DOI: 10.5061/dryad.fxpnvx100
title: Parcel level temporal variance of remotely sensed spectral reflectance predicts plant diversity

description: Over the last two decades, considerable research has built on remote
 sensing of spectral diversity to assess plant diversity. The spectral
 variation hypothesis (SVH) proposes that spatial variation in reflectance
 data of an area is positively associated with plant diversity. While the
 SVH has exhibited validity in dense forests, it performs poorly in highly
 fragmented and temporally dynamic agricultural landscapes covered mainly
 by grasslands. Such underperformance can be attributed to the mosaic-like
 spatial structure of human-dominated landscapes with fields in varying
 phenological and management stages. Therefore, we argued for re-evaluating
 SVH's flawed window-based spatial analysis and underutilized temporal
 component. In particular, In particular, we captured the spatial and
 temporal variation in reflectance and assessed the relationships between
 spatial and temporal components of spectral diversity and plant diversity
 at the parcel level as a unit that relates to management patterns. Our
 investigation spanned three grasslands on two continents covering a wide
 spectrum of agricultural usage intensities. To calculate different
 components of spectral diversity, we used multi-temporal spaceborne
 Sentinel-2 data. We showed that plant diversity was negatively associated
 with the temporal component of spectral diversity across all sites. In
 contrast, the spatial component of spectral diversity was related to plant
 diversity in sites with larger parcels. Our findings highlighted that in
 agricultural landscapes, the temporal component of spectral diversity
 drives the spectral diversityplant diversity associations. Consequently,
 our results offer a novel perspective for remote sensing of plant
 diversity globally.
description: We calculated the spectral diversity and its spatial, temporal
 and spatio-temporal components from multi-temporal Sentinel-2 data in
 three grasslands utilized for agricultural production, two in Europe and
 one in the United States, using the function divcom from the stdiversity
 v1.1.0 package (Rossi et al., 2021). For the same parcels, we
 collected <em>in situ</em> species inventories and used
 species richness to express the plant diversity of each parcel. A
 detailetd description of data collection and processing, as well as the
 method used, can be found in Rossi et al. (2024).
 <pre> </pre>
description: # Spectral and species diversity on a parcel level ## Description of the
 file structure species_spectral_diversity.csv includes the spectral
 richness per parcel and the calculated spectral diversity components
 including the temporal, spatial and spatio-temporal components for three
 different grassland sites in Switzerland, Netherlands and Oklahoma (United
 States). * *ID*: The ID of the parcel * *Species_richness*: The species
 richness per parcel corresponded to the total count of observed plant
 species within all the given parcel plots. For each study area, a
 different sampling design was used. * *Parcel_size:* The size in m2 of the
 parcel after negative buffering of 50 m for Oklahoma and 10 m in the other
 locations. * *Study_area:* The state where the field and remote sensing
 data were collected. * *Lat_centroid: Latitude of the parcel centroid
 (EPSG:4326)* * *Lon_centroid: Longitude of the parcel centroid
 (EPSG:4326)* * *Sampling_year*: The year of in-situ and remote sensing
 data collection. * *Temporal_component:* The temporal component of
 spectral diversity was calculated as the spectral variance of the average
 parcel value over a growing season from three Sentinel-2 vegetation
 indices. * *Spatial_component:* The spatial component of spectral
 diversity a parcel accounted for the spectral variance between pixels
 after averaging their temporal variability. As spectral feature three
 vegetation indices derived from Sentinel-2 data were used. * *Spatio
 temporal:* The spatio-temporal component of spectral diversity quantified
 the spectral variance among pixels over both space and time within a
 parcel, encompassing the variance not captured by the other two
 components. * *Total spectral diversity*: Is the sum of the three spectral
 diversity components per parcel. The data was used to produce Figure 3 and
 the analysis in section 3.2 in Rossi et al. (2024). --- specdiv_parcel.R
 provides the R code to calculate the spectral diversity components on a
 parcel level based on multi-temporal Sentinel-2 images. In addition to the
 remote sensing raster files, the code requires a vector file representing
 the parcels as input. The script was created using the R version 4.2.0.
 --- spectral_diversity_CH.csv includes the management information per
 parcel and the calculated spectral diversity components for the larger
 Swiss study site, based on Sentinel-2 data from the growing season 2021. *
 *ID*: The ID of the parcel * *NUTZUNGSAR:* Each grassland parcel was
 associated with one of seven distinct management types: (1) artificial
 meadows, (2) permanent meadows, (3) low-intensity meadows, extensively
 used meadows, (5) pastures, (6) extensively used pastures, and (7)
 protected grasslands * *Lat_centroid: Latitude of the parcel centroid
 (EPSG:4326)* * *Lon_centroid: Longitude of the parcel centroid
 (EPSG:4326)* * *Shape_Leng*: The perimeter in m of the parcel after
 negative buffering of 10 m. * *Shape_Area:* The size in m2 of the parcel
 after negative buffering of 10 m. * *Temporal component:* The temporal
 component of spectral diversity was calculated as the spectral variance of
 the average parcel value over a growing season from three Sentinel-2
 vegetation indices. * *Spatial component:* The spatial component of
 spectral diversity a parcel accounted for the spectral variance between
 pixels after averaging their temporal variability. As spectral feature
 three vegetation indices derived from Sentinel-2 data were used. *
 *Spatiotemporal component:* The spatio-temporal component of spectral
 diversity quantified the spectral variance among pixels over both space
 and time within a parcel, encompassing the variance not captured by the
 other two components. * *Total*: Is the sum of the three spectral
 diversity components per parcel.   The data was used to produce Figure 2
 and the analysis in section 3.1 of Rossi et al. (2024). --- plot_fig_2_3.R
 provides the R code to produce Figures 2 and 3 in Rossi et al. (2024). The
 script was created using the R version 4.2.0. ## Code/Software To perform
 the Welch's ANOVA test and post-hoc Games-Howell test in section 3.1,
 we used the following R code: ```Rscript library(onewaytests)
 library(userfriendlyscience) data_parcel_CH &lt;- read.table(
 "spectral_diversity_CH.csv, sep = ";", header = TRUE )
 welch.test(data_parcel_CH$`Spatial component`~NUTZUNGSAR,data=data_parcel)
 welch.test(data_parcel_CH$`Temporal
 component`~NUTZUNGSAR,data=data_parcel)
 welch.test(data_parcel_CH$`Spatiotemporal
 component`~NUTZUNGSAR,data=data_parcel) #Games Howell posthoc test our
 data not meet the homogeneity of variances assumption #tells you specific
 group difference posthocTGH(y=data_parcel_CH$`Spatial
 component`,x=data_parcel$NUTZUNGSAR, method="games-howell",
 conf.level = 0.95, digits=2, formatPvalue = TRUE)
 posthocTGH(y=data_parcel_CH$`Temporal component`,x=data_parcel$NUTZUNGSAR,
 method="games-howell", conf.level = 0.95, digits=2, formatPvalue
 = TRUE) ```