1/10/2024 0 Comments Multispec camera problem pix4d![]() ![]() Despite the utility of sUAS and handheld SfM for monitoring vegetation phenology and structure, their spatial extents are small relative to manned aircraft. The post-processing of SfM photogrammetry data became the limiting factor at larger spatial scale and temporal repetition. Availability and costs of manned aircraft lidar collection preclude high frequency repeatability but this is less limiting for terrestrial lidar, sUAS and handheld SfM. Combining point cloud data and derivatives (i.e., meshes and rasters) from two or more platforms allowed for more accurate measurement of herbaceous and woody vegetation (height and canopy cover) than any single technique alone. UAS and handheld SfM photogrammetry in near-distance high resolution collections had similar accuracy to terrestrial lidar for vegetation, but difficulty at measuring bare earth elevation beneath dense herbaceous cover. UAS SfM photogrammetry at lower spatial resolution under-estimated maximum heights in grass and shrubs. Conversely, the manned aerial lidar did not detect grass and fine woody vegetation while the terrestrial lidar and high resolution near-distance (ground and sUAS) SfM photogrammetry detected these and were accurate. We found aerial lidar to be more accurate for characterizing the bare earth (ground) in dense herbaceous vegetation than either terrestrial lidar or aerial SfM photogrammetry. To explore the potential for combining platforms, we compared detection bias amongst two 3D remote sensing techniques (lidar and SfM) using three different platforms. ![]() Yet, a combination of platforms and techniques might provide solutions that overcome the weakness of a single platform. Three dimensional (3D) remote sensing technologies like lidar, and techniques like structure from motion (SfM) photogrammetry, each have strengths and weaknesses at detecting vegetation volume and extent, given the instrument's ground sample distance and ease of acquisition. Remotely sensing recent growth, herbivory, or disturbance of herbaceous and woody vegetation in dryland ecosystems requires high spatial resolution and multi-temporal depth. 4USDA Agricultural Research Service, Southwest Watershed Research Center, Tucson, AZ, United States.3Informatics and Computing Program, Northern Arizona University, Flagstaff, AZ, United States.2School of Natural Resource and Environment, University of Arizona, Tucson, AZ, United States.1BIO5 Institute, University of Arizona, Tucson, AZ, United States.Nichols 4, Philip Heilman 4 and Jason McVay 3 Scientific cameras certainly have their abilities and value but the modified consumer cameras are proving their abilities and usefulness as well.Tyson L. With the advances and availability of new filters to modify a consumer camera I think the gap is closing. The Canon set is on the left of the above image and the RedEdge set is on the right. Can you tell which image set came from a near $7000 camera and which came from a camera that cost under $200? The results are very similar especially in terms of sensitivity. The image sets were processed with Pix4D Pro with the only difference being a slight modification to the NDVI formula with the Canon to account for the NIR contamination in the red channel. The flight with the Canon camera was flown within five minutes of landing with the RedEdge camera. The images were obtained on the same day at around solar noon. The scientific camera used was the Micasense RedEdge 3( )īoth sets of camera images were calibrated against reflectance panels with known values. The consumer camera used was the Canon S100 with the internal IR filter removed and replaced with the MidOpt DB 660/850 filter( ) It is calculated by dividing the difference in the near-infrared and red color bands by the sum of near-infrared and red bands for each pixel. It is often used in agriculture to measure general crop health and changes. NDVI is a vegetation index that uses a numerical indicator using visible and near-infrared bands to help analyze remote sensing measurements. I wanted to see if it was possible to get meaningful and comparable NDVI data from an affordable consumer grade camera vs a scientific multispectral camera and if the difference in price was really worth it. This note is related to NDVI only as the modified consumer camera tested only has two bands to compare with(Red and NIR). I wanted to share some of my results in comparing a modified consumer camera with a scientific grade multispectral camera when evaluating plant health with NDVI. ![]()
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