scholarly journals A multiscale subpixel mixture analysis applied for melt detection using passive microwave and radar scatterometer image time series of the Antarctic Peninsula (1999–2009)

2017 ◽  
Vol 59 (76pt1) ◽  
pp. 16-28 ◽  
Author(s):  
Marcos W. D. De Freitas ◽  
Cláudio W. Mendes Júnior ◽  
Jorge Arigony-Neto ◽  
Juliana Costi ◽  
Jefferson C. Simões
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Antarctic Peninsula ◽  
Temporal Dynamics ◽  
Snow Melt ◽  
Mixture Analysis ◽  
Entire Area ◽  
Wet Snow ◽  
The Antarctic ◽  
Fraction Images

ABSTRACTThis paper reports a comparative analysis performed on a fraction-image time series of the Antarctic Peninsula from the period 1999–2009 generated by multiresolution remote-sensing images (SSM/I and SSMI/S with 25 km and QuikSCAT with 2.225 km spatial resolutions) for snow-melt detection. Our method is based on the (a) preprocessing of multitemporal remote-sensing data, (b) subpixel mixture analysis of SSMI and QuikSCAT image time series, and (c) evaluation of subpixel analysis, including an assessment of fraction images of wet snow using an independent ASAR dataset and sensitivity analysis on the melt metrics measured by these images. The temporal dynamics of the melt indices derived from the wet-snow fraction images presented a more realistic pattern than the traditional melt metrics measured by Boolean snow-melt detection approaches. Because the snow melt actually occurs at the pixel fractions, the multiscale analysis that was performed suggests an overestimation of the melt metrics calculated using Boolean approaches (which assume that the entire area of the detected pixel shows snow melt). The melt metrics measurements show an overestimation according to the decrease in spatial resolution related to the multiplicative effect of a larger pixel area.

scholarly journals Identification of catchment functional units by time series of thermal remote sensing images

2014 ◽  
Vol 18 (12) ◽  
pp. 5345-5359 ◽  
Author(s):  
B. Müller ◽  
M. Bernhardt ◽  
K. Schulz
Keyword(s):  
Remote Sensing ◽  
Principal Component Analysis ◽  
Time Series ◽  
Land Surface ◽  
Temporal Dynamics ◽  
Principal Component ◽  
Component Analysis ◽  
Land Surface Models ◽  
Surface Models ◽  
Functional Units

Abstract. The identification of catchment functional behavior with regards to water and energy balance is an important step during the parameterization of land surface models. An approach based on time series of thermal infrared (TIR) data from remote sensing is developed and investigated to identify land surface functioning as is represented in the temporal dynamics of land surface temperature (LST). For the mesoscale Attert catchment in midwestern Luxembourg, a time series of 28 TIR images from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) was extracted and analyzed, applying a novel process chain. First, the application of mathematical–statistical pattern analysis techniques demonstrated a strong degree of pattern persistency in the data. Dominant LST patterns over a period of 12 years were then extracted by a principal component analysis. Component values of the two most dominant components could be related for each land surface pixel to land use data and geology, respectively. The application of a data condensation technique ("binary words") extracting distinct differences in the LST dynamics allowed the separation into landscape units that show similar behavior under radiation-driven conditions. It is further outlined that both information component values from principal component analysis (PCA), as well as the functional units from the binary words classification, will highly improve the conceptualization and parameterization of land surface models and the planning of observational networks within a catchment.

Assessing the potential of multispectral remote sensing for lithological mapping on the Antarctic Peninsula: case study from eastern Adelaide Island, Graham Land

2010 ◽  
Vol 22 (3) ◽  
pp. 299-318 ◽  
Author(s):  
C.E. Haselwimmer ◽  
T.R. Riley ◽  
J.G. Liu
Keyword(s):  
Remote Sensing ◽  
Antarctic Peninsula ◽  
Spectral Properties ◽  
Volcanic Rocks ◽  
Lithological Mapping ◽  
Aster Data ◽  
Intrusive Rocks ◽  
Multispectral Remote Sensing ◽  
The Antarctic ◽  
Adelaide Island

AbstractThe results of lithological mapping using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for the Wright Peninsula region of Adelaide Island, Antarctic Peninsula are compared with existing geological maps and recent field observations to assess the potential of multispectral remote sensing to undertake lithological mapping on the Antarctic Peninsula. The Wright Peninsula comprises calc-alkaline intrusive rocks ranging from granite to gabbro, volcanic rocks of acidic to intermediate composition, and arc-related sediments. The reflective and thermal bands of a single ASTER image were analysed with reference to reflectance spectra of rock samples from the study area. Assessment of the ASTER mapping outcomes was undertaken with a newly compiled geological map of Adelaide Island and observations made during recent fieldwork. The results demonstrate that ASTER can uniquely discriminate granitoid intrusive rocks and altered rhyolitic volcanic rocks that display distinctive spectral properties. The results are more ambiguous at discriminating more intermediate/mafic rocks such as diorite/gabbro, andesite/basalt and chlorite-bearing sediments due to the similarity in spectral properties. These results demonstrate that although ASTER data are limited in their ability to uniquely discriminate lithologies they can provide important lithological information in support of geological mapping on the Antarctic Peninsula.

scholarly journals Dynamic Response of Sjögren Inlet Glaciers, Antarctic Peninsula, to Ice Shelf Breakup Derived from Multi-Mission Remote Sensing Time Series

2016 ◽  
Vol 4 ◽  
Author(s):  
Thorsten C. Seehaus ◽  
Sebastián Marinsek ◽  
Pedro Skvarca ◽  
Jan M. van Wessem ◽  
Carleen H. Reijmer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Dynamic Response ◽  
Antarctic Peninsula ◽  
Ice Shelf ◽  
Sensing Time

scholarly journals Fast recession of the northern Larsen Ice Shelf monitored by space images

1993 ◽  
Vol 17 ◽  
pp. 317-321 ◽  
Author(s):  
Pedro Skvarca
Keyword(s):  
Remote Sensing ◽  
Antarctic Peninsula ◽  
Climatic Warming ◽  
Ice Shelf ◽  
Space Images ◽  
Front Position ◽  
Satellite Photograph ◽  
The Antarctic ◽  
Larsen Ice Shelf

The rapid retreat and disintegration of the Larsen Ice Shelf sector extending north of Seal Nunataks (65° S), documented from the mid 1970s onwards by remote sensing, is presented and related to the Antarctic Peninsula climatic warming recorded over several past decades. A 1975 KOSMOS satellite photograph and a series of LANDSAT MSS and TM images taken in 1978, 1979, 1986, 1988 and 1989 were used to monitor the retreat of the ice shelf between Seal Nunataks and Prince Gustav Channel. The ice shelf has decreased by more than 30% during the period 1975–89 within the Christensen Island to Cape Longing region. Measurements of the ice front position carried out in the field during late 1991 indicate that the recession between Lindenberg Island and Sobral Peninsula is still continuing, in some places at a rate of up to 2.5 km a−1.

scholarly journals Identification of catchment functional units by time series of thermal remote sensing images

2014 ◽  
Vol 11 (6) ◽  
pp. 7019-7052 ◽  
Author(s):  
B. Müller ◽  
M. Bernhardt ◽  
K. Schulz
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Surface ◽  
Temporal Dynamics ◽  
Principal Component ◽  
Land Surface Models ◽  
Principal Component Analysis Component ◽  
Functional Behavior ◽  
Surface Models ◽  
Functional Units

Abstract. The identification of catchment functional behavior with regard to water and energy balance is an important step during the parameterization of land surface models. An approach based on time series of thermal infrared (TIR) data from remote sensing is developed and investigated to identify land surface functioning as is represented in the temporal dynamics of land surface temperature (LST). For the meso-scale Attert catchment in midwestern Luxembourg, a time series of 28 TIR images from ASTER was extracted and analyzed. The application mathematical-statistical pattern analysis techniques demonstrated a strong degree of pattern persistency in the data. Dominant LST patterns over a period of 12 years were extracted by a principal component analysis. Component values of the 2 most dominant components could be related for each land surface pixel to vegetation/land use data, and geology, respectively. A classification of the landscape by introducing "binary word", representing distinct differences in LST dynamics, allowed the separation into functional units under radiation driven conditions. It is further outlined that both information, component values from PCA as well as the functional units from "binary words" classification, will highly improve the conceptualization and parameterization of land surface models and the planning of observational networks within a catchment.

scholarly journals Long melt seasons on ice shelves of the Antarctic Peninsula: an analysis using satellite-based microwave emission measurements

2002 ◽  
Vol 34 ◽  
pp. 127-133 ◽  
Author(s):  
Mark A. Fahnestock ◽  
Waleed Abdalati ◽  
Christopher A. Shuman
Keyword(s):  
Time Series ◽  
Antarctic Peninsula ◽  
Threshold Value ◽  
Microwave Emission ◽  
Ice Shelf ◽  
Ice Shelves ◽  
Gradient Ratio ◽  
The Antarctic ◽  
Larsen Ice Shelf ◽  
Possible Cause

AbstractWe have examined the record of melt-season duration on the Antarctic Peninsula using two techniques for detecting the presence of a melt signal in microwave-emission time series covering the period 1978–2000. We have obtained similar estimates of melt-season length using the cross-polarized gradient ratio (XPGR) technique and calibrations previously applied in Greenland and a technique which detects the jump in emission caused by melt without using a sensor- and frequency-dependent threshold value. The close correspondence between results from the two techniques on peninsula ice shelves suggests that the XPGR analysis can be used over the length of the time series. The results show that the long melt seasons of 1992/93 and several later years were exceptional occurrences on the northern parts of the Larsen Ice Shelf. These melt seasons were followed by disintegration events, supporting a possible cause-and-effect relationship.

scholarly journals A Machine Learning Approach for Remote Sensing Data Gap-Filling with Open-Source Implementation: An Example Regarding Land Surface Temperature, Surface Albedo and NDVI

2020 ◽  
Vol 12 (23) ◽  
pp. 3865
Author(s):  
Mikhail Sarafanov ◽  
Eduard Kazakov ◽  
Nikolay O. Nikitin ◽  
Anna V. Kalyuzhnaya
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temporal Dynamics ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Terra Modis

Satellite remote sensing has now become a unique tool for continuous and predictable monitoring of geosystems at various scales, observing the dynamics of different geophysical parameters of the environment. One of the essential problems with most satellite environmental monitoring methods is their sensitivity to atmospheric conditions, in particular cloud cover, which leads to the loss of a significant part of data, especially at high latitudes, potentially reducing the quality of observation time series until it is useless. In this paper, we present a toolbox for filling gaps in remote sensing time-series data based on machine learning algorithms and spatio-temporal statistics. The first implemented procedure allows us to fill gaps based on spatial relationships between pixels, obtained from historical time-series. Then, the second procedure is dedicated to filling the remaining gaps based on the temporal dynamics of each pixel value. The algorithm was tested and verified on Sentinel-3 SLSTR and Terra MODIS land surface temperature data and under different geographical and seasonal conditions. As a result of validation, it was found that in most cases the error did not exceed 1 °C. The algorithm was also verified for gaps restoration in Terra MODIS derived normalized difference vegetation index and land surface broadband albedo datasets. The software implementation is Python-based and distributed under conditions of GNU GPL 3 license via public repository.

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