Landscape Assessment (LA) hyperspectral vegetation indices

Summary statistics on 12 vegetation indices for the 94 Landscape Assessment plots within hyperspectral coverage (2020-02-05). introduction Information on 12 vegetation indices derived from hyperspectral data on 94 landscape assessment plots. The spreadsheet contains information on the minimum, maximum, mean, median, range and standard deviation (sd) of vegetation indices values, as well as land use type and plot Id. All the metrics provided here are extracted from a circular area of 1000 m2 surrounding each of the landscape assessment plots (using the centre of the plot with a radius of 17.84 m). Metrics calculation and extraction was carried out in QGIS. measurements Remote sensing products provision for the CRC 990 project. The acquisition flight was done using a BN2T fix-wing aircraft based in Jambi and operated by a local Indonesian aircrew. equipment Neo Hyspex 1600 pushbroom sensor: The Neo Hyspex 1600 pushbroom sensor operates at a spectral range of 400 to 1000 nm. The hyperspectral imageries were collected on 7 separate days throughout between the 24th of January and 5th of February 2020. The flying days are not consecutive due to bad weather conditions in the survey areas. 1. Converted raw data (hslvl0) to imageries in radiance unit (hslvl1) using Hyspex RAD software. 2. Generated hyperspectral EO data (hseo data) from log file, event file and gpsins file using Hyspex NAV software. 3. Marked Ground Control Points (GCP) on hyperspectral imageries using the reference map, DSM at 1m, and then created the boresight file for georectification of hslvl1 imageries in PARGE software – in hssupport 4. Created the boresight file by eliminating those GCP with high d-pitch and d-roll values as well as high d-X and d-Y values, and then optimizing data offsets (pitch, roll and heading values). Refined the boresight file according to accuracy assessment of one-band georectified imageries. This procedure was repeated until the satisfactory georectfied results were attained 5. If the georectified results are still unsatisfactory, extra GCP are added and Point 4 (as above) is repeated. 6. Produced full-band georectfied imageries (hslvl2) by boresight files, DSM from Lidar, hseo data and the corresponding sensormodel file using PARGE software. 7. The georectified imageries were atmospherically corrected (hslvl3) by mountain model, scan angle file, atmospheric type (rural area and water vapour column at 2.0g/cm2) using ATCOR4 software. Vegetation indices were calculated using the EnMap toolbox user interface, within QGIS. The 12 indices obtained are the following (grouped according to the main functional trait described): - Structural hNDVI (hyperspectral Normalized Difference Vegetation Index) – (Oppelt, 2002) - Chlorophyll CSI1 (Carter Stress Index 1) – (Carter, 1994) Greeness Index (G) - (Zarco-Tejada et al., 2005) MCARI (Modified Chlorophyll Absorption in Reflectance Index) – (Daughtry et al., 2000) REP (Red Edge Point) – (Dawson & Curran, 1998) SRtot (Simple Ratio Chl tot) - (Datt, 1999) PRI (Photochemical Reflectance Index) – (Gamon et al., 1992) - Carotenoids and Anthocyanin ARI (Anthocyanin Reflectance Index) – (Gitelson et al., 2001) CRI2 (Carotenoid Reflectance Index 2) – (Gitelson et al., 2002) SIPI (Structural Independent Pigment Index) – (Josep Peñuelas & Filella, 1998) - Dry Matter NPCI (Normalized Pigment Chlorophyll Index – (J. Peñuelas et al., 1994) BRI (Blue red Pigment Index) – (Zarco-Tejada et al., 2005) References: Carter, G. A. (1994). Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing, 15(3), 517–520. https://doi.org/10.1080/01431169408954109 Datt, B. (1999). A New Reflectance Index for Remote Sensing of Chlorophyll Content in Higher Plants: Tests using Eucalyptus Leaves. Journal of Plant Physiology, 154(1), 30–36. https://doi.org/10.1016/S0176-1617(99)80314-9 Daughtry, C. S. T., Walthall, C. L., Kim, M. S., Brown de Colstoun, E., & McMurtrey III, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74, 229–239. https://doi.org/10.3184/174751911X556684 Dawson, T. P., & Curran, P. J. (1998). Technical note A new technique for interpolating the reflectance red edge position. International Journal of Remote Sensing, 19(11), 2133–2139. https://doi.org/10.1080/014311698214910 Gamon, J. A., Peñuelas, J., & Field, C. B. (1992). A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41, 35–44. Gitelson, A. A., Merzlyak, M. N., & Chivkunova, O. B. (2001). Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and Photobiology, 74(1), 38. https://doi.org/10.1562/0031-8655(2001)074<0038:OPANEO>2.0.CO;2 Gitelson, A. A., Zur, Y., Chivkunova, O. B., & Merzlyak, M. N. (2002). Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy. Photochemistry and Photobiology, 75(3), 272. https://doi.org/10.1562/0031-8655(2002)075<0272:accipl>2.0.co;2 Oppelt, N. (2002). Monitoring of Plant Chlorophyll and Nitrogen Status Using the Airborne Imaging Spectrometer AVIS (Issue April). Ludwig-Maximilians-University Munich. Peñuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J., & Field, C. B. (1994). Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sensing of Environment, 48(2), 135–146. https://doi.org/10.1016/0034-4257(94)90136-8 Peñuelas, Josep, & Filella, I. (1998). Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science, 3(4), 151–156. https://doi.org/10.1016/S1360-1385(98)01213-8 Zarco-Tejada, P. J., Berjón, A., López-Lozano, R., Miller, J. R., Martín, P., Cachorro, V., González, M. R., & De Frutos, A. (2005). Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sensing of Environment, 99(3), 271–287. https://doi.org/10.1016/j.rse.2005.09.002