable insights into the historical vegetation patterns. Our research focuses on the El Alto Ancasti mountain range, a region characterized by its rich environmental diversity, and archaeological evidence of pre-Hispanic agricultural village landscapes, showcasing the knowledge and management of diverse environments and their fluctuations. To reconstruct the paleo-NDVI, we employed a Neuronal Network trained with two datasets. First, we utilized NDVI data extracted from AVHRR/NOAA satellite images spanning from 1982 to 2015, representing 8 km pixels from the Global Inventory Modeling and Mapping Studies. Second, we incorporated growth ring chronologies from Northwest Argentina sourced from the International Tree Ring Data Bank, enabling the calibration of the NDVI-ring relationship. This methodology allowed us to reconstruct the paleo-NDVI back to 442 AD and to calculate anomalies. Our analysis revealed significant negative anomalies around 600 AD, coinciding with previous research conducted in the nearby Ambato valley. These anomalies may be attributed to the eruption of Tres Cruces volcano in Catamarca during that period. Additionally, extensive positive anomalies emerged from 1600 AD onwards, which align with anomalies detected in earlier studies. The eruption of Huaynaputina volcano in southern Peru in 1600 AD is a possible factor contributing to these positive anomalies. Ongoing investigations focusing on the paleoenvironment and human occupations in the El Alto-Ancasti area aim to provide further insights into how these detected changes could have influenced environmental management decisions. By integrating multidisciplinary approaches, we can better comprehend the complex relationship between vegetation dynamics, climatic events and human activities, ultimately enhancing our understanding of past environmental transformations and their implications for sustainable management strategies.
