Coops, Nicholas

Nicholas and his research team in the Integrated Remote Sensing Studio (IRSS) overall research program is focused on increasing understanding of the interaction between vegetation pigments, biochemistry and structure and how remote sensing technologies can be used to estimate properties of vegetation at a range of spatial and temporal scales. The IRSS key focus area is forestry and the application of remote sensing to conservation, management and production issues.

Key areas of interest include:

1. Increased awareness that high spectral resolution optical sensors, capable of detecting changes in leaf spectral properties with high temporal frequency can be used to augment a number of established approaches for modeling growth from drone, aircraft or space. His research has developed new approaches for incorporating remotely sensed data as inputs into plant physiological models, with the ultimate aim of improving models from satellite based observations. Key to this approach is estimating light-use efficiency (LUE) which is determined by the most limiting of a number of environmental stresses restraining the photochemical reaction process, such as nutrition, water, and temperature. The IRSS lab has been focused on moving from individual sites with single sensors, to more diverse sites which cover a range of species and structural stages and the deployment of more instruments across Canada. This work is also highly relevant for forestry, phenotyping and agriculture. We have designed and installed a range of near remote sensing spectro-radiometers on tower based systems to observe and monitor plant growth. Likewise the installation of cameras on UAV’s to detect changes in tree physiological behaviour.
2. Airborne and space-borne LiDAR technology has revolutionized the measurement of tree height and development of highly accurate digital elevation models (DEM). The application of LiDAR data to sustainable forest management continues to be a major theme of our research. With collaborators from these universities/governments/industry partners, we have demonstrated how LiDAR data can best be utilized across a range of forest types, silvicultural regimes and acquisition platforms. We have contributed to best practice guides with the Canadian Forest Service (CFS) documenting our discoveries and development of the technology, which is the most downloaded CFS information guide ever and for which we were recognized with the CFS collaboration award for 2013.
3. We also have a strong and productive collaboration with the remote sensing team, at the Canadian Forest Service, Pacific Forestry Centre on the development of Canada-wide land cover, disturbance and forest attribute datasets at 30m resolution for national level reporting. We develop with CFS new algorithms, approaches and analyses to examine how and where Canada’s forests are changing distributing a wide range of published, free and open datasets via the Canada Forest Information System.
4. My interest in linking remote sensing to forest growth has its origins as a co-developer of the 3PGS model, a simple physiological-based forest growth model that can utilize remote sensing data as an input to predict stand growth primary production. I have been further developing the model, and its underlying basis (3PG) to model growth rates, and limits to growth in the Canadian Boreal and Pacific Northwest (PNW) and mapping species distribution under current and future climate.
5. Lastly our work also focuses on Biodiversity and remote sensing. We work with international collaborators to define the critical aspects of biodiversity that need to be monitored in Science (Pereira al. 2015) as well as examine how remote sensing approaches can offer solutions in Nature (Skidmore et al., 2016). In addition to these reviews and opinion pieces , I have continued to develop the Dynamic Habitat Index (DHI) which I adapted and applied across Canada, Australia and globally. This index provides an integrated response of vegetation to climate using landscape greenness (fraction of photosythetically active radiation, fPAR) at monthly time steps, to assess how biomass is partitioned and made available as food and other habitat resources for fauna.

Awards

Nicholas Coops has been listed in the “Highly Cited Researcher” list for 2019, 2020, 2021, 2022, 2023 and 2024, indicating he has authored multiple Highly Cited Papers which rank in the top 1% by citations for their field and publication year in the Web of Science over the past decade.

• Canada Research Chair in Remote Sensing
• Fellow of the Royal Society of Canada 2022
• 2022 Canadian Institute of Forestry Scientific Award
• Joint Winner. Marcus Wallenberg Prize 2020
• Gold Medal – Canadian Remote Sensing Society 2020
• 2020 UBC Killam Research Prize