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Hi 👋

I am Jonathan Solórzano, a biologist interested in using remote sensing to study and monitor tropical forests 🌳🛰️.

I am particularly keen on using multispectral, SAR, and LiDAR data with machine/deep learning algorithms to monitor land use/land cover (LULC) and aboveground biomass (AGB) changes 🤓.

I usually use R and GEE to make my analyses, and QGIS to visualize or perform other simple tasks.

You can visit this page to learn more about my research or take a look at my posts (mainly R code): https://jonathanvsv.github.io/Ppage2/

Jonathan V. Solórzano's Projects

class-hpv-deg icon class-hpv-deg

Classifies Landsat surface relectance annual mosaics (can be done with the scripts found here) in degraded and non degraded high productivity vegetation

cursoqgis icon cursoqgis

Curso básico para aprender a utilizar QGIS 3.4

ee-tensorflow-notebooks icon ee-tensorflow-notebooks

Repository to place example notebooks for Deep Learning applications with TensorFlow and Earth Engine.

eo-flow icon eo-flow

Collection of TensorFlow 2.0 code for Earth Observation applications

gee-ccdc-tools icon gee-ccdc-tools

Tools and Earth Engine apps to interact with the outputs from the CCDC algorithm

geocompr icon geocompr

Open source book: Geocomputation with R

landsat-sentinel-obs icon landsat-sentinel-obs

GEE Javascript API scripts to obtain the number of cloudless observations (using Landsat 4, 5, 7, 8 and Sentinel-2)

mxmosaic_ee icon mxmosaic_ee

Ayuda para correr la rutina en GEE que crea mosaicos

perpixelobssentinel-2 icon perpixelobssentinel-2

GEE Javascript API script for obtaining the number of cloudless observations using Sentinel-2 in MX (by ecoregion)

rsmodels icon rsmodels

Contains functions that enable the construction of linear models with different parameters, cross validation implementation and the calculation of a random goodness-of-fit ditribution. These models are specially useful for studies that use remote sensing variables as independent variables to describe or predict measured-in-field community attributes.

unet icon unet

Keras implementation of U-Net using R

vegcommunity icon vegcommunity

Calculate basic plant community structural and diversity attributes

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