In the Maipo watershed, situated in central Chile, mining activities are impacting high altitude Andean wetlands through the consumption and exploitation of water and land. As wetlands are vulnerable and particularly susceptible to changes of water supply, alterations and modifications in the hydrological regime have direct effects on their ecophysiological condition and vegetation cover. The aim of this study was to evaluate the potential of Worldview-2 and Sentinel-2 sensors to identify and map Andean wetlands through the use of the one-class classifier Bias support vector machines (BSVM), and then to estimate soil moisture content of the identified wetlands during snow-free summer using partial least square regression.

The results obtained in this research showed that the combination of remote sensing data and a small sample of ground reference measurements enables to map Andean high altitude wetlands with high accuracies. BSVM was capable to classify the meadow areas with an overall accuracy of over ∼78% for both sensors. Our results also indicate that it is feasible to map surface soil moisture with optical remote sensing data and simple regression approaches in the examined environment. Surface soil moisture estimates reached r² values of up to 0.58, and normalized mean square errors of 19% using Sentinel-2 data, while Worldview-2 estimates resulted in non-satisfying results. The presented approach is particularly valuable for monitoring high-mountain wetland areas with limited accessibility such as in the Andes.

在智利中部的迈坡流域，采矿活动通过对水和土地的消耗和利用，对高海拔的安第斯湿地产生了影响。由于湿地非常脆弱，尤其容易受到供水变化的影响，因此水文状况的变化和改变直接影响其生态生理状况和植被覆盖。这项研究的目的是评估Worldview-2和Sentinel-2传感器通过使用一类分类器偏倚支持向量机（BSVM）来识别和绘制安第斯湿地的潜力，然后评估该地区的土壤含水量。使用偏最小二乘回归法在无雪夏季确定的湿地。

这项研究获得的结果表明，结合遥感数据和少量的地面参考测量样本，可以绘制出高精度的安第斯山脉高海拔湿地。BSVM能够对两个传感器的草地区域进行分类，总精度超过78％。我们的结果还表明，在被检查的环境中，利用光学遥感数据和简单的回归方法来绘制地表土壤水分是可行的。根据Sentinel-2数据，地表土壤水分估计值的r ²值高达0.58，归一化均方误差为19％，而Worldview-2估计值的结果令人不满意。所提出的方法对于监测诸如安第斯山脉等交通不便的高山湿地地区特别有价值。