Assessing potential future changes in arctic and boreal plant species productivity, ecosystem composition, and canopy complexity is essential for understanding environmental responses under expected altered climate forcing. We examined potential changes in the dominant plant functional types (PFTs) of the sedge tundra, shrub tundra, and boreal forest ecosystems in ecotonal northern Alaska, USA, for the years 2003–2100. We compared energy feedbacks associated with increases in biomass to energy feedbacks associated with changes in the duration of the snow‐free season. We based our simulations on nine input climate scenarios from the Intergovernmental Panel on Climate Change (IPCC) and a new version of the Terrestrial Ecosystem Model (TEM) that incorporates biogeochemistry, vegetation dynamics for multiple PFTs (e.g., trees, shrubs, grasses, sedges, mosses), multiple vegetation pools, and soil thermal regimes. We found mean increases in net primary productivity (NPP) in all PFTs. Most notably, birch (Betulaspp.) in the shrub tundra showed increases that were at least three times larger than any other PFT. Increases in NPP were positively related to increases in growing‐season length in the sedge tundra, but PFTs in boreal forest and shrub tundra showed a significant response to changes in light availability as well as growing‐season length. Significant NPP responses to changes in vegetation uptake of nitrogen by PFT indicated that some PFTs were better competitors for nitrogen than other PFTs. While NPP increased, heterotrophic respiration (R_H) also increased, resulting in decreases or no change in net ecosystem carbon uptake. Greater aboveground biomass from increased NPP produced a decrease in summer albedo, greater regional heat absorption (0.34 ± 0.23 W·m⁻²·10 yr⁻¹[mean ± SD]), and a positive feedback to climate warming. However, the decrease in albedo due to a shorter snow season (−5.1 ± 1.6 d/10 yr) resulted in much greater regional heat absorption (3.3 ± 1.24 W·m⁻²·10 yr⁻¹) than that associated with increases in vegetation. Through quantifying feedbacks associated with changes in vegetation and those associated with changes in the snow season length, we can reach a more integrated understanding of the manner in which climate change may impact interactions between high‐latitude ecosystems and the climate system.

Fractional vegetation cover (FVC) is an important parameter in studies of ecosystem balance, soil erosion, and climate change. Remote-sensing inversion is a common approach to estimating FVC. However, there is an important gap between ground-based surveys (quadrat level) and remote-sensing imagery (satellite image pixel scale) from satellites. In this study we evaluated that gap with unmanned aerial vehicle (UAV) aerial images of alpine grassland on the Qinghai–Tibetan Plateau (QTP). The results showed that: (1) the most accurate estimations of FVC came from UAV (FVC_UAV) at the satellite image pixel scale, and when FVC was estimated using ground-based surveys (FVC_ground), the accuracy increased as the number of quadrats used increased and was inversely proportional to the heterogeneity of the underlying surface condition; (2) the UAV method was more efficient than conventional ground-based survey methods at the satellite image pixel scale; and (3) the coefficient of determination (R²) between FVC_UAV and vegetation indices (VIs) was significantly greater than that between FVC_ground and VIs (p < 0.05, n = 5). Our results suggest that the use of UAV to estimate FVC at the satellite image pixel scale provides more accurate results and is more efficient than conventional ground-based survey methods.

Aboveground biomass is a key indicator of a grassland ecosystem. Accurate estimation from remote sensing is important for understanding the response of grasslands to climate change and disturbance at a large scale. However, the precision of remote sensing inversion is limited by a lack in the ground truth and scale mismatch with satellite data. In this study, we first tried to establish a grassland aboveground biomass estimation model at 1 m² quadrat scale by conducting synchronous experiments of unmanned aerial vehicle (UAV) and field measurement in three different grassland ecosystems. Two flight modes (the new QUADRAT mode and the commonly used MOSAIC mode) were used to generate point clouds for further processing. Canopy height metrics of each quadrat were then calculated using the canopy height model (CHM). Correlation analysis showed that the mean of the canopy height model (CHM_mean) had a significant linear relationship with field height (R² = 0.90, root mean square error (RMSE) = 19.79 cm, rRMSE = 16.5%, p < 0.001) and a logarithmic relationship with field aboveground biomass (R² = 0.89, RMSE = 91.48 g/m², rRMSE = 16.11%, p < 0.001). We concluded our study by conducting a preliminary application of estimation of the aboveground biomass at a plot scale by jointly using UAV and the constructed 1 m² quadrat scale estimation model. Our results confirmed that UAV could be used to collect large quantities of ground truths and bridge the scales between ground truth and remote sensing pixels, which were helpful in improving the accuracy of remote sensing inversion of grassland aboveground biomass.

Permafrost on the Qinghai-Tibetan Plateau (QTP) has degraded over the last few decades. Its ecological effects have attracted great concern. Previous studies focused mostly at plot scale, and hypothesized that degradation of permafrost would cause lowering of the water table and drying of shallow soil and then degradation of alpine grassland. However, none has been done to test the hypothesis at basin scale. In this study, for the first time, we investigated the relationships between land surface temperature (LST) and fractional vegetation cover (FVC) in different types of permafrost zone to infer the limiting condition (water or energy) of grassland growth on the source region of Shule River Basin, which is located in the northeastern edge of the QTP. LST was obtained from MODIS Aqua products at 1 km resolution, while FVC was upscaled from quadrat (50 cm) to the same resolution as LST, using 30 m resolution NDVI data of the Chinese HJ satellite. FVC at quadrat scale was estimated by analyzing pictures taken with a multi-spectral camera. Results showed that (1) retrieval of FVC at quadrat scale using a multi-spectral camera was both more accurate and more efficient than conventional methods and (2) the limiting factor of vegetation growth transitioned from energy in the extreme stable permafrost zone to water in the seasonal frost zone. Our study suggested that alpine grassland would respond differently to permafrost degradation in different types of permafrost zone. Future studies should consider overall effects of permafrost degradation, and avoid the shortcomings of existing studies, which focus too much on the adverse effects.