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

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.

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.

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.

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.

ABSTRACT:

The images generated by high-resolution spectral and thermal sensors equipped on small unmanned aerial vehicles (sUAV) make possible estimation of energy flux for California vineyards via the two-source energy balance (TSEB) model. Temperature (thermal) image plays an important role in the TSEB model, and the high-resolution provides an opportunity for temperature separation, which may better delineate the energy flux between canopy and soil. However, with the exception of shadow effects, outliers are another major concern during data processing with a previous temperature separation algorithm that uses the relationship between the normalized difference vegetation index (NDVI) and the corresponding temperature pixel for temperature separation. An upgraded algorithm for temperature separation was introduced in the paper titled “The suitability of the TSEB model as a tool to estimate ET partitioning using improved LAI considering the difference of climate, soil, vine variety, and seasons for research areas across California,” and this research provides example data and the upgraded algorithm (a python programmed function) to demonstrate how we finished the temperature separation process.

ABSTRACT:

This is a project aiming at using python program to generate fractional cover, canopy height, and canopy width over canopy height for the TSEB model based on images collected via the AggieAir small unmanned aerial system (sUAS) platform over California vineyards.

ABSTRACT:

This project aims to use remote sensing data from the Landsata database from Google Earth Engine to evaluate the spatial extent changes in the Bear Lake located between the US states of Utah and Idaho. This work is part of a term project submitted to Dr Alfonso Torres-Rua as a requirment to pass the Remote Sensing of Land Surfaces class (CEE6003). More information about the course is provided below. This project uses the geemap Python package (https://github.com/giswqs/geemap) for dealing with the google earth engine datasets. The content of this notebook can be used to:

learn how to retrive the Landsat 8 remote sensed data. The same functions and methodology can also be used to get the data of other Landsat satallites and other satallites such as Sentinel-2, Sentinel-3 and many others. However, slight changes might be required when dealing with other satallites then Landsat.
Learn how to create time lapse images that visulaize changes in some parameters over time.
Learn how to use supervised classification to track the changes in the spatial extent of water bodies such as Bear Lake that is located between the US states of Utah and Idaho.
Learn how to use different functions and tools that are part of the geemap Python package. More information about the geemap Pyhton package can be found at https://github.com/giswqs/geemap and https://github.com/diviningwater/RS_of_Land_Surfaces_laboratory
Course information:

Name: Remote Sensing of Land Surfaces class (CEE6003)
Instructor: Alfonso Torres-Rua (alfonso.torres@usu.edu)
School: Utah State University
Semester: Spring semester 2023

ABSTRACT:

This is a resource that compiles the term projects done by the students of the CEE 5003 course Remote Sensing of Land Surfaces, Spring 2024. Presentation recordings are here https://www.youtube.com/playlist?list=PLOP6OF1n-WBGsQv4m0O3uNgL64yrF6cOZ