Center for Precision Agricultural Systems

Research

A New Harvest Mapping Tool? A First Look at Canopy Density from Active Optical Measurements.

Eileen Perry, WSU Center for Precision Agricultural Systems

Jennifer Smithyman, Ste. Michelle Wine Estates, Prosser WA

 

Abstract- Methods- Results

 

Abstract:

Normalized Difference Vegetation Index (NDVI) maps from airborne images have been used by Ste. Michelle Wine Estates to guide selective harvests of several vineyard blocks in south-central Washington State.  During the 2006 season, a new approach for mapping the vineyard blocks was evaluated.  Ground based measurements were made with a commercially available active optical sensor, the GreenseekerÒ (Ntech Industries, Ukiah CA, USA).  This instrument illuminates the canopy with light in the red and near infrared wavelengths and measures the return reflectance.  Because the light is pulsed, the reflectance measured can be distinguished from natural light.  The sensor can be mounted on an ATV or tractor and combined with GPS to generate maps of a vineyard.  Active optical systems are intriguing because the measurements should be independent of time of day or cloud cover; in fact, the measurements could be made at night.  The 2006 GreenseekerÒ (GS) NDVI measurements were acquired along with the airborne imagery for a WSU research vineyard and several commercial vineyard blocks.  In this poster we compare the GS measurements for a research block for several dates in 2006, and compare concurrent GS maps and airborne NDVI maps for several blocks.

 

Whats the Difference?

 

 

Airborne imagery measures the sunlight reflected off the top of the grape canopy.  If there is vegetation growing between the rows, that will show up too.  (This extra vegetation can be edited from the image.)  The NDVI values in the imagery show us both the amount of chlorophyll, and the extent (i.e., the vigor) of the canopy.

 

 

 

 

The GreenseekerÒ actively pulses light in the red and near infrared wavelengths, making it independent of sky conditions.  We set the sensors up to view the sides of the grape canopy, with each NDVI measurement  (taken at 1 second intervals) representing a narrow vertical profile about 3 feet in height.  The GPS location of the ATV are collected simultaneously, so that by driving up and down the rows we generate NDVI maps.

 

Methods:

2006 Measurements

  • Multiple Sites: WSU research block and several commercial blocks of Ste. Michelle

  • NDVI from imagery: multiple dates from June-Oct

  • NDVI from GS: July-Oct for research block, July for commercial block

Example 1

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Example 2

GS measurements collected about 1 week apart on juice grapes (top & bottom) show some of the same patterns of canopy density as the imagery (middle).
The GS measurements made on the first date show obvious differences between the sides of the canopy (below).

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Example 3

This is an example with good agreement between the GS and imagery NDVI maps.  The individual GS measurements (left middle) from both sides of the canopy were spatially interpolated (using an inverse distance with 30.5 m search radius) to produce the map in the lower left.  The harvest zone map shown at right was generated from the imagery and was used to selectively harvest.

 

Results:

What we've learned so far

•We do see similarities between airborne and GS data for large areas or trends within some blocks

•GS data shows differences between sides of the canopy for many blocks

•Technical problems include losing GPS signal and missing measurements

•More research is needed on how best to collect and process the GS data.

•What methods are best for interpolation?

•How many rows can we skip for field measurements?

•Can we generate harvest maps using both sides of canopy?

•Results are promising, but more experience is needed before we can use GS data reliably to make harvest maps.

 

Acknowledgements

The authors would like to thank our grower collaborators for permission to analyze and present data from their vineyards, and Ntech Industries for providing technical support.

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