A new glacier inventory for Svalbard from Sentinel-2 and Landsat 8 for improved calculation of climate change impacts

2021 ◽  
Author(s):  
Frank Paul ◽  
Franz Goerlich ◽  
Philipp Rastner
Keyword(s):  
Climate Change Impacts ◽  
Weather Conditions ◽  
Landsat 8 ◽  
Related Area ◽  
Band Ratio ◽  
Glacier Inventory ◽  
Swir Band ◽  
Specific Calculations ◽  
Glacier Mapping ◽  
Sentinel 2

<p><span>Svalbard is famous for its numerous surge-type glaciers as well as for the harsh weather conditions of a highly maritime Arctic island, making regular observations of its glaciers challenging. However, the rapid changes of glacier geometry require a frequent update of their extent to perform accurate glacier-specific calculations such as their mass balance or contribution to sea level. The last inventory for Svalbard has been compiled by Nuth et al. (2013) from about 40 satellite scenes acquired by three different sensors (ASTER, Landsat, SPOT) on 30 unique days over a period of 10 years. Accordingly, any change assessment or other time dependent calculations are difficult to perform and a temporarily more consistent dataset is urgently required.</span></p><p><span>In this study we present the results of a new glacier inventory for Svalbard that has been derived from two Sentinel-2 swaths acquired for the main island within 3 days of 2017 and on 1 day in 2016 from Landsat 8 for Nordaustlandet. The images had overall very good snow conditions but in some regions late seasonal snow was hiding glaciers. Glacier mapping under local clouds in the very north and south could be performed by using further scenes from 2017 processed with GEE. We applied a simple red/SWIR band ratio to map clean ice and corrected wrong classifications (sea ice, lakes) or missing parts (debris cover) manually. New drainage divides and topographic parameters were derived from the ArcticDEM. </span></p><p>The new inventory counts 3136 glaciers >0.01 km<sup>2</sup> covering an area of 32,948 km<sup>2</sup>. Of these, glaciers < 1 km<sup>2</sup> cover 1.3% of the area but nearly 44% of the number whereas glac-iers >10 km<sup>2</sup> cover 91% of the area and 10% by number. Compared to the previous inventory we have 1468 glaciers more and 2.5% area less. However, when excluding the 2025 glaciers <1 km<sup>2</sup>, we only identified 1111 glaciers, i.e. 557 less than in the previous inventory. The differences are mostly due to newly considered entities, different drainage divides, glacier retreat and advance/surging. By excluding surge-type glaciers, a more meaningful determination of climate-related area changes can be performed. The presentation will discuss the differences of the new inventory to the RGI dataset, the specific glacier mapping challenges and our approach to solve them.</p>

scholarly journals A new glacier inventory on southern Baffin Island, Canada, from ASTER data: I. Applied methods, challenges and solutions

2009 ◽  
Vol 50 (53) ◽  
pp. 11-21 ◽  
Author(s):  
Felix Svoboda ◽  
Frank Paul
Keyword(s):  
Climate Change Impacts ◽  
Ice Age ◽  
Baffin Island ◽  
Glacier Inventory ◽  
Ice Caps ◽  
Digital Elevation ◽  
Glacier Length ◽  
Length And Area ◽  
Shortwave Infrared ◽  
Glacier Mapping

AbstractThe quantitative assessment of glacier changes as well as improved modeling of climate-change impacts on glaciers requires digital vector outlines of individual glacier entities. Unfortunately, such a glacier inventory is still lacking in many remote but extensively glacierized gions such as the Canadian Arctic. Multispectral satellite data in combination with digital elevation models (DEMs) a particularly useful for creating detailed glacier inventory data including topographic information for each entity. In this study, we extracted glacier outlines and a DEM using two adjacent Terra ASTER scenes acquired in August 2000 for a remote region on southern Baffin Island, Canada. Additionally, Little Ice Age (LIA) extents we digitized from trimlines and moraines visible on the ASTER scenes, and Landsat MSS and TM scenes from the years 1975 and 1990 we used to assess changes in glacier length and area. Because automated delineation of glaciers is based on a band in the shortwave infrared, we have developed a new semi-automated glacier-mapping approach for the MSS sensor. Wrongly classified debris-coved glaciers, water bodies and attached snowfields we corrected manually for both ASTER and MSS. Glacier drainage divides we manually digitized by combining visual interptation with DEM information. In this first paper, we describe the applied methods for glacier mapping and the glaciological challenges encounted (e.g. data voids, snow cover, ice caps, tributaries), while the second paper ports the data analyses and the derived changes.

scholarly journals Uma abordagem metodológica de Sensoriamento Remoto para o monitoramento da contaminação do rio Paraopeba pós-desastre de Brumadinho-MG

2021 ◽  
Vol 43 ◽  
pp. e36
Author(s):  
Neison Cabral Ferreira Freire ◽  
Admilson Da Penha Pacheco ◽  
Vinícius D'Lucas Bezerra Queiroz
Keyword(s):  
Remote Sensing ◽  
Spectral Band ◽  
Ratio Method ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Band Ratio ◽  
Hydrographic Basin ◽  
Search Area ◽  
Brazilian History ◽  
Sentinel 2

The following article aims to present and discuss the monitoring, through Remote Sensing, of the dirt displacement caused by the collapse of the Córrego do Feijão’s dam I of mining waste, which occurred on January 25, 2019, in the rural area of Brumadinho, a city located in the state of Minas Gerais, Brazil. This event is considered one of the greatest technoindustrial disasters in Brazilian history, placing in danger one of the largest hydrographic basin in Brazil: the São Francisco river basin. The search area comprises from where the sludge mud got in contact with the Paraopeba’s right bank to its mouth into the Três Marias Dam, adding up to approximately 315 km. For this monitoring the spectral band ratio method was utilized,  using images from the sensors MSI/Sentinel-2 and OLI/Landsat-8 captured at different dates, employing standardization of means and variances to harmonize the range of the surface reflectance values in each image.

scholarly journals Comparison of Different Multispectral Images to Map Hydrothermal Alteration Zones in Kokap, Kulon Progo

2021 ◽  
Vol 6 (2) ◽  
pp. 86
Author(s):  
Bayu Raharja ◽  
Agung Setianto ◽  
Anastasia Dewi Titisari
Keyword(s):  
Remote Sensing ◽  
Hydrothermal Alteration ◽  
Remote Sensing Data ◽  
Component Analysis ◽  
Landsat 8 ◽  
Accuracy Evaluation ◽  
Multispectral Images ◽  
Band Ratio ◽  
Alteration Zones ◽  
Sentinel 2

Using remote sensing data for hydrothermal alteration mapping beside saving time and reducing  cost leads to increased accuracy. In this study, the result of multispectral remote sensing tehcniques has been compare for manifesting hydrothermal alteration in Kokap, Kulon Progo. Three multispectral images, including ASTER, Landsat 8, and Sentinel-2, were compared in order to find the highest overall accuracy using principle component analysis (PCA) and directed component analysis (DPC). Several subsets band combinations were used as PCA and DPC input to targeting the key mineral of alteration. Multispectral classification with the maximum likelihood algorithm was performed to map the alteration types based on training and testing data and followed by accuracy evaluation. Two alteration zones were succeeded to be mapped: argillic zone and propylitic zone. Results of these image classification techniques were compared with known alteration zones from previous study. DPC combination of band ratio images of 5:2 and 6:7 of Landsat 8 imagery yielded a classification accuracy of 56.4%, which was 5.05% and 10.13% higher than those of the ASTER and Sentinel-2 imagery. The used of DEM together with multispectral images was increase the accuracy of hydrothermal alteration mapping in the study area.

Glacier mapping with Sentinel-2 in Svalbard: Challenges when creating a new glacier inventory in the Artic

2020 ◽  
Author(s):  
Frank Paul ◽  
Philipp Rastner
Keyword(s):  
The Arctic ◽  
Minimum Size ◽  
Ratio Method ◽  
Large Area ◽  
Band Ratio ◽  
Glacier Inventory ◽  
Ice Caps ◽  
Global Sea Level ◽  
Sentinel 2 ◽  
Snow And Ice

<p>Svalbard is dominated by large (often calving) glaciers and ice caps with a strong contribution to global sea-level rise. Due to many surge-type glaciers, large changes of glacier extents are common and determination of their mass balance requires a regular update of their outlines. However, frequent cloud cover prevents accurate repeat mapping. In consequence, the last glacier inventory for Svalbard was compiled from satellite scenes acquired over a period of 11 years, making change assessment and other applications difficult. Due to long-lasting seasonal snow and confusion with large perennial snow patches, the minimum size of this inventory has been set to 1 km<sup>2</sup>.</p><p>Here we present a new glacier inventory for Svalbard that has been compiled at 10 m resolution from two Sentinel-2 scenes that were acquired only two days apart. Sea ice, ice-bergs, lakes and turbid water were wrongly classified as glaciers by the applied band ratio method and manually removed. Debris cover, snow and ice under some clouds but also polluted (very dark) clean ice was not mapped as thresholds were optimized to get snow and ice in shadow properly mapped. These missing regions were manually added. Snow patches were removed with a 5 by 5 majority filter applied to the binary glacier map and a minimum size of 0.05 km<sup>2</sup>. Outlines from the previous inventory as available in the RGI were used to guide the corrections. After careful comparison, we used the Arctic DEM to derive surface drainage divides and topographic attributes for all glaciers.</p><p>The largest challenges for accurate glacier delineation are discrimination of debris-covered glaciers from peri-glacial debris and rock glaciers, handling of attached seasonal or perennial snowfields, and identifying disintegrating tongues of down-wasting and often debris-covered ice masses remaining after a surge. Compared to the previous inventory, the large area gains and losses of surge-type glaciers are remarkable, but area differences result also from a different interpretation of debris-covered glaciers, inclusion of snow-filled couloirs and several new glaciers that were excluded in the previous inventory.</p>

scholarly journals Glacier shrinkage in the Alps continues unabated as revealed by a new glacier inventory from Sentinel-2

2020 ◽  
Vol 12 (3) ◽  
pp. 1805-1821 ◽  
Author(s):  
Frank Paul ◽  
Philipp Rastner ◽  
Roberto Sergio Azzoni ◽  
Guglielmina Diolaiuti ◽  
Davide Fugazza ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Uncertainty Assessment ◽  
Ratio Method ◽  
Band Ratio ◽  
Glacier Inventory ◽  
Glacier Shrinkage ◽  
Run Off ◽  
Glacier Ice ◽  
The Alps ◽  
Sentinel 2

Abstract. The ongoing glacier shrinkage in the Alps requires frequent updates of glacier outlines to provide an accurate database for monitoring, modelling purposes (e.g. determination of run-off, mass balance, or future glacier extent), and other applications. With the launch of the first Sentinel-2 (S2) satellite in 2015, it became possible to create a consistent, Alpine-wide glacier inventory with an unprecedented spatial resolution of 10 m. The first S2 images from August 2015 already provided excellent mapping conditions for most glacierized regions in the Alps and were used as a base for the compilation of a new Alpine-wide glacier inventory in a collaborative team effort. In all countries, glacier outlines from the latest national inventories have been used as a guide to compile an update consistent with the respective previous interpretation. The automated mapping of clean glacier ice was straightforward using the band ratio method, but the numerous debris-covered glaciers required intense manual editing. Cloud cover over many glaciers in Italy required also including S2 scenes from 2016. The outline uncertainty was determined with digitizing of 14 glaciers several times by all participants. Topographic information for all glaciers was obtained from the ALOS AW3D30 digital elevation model (DEM). Overall, we derived a total glacier area of 1806±60 km2 when considering 4395 glaciers >0.01 km2. This is 14 % (−1.2 % a−1) less than the 2100 km2 derived from Landsat in 2003 and indicates an unabated continuation of glacier shrinkage in the Alps since the mid-1980s. It is a lower-bound estimate, as due to the higher spatial resolution of S2 many small glaciers were additionally mapped or increased in size compared to 2003. Median elevations peak around 3000 m a.s.l., with a high variability that depends on location and aspect. The uncertainty assessment revealed locally strong differences in interpretation of debris-covered glaciers, resulting in limitations for change assessment when using glacier extents digitized by different analysts. The inventory is available at https://doi.org/10.1594/PANGAEA.909133 (Paul et al., 2019).

scholarly journals Remote Sensing and GIS for Thematic Mapping of the Ak-Suu and Isfana River Basins in Kyrgyzstan

2021 ◽  
pp. 57-64
Author(s):  
A. Chymyrov ◽  
N. Ismailov
Keyword(s):  
Remote Sensing ◽  
River Basins ◽  
Remote Sensing And Gis ◽  
Landsat 8 ◽  
Soil Maps ◽  
Basin Boundary ◽  
Central Asian ◽  
Thematic Mapping ◽  
Glacier Mapping ◽  
Sentinel 2

Geographic information systems (GIS) play a significant role in the thematic mapping to collect, store, analyze, visualize and deliver geospatial data today. The Ak-Suu and Isfana rivers flow into the Syrdarya river, which is used for irrigation and other purposes in Uzbekistan, Tajikistan and Kazakhstan. Thematic mapping of the river basins allow efficient use of natural and water resources in the region to mitigate the existing conflicts over water use by four Central Asian neighboring countries. SRTMGL1 DEM is applied in terrain modeling and river basin boundary delineation. Multispectral Landsat 8 and Sentinel-2 images were used in land use and glacier mapping. DEM, glacier, ecosystem, emergency and soil maps are designed and updated based on the cartographic materials, remote sensing, infrastructure and statistical datasets.

scholarly journals Glacier shrinkage in the Alps continues unabated as revealed by a new glacier inventory from Sentinel-2

2019 ◽  
Author(s):  
Frank Paul ◽  
Philipp Rastner ◽  
Roberto Sergio Azzoni ◽  
Guglielmina Diolaiuti ◽  
Davide Fugazza ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Uncertainty Assessment ◽  
Ratio Method ◽  
Band Ratio ◽  
Glacier Inventory ◽  
Glacier Shrinkage ◽  
Run Off ◽  
Glacier Ice ◽  
The Alps ◽  
Sentinel 2

Abstract. The on-going glacier shrinkage in the Alps requires frequent updates of glacier outlines to provide an accurate database for monitoring or modeling purposes (e.g. determination of run-off, mass balance, or future glacier extent) and other applications. With the launch of the first Sentinel-2 (S2) satellite in 2015, it became possible to create a consistent, Alpine-wide glacier inventory with an unprecedented spatial resolution of 10 m. Fortunately, already the first S2 images acquired in August 2015 provided excellent mapping conditions for most of the glacierised regions in the Alps. We have used this opportunity to compile a new Alpine-wide glacier inventory in a collaborative team effort. In all countries, glacier outlines from the latest national inventories have been used as a guide to compile a consistent update. However, cloud cover over many glaciers in Italy required including also S2 scenes from 2016. Whereas the automated mapping of clean glacier ice was straightforward using the band ratio method, the numerous debris-covered glaciers required in-tense manual editing. The uncertainty in the outlines was determined with a multiple digitising experiment of 14 glaciers by all participants. Topographic information for all glaciers was derived from the ALOS AW3D30 DEM. Overall, we derived a total glacier area of 1806 ± 60 km2 when considering 4394 glaciers > 0.01 km2. This is 14 % (−1.2 %/a) less than the 2100 km2 derived from Landsat scenes acquired in 2003 and indicating an unabated continuation of glacier shrinkage in the Alps since the mid-1980s. Due to the higher spatial resolution of S2 many small glaciers were additionally mapped in the new inventory or increased in size compared to 2003. An artificial reduction to the former extents would thus result in an even higher overall area loss. Still, the uncertainty assessment revealed locally considerable differences in interpretation of debris-covered glaciers, resulting in limitations for change assessment when using glacier extents digitised by different analysts. The inventory is available at: https://doi.pangaea.de/10.1594/PANGAEA.909133 (Paul et al., 2019).

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

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