Polarimetric Alos/Palsar-2 data for retrieving aboveground biomass of secondary forest in the Brazilian Amazon

Secondary forests (SFs) are one of the major carbon sink in the Neotropics due to the rapid carbon assimilating in their aboveground biomass (AGB). However, the accurate contribution of the SFs to the carbon cycle is a great challenge because of the uncertainty in AGB estimates. In this context, the main objective of this work is to explore polarimetric Alos/Palsar-2 data from to model AGB in the SFs of the Central Amazon, Amazonas State. Forest inventory was conducted in 2014 with the measured of 23 field plots. Multiple linear regression analysis was performed to select the best model by corrected AIC w and validated by leave-one-out bootstrapping method. The best fitted model has six parameters and explained 65% of the aboveground biomass variability. The prediction error was calculated to be RMSEP = 8.8 ± 2.98 Mg.ha -1 (8.75%). The main polarimetric attributes in the model were those direct related to multiple scattering mechanisms as the Shannon Entropy and the volumetric mechanism of Bhattacharya decomposition; and those related to increase in double-bounce as the co-polarization ratio (VV/HH) resulted of soil-trunk interactions. Such models are intended to improve accuracy for mapping SFs AGB in often cloudy environments as in the Brazilian Amazon

Retrieving secondary forest aboveground biomass from polarimetric ALOS-2 PALSAR-2 data in the Brazilian Amazon

Secondary forests (SF) are important carbon sinks, removing CO2 from the atmosphere through the photosynthesis process and storing photosynthates in their aboveground live biomass (AGB). This process occurring at large-scales partially counteracts C emissions from land-use change, playing, hence, an important role in the global carbon cycle. The absorption rates of carbon in these forests depend on forest physiology, controlled by environmental and climatic conditions, as well as on the past land use, which is rarely considered for retrieving AGB from remotely sensed data. In this context, the main goal of this study is to evaluate the potential of polarimetric (quad-pol) ALOS-2 PALSAR-2 data for estimating AGB in a SF area. Land-use was assessed through Landsat time-series to extract the SF age, period of active land-use (PALU), and frequency of clear cuts (FC) to randomly select the SF plots. A chronosequence of 42 SF plots ranging 3-28 years (20 ha) near the Tapajós National Forest in Pará state was surveyed to quantifying AGB growth. The quad-pol data was explored by testing two regression methods, including non-linear (NL) and multiple linear regression models (MLR).We also evaluated the influence of the past land-use in the retrieving AGB through correlation analysis. The results showed that the biophysical variables were positively correlated with the volumetric scattering, meaning that SF areas presented greater volumetric scattering contribution with increasing forest age. Mean diameter, mean tree height, basal area, species density, and AGB were significant and had the highest Pearson coefficients with the Cloude decomposition (λ3), which in turn, refers to the volumetric contribution backscattering from cross-polarization (HV) (ρ = 0.57-0.66, p-value < 0.001). On the other hand, the historical use (PALU and FC) showed the highest correlation with angular decompositions, being the Touzi target phase angle the highest correlation (Φs) (ρ = 0.37 and ρ = 0.38, respectively). The combination of multiple prediction variables with MLR improved the AGB estimation by 70% comparing to the NL model (R2 adj. = 0.51; RMSE = 38.7 Mg ha-1) bias = 2.1 ± 37.9 Mg ha-1 by incorporate the angular decompositions, related to historical use, and the contribution volumetric scattering, related to forest structure, in the model. The MLR uses six variables, whose selected polarimetric attributes were strongly related with different structural parameters such as the mean forest diameter, basal area, and the mean forest tree height, and not with the AGB as was expected. The uncertainty was estimated to be 18.6% considered all methodological steps of the MLR model. This approach helped us to better understand the relationship between parameters derived from SAR data and the forest structure and its relation to the growth of the secondary forest after deforestation events

Amazonian forest degradation must be incorporated into the COP26 agenda

Nations will reaffirm their commitment to reducing greenhouse gas (GHG) emissions during the 26th United Nations Climate Change Conference (COP26; www.ukcop26.org), in Glasgow, Scotland, in November 2021. Revision of the national commitments will play a key role in defining the future of Earth’s climate. In past conferences, the main target of Amazonian nations was to reduce emissions resulting from land-use change and land management by committing to decrease deforestation rates, a well-known and efficient strategy1,2. However, human-induced forest degradation caused by fires, selective logging, and edge effects can also result in large carbon dioxide (CO2) emissions1,2,3,4,5, which are not yet explicitly reported by Amazonian countries. Despite its considerable impact, forest degradation has been largely overlooked in previous policy discussions5. It is vital that forest degradation is considered in the upcoming COP26 discussions and incorporated into future commitments to reduce GHG emissions

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost