رسالة جامعية
Observing the Seasonal Evolution of Supraglacial Ponds in High Mountain Asia: A Supervised Classification Approach
| العنوان: | Observing the Seasonal Evolution of Supraglacial Ponds in High Mountain Asia: A Supervised Classification Approach |
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| المؤلفون: | Smith, Caroline Sophia Rose |
| بيانات النشر: | Scott Polar Research Institute University of Cambridge |
| سنة النشر: | 2022 |
| المجموعة: | Apollo - University of Cambridge Repository |
| مصطلحات موضوعية: | Supraglacial ponds |
| الوصف: | Supraglacial ponds on debris covered glaciers in High Mountain Asia (HMA) can locally store surface runoff and prolong water delivery to downstream river basins (Benn et al., 2012; Miles et al., 2019). Temporal variation in the extent of supraglacial ponding therefore affects the timing and availability of water supplies to communities downstream. Large supraglacial ponds can rapidly drain during Glacial Lake Outburst Flood (GLOF) events, which presents a hazard risk to local populations (Nie et al., 2018). Furthermore, the rate of glacier surface mass loss is locally enhanced by supraglacial ponds, so that supraglacial ponds influence the sensitive response of HMA glaciers to climate change (Sakai et al., 2000). Region-wide, multi-temporal maps of supraglacial pond cover are therefore required as inputs to glacier hydrology and mass balance models (Miles et al., 2020). Remote sensing techniques are often used for supraglacial pond mapping because of their wide spatial coverage and capacity for repeat observations. However, current approaches are limited by factors including efficiency and poor transferability throughout time and space (Watson et al., 2018; Wangchuk and Bolch, 2020). This study develops an efficient, accurate pond mapping approach that is widely spatiotemporally applicable in the HMA region. An unsupervised k-means classifier is used to train a supervised Random Forest Classifier (RFC) in the Google Earth Engine platform. This study adapts algorithms used by Dell et al., (2021) for application in the HMA region. The classifier is trained on four spatially distal glaciers within the Himalaya region, with Sentinel-2 optical satellite imagery obtained from April 2017 to October 2021 and 8m resolution HMA DEM data. The RFC is validated against manually derived pond outlines at these glaciers. The RFC achieves accuracy of 86.3-99.6% against manually derived outlines for supraglacial ponds with an area greater than 1000m2. A Root Mean Square Error of 978.4m2 is calculated across 222 overlapping pond ... |
| نوع الوثيقة: | master thesis |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | https://www.repository.cam.ac.uk/handle/1810/342294Test |
| DOI: | 10.17863/CAM.89716 |
| الإتاحة: | https://doi.org/10.17863/CAM.89716Test https://www.repository.cam.ac.uk/handle/1810/342294Test |
| حقوق: | All Rights Reserved ; https://www.rioxx.net/licenses/all-rights-reservedTest/ |
| رقم الانضمام: | edsbas.519B7C1C |
| قاعدة البيانات: | BASE |
| DOI: | 10.17863/CAM.89716 |
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