Development of a Forest Carbon Monitoring System in Peru

Forests are the lungs of the Earth: they take in and store carbon dioxide, and release the oxygen we breathe. This is something we intuitively understand, but we have not yet developed practical and cost-effective ways of accounting for this value in our economic decisions and transactions. The result is that tropical countries are incentivized to clear their forests in order to develop economically, and we collectively lose global forests at a rate of 48 football fields every minute – turning the world’s largest terrestrial carbon sink into our second largest source of global greenhouse gas emissions.

Fortunately, the United Nations Framework Convention on Climate Change (UNFCCC) has forged incredible international leadership addressing this problem. Under the UNFCCC, REDD+ is a mechanism to quantify and value the climate change mitigation services forests provide, creating sustainable pathways to economic development for tropical countries. Nearly seventy developing countries are in various phases of REDD+ implementation and must develop and execute Measurement, Reporting, and Verification (MRV) systems in order to receive performance-based payments for REDD+.

While this is a huge step forward, public financing alone is not sufficient to shift economic incentives around land use in tropical countries. The required volume of performance-based payments to sustain REDD+ globally is estimated to be roughly $20B – $40B per year, or several times the total capital that has been pledged by donor governments to date. To meet global development and climate goals, the world needs to mobilize significant funds for performance-based payments in REDD+ over the next decade – beyond the scale of which the public sector alone can provide.

Technological breakthroughs in Earth observation can help address this challenge. Earth observation satellites are rapidly shrinking in size and cost while simultaneously improving in resolution and revisit rates – unlocking unprecedented opportunities for timely, high-resolution, wall-to-wall mapping of the world’s tropical forests. This offers new possibilities to develop different MRV systems for REDD+. ‘Next-generation’ (automated, analytical) MRV tools can be developed to not only improve accuracy, efficiency, and capacity but also to inform low-carbon financial product innovation in order to unlock sustainable private financing for REDD+.

In partnership with Plane and The Erol Foundation, The Asner Lab is developing such a ‘next- generation’ REDD+ MRV tool. The objective of this project is to create an automated monthly, spatially explicit indicator of forest carbon stocks and emissions for the country of Peru.

The Asner Lab and Planet each bring something powerful to the challenge of developing a spatially explicit forest carbon indicator. The Asner Lab’s Global Airborne Observatory (GAO) is the world’s leading research lab using LiDAR (light detection and ranging) to assess forest carbon from the sky. In 2014, the Asner Lab developed a high-resolution carbon map of the country of Peru and submitted the findings to the country’s Ministry of Environment. The map was notable as the first 1-ha resolution carbon stock assessment for a large tropical forest country but had two key areas for improvement. First, while approximately 5% of Peru was directly sampled with carbon-sensitive LiDAR observations, the remaining 95% of carbon estimates were modeled, based on the intersection of LiDAR data with Landsat and other coarse-resolution satellite inputs. Second, the map reflected a single moment in time.

Planet operates the largest constellation of Earth-observing satellites in human history, collecting high-resolution (3.7m) imagery of the entire Earth’s surface each day the sky is clear. The Asner Lab is the biggest user of Planet data in the academic sector

Intersecting Planet’s satellite data source with the GAO’s LiDAR allows our teams to do two revolutionary things: First, we are dramatically increasing per-pixel accuracy of our static carbon assessment for Peru: using machine learning techniques, we will develop algorithms to integrate the GAO’s high resolution (1m) LiDAR data with Planet’s optical image data (3.7m). Second, we will animate this map in time, by synthesizing airborne data with a dynamic, high-resolution monitoring effort in nation-wide monitoring and reporting, creating a monthly updated, spatially-explicit indicator of stocks and emissions.

Project Website

The Erol Foundation


Peruvian Ministry of Environment

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