Eintrag weiter verarbeiten
UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION
Gespeichert in:
| Zeitschriftentitel: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
|---|---|
| Personen und Körperschaften: | , , |
| In: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1/W4, 2015, S. 249-256 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Copernicus GmbH
|
| Schlagwörter: |
| author_facet |
Marcaccio, J. V. Markle, C. E. Chow-Fraser, P. Marcaccio, J. V. Markle, C. E. Chow-Fraser, P. |
|---|---|
| author |
Marcaccio, J. V. Markle, C. E. Chow-Fraser, P. |
| spellingShingle |
Marcaccio, J. V. Markle, C. E. Chow-Fraser, P. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION General Earth and Planetary Sciences General Environmental Science |
| author_sort |
marcaccio, j. v. |
| spelling |
Marcaccio, J. V. Markle, C. E. Chow-Fraser, P. 2194-9034 Copernicus GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.5194/isprsarchives-xl-1-w4-249-2015 <jats:p>Abstract. With recent advances in technology, personal aerial imagery acquired with unmanned aerial vehicles (UAVs) has transformed the way ecologists can map seasonal changes in wetland habitat. Here, we use a multi-rotor (consumer quad-copter, the DJI Phantom 2 Vision+) UAV to acquire a high-resolution (< 8 cm) composite photo of a coastal wetland in summer 2014. Using validation data collected in the field, we determine if a UAV image and SWOOP (Southwestern Ontario Orthoimagery Project) image (collected in spring 2010) differ in their classification of type of dominant vegetation type and percent cover of three plant classes: submerged aquatic vegetation, floating aquatic vegetation, and emergent vegetation. The UAV imagery was more accurate than available SWOOP imagery for mapping percent cover of submergent and floating vegetation categories, but both were able to accurately determine the dominant vegetation type and percent cover of emergent vegetation. Our results underscore the value and potential for affordable UAVs (complete quad-copter system < $3,000 CAD) to revolutionize the way ecologists obtain imagery and conduct field research. In Canada, new UAV regulations make this an easy and affordable way to obtain multiple high-resolution images of small (< 1.0 km2) wetlands, or portions of larger wetlands throughout a year. </jats:p> UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| doi_str_mv |
10.5194/isprsarchives-xl-1-w4-249-2015 |
| facet_avail |
Online Free |
| format |
ElectronicArticle |
| fullrecord |
blob:ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuNTE5NC9pc3Byc2FyY2hpdmVzLXhsLTEtdzQtMjQ5LTIwMTU |
| id |
ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuNTE5NC9pc3Byc2FyY2hpdmVzLXhsLTEtdzQtMjQ5LTIwMTU |
| institution |
DE-Zwi2 DE-D161 DE-Gla1 DE-Zi4 DE-15 DE-Rs1 DE-Pl11 DE-105 DE-14 DE-Ch1 DE-L229 DE-D275 DE-Bn3 DE-Brt1 |
| imprint |
Copernicus GmbH, 2015 |
| imprint_str_mv |
Copernicus GmbH, 2015 |
| issn |
2194-9034 |
| issn_str_mv |
2194-9034 |
| language |
English |
| mega_collection |
Copernicus GmbH (CrossRef) |
| match_str |
marcaccio2015unmannedaerialvehiclesproducehighresolutionseasonallyrelevantimageryforclassifyingwetlandvegetation |
| publishDateSort |
2015 |
| publisher |
Copernicus GmbH |
| recordtype |
ai |
| record_format |
ai |
| series |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| source_id |
49 |
| title |
UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION |
| title_unstemmed |
UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION |
| title_full |
UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION |
| title_fullStr |
UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION |
| title_full_unstemmed |
UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION |
| title_short |
UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION |
| title_sort |
unmanned aerial vehicles produce high-resolution, seasonally-relevant imagery for classifying wetland vegetation |
| topic |
General Earth and Planetary Sciences General Environmental Science |
| url |
http://dx.doi.org/10.5194/isprsarchives-xl-1-w4-249-2015 |
| publishDate |
2015 |
| physical |
249-256 |
| description |
<jats:p>Abstract. With recent advances in technology, personal aerial imagery acquired with unmanned aerial vehicles (UAVs) has transformed the way ecologists can map seasonal changes in wetland habitat. Here, we use a multi-rotor (consumer quad-copter, the DJI Phantom 2 Vision+) UAV to acquire a high-resolution (< 8 cm) composite photo of a coastal wetland in summer 2014. Using validation data collected in the field, we determine if a UAV image and SWOOP (Southwestern Ontario Orthoimagery Project) image (collected in spring 2010) differ in their classification of type of dominant vegetation type and percent cover of three plant classes: submerged aquatic vegetation, floating aquatic vegetation, and emergent vegetation. The UAV imagery was more accurate than available SWOOP imagery for mapping percent cover of submergent and floating vegetation categories, but both were able to accurately determine the dominant vegetation type and percent cover of emergent vegetation. Our results underscore the value and potential for affordable UAVs (complete quad-copter system < $3,000 CAD) to revolutionize the way ecologists obtain imagery and conduct field research. In Canada, new UAV regulations make this an easy and affordable way to obtain multiple high-resolution images of small (< 1.0 km2) wetlands, or portions of larger wetlands throughout a year.
</jats:p> |
| container_start_page |
249 |
| container_title |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| container_volume |
XL-1/W4 |
| format_de105 |
Article, E-Article |
| format_de14 |
Article, E-Article |
| format_de15 |
Article, E-Article |
| format_de520 |
Article, E-Article |
| format_de540 |
Article, E-Article |
| format_dech1 |
Article, E-Article |
| format_ded117 |
Article, E-Article |
| format_degla1 |
E-Article |
| format_del152 |
Buch |
| format_del189 |
Article, E-Article |
| format_dezi4 |
Article |
| format_dezwi2 |
Article, E-Article |
| format_finc |
Article, E-Article |
| format_nrw |
Article, E-Article |
| _version_ |
1792345224750563335 |
| geogr_code |
not assigned |
| last_indexed |
2024-03-01T17:19:34.388Z |
| geogr_code_person |
not assigned |
| openURL |
url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fvufind.svn.sourceforge.net%3Agenerator&rft.title=UNMANNED+AERIAL+VEHICLES+PRODUCE+HIGH-RESOLUTION%2C+SEASONALLY-RELEVANT+IMAGERY+FOR+CLASSIFYING+WETLAND+VEGETATION&rft.date=2015-08-26&genre=article&issn=2194-9034&volume=XL-1%2FW4&spage=249&epage=256&pages=249-256&jtitle=The+International+Archives+of+the+Photogrammetry%2C+Remote+Sensing+and+Spatial+Information+Sciences&atitle=UNMANNED+AERIAL+VEHICLES+PRODUCE+HIGH-RESOLUTION%2C+SEASONALLY-RELEVANT+IMAGERY+FOR+CLASSIFYING+WETLAND+VEGETATION&aulast=Chow-Fraser&aufirst=P.&rft_id=info%3Adoi%2F10.5194%2Fisprsarchives-xl-1-w4-249-2015&rft.language%5B0%5D=eng |
| SOLR | |
| _version_ | 1792345224750563335 |
| author | Marcaccio, J. V., Markle, C. E., Chow-Fraser, P. |
| author_facet | Marcaccio, J. V., Markle, C. E., Chow-Fraser, P., Marcaccio, J. V., Markle, C. E., Chow-Fraser, P. |
| author_sort | marcaccio, j. v. |
| container_start_page | 249 |
| container_title | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| container_volume | XL-1/W4 |
| description | <jats:p>Abstract. With recent advances in technology, personal aerial imagery acquired with unmanned aerial vehicles (UAVs) has transformed the way ecologists can map seasonal changes in wetland habitat. Here, we use a multi-rotor (consumer quad-copter, the DJI Phantom 2 Vision+) UAV to acquire a high-resolution (< 8 cm) composite photo of a coastal wetland in summer 2014. Using validation data collected in the field, we determine if a UAV image and SWOOP (Southwestern Ontario Orthoimagery Project) image (collected in spring 2010) differ in their classification of type of dominant vegetation type and percent cover of three plant classes: submerged aquatic vegetation, floating aquatic vegetation, and emergent vegetation. The UAV imagery was more accurate than available SWOOP imagery for mapping percent cover of submergent and floating vegetation categories, but both were able to accurately determine the dominant vegetation type and percent cover of emergent vegetation. Our results underscore the value and potential for affordable UAVs (complete quad-copter system < $3,000 CAD) to revolutionize the way ecologists obtain imagery and conduct field research. In Canada, new UAV regulations make this an easy and affordable way to obtain multiple high-resolution images of small (< 1.0 km2) wetlands, or portions of larger wetlands throughout a year. </jats:p> |
| doi_str_mv | 10.5194/isprsarchives-xl-1-w4-249-2015 |
| facet_avail | Online, Free |
| format | ElectronicArticle |
| format_de105 | Article, E-Article |
| format_de14 | Article, E-Article |
| format_de15 | Article, E-Article |
| format_de520 | Article, E-Article |
| format_de540 | Article, E-Article |
| format_dech1 | Article, E-Article |
| format_ded117 | Article, E-Article |
| format_degla1 | E-Article |
| format_del152 | Buch |
| format_del189 | Article, E-Article |
| format_dezi4 | Article |
| format_dezwi2 | Article, E-Article |
| format_finc | Article, E-Article |
| format_nrw | Article, E-Article |
| geogr_code | not assigned |
| geogr_code_person | not assigned |
| id | ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuNTE5NC9pc3Byc2FyY2hpdmVzLXhsLTEtdzQtMjQ5LTIwMTU |
| imprint | Copernicus GmbH, 2015 |
| imprint_str_mv | Copernicus GmbH, 2015 |
| institution | DE-Zwi2, DE-D161, DE-Gla1, DE-Zi4, DE-15, DE-Rs1, DE-Pl11, DE-105, DE-14, DE-Ch1, DE-L229, DE-D275, DE-Bn3, DE-Brt1 |
| issn | 2194-9034 |
| issn_str_mv | 2194-9034 |
| language | English |
| last_indexed | 2024-03-01T17:19:34.388Z |
| match_str | marcaccio2015unmannedaerialvehiclesproducehighresolutionseasonallyrelevantimageryforclassifyingwetlandvegetation |
| mega_collection | Copernicus GmbH (CrossRef) |
| physical | 249-256 |
| publishDate | 2015 |
| publishDateSort | 2015 |
| publisher | Copernicus GmbH |
| record_format | ai |
| recordtype | ai |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| source_id | 49 |
| spelling | Marcaccio, J. V. Markle, C. E. Chow-Fraser, P. 2194-9034 Copernicus GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.5194/isprsarchives-xl-1-w4-249-2015 <jats:p>Abstract. With recent advances in technology, personal aerial imagery acquired with unmanned aerial vehicles (UAVs) has transformed the way ecologists can map seasonal changes in wetland habitat. Here, we use a multi-rotor (consumer quad-copter, the DJI Phantom 2 Vision+) UAV to acquire a high-resolution (< 8 cm) composite photo of a coastal wetland in summer 2014. Using validation data collected in the field, we determine if a UAV image and SWOOP (Southwestern Ontario Orthoimagery Project) image (collected in spring 2010) differ in their classification of type of dominant vegetation type and percent cover of three plant classes: submerged aquatic vegetation, floating aquatic vegetation, and emergent vegetation. The UAV imagery was more accurate than available SWOOP imagery for mapping percent cover of submergent and floating vegetation categories, but both were able to accurately determine the dominant vegetation type and percent cover of emergent vegetation. Our results underscore the value and potential for affordable UAVs (complete quad-copter system < $3,000 CAD) to revolutionize the way ecologists obtain imagery and conduct field research. In Canada, new UAV regulations make this an easy and affordable way to obtain multiple high-resolution images of small (< 1.0 km2) wetlands, or portions of larger wetlands throughout a year. </jats:p> UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spellingShingle | Marcaccio, J. V., Markle, C. E., Chow-Fraser, P., The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION, General Earth and Planetary Sciences, General Environmental Science |
| title | UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION |
| title_full | UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION |
| title_fullStr | UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION |
| title_full_unstemmed | UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION |
| title_short | UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION |
| title_sort | unmanned aerial vehicles produce high-resolution, seasonally-relevant imagery for classifying wetland vegetation |
| title_unstemmed | UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION |
| topic | General Earth and Planetary Sciences, General Environmental Science |
| url | http://dx.doi.org/10.5194/isprsarchives-xl-1-w4-249-2015 |