Using remote sensing to map degraded mountain peatlands with high climate mitigation potential in Colombia's Central Cordillera

IF 3.3 Q2 ENVIRONMENTAL SCIENCES
Michael J. Battaglia, Angela Lafuente, Juan C. Benavides, E. Lilleskov, R. Chimner, L. Bourgeau-Chavez, Patrick Nicolás Skillings-Neira
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Abstract

Peatlands are the most carbon-dense ecosystems on earth. In tropical mountains, peatlands are numerous and susceptible to rapid degradation and carbon loss after human disturbances. Quantifying where peatlands are located and how they are affected by land use is key in creating a baseline of carbon stocks and greenhouse gas fluxes from tropical mountain peatlands. However, mapping peatlands in the páramo of the Northern Andes is difficult because they are in a topographically complex environment with nearly continuous cloud cover and frequent conversion to pastures or cropland. The goal of this effort was to identify the different types of páramo peatlands and their degradation patterns in the Colombian Central Cordillera. Moderate resolution cloud-free composites of optical imagery, temporal variance in ALOS- PALSAR L-band SAR, Sentinel-1 C-band SAR, and topography data were used as inputs in a machine learning classifier to identify was used to map 12 land cover classes including peatlands with natural vegetation and peatlands converted to pasture. Field data from 507 control points collected across the study area, including information on the vegetation and carbon content on the top 20 cm of the soil, were used to train and validate the classifier. Results show that the use of multiple platforms and image dates, including variance of the radar returns, is necessary for a clear separation of disturbed and undisturbed peatland classes. Peatland area varied across the study region, covering 7% and 20% of the landscape in the northern and southern portions of the study area, respectively. Disturbed peatlands with exotic grasses cover nearly 2% of the area. The overall accuracy of the peatland classes was 82.6%. Disturbed peatlands with exotic grasses had less carbon in the top 20 cm than undisturbed peatlands with natural vegetation. These results highlight the prevalence of peatlands in the tropical Andes and a promising approach to detecting peatlands converted to agriculture. Understanding the distribution and extent of these carbon dense ecosystems can facilitate the restoration and protection of peatlands in the northern Andes, with implications for the future trajectories of the national greenhouse gas inventory.
利用遥感技术绘制哥伦比亚中科迪勒拉山脉具有较高气候减缓潜力的退化山地泥炭地地图
泥炭地是地球上碳密度最高的生态系统。在热带山区,泥炭地数量众多,在人为干扰后很容易迅速退化和碳流失。量化泥炭地的位置及其如何受到土地利用的影响,是建立热带山区泥炭地碳储量和温室气体通量基线的关键。然而,绘制北安第斯山脉páramo地区泥炭地的地图十分困难,因为这些泥炭地所处的地形环境十分复杂,云层几乎连绵不绝,而且经常被改造成牧场或耕地。这项工作的目标是确定哥伦比亚中科迪勒拉山系不同类型的páramo泥炭地及其退化模式。将光学图像的中分辨率无云合成图、ALOS- PALSAR L 波段合成孔径雷达的时间差异、哨兵-1 C 波段合成孔径雷达和地形数据作为机器学习分类器的输入,用于识别 12 个土地覆被类别,包括具有天然植被的泥炭地和转化为牧场的泥炭地。在整个研究区域收集的 507 个控制点的实地数据(包括植被信息和土壤顶部 20 厘米的碳含量)被用于训练和验证分类器。结果表明,要明确区分扰动和未扰动泥炭地类别,必须使用多个平台和图像日期,包括雷达回波的方差。整个研究区域的泥炭地面积各不相同,研究区域北部和南部的泥炭地面积分别占地形面积的 7% 和 20%。生长着外来草类的受干扰泥炭地占地面积的近 2%。泥炭地分类的总体准确率为 82.6%。与自然植被未受干扰的泥炭地相比,外来牧草干扰的泥炭地顶部 20 厘米的碳含量较低。这些结果突显了泥炭地在热带安第斯山脉的普遍性,以及一种检测农用泥炭地的有效方法。了解这些碳密集生态系统的分布和范围有助于安第斯山脉北部泥炭地的恢复和保护,并对国家温室气体清单的未来轨迹产生影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Frontiers in Climate
Frontiers in Climate Environmental Science-Environmental Science (miscellaneous)
CiteScore
4.50
自引率
0.00%
发文量
233
审稿时长
15 weeks
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