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Remote Sensing Analytics 

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Analyzed AggieAir imagery is used to support a wide array of scientific research in water, agricultural, and natural resource managment. Analytic approaches in these areas developed by AggieAir researchers has been published and widely cited in professional remote sensing literature. 12 years of experience has made AggieAir a leader in a nation-wide call for scientific standards in UAV remote sensing applications and technology. 

 

Ground Cover Classification Analytics Analytics

Since the beginning, AggieAir researchers have advanced the use of remote sensing technology coupled with machine learning approaches to develop highly accurate classification methods. This is illustrated in the use of AggieAir imagery and machine learning approaches to classify invasive vegetation in wetlands, for example. Some of AggieAir's earliest work tracked the spread of Phragmites australis, in invasive reed causing serious damage to North American wetlands, using remote sensing techniques and machine learning. This research also resulted in a new understanding about the rate at which clones of Phragmites spread in dense, genetically identical patches.

In addition AggieAir remote sensing technology has seen the successful classification of submerged aquatic vegetation in a high-mountain lake, providing valuable information for management efforts to monitor and control the spreading of invasive milfoil. 
 

Ground Cover Analytics

Quantification of Biophyiscal Processes

Scientific-grade data generated through the application of AggieAir technology and protocols has led to a better understanding biophysical processes whose characteristics can be measured through the acquisition and analysis of high-resolution, multispectral imagery.

Below are a few examples:

  • AggieAir researchers are involved in a multi-year partnership with the agricultural Research Service in USDA and E&J Gallo Winery to develop better models for remote sensing of water consumption in wine grape vineyards.
  • AggieAir has developed machine learning models that uses AggieAir imagery to estimate surface and root-zone soil moisture in irrigation fields and to optimize the application of irrigation water.
  • AggieAir imagery and machine learning approaches have been used to remotely sense crop tissue chlorophyll content.
  • AggieAir have begun to research new analytic approaches for upscaling and downscaling methods to reconcile high-resolution UAV data with geographically coarse-scale satellite data.
BioPhysical Processes

The Need for Scientific-Grade Remote Sensing Standards

The current state of UAV-based remote sensing technology, especially with respect to commercial applications in agricultural management leaves much to be desired, particularly in regards to scientific content. AggieAir researchers have begun to advocate the identification of scient-based standards for agricultural remote sensing. 

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