Evaluating the Differences in Modeling Biophysical Attributes between Deciduous Broadleaved and Evergreen Conifer Forests Using Low-Density Small-Footprint LiDAR Data
Airborne light detection and ranging (LiDAR) has been used for forest biomass estimation for the past three decades. The performance of estimation, in particular, has been of great interest. However, the difference in the performance of estimation between stem volume (SV) and total dry biomass (TDB)...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-06-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/9/6/572 |
| _version_ | 1828883302517309440 |
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| author | Yoshio Awaya Tomoaki Takahashi |
| author_facet | Yoshio Awaya Tomoaki Takahashi |
| author_sort | Yoshio Awaya |
| collection | DOAJ |
| description | Airborne light detection and ranging (LiDAR) has been used for forest biomass estimation for the past three decades. The performance of estimation, in particular, has been of great interest. However, the difference in the performance of estimation between stem volume (SV) and total dry biomass (TDB) estimations has been a priority topic. We compared the performances between SV and TDB estimations for evergreen conifer and deciduous broadleaved forests by correlation and regression analyses and by combining height and no-height variables to identify statistically useful variables. Thirty-eight canopy variables, such as average and standard deviation of the canopy height, as well as the mid-canopy height of the stands, were computed using LiDAR point data. For the case of conifer forests, TDB showed greater correlation than SV; however, the opposite was the case for deciduous broadleaved forests. The average- and mid-canopy height showed the greatest correlation with TDB and SV for conifer and deciduous broadleaved forests, respectively. Setting the best variable as the first and no-height variables as the second variable, a stepwise multiple regression analysis was performed. Predictions by selected equations slightly underestimated the field data used for validation, and their correlation was very high, exceeding 0.9 for coniferous forests. The coefficient of determination of the two-variable equations was smaller than that of the one-variable equation for broadleaved forests. It is suggested that canopy structure variables were not effective for broadleaved forests. The SV and TDB maps showed quite different frequency distributions. The ratio of the stem part of the broadleaved forest is smaller than that of the coniferous forest. This suggests that SV was relatively smaller than TDB for the case of broadleaved forests compared with coniferous forests, resulting in a more even spatial distribution of TDB than that of SV. |
| first_indexed | 2024-12-13T10:46:04Z |
| format | Article |
| id | doaj.art-6b3fb14bbe5f4b2e87355da74597aa75 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-13T10:46:04Z |
| publishDate | 2017-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6b3fb14bbe5f4b2e87355da74597aa752022-12-21T23:50:09ZengMDPI AGRemote Sensing2072-42922017-06-019657210.3390/rs9060572rs9060572Evaluating the Differences in Modeling Biophysical Attributes between Deciduous Broadleaved and Evergreen Conifer Forests Using Low-Density Small-Footprint LiDAR DataYoshio Awaya0Tomoaki Takahashi1River Basin Research Center, Gifu University, 1-1 Yanagido, Gifu 501-1193, JapanKyushu Research Center, Forestry and Forest Products Research Institute, 4-11-16 Kurokami, Chuo-ku, Kumamoto 860-0862, JapanAirborne light detection and ranging (LiDAR) has been used for forest biomass estimation for the past three decades. The performance of estimation, in particular, has been of great interest. However, the difference in the performance of estimation between stem volume (SV) and total dry biomass (TDB) estimations has been a priority topic. We compared the performances between SV and TDB estimations for evergreen conifer and deciduous broadleaved forests by correlation and regression analyses and by combining height and no-height variables to identify statistically useful variables. Thirty-eight canopy variables, such as average and standard deviation of the canopy height, as well as the mid-canopy height of the stands, were computed using LiDAR point data. For the case of conifer forests, TDB showed greater correlation than SV; however, the opposite was the case for deciduous broadleaved forests. The average- and mid-canopy height showed the greatest correlation with TDB and SV for conifer and deciduous broadleaved forests, respectively. Setting the best variable as the first and no-height variables as the second variable, a stepwise multiple regression analysis was performed. Predictions by selected equations slightly underestimated the field data used for validation, and their correlation was very high, exceeding 0.9 for coniferous forests. The coefficient of determination of the two-variable equations was smaller than that of the one-variable equation for broadleaved forests. It is suggested that canopy structure variables were not effective for broadleaved forests. The SV and TDB maps showed quite different frequency distributions. The ratio of the stem part of the broadleaved forest is smaller than that of the coniferous forest. This suggests that SV was relatively smaller than TDB for the case of broadleaved forests compared with coniferous forests, resulting in a more even spatial distribution of TDB than that of SV.http://www.mdpi.com/2072-4292/9/6/572stem volumedry biomassconiferbroadleaveslight detection and ranging (LiDAR)regression analysiscorrelation coefficient |
| spellingShingle | Yoshio Awaya Tomoaki Takahashi Evaluating the Differences in Modeling Biophysical Attributes between Deciduous Broadleaved and Evergreen Conifer Forests Using Low-Density Small-Footprint LiDAR Data Remote Sensing stem volume dry biomass conifer broadleaves light detection and ranging (LiDAR) regression analysis correlation coefficient |
| title | Evaluating the Differences in Modeling Biophysical Attributes between Deciduous Broadleaved and Evergreen Conifer Forests Using Low-Density Small-Footprint LiDAR Data |
| title_full | Evaluating the Differences in Modeling Biophysical Attributes between Deciduous Broadleaved and Evergreen Conifer Forests Using Low-Density Small-Footprint LiDAR Data |
| title_fullStr | Evaluating the Differences in Modeling Biophysical Attributes between Deciduous Broadleaved and Evergreen Conifer Forests Using Low-Density Small-Footprint LiDAR Data |
| title_full_unstemmed | Evaluating the Differences in Modeling Biophysical Attributes between Deciduous Broadleaved and Evergreen Conifer Forests Using Low-Density Small-Footprint LiDAR Data |
| title_short | Evaluating the Differences in Modeling Biophysical Attributes between Deciduous Broadleaved and Evergreen Conifer Forests Using Low-Density Small-Footprint LiDAR Data |
| title_sort | evaluating the differences in modeling biophysical attributes between deciduous broadleaved and evergreen conifer forests using low density small footprint lidar data |
| topic | stem volume dry biomass conifer broadleaves light detection and ranging (LiDAR) regression analysis correlation coefficient |
| url | http://www.mdpi.com/2072-4292/9/6/572 |
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