Aboveground forest biomass varies across continents, ecological zones and successional stages: refined IPCC default values for tropical and subtropical forests

For monitoring and reporting forest carbon stocks and fluxes, many countries in the tropics and subtropics rely on default values of forest aboveground biomass (AGB) from the Intergovernmental Panel on Climate Change (IPCC) guidelines for National Greenhouse Gas (GHG) Inventories. Default IPCC forest AGB values originated from 2006, and are relatively crude estimates of average values per continent and ecological zone. The 2006 default values were based on limited plot data available at the time, methods for their derivation were not fully clear, and no distinction between successional stages was made. As part of the 2019 Refinement to the 2006 IPCC Guidelines for GHG Inventories, we updated the default AGB values for tropical and subtropical forests based on AGB data from >25 000 plots in natural forests and a global AGB map where no plot data were available. We calculated refined AGB default values per continent, ecological zone, and successional stage, and provided a measure of uncertainty. AGB in tropical and subtropical forests varies by an order of magnitude across continents, ecological zones, and successional stage. Our refined default values generally reflect the climatic gradients in the tropics, with more AGB in wetter areas. AGB is generally higher in old-growth than in secondary forests, and higher in older secondary (regrowth >20 years old and degraded/logged forests) than in young secondary forests (20 years old). While refined default values for tropical old-growth forest are largely similar to the previous 2006 default values, the new default values are 4.0-7.7-fold lower for young secondary forests. Thus, the refined values will strongly alter estimated carbon stocks and fluxes, and emphasize the critical importance of old-growth forest conservation. We provide a reproducible approach to facilitate future refinements and encourage targeted efforts to establish permanent plots in areas with data gaps

Estimating aboveground net biomass change for tropical and subtropical forests: Refinement of IPCC default rates using forest plot data

© 2019 The Authors. Global Change Biology Published by John Wiley & Sons Ltd As countries advance in greenhouse gas (GHG) accounting for climate change mitigation, consistent estimates of aboveground net biomass change (∆AGB) are needed. Countries with limited forest monitoring capabilities in the tropics and subtropics rely on IPCC 2006 default ∆AGB rates, which are values per ecological zone, per continent. Similarly, research into forest biomass change at a large scale also makes use of these rates. IPCC 2006 default rates come from a handful of studies, provide no uncertainty indications and do not distinguish between older secondary forests and old-growth forests. As part of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, we incorporate ∆AGB data available from 2006 onwards, comprising 176 chronosequences in secondary forests and 536 permanent plots in old-growth and managed/logged forests located in 42 countries in Africa, North and South America and Asia. We generated ∆AGB rate estimates for younger secondary forests (≤20 years), older secondary forests (>20 years and up to 100 years) and old-growth forests, and accounted for uncertainties in our estimates. In tropical rainforests, for which data availability was the highest, our ∆AGB rate estimates ranged from 3.4 (Asia) to 7.6 (Africa) Mg ha−1 year−1 in younger secondary forests, from 2.3 (North and South America) to 3.5 (Africa) Mg ha−1 year−1 in older secondary forests, and 0.7 (Asia) to 1.3 (Africa) Mg ha−1 year−1 in old-growth forests. We provide a rigorous and traceable refinement of the IPCC 2006 default rates in tropical and subtropical ecological zones, and identify which areas require more research on ∆AGB. In this respect, this study should be considered as an important step towards quantifying the role of tropical and subtropical forests as carbon sinks with higher accuracy; our new rates can be used for large-scale GHG accounting by governmental bodies, nongovernmental organizations and in scientific research

Aboveground carbon stocks and sinks in recovering tropical forests

Tropical and subtropical forests have many valuable roles, one of them within the carbon cycle. Within this cycle they are an essential terrestrial component, functioning as carbon reservoirs and sinks. The importance of (sub)tropical forests in climate change mitigation has been highlighted in recent climate change policies, such as the Paris Agreement, with signatory countries working towards a robust monitoring of their forest carbon stocks and sinks. Additionally, efforts to enhance forest carbon sinks through the restoration of degraded land has been highlighted by the Bonn Challenge, with currently more than 70 pledges in 60 countries underway.Until recently, large-scale assessments and country-level reporting of forest carbon stocks and sinks have been relying on coarse estimates provided in 2006 by the Intergovernmental Panel on Climate Change (IPCC). However, these estimates were based on a handful of studies per global ecological zone (also known as ecozone), did not provide methods for their derivation nor measures of uncertainty, and did not distinguish between forest successional stages.Furthermore, little is known about the drivers of variations in carbon stocks and sinks across (sub)tropical forests, particularly in forests with limited forest plot data availability. In this respect, insights for variations in forests and woodlands in the African dry tropics and in forests recovering from recent disturbance remains limited. Understanding how forest carbon stocks and sinks vary is essential for monitoring greenhouse gas (GHG) fluxes as well as for improving forest conservation and restoration endeavours.Over time, research on (sub)tropical forest carbon stocks and sinks has progressed, as well as country-level monitoring efforts to improve forest GHG reporting. This has led to the increase in availability of forest plot data. Simultaneously, large-scale remote sensing products have become available and region-specific methods for the monitoring of forest disturbance/recovery dynamics have improved over time. Thus, the opportunity to combine forest plot data with remote sensing to evaluate carbon stocks and sinks in (sub)tropical forests at different stages of recovery arises.The overall aim of this thesis is to integrate forest plot data with remote sensing to contribute towards understanding and quantifying aboveground forest carbon stocks (aboveground biomass; AGB) and sinks (aboveground biomass change; ΔAGB) in (sub)tropical forests. More specifically, this thesis has the objectives of (1) improving estimations of (sub)tropical aboveground forest carbon stocks and sinks under varying disturbance types for GHG reporting and of (2) understanding the drivers of aboveground carbon stocks and sinks in recovering forests in the (sub)tropic

Characterizing aboveground biomass and tree cover of regrowing forests in Brazil using multi‐source remote sensing data

Characterization of regrowing forests is vital for understanding forest dynamics to assess the impacts on carbon stocks and to support sustainable forest management. Although remote sensing is a key tool for understanding and monitoring forest dynamics, the use of exclusively remotely sensed data to explore the effects of different variables on regrowing forests across all biomes in Brazil has rarely been investigated. Here, we analyzed how environmental and human factors affect regrowing forests. Based on Brazil's secondary forest age map, 3060 locations disturbed between 1984 and 2018 were sampled, interpreted and analyzed in different biomes. We interpreted the time since disturbance for the sampled pixels in Google Earth Engine. Elevation, slope, climatic water deficit (CWD), the total Nitrogen of soil, cation exchange capacity (CEC) of soil, surrounding tree cover, distance to roads, distance to settlements and fire frequency were analyzed in their importance for predicting aboveground biomass (AGB) and tree cover derived from global forest aboveground biomass map and tree cover map, respectively. Results show that time since disturbance interpreted from satellite time series is the most important predictor for characterizing AGB and tree cover of regrowing forests. AGB increased with increasing time since disturbance, surrounding tree cover, soil total N, slope, distance to roads, distance to settlements and decreased with larger fire frequency, CWD and CEC of soil. Tree cover increased with larger time since disturbance, soil total N, surrounding tree cover, distance to roads, distance to settlements, slope and decreased with increasing elevation and CWD. These results emphasize the importance of remotely sensing products as key opportunities to improve the characterization of forest regrowth and to reduce data gaps and uncertainties related to forest carbon sink estimation. Our results provide a better understanding of regional forest dynamics, toward developing and assessing effective forest-related restoration and climatic mitigation strategies

Towards the use of satellite-based tropical forest disturbance alerts to assess selective logging intensities

Illegal logging is an important driver of tropical forest loss. A wide range of organizations and interested parties wish to track selective logging activities and verify logging intensities as reported by timber companies. Recently, free availability of 10 m scale optical and radar Sentinel data has resulted in several satellite-based alert systems that can detect increasingly small-scale forest disturbances in near-real time. This paper provides insight in the usability of satellite-based forest disturbance alerts to track selective logging in tropical forests. We derive the area of tree cover loss from expert interpretations of monthly PlanetScope mosaics and assess the relationship with the RAdar for Detecting Deforestation (RADD) alerts across 50 logging sites in the Congo Basin. We do this separately for various aggregation levels, and for tree cover loss from felling and skidding, and logging roads. A strong linear relationship between the alerts and visually identified tree cover loss indicates that with dense time series satellite data at 10 m scale, the area of tree cover loss in logging concessions can be accurately estimated. We demonstrate how the observed relationship can be used to improve near-real time tree cover loss estimates based on the RADD alerts. However, users should be aware that the reliability of estimations is relatively low in areas with few disturbances. In addition, a trade-off between aggregation level and accuracy requires careful consideration. An important challenge regarding remote verification of logging activities remains: as opposed to tree cover loss area, logging volumes cannot yet be directly observed by satellites. We discuss ways forward towards satellite-based assessment of logging volumes at high spatial and temporal detail, which would allow for better remote sensing based verification of reported logging intensities and tracking of illegal activities

Precision of subnational forest AGB estimates within the Peruvian Amazonia using a global biomass map

National forest inventories (NFI) provide essential forest-related biomass and carbon information for country greenhouse gas (GHG) accounting systems. Several tropical countries struggle to execute their NFIs while the extent to which space-based global information on aboveground biomass (AGB) can support national GHG accounting is under investigation. We assess whether the use of a global AGB map as auxiliary information produces a gain in precision of subnational AGB estimates for the Peruvian Amazonia. We used model-assisted estimators with data from the country’s NFI and explored hybrid inferential techniques to account for the sources of uncertainty associated with the integration of remote sensing-based products and NFI plot data.Our results show that the selected global biomass map tends to overestimate AGB values across the Peruvian Amazonia. For most strata, directly using the map in its published form did not reduce the precision of AGB estimates. However, after calibrating the map using the NFI data, the precision of our map-assisted AGB estimates increased by up to 50% at stratum level and 20% at Amazonia level. We further demonstrate how different sources of uncertainties can be incorporated in the map-NFI integrated estimates. With the hybrid inferential analysis, we found that the small spatial support of the NFI plots compared to the remote sensing-based sample units of aggregated pixels (within block variability) contributed the most to the total uncertainty associated with the AGB estimates from our map-NFI integration. Uncertainties caused by measurement variability and allometric model prediction uncertainty were the second largest contributors. When these uncertainties were incorporated, the increase in precision of our calibrated map-assisted AGB estimates was negligible, probably hindered by the great contribution of the within block variability to our map-plot assessment. We developed a reproducible method that countries can build upon and further improve while the global biomass products continue to evolve and better characterize the AGB distribution under large biomass conditions. We encourage further cross-country case studies that reflect a wider range of AGB distributions, especially within humid tropical forests, to further assess the contribution of global biomass maps to (sub)national AGB estimates and finally GHG monitoring and reporting

Data and Code underlying the article: "Forest Disturbance and Recovery in Peruvian Amazonia"

The data and code in this repository can be used to reproduce the analysis Requena Suarez et al. (2023), "Forest Disturbance and Recovery in Peruvian Amazonia". Spatial datasets used in this study are accessible from the sources cited in Table 1 of the main study. Estimation of disturbance and time since disturbance was done using the AVOCADO algorithm (Decuyper et al, 2022, https://doi.org/10.1016/j.rse.2021.112829), and Landsat imagery downloaded from Google Earth Engine. The underlying code for AVOCADO can be found in the following GitHub repository: https://github.com/MDecuy/AVOCADO, as well as a tutorial: https://www.pucv.cl/uuaa/labgrs/proyectos/avocado