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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Online Access: | https://dx.doi.org/10.5281/zenodo.10223402 https://zenodo.org/doi/10.5281/zenodo.10223402 |
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ftdatacite:10.5281/zenodo.10223402 2023-12-31T10:02:27+01:00 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 https://dx.doi.org/10.5281/zenodo.10223402 https://zenodo.org/doi/10.5281/zenodo.10223402 en eng Zenodo https://dx.doi.org/10.5281/zenodo.7348971 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 landscape hydrology remote sensing clustering Subarctic watershed Arctic greening Dataset dataset 2023 ftdatacite https://doi.org/10.5281/zenodo.1022340210.5281/zenodo.7348971 2023-12-01T12:21:08Z 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 km2 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 ... Dataset Arctic Greening Arctic Subarctic Nunavik DataCite Metadata Store (German National Library of Science and Technology) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
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 km2 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 ... |
format |
Dataset |
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://dx.doi.org/10.5281/zenodo.10223402 https://zenodo.org/doi/10.5281/zenodo.10223402 |
genre |
Arctic Greening Arctic Subarctic Nunavik |
genre_facet |
Arctic Greening Arctic Subarctic Nunavik |
op_relation |
https://dx.doi.org/10.5281/zenodo.7348971 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.5281/zenodo.1022340210.5281/zenodo.7348971 |
_version_ |
1786811208676933632 |