David Macias Jr.

David Macias Jr.
B.S, Civil Engineering, Undergraduate, 12/12/2021

Cohort Level: Cohort - V

Expected Graduation Date: December 12, 2021

Degree: B.S Civil Engineering

Research Title: Drone-based environmental monitoring of vegetation

Research Synopsis: Environmental monitoring plays a central role in the management of agricultural and environmental systems. Unmanned aerial vehicles (UAVs) with imaging sensors can obtain precise and real-time measurements, including generating graphical system models for environmental management and precision agriculture. The purpose of this research study is to use UAVs with mounting spectral sensors for environmental monitoring of vegetation in agricultural fields. Detail objectives include determining specific spectral features corresponding to specific crops from spectral images and assessing crop health. In this research project, a DJI Matrices 100 UAV equipped with a hyperspectral sensor (400 – 1000nm) is being used to collect vegetation measurements at the Texas A&M AgriLife Research Station in Socorro, Texas. Special attention is being directed to pre-flight procedures, field operations, and post-processing methods. UgCS for DJI and HyperSpec III from Headwall are assisting in mission planning and sensory configuration that includes radiometric adjustments. SpectralView from Headwall is being used for processing raw vegetation data into calibrated ortho-rectified hyperspectral data. SpectralView is also being used to create geometric corrections, image mosaics, white-reference calibrations, and extract relevant metrics for remote sensing applications. The first phase of the research project specifically targets chlorophyll and NDVI (Normalized Difference Vegetation Index) as indicators of crop health. ENVI (Environment for Visualizing Images) software is assisting in the processing and analysis of geospatial imagery, including using its NDVI tool to measure vegetation density. UAVs with mounting sensors can effectively collect environmental data with a high spatial resolution for environmental and agricultural monitoring.