Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring
Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring
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DOI：
10.3390/rs6010716
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发表时间：
2014-01-01
期刊：
影响因子：
5
通讯作者：
Evans, Martin
中科院分区：
文献类型：
--
作者：
Cole, Beth;McMorrow, Julia;Evans, Martin
泥炭地是重要的陆地碳库。恢复退化的泥炭地以恢复生态系统服务是保护工作的一个主要领域。监测是判断修复成功与否的关键。遥感是一种潜在的工具，可以提供关于生境条件的景观尺度信息。本文利用航空高光谱影像数据和地面植被调查数据，采用经验建模方法，对正在恢复中的英国退化旱地盖层沼泽的植被丰富度进行了建模。利用偏最小二乘回归(PLSR)建立了植被功能型(PFT)丰富度预测模型，并将其应用于整个恢复地。对PFT和单物种水平上的光谱数据与植被丰度之间的关系进行了敏感性试验，证实了PFT是正确的分析尺度。PLSR模型允许根据单个光谱波段的加权回归系数选择变量，显示哪些波段对模型影响最大。这些结果表明，SWIR在从高光谱图像监测泥炭地植被方面的价值低于最初的预测。验证数据的RMSE值介于10%到16%之间，表明考虑到现有植被调查结果的主观性，模型可以作为一种可操作的工具。这些预测的覆盖图像是为该地点产生的第一个定量景观规模监测结果。PFTs的高分辨率高光谱制图首次有可能在景观尺度上评估泥炭地系统的恢复。
Peatlands are important terrestrial carbon stores. Restoration of degraded peatlands to restore ecosystem services is a major area of conservation effort. Monitoring is crucial to judge the success of this restoration. Remote sensing is a potential tool to provide landscape-scale information on the habitat condition. Using an empirical modelling approach, this paper aims to use airborne hyperspectral image data with ground vegetation survey data to model vegetation abundance for a degraded upland blanket bog in the United Kingdom (UK), which is undergoing restoration. A predictive model for vegetation abundance of Plant Functional Types (PFT) was produced using a Partial Least Squares Regression (PLSR) and applied to the whole restoration site. A sensitivity test on the relationships between spectral data and vegetation abundance at PFT and single species level confirmed that PFT was the correct scale for analysis. The PLSR modelling allows selection of variables based upon the weighted regression coefficient of the individual spectral bands, showing which bands have the most influence on the model. These results suggest that the SWIR has less value for monitoring peatland vegetation from hyperspectral images than initially predicted. RMSE values for the validation data range between 10% and 16% cover, indicating that the models can be used as an operational tool, considering the subjective nature of existing vegetation survey results. These predicted coverage images are the first quantitative landscape scale monitoring results to be produced for the site. High resolution hyperspectral mapping of PFTs has the potential to assess recovery of peatland systems at landscape scale for the first time.