Using drones to assess above-ground carbon stock

3D models of mangrove islands created by applying photogrammetric techniques on UAV imagery

In our previous posts about assessing ecosystem carbon stocks through remote sensing, we discussed carbon estimation methods which involved the use of vegetation indices applied to satellite imagery, as well as those that utilized volumetric assessments of biomass derived from aerial surveys using LiDAR and RGB sensors. Several papers discussed the potential of using Unmanned Aerial Vehicles (UAVs) to study forest health and carbon estimation, which we take a closer look at in this post.

Methods

1.      Carbon estimation through Above Ground Biomass (AGB) derived from Canopy Height Model (CHM)

This method uses RGB data collected from UAVs to create 3D models of tree clusters. From this information, a Digital Terrain Model (DTM) and Digital Surface Model (DSM) are computed, and a Canopy Height Model (CHM) is created. This CHM can be used to deduce median values for height and Diameter at Breast Height (DBH), from which AGB and subsequently carbon stock can be calculated.

Based on existing literature (Ota et al. 2015; González-Jaramillo, Fries, & Bendix 2019), it appears that using CHMs and hence AGB values derived completely from RGB data is not very accurate, primarily because imagery from UAVs cannot be used to build accurate DTMs. Since forest canopies are opaque to RGB imagery, getting good information on the varying height of the terrain at ground level is difficult. González-Jaramillo et al. (2019) found that the correlation between a CHM derived from RGB data and a CHM derived from LiDAR data was very low. Both these studies thus proceeded to use a DTM derived through LiDAR surveys of the area along with RGB based DSMs to derive CHMs. Values for AGB derived from this combination of sensors showed high correlation with those derived only by LiDAR, as well as with those derived through more traditional allometric methods.

It’s interesting to note that González-Jaramillo et al. (2019), did not conduct LiDAR based data collection for this study. They compared the accuracy of UAV based methods against results from a previous study in the same area done using LiDAR. Having access to this data allowed them to create a CHM model that combined LiDAR and RGB imagery. Due to the high costs and computational power required for LiDAR, they recommend that having a single accurate LiDAR-based DTM for an area would be sufficient to do subsequent studies using only UAVs. This is especially useful for forests that require repeated volume estimations, such as for REDD+ or carbon credit programs. Based on these studies, this method seems like a useful application to derive UAV based carbon estimates, provided that a high resolution DTM is available for the area.

2.      REDD+ Protocol, as developed by Goslee et al. (2016).

This method is based on field work and allometric equations. Sample circular plots are identified in the area for which carbon is to be estimated. The area of these plots is calculated and used to derive a scaling factor. Field work is then conducted in accordance with established protocols to estimate the AGB of sample trees using allometric equations. The scaling factor is used to extrapolate to the plot and entire area of concern. It may be possible to replace some of the tedious fieldwork with data obtained from UAV imagery.

The basic formula is:
𝐶𝑝 = 𝐷𝑀 ∗ 𝐶𝐹 
where
𝐶𝑝 = carbon stock in plot 
DM = dry biomass in plot
CF = carbon fraction

Based on this formula, it would be interesting to test how dry biomass values derived from UAV imagery would compare to results based on field work. RGB images from a UAV could be used to generate a 3D model of the relevant forest plot, which could then be used to derive volume and hence mass in the plot. Estimating dry biomass from this could be done based on the wet vs dry ratio of the tree species. Standardised carbon fractions are publicly available for most tree species.

Since this method specifies which field work protocols are to be used, replacing variables by deriving them through UAV-based RGB data would need to be tested adequately. Results from both methods on the same plots would need to be correlated to check accuracy and determine whether this is truly an option. The scale of the area used in this method varies. Especially for larger areas, using UAVs could potentially increase  both the speed and accuracy of the surveys, since it would reduce the time spent on field work as well as provide estimates on the volume of entire plots, rather than having to sample individual trees per plot for extrapolation.

3.      Biomass Expansion Factor (BEF) Method:

In this method, above-ground biomass density is derived from the volume of biomass, wood density and BEF (Brown 1997).  This method is generally based on data from National Forest Inventories (NFI). These are records of forest attributes such as species, DBH, age, class, etc generally collected by governments which is used to estimate volume of the biomass of the given area. This is then combined with the species-specific wood density (standardised and available for different tree species) as well as BEF to estimate Aboveground Biomass and then carbon. BEF is the ratio of aboveground oven-dry biomass of trees to oven-dry biomass of inventoried volume. Standardised constants of BEF have been developed by the IPCC (2014) for use across various forest types.

Volumetric assessments derived through NFI data have been cited as one of the shortcomings of this method (Shi & Liu 2017) since the resolution and relevance of the NFI data varies greatly across the world. We see merit in experimenting to see whether using UAVs could fill this gap. Deriving volume data of forests by creating 3D models with UAVs could be useful in situations that require updated and accurate carbon estimations of relatively smaller forested areas that can be mapped by UAVs. For such areas, this could be an efficient way to estimate carbon depending on the accuracy of the results.

4. Normalized Difference Vegetation Index (NDVI) Method:

Carbon estimation through satellite-derived vegetation indices are well documented. The same methods can be applied using multispectral UAVs for higher resolution and accuracy. Tian et al. (2017) tested the correlation of AGB derived by various vegetation indices using a multispectral UAV and found that NDVI showed the highest correlation with AGB. However, even their best model only showed a moderate correlation with the AGB (r^2=0.67). Following this, Gonzalez et al (2019) compared results of AGB derived through a UAV based NDVI with that derived from LiDAR for the same area, and showed no correlation. According to them, this was because the UAV-based sensors they were using were saturated, and the resulting NDVI wasn’t suitable to provide forest structure information. Based on this, it seems possible to use UAVs to derive carbon estimates through vegetation indices in contexts where the NDVI varies significantly within the plot.

Experiment

Recently, we’ve been experimenting with collecting data on above-ground biomass from UAV based volumetric assessments over mangrove forests, and have some preliminary results. On the mangrove islands indicated below, we’ve calculated that there’s approximately 57,000 cubic metres of above-ground biomass over an area of 3 acres.

Three acres of mangrove forest across two islands, sequestering ~ 27,000 tons of carbon (with huge caveats!).

Using the REDD+ protocol, and standard values for the carbon fraction, we estimate that there’s approximately 27,000 tons of above-ground carbon in this plot (calculated value of 8,837 tons/acre). This is definitely an overestimation, possibly by an order of magnitude; the two main factors being due to all the empty space being accounted for in the canopy, as well as because these equations use dry biomass values. With more field work and research, we’ll be able to obtain site-specific scaling factors that we can use to estimate a stronger relationship between the volume of a mangrove forest and the carbon sequestered within it.

In conclusion, with UAV technology more accessible, it seems like the right time to test what is possible to better understand forest health using this platform. Using UAV data in combination with other data sources and testing various methods may produce interesting results and take us a few steps closer to efficient and accessible monitoring of forest carbon and health through remote sensing. If you have any comments or suggestions regarding the suitability of these methods, or could recommend additional methods, please let us know!

References

·         Ota, T., Ogawa, M., Shimizu, K., Kajisa, T., Mizoue, N., Yoshida, S., … Ket, N. (2015). Aboveground Biomass Estimation Using Structure  from Motion Approach with Aerial Photographs in a  Seasonal Tropical Forest. Forests, 6(12), 3882–3898. https://doi.org/10.3390/f6113882

·         González-Jaramillo, V., Fries, A., & Bendix, J. (2019). AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV). Remote Sensing, 11(12), 1413. https://doi.org/10.3390/rs11121413

·         Lei Shi and Shirong Liu (February 22nd 2017). Methods of Estimating Forest Biomass: A Review, Biomass Volume Estimation and Valorization for Energy, Jaya Shankar Tumuluru, IntechOpen, DOI: 10.5772/65733.

·         Kauppi, P. E., Ausubel, J. H., Fang, J., Mather, A. S., Sedjo, R. A., & Waggoner, P. E. (2006). Returning forests analyzed with the forest identity. Proceedings of the National Academy of Sciences, 103(46), 17574–17579. https://doi.org/10.1073/pnas.0608343103

·         Goslee, K., Walker, S. M., Grais, A., Murray, L., Casarim, F., & Brown, S. (2016). Module C-CS: calculations for estimating carbon stocks. Leaf technical guidance series for the development of a forest carbon monitoring system for REDD+. Winrock International.          https://www.leafasia.org/sites/default/files/tools/Winrock_LEAF_REDD_TechSeries_C-CS_0.pdf

·         Inter Governmental Panel on Climate Change. (2014). IPCC Good Practice Guidance for LULUCF (Annex 3A.1 Biomass Default Tables for Section 3.2 Forest Land). Retrieved from https://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf_files/Chp3/Anx_3A_1_Data_Tables.pdf

·         Brown, S. (1997). Estimating biomass and biomass change of tropical forests: a primer (Vol. 134). Food & Agriculture Org. https://books.google.com/books?hl=en&lr=&id=uv-ISezvitwC&oi=fnd&pg=PA1&dq=Estimating+Biomass+and+Biomass+Change+of+Tropical+Forests:+a+Primer.+(FAO+Forestry+Paper+-+134)&ots=OCwbWs7WzH&sig=xaDzjevMu6Yg5jwbBuciglfkeIQ

·         Brown, Sandra., & Lugo, A. E. (1992). Aboveground biomass estimates for tropical moist forests of the Brazilian Amazon. Interciencia. Caracas, 17(1), 8-18.

·         Tian, J., Wang, L., Li, X., Gong, H., Shi, C., Zhong, R., & Liu, X. (2017). Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest. International Journal of Applied Earth Observation and Geoinformation, 61, 22–31. https://doi.org/10.1016/j.jag.2017.05.002

August 28th (Ladakh Lakes #7)

Shashank’s birthday might have been the best day of the trip for all of us! We had been working to put together a little card for him- with Gabriella’s doodles and messages from us all that we glued together with candle wax. We were heading to a new field site that day. It was the first time we were leaving the tourist circuit and we were all excited to see what we would find. The journey was incredible. Stoic brown mountains would open out in bits into green meadows and we could see marmots popping in and out of the ground like they were performing a song in a Disney Movie.Soon, we reached the Indus. It was the first time any of us was encountering this fierce river that has shaped so much of the sub-continent’s history. Almost as soon as we crossed the river, it felt like the landscape began to change. Like most other people, I’d seen hundreds of photographs of Ladakh before ever going there and this was not even my first trip up there. Besides, we had already been in the field for over a week so I didn’t expect to be surprised. But  the landscape changed fast and I was completely stunned. Brown mountains turned to purple and then white and then black. I had never imagined rocks and sand or mountains could ever look like this. As if the place wasn't stunning enough, we spotted two black necked cranes just by the side of the highway, at a spot that isn’t commonly known to have them and were able to get  a closer look than even Tso Kar. Shashank managed to get some beautiful photographs too- talk about a happy birthday!

Onwards, the landscape only got more magical. Small towns, propped up on jagged hills looked like mystical kingdoms and herds of Kiang ran across the valley and  I couldn’t believe what I was seeing. But it was about to get better still. As we ascended towards our field site, clouds covered the sky and it began to hail. Suddenly, the vast brown landscape was speckled with white and just as we turned a corner, we saw a lake literally sparkling in the distance. We made our way towards the most surreal place I’ve ever been. The lake was a crystal blue that I’ve never seen before in a natural environment. We were freezing but were very excited to take the Trident out because it looked like there could be a dragon living in the depths!

We completed our field work and needed to head to another lake for a survey. But delayed by road blocks and hail; we were way behind schedule and had not had a chance to eat lunch. There was a lone tent by the side of this lake and Topden, our resourceful driver had no intention of skipping lunch. He was familiar with this area because he used to come here to assist with medical camps and vaccination programs. He spoke with the lady at the tent who graciously agreed to have us in for lunch. The tent was beautiful. IT was bright , so clean and well organized and the lake listened through the entrance. We communicated in pieces and smiles through Topden and she fed us fresh butter and curd and this was probably my favourite moment in the whole trip.

Having completed the other lake survey we returned to our rest house for the night which was at a monastery. Situated in the midst of barren brown lands, this bright and colourful building stood out magnificently. We were looking at this royal structure when Shashank called out to us to rush to come see something. The excitement was well worth it because a puppy about the size of his face was waddling towards us. He was a ball of fluff and excitement- he looked like an animation of a puppy. The rest of our time there was spent playing with and squishing this puppy..

After a fresh and warm dinner, cooked so kindly and eaten in the warm kitchen, we created an ad-hoc (but as indulgent) version of a serradura as Shashank’s birthday cake with condensed milk, marie biscuits, Dairy Milk chocolate and coffee powder. We then sat around together after dinner feasting on this bowl of (mostly) cream, and playing cards. At one point, Gaby, Raghav and I walked out of the monastery to use the washroom and when we were walking back in, we all paused at the entrance of the Monastery. I forget why we stopped there in the first place, but once we were there, we couldn’t leave. We stopped talking and it was one of those moments, when you could feel your mind making a memory. It was pitch dark and the you could just about distinguish the structure of the monastery but we could smell the incense that had been burning in the evening. Above was a sky densely full of stars. I’m not even sure how long we stood there but I definitely felt very grateful to be there that crisp night. Also grateful to then return  to the warmth of the room to play rummy and beat Raghav in every round :)

High-altitude Desserts (Ladakh Lakes #6)

Driving through the mountains for hours on end makes one hungry. After nine hours of being on the road, the Ladhaki landscape appeared edible.  Dusty brown chocolate truffle mountains, sharp Black Forest cake mountains, purple blueberry pie mountains and soft tiramisu-like caramel colored mountains topped with snow. Running through this delicious landscape is the River Indus: chocolate milkshake making its way through pancake plains. Ladakh is truly the land of high-altitude desserts.

Tso Kar lay at the end of one of these delectable drives, where a hot bowl of Maggie noodles put an end to my food-related hallucinations. Tso Kar, a vast salt water lake, sits at the center of a mosaic of bright green marshland and smooth white salt mounds hosting a range of rare species such as the black-necked crane and the kiang.

The salt mounds and wetlands of the Tso Kar basin

Here in Tso Kar, everything is beautiful. The elegant birds that inhabit the plains, the local people whose sun-wrinkled skin deepens their smiles, the ornate monastery with bright colored murals… I could go on. What I found most beautiful of all was that the community that lives around Tso Kar still follows its nomadic traditions. I found it fascinating that close to a hundred families should pack up their belongings, gather the old and the young and move across the plains through the summer, herding their animals till winter came. Without romanticizing the hardships of their lives, I am in awe of their connection to the landscape and the seasons, their local knowledge and their willingness to follow a nomadic existence in spite of the influence that tourism has had on the region. I wonder how many of the younger nomads value this hard yet beautiful way of living and how many of them have desires of living in a city with access to a school, a hospital and an internet connection.

The Thukje gonpa

With the melting of the snow and the opening of the mountain passes, visitors like myself pour into Ladakh, bringing with us our fast paced, mall-cultured, Swiggy- assisted lives. I wonder if there is a way of amalgamating these two worlds in such a way that the communities that inhabit Tso Kar are able to choose the future they want. I am so grateful to have witnessed this stunning alien landscape at this pivotal moment in time.

The author, with edible mountains behind her.