Multi-Scale Mapping and Analysis of Broadleaf Species Distribution Using Remotely Piloted Aircraft and Satellite Imagery
Tree species mapping from the individual crown to landscape scales provides crucial information on the diversity and richness of forest ecosystems, supporting major conservation decisions under ongoing climate change. With the emergence of Remote Piloted Aircraft (RPA), high spatial resolution datas...
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/24/4809 |
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| author | Aishwarya Chandrasekaran Joseph P. Hupy Guofan Shao |
| author_facet | Aishwarya Chandrasekaran Joseph P. Hupy Guofan Shao |
| author_sort | Aishwarya Chandrasekaran |
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| description | Tree species mapping from the individual crown to landscape scales provides crucial information on the diversity and richness of forest ecosystems, supporting major conservation decisions under ongoing climate change. With the emergence of Remote Piloted Aircraft (RPA), high spatial resolution datasets can be obtained and analyzed to inherently improve the current understanding of broadleaf tree species distribution. The utility of RPA for mapping broadleaf species at broader scales using satellite data needs to be explored. This study investigates the use of RPA RGB imagery captured during peak fall foliage to leverage coloration commonly exhibited by different broadleaf tree species during phenology transition to delineate individual tree crowns and map species distribution. Initially, a two-step hybrid segmentation procedure was designed to delineate tree crowns for two broadleaf forests using RPA imagery collected during the fall season. With the tree crowns, a subsequent Object-based Random Forest (ORF) model was tested for classifying common and economically important broadleaf tree species groups. The classified map was further utilized to improve ground reference data for mapping species distribution at the stand and landscape scales using multispectral satellite imagery (1.4 m to 10 m). The results indicated an improvement in the overall accuracy of 0.13 (from 0.68 to 0.81) and a MICE metric of 0.14 (from 0.61 to 0.75) using reference samples derived from RPA data. The results of this preliminary study are promising in utilizing RPA for multi-scale mapping of broadleaf tree species effectively. |
| format | Article |
| id | doaj-art-d2d11c8e706c49eb8585f94e1f322d64 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-d2d11c8e706c49eb8585f94e1f322d642025-08-20T02:01:14ZengMDPI AGRemote Sensing2072-42922024-12-011624480910.3390/rs16244809Multi-Scale Mapping and Analysis of Broadleaf Species Distribution Using Remotely Piloted Aircraft and Satellite ImageryAishwarya Chandrasekaran0Joseph P. Hupy1Guofan Shao2Department of Environment and Society, Utah State University, Logan, UT 84322, USASchool of Aviation Technology and Transportation, Purdue University, West Lafayette, IN 47907, USADepartment of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907, USATree species mapping from the individual crown to landscape scales provides crucial information on the diversity and richness of forest ecosystems, supporting major conservation decisions under ongoing climate change. With the emergence of Remote Piloted Aircraft (RPA), high spatial resolution datasets can be obtained and analyzed to inherently improve the current understanding of broadleaf tree species distribution. The utility of RPA for mapping broadleaf species at broader scales using satellite data needs to be explored. This study investigates the use of RPA RGB imagery captured during peak fall foliage to leverage coloration commonly exhibited by different broadleaf tree species during phenology transition to delineate individual tree crowns and map species distribution. Initially, a two-step hybrid segmentation procedure was designed to delineate tree crowns for two broadleaf forests using RPA imagery collected during the fall season. With the tree crowns, a subsequent Object-based Random Forest (ORF) model was tested for classifying common and economically important broadleaf tree species groups. The classified map was further utilized to improve ground reference data for mapping species distribution at the stand and landscape scales using multispectral satellite imagery (1.4 m to 10 m). The results indicated an improvement in the overall accuracy of 0.13 (from 0.68 to 0.81) and a MICE metric of 0.14 (from 0.61 to 0.75) using reference samples derived from RPA data. The results of this preliminary study are promising in utilizing RPA for multi-scale mapping of broadleaf tree species effectively.https://www.mdpi.com/2072-4292/16/24/4809multi-scale analysisremotely piloted aircraftbroadleafhybrid segmentationobject-based classificationstand and landscape scales |
| spellingShingle | Aishwarya Chandrasekaran Joseph P. Hupy Guofan Shao Multi-Scale Mapping and Analysis of Broadleaf Species Distribution Using Remotely Piloted Aircraft and Satellite Imagery Remote Sensing multi-scale analysis remotely piloted aircraft broadleaf hybrid segmentation object-based classification stand and landscape scales |
| title | Multi-Scale Mapping and Analysis of Broadleaf Species Distribution Using Remotely Piloted Aircraft and Satellite Imagery |
| title_full | Multi-Scale Mapping and Analysis of Broadleaf Species Distribution Using Remotely Piloted Aircraft and Satellite Imagery |
| title_fullStr | Multi-Scale Mapping and Analysis of Broadleaf Species Distribution Using Remotely Piloted Aircraft and Satellite Imagery |
| title_full_unstemmed | Multi-Scale Mapping and Analysis of Broadleaf Species Distribution Using Remotely Piloted Aircraft and Satellite Imagery |
| title_short | Multi-Scale Mapping and Analysis of Broadleaf Species Distribution Using Remotely Piloted Aircraft and Satellite Imagery |
| title_sort | multi scale mapping and analysis of broadleaf species distribution using remotely piloted aircraft and satellite imagery |
| topic | multi-scale analysis remotely piloted aircraft broadleaf hybrid segmentation object-based classification stand and landscape scales |
| url | https://www.mdpi.com/2072-4292/16/24/4809 |
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