Worldwide LiDAR of Rainforest Biomass for REDD+

We at rapidlasso have long been big fans of the biomass and biodiversity work done by Greg Asner’s group and their Carnegie Airborne Observatory. They were, in fact, the earliest „power-users“ of the BLAST extension of LAStools and helped finding all the bugs when rasterizing billions of LiDAR points collected during large-scale surveys in the Amazon into Digital Elevation Models (DEMs) with blast2dem. Below a video fly-through of some of the LiDAR they collected.

http://www.youtube.com/watch?v=2eDBfdomQ-w

A few days ago, Greg Asner together with his colleagues Joseph Mascaro, Stuart Davies, Alex Dehgan, and Sassan Saatchi published a thought-provoking article called „These are the days of lasers in the jungle“ which is essentially a „call for action“ to map the world’s tropical forests with a fleet of airplanes outfitted with advanced LiDAR to rapidly and to accurately assess global forest carbon stocks.

Why would anyone want to do this? In order to properly quantify actual emissions savings for REDD+ (Reduced Emissions from Deforestation and Degradation). REDD+ is a tropical forest carbon accounting program of the United Nations Framework Convention on Climate Change that aims to compensate tropical countries for reducing carbon emissions from deforestation and forest degradation that account for roughly 10 percent of global greenhouse gas emissions. The key to implement such a program is the ability to accurately and affordably estimate the actual emissions savings and a worldwide LiDAR inventory of tropical forests will accomplish just that argues the paper. This „call for action“ has since been picked up by Mongabay – a site that examines emerging trends in climate, technology, and finance on conservation and development.

Interesting is the price tag that they estimate, which is a fraction of the cost of a typical Earth observation satellite mission, They claim: „Our ambitious plan can be accomplished for far less than what we have already spent on avoided deforestation. Aircraft leasing, data collection and processing costs for 30 days of flying can reasonably be limited to USD 500,000 Using this monthly sampling unit, collecting at an average of 100,000 hectares per day, a fleet of ten aircraft could do the job in four years at USD 250 million, or just 5% of pledged REDD + funding.“

The authors state „The time has come for a brute-force effort to directly assess the carbon stock for all of the world’s tropical forests by 2020“ because „airborne LiDAR is uniquely suited for this role because it can be collected, standardized, reported and verified in a simple manner by both a landholder and any third party“. Should such a campaign turn out to be a viable option to implement REDD+ we hope full waveform LiDAR – not just discrete returns – will be collected.

Waveform digitizers have become popular because they can capture the reflection of the emitted laser pulse with much more detail than a discrete return system. The intensity of the signal returning to the plane is digitized up to one billion times per second, giving a vertical resolution of one digitized amplitude each 15 centimeters. As this can capture the interactions between each laser pulse and the vegetation with much greater detail, it would seem that having full waveform instead of discrete returns will prove especially useful for biomass studies. A future blog post will talk about our own experiences of scanning tropical rainforest to produce full waveform LiDAR in PulseWaves format.

Another approach with a similar objective is being taken by NASA with their future GEDI space LiDAR. The Global Ecosystem Dynamics Investigation (GEDI) instrument will be the first to systematically probe the depths of the forests from space to reveal their 3D structure, as depicted in the artist’s concept below, and provide crucial information about the impact that trees have on the amount of carbon in the atmosphere. „One of the most poorly quantified components of the carbon cycle is the net balance between forest disturbance and regrowth“, said Ralph Dubayah, the GEDI principal investigator at the University of Maryland. „GEDI will help scientists fill in this missing piece by revealing the vertical structure of the forest, which is information we really can’t get with sufficient accuracy any other way“. The instrument will be built at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. Not sure about the price tag.

nasa_gedi
Image Credit: NASA’s Goddard Space Flight Center

Until either of these projects become reality, „enjoy“ this video by Carnegie Airborne Observatory of a 3D flyover that shows rapidly expanding palm oil plantations in the Peruvian Amazon rainforest that are contributing to deforestation.

http://www.youtube.com/watch?v=MxV7YKyem8k

0 Kommentare zu „Worldwide LiDAR of Rainforest Biomass for REDD+“

  1. Pingback: REDD in the news: 1-7 September 2014 | redd-monitor.org

  2. I’m not quite sure how they calculated this but since it took my company something like 2 months of flying to capture over 4000km2, I wonder if they have properly accounted for the low lying cloud cover and terrain conditions for which rainforests are notorious and prevent high altitude acquisition so data capture will be much slower than they realize.

  3. Yes, seems to be more of a random thought bubble than any kind of sensible plan. Geography aside, one thing that I’m quite sure they haven’t accounted for are the complex political and economic realities of the (often impoverished and dysfunctional) tropical nations in which these forests lie. There are immense difficulties involved in terms of nationalism and heritage, strategic military implications, ownership and control over data, deploying low-flying aircraft over conflict zones, the fact that powerful interests will not want a fair an honest accounting of carbon stocks, and so on. A grand idea, but practically impossible to implement and therefore doomed. The future here surely lies with projects like GEDI which will allow the research community to transcend the narrow and parochial agendas of various parties on the ground.

  4. As a part of the CAO team, I’d like to dispel any notion that we have not accounted for weather, logistics, political or economic dynamics that come into play in planning this kind of survey. We recently released a 1-ha scale carbon map for the whole country of Peru, where we spent 3 summers working. We mapped over 6 million hectares over three two-month periods from 2011-2013. We then upscaled biomass data from the ground to our aiborne LiDAR, and from our LiDAR to full satellite coverage. The complete report can be found here, including the methods:

    ftp://dge.stanford.edu/pub/asner/carbonreport/CarnegiePeruCarbonReport-English.pdf

    As we have surveyed in Peru, Colombia, Panama, Hawaii and South Africa, I would suggest we are quite capable of understanding the limits of our equipment, our environments, and our partnering countries. I flew those flight lines; I know what we’re up against.

    I think our major downfall will be framing all of our science policy goals in the context of Paul Simon songs.

    I am certainly a fan of the GEDI program, but it is not the only solution to the kind science that we are already doing. We can do global coverage. If nobody else wants to, we will keep doing it.

    Martin, still huge fans of your work over here.

  5. Thanks for that, but frankly speaking it comes as no surprise to learn that the authors have no experience at all with lidar campaigns in tropical forests outside the Americas.

    What’s been achieved in Peru is terrific, and is a model that could clearly be applied elsewhere. However, there are a whole range of very complex and quite specific reasons why lidar coverage is extremely limited in tropical Asia and Africa, and with all due respect, it’s not simply because the CAO team hasn’t turned its attention to the issue yet.

    A very strong case can be made that sensors that operate above sovereign airspace are the only viable path to reasonably comprehensive global coverage and monitoring applications, and I think we might be doing a disservice to that broader agenda by trying to divert limited resources to apparently unrealistic alternatives.

    Maybe the authors do have a plan that takes into full account the full political, cultural and economic complexity of the situation, but it’s difficult to draw much reassurance from the total absence of any detail in the publication, and it would be nice to eventually see it laid out.

  6. Sorry about that. Here are a couple of publications by one of the authors, a collaborator, and a stranger doing large-scale airborne LiDAR outside of the Americas.

    A current plan to map the Congo (Saatchi)
    http://adsabs.harvard.edu/abs/2014EGUGA..16.2678S

    Mapping biodiversity in Sierra Leone using hyperspectral and LiDAR (Laurin)
    http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097910

    Monitoring and biomass and comparing with SAR in Indonesia (Englhart)
    http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6104195

    We know many researchers and survey companies working in these areas and neighboring countries. This is all possible. And while you are correct in asserting that we did not lay out a plan in our two page call to action, I believe you are incorrect in asserting that it is not possible.

    We are not suggesting that we will be the ones to solely undertake this project. As we have a team of five, establishing global coverage is outside of our reach. This paper is about reaching out the community and saying ‚this is possible!‘.

    Your vague concerns that there are ’specific reasons‘ this isn’t happening don’t contribute to the real logistics challenges we face, which I am not trying to avoid. We have had to face them in each of our international campaigns. Others are currently facing them. They are being overcome. If we work together to push this forward and get support to overcome the biggest ’specific reason‘ this isn’t being done (money) then we can work towards lofty goal of global high-resolution biomass mapping.

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