Comprehensive forest report

Flown by Phil Woolery, Agriculture & Natural Resource Educator, Purdue Extension – Starke and Pulaski Counties

To explore UAV uses in forestry applications, several forests were flown over the course of 2019. One forest had documented history of planting and harvests and flown in two missions in August using a Phantom 4 Pro V2.0 with standard RGB and Sentera Double 4K cameras.

This stitched map was produced of the forest and subsequently analyzed. As you scroll down, you'll see RGB images allow identification of specific tree species upon color and texture.

Image of a forest, RGB images allow identification of specific tree species upon color and texture

RGB images allow identification of specific tree species upon color and texture Tree identification
RGB images allow identification of specific tree species upon color and texture Tree mortality

Tree stand and growing conditions

By using mapping tools such as the elevation tool in Drone Deply, we get values for total tree heights and location of young and short trees. We can also locate the best growing conditions.

Image of map with values for total tree heights and location of young and short trees

Image of The canopy gaps in the forest show up by elevation The canopy gaps show up by elevation, and in this instance, are due to a recent timber harvest and sale. This can be useful to compare pre- and post-harvest to evaluate goals.
Image of a closeup and map of canopy gap along with square feet. Closeup and map of canopy gap along with square feet.

Plant health features

Differences can be seen when comparing plant health maps, such as VARI, NDVI and NDRE.

In comparing the three models, we can see similar patterns. The NDVI and NDRE show a few more differences than VARI. All three do a nice job in highlighting trees.

Disease and stress pressure

NDVI indicated unhealthy trees in a few instances, while the RGB did not indicate any obvious sign of stress or disease.

Ground truthing:

Several of the trees were checked by ground in September. Overall, it appears the NDVI is doing a good job pinpointing stress. Further investigation will evaluate potential disease spotting and susceptibility in tree breeding.

A black walnut was planted 33 years ago with a diameter of 14 inches – a growth rate of about a half inch per year. The crown looked large and healthy. This particular tree showed signs of stress in the NDVI map, but may be a result of growth pattern. Vigorous growing black walnut trees will continue growth late into the summer, and the new growth may be a result of the NDVI data.

Image of eastern white pine The eastern white pine in this photo showed signs of stress in the NDVI. It’s likely suffering from allelopathy from the black walnut.
Image of a hackberry tree The third tree identified is a hackberry. It appeared healthy but was in a suppressed canopy position. It could be the leaf color that indicated stress in the NDVI.

Natural Resources Examples

Image of a tree plantation the VARI algorithm
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Aerial RBG image of a shallow section of Bass Lake
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Invasive species: honeysuckle

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Oak Wilt

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Image dead trees cause by the Emerald Ash Borer
Tree loss from invasive species

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RGB images allow identification of specific tree species upon color and texture
Comprehensive forest report

To explore UAV uses in forestry applications, several forests were flown over the course of 2019....

Learn More