Abstract
Modern satellite and airborne optical images have increasingly higher resolutions and enable the study of all layers of forests, not just the forest canopy. To understand the contribution of different types of understory on the overall spectral reflectance signal, ground reference data are needed from different types of forests. In this paper, we present the analysis of spectral reflectance factors (350-2300 nm) and fractional covers of understory from 36 boreal forest stands. The data were collected during peak growing season in a southern boreal forest area in Finland. The study stands represent four different forest site fertility types. We used a spectrometer to measure understory spectra in nadir and vegetation quadrats to estimate fractional cover. We showed that the understory has specific spectral features related to the site fertility type and fractional cover. Our results suggest that remote sensing can be used to differentiate forest site fertility types and estimate understory green fractional cover in northern European boreal forests. The collected data are openly available in an open data repository.
Highlights
Satellite and airborne images may be used for studying the optical and structural properties of all forest layers, not just canopies
The most fertile site type Oxalis-Myrtillus type (OMT) had the largest variation in the fractional cover between the transects and the less fertile site type Vaccinium type (VT) the smallest variation
There were similarities as well as distinguishing differences between the nadir view mean spectra of the forest site types (Fig. 2a). In both VIS (350–700 nm and red edge 700–800 nm) and NIR (800–1300 nm), the spectra of OMT, Myrtillus type (MT), and VT sites resembled that of healthy green leaves, while all site types, including Cal luna type (CT) showed decreasing trend of reflectance factor (RF) in SWIR (1300–2300 nm)
Summary
Satellite and airborne images may be used for studying the optical and structural properties of all forest layers, not just canopies. Retrieval of understory properties from optical remote sensing data has been shown to be feasible (e.g., Canisius and Chen, 2007; Pisek et al, 2012; Pisek et al, 2016; Kuusk et al, 2018, Markiet & Mottus, 2020; Pisek et al, 2021). In Finland, the classification of forests is based on the theory of forest site types (Cajander, 1926), which directly relates understory species composition to forest productivity. We hypothesize that understory in different boreal forest site types should have distinct spectral features. If such dependencies exist, they could be applied in future remote sensing applications to map forest site fertility type
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