Rangeland Ecology & Management

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REMOTE SENSING TECHNOLOGY AND UNMANNED AERIAL SYSTEMS (UAS) FOR THE PRECISION MANAGEMENT OF CROPS AND NATURAL RESOURCES.
Author
Landivar, Juan
Jung, Jinha
Maeda, Murilo
Chang, Anjin
Publisher
Society for Range Management
Publication Year
2016
Body

Crops are often unable to express their full genetic potential due to unfavorable environmental conditions. Timely identification of both biotic and abiotic sources of crop stress (e.g. drought, high temperature, and insect & disease pressure) has the potential to increase crop yields and decrease production costs by improving water, fertilizer, and pesticide management. Unmanned Aerial Systems (UAS) when properly equipped with remote sensors enable the acquisition of agronomic data at significantly higher spatial and temporal resolutions at much lower cost when compared to other traditional airborne and satellite platforms. Passive (multispectral and thermal) and active sensors (LiDAR) can collect valuable information on crop growth, development, and overall plant health. Crop canopy temperature is a simple measurement that has long been associated with plant water status. NDVI from multispectral data is also known to provide useful information on crop growth rates, canopy cover, and ultimately, crop photosynthetic efficiency. Measurements from the remote sensors are then integrated with position (recorded from GPS) and orientation (measured from Inertial Measurement Unit) of the UAS platform to generate seamless geospatial data products such as orthomosaic and Digital Surface Model (DSM). These geospatial data products can be used to 1) provide visual clues to agricultural scientists on the overall crop status, 2) monitor growth patterns at individual crop level, and 3) estimate above ground biomass throughout the crop's life cycle. The added capability to quantify crop stress values at any point during the growing season is a powerful tool for both management and screening purposes. Currently, the acquisition of temporal and spatial biomass data is performed by strenuous, destructive, and labor-intensive hand sampling practices. Our objective at Texas A&M AgriLife is to develop an UAS remote sensing data platform and processing/visualization tool that will enable the discovery and quantification of genotype, phenotype, and environment interactions.

Language
English
Resource Type
Text
Document Type
Conference Proceedings
Conference Name
SRM Corpus Christi, TX