Manual of Digital Earth

doi:10.1007/978-981-32-9915-3

Cited by 110 publications

(14 citation statements)

References 69 publications

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“…However, mining large-scale and time series land information from high spatiotemporal resolution remote sensing data was found to be a computationally intensive task, requiring powerful computing platforms for analysis. Fortunately, some geospatial cloud-computing platforms are emerging that meet this demand, such as Google Earth Engine GEE, Amazon Web Services, Earth Server, and the Earth Observation Data Centre (Guo et al, 2020). Among these, GEE has advantages because it is an open-source, cloud-based platform for planetary-scale geospatial analysis that integrates mainstream free satellite data, such as the Landsat archive, Sentinel series imagery, and other terrain products and climate data (Gorelick et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Upward Expansion of Supra-Glacial Debris Cover in the Hunza Valley, Karakoram, During 1990 ∼ 2019

Xie

Liu

et al. 2020

Front. Earth Sci.

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Supra-glacial debris cover is key to glacier ablation through increasing (thin debris layer) or decreasing (thick debris layer) melt rates, thereby regulating the mass balance of a glacier and its meltwater runoff. The thickening or lateral expansion of supra-glacial debris cover correlates with a reduction of glacier ablation and, consequently, runoff generation, which is also considered to be an influential factor on the rheology and dynamics of a glacierized system. Studies on supra-glacial debris cover have recently attracted wide attention especially for glaciers in the Himalayas and Karakoram, where the glaciers have heterogeneously responded to climate change. In this study, we used 32 images from the Landsat Thematic Mapper, Enhanced Thematic Mapper Plus, and Operational Land Imager archive, going back to 1990, which are available on the Google Earth Engine cloud-computing platform, to map the supra-glacial debris cover in the Hunza Valley, Karakoram, Pakistan, based on a band ratio segmentation method (normalized difference snow index [NDSI] < 0.4), Otsu thresholding, and machine learning algorithms. Compared with manual digitization, the random forest (RF) model was found to have the greatest accuracy in identifying supra-glacial debris, with a Kappa coefficient of 0.94 ± 0.01 and an overall accuracy of 95.5 ± 0.9%. Overall, the supraglacial debris cover in the study area showed an increasing trend, and the total area expanded by 8.1-21.3% for various glaciers from 1990 to 2019. The other two methods (Otsu thresholding and NDSI < 0.4) generally overestimated the supra-glacial debris covered area, by 36.3 and 18.8%, respectively, compared to that of the RF model. The supra-glacial debris cover has migrated upward on the glaciers, with intensive variation near the equilibrium-line altitude zone (4,500-5,500 m a.s.l.). The increase in ice or snow avalanche activity at high altitudes may be responsible for this upward expansion of supra-glacial debris cover in the Hunza Valley, which is attributed to the combined effect of temperature decrease and precipitation increase in the study area.

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Section: Introductionmentioning

confidence: 99%

Upward Expansion of Supra-Glacial Debris Cover in the Hunza Valley, Karakoram, During 1990 ∼ 2019

Xie

Liu

et al. 2020

Front. Earth Sci.

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“…Instead of hiding behind academic walls and difficult to access scientific journals than can perpetuate distrust of science (Cooper 2016), it creates stronger public engagement and greater interest and knowledge transfer (Carleton et al 2020). Challenges regarding citizen science include quality, equity, inclusion, and governance (Brovelli et al 2020) and, increasingly, legal issues relating to privacy, ethics and licensing (Mooney et al 2019). However, how professional scientists perceive the value of participatory science represents a reliable indicator of their likelihood to engage and collaborate.…”

Section: Monitoring With Citizen Sciencementioning

confidence: 99%

A new generation of sensors and monitoring tools to support climate-smart forestry practices

et al. 2021

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Climate-smart forestry (CSF) is an emerging branch of sustainable adaptive forest management aimed at enhancing the potential of forests to adapt to and mitigate climate change. It relies on much higher data requirements than traditional forestry. These data requirements can be met by new devices that support continuous, in-situ monitoring of forest conditions in real time. We propose a comprehensive network of sensors, i.e. a wireless sensor network (WSN), that can be part of a world-wide network of interconnected uniquely addressable objects, an Internet of Things (IoT), which can make data available in near real time to multiple stakeholders, including scientists, foresters, and forest managers, and may partially motivate citizens to participate in big data collection. The use of in-situ sources of monitoring data as ground-truthed training data for remotely sensed data can boost forest monitoring by increasing the spatial and temporal scale of the monitoring, leading to a better understanding of forest processes and potential threats. Here, some of the key developments and applications of these sensors are outlined, together with guidelines for data management. Examples are given of their deployment to detect early warning signals (EWS) of ecosystem regime-shifts in terms of forest productivity, health and biodiversity. Analysis of the strategic use of these tools highlights the opportunities for engaging citizens and forest managers in this new generation of forest monitoring.

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“…Machine learning has advantages for extracting land surface information from remote sensing images (Maxwell et al, 2018) because mining largescale and time-series land information from massive remote sensing data is a computationally intensive task and requires powerful computing platforms for analysis. Fortunately, some geospatial cloud-computing platforms are emerging to meet this demand, such as Google Earth Engine (GEE), Amazon Web Services (AWS), Earth Server (ES), and Earth Observation Data Centre (EODC) (Guo et al, 2020). Among these, GEE has obvious advantages because it is an open-source cloud-based platform for planetary-scale geospatial analysis that integrates mainstream free satellite data such as the Landsat archive, Sentinel series imagery, and other terrain products and climate data (Gorelick et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Derivation of Supraglacial Debris Cover by Machine Learning Algorithms on the Gee Platform: A Case Study of Glaciers in the Hunza Valley

Xie

Liu

Gao

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Calculating the spatial-temporal distribution of supraglacial debris cover on glaciers is essential for understanding mass balance processes, glacier lake outburst floods, hydrological predictions, and glacier fluctuations that have attracted attention in recent years. However, due to the reflectance of supraglacial debris is similar to that of non-glacier slopes, mapping supraglacial debris cover based on optical remote sensing remains challenging. In this paper, we used NDSI and machine learning algorithm to extract debris cover on glaciers in Hunza Valley, Pakistan. Our result showed that the RF model has the best classification accuracy with kappa coefficient of 0.94 and overall accuracy of 96%. The debris-covered area increased by 21.31% from 1990 to 2019 (394.76 km2 – 478.88 km2) in the study area. Results and the method are of significance in the assessment of meltwater modeling for glaciers with debris cover.

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Manual of Digital Earth

Cited by 110 publications

References 69 publications

Upward Expansion of Supra-Glacial Debris Cover in the Hunza Valley, Karakoram, During 1990 ∼ 2019

Upward Expansion of Supra-Glacial Debris Cover in the Hunza Valley, Karakoram, During 1990 ∼ 2019

A new generation of sensors and monitoring tools to support climate-smart forestry practices

Derivation of Supraglacial Debris Cover by Machine Learning Algorithms on the Gee Platform: A Case Study of Glaciers in the Hunza Valley

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