Alfonso Faustino Torres-Rua

Utah State University | Assistant Professor

Subject Areas: UAV, remote sensing, satellites, agriculture

 Recent Activity

ABSTRACT:

Leaf area index (LAI) plays an important role in land-surface models to describe the energy, carbon, and water fluxes between the soil and canopy vegetation. Indirect ground LAI measurements, such as using the LAI2200C Plant Canopy Analyzer (PCA), can not only increase the measurement efficiency but also protect the vegetation compared with the direct and destructive ground LAI measurement. Additionally, indirect measurements provide opportunities for remote-sensing-based LAI monitoring. This project focuses on the extraction of several features observed using the LAI2200C PCA because the extracted features can help to explore the relationship between the ground measurements and remote sensing data. Although FV2200 software can provide convenient data calculation, data visualization, etc., it cannot generate features such as time, coordinates, and LAI from the data log for deeper exploration, especially when facing a large amount of collected data that needs to process. In order to increase efficiency, this project developed a simple python script for feature extraction, and demo data are provided.

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ABSTRACT:

The widely used two-source energy balance (TSEB) model coupled with AggieAir (https:// uwrl.usu.edu/aggieair/, a type of small Unmanned Aerial System) data can provide high-resolution modeled energy components, evapotranspiration partitioning, etc., at a subfield scale. When the research area is equipped with eddy-covariance (EC) towers, researchers often want to compare between the modeling results and the EC monitoring data within the footprint area once they run the TSEB model. However, we found the process to be time-consuming due to the large number of AggieAir flight images in the archive, as well as the uncertainty of some parameters (e.g., the G ratio). In order to increase the efficiency of modeling and verifying modeling results, this project adds some python scripts after the published TSEB model runs that consider the connection among the modeling results, the AggieAir platform, and the EC tower. This project is a part of our pending paper. Other researchers can also consider using this project if the available data are similar to ours.

Show More

ABSTRACT:

Energy flux and evapotranspiration modeling via the widely used two-source energy balance (TSEB) model at a subfield scale for vineyards based on the high-resolution images gained by the small Unmanned Aerial System (sUAS) is a critical tool for vine-growers and researchers to better understand the water and energy exchange between the land surface and air. The footprint area of the eddy-covariance (EC) tower is a crucial factor that can provide an efficient and effective channel for verification of modeling results (e.g., evapotranspiration and energy components). This project provides an efficient way to search parameters from the available dataset provided by the Grape Remote sensing Atmospheric Profiling and Evapotranspiration eXperiment (GRAPEX) team according to the AggieAir (https://uwrl.usu.edu/aggieair/, a type of sUAS) flight time, which can help in footprint area calculation. The list of footprint areas generated are intended to efficiently support research and promote a better understanding of the water and energy exchange. This project is also a part of our pending paper. Other researchers can also consider using this project if the available data are similar.

Show More

ABSTRACT:

Accurate leaf area index (LAI) estimation through machine learning (ML) algorithms is a channel for better understanding and monitoring the existing biomass and it relates to the distribution of energy fluxes and evapotranspiration partitioning. In order to support the ML algorithm for accurate LAI estimation, the supporting data (or features) gained from the sUAS platform are challenging in terms of variety, quantity, and quality. This project provides two types of feature-extraction approaches and the demo data to show how a variety of features are generated based on the sUAS platform via the python language. This project is also part of our pending paperwork. Other researchers can also use this project based on their sUAS platform to gain the features for estimation of their interested parameters, such as biomass and leaf water potential.

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ABSTRACT:

A first trial for merging the Google Earth Engine API into HydroShare.

Google EarthEngine API <a href="http://href="https://developers.google.com/earth-engine/api_docs" rel="nofollow">http://href="https://developers.google.com/earth-engine/api_docs</a>

The code here only provides connection to Earth Engine API yet. Does not interact with HS data or functions yet.

It uses git code published by Erik Tyler <a <a href="http://href="https://github.com/tylere/eeus2017-python" rel="nofollow">href="https://github.com/tylere/eeus2017-python</a>" <a href="http://rel="nofollow">https://github.com/tylere/eeus2017-python%3C/a" rel="nofollow">rel="nofollow">https://github.com/tylere/eeus2017-python</a</a>> to demonstrate adequate installation and setup

To Do:

Import /Export data from HS to Earth Engine
Permanent storing of Google EE key
Fix weird behavior of leaflet python module
Separate code in folders

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Resources
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Composite Resource 0
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Geographic Feature 0
Geographic Raster 0
HIS Referenced Time Series 0
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Multidimensional (NetCDF) 0
Script Resource 0
SWAT Model Instance 0
Time Series 0
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Composite Resource Composite Resource
Hydroshare-GoogleEarthEngine
Created: Nov. 10, 2017, 8:59 p.m.
Authors: Alfonso Torres-Rua

ABSTRACT:

A first trial for merging the Google Earth Engine API into HydroShare.

Google EarthEngine API <a href="http://href="https://developers.google.com/earth-engine/api_docs" rel="nofollow">http://href="https://developers.google.com/earth-engine/api_docs</a>

The code here only provides connection to Earth Engine API yet. Does not interact with HS data or functions yet.

It uses git code published by Erik Tyler <a <a href="http://href="https://github.com/tylere/eeus2017-python" rel="nofollow">href="https://github.com/tylere/eeus2017-python</a>" <a href="http://rel="nofollow">https://github.com/tylere/eeus2017-python%3C/a" rel="nofollow">rel="nofollow">https://github.com/tylere/eeus2017-python</a</a>> to demonstrate adequate installation and setup

To Do:

Import /Export data from HS to Earth Engine
Permanent storing of Google EE key
Fix weird behavior of leaflet python module
Separate code in folders

Show More
Composite Resource Composite Resource
Feature extraction approaches for leaf area index estimation in California vineyards via machine learning algorithms
Created: Oct. 25, 2021, 4:35 p.m.
Authors: Rui Gao · Torres-Rua, Alfonso Faustino · Aboutalebi, Mahyar · William A. White · Martha Anderson · William P. Kustas · Nurit Agam · Maria Mar Alsina · Joseph Alfieri · Lawrence Hipps · Nick Dokoozlian · Hector Nieto · Feng Gao · Lynn McKee · John H. Prueger · Luis Sanchez · Andrew J. Mcelrone · Nicolas Bambach Ortiz · Ian Gowing · Calvin Coopmans

ABSTRACT:

Accurate leaf area index (LAI) estimation through machine learning (ML) algorithms is a channel for better understanding and monitoring the existing biomass and it relates to the distribution of energy fluxes and evapotranspiration partitioning. In order to support the ML algorithm for accurate LAI estimation, the supporting data (or features) gained from the sUAS platform are challenging in terms of variety, quantity, and quality. This project provides two types of feature-extraction approaches and the demo data to show how a variety of features are generated based on the sUAS platform via the python language. This project is also part of our pending paperwork. Other researchers can also use this project based on their sUAS platform to gain the features for estimation of their interested parameters, such as biomass and leaf water potential.

Show More
Composite Resource Composite Resource
Footprint area generating based on eddy covariance records
Created: Oct. 25, 2021, 5:31 p.m.
Authors: Rui Gao · Nassar, Ayman · Torres-Rua, Alfonso Faustino · Lawrence Hipps · Mahyar Aboutalebi · William A. White · Martha Anderson · William P. Kustas · Maria Mar Alsina · Joseph Alfieri · Nick Dokoozlian · Feng Gao · Hector Nieto · Lynn McKee · John H. Prueger · Luis Sanchez · Andrew J. Mcelrone · Nicolas Bambach Ortiz · Ian Gowing · Calvin Coopmans

ABSTRACT:

Energy flux and evapotranspiration modeling via the widely used two-source energy balance (TSEB) model at a subfield scale for vineyards based on the high-resolution images gained by the small Unmanned Aerial System (sUAS) is a critical tool for vine-growers and researchers to better understand the water and energy exchange between the land surface and air. The footprint area of the eddy-covariance (EC) tower is a crucial factor that can provide an efficient and effective channel for verification of modeling results (e.g., evapotranspiration and energy components). This project provides an efficient way to search parameters from the available dataset provided by the Grape Remote sensing Atmospheric Profiling and Evapotranspiration eXperiment (GRAPEX) team according to the AggieAir (https://uwrl.usu.edu/aggieair/, a type of sUAS) flight time, which can help in footprint area calculation. The list of footprint areas generated are intended to efficiently support research and promote a better understanding of the water and energy exchange. This project is also a part of our pending paper. Other researchers can also consider using this project if the available data are similar.

Show More
Composite Resource Composite Resource
TSEB modeling and the comparison between the model results and the eddy-covariance monitored data within the footprint area
Created: Oct. 25, 2021, 6:03 p.m.
Authors: Rui Gao · Torres-Rua, Alfonso Faustino · Nassar, Ayman · Lawrence Hipps · Hector Nieto · Mahyar Aboutalebi · William A. White · Martha Anderson · William P. Kustas · Maria Mar Alsina · Joseph Alfieri · Nick Dokoozlian · Feng Gao · Lynn McKee · John H. Prueger · Luis Sanchez · Andrew J. Mcelrone · Nicolas Bambach Ortiz · Ian Gowing · Calvin Coopmans

ABSTRACT:

The widely used two-source energy balance (TSEB) model coupled with AggieAir (https:// uwrl.usu.edu/aggieair/, a type of small Unmanned Aerial System) data can provide high-resolution modeled energy components, evapotranspiration partitioning, etc., at a subfield scale. When the research area is equipped with eddy-covariance (EC) towers, researchers often want to compare between the modeling results and the EC monitoring data within the footprint area once they run the TSEB model. However, we found the process to be time-consuming due to the large number of AggieAir flight images in the archive, as well as the uncertainty of some parameters (e.g., the G ratio). In order to increase the efficiency of modeling and verifying modeling results, this project adds some python scripts after the published TSEB model runs that consider the connection among the modeling results, the AggieAir platform, and the EC tower. This project is a part of our pending paper. Other researchers can also consider using this project if the available data are similar to ours.

Show More
Composite Resource Composite Resource

ABSTRACT:

Leaf area index (LAI) plays an important role in land-surface models to describe the energy, carbon, and water fluxes between the soil and canopy vegetation. Indirect ground LAI measurements, such as using the LAI2200C Plant Canopy Analyzer (PCA), can not only increase the measurement efficiency but also protect the vegetation compared with the direct and destructive ground LAI measurement. Additionally, indirect measurements provide opportunities for remote-sensing-based LAI monitoring. This project focuses on the extraction of several features observed using the LAI2200C PCA because the extracted features can help to explore the relationship between the ground measurements and remote sensing data. Although FV2200 software can provide convenient data calculation, data visualization, etc., it cannot generate features such as time, coordinates, and LAI from the data log for deeper exploration, especially when facing a large amount of collected data that needs to process. In order to increase efficiency, this project developed a simple python script for feature extraction, and demo data are provided.

Show More