This note proposes a Light Emitting Diode (LED) simulator that would assist in the collection and analysis of normalized difference vegetation index (NDVI) images. A prototype simulator and a NIR/red vs NIR/white light NDVI comparison example is described.
The packaging of color filters in Raspberry Pi NoIR cameras, Infragram starter kits and the development of open source image processing software has provided increase access to DIY NDVI remote sensing technology. While these tools are easy to use, NDVI applications are often faced with challenges that frustrate successful NDVI data analysis. These challenges include correct selection of multiple camera and processing settings, variable lighting/atmospheric conditions, changes in vegetation over time, inexperienced users, and images limited by optical distortions. The note proposes a general purpose NDVI light simulator that can be used to test different techniques/camera designs and help reduce the challenges associated with multispectral DIY remote sensing.
Potential NDVI LED simulator applications:
--Demonstration of image collection/processing pipeline before field trials
--Permit real time NDVI measurements for classroom demonstrations
--Provide feedback for optimization of camera or processing features.
--Identify impact of lighting dynamic range on NDVI images.
--Ability to demonstrate NDVI indoors, at night and independent of season
--Consistent color reference with LANDSAT 8 data
--Practice calibration techniques required for NDVI analysis
--Create high resolution NDVI images
NDVI LED simulator design concept:
--Lighting: The lighting approach consists of LED strip lights that mimic LANDSAT 8 spectral properties (see picture below). In particular, LEDs that produce light in band 4 (640-670nm) and band 5 (850-880nm) https://landsat.usgs.gov/spectral-characteristics-viewer. LEDs are also considered safer (cool to the touch) than other infrared light sources.
--Test Materials: A mixture of photographic reference objects mixed with green vegetation samples. The concept is to use well known color reference targets for immediate visible light analysis and conduct additional measurements which would update their NIR (near infrared) spectral characteristics. The goal is to extend the range of commercial products to enable NDVI analysis. In addition, other calibration options could also be included: https://publiclab.org/notes/nedhorning/05-01-2014/improved-diy-nir-camera-calibration
--Software Processing: Processing via Spectral Workbench. https://publiclab.org/wiki/image-sequencer.
Prototype NDVI simulator:
The design consists of a horizontal wooden board that is laced with different wavelength LED strips. The LED strips were selected to match Red/ NIR Landsat spectral bands as well as to permit evaluation of different NDVI camera designs (NRG/NRB). Picture below describes the wooden board mounted over imaging objects (left) and a close up of the LED strips (right). Holes in the center of the board permit a camera to view objects directly below.
Imaging objects (pictured below) were selected to illustrate a wide range of spatial and spectral properties:
-5 dollar US bill -- NIR counterfeit markings provide example of high NIR reflectivity
-Roscolux color filters - Red (19), Orange (21), Yellow (10), Green (89), Blue (83), Indigo(59), Violet (54). Spectral transmission graphs at http://us.rosco.com/en/mycolor
-Resolution test target
-Vivitar 2,4,8 Neutral Density (ND) filters over green and blue filters -- use to study impact of different light levels on NDVI ratio.
-Green leaf samples
NDVI LED simulator demonstration
Combined with the Public Lab's image-sequencer, the NDVI LED simulator permits rapid evaluation of different NDVI imaging scenarios. Example below is a comparison study of NIR/wht vs NIR/red NDVI images taken with a Raspberry Pi NoIR camera (AUTO settings). The comparison begins by taking a NIR, red and wht picture of the same objects. The picture below shows the NIR/wht light processing steps with different colormaps.
NIR/red NDVI processing is shown below. Two noticeable differences can be observed. The first is that the Raspberry Pi camera lens shading correction is not adjusted for red only lighting, The second is that the colormaps display bright red (purple for fastie) indicating NDVI ratios near 1. My interpretation is that the red scene (and auto camera settings) provides higher red signal levels (creating higher NDVI ratios) that are near the red peak in colormap displays. Future tests should consider reducing the red gain which might also help correct the lens shading problem.
The prototype NDVI LED simulator can be adapted for different imaging systems as needed. For example, other Landsat 8 bands could be matched or UV LEDS could be added.
A major limitation is the lack of (low cost) calibrated NIR objects. It would be great if we could pick a few targets and crowd source measurements.
Please provide any comments on how to improve the prototype or make recommendations on alternative designs.
Simulators are like training wheels and should not be considered a replacement for NDVI outdoor collection (ideally with calibrated reference targets).
Calibration discussion: https://publiclab.org/notes/nedhorning/06-30-2015/automating-ndvi-calibration
Colormap discussion https://publiclab.org/notes/cfastie/08-26-2014/new-ndvi-colormap
Red/blue filter discussion
Color balance discussion
Raspberry Pi NoIR imaging
CameraTrax spectrum card (similar to Macbeth Chart) (paper below provides some reflectance data)