ABSTRACT

Secondary forests (SFs) are one of the major carbons sinks in the Neotropics due to the rapid carbon assimilation in their above-ground biomass (AGB). However, the accurate contribution of SFs to the carbon cycle is a great challenge because of the uncertainty in AGB estimates. In this context, the main objective of this study is to explore full polarimetric Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar-2 (ALOS/PALSAR-2) data to model SFs AGB in the Central Amazon. We carried out the forest inventory in 2014, measuring 23 field plots. Supplementary land-use classification history was used to create 120 additional independent sample plots by adjusting growth curves using SFs age and previous land-use intensity from field plots and literature database. Multiple Linear Regression (MLR) analysis was performed to select the best model by corrected weighted Akaike Information Criterion (AICw) and validated by the leave-one-out bootstrapping method. The best-fitted model has six parameters and explained 65% of the above-ground biomass variability. The prediction error was of Root Mean Square Error of the Prediction (RMSEP) = 8.8 ± 3.0 tonnes ha⁻¹ (8.8%). The most explanatory variables for modelling secondary forest AGB were those that result from multiple scattering (Shannon Entropy), volumetric scattering (Bhattacharya decomposition), and double-bounce scattering (ratio VV/HH, vertically transmitted and received polarization/horizontally transmitted and received polarization). Including past-use of SF areas in the model with the Landsat time series classification, as the frequency of clear cuts and the number of years of active land-use before abandonment, the MLR has increased by 10%, achieving 71% of the variability explained by the model. The uncertainty report showed that ground truth AGB estimation (inventory, allometry, and plot expansion factors) might represent 50% of the errors in the modelling estimation. In contrast, Synthetic Aperture Radar (SAR) inversion models (SAR error and regression) have achieved 20%. The results showed that additional information on secondary forest land-use history could improve the performance of AGB recovery models, as well as can be used to expand the sampling units on tropical forests.

摘要

次生林（SFs）是新热带地区主要的碳汇之一，这是由于其地上生物量（AGB）中的碳迅速吸收。但是，由于AGB估算值的不确定性，SF对碳循环的准确贡献是一个巨大的挑战。在这种情况下，本研究的主要目的是探索全极化高级陆地观测卫星/相控阵L波段合成孔径雷达2（ALOS / PALSAR-2）数据，以模拟亚马逊中部的SFs AGB。2014年，我们进行了森林清查，测量了23个田地。补充的土地利用分类历史用于通过使用SF年龄和来自田间地块和文献数据库的先前土地利用强度来调整生长曲线，从而创建120个额外的独立样本地块。通过校正的加权Akaike信息准则（AICw）进行了多元线性回归（MLR）分析，以选择最佳模型，并通过留一法自举方法进行了验证。最佳拟合模型具有六个参数，可以解释65％的地上生物量变异性。预测误差为预测的均方根误差（RMSEP）= 8.8±3.0吨公顷^-1（8.8％）。对次生林AGB建模的最具解释性的变量是那些由多重散射（香农熵），体积散射（Bhattacharya分解）和双反弹散射（比率VV / HH，垂直传输和接收极化/水平传输和接收极化）产生的变量。 ）。在具有Landsat时间序列分类的模型中，包括SF区域的过去使用情况，由于明确砍伐的频率和废弃前活跃土地使用的年限，MLR增加了10％，实现了71％的可变性由模型解释。不确定性报告显示，地面真实AGB估计（库存，异度法和地块扩展因子）可能代表建模估计中50％的误差。相比之下，合成孔径雷达（SAR）反演模型（SAR误差和回归）已达到20％。结果表明，有关次生林地使用历史的更多信息可以提高AGB恢复模型的性能，并可用于扩展热带森林的采样单位。