Analyzing the feasibility of a space-borne sensor (SPOT-6) to estimate the height of submerged aquatic vegetation (SAV) in inland waters
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.isprsjprs.2018.07.011 http://hdl.handle.net/11449/176708 |
Resumo: | Remote sensing based approaches have been widely used over the years to monitor and manage submerged aquatic vegetation (SAV) or aquatic macrophytes mainly by mapping their spatial distribution and at the most, modeling SAV biomass. Remote sensing based studies to map SAV heights are rare because of the complexities in modeling water column optical proprieties. SAV height is a proxy for biomass and can be used to estimate plant volume when combined with percent cover. The objective of this study was to explore the feasibility of a satellite sensor to estimate the SAV height distribution in an inland reservoir. Also to test different radiative transfer theory based bio-optical models for estimating SAV heights using SPOT-6 data. The satellite-based multispectral data have rarely been used and SPOT-6 data, to the best of our knowledge, have never been used to estimate SAV heights in inland water bodies. In addition to depth and hydroacoustic data, in situ hyperspectral radiance and irradiance were measured at different depths to compute remote sensing reflectance (Rrs) and the attenuation coefficients (Kd and KLu). Two models, Palandro et al. (2008) and Dierssen et al. (2003), were used to derive bottom reflectance from both in situ and atmospherically corrected SPOT-6 Rrs. Bottom reflectance-based vegetation indices (green-red index, slope index, and simple ratio) were used to estimate SAV heights. Validation was performed using echosounder acquired hydroacoustic data. In situ model calibration produced an R2 of 0.7, however, the validation showed a systematic underestimation of SAV heights and high Root Mean Square Error (RMSE); indicating that there is a greater sensitivity in in situ models to localized variations in water column optical properties. The model based on SPOT-6 data presented higher accuracy, with R2 of 0.54 and RMSE of 0.29 m (NRMSE = 15%). Although the models showed a decreased sensitivity for SAVs at depths greater than 5 m with a height of 1.5 m, the finding nonetheless is significant because it proves that re-calibration of existing bottom reflectance models with more field data can enhance the accuracy to be able to periodically map SAV heights and biomass in inland waters. Although the initial results presented in this study are encouraging, further calibration of the model is required across different species, seasons, sites, and turbidity regime in order to test its application potential. |
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Analyzing the feasibility of a space-borne sensor (SPOT-6) to estimate the height of submerged aquatic vegetation (SAV) in inland watersAttenuationBottom albedoHydroacoustic dataInvasive speciesRadiative transfer modelsReflectanceReservoir managementWater column correctionRemote sensing based approaches have been widely used over the years to monitor and manage submerged aquatic vegetation (SAV) or aquatic macrophytes mainly by mapping their spatial distribution and at the most, modeling SAV biomass. Remote sensing based studies to map SAV heights are rare because of the complexities in modeling water column optical proprieties. SAV height is a proxy for biomass and can be used to estimate plant volume when combined with percent cover. The objective of this study was to explore the feasibility of a satellite sensor to estimate the SAV height distribution in an inland reservoir. Also to test different radiative transfer theory based bio-optical models for estimating SAV heights using SPOT-6 data. The satellite-based multispectral data have rarely been used and SPOT-6 data, to the best of our knowledge, have never been used to estimate SAV heights in inland water bodies. In addition to depth and hydroacoustic data, in situ hyperspectral radiance and irradiance were measured at different depths to compute remote sensing reflectance (Rrs) and the attenuation coefficients (Kd and KLu). Two models, Palandro et al. (2008) and Dierssen et al. (2003), were used to derive bottom reflectance from both in situ and atmospherically corrected SPOT-6 Rrs. Bottom reflectance-based vegetation indices (green-red index, slope index, and simple ratio) were used to estimate SAV heights. Validation was performed using echosounder acquired hydroacoustic data. In situ model calibration produced an R2 of 0.7, however, the validation showed a systematic underestimation of SAV heights and high Root Mean Square Error (RMSE); indicating that there is a greater sensitivity in in situ models to localized variations in water column optical properties. The model based on SPOT-6 data presented higher accuracy, with R2 of 0.54 and RMSE of 0.29 m (NRMSE = 15%). Although the models showed a decreased sensitivity for SAVs at depths greater than 5 m with a height of 1.5 m, the finding nonetheless is significant because it proves that re-calibration of existing bottom reflectance models with more field data can enhance the accuracy to be able to periodically map SAV heights and biomass in inland waters. Although the initial results presented in this study are encouraging, further calibration of the model is required across different species, seasons, sites, and turbidity regime in order to test its application potential.Department of Cartography São Paulo State University (UNESP)Center for Geospatial Research Department of Geography University of Georgia (UGA)Department of Environment Engineering São Paulo State University (UNESP)Department of Cartography São Paulo State University (UNESP)Department of Environment Engineering São Paulo State University (UNESP)Universidade Estadual Paulista (Unesp)University of Georgia (UGA)Rotta, Luiz Henrique S. [UNESP]Mishra, Deepak R.Watanabe, Fernanda S.Y. [UNESP]Rodrigues, Thanan W.P [UNESP]Alcântara, Enner H. [UNESP]Imai, Nilton N. [UNESP]2018-12-11T17:22:09Z2018-12-11T17:22:09Z2018-10-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article341-356application/pdfhttp://dx.doi.org/10.1016/j.isprsjprs.2018.07.011ISPRS Journal of Photogrammetry and Remote Sensing, v. 144, p. 341-356.0924-2716http://hdl.handle.net/11449/17670810.1016/j.isprsjprs.2018.07.0112-s2.0-850514107942-s2.0-85051410794.pdf66913103944104900000-0002-8077-2865Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengISPRS Journal of Photogrammetry and Remote Sensing3,169info:eu-repo/semantics/openAccess2024-01-23T07:11:48Zoai:repositorio.unesp.br:11449/176708Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-01-23T07:11:48Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Analyzing the feasibility of a space-borne sensor (SPOT-6) to estimate the height of submerged aquatic vegetation (SAV) in inland waters |
title |
Analyzing the feasibility of a space-borne sensor (SPOT-6) to estimate the height of submerged aquatic vegetation (SAV) in inland waters |
spellingShingle |
Analyzing the feasibility of a space-borne sensor (SPOT-6) to estimate the height of submerged aquatic vegetation (SAV) in inland waters Rotta, Luiz Henrique S. [UNESP] Attenuation Bottom albedo Hydroacoustic data Invasive species Radiative transfer models Reflectance Reservoir management Water column correction |
title_short |
Analyzing the feasibility of a space-borne sensor (SPOT-6) to estimate the height of submerged aquatic vegetation (SAV) in inland waters |
title_full |
Analyzing the feasibility of a space-borne sensor (SPOT-6) to estimate the height of submerged aquatic vegetation (SAV) in inland waters |
title_fullStr |
Analyzing the feasibility of a space-borne sensor (SPOT-6) to estimate the height of submerged aquatic vegetation (SAV) in inland waters |
title_full_unstemmed |
Analyzing the feasibility of a space-borne sensor (SPOT-6) to estimate the height of submerged aquatic vegetation (SAV) in inland waters |
title_sort |
Analyzing the feasibility of a space-borne sensor (SPOT-6) to estimate the height of submerged aquatic vegetation (SAV) in inland waters |
author |
Rotta, Luiz Henrique S. [UNESP] |
author_facet |
Rotta, Luiz Henrique S. [UNESP] Mishra, Deepak R. Watanabe, Fernanda S.Y. [UNESP] Rodrigues, Thanan W.P [UNESP] Alcântara, Enner H. [UNESP] Imai, Nilton N. [UNESP] |
author_role |
author |
author2 |
Mishra, Deepak R. Watanabe, Fernanda S.Y. [UNESP] Rodrigues, Thanan W.P [UNESP] Alcântara, Enner H. [UNESP] Imai, Nilton N. [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) University of Georgia (UGA) |
dc.contributor.author.fl_str_mv |
Rotta, Luiz Henrique S. [UNESP] Mishra, Deepak R. Watanabe, Fernanda S.Y. [UNESP] Rodrigues, Thanan W.P [UNESP] Alcântara, Enner H. [UNESP] Imai, Nilton N. [UNESP] |
dc.subject.por.fl_str_mv |
Attenuation Bottom albedo Hydroacoustic data Invasive species Radiative transfer models Reflectance Reservoir management Water column correction |
topic |
Attenuation Bottom albedo Hydroacoustic data Invasive species Radiative transfer models Reflectance Reservoir management Water column correction |
description |
Remote sensing based approaches have been widely used over the years to monitor and manage submerged aquatic vegetation (SAV) or aquatic macrophytes mainly by mapping their spatial distribution and at the most, modeling SAV biomass. Remote sensing based studies to map SAV heights are rare because of the complexities in modeling water column optical proprieties. SAV height is a proxy for biomass and can be used to estimate plant volume when combined with percent cover. The objective of this study was to explore the feasibility of a satellite sensor to estimate the SAV height distribution in an inland reservoir. Also to test different radiative transfer theory based bio-optical models for estimating SAV heights using SPOT-6 data. The satellite-based multispectral data have rarely been used and SPOT-6 data, to the best of our knowledge, have never been used to estimate SAV heights in inland water bodies. In addition to depth and hydroacoustic data, in situ hyperspectral radiance and irradiance were measured at different depths to compute remote sensing reflectance (Rrs) and the attenuation coefficients (Kd and KLu). Two models, Palandro et al. (2008) and Dierssen et al. (2003), were used to derive bottom reflectance from both in situ and atmospherically corrected SPOT-6 Rrs. Bottom reflectance-based vegetation indices (green-red index, slope index, and simple ratio) were used to estimate SAV heights. Validation was performed using echosounder acquired hydroacoustic data. In situ model calibration produced an R2 of 0.7, however, the validation showed a systematic underestimation of SAV heights and high Root Mean Square Error (RMSE); indicating that there is a greater sensitivity in in situ models to localized variations in water column optical properties. The model based on SPOT-6 data presented higher accuracy, with R2 of 0.54 and RMSE of 0.29 m (NRMSE = 15%). Although the models showed a decreased sensitivity for SAVs at depths greater than 5 m with a height of 1.5 m, the finding nonetheless is significant because it proves that re-calibration of existing bottom reflectance models with more field data can enhance the accuracy to be able to periodically map SAV heights and biomass in inland waters. Although the initial results presented in this study are encouraging, further calibration of the model is required across different species, seasons, sites, and turbidity regime in order to test its application potential. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-11T17:22:09Z 2018-12-11T17:22:09Z 2018-10-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.isprsjprs.2018.07.011 ISPRS Journal of Photogrammetry and Remote Sensing, v. 144, p. 341-356. 0924-2716 http://hdl.handle.net/11449/176708 10.1016/j.isprsjprs.2018.07.011 2-s2.0-85051410794 2-s2.0-85051410794.pdf 6691310394410490 0000-0002-8077-2865 |
url |
http://dx.doi.org/10.1016/j.isprsjprs.2018.07.011 http://hdl.handle.net/11449/176708 |
identifier_str_mv |
ISPRS Journal of Photogrammetry and Remote Sensing, v. 144, p. 341-356. 0924-2716 10.1016/j.isprsjprs.2018.07.011 2-s2.0-85051410794 2-s2.0-85051410794.pdf 6691310394410490 0000-0002-8077-2865 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
ISPRS Journal of Photogrammetry and Remote Sensing 3,169 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
341-356 application/pdf |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1799965707717312512 |