Applying both Landsat and Sentinel-2 data to LandTrendr for detection of landscape change trends in Arctic permafrost regions

Landscape changes in the terrestrial Arctic occur on different time scales. One can normally distinguish between gradual and abrupt changes. Permafrost top-down thaw, changes in pedological processes and vegetation structure as well as aridification and paludification are gradual changes. In contras...

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Main Authors: Runge, Alexandra, Grosse, Guido
Format: Conference Object
Language:unknown
Published: 2019
Subjects:
Online Access:https://epic.awi.de/id/eprint/49676/
https://hdl.handle.net/10013/epic.beb27b6b-f500-4bb3-b5fe-8e8aec51854b
id ftawi:oai:epic.awi.de:49676
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spelling ftawi:oai:epic.awi.de:49676 2024-09-15T17:51:05+00:00 Applying both Landsat and Sentinel-2 data to LandTrendr for detection of landscape change trends in Arctic permafrost regions Runge, Alexandra Grosse, Guido 2019-05-17 https://epic.awi.de/id/eprint/49676/ https://hdl.handle.net/10013/epic.beb27b6b-f500-4bb3-b5fe-8e8aec51854b unknown Runge, A. and Grosse, G. orcid:0000-0001-5895-2141 (2019) Applying both Landsat and Sentinel-2 data to LandTrendr for detection of landscape change trends in Arctic permafrost regions , ESA Living Planet Symposium 2019, Milan, 13 May 2019 - 17 May 2019 . hdl:10013/epic.beb27b6b-f500-4bb3-b5fe-8e8aec51854b EPIC3ESA Living Planet Symposium 2019, Milan, 2019-05-13-2019-05-17 Conference notRev 2019 ftawi 2024-06-24T04:22:11Z Landscape changes in the terrestrial Arctic occur on different time scales. One can normally distinguish between gradual and abrupt changes. Permafrost top-down thaw, changes in pedological processes and vegetation structure as well as aridification and paludification are gradual changes. In contrast to that, wildfires, thermokarst formation, thermokarst lake drainage, flooding and rapid soil erosion are abrupt and sometimes even discrete change events. A high temporal resolution in observations is required to properly monitor both types of changes. Particularly discrete change events that happen abruptly can be identified and characterized more precisely with better temporal resolution. However, a better understanding of the nature of gradual changes can also be gained since short periods of increased change or stagnation can also be identified with higher temporal resolution. LandTrendr is an algorithm developed by Kennedy et al. (2010) to capture a wide range of forest disturbance and recovery occurrences based on Landsat time series. The algorithm creates cloud-free mosaics from multiple images and extracts the temporal trajectory of spectral data on a pixel-by-pixel basis. Furthermore, it segments the temporal trajectories based on regressions and point-to-point fittings of spectral indices, depicting then both long-term trends and abrupt changes (Kennedy et al., 2010). As optical remote sensing in the Arctic is highly restricted by frequent cloud cover and low illumination angles, the number of useable images per summer season is low. However, combining data of multiple optical systems bypasses this limitation. Combining data from Landsat and the newly launched ESA Copernicus Sentinel-2 mission, enables the use of data from three satellites (L-8, S-2A, S-2B), which shortens the revisit time to less than five days in high latitudes, enhances the likelihood of cloud-free images considerably, and increase the number of images per summer season dramatically. Using both Landsat and Sentinel-2 images will lead ... Conference Object Arctic permafrost Thermokarst Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center)
institution Open Polar
collection Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center)
op_collection_id ftawi
language unknown
description Landscape changes in the terrestrial Arctic occur on different time scales. One can normally distinguish between gradual and abrupt changes. Permafrost top-down thaw, changes in pedological processes and vegetation structure as well as aridification and paludification are gradual changes. In contrast to that, wildfires, thermokarst formation, thermokarst lake drainage, flooding and rapid soil erosion are abrupt and sometimes even discrete change events. A high temporal resolution in observations is required to properly monitor both types of changes. Particularly discrete change events that happen abruptly can be identified and characterized more precisely with better temporal resolution. However, a better understanding of the nature of gradual changes can also be gained since short periods of increased change or stagnation can also be identified with higher temporal resolution. LandTrendr is an algorithm developed by Kennedy et al. (2010) to capture a wide range of forest disturbance and recovery occurrences based on Landsat time series. The algorithm creates cloud-free mosaics from multiple images and extracts the temporal trajectory of spectral data on a pixel-by-pixel basis. Furthermore, it segments the temporal trajectories based on regressions and point-to-point fittings of spectral indices, depicting then both long-term trends and abrupt changes (Kennedy et al., 2010). As optical remote sensing in the Arctic is highly restricted by frequent cloud cover and low illumination angles, the number of useable images per summer season is low. However, combining data of multiple optical systems bypasses this limitation. Combining data from Landsat and the newly launched ESA Copernicus Sentinel-2 mission, enables the use of data from three satellites (L-8, S-2A, S-2B), which shortens the revisit time to less than five days in high latitudes, enhances the likelihood of cloud-free images considerably, and increase the number of images per summer season dramatically. Using both Landsat and Sentinel-2 images will lead ...
format Conference Object
author Runge, Alexandra
Grosse, Guido
spellingShingle Runge, Alexandra
Grosse, Guido
Applying both Landsat and Sentinel-2 data to LandTrendr for detection of landscape change trends in Arctic permafrost regions
author_facet Runge, Alexandra
Grosse, Guido
author_sort Runge, Alexandra
title Applying both Landsat and Sentinel-2 data to LandTrendr for detection of landscape change trends in Arctic permafrost regions
title_short Applying both Landsat and Sentinel-2 data to LandTrendr for detection of landscape change trends in Arctic permafrost regions
title_full Applying both Landsat and Sentinel-2 data to LandTrendr for detection of landscape change trends in Arctic permafrost regions
title_fullStr Applying both Landsat and Sentinel-2 data to LandTrendr for detection of landscape change trends in Arctic permafrost regions
title_full_unstemmed Applying both Landsat and Sentinel-2 data to LandTrendr for detection of landscape change trends in Arctic permafrost regions
title_sort applying both landsat and sentinel-2 data to landtrendr for detection of landscape change trends in arctic permafrost regions
publishDate 2019
url https://epic.awi.de/id/eprint/49676/
https://hdl.handle.net/10013/epic.beb27b6b-f500-4bb3-b5fe-8e8aec51854b
genre Arctic
permafrost
Thermokarst
genre_facet Arctic
permafrost
Thermokarst
op_source EPIC3ESA Living Planet Symposium 2019, Milan, 2019-05-13-2019-05-17
op_relation Runge, A. and Grosse, G. orcid:0000-0001-5895-2141 (2019) Applying both Landsat and Sentinel-2 data to LandTrendr for detection of landscape change trends in Arctic permafrost regions , ESA Living Planet Symposium 2019, Milan, 13 May 2019 - 17 May 2019 . hdl:10013/epic.beb27b6b-f500-4bb3-b5fe-8e8aec51854b
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