Pairing Remote Sensing and Clustering in Landscape Hydrology for Large-Scale Changes Identification. Applications to the Subarctic Watershed of the George River (Nunavik, Canada). Dataset and Code.

For remote and vast northern watersheds, hydrological data are often sparse and incomplete. Landscape hydrology provides useful approaches for the indirect assessment of the hydrological characteristics of watersheds through analysis of landscape properties. In this study, we used unsupervised Geogr...

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Main Authors: Sicaud, Eliot, Fortier, Daniel, Dedieu, Jean-Pierre, Franssen, Jan
Format: Other/Unknown Material
Language:English
Published: Zenodo 2023
Subjects:
Online Access:https://doi.org/10.5281/zenodo.10223402
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spelling ftzenodo:oai:zenodo.org:10223402 2024-09-15T17:34:56+00:00 Pairing Remote Sensing and Clustering in Landscape Hydrology for Large-Scale Changes Identification. Applications to the Subarctic Watershed of the George River (Nunavik, Canada). Dataset and Code. Sicaud, Eliot Fortier, Daniel Dedieu, Jean-Pierre Franssen, Jan 2023-07-03 https://doi.org/10.5281/zenodo.10223402 eng eng Zenodo https://doi.org/10.5281/zenodo.7348971 https://doi.org/10.5281/zenodo.10223402 oai:zenodo.org:10223402 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode landscape hydrology remote sensing clustering Subarctic watershed Arctic greening info:eu-repo/semantics/other 2023 ftzenodo https://doi.org/10.5281/zenodo.1022340210.5281/zenodo.7348971 2024-07-25T20:46:54Z For remote and vast northern watersheds, hydrological data are often sparse and incomplete. Landscape hydrology provides useful approaches for the indirect assessment of the hydrological characteristics of watersheds through analysis of landscape properties. In this study, we used unsupervised Geographic Object-Based Image Analysis (GeOBIA) paired with the Fuzzy C-Means (FCM) clustering algorithm to produce seven high-resolution territorial classifications of key remotely sensed hydro-geomorphic metrics for the 1985-2019 time-period, each spanning five years. Our study site is the George River watershed (GRW), a 42,000 km 2 watershed located in Nunavik, northern Quebec (Canada). The subwatersheds within the GRW, used as the objects of the GeOBIA, were classified as a function of their hydrological similarities. Classification results for the period 2015-2019 showed that the GRW is composed of two main types of subwatersheds distributed along a latitudinal gradient, which indicates broad-scale differences in hydrological regimes and water balances across the GRW. Six classifications were computed for the period 1985-2014 to investigate past changes in hydrological regime. The seven-classification time series showed a homogenization of subwatershed types associated to increases in vegetation productivity and in water content in soil and vegetation, mostly concentrated in the northern half of the GRW, which were the major changes occurring in the land cover metrics of the GRW. An increase in vegetation productivity likely contributed to an augmentation in evapotranspiration and may be a primary driver of fundamental shifts in the GRW water balance, potentially explaining a measured decline of about 1 % (∼ 0.16 km 3 y −1 ) in the George River’s discharge since the mid-1970s. Permafrost degradation over the study period also likely affected the hydrological regime and water balance of the GRW. However, the shifts in permafrost extent and active layer thickness remain difficult to detect using remote sensing based ... Other/Unknown Material Active layer thickness Arctic Greening permafrost Subarctic Nunavik Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language English
topic landscape hydrology
remote sensing
clustering
Subarctic watershed
Arctic greening
spellingShingle landscape hydrology
remote sensing
clustering
Subarctic watershed
Arctic greening
Sicaud, Eliot
Fortier, Daniel
Dedieu, Jean-Pierre
Franssen, Jan
Pairing Remote Sensing and Clustering in Landscape Hydrology for Large-Scale Changes Identification. Applications to the Subarctic Watershed of the George River (Nunavik, Canada). Dataset and Code.
topic_facet landscape hydrology
remote sensing
clustering
Subarctic watershed
Arctic greening
description For remote and vast northern watersheds, hydrological data are often sparse and incomplete. Landscape hydrology provides useful approaches for the indirect assessment of the hydrological characteristics of watersheds through analysis of landscape properties. In this study, we used unsupervised Geographic Object-Based Image Analysis (GeOBIA) paired with the Fuzzy C-Means (FCM) clustering algorithm to produce seven high-resolution territorial classifications of key remotely sensed hydro-geomorphic metrics for the 1985-2019 time-period, each spanning five years. Our study site is the George River watershed (GRW), a 42,000 km 2 watershed located in Nunavik, northern Quebec (Canada). The subwatersheds within the GRW, used as the objects of the GeOBIA, were classified as a function of their hydrological similarities. Classification results for the period 2015-2019 showed that the GRW is composed of two main types of subwatersheds distributed along a latitudinal gradient, which indicates broad-scale differences in hydrological regimes and water balances across the GRW. Six classifications were computed for the period 1985-2014 to investigate past changes in hydrological regime. The seven-classification time series showed a homogenization of subwatershed types associated to increases in vegetation productivity and in water content in soil and vegetation, mostly concentrated in the northern half of the GRW, which were the major changes occurring in the land cover metrics of the GRW. An increase in vegetation productivity likely contributed to an augmentation in evapotranspiration and may be a primary driver of fundamental shifts in the GRW water balance, potentially explaining a measured decline of about 1 % (∼ 0.16 km 3 y −1 ) in the George River’s discharge since the mid-1970s. Permafrost degradation over the study period also likely affected the hydrological regime and water balance of the GRW. However, the shifts in permafrost extent and active layer thickness remain difficult to detect using remote sensing based ...
format Other/Unknown Material
author Sicaud, Eliot
Fortier, Daniel
Dedieu, Jean-Pierre
Franssen, Jan
author_facet Sicaud, Eliot
Fortier, Daniel
Dedieu, Jean-Pierre
Franssen, Jan
author_sort Sicaud, Eliot
title Pairing Remote Sensing and Clustering in Landscape Hydrology for Large-Scale Changes Identification. Applications to the Subarctic Watershed of the George River (Nunavik, Canada). Dataset and Code.
title_short Pairing Remote Sensing and Clustering in Landscape Hydrology for Large-Scale Changes Identification. Applications to the Subarctic Watershed of the George River (Nunavik, Canada). Dataset and Code.
title_full Pairing Remote Sensing and Clustering in Landscape Hydrology for Large-Scale Changes Identification. Applications to the Subarctic Watershed of the George River (Nunavik, Canada). Dataset and Code.
title_fullStr Pairing Remote Sensing and Clustering in Landscape Hydrology for Large-Scale Changes Identification. Applications to the Subarctic Watershed of the George River (Nunavik, Canada). Dataset and Code.
title_full_unstemmed Pairing Remote Sensing and Clustering in Landscape Hydrology for Large-Scale Changes Identification. Applications to the Subarctic Watershed of the George River (Nunavik, Canada). Dataset and Code.
title_sort pairing remote sensing and clustering in landscape hydrology for large-scale changes identification. applications to the subarctic watershed of the george river (nunavik, canada). dataset and code.
publisher Zenodo
publishDate 2023
url https://doi.org/10.5281/zenodo.10223402
genre Active layer thickness
Arctic Greening
permafrost
Subarctic
Nunavik
genre_facet Active layer thickness
Arctic Greening
permafrost
Subarctic
Nunavik
op_relation https://doi.org/10.5281/zenodo.7348971
https://doi.org/10.5281/zenodo.10223402
oai:zenodo.org:10223402
op_rights info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
op_doi https://doi.org/10.5281/zenodo.1022340210.5281/zenodo.7348971
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