Javier Lopatin

Javier Lopatin

@JavierLopatin

Remote sensing of Vegetation; UAVs; Hyperspectral; Structural Equation modeling

Universidad Adolfo Ibáñez Santiago, Chile
76
Followers
31
Following
31
Public Repos
0
Private Repos

Language Breakdown

Lines of code distribution across 28 owned repositories

45.3M Total LOC
Jupyter Notebook
43,138,170 lines
95.1%
N/A
R
691,640 lines
1.5%
N/A
HTML
686,510 lines
1.5%
N/A
Python
342,135 lines
0.8%
N/A
SCSS
261,406 lines
0.6%
N/A
Other
219,138 lines
0.5%
N/A
I

I-Shaped Developer

I-shaped

Specialist — deep expertise in Jupyter Notebook

Jupyter Notebook
R
HTML
Python
SCSS

Collaboration Network

Global Impact visualization

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Javier Lopatin
0 active collaborators

Repos

32

PRs

0

Growth

+18%

Top Collaborators

No collaborator data yet.

Coding Streak

Contribution activity over the past year

3 days
70
Contributions
69
Commits
1
Pull Requests
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Top Repositories

Python-Remote-Sensing-Scripts

Python 3.X scripts for remote sensing processing

44 24
Python
PhenoSensing

Phenological analysis of Remote Sensing data with Python

29 3
Python
UAV-InvasiveSpp

Mapping invasive tree species in Chile using UAV

8 3
R
plspmSpatial

Set of functions to help in Partial Least Square Path Modeling (PLS-PM) analysis in spatial ecology applications

5 0
R
Grassland-Species-Classification

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.

5 2
R
PLS-Path-Modeling

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

5 3
Jupyter Notebook
Peatland-carbon-stock

Codes for: Lopatin, J., et al. (2019). Using aboveground vegetation attributes as proxies for mapping peatland belowground carbon stocks. Remote Sens. Environ. 231, 111217

4 0
R
SpeciesRichness-GLMvsRF-LiDAR

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

4 9
Jupyter Notebook
Wetland_Phenology_Python

Phenology metrics for the San Francisco Bay area using Time Series of Sentinel-2 data

3 3
Jupyter Notebook
R-codes-and-functions

R-codes for Ecology and Remote Sensing Modeling

3 1
R

Open Source Impact

Contributions to external projects

18 merged PRs

No external contributions found.