An NDVI image provides critical crop information that is not visible in an RGB or NIR image of the same scene. For example, plants may appear green

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2 Normalized Difference Vegetation Index (NDVI) Spectral Band calculation that uses the visible (RGB) and near-infrared (NIR) bands of the electromagnetic spectrum NDVI= +

3 An NDVI image provides critical crop information that is not visible in an RGB or NIR image of the same scene. For example, plants may appear green in an RGB image but if the plants are no longer photosynthetically active the same plants will appear black in an NDVI image.

4 Leaf optical responses to a wide range of biotic and abiotic stresses have been widely researched. Some researched plant stressors include[1]: Increased CO2 and other gaseous pollutants Heat stress Heavy metal toxicity Exposure to ultraviolet radiation Water status Insect pest attack Herbicide treatment Salinity effects Extremes in nutrient availability

5 Leaf spectral reflectance provides a vast data resource for assessing plant health based on the impact of biotic and abiotic stresses on leaf biochemistry and anatomy which in turn produces distinct changes in leaf optical properties. Unfavorable growing conditions result in morphological, physiological and/or biochemical changes that impact the manner with which plants interact with light. [1] Key regions of a reflectance spectrum are: 1. Blue region ( nm) which is strongly influenced by absorption of chlorophylls and carotenoids. 2. Blue-green edge ( nm) leading to the green peak at 550 nm. 3. Red edge ( nm) associated with strong chlorophyll absorption nm range the reflectance characteristics are influenced by cell structure nm range the reflectance characteristics are influenced by water content in the tissues

6 Light falling on a leaf can be reflected, absorbed or transmitted. Absorption in the visible (VIS) and infrared (IR) regions of the spectrum is primarily driven by stretching and bending of covalent bonds between oxygen, carbon, hydrogen and nitrogen present in plant biochemical components like sugar, lignin, cellulose and proteins. [1] In addition, pigments responsible for leaf color also constitute principal absorbing molecules. Because of the central function of these pigments in photosynthesis, chlorophyll content is generally regarded as a good indicator of plant physiological health. [1] Many nutrient deficiencies result in a decrease in chlorophyll content, a concomitant increase in reflectance in the visible ( nm) and infrared ( nm) ranges and blue shift in the red edge inflection point. Visually, chlorotic changes are perceived as yellowing of leaves. [1]

7 Reflectance patterns are influenced by leaf surface features, internal architecture and biochemical composition. [1] Strong Reflectance in intercellular space in Near Infrared (NIR) key anatomical structures in relation to their mode of interaction with light.

8 Reference [5]

9 Extensive research has been conducted over the past 40 years on the actionable information that can be extracted from NDVI imagery. [2] NDVI can be used to detect plant stress due to: Water deficiency Disease Leaf nitrogen content Photosynthetic activity. NDVI can be used to identify locations in a field that may need attention; where leaf and soil samples should be taken for more detailed information.

10 Location and dosage recommendations for variable rate applications of fertilizers (i.e. Nitrogen), pesticides, and herbicides (i.e. Kg/hectare) Grazing potential of pastures based on relative photosynthetic activity Evaluating the impact of recommended applications and dosages on plant health Recommended water applications and dosages Detecting and locating water drainage problems Detecting and locating locations requiring re-seeding (i.e. GPS coordinates of sections of rows needing re-seeding, missed areas or water logged areas needing leveling, tiling and reseeding) Standcount/Yield estimation Damage assessment due to herbicide, hail, wind, flooding, draught, insect infestation, and plant disease NDVI has strong correlation to Nitrogen and Chlorophyll contents in the plants. Closely monitoring NDVI with real-time feedback allows rapid response from the farmer to prevent and take corrective action.

11 Unlike satellite remote sensing, UAV remote sensing is timely (i.e. can be taken precisely when needed for example during critical growing season events such as plant emergence or pollination), high resolution (i.e. centimeters squared versus tens or hundreds of meters squared),and less affected by environmental conditions (i.e. can fly under cloud cover). The progression of the crop can be altered by remote sensing by: 1. Detecting and locating gaps in the crop during plant emergence and providing the information needed to reseed the affected rows 2. Detecting and locating drainage problems and providing information needed to take corrective action (i.e. reseed affected areas, level fields, insert drainage tiles etc.) 3. Detecting and locating diseased or stressed crops so that corrective action can be taken (i.e. identify where pesticides, fertilizer or water should be applied). Early detection and corrective action will impact crop yield and the progression of the crop.

12 Image Number: 246 Height above ground = m ( ft) Speed = m/s ( mph) GSD = cm ( inches) RGB Info: Exposure = ms Distance Traveled During Exposure = cm Translation-based smearing = pixels Mono Info: Exposure: ms Distance Traveled During Exposure = cm Translation-based smearing = pixels GEMS Imagery: Photosynthetic Activity 2014 Sentek Systems LLC Released 12

13 GEMS Imagery: Photosynthetic Activity 2014 Sentek Systems LLC Released 13

14 GEMS Imagery: Photosynthetic Activity 2014 Sentek Systems LLC Released 14

15 Image Number: 245 Height above ground = m ( ft) Speed = m/s ( mph) GSD = cm ( inches) RGB Info: Exposure = ms Distance Traveled During Exposure = cm Translation-based smearing = pixels Mono Info: Exposure: ms Distance Traveled During Exposure = cm Translation-based smearing = pixels GEMS Imagery: Photosynthetic Activity 2014 Sentek Systems LLC Released 15

16 GEMS Imagery: Photosynthetic Activity 2014 Sentek Systems LLC Released 16

17 GEMS Imagery: Photosynthetic Activity 2014 Sentek Systems LLC Released 17

18 GEMS Imagery: Pasture Grazing Potential Un-mowed Pasture Mowed Pasture

19 GEMS Imagery: Pasture Grazing Potential Un-mowed Pasture Mowed Pasture

20 GEMS Imagery: Pasture Grazing Potential Un-mowed Pasture Mowed Pasture

21 Un-mowed Pasture GEMS Imagery: Pasture Grazing Potential Mowed Pasture Drying Corn

22 Un-mowed Pasture GEMS Imagery: Pasture Grazing Potential Mowed Pasture Drying Corn

23 Un-mowed Pasture GEMS Imagery: Pasture Grazing Potential Mowed Pasture Drying Corn

24 GEMS Imagery: Field Drainage

25 GEMS Imagery: Field Drainage

26 GEMS Imagery: Field Drainage

27 GEMS GEMS Imagery: Imagery: Field Field Drainage Drainage

28 GEMS GEMS Imagery: Imagery: Field Field Drainage Drainage

29 GEMS Imagery: Field Drainage

30 GEMS Imagery: Gaps GEMS in Imagery: Field Seeding Field Drainage

31 GEMS Imagery: Gaps Field Seeding GEMSinImagery: Field Drainage

32 GEMS Imagery: Gaps GEMS in Imagery: Field Seeding Field Drainage

33 GEMS Imagery: Gaps GEMS in Imagery: Field Seeding Field Drainage

34 GEMS Imagery: Gaps GEMS in Imagery: Field Seeding Field Drainage

35 GEMS Imagery: Gaps GEMS in Imagery: Field Seeding Field Drainage

36 Fly Analyze Data on Laptop Take Corrective Action Identify trouble areas in field with GPS Coordinates GEMS optimizes the workflow Take Samples in Field The GEMS hardware and software allows for rapid processing and analyzing of the data to identify trouble areas immediately with GPS coordinates. The removal of having to upload the imagery to the cloud for computing or a 3 rd party is eliminated.

37 GEMS Imagery Frequency of Imagery Everyday or as much as farmer demands. Can be flown daily during critical periods of the growing season Spatial Resolution of Airborne Data Affects of weather Filters Sub cm level. ( < 6 Only cant fly in rain but obtains accurate NDVI with cloud coverage Narrowband spectral band filters Satellite Imagery On average 4 times a month. Every 16 days for Landsat 4-5 TM and Landsat -7 ETM + 30 meters for Landsat 4-5 TM and Landsat-7 ETM+ Cannot get NDVI imagery with any cloud coverage or rain. Fixed wideband spectral band filters UAVs can obtain faster and more accurate NDVI for timely assessment of crop health to provide immediate feedback to the farmer to take action.

38 UAV Versus Satellite NDVI A single pixel in the satellite focal plane array senses reflected light from a patch of ground of size 30m x 30m = 900 meter squared A single pixel in the UAV focal plane array senses reflected light from a patch of ground of size 1cm x 1cm = 1 centimeter squared. Hence, the NDVI value of a single pixel in a satellite NDVI image is the average value of NDVI over a 900 square meter patch of ground and the NDVI value in a single pixel of a UAV NDVI image is the average value of NDVI over a 1 centimeter square patch of ground. Early in the growing season, when the crop has not fully covered the soil, an NDVI value from a satellite NDVI image pixel is an average of crop and soil NDVI values over the 900 square meter patch of ground. Hence, it is not an accurate measure of crop photosynthetic activity since the crop has been mixed with the soil. In contrast, the NDVI value from a single pixel of a UAV NDVI image with 1 square centimeter resolution will typically correspond to a patch of ground which contains either crop or soil but not a mixture of both (i.e. observe in the limit as resolution goes to zero there will either be crop or soil but not both at a single point in space).

39 UAV Versus Satellite NDVI Early detection, diagnosis, and corrective action, requires high resolution NDVI imagery which will detect crop stress over much smaller areas when the problems are just emerging. High resolution UAV based NDVI imagery enables much earlier detection, diagnosis, continuous monitoring, and corrective action than low resolution satellite imagery. It is known from prior research that field nitrogen content can vary significantly over distances of one meter. NDVI and other vegetation indices have been correlated with nitrogen content. Modern variable rate spreaders can take advantage of high resolution data to apply fertilizer or pesticides at a one meter square level of resolution. Hence, UAV based NDVI imagery can support variable rate spreaders operating at the 1 meter squared level of resolution whereas satellite based NDVI imagery can only support variable rate spreaders operating at 900 square meter level of resolution. Significant cost savings in fertilizer and pesticides is possible with higher resolution NDVI imagery and variable rate spreaders. Having the capability to vary the application of pesticide or fertilizer at the 1 meter squared level of resolution should provide cost savings over varying the application of pesticides or fertilizer at the 900 square meter level of resolution. Hence, UAV based high resolution NDVI imagery has the potential to increase cost savings compared to lower resolution satellite based NDVI imagery.

40 UAV NDVI

41 Satellite NDVI

42 References 1. Signature Optical Cues: Emerging Technologies for Monitoring Plant Health, Sensors 2008, 8, ; DOI: /s Hyperspectral Remote Sensing of Vegetation Edited by Prasad S. Thenkabail, John G. Lyon, Alfredo Huete, CRC Press Remote Sensing for Crop Management, Photogrammetric Engineering & Remote Sensing Vol. 69, No. 6, June 2003, pp Airborne imaging aids vineyard canopy evaluation California Agriculture 50(4): DOI: /ca.v050n04p14. July-August