Multi‐temporal assessment of grassland α‐ and β‐diversity using hyperspectral imaging

Abstract

While more and more studies are exploring the application of remote sensing in assessing biodiversity for different ecosystems, most consider biodiversity at one point in time. Using several remote-sensing-based metrics, we asked how well remote sensing can detect biodiversity (both aand b-diversity) in a prairie grassland across time using airborne hyperspectral data collected in two successive years (2017 and 2018) and at different periods in the growing season (2018). The ability to detect biodiversity using "spectral diversity" and "spectral species" types indeed varied significantly over a 2-yr timespan. Toward the end of the growing season in 2018, the relationship between field-and remote-sensing-based aand b-diversity weakened compared to data collected from the same season in the previous year. This contrasting pattern between the two years was likely influenced by prescribed fire, altered weather, and the resulting shifting species composition and phenology. These findings indicate that direct detection of aand b-diversity in grasslands should be multi-temporal when possible and should consider the effect of disturbances, climate variables, and phenology. We demonstrate an essential role for airborne platforms in developing a global biodiversity monitoring system involving forthcoming space-borne hyperspectral sensors.

Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing

“…However, also non-peak season features and RGB drone features from two different years (2016 and 2018) were important in the different regressions Figure 4. This indicates that multitemporal data can alleviate problems related to temporal mismatches and increase model performance (Räsänen et al 2020;Halabisky, Babcock, and Moskal 2018;Gholizadeh et al 2020;Poley and McDermid 2020). Nevertheless, there is some uncertainty when comparing the relative benefit of datasets with divergent spectral and spatial resolutions as the data were acquired at different times; in an ideal situation, all datasets would have been collected simultaneously.…”

“…In particular, with multi-source data, different aspects of the landscape, such as spectral, topographic, and vegetation height properties at multiple spatial resolutions can be captured. Thereby, the explanatory capacities are typically higher than when using a single data source Chen, Huang, and Xu 2017;Arroyo-Mora et al 2017;Luo et al 2016;Sankey et al 2018;Poley and McDermid 2020), and further boosts in model performance can be obtained with multi-temporal data (Halabisky, Babcock, and Moskal 2018;Gholizadeh et al 2020;Poley and McDermid 2020).…”

Investigating the Relationship between Tree Species Diversity and Landsat-8 Spectral Heterogeneity across Multiple Phenological Stages

“…Compared to the scale issue, which has shown that its increase could impact positively or negatively on SVH [17,19,21] (Schmidtlein and Fassnacht, 2017;Oldeland et al, 2010;Rocchini et al, 2004), little is known about the impact of phenology on the relationship between spectral heterogeneity and species diversity, especially in the savannah ecosystem. In an experiment in prairie grasslands, Gholizadeh et al (2020) [24] observed that a shift in phenology affected the relationship between spectral heterogeneity metrics and species richness and that the relationship between spectral heterogeneity metrics and species richness may change between years, regardless of the phenology. However, it must be noted that the experiment was conducted in an ecosystem where (i) a shift in phenology was accompanied by a change in species richness and (ii) there was prescribed burning, which might have affected the spectral reflectance, as it alters the percentage cover of individual species and the background characteristics of the landscape [24,25] (Flanagan et al, 2015;Gholizadeh et al, 2020).…”

“…In an experiment in prairie grasslands, Gholizadeh et al (2020) [24] observed that a shift in phenology affected the relationship between spectral heterogeneity metrics and species richness and that the relationship between spectral heterogeneity metrics and species richness may change between years, regardless of the phenology. However, it must be noted that the experiment was conducted in an ecosystem where (i) a shift in phenology was accompanied by a change in species richness and (ii) there was prescribed burning, which might have affected the spectral reflectance, as it alters the percentage cover of individual species and the background characteristics of the landscape [24,25] (Flanagan et al, 2015;Gholizadeh et al, 2020). In an alpine coniferous forest, Torresani et al (2019) observed that the relationship between spectral heterogeneity and tree species diversity was highest during the summer period when the NDVI reached its peak and lowest during the winter period.…”

“…This study builds on the work of Oldeland et al (2010) [19], who tested the SVH in the African savannah and observed promising results between the Shannon diversity index and spectral heterogeneity. Other studies [17,24,30,31] (Schmidtlein and Fassnacht, 2017;Wang et al, 2018;Torresani et al, 2019;Gholizadeh et al, 2020), however, were conducted in different ecosystems.…”