A 3D perspective on the effects of topography and wind on forest height and dynamics

Lead Research Organisation: University of Cambridge
Department Name: Plant Sciences

Abstract

Strong winds can unleash huge destructive power on forests. For example, Britain's Great Storm of 1987 blew over about 15 million trees. Most research into wind damage occurs in temperate plantations, because of their social and economic value, but much less is known about the impact of wind on tropical rainforest. We recently used survey forests in Sabah (a Malaysian state on the island of Borneo) using airborne laser scanning, as part of a NERC-funded programme into human-modified tropical forests. We spotted the tallest tree ever found in the tropics in the laser scans: an 89-m tall individual in the Dipterocarpaceae family of trees, which are renowned for producing giants. This discovery raises questions about the mechanical limits of height in tropical forests and the reason why Sabah's lowland forests grow so much taller than those of Amazonia and Africa. One idea is that trees are particularly tall in sheltered hillsides in Sabah because they seldom experience strong winds. Another idea is that Dipterocarps have evolved a unique set of mechanical properties that enable them to withstand strong wind better than other tropical trees. Both of these theories need critical evaluation.

The project addresses these ideas, by studying wind flow and tree responses in 40 square kilometres of old-growth hill forest situated in Sabah, where NERC's airborne research facility has already mapped ground elevation, canopy height and foliar nutrient concentrations by laser scanning (ALS) and imaging spectroscopy. The tallest forests in the tropics juxtapose with stunted heath forests in these hills, making the landscape ideal for evaluating the role of wind in determining forest height and dynamics. We will compare wind flows in the immensely tall forests of Borneo with those found in much shorter forests of South America (French Guiana) and the hurricane-impacted Caribbean (Puerto Rico), including an analysis of how wind is funneled through hilly landscapes resulting in strong wind forces on forests in exposed sites. We will also quantify the susceptibility of individual trees to windthrow by a novel approach involving 3D reconstruction of hundreds of rainforest trees using terrestrial laser scanning and simulation modelling of stresses within their trunks when exposed to wind gusts. Combining the wind flow maps with the tree susceptibility will allow us to produce maps showing where we think trees are most at risk of windthrow in each tropical region. The key question is whether sites predicted to be at high risk and found to be wind damaged. To test this, we will use the latest advances in repeat-survey ALS to track growth and mortality of about 200,000 tall trees (>30 m). Measuring tree demography at this unprecedented scale will provide a unique and powerful data set with which to test whether wind is a major determinant of tree death and canopy height. Finally, we will put this work in a global context. Wood density, modulus of elasticity and tree architecture of Bornean trees will be compared with values from French Guianan and pan-tropical datasets, to evaluate whether the exceptional tall trees are mechanically distinct. Simulations will be run using wind regimes from other tropical regions to assess whether a lack of extreme wind events may have contributed to the development of such tall forests in Borneo. ALS-measured canopy height is closely related to the aboveground carbon density, so these analyses will provide a wall-to-wall perspective on carbon dynamics in the context of climate change.

Planned Impact

A primary beneficiary will be Sabah Forestry Department (SFD), which is committed to protecting and restoring the state's remaining areas of high conservation value forest. They have taken a keen interest in the discovery of very tall trees in the state, using airborne laser scanning, and are already promoting visits to the sites for ecotourism. We will produce maps of tall tree groves in collaboration with SFD to aid conservation planning. Borneo has lost much of its lowland forests to agriculture in recent decades, but the state of Sabah is at the forefront of conservation and restoration efforts in the region. Knowing the location of very large trees would be useful for conservation planning in the region. We will run our tree segmentation algorithms over the entire laser-scanned areas of Sabah, to locate all trees over 60 m tall. These tall-tree maps will be used by SFD and presented to conservation practitioners at the 2020 Heart of Borneo conference, a key regional meeting that brings together government officials, researchers, conservationists and agribusiness leaders and provides an important opportunity to engage with stakeholders.

We plan to produce short films which will benefit SFD by providing them material for use in their education centres and will benefit the wider public via social media. Large trees are among the longest-lived and largest organisms on the planet, and as such are a source of inspiration, often appearing in artwork, films as well as news outlets. For example, our discovery of the largest trees ever found in the tropics have attracted over 100,000 views on short videos we produced, and was selected as a top ten news story (news.mongabay.com/2016/12/top-10-happy-environmental-stories-of-2016/ ). We plan to capitalise on this public interest in tall trees to communicate the wider scientific story, producing films that discuss (a) the loss of forests in SE Asia and recent initiatives aimed at protection of remaining old-growth forests and restoration of secondary forests; (b) the science of tall trees; (c) the science of airborne and terrestrial laser scanning in the context of forest science. These films will bring a sense of adventure and wonder to the ongoing efforts to conserve the world's dwindling natural resources. We will also present our work live at science festivals to inspire young people in the application of science to address pressing environmental issues.

The PI is part of the Cambridge Conservation Initiative, working in the same building as major conservation organisations that manage reserves in SE Asia. These organisations have expressed interest in our work because they have identified a need to monitor reserves more effectively and recognise the value of new remote sensing tools. We will hold a workshop with these partners to explain what we are doing and foster collaboration.

Publications

10 25 50
 
Description Wind influences forest structure and carbon storage. The risk of snapping increased with tree height in Danum Valley, Malaysia. In Barro Colorado, Panama, the risk of snapping increased with tree slenderness and decreased with wood density. Trees with a higher risk of snapping will be more limited in their ability to grow taller and compete for light.
Tree mortality and canopy disturbance can be mapped using airborne laser scanning. Across the Brazilian Amazon, there are more gaps in the forest canopy in areas with high wind speeds and lightning intensities and areas with low water availability. We also found that the tallest trees in the Amazon are located in areas with low wind speeds and high fertility.
Tree segmentation is an important methodological challenge in forest remote sensing. We showed that current methods can only accurately segment the canopy trees in airborne laser scanning data. We also showed that machine learning methods can accurately segment canopy trees in low cost RGB imagery. Segmenting understory trees remains a challenge.
Exploitation Route The finding are fascinating and very worthy of further work in other parts of the tropics (i.e. doing what was planned before covid struck)
Sectors Environment

 
Description Policy impact. We co-authored a COP26 briefing note on 'Space-based earth observation for climate security' which was used by policymakers at the summit in Glasgow in 2021. Public engagement. We produced a short video, an article in The Conversation about the discovery of the tallest trees in the Amazon and the importance of conserving them. This story was covered by multiple news outlets and reached a large audience. Scientific impact. Our work is helping researchers understand the role of wind as a driver of forest structure, which could help improve forest models. We are also contributing to international efforts to better quantify large tree mortality rates, which are a key uncertainty in current forest models. Technical impact. We have provided a number of open-source data sets and software packages which are being used by other researchers.
First Year Of Impact 2020
Sector Environment
Impact Types Policy & public services

 
Title Airborne LiDAR and RGB imagery from Sepilok Reserve and Danum Valley in Malaysia in 2020 
Description This dataset contains LiDAR and RedGreenBlue (RGB) Imagery data collected from a helicopter over two forest sites in Sabah, Malaysia in February 2020. Point cloud data are included in LAS (LASer) format as well as RGB data summary rasters in .tif format. The raster images were processed with LAStools using default parameters. Canopy Height Model (CHM), Digital Surface Model (DSM), Digital Terrain Model (DTM) and pulse density (pd) are also present. The RGB data are provided as jpgs and are organised by flight julian day (JD). The Sepilok Reserve was scanned in full between 15 February 2020 (julian day 46). This is a total area of 27 square kilometres. In Danum Valley the scanning was distributed into two contiguous areas, the protected area (20 square kilometres) and the reduced impact logging area (9 square kilometres) on the 19-22 February 2020 (julian day 50-53). Importantly, these areas were chosen because of the availability of prior airborne LiDAR data collected by NERC in 2014 and by Ground Data Solutions in 2013. The helicopter flew at approximately 350 m altitude above the forest canopy and at a speed of approximately 100 km/hr. The data were georeferenced using ground control points and are provided in the UTM 50N coordinate system. 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
Impact Premature, this has only recently been made public. 
URL https://catalogue.ceda.ac.uk/uuid/dd4d20c8626f4b9d99bc14358b1b50fe
 
Title L2C - Canopy height models across the Brazilian Amazon 
Description Canopy height models derived from LiDAR data collected across the Brazilian Amazon. The files are provided in .tiff format in 7 zip folders. A full description of the data set is available here: https://zenodo.org/record/4968706#.YzB693ZKg5s We also provide the summary data used for statistical analysis in the associated publication: Reis and Jackson et al 2022. Forest disturbance and growth processes are reflected in the geographic distribution of large canopy gaps across the Brazilian Amazon. Journal of Ecology. Each transect covered 375 ha (12.5 km × 300 m) by emitting full-waveform laser pulses from a Trimble Harrier 68i airborne sensor (Trimble; Sunnyvale, CA) aboard a Cessna aircraft (model 206). The average point density was set at four returns per square meters, the field of view was equal to 30°, the flying altitude was 600 m, and transect width on the ground was approximately 494 m. Global Navigation Satellite System (GNSS) data were collected on a dual-frequency receiver (L1/L2). The pulse footprint was set to be below 30 cm, based on a divergence angle between 0.1 and 0.3 milliradians. Horizontal and vertical accuracy were controlled to be under 1 m and under 0.5 m, respectively. The data collection was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Brasil (CAPES; Finance Code 001); Conselho Nacional de Desenvolvimento Científico e Tecnológico (Processes 403297/2016-8 and 301661/2019-7); Amazon Fund (grant 14.2.0929.1) The research project was funded by the UK Natural Environment Research Council project number NE/S010750/1 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
Impact This data set enables researchers to easily access an unprecedented data set of canopy height measurements randomly located across the Brazilian Amazon. As of 10/03/2023, it has been downloaded 90 times. 
URL https://zenodo.org/record/7104044
 
Title Map of the probability of giant trees occurrence (> 70 m) in the Brazilian Amazon 
Description The probability of giant trees occurrence (> 70m) based on environmental conditions. The observations higher than 70 m were filtered out and used to adjust an envelope model based on maximum entropy. In its optimization routine, the algorithm tracked how much the model gain was improved when small changes were made to each coefficient value associated with a particular variable. The resulting map of predicted occurrence of the tallest trees in the Amazon from the MaxEnt model shows that the probability of maximum tree height occurrence is highest in the northeastern Amazon (Fig. 6), more specifically in the Roraima and Guianan Lowlands. We considered 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m); (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s -1); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s -1); (5) the number of days not affected by cloud cover (clear days; in days yr -1); (6) the number of days with precipitation above 20 mm (days > 20mm; in days yr -1 ); (7) the number of months with precipitation below 100 mm (months < 100mm; in months yr -1 ) ; (8) lightning frequency (flashes rate); (9) annual precipitation (in mm); (10) potential evapotranspiration (in mm); (11) coefficient of variation of precipitation (precipitation seasonality; in %); (12) amount of precipitation on the wettest month (precip. wettest; in mm); (13) amount of precipitation on the driest month (precip. driest; in mm); (14) mean annual temperature (in °C); (15) standard deviation of temperature (temp. seasonality; in °C); (16) annual maximum temperature (in °C); (17) soil clay content (in %); and (18) soil water content (in %). 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
URL https://zenodo.org/record/4037100
 
Title Maximum tree height map for the Brazilian Amazon 
Description Maximum tree height distribution estimated by the Random Forest model based on the environmental variables. To explore the influence and importance of the environmental variables for development in tree height, we employed Random Forest modeling, which consists of generating a large number of regression trees, each constructed considering a random data subset. The regression trees are used to identify the best sequence to split the solution space to estimate the output. Were considered 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m); (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s -1); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s -1); (5) the number of days not affected by cloud cover (clear days; in days yr -1); (6) the number of days with precipitation above 20 mm (days > 20mm; in days yr -1 ); (7) the number of months with precipitation below 100 mm (months < 100mm; in months yr -1 ) ; (8) lightning frequency (flashes rate); (9) annual precipitation (in mm); (10) potential evapotranspiration (in mm); (11) coefficient of variation of precipitation (precipitation seasonality; in %); (12) amount of precipitation on the wettest month (precip. wettest; in mm); (13) amount of precipitation on the driest month (precip. driest; in mm); (14) mean annual temperature (in °C); (15) standard deviation of temperature (temp. seasonality; in °C); (16) annual maximum temperature (in °C); (17) soil clay content (in %); and (18) soil water content (in %). 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
URL https://zenodo.org/record/4036988
 
Title Measurements of tree motion in the wind collated from multiple studies undertaken in the UK, Puerto Rico and Australia between 1987-2015 
Description This dataset contains high resolution measurements of tree motion in response to wind at six sites. The data was collected using sensors mounted directly onto the tree trunk. The sites are: Rivox, Kershope, Kyloe and Clocaenog in the UK. Guanica in Puerto Rico and six open-grown trees from Australia. 
Type Of Material Database/Collection of data 
Year Produced 2021 
Provided To Others? Yes  
URL https://catalogue.ceh.ac.uk/id/fecff8c2-983e-4883-a0f8-ac65deba02cf
 
Title The motion of trees in the wind - a collection of multiple data sets 
Description The tree motion data in this repository were collected from multiple sites with different sensors. There are two types of data: (a) 1-hour sample data from 27 sites (/one_hour_samples/*.csv). Each file represents a single tree and contains two columns representing the two horizontal axes of tree motion. These data are uncalibrated and do not have a time-stamp. File names refer to: OwnersInitials_Site_treeID_resolution_SensorType for example TJ_MY_7_4Hz_Strain These filenames match the first column in the 'TreeSummary.csv' table (b) long-term data from 9 data sets (/site_name.zip/) The wind and tree data are stored in separate files for each day of data, labelled with site name, tree ID or wind, year, month, day. For example Kershope_T25_1990_5_23 In each file, the first column is the time and date in format YYYY-MM-DD HH:mm:ss.SSS The following columns are tree motion data or wind data with units in the variable names. Some of the data were calibrated by pulling the tree with a known force, in these cases calibration coefficients are given in the 'TreeSummary' table Wind data for the Australian data set is stored in the folder with data for each tree, because the trees were not close together enough to share a wind measurement. The data for Orange, Storrs and Torrington were collected with inclinometers. The data for all trees is stored together in each daily file. The long-term tree motion data have not been pre-processed because user choices affect the results. Importantly, you will need to remove a varying offset (caused by sensor drift) from the tree motion data. See discussion in the accompanying publication and references therein. References: Publication describing all collated data and necessary pre-processing. Jackson, T. et al (2021) The motion of trees in the wind: a data synthesis. Biogeosciences. Australian data decsribed in: James, K. et al (2006).: Mechanical stability of trees under dynamic loads, Am. J. Bot. doi:10.3732/ajb.93.10.1522 Kershope and Rivox data described in: Gardiner, B. A., et al. "Field and wind tunnel assessments of the implications of respacing and thinning for tree stability." Forestry: An International Journal of Forest Research 70.3 (1997): 233-252. Kyloe and Clocaenog described in: Hale, Sophie E., et al. "Wind loading of trees: influence of tree size and competition." European Journal of Forest Research 131.1 (2012): 203-217. Orange, Storrs and Torrington described in: Bunce, Amanda, et al. "Determinants of tree sway frequency in temperate deciduous forests of the Northeast United States." Agricultural and Forest Meteorology 266 (2019): 87-96. Other large tree motion data sets available at the time of writing (June 2021): https://doi.org/10.7910/DVN/FHJBYG http://www.hydroshare.org/resource/38ae9d9fb88d49f9ad2eed1ee07475c0 https://doi.org/10.5683/SP2/WZIKSR https://data.4tu.nl/articles/dataset/Tree_sway_of_19_Amazon_trees/12714989/1 https://doi.org/10.5285/657f420e-f956-4c33-b7d6-98c7a18aa07a https://doi.org/10.5285/533d87d3-48c1-4c6e-9f2f-fda273ab45bc 
Type Of Material Database/Collection of data 
Year Produced 2021 
Provided To Others? Yes  
Impact This data has been used by many other researchers, and has been downloaded 788 times, as of 10/03/2023 
URL https://zenodo.org/record/4915883
 
Description Developing low cost sensors to measure tree biomechanics 
Organisation University of Exeter
Country United Kingdom 
Sector Academic/University 
PI Contribution We purchased materials and helped build prototype sensors. We also tested these prototype sensors in the field. Finally, we made a batch order of 300 sensors and used them for our fieldwork.
Collaborator Contribution Dr Paulo Bittencourt at the University of Exeter shared his custom built data logger design with dramatically reduced the cost of the sensor system. He also helped us order all the necessary components, build the sensors and test them.
Impact Collected data on wind damage risk for >100 trees in Barro Colorado Island. This will result in a publication in due course. This collaboration is multi-disciplinary, combining forest ecology, electrical engineering and physics.
Start Year 2020
 
Description Eric Gorgen 
Organisation Federal University of Jequitinhonha and Mucuri Valleys
Country Brazil 
Sector Academic/University 
PI Contribution We provided expertise in lidar process and tropical ecology
Collaborator Contribution The provided access to an airborne lidar survey of brazil which cost over US$ 10,000,000 to collect and hasn't been that extensively utilised since it was collected
Impact My postdoc on the project - Toby Jackson - travelled to Brazil to film the largest tree ever discovered in South America and record aspects of its ecology. We have two papers published and another couple almost ready from this work. It has helped form a trusting relationship with INPE and Brazilian scientists which has made access to further datasets more possible.
Start Year 2019
 
Description ForestScan (lidar data synthesis across multiple partners) 
Organisation University College London
Country United Kingdom 
Sector Academic/University 
PI Contribution Refining estimates of carbon storage and flux in tropical rainforests is a vital component of improving understanding of the global carbon cycle, with important implications for climate change mitigation. This collaboration brought partners together with dataset and expertise from sites across the three regions, to synthesise data and generate improved knowledge of the carbon cycle
Collaborator Contribution We shared data from airborne lidar scans in Malaysian Sabah (originally collected under this project and a previous grant)
Impact Premature.
Start Year 2020
 
Title Maximum entropy model trained to estimate probability to host trees taller then 70 m based on enviromental factors 
Description Focusing only on the tallest trees - those over 70 m in height - we built an environmental envelope model to assess the conditions which allow them to occur. We employed the maximum entropy approach (MaxEnt) commonly applied to modelling species geographic distributions with presence-only data to discriminate suitable versus unsuitable areas for the species. We initially considered a total of 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m); (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s -1); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s -1); (5) the number of days not affected by cloud cover (clear days; in days yr -1); (6) the number of days with precipitation above 20 mm (days > 20mm; in days yr -1 ); (7) lightning frequency (flashes rate); (8) annual precipitation (in mm); (9) potential evapotranspiration (in mm); (10) coefficient of variation of precipitation (precipitation seasonality; in %); (11) amount of precipitation on the wettest month (precip. wettest; in mm); (12) mean annual temperature (in °C); (13) standard deviation of temperature (temp. seasonality; in °C); (14) annual maximum temperature (in °C); (15) soil clay content (in %); and (16) soil water content (in %). 
Type Of Technology Software 
Year Produced 2020 
Open Source License? Yes  
URL https://zenodo.org/record/4066653
 
Title Maximum entropy model trained to estimate probability to host trees taller then 70 m based on enviromental factors 
Description Focusing only on the tallest trees - those over 70 m in height - we built an environmental envelope model to assess the conditions which allow them to occur. We employed the maximum entropy approach (MaxEnt) commonly applied to modelling species geographic distributions with presence-only data to discriminate suitable versus unsuitable areas for the species. We initially considered a total of 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m); (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s -1); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s -1); (5) the number of days not affected by cloud cover (clear days; in days yr -1); (6) the number of days with precipitation above 20 mm (days > 20mm; in days yr -1 ); (7) lightning frequency (flashes rate); (8) annual precipitation (in mm); (9) potential evapotranspiration (in mm); (10) coefficient of variation of precipitation (precipitation seasonality; in %); (11) amount of precipitation on the wettest month (precip. wettest; in mm); (12) mean annual temperature (in °C); (13) standard deviation of temperature (temp. seasonality; in °C); (14) annual maximum temperature (in °C); (15) soil clay content (in %); and (16) soil water content (in %). 
Type Of Technology Software 
Year Produced 2020 
Open Source License? Yes  
URL https://zenodo.org/record/4066652
 
Title Random Forest trained to estimate Amazon maximum height based on enviromental factors 
Description The Random Forest model obtained MAE = 3.62 m, RMSE = 4.92 m, and R² = 0.735. we initially considered a total of 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m); (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s -1); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s -1); (5) the number of days not affected by cloud cover (clear days; in days yr -1); (6) the number of days with precipitation above 20 mm (days > 20mm; in days yr -1 ); (7) the number of months with precipitation below 100 mm (months < 100mm; in months yr -1 ) ; (8) lightning frequency (flashes rate); (9) annual precipitation (in mm); (10) potential evapotranspiration (in mm); (11) coefficient of variation of precipitation (precipitation seasonality; in %); (12) amount of precipitation on the wettest month (precip. wettest; in mm); (13) amount of precipitation on the driest month (precip. driest; in mm); (14) mean annual temperature (in °C); (15) standard deviation of temperature (temp. seasonality; in °C); (16) annual maximum temperature (in °C); (17) soil clay content (in %); and (18) soil water content (in %). Among the initial 18 environmental variables, two of them (precipitation on driest month and months < 100mm) were excluded due to high correlation (> 0.80) to other independent variables. 
Type Of Technology Software 
Year Produced 2020 
Open Source License? Yes  
URL https://zenodo.org/record/4061838
 
Title Random Forest trained to estimate Amazon maximum height based on enviromental factors 
Description The Random Forest model obtained MAE = 3.62 m, RMSE = 4.92 m, and R² = 0.735. we initially considered a total of 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m); (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s -1); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s -1); (5) the number of days not affected by cloud cover (clear days; in days yr -1); (6) the number of days with precipitation above 20 mm (days > 20mm; in days yr -1 ); (7) the number of months with precipitation below 100 mm (months < 100mm; in months yr -1 ) ; (8) lightning frequency (flashes rate); (9) annual precipitation (in mm); (10) potential evapotranspiration (in mm); (11) coefficient of variation of precipitation (precipitation seasonality; in %); (12) amount of precipitation on the wettest month (precip. wettest; in mm); (13) amount of precipitation on the driest month (precip. driest; in mm); (14) mean annual temperature (in °C); (15) standard deviation of temperature (temp. seasonality; in °C); (16) annual maximum temperature (in °C); (17) soil clay content (in %); and (18) soil water content (in %). Among the initial 18 environmental variables, two of them (precipitation on driest month and months < 100mm) were excluded due to high correlation (> 0.80) to other independent variables. 
Type Of Technology Software 
Year Produced 2020 
Open Source License? Yes  
URL https://zenodo.org/record/4061837
 
Description COP26 Universities Network - Space-Based Earth Observations for Climate Security 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Policymakers/politicians
Results and Impact In preparation for COP26 in Glasgow, a network of UK universities prepared a number of briefing papers to inform and advise policy makers on key issues. I was part of the group which co-authored 'Space-Based Earth Observations for Climate Security'
Year(s) Of Engagement Activity 2021
URL https://uucn.ac.uk/uucn_briefings/space-based-earth-observations-for-climate-security/
 
Description Short video on tallest trees in the Amazon and the importance of forest conservation 
Form Of Engagement Activity A broadcast e.g. TV/radio/film/podcast (other than news/press)
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Media (as a channel to the public)
Results and Impact We filmed a trip up the Jari river to confirm the discovery of the tallest known tree in the Amazon. We then produced a short video of the trip, which was amplified by various media outlets. We also wrote an article for The Conversation on the subject.
Year(s) Of Engagement Activity 2019
URL https://theconversation.com/the-amazons-tallest-tree-just-got-50-taller-and-scientists-dont-know-how...
 
Description Video released on University Webite 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact The University produced a video explaining how Toby Jackson had travelled to a remote region of Brazil to survey a massive tropical tree discovered by laser scanning
Year(s) Of Engagement Activity 2019
URL https://www.cam.ac.uk/research/news/expedition-finds-tallest-tree-in-the-amazon