基于SAR和光学卫星影像的亚热带森林地上生物量制图的机器学习模型

IF 4.5 Q2 ENVIRONMENTAL SCIENCES
Abraham Aidoo Borsah , Man Sing Wong , Majid Nazeer , Guoqiang Shi
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引用次数: 0

摘要

森林生物量评估是影响参与森林管理的利益攸关方决策的关键因素。在热带和亚热带生物多样性热点地区,准确测量地上生物量(AGB)对生态系统的可持续性至关重要。然而,由于这些森林的植被种类复杂,估计这些森林的AGB具有挑战性,需要整合来自各种来源的数据。因此,本研究旨在探讨将地面测量数据与SAR和光学遥感数据相结合用于估算香港亚热带森林AGB的可行性,并比较逐步线性回归(SLR)、k近邻回归(KNN)和梯度增强回归树(GBRT)等不同建模方法在AGB制图中的有效性。收集了大量的野外数据,然后使用当地开发的异速生长模型将其转换为每个地块的生物量值,该模型旨在促进地上生物量(AGB)制图。结果表明,Sentinel-1和Sentinel-2数据集的组合显著提高了GBRT模型的模型性能(R2 = 0.84, RMSE = 26.50 t /ha),优于KNN模型(R2 = 0.67, RMSE = 38.33 t /ha)和SLR模型(R2 = 0.57, RMSE = 43.88 t /ha)。此外,GBRT建模方法的偏差较小,综合数据集的AGB预测的残差变异性较小,其次是Sentinel-2数据集,然后是Sentinel-1数据集。季节分析表明,AGB与NDVI具有较强的相关性,涉及Sentinel-2植被红边带(SR74, SR85)的频带比是生物量估算的重要预测因子。相比之下,Sentinel-1雷达后向散射预测因子对生物量估算的影响较弱。这项研究强调了机器学习方法与卫星遥感相结合的潜力,可以在亚热带森林中进行准确的AGB测绘,为森林管理和保护提供有价值的见解。这些发现不仅有助于不断发展的遥感应用领域,而且通过应对气候变化和促进城市可持续性和减轻环境风险,与可持续发展目标(SDG) 13和可持续发展目标11保持一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning models for subtropical forest aboveground biomass mapping using combined SAR and optical satellite imagery
Forest biomass assessment is a critical element influencing the decisions of stakeholders involved in forest management. In tropical and subtropical biodiversity hotspots, accurately measuring aboveground biomass (AGB) is crucial for ecosystem sustainability. However, estimating AGB in these forests is challenging due to the complex vegetation species, necessitating the integration of data from various sources. Therefore, this study aims to investigate the feasibility of integrating ground-based measurements with SAR and optical remote sensing data for estimating AGB in the subtropical forest of Hong Kong and compare various modeling approaches - Stepwise linear regression (SLR), K-nearest neighbors' regression (KNN), and Gradient boosted regression trees (GBRT) - in terms of their effectiveness for AGB mapping. Extensive field data were collected and then converted into biomass values per plot using a locally developed allometric model, designed to facilitate aboveground biomass (AGB) mapping. From the results, we observed that the combination of Sentinel-1 and Sentinel-2 datasets significantly enhanced our model's performance with the GBRT model (R2 = 0.84, RMSE = 26.50 tons/ha), outperforming the KNN (R2 = 0.67, RMSE = 38.33 tons/ha) and SLR (R2 = 0.57, RMSE = 43.88 tons/ha). Furthermore, the GBRT modelling approach demonstrated fewer deviations, with residuals exhibiting less variability in the AGB predictions from the combined dataset, followed by the Sentinel-2 dataset and then the Sentinel-1 dataset. Seasonal analysis revealed a strong correlation between AGB and NDVI, with band ratios involving Sentinel-2 vegetation red-edge bands (SR74, SR85) serving as influential predictors for biomass estimation. In contrast, Sentinel-1 radar backscatter predictors demonstrated a weaker impact on biomass estimation. This research highlights the potential of machine learning approaches in conjunction with satellite remote sensing for accurate AGB mapping in subtropical forests, providing valuable insights for forest management and conservation. The findings not only contribute to the growing field of remote sensing applications but also align with Sustainable Development Goals (SDG) 13 by addressing climate change and SDG 11 by promoting urban sustainability and mitigating environmental risks.
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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