scholarly journals Surface composition of debris-covered glaciers across the Himalaya using linear spectral unmixing of Landsat 8 OLI imagery

2021 ◽  
Vol 15 (9) ◽  
pp. 4557-4588
Author(s):  
Adina E. Racoviteanu ◽  
Lindsey Nicholson ◽  
Neil F. Glasser
Keyword(s):  
Large Scale ◽  
Surface Composition ◽  
Spectral Unmixing ◽  
Eastern Himalaya ◽  
Landsat 8 ◽  
Mountain Range ◽  
Dynamic Topography ◽  
Debris Cover ◽  
Linear Spectral Unmixing ◽  
The Himalaya

Abstract. The Himalaya mountain range is characterized by highly glacierized, complex, dynamic topography. The ablation area of Himalayan glaciers often features a highly heterogeneous debris mantle comprising ponds, steep and shallow slopes of various aspects, variable debris thickness, and exposed ice cliffs associated with differing ice ablation rates. Understanding the composition of the supraglacial debris cover is essential for a proper understanding of glacier hydrology and glacier-related hazards. Until recently, efforts to map debris-covered glaciers from remote sensing focused primarily on glacier extent rather than surface characteristics and relied on traditional whole-pixel image classification techniques. Spectral unmixing routines, rarely used for debris-covered glaciers, allow decomposition of a pixel into constituting materials, providing a more realistic representation of glacier surfaces. Here we use linear spectral unmixing of Landsat 8 Operational Land Imager (OLI) images (30 m) to obtain fractional abundance maps of the various supraglacial surfaces (debris material, clean ice, supraglacial ponds and vegetation) across the Himalaya around the year 2015. We focus on the debris-covered glacier extents as defined in the database of global distribution of supraglacial debris cover. The spectrally unmixed surfaces are subsequently classified to obtain maps of composition of debris-covered glaciers across sample regions. We test the unmixing approach in the Khumbu region of the central Himalaya, and we evaluate its performance for supraglacial ponds by comparison with independently mapped ponds from high-resolution Pléiades (2 m) and PlanetScope imagery (3 m) for sample glaciers in two other regions with differing topo-climatic conditions. Spectral unmixing applied over the entire Himalaya mountain range (a supraglacial debris cover area of 2254 km2) indicates that at the end of the ablation season, debris-covered glacier zones comprised 60.9 % light debris, 23.8 % dark debris, 5.6 % clean ice, 4.5 % supraglacial vegetation, 2.1 % supraglacial ponds, and small amounts of cloud cover (2 %), with 1.2 % unclassified areas. The spectral unmixing performed satisfactorily for the supraglacial pond and vegetation classes (an F score of ∼0.9 for both classes) and reasonably for the debris classes (F score of 0.7). Supraglacial ponds were more prevalent in the monsoon-influenced central-eastern Himalaya (up to 4 % of the debris-covered area) compared to the monsoon-dry transition zone (only 0.3 %) and in regions with lower glacier elevations. Climatic controls (higher average temperatures and more abundant precipitation), coupled with higher glacier thinning rates and lower average glacier velocities, further favour pond incidence and the development of supraglacial vegetation. With continued advances in satellite data and further method refinements, the approach presented here provides avenues towards achieving large-scale, repeated mapping of supraglacial features.

scholarly journals Surface composition of debris-covered glaciers across the Himalaya using spectral unmixing and multi-sensor imagery

2021 ◽  
Author(s):  
Adina E. Racoviteanu ◽  
Lindsey Nicholson ◽  
Neil F. Glasser
Keyword(s):  
Surface Composition ◽  
Spatial Scales ◽  
Spectral Unmixing ◽  
Eastern Himalaya ◽  
Mountain Range ◽  
Dynamic Topography ◽  
Glacier Surface ◽  
Proper Understanding ◽  
Debris Cover ◽  
The Himalaya

Abstract. The Hindu-Kush Himalaya mountain range is characterized by highly glacierized, complex, dynamic topography. The ablation area of these glaciers is often covered a highly heterogeneous debris cover mantle comprising ponds, steep and shallow slopes of various aspects, variable debris thickness and exposed ice cliffs. These surface elements are associated with differing ice ablation rates, and understanding the composition of the glacier surface is essential for a proper understanding of glacier hydrology and glacier-related hazards. Here we use high-resolution Pleiades (2 m) and RapidEye imagery (5 m) combined with Landsat Operational Land Imager (OLI) imagery (30 m) to estimate the composition of debris-covered glacier tongues across the Himalaya around the year 2015. We use linear spectral unmixing to map various types of debris, clean ice, supraglacial ponds and vegetation on debris-covered glaciers across the mountain range. We develop the spectral unmixing methods in the Khumbu region of eastern Nepal, and then apply them over the entire Himalaya (a glacier area of 2,254 km2). This allowed us to convert 30 m fractional maps into finer classification maps and to estimate the composition of debris-covered glaciers at various spatial scales. Debris-covered glaciers across the mountain range comprised 2.1 % supraglacial ponds, 12.8 % dark debris, 60.9 % light debris and 4.5 % supra glacial vegetation, with negligible amounts of clean ice and clouds and unclassified areas. Supraglacial ponds were more prevalent in the monsoon-influenced central-eastern Himalaya (up to 4 % of the debris cover area) compared to the monsoon-dry transition zone (only 0.3 %). The automated fractional supraglacial pond maps developed here serve to complement and improve the accuracy of existing regional lake datasets. They also provide a basis for exploring the turbidity of lakes and ponds as indicators of glacier change processes, and to monitor the evolution of ponds in the context of glacial hazards.

scholarly journals Mapping Mangrove Zonation Changes in Senegal with Landsat Imagery Using an OBIA Approach Combined with Linear Spectral Unmixing

2021 ◽  
Vol 13 (10) ◽  
pp. 1961
Author(s):  
Florent Lombard ◽  
Julien Andrieu
Keyword(s):  
Forest Cover ◽  
Landsat Imagery ◽  
Regional Scale ◽  
Rhizophora Mangle ◽  
Spectral Unmixing ◽  
Avicennia Germinans ◽  
Landsat 8 ◽  
Conservation Policies ◽  
Linear Spectral Unmixing ◽  
Mangrove Zonation

The mangrove areas in Senegal have fluctuated considerably over the last few decades, and it is therefore important to monitor the evolution of forest cover in order to orient and optimise forestry policies. This study presents a method for mapping plant formations to monitor and study changes in zonation within the mangroves of Senegal. Using Landsat ETM+ and Landsat 8 OLI images merged to a 15-m resolution with a pansharpening method, a processing chain that combines an OBIA approach and linear spectral unmixing was developed to detect changes in mangrove zonation through a diachronic analysis. The accuracy of the discriminations was evaluated with kappa indices, which were 0.8 for the Saloum delta and 0.83 for the Casamance estuary. Over the last 20 years, the mangroves of Senegal have increased in surface area. However, the dynamics of zonation differ between the two main mangrove hydrosystems of Senegal. In Casamance, a colonisation process is underway. In the Saloum, Rhizophora mangle is undergoing a process of densification in mangroves and appears to reproduce well in both regions. Furthermore, this study confirms, on a regional scale, observations in the literature noting the lack of Avicennia germinans reproduction on a local scale. In the long term, these regeneration gaps may prevent the mangrove from colonising the upper tidal zones in the Saloum. Therefore, it would be appropriate to redirect conservation policies towards reforestation efforts in the Saloum rather than in Casamance and to focus these actions on the perpetuation of Avicennia germinans rather than Rhizophora mangle, which has no difficulty in reproducing. From this perspective, it is necessary to gain a more in-depth understanding of the specific factors that promote the success of Avicennia germinans seeding.

scholarly journals The Greater Caucasus Glacier Inventory (Russia, Georgia and Azerbaijan)

2018 ◽  
Vol 12 (1) ◽  
pp. 81-94 ◽  
Author(s):  
Levan G. Tielidze ◽  
Roger D. Wheate
Keyword(s):  
Surface Area ◽  
Large Scale ◽  
Thermal Emission ◽  
Landsat 8 ◽  
Mountain Range ◽  
Height Distribution ◽  
Glacier Surface ◽  
Data Set ◽  
Glacier Inventory ◽  
Greater Caucasus

Abstract. There have been numerous studies of glaciers in the Greater Caucasus, but none that have generated a modern glacier database across the whole mountain range. Here, we present an updated and expanded glacier inventory at three time periods (1960, 1986, 2014) covering the entire Greater Caucasus. Large-scale topographic maps and satellite imagery (Corona, Landsat 5, Landsat 8 and ASTER) were used to conduct a remote-sensing survey of glacier change, and the 30 m resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM; 17 November 2011) was used to determine the aspect, slope and height distribution of glaciers. Glacier margins were mapped manually and reveal that in 1960 the mountains contained 2349 glaciers with a total glacier surface area of 1674.9 ± 70.4 km2. By 1986, glacier surface area had decreased to 1482.1 ± 64.4 km2 (2209 glaciers), and by 2014 to 1193.2 ± 54.0 km2 (2020 glaciers). This represents a 28.8 ± 4.4 % (481 ± 21.2 km2) or 0.53 % yr−1 reduction in total glacier surface area between 1960 and 2014 and an increase in the rate of area loss since 1986 (0.69 % yr−1) compared to 1960–1986 (0.44 % yr−1). Glacier mean size decreased from 0.70 km2 in 1960 to 0.66 km2 in 1986 and to 0.57 km2 in 2014. This new glacier inventory has been submitted to the Global Land Ice Measurements from Space (GLIMS) database and can be used as a basis data set for future studies.

scholarly journals Spatial patterns in glacier characteristics and area changes from 1962 to 2006 in the Kanchenjunga–Sikkim area, eastern Himalaya

2015 ◽  
Vol 9 (2) ◽  
pp. 505-523 ◽  
Author(s):  
A. E. Racoviteanu ◽  
Y. Arnaud ◽  
M. W. Williams ◽  
W. F. Manley
Keyword(s):  
Spatial Patterns ◽  
Thermal Emission ◽  
Eastern Himalaya ◽  
Slope Aspect ◽  
Glacier Area ◽  
Glacier Surface ◽  
Area Loss ◽  
Debris Cover ◽  
Landsat 7 ◽  
The Himalaya

Abstract. This study investigates spatial patterns in glacier characteristics and area changes at decadal scales in the eastern Himalaya – Nepal (Arun and Tamor basins), India (Teesta basin in Sikkim) and parts of China and Bhutan – based on various satellite imagery: Corona KH4 imagery, Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Advanced Spaceborne Thermal Emission Radiometer (ASTER), QuickBird (QB) and WorldView-2 (WV2). We compare and contrast glacier surface area changes over the period of 1962–2000/2006 and their dependency on glacier topography (elevation, slope, aspect, percent debris cover) and climate (solar radiation, precipitation) on the eastern side of the topographic barrier (Sikkim) versus the western side (Nepal). Glacier mapping from 2000 Landsat ASTER yielded 1463 ± 88 km2 total glacierized area, of which 569 ± 34 km2 was located in Sikkim and 488 ± 29 km2 in eastern Nepal. Supraglacial debris covered 11% of the total glacierized area, and supraglacial lakes covered about 5.8% of the debris-covered glacier area alone. Glacier area loss (1962 to 2000) was 0.50 ± 0.2% yr−1, with little difference between Nepal (0.53 ± 0.2% yr−1) and Sikkim (0.44 ± 0.2% yr−1. Glacier area change was controlled mostly by glacier area, elevation, altitudinal range and, to a smaller extent, slope and aspect. In the Kanchenjunga–Sikkim area, we estimated a glacier area loss of 0.23 ± 0.08% yr−1 from 1962 to 2006 based on high-resolution imagery. On a glacier-by-glacier basis, clean glaciers exhibit more area loss on average from 1962 to 2006 (34%) compared to debris-covered glaciers (22%). Glaciers in this region of the Himalaya are shrinking at similar rates to those reported for the last decades in other parts of the Himalaya, but individual glacier rates of change vary across the study area with respect to local topography, percent debris cover or glacier elevations.

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.

Lithologic variation within bright material on Vesta revealed by linear spectral unmixing

Icarus ◽  
2016 ◽  
Vol 272 ◽  
pp. 16-31 ◽  
Author(s):  
F. Zambon ◽  
F. Tosi ◽  
C. Carli ◽  
M.C. De Sanctis ◽  
D.T. Blewett ◽  
...  
Keyword(s):  
Spectral Unmixing ◽  
Linear Spectral Unmixing

Fire history on a desert mountain range: Rincon Mountain Wilderness, Arizona, U.S.A.

1990 ◽  
Vol 20 (10) ◽  
pp. 1559-1569 ◽  
Author(s):  
Christopher H. Baisan ◽  
Thomas W. Swetnam
Keyword(s):  
Tree Ring ◽  
Large Scale ◽  
Fire History ◽  
Fire Regime ◽  
Forest Type ◽  
Drought Severity ◽  
Mountain Range ◽  
Mixed Conifer ◽  
The Mean ◽  
Mean Fire Interval

Modern fire records and fire-scarred remnant material collected from logs, snags, and stumps were used to reconstruct and analyze fire history in the mixed-conifer and pine forest above 2300 m within the Rincon Mountain Wilderness of Saguaro National Monument, Arizona, United States. Cross-dating of the remnant material allowed dating of fire events to the calendar year. Estimates of seasonal occurrence were compiled for larger fires. It was determined that the fire regime was dominated by large scale (> 200 ha), early-season (May–July) surface fires. The mean fire interval over the Mica Mountain study area for the period 1657–1893 was 6.1 years with a range of 1–13 years for larger fires. The mean fire interval for the mixed-conifer forest type (1748–1886) was 9.9 years with a range of 3–19 years. Thirty-five major fire years between 1700 and 1900 were compared with a tree-ring reconstruction of the Palmer drought severity index (PDSI). Mean July PDSI for 2 years prior to fires was higher (wetter) than average, while mean fire year PDSI was near average. This 490-year record of fire occurrence demonstrates the value of high-resolution (annual and seasonal) tree-ring analyses for documenting and interpreting temporal and spatial patterns of past fire regimes.

scholarly journals Use of recent geoid models to estimate mean dynamic topography and geostrophic currents in South Atlantic and Brazil Malvinas confluence

2012 ◽  
Vol 60 (1) ◽  
pp. 41-48
Author(s):  
Alexandre Bernardino Lopes ◽  
Joseph Harari
Keyword(s):  
Numerical Model ◽  
Large Scale ◽  
Singular Spectrum Analysis ◽  
Satellite System ◽  
South Atlantic ◽  
Dynamic Topography ◽  
Geostrophic Currents ◽  
Mean Dynamic Topography ◽  
Level Model

The use of geoid models to estimate the Mean Dynamic Topography was stimulated with the launching of the GRACE satellite system, since its models present unprecedented precision and space-time resolution. In the present study, besides the DNSC08 mean sea level model, the following geoid models were used with the objective of computing the MDTs: EGM96, EIGEN-5C and EGM2008. In the method adopted, geostrophic currents for the South Atlantic were computed based on the MDTs. In this study it was found that the degree and order of the geoid models affect the determination of TDM and currents directly. The presence of noise in the MDT requires the use of efficient filtering techniques, such as the filter based on Singular Spectrum Analysis, which presents significant advantages in relation to conventional filters. Geostrophic currents resulting from geoid models were compared with the HYCOM hydrodynamic numerical model. In conclusion, results show that MDTs and respective geostrophic currents calculated with EIGEN-5C and EGM2008 models are similar to the results of the numerical model, especially regarding the main large scale features such as boundary currents and the retroflection at the Brazil-Malvinas Confluence.

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