Uncovering the Hidden Carbon Treasures of the Philippines’ Towering Mountains: A Synergistic Exploration Using Satellite Imagery and Machine Learning

IF 2.1 4区 地球科学 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
Richard Dein D. Altarez, Armando Apan, Tek Maraseni
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Abstract

Tropical montane forests (TMFs) are highly valuable for their above-ground biomass (AGB) and their potential to sequester carbon, but they remain understudied. Sentinel-1, -2, biophysical data and Machine Learning were used to estimate and map the AGB and above-ground carbon (AGC) stocks in Benguet, Philippines. Non-destructive field AGB measurements were collected from 184 plots, revealing that pine forests had 33.57% less AGB than mossy forests (380.33 Mgha−1), whilst the grassland summit had 39.93 Mgha−1. In contrast to the majority of literature, AGB did not decrease linearly with elevation. NDVI, LAI, fAPAR, fCover and elevation were the most effective predictors of field-derived AGB as determined by Random Forest (RF) feature selection in R. WEKA was used to evaluate and validate 26 Machine Learning algorithms. The results show that the Machine Learning K star (K*) (r = 0.213–0.832; RMSE = 106.682 Mgha−1–224.713 Mgha−1) and RF (r = 0.391–0.822; RMSE = 108.226 Mgha−1–175.642 Mgha−1) exhibited high modelling capabilities to estimate AGB across all predictor categories. Consequently, spatially explicit models were carried out in Whitebox Runner software to map the study site’s AGB, demonstrating RF with the highest predictive performance (r = 0.982; RMSE = 53.980 Mgha−1). The study area’s carbon stock map ranged from 0 to 434.94 Mgha−1, highlighting the significance of forests at higher elevations for forest conservation and carbon sequestration. Carbon-rich mountainous regions of the county can be encouraged for carbon sequestration through REDD + interventions. Longer wavelength radar imagery, species-specific allometric equations and soil fertility should be tested in future carbon studies. The produced carbon maps can help policy makers in decision-planning, and thus contribute to conserve the natural resources of the Benguet Mountains.

Abstract Image

揭开菲律宾高耸山脉中隐藏的碳宝藏:使用卫星图像和机器学习的协同探索
热带山地森林(TMFs)因其地上生物量(AGB)和固碳潜力而具有很高的价值,但它们仍未得到充分研究。利用Sentinel-1、2、生物物理数据和机器学习对菲律宾Benguet地区的AGB和地上碳(AGC)储量进行了估算和绘制。184个样地的非破坏性野外AGB测量结果显示,松林的AGB值比苔藓林(380.33 Mgha−1)低33.57%,而草地峰顶的AGB值为39.93 Mgha−1。与大多数文献相反,AGB并没有随着海拔的升高而线性下降。NDVI、LAI、fAPAR、fCover和elevation是r中随机森林(Random Forest, RF)特征选择确定的最有效的野外衍生AGB预测因子。结果表明,机器学习K* (K*) (r = 0.213-0.832;RMSE = 106.682 Mgha−1 - 224.713 Mgha−1)和RF (r = 0.391-0.822;RMSE = 108.226 Mgha−1 - 175.642 Mgha−1)在所有预测器类别中显示出很高的建模能力来估计AGB。因此,在Whitebox Runner软件中建立空间显式模型来绘制研究地点的AGB,结果表明,RF具有最高的预测性能(r = 0.982;RMSE = 53.980 mha−1)。研究区碳储量分布范围为0 ~ 434.94 Mgha−1,表明高海拔森林对森林保护和碳汇具有重要意义。可以通过REDD +干预措施鼓励该县富含碳的山区进行碳固存。在未来的碳研究中,应该测试长波雷达图像、物种特异性异速生长方程和土壤肥力。制作的碳地图可以帮助决策者进行决策规划,从而有助于保护本盖特山脉的自然资源。
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来源期刊
CiteScore
8.20
自引率
2.40%
发文量
38
期刊介绍: PFG is an international scholarly journal covering the progress and application of photogrammetric methods, remote sensing technology and the interconnected field of geoinformation science. It places special editorial emphasis on the communication of new methodologies in data acquisition and new approaches to optimized processing and interpretation of all types of data which were acquired by photogrammetric methods, remote sensing, image processing and the computer-aided interpretation of such data in general. The journal hence addresses both researchers and students of these disciplines at academic institutions and universities as well as the downstream users in both the private sector and public administration. Founded in 1926 under the former name Bildmessung und Luftbildwesen, PFG is worldwide the oldest journal on photogrammetry. It is the official journal of the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF).
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