Forested ecosystems provide a significant carbon sink, absorbing roughly 3 billion tons of anthropogenic carbon annually (Canadell & Raupach, 2008). The boreal represents the largest biome in the world, 552 Mha of which is found in Canada, accounting for 28% of the boreal ecosystem globally (Brandt et al., 2013). Understanding the Earth System processes that drive land cover change and vegetation productivity in Canadian boreal ecosystems is therefore critical for accurate assessments of carbon dynamics and accumulation. Although many studies have been undertaken to understand the productivity and carbon cycle in managed forests in southern Canada (Kurz et al., 2009), less is known about carbon dynamics and land cover change transitions in other key ecosystems. In the Canadian boreal, three main sources of uncertainty stand out from the literature: the impact of the warming climate on the northern treeline, carbon estimates in wetland landscapes, and the implications of permafrost thaw. Understanding changes in carbon dynamics and land cover transition in these environments is of paramount concern, yet our carbon balance estimates for these environments are limited due to several key reasons. Lack of accessibility and significant cost are key drivers behind the lack of field studies in the remote environments in question, which has led to a lack of temporally and spatially dense ground based datasets of carbon dynamics (Lees et al., 2019; Srinet et al., 2020).
Current models of ecosystem carbon exchange driven by remote sensing still require input of ground based meteorological measurements and utilize look-up tables based on plant functional type, which limits their utility in remote areas where ground-based observations do not exist (Jones et al., 2017). In addition, there is often a scale mismatch between ground-based observations and remote sensing drivers introducing possible errors or limits when using models to make carbon exchange estimates in highly heterogenous landscapes such as the Canadian boreal. Exclusively remote sensing based methods represent an approach by which we can more directly assess changes in carbon dynamics and land cover transitions without the need for ground-based inputs, and offer a significant opportunity for addressing the sources of uncertainty and improving our predictions of future changes in these heterogenous environments (Lees et al., 2020; Schimel et al., 2015; Sims et al., 2008).
In this paper we present some key components of a new analytical model for Canadian terrestrial vegetation carbon productivity mapping and monitoring. We exploit well established links between vegetation greenness and land surface temperature (Sims et al., 2008), and apply these to the data acquired by the European Space Agency (ESA) Sentinel-2 and -3 satellite datasets and compare these to longer time series acquisitions from the National Aeronautics and Space Administration’s (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) data. We also examine the information conveyed by microwave remote sensing data layers, from the ESA Soil Moisture and Ocean Salinity (SMOS) mission in regulating these productivity estimates under changing freeze / thaw conditions using a Hidden Markov Model (HMM) algorithm applied to the SMOS freeze/thaw data product. The SMOS data is utilized to inform on modelling transitions of freeze/thaw, estimates of growing season length, and the impacts of permafrost thaw on boreal vegetation dynamics.
Using existing land cover information to stratify key wetland and tree line focus sites across the boreal regions of the Yukon, Quebec, and Ontario, we applied our model at a continuous 7-day time-step at 30m spatial resolution from 2016-2020. Seasonal and annual photosynthetic terrestrial carbon sequestration and freeze / thaw dynamics by land cover class were then examined to improve our understanding of land cover transitions and their implications regarding vegetation productivity. We end with a discussion on the future integration of other recently acquired remote sensing datasets which can inform on the influence of soil moisture and other processes on terrestrial carbon sequestration and accumulation in the Canadian boreal.
Brandt, J. P., Flannigan, M. D., Maynard, D. G., Thompson, I. D., & Volney, W. J. A. (2013). An introduction to Canada’s boreal zone: ecosystem processes, health, sustainability, and environmental issues. Environmental Reviews, 21(4), 207–226. https://doi.org/10.1139/er-2013-0040
Canadell, J. G., & Raupach, M. R. (2008). Managing Forests for Climate Change Mitigation . In Science (American Association for the Advancement of Science) (Vol. 320, Issue 5882, pp. 1456–1457). American Association for the Advancement of Science . https://doi.org/10.1126/science.1155458
Jones, L. A., Kimball, J. S., Reichle, R. H., Madani, N., Glassy, J., Ardizzone, J. V, Colliander, A., Cleverly, J., Desai, A. R., Eamus, D., Euskirchen, E. S., Hutley, L., Macfarlane, C., & Scott, R. L. (2017). The SMAP Level 4 Carbon Product for Monitoring Ecosystem Land-Atmosphere CO2 Exchange . In IEEE transactions on geoscience and remote sensing (Vol. 55, Issue 11, pp. 6517–6532). IEEE . https://doi.org/10.1109/TGRS.2017.2729343
Kurz, W. A., Dymond, C. C., White, T. M., Stinson, G., Shaw, C. H., Rampley, G. J., Smyth, C., Simpson, B. N., Neilson, E. T., Trofymow, J. A., Metsaranta, J., & Apps, M. J. (2009). CBM-CFS3: A model of carbon-dynamics in forestry and land-use change implementing IPCC standards . In Ecological modelling (Vol. 220, Issue 4, pp. 480–504). Elsevier B.V . https://doi.org/10.1016/j.ecolmodel.2008.10.018
Lees, K., Khomik, M., Quaife, T., Clark, J., Hill, T., Klein, D., Ritson, J., & Artz, R. (2020). Assessing the reliability of peatland GPP measurements by remote sensing: From plot to landscape scale. In The Science of the total environment (Vol. 766, p. 142613). Elsevier B.V. https://doi.org/10.1016/j.scitotenv.2020.142613
Lees, K., Quaife, T., Artz, R. R. E., Khomik, M., Sottocornola, M., Kiely, G., Hambley, G., Hill, T., Saunders, M., Cowie, N. R., Ritson, J., & Clark, J. M. (2019). A model of gross primary productivity based on satellite data suggests formerly afforested peatlands undergoing restoration regain full photosynthesis capacity after five to ten years. In Journal of environmental management (Vol. 246, pp. 594–604). Elsevier Ltd. https://doi.org/10.1016/j.jenvman.2019.03.040
Schimel, D., Pavlick, R., Fisher, J. B., Asner, G. P., Saatchi, S., Townsend, P., Miller, C., Frankenberg, C., Hibbard, K., Cox, P., & Pacific Northwest National Lab. (PNNL) WA (United States), R. (2015). Observing terrestrial ecosystems and the carbon cycle from space . In Global change biology (Vol. 21, Issue 5, pp. 1762–1776). Blackwell Publishing Ltd . https://doi.org/10.1111/gcb.12822
Sims, D. A., Rahman, A. F., Cordova, V. D., El-Masri, B. Z., Baldocchi, D. D., Bolstad, P. V, Flanagan, L. B., Goldstein, A. H., Hollinger, D. Y., Misson, L., Monson, R. K., Oechel, W. C., Schmid, H. P., Wofsy, S. C., & Xu, L. (2008). A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS . In Remote sensing of environment (Vol. 112, Issue 4, pp. 1633–1646). Elsevier Inc . https://doi.org/10.1016/j.rse.2007.08.004
Srinet, R., Nandy, S., Watham, T., Padalia, H., Patel, N. R., & Chauhan, P. (2020). Spatio-temporal variability of gross primary productivity in moist and dry deciduous plant functional types of Northwest Himalayan foothills of India using temperature-greenness model . In Geocarto international (pp. 1–13). https://doi.org/10.1080/10106049.2020.1801855