Global warming will affect arctic and subarctic regions more than any other area in the world [1
]. It is expected to increase the productivity of subarctic and arctic ecosystems [2
]. Increasing productivity and biomass is generally known as ‘greening’ [5
]. Major drivers are a longer growing season and increasing summer warming [6
]. However, negative trends in productivity and biomass, known as ‘browning’, have also been reported [6
]. For the Arctic as a whole, trends are complex, as Myers-Smith et al. state: “Figures vary from 42% greening and 2.5% browning from 1982 to 2014 in the GIMMS3g AVHRR dataset to 20% greening and 4% browning from 2000 to 2016 in Landsat data, and to estimates of 13% greening and 1% browning for the MODIS trends calculated for 1,000 random points in the tundra polygon from 2000 to 2018.” ([7
], p. 107).
In the subarctic region of Scandinavia, i.e. Norway, Finland, and Sweden north of the Arctic Circle, the main drivers of browning are winter warming events and pest outbreaks [8
]. Winter warming events can melt the insulating snow cover that normally protects photosynthetic short-statured organisms overwintering with aboveground tissue (e.g. prostrate shrubs, cushion plants, bryophytes, and lichens) from the harsh ambient winter weather conditions. After a few thaw days, ground vegetation becomes exposed to ambient air and hibernation is interrupted, thus reducing the protection of photosynthetic organisms against frost, which may easily lead to freezing damage upon return of normal winter weather [9
]. Soil communities, including both micro-arthropods and bacteria, can also be severely affected [10
]. Overall, a warmer winter climate changes species compositions and reduces carbon cycling [11
]. In subarctic and arctic regions evergreen plants in particular are sensitive to changing winter climate and reduced snow cover [12
]. This includes the widespread dwarf shrubs Empetrum nigrum
L., Vaccinium vitis-idaea
L., Cassiope tetragona
(L.) D.Don, and Calluna vulgaris
(L.) Hull, as well as the tall coniferous shrub Juniperus communis
]. Bryophytes, such as the widespread feathermoss Hylocomium splendens
(Hedw.) Schimp., deciduous shrubs, such as Vaccinium myrtillus
L., evergreen horsetails (Equisetum
spp.), as well as small cushion plants show reduced growth following exposure to winter warming [9
]. The other major factor causing browning are pest outbreaks. Recently, increasing frequency and intensity of outbreaks of leaf-defoliating geometrid moths led to massive canopy defoliation of their preferred host tree Betula pubescens
Ehrh. and understory plants [13
]. Overall, multiple stress events are main drivers of browning. Given the high focus on climate change-induced changes in northern primary productivity, it is important to develop easy and reliable methods for assessment of plant vitality.
For a long time, satellites have monitored the global vegetation status [2
]. Spectral sensors operated near the target vegetation are increasingly applied for assessing the plant status [18
]. However, near-remote time series of the plant status are still uncommon, which is partly due to the need for expensive equipment, for example spectroradiometers [20
]. In recent years, several new and low-cost active and passive proximal sensors were developed. This includes sensors measuring the normalized difference vegetation index (NDVI). NDVI is a radiometric measure of the amount of radiation (≈∼400–700 nm) absorbed by vegetation during photosynthesis. It is calculated from contrasting reflectance at near-infrared (NIR) and red bands [21
NDVI has been widely used in studies of phenology, productivity, biomass, and disturbance monitoring, as it has proven to be a good proxy of the vegetation’s photosynthetic activity [19
]. NDVI works well for subarctic ecosystem monitoring and is widely used on different scales and as a vegetation marker [24
Previously, modified cameras—with the infrared filter removed—were found to be good NDVI surrogates. In such cameras, the NDVI proxy is commonly calculated by using the enhanced red channel and the blue channel (BNDVI) [27
]. However, a combination of the enhanced red channel and the green channel might also be of interest due to a strong linear correlation with the chlorophyll content (GNDVI) [28
]. Additionally, ordinary cameras were increasingly applied for vegetation analysis and phenology studies in recent years. Greenness indices based on ordinary RGB images from such cameras are promising NDVI substitutes [28
], even for high-arctic vegetation [30
In subarctic forests, the contribution of understory vegetation (i.e. dwarf shrubs, herbs, graminoids, bryophytes and lichens) to the total ecosystem productivity is similar to that of trees [31
]. Moreover, biodiversity of vascular plants at high latitudes is relatively low, which makes research into dominant species and their vulnerability to environmental change even more important [32
]. We hypothesized that in situ estimates of plant damage would be correlated to optical measurements of plant greenness, but that greenness indices would vary in their explanatory power. Our second hypothesis was that plant stress would vary over short distances in a rolling subarctic landscape and that this would be detectable both by near-remote sensing measurements and by Sentinel. To this end, we combined near-remote sensing approaches with classical determination of plant traits of understory vegetation to address the following research questions:
What is the range of intraspecific variability of common traits of dwarf shrubs and mosses in subarctic spring?
Which traits are reliable indicators of plant stress?
How do the indices derived from ordinary and modified RGB cameras correlate with common plant traits?
To answer these questions, we made analyses in a widespread subarctic heath ecosystem, focusing on vegetation plots dominated by two evergreen dwarf shrubs and a mat-forming moss.
The range of intraspecific trait variability (1st
research question), is attributed well during the study. Some plant traits remained relatively stable during spring (SLA, Plant height, Flav), while others showed more variations during the season and to environmental circumstances (NDVI, ChannelG% and Chl). The small leaves of E. nigrum
and shoots of H. splendens
made the SLA measurements challenging. However, the infrequently used method that we decided to apply seemed to work well, as our results are comparable to SLA values retrieved in previous studies [48
]. To our knowledge, our plots did not suffer from any major stress (browning) events during the last 3 years prior to our measurements, except that Vaccinium myrtillus
in the area had been partly defoliated by larvae of geometrid moths [51
], but this species was rare or absent in our plots. In the early growing season, plants are especially vulnerable to winter-related stress and are showing accumulated stress responses from the previous years [13
]. Hence, we monitored the natural range of trait variabilities from start of the growing season (DOY 130) onwards. Chlorophyll content was dropping significantly when temperature fell to almost freezing point. However, more research is needed to validate this result. It might be that the slight snowfall, or both parameters jointly, instigated the decline in chlorophyll concentrations.
The second research question was to assess whether any of the studied plant traits are suitable for stress monitoring. Our data show that the stress level differed between plots; we found that plant height was related to soil depth and that soil depth was also related to NBI. Although we did not find any significant correlation between plant height and NBI, we assume that soil depth is a limiting factor for this ecosystem. Lower soil depth affects water and nutrient availability as well as soil temperature [52
] and is also associated with areas of low snow accumulation during winter [13
]. This is supported by the fact that the stress level decreased with increasing soil depth and that NDVI increased with increasing soil depth.
In general, the flavonol content is associated with plant stress reactions [53
]. However, we could not relate the flavonol absorbance to our stress level estimates. As the Dualex device estimates the flavonol content from spectral properties, it might not be able to measure the relevant flavonols in relation to the types of stress occurring in these subarctic plants. Dualex flavonol measurements are performed at the wavelengths 375 nm (UV-A) and 650 nm (red) [34
]. This results in screening of mainly kaempferol, quercetin, and myricetin [53
]. Our results are in agreement with Lefebvre et al. [54
], who concluded that the Dualex device could not accurately predict the flavonol content in the three alpine plants they studied.
Concerning the third research question, our results show that ordinary RGB cameras may be used as NDVI surrogates and that they reflect various plant traits well. They performed equally well as modified cameras (with the infrared filter removed) for near-remote sensing approaches in the subarctic ecosystem. We found that a normal gray card, as used by professional photographers, was sufficient for the calibration process. Based on our findings, we recommend a simple white balance. Even if correlations to NDVI were slightly higher for BNDVI (r
= 0.779) than for RGB greenness indices (0.689−0.749), one of the main strengths of the RGB cameras is that they are easier to operate than the modified devices. Sonnentag et al. [46
] showed that different RGB cameras produce comparable results and that the choice of file format is not that important. Also, Nijland et al. [55
] identified band separation and dynamic range as main problems when using converted cameras and therefore recommended the use of true color imaging. Another aspect is that the distribution of RGB cameras via smartphones is enormous and might be valuable for citizen science projects or app development [56
]. In general, our greenness measurements are in agreement with existing reports on phenology at higher latitudes [30
Moreover, the Channel G% index performed better than NDVI in characterizing some plant traits. This includes the stress level which showed a stronger correlation to Channel G% (linear: r
= −0.768 vs. r
= −0.600; logistic: r
= −0.833 vs. r
= −0.651) and NBI which showed a significant correlation (r
= −0.354) to Channel G%, but not to NDVI. Consequently, the Channel G% index is of additional value for screening plant stress (2nd
research question). The significant correlation between RGB indices and chlorophyll meter readings (r
= 0.38, p
< 0.05) also implies that the RGB-based indices could be potential NDVI surrogates (see Table 4
). In contrast to previous studies [28
], our GNDVI data did not show any significant correlation with chlorophyll content or other plant traits. Correlations between chlorophyll and NDVI showed reasonable results [29
], indicating that chlorophyll measurements are valid in spite of the untypical leaf structures of H. splendens
and E. nigrum
We found a high correlation between spaceborne NDVI and ground-sampled NDVI measured by the active Greenseeker device (maximum r
= 0.968 for the Sentinel-2 NDVI calculated with bands 4 and 7). Nevertheless, despite of the strongly significant correlation, it is based only on 14 data points, implying that relationships have to be handled with care. It is a higher correlation than retrieved in previous studies, where near-remotely sensed NDVI data were compared to NDVI from Sentinel-2 and Landsat 8 [59
]. A likely reason for the very strong correlation is that this study was carried out in a very open subarctic woodland (in parts nearly treeless and then considered as heath) where understory vegetation contributes very much to the NDVI detected by the satellites. We did not find major differences in the correlations, even when spectral properties (bandwidth and wavelength peaks) were not similar. This strongly suggests that active sensors can be used for validation of spaceborne data, for example, from Sentinel-2.