Measured and modelled albedos in Finnish boreal forest stands of different species, structure and understory

Fine-Spatial Boreal–Alpine Single-Tree Albedo Measured by UAV: Experiences and Challenges

The boreal–alpine treeline is a fine-spatial heterogeneous ecotone with small single trees, tree clusters and open snow surfaces during wintertime. Due to climate change and decreased grazing of domestic animals, the treelines expand both upwards into the mountains and northwards into the tundra. To quantify and understand the biophysical radiative climatic feedback effect due to this expansion, it is necessary to establish measurement strategies of fine-spatial albedo by which relationships with the tree structure and snow-masking effect can be quantified. In this study, we measured single-tree Norway spruce albedo for small trees using an unmanned aerial vehicle (UAV). The platform allows the measurement of proximal remotely sensed albedo, enabling the provision of fine-spatial reflectance distributed over larger geographical areas. The albedo measurements varied from 0.39 to 0.99. The interaction between the diurnal course of the sun and sloping terrain constituted the most important driving factor on the albedo. Surprisingly, all tree structural variables revealed smaller correlations with the albedo than typically found for boreal and boreal–alpine summertime albedo. The snow-masking effect of the trees on the albedo was statistically significant and was found to be stronger than the effects of tree structural variables. Only the canopy density had a statistically significant effect on the albedo among the tree structural variables. This was likely explained by the imprecise heading of the hoovering positions of the UAV and insufficient spatial resolution of the reflected radiation measurements. For further development of fine-spatial UAV-measured albedo, we recommend the use of UAVs with high-precision navigation systems and field-stop devices to limit the spatial size of the measured reflected radiation.

Trade-offs between three forest ecosystem services across the state of New Hampshire, USA: timber, carbon, and albedo

Forests are more frequently being managed to store and sequester carbon for the purposes of climate change mitigation. Generally, this practice involves long-term conservation of intact mature forests and/or reductions in the frequency and intensity of timber harvests. However, incorporating the influence of forest surface albedo often suggests that long rotation lengths may not always be optimal in mitigating climate change in forests characterized by frequent snowfall. To address this, we investigated trade-offs between three ecosystem services: carbon storage, albedo-related radiative forcing, and timber provisioning. We calculated optimal rotation length at 498 diverse Forest Inventory and Analysis forest sites in the state of New Hampshire, USA. We found that the mean optimal rotation lengths across all sites was 94 yr (standard deviation of sample means = 44 yr), with a large cluster of short optimal rotation lengths that were calculated at high elevations in the White Mountain National Forest. Using a regression tree approach, we found that timber growth, annual storage of carbon, and the difference between annual albedo in mature forest vs. a post-harvest landscape were the most important variables that influenced optimal rotation. Additionally, we found that the choice of a baseline albedo value for each site significantly altered the optimal rotation lengths across all sites, lowering the mean rotation to 59 yr with a high albedo baseline, and increasing the mean rotation to 112 yr given a low albedo baseline. Given these results, we suggest that utilizing temperate forests in New Hampshire for climate mitigation purposes through carbon storage and the cessation of harvest is appropriate at a site-dependent level that varies significantly across the state.

Land Surface Albedo Estimation from Chinese HJ Satellite Data Based on the Direct Estimation Approach

Monitoring surface albedo at medium-to-fine resolution (<100 m) has become increasingly important for medium-to-fine scale applications and coarse-resolution data evaluation. This paper presents a method for estimating surface albedo directly using top-of-atmosphere reflectance. This is the first attempt to derive surface albedo for both snow-free and snow-covered conditions from medium-resolution data with a single approach. We applied this method to the multispectral data from the wide-swath Chinese HuanJing (HJ) satellites at a spatial resolution of 30 m to demonstrate the feasibility of this data for surface albedo monitoring over rapidly changing surfaces. Validation against ground measurements shows that the method is capable of accurately estimating surface albedo over both snow-free and snow-covered surfaces with an overall root mean square error (RMSE) of 0.030 and r-square (R2) of 0.947. The comparison between HJ albedo estimates and the Moderate Resolution Imaging Spectral Radiometer (MODIS) albedo product suggests that the HJ data and proposed algorithm can generate robust albedo estimates over various land cover types with an RMSE of 0.011–0.014. The accuracy of HJ albedo estimation improves with the increase in view zenith angles, which further demonstrates the unique advantage of wide-swath satellite data in albedo estimation.