Home > Research > Publications & Outputs > Fine temporal resolution satellite sensors with...

Links

Text available via DOI:

View graph of relations

Fine temporal resolution satellite sensors with global coverage: an opportunity for landscape ecologists

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

Standard

Fine temporal resolution satellite sensors with global coverage: an opportunity for landscape ecologists. / Pazúr, R.; Price, B.; Atkinson, P.M.
In: Landscape Ecology, Vol. 36, 31.08.2021, p. 2199-2213.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

APA

Vancouver

Pazúr R, Price B, Atkinson PM. Fine temporal resolution satellite sensors with global coverage: an opportunity for landscape ecologists. Landscape Ecology. 2021 Aug 31;36:2199-2213. Epub 2021 Jul 16. doi: 10.1007/s10980-021-01303-w

Author

Bibtex

@article{3586656cd9c44e92a219312184872803,
title = "Fine temporal resolution satellite sensors with global coverage: an opportunity for landscape ecologists",
abstract = "Context: Open data policies and accessible computation platforms allow efficient extraction of information from remote sensing data for landscape research. Landscape ecology is strongly influenced by remote sensing, and the value of fine resolution temporal information for characterising landscapes is under-explored. Objectives: We highlighted the importance of temporal information extracted from remote sensing data gathered over a period of time for landscape research. A case study approach was used to show how time-series information can benefit the mapping of land cover and landscape elements in a heterogeneous landscape dominated by agricultural land use. Methods: We constructed four composite images of the study area, each incorporating different levels of temporal information. The images either represent a single date or summarise temporal information into single values as the median of spectral bands or vegetation indices. Random forest and k-means clustering methods were used to classify the images. Results: The overall accuracy of the landscape classifications ranged between 0.3 to 0.8, increasing substantially when including temporal information, for mapping both land cover and small landscape elements. Using temporal information and a RF-based classification it was generally possible to map crop and forest types. The size of landscape elements was overestimated, although the clustering model predicted elements close to their true size and complexity. Conclusions: The approach highlights the importance of temporal resolution for landscape ecology research. The easy-to-implement methodology offers an opportunity for landscape ecologists to increase the accuracy of landscape mapping and identify ecologically important landscape elements that might otherwise be missed. {\textcopyright} 2021, The Author(s).",
keywords = "Accuracy, Land cover, Landscape elements, Phenology, Sentinel-2, Time-series",
author = "R. Paz{\'u}r and B. Price and P.M. Atkinson",
year = "2021",
month = aug,
day = "31",
doi = "10.1007/s10980-021-01303-w",
language = "English",
volume = "36",
pages = "2199--2213",
journal = "Landscape Ecology",
issn = "0921-2973",
publisher = "Springer Netherlands",

}

RIS

TY - JOUR

T1 - Fine temporal resolution satellite sensors with global coverage

T2 - an opportunity for landscape ecologists

AU - Pazúr, R.

AU - Price, B.

AU - Atkinson, P.M.

PY - 2021/8/31

Y1 - 2021/8/31

N2 - Context: Open data policies and accessible computation platforms allow efficient extraction of information from remote sensing data for landscape research. Landscape ecology is strongly influenced by remote sensing, and the value of fine resolution temporal information for characterising landscapes is under-explored. Objectives: We highlighted the importance of temporal information extracted from remote sensing data gathered over a period of time for landscape research. A case study approach was used to show how time-series information can benefit the mapping of land cover and landscape elements in a heterogeneous landscape dominated by agricultural land use. Methods: We constructed four composite images of the study area, each incorporating different levels of temporal information. The images either represent a single date or summarise temporal information into single values as the median of spectral bands or vegetation indices. Random forest and k-means clustering methods were used to classify the images. Results: The overall accuracy of the landscape classifications ranged between 0.3 to 0.8, increasing substantially when including temporal information, for mapping both land cover and small landscape elements. Using temporal information and a RF-based classification it was generally possible to map crop and forest types. The size of landscape elements was overestimated, although the clustering model predicted elements close to their true size and complexity. Conclusions: The approach highlights the importance of temporal resolution for landscape ecology research. The easy-to-implement methodology offers an opportunity for landscape ecologists to increase the accuracy of landscape mapping and identify ecologically important landscape elements that might otherwise be missed. © 2021, The Author(s).

AB - Context: Open data policies and accessible computation platforms allow efficient extraction of information from remote sensing data for landscape research. Landscape ecology is strongly influenced by remote sensing, and the value of fine resolution temporal information for characterising landscapes is under-explored. Objectives: We highlighted the importance of temporal information extracted from remote sensing data gathered over a period of time for landscape research. A case study approach was used to show how time-series information can benefit the mapping of land cover and landscape elements in a heterogeneous landscape dominated by agricultural land use. Methods: We constructed four composite images of the study area, each incorporating different levels of temporal information. The images either represent a single date or summarise temporal information into single values as the median of spectral bands or vegetation indices. Random forest and k-means clustering methods were used to classify the images. Results: The overall accuracy of the landscape classifications ranged between 0.3 to 0.8, increasing substantially when including temporal information, for mapping both land cover and small landscape elements. Using temporal information and a RF-based classification it was generally possible to map crop and forest types. The size of landscape elements was overestimated, although the clustering model predicted elements close to their true size and complexity. Conclusions: The approach highlights the importance of temporal resolution for landscape ecology research. The easy-to-implement methodology offers an opportunity for landscape ecologists to increase the accuracy of landscape mapping and identify ecologically important landscape elements that might otherwise be missed. © 2021, The Author(s).

KW - Accuracy

KW - Land cover

KW - Landscape elements

KW - Phenology

KW - Sentinel-2

KW - Time-series

U2 - 10.1007/s10980-021-01303-w

DO - 10.1007/s10980-021-01303-w

M3 - Journal article

VL - 36

SP - 2199

EP - 2213

JO - Landscape Ecology

JF - Landscape Ecology

SN - 0921-2973

ER -