Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data
Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context of the unique environments o...
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ftdoajarticles:oai:doaj.org/article:8d815ee7aa9b44f59280d2b6fca7b702 2023-05-15T14:43:16+02:00 Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data Adam Collingwood Paul Treitz Francois Charbonneau David M. Atkinson 2014-03-01T00:00:00Z https://doi.org/10.3390/rs6032134 https://doaj.org/article/8d815ee7aa9b44f59280d2b6fca7b702 EN eng MDPI AG http://www.mdpi.com/2072-4292/6/3/2134 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs6032134 https://doaj.org/article/8d815ee7aa9b44f59280d2b6fca7b702 Remote Sensing, Vol 6, Iss 3, Pp 2134-2153 (2014) Arctic synthetic aperture radar phytomass artificial neural network Science Q article 2014 ftdoajarticles https://doi.org/10.3390/rs6032134 2022-12-31T15:19:38Z Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context of the unique environments of the Canadian High Arctic. In this paper, Artificial Neural Networks (ANNs) were created to model the relationship between variables derived from high resolution multi-incidence angle RADARSAT-2 SAR data and optically-derived (GeoEye-1) Soil Adjusted Vegetation Index (SAVI) values. The modeled SAVI values (i.e., from SAR variables) were then used to create maps of above-ground phytomass across the study area. SAVI model results for individual ecological classes of polar semi-desert, mesic heath, wet sedge, and felsenmeer were reasonable, with r2 values of 0.43, 0.43, 0.30, and 0.59, respectively. When the outputs of these models were combined to analyze the relationship between the model output and SAVI as a group, the r2 value was 0.60, with an 8% normalized root mean square error (% of the total range of phytomass values), a positive indicator of a relationship. The above-ground phytomass model also resulted in a very strong relationship (r2 = 0.87) between SAR-modeled and field-measured phytomass. A positive relationship was also found between optically derived SAVI values and field measured phytomass (r2 = 0.79). These relationships demonstrate the utility of SAR data, compared to using optical data alone, for modeling above-ground phytomass in a high arctic environment possessing relatively low levels of vegetation. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Remote Sensing 6 3 2134 2153 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic synthetic aperture radar phytomass artificial neural network Science Q |
spellingShingle |
Arctic synthetic aperture radar phytomass artificial neural network Science Q Adam Collingwood Paul Treitz Francois Charbonneau David M. Atkinson Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data |
topic_facet |
Arctic synthetic aperture radar phytomass artificial neural network Science Q |
description |
Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context of the unique environments of the Canadian High Arctic. In this paper, Artificial Neural Networks (ANNs) were created to model the relationship between variables derived from high resolution multi-incidence angle RADARSAT-2 SAR data and optically-derived (GeoEye-1) Soil Adjusted Vegetation Index (SAVI) values. The modeled SAVI values (i.e., from SAR variables) were then used to create maps of above-ground phytomass across the study area. SAVI model results for individual ecological classes of polar semi-desert, mesic heath, wet sedge, and felsenmeer were reasonable, with r2 values of 0.43, 0.43, 0.30, and 0.59, respectively. When the outputs of these models were combined to analyze the relationship between the model output and SAVI as a group, the r2 value was 0.60, with an 8% normalized root mean square error (% of the total range of phytomass values), a positive indicator of a relationship. The above-ground phytomass model also resulted in a very strong relationship (r2 = 0.87) between SAR-modeled and field-measured phytomass. A positive relationship was also found between optically derived SAVI values and field measured phytomass (r2 = 0.79). These relationships demonstrate the utility of SAR data, compared to using optical data alone, for modeling above-ground phytomass in a high arctic environment possessing relatively low levels of vegetation. |
format |
Article in Journal/Newspaper |
author |
Adam Collingwood Paul Treitz Francois Charbonneau David M. Atkinson |
author_facet |
Adam Collingwood Paul Treitz Francois Charbonneau David M. Atkinson |
author_sort |
Adam Collingwood |
title |
Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data |
title_short |
Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data |
title_full |
Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data |
title_fullStr |
Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data |
title_full_unstemmed |
Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data |
title_sort |
artificial neural network modeling of high arctic phytomass using synthetic aperture radar and multispectral data |
publisher |
MDPI AG |
publishDate |
2014 |
url |
https://doi.org/10.3390/rs6032134 https://doaj.org/article/8d815ee7aa9b44f59280d2b6fca7b702 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Remote Sensing, Vol 6, Iss 3, Pp 2134-2153 (2014) |
op_relation |
http://www.mdpi.com/2072-4292/6/3/2134 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs6032134 https://doaj.org/article/8d815ee7aa9b44f59280d2b6fca7b702 |
op_doi |
https://doi.org/10.3390/rs6032134 |
container_title |
Remote Sensing |
container_volume |
6 |
container_issue |
3 |
container_start_page |
2134 |
op_container_end_page |
2153 |
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1766314957583941632 |