Using Optical Satellite Data and Airborne Lidar Data for a Nationwide Sampling Survey
A workflow for combining airborne lidar, optical satellite data and National Forest Inventory (NFI) plots for cost efficient operational mapping of a nationwide sample of 5 × 5 km squares in the National Inventory of Landscapes in Sweden (NILS) landscape inventory in Sweden is presented. Since the a...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/7/4/4253 |
| _version_ | 1818320256888733696 |
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| author | Nils Lindgren Pernilla Christensen Björn Nilsson Marianne Åkerholm Anna Allard Heather Reese Håkan Olsson |
| author_facet | Nils Lindgren Pernilla Christensen Björn Nilsson Marianne Åkerholm Anna Allard Heather Reese Håkan Olsson |
| author_sort | Nils Lindgren |
| collection | DOAJ |
| description | A workflow for combining airborne lidar, optical satellite data and National Forest Inventory (NFI) plots for cost efficient operational mapping of a nationwide sample of 5 × 5 km squares in the National Inventory of Landscapes in Sweden (NILS) landscape inventory in Sweden is presented. Since the areas where both satellite data and lidar data have a common data quality are limited, and impose a constraint on the number of available NFI plots, it is not feasible to perform classifications in a single step. Instead a stratified approach where canopy cover and canopy height are first predicted from lidar data trained with NFI plots is proposed. From the lidar predictions a forest stratum is defined as grid cells with more than 3 m mean tree height and more than 10% vertical canopy cover, the remaining grid cells are defined as open land. Both forest and open land are then classified into broad vegetation classes using optical satellite data. The classification of open land is trained with aerial photo interpretation and the classification of the forest stratum is trained with a new set of NFI plots. The result is a rational procedure for nationwide sample based vegetation characterization. |
| first_indexed | 2024-12-13T10:22:07Z |
| format | Article |
| id | doaj.art-6f6aeb7ced1745d2a8716dc1fade4538 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-13T10:22:07Z |
| publishDate | 2015-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6f6aeb7ced1745d2a8716dc1fade45382022-12-21T23:51:09ZengMDPI AGRemote Sensing2072-42922015-04-01744253426710.3390/rs70404253rs70404253Using Optical Satellite Data and Airborne Lidar Data for a Nationwide Sampling SurveyNils Lindgren0Pernilla Christensen1Björn Nilsson2Marianne Åkerholm3Anna Allard4Heather Reese5Håkan Olsson6Department of Forest Resource Management, Swedish University of Agricultural Sciences, SE 90183 Umeå, SwedenDepartment of Forest Resource Management, Swedish University of Agricultural Sciences, SE 90183 Umeå, SwedenDepartment of Forest Resource Management, Swedish University of Agricultural Sciences, SE 90183 Umeå, SwedenDepartment of Forest Resource Management, Swedish University of Agricultural Sciences, SE 90183 Umeå, SwedenDepartment of Forest Resource Management, Swedish University of Agricultural Sciences, SE 90183 Umeå, SwedenDepartment of Forest Resource Management, Swedish University of Agricultural Sciences, SE 90183 Umeå, SwedenDepartment of Forest Resource Management, Swedish University of Agricultural Sciences, SE 90183 Umeå, SwedenA workflow for combining airborne lidar, optical satellite data and National Forest Inventory (NFI) plots for cost efficient operational mapping of a nationwide sample of 5 × 5 km squares in the National Inventory of Landscapes in Sweden (NILS) landscape inventory in Sweden is presented. Since the areas where both satellite data and lidar data have a common data quality are limited, and impose a constraint on the number of available NFI plots, it is not feasible to perform classifications in a single step. Instead a stratified approach where canopy cover and canopy height are first predicted from lidar data trained with NFI plots is proposed. From the lidar predictions a forest stratum is defined as grid cells with more than 3 m mean tree height and more than 10% vertical canopy cover, the remaining grid cells are defined as open land. Both forest and open land are then classified into broad vegetation classes using optical satellite data. The classification of open land is trained with aerial photo interpretation and the classification of the forest stratum is trained with a new set of NFI plots. The result is a rational procedure for nationwide sample based vegetation characterization.http://www.mdpi.com/2072-4292/7/4/4253lidaroperationalLandsatnationwidesamplingmapping |
| spellingShingle | Nils Lindgren Pernilla Christensen Björn Nilsson Marianne Åkerholm Anna Allard Heather Reese Håkan Olsson Using Optical Satellite Data and Airborne Lidar Data for a Nationwide Sampling Survey Remote Sensing lidar operational Landsat nationwide sampling mapping |
| title | Using Optical Satellite Data and Airborne Lidar Data for a Nationwide Sampling Survey |
| title_full | Using Optical Satellite Data and Airborne Lidar Data for a Nationwide Sampling Survey |
| title_fullStr | Using Optical Satellite Data and Airborne Lidar Data for a Nationwide Sampling Survey |
| title_full_unstemmed | Using Optical Satellite Data and Airborne Lidar Data for a Nationwide Sampling Survey |
| title_short | Using Optical Satellite Data and Airborne Lidar Data for a Nationwide Sampling Survey |
| title_sort | using optical satellite data and airborne lidar data for a nationwide sampling survey |
| topic | lidar operational Landsat nationwide sampling mapping |
| url | http://www.mdpi.com/2072-4292/7/4/4253 |
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