TECHNICAL
became visible to the human eye. Sports fi elds and golf courses are often criticised for their use of pesticides, fertilisers and water consumption. If turf problems are detected at an early stage, it should be possible to strengthen the plants through mechanical procedures and biological inputs and to avoid excessive addition of chemicals, fertilisers and water. Remote sensing techniques could be benefi cial in this early detection. To this end, an initial pilot project, lasting over a year, was launched at the end of 2018 in collaboration with the Sports Department of the City of Basel and FC Basel 1893.
The work was undertaken between February 2019 and January 2020. The aim of this pilot project was to investigate the potential applications of remote sensing technology in sports turf maintenance, using practical examples to fi nd out how eff ective it is in the early detection of plant stress - with the ultimate aim of reducing the use of pesticides and irrigation.
Materials and Methods
Locations and test set-up In the summer of 2011, the fi eld 11 (main FCB match fi eld - Figure 1) at the St. Jakob sports facility in Basel was completely rebuilt as a Lavaterr pitch, including new drainage pipes, in-ground heating, automatic irrigation and seeded with a sports turf mixture consisting
of Lolium perenne and Poa pratensis. After eight years, the pitch also contained a high percentage of Poa annua. In autumn 2018, the entire middle third of the pitch was repaired and sodded with new turf with a high proportion of Lolium perenne (see dark green area in Figure 2). To begin with, a test fl ight was undertaken over the entire St. Jakob sports facility in order to establish a “zero measurement” and 16 georeferenced control points (GCP), measured in Basel and stored in the project database in order to locate, overlay and compare the fl own results precisely.
Drone Flights
Following the test fl ight, the offi cial data collection fl ights were fl own once a month with the drone (DJI Matrice 210), equipped with both multispectral camera sensors (MicaSense RedEdge-M) and an RGB camera (DJI Zenmuse X5s). All recordings were made automatically, using pre-programmed fl ight paths and the following parameters: fl ight altitude of 80m, overlap of 78% / 78%, 20 rows (north-south), total distance covered ~14km, fl ight time ~38 minutes (2 fl ights + battery change), approx. 8’800 single images per fl ight (5 channels with 1700 images each). Flights were always done in the middle of the day between 12:00 to 13:00 and, to compensate for any changes in the light quality, a calibration measurement was
carried out before and after the fl ight using a reference disc from MicaSense. This made it possible to adjust the acquired images to the specifi ed values during processing and to obtain a comparable result.
Multispectral camera and processing of the acquired data
The images were captured by the fi ve spectral-specifi c designed and calibrated lenses and stored as jpg fi les. Multispectral image data is composed of several spectral channels that capture refl ected electromagnetic radiation in green (497- 530nm), red (620-780nm) and near infrared light (780-1’400nm), however, before processing, the image fi les initially appear in pale grey scales. The representation in the known colour gradients of the NDVI (Normalized Diff erence Vegetation Index) results from the algorithm-based calculation of the individual spectral ranges and the programmed colour presets. The fi nal NDVI images illustrate the refl ection of the green, red and near infrared light refl ected off the turf surface, with greener colours indicating healthier plants with, for example, a greater percentage of chlorophyll in the plant leaves, and red indicating stressed or sick plants with a weaker vitality. The photogrammetric evaluation was then done with Pix4Dmapper and from there the software Pix4Dfi elds allowed the superimposition and comparison
PC February/March 2021 115
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132
