Javier Lopatin's Projects
R-scripts entitled "Using Multi-Sensor Data to Derive a Landscape-Level Biomass Map Covering Multiple Vegetation Types"
Theoretical and practical classes
Examples using Data Cube Chile, and Xarray
A set of miscellaneous functions for working in XArray and Data Cube environments.
Codes for: Javier Lopatin, Fabian E. Fassnacht, Teja Kattenborn, Sebastian Schmidtlein. Mapping plant species in mixed grassland communities using close range imaging spectroscopy. Remote Sensing of Environment 201, 12-23.
Package for the Satellite Alert and Monitoring System for Areas of Environmental Relevance (SAMSARA).
Python scripts for remote sensing analysis using the Data Cube Chile architecture
Codes for: Lopatin, J., et al. (2019). Using aboveground vegetation attributes as proxies for mapping peatland belowground carbon stocks. Remote Sens. Environ. 231, 111217
Data and R scripts for the manuscript 'Disturbance alters ecological links between soil peatland carbon pools and aboveground vegetation attributes'
Here, we explore the above- belowground interdependencies differences between a peatland with conservation and productive managements in their carbon stock
Phenological analysis of Remote Sensing data with Python
R-Codes for the paper 'Using a Multistructural Object-Based LiDAR Approach to Estimate Vascular Plant Richness in Mediterranean Forests With Complex Structure', IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 12, NO. 5
Set of functions to help in Partial Least Square Path Modeling (PLS-PM) analysis in spatial ecology applications
Python 3.X scripts for remote sensing processing
R-codes for Ecology and Remote Sensing Modeling
Repository with functions and tools for Radiative Transfer Models and Remote Sensing
R functions for the simulation of multispectral data based on hyperspectral
R-codes for: Lopatin, J., Dolos, K., Hernández, J., Galleguillos, M., Fassnacht, F. E. (2016): Comparing Generalized Linear Models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile. Remote Sensing of Environment 173, pp. 200–210. 10.1016/j.rse.2015.11.029
Example of PLS-PM models with stratified bootstrapping
Earth Observation Research Group, Universidad Adolfo Ibñañez
Curso electivo del programa de Master in Science of Data Science (MSDS), Universidad Adolfo Ibáñez, Chile
Mapping invasive tree species in Chile using UAV
Phenology metrics for the San Francisco Bay area using Time Series of Sentinel-2 data