Characterizing degradation of palm swamp peatlands from space and on the ground: An exploratory study in the Peruvian Amazon
Characterizing degradation of palm swamp peatlands from space and on the ground: An exploratory study in the Peruvian Amazon
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DOI：
10.1016/j.foreco.2017.03.016
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发表时间：
2017-06-01
影响因子：
3.7
通讯作者：
Verchot, Louis V.
中科院分区：
文献类型：
--
作者：
Hergoualc'h, Kristell;Gutierrez-Velez, Victor Hugo;Verchot, Louis V.
秘鲁拥有热带地区第四大泥炭地面积。其最具代表性的泥炭土地覆盖是毛里求斯以毛里求斯为主的棕榈沼泽（以下称为稠密 PS），几十年来，由于对毛里求斯毛里求斯果实的大量需求（通常通过砍伐整个棕榈树来收集），该沼泽一直承受着人类的压力。这些碳密集森林的退化会严重影响温室气体的排放并导致气候变化。本研究的首要目标是评估密集 PS 降解对森林结构和生物量碳储量的影响。第二个是探索利用遥感数据和方法绘制不同退化水平的密集 PS 分布图的潜力。在高密度 PS 区域建立的 0.25 公顷样地中测量了生物量储量，其中低退化（n = 2 样地）、中度退化（n = 2）和高退化（n = 4）。我们结合了来自 Landsat TM 和 ALOS/PALSAR 卫星的实地和遥感数据，以区分低、中、高退化的密集 PS 森林和陆地、restinga 和混合 PS（不是 M. flexuosa 为主）森林的典型区域。为此，我们使用了随机森林机器学习分类算法。结果表明，退化后森林组成从棕榈树为主转变为以木本树为主的森林。我们还发现，人类对密集 PS 的干预会导致树木碳储量显着减少，在中度和高度退化后，初始（地上和地下）生物量储量（135.4 +/- 4.8 Mg C ha(-1)）减少了 11% 和 17%。遥感分析表明，密集 PS 与所有其他类别的低退化之间具有高度可分离性。除了雷斯汀加森林和混合 PS 之外，中度和高度退化的密集 PS 与大多数类别高度分离。结果还表明，来自主动和被动遥感传感器的数据对于绘制密集 PS 退化图非常重要。总体土地覆盖分类准确度较高（91%）。这项试点分析的结果令人鼓舞，可以进一步探索使用遥感数据和方法来监测秘鲁亚马逊更广泛范围内的密集 PS 退化。需要对密集PS退化的空间范围以及生物质和泥炭衍生的排放进行精确估计，以评估秘鲁森林退化造成的国家排放，并且对于支持旨在减少退化活动的举措至关重要。 (C) 2017 年作者。由 Elsevier B.V. 出版
Peru has the fourth largest area of peatlands in the Tropics. Its most representative land cover on peat is a Mauritia flexuosa dominated palm swamp (thereafter called dense PS), which has been under human pressure over decades due to the high demand for the M. flexuosa fruit often collected by cutting down the entire palm. Degradation of these carbon dense forests can substantially affect emissions of greenhouse gases and contribute to climate change. The first objective of this research was to assess the impact of dense PS degradation on forest structure and biomass carbon stocks. The second one was to explore the potential of mapping the distribution of dense PS with different degradation levels using remote sensing data and methods. Biomass stocks were measured in 0.25 ha plots established in areas of dense PS with low (n = 2 plots), medium (n = 2) and high degradation (n = 4). We combined field and remote sensing data from the satellites Landsat TM and ALOS/PALSAR to discriminate between areas typifying dense PS with low, medium and high degradation and terra firme, restinga and mixed PS (not M. flexuosa dominated) forests. For this we used a Random Forest machine learning classification algorithm. Results suggest a shift in forest composition from palm to woody tree dominated forest following degradation. We also found that human intervention in dense PS translates into significant reductions in tree carbon stocks with initial (above and below-ground) biomass stocks (135.4 +/- 4.8 Mg C ha(-1)) decreased by 11 and 17% following medium and high degradation. The remote sensing analysis indicates a high separability between dense PS with low degradation from all other categories. Dense PS with medium and high degradation were highly separable from most categories except for restinga forests and mixed PS. Results also showed that data from both active and passive remote sensing sensors are important for the mapping of dense PS degradation. Overall land cover classification accuracy was high (91%). Results from this pilot analysis are encouraging to further explore the use of remote sensing data and methods for monitoring dense PS degradation at broader scales in the Peruvian Amazon. Providing precise estimates on the spatial extent of dense PS degradation and on biomass and peat derived emissions is required for assessing national emissions from forest degradation in Peru and is essential for supporting initiatives aiming at reducing degradation activities. (C) 2017 The Authors. Published by Elsevier B.V.