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A COMPARATIVE ANALYSIS OF PIXEL-BASED AND OBJECT-BASED APPROACHES FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING RANDOM FOREST MODEL
| Content Provider | Scilit |
|---|---|
| Author | Tamiminia, H. Salehi, B. Mahdianpari, M. Beier, C. M. Johnson, L. |
| Copyright Year | 2022 |
| Description | Providing an accurate above-ground biomass (AGB) map is of paramount importance for carbon stock and climate change monitoring. The main objective of this study is to compare the performance of pixel-based and object-based approaches for AGB estimation of temperate forests in north-eastern of New York State. Second, the capabilities of optical, SAR, and optical + SAR data were investigated. To achieve the goals, the random forest (RF) regression algorithm was used to model and predict the AGB values. Optical (i.e. Landsat 5TM, Landsat 8 OLI, and Sentinel-2), synthetic aperture radar (SAR) (Sentinel-1 and global phased array type L-band SAR (PALSAR/PALSAR-2)), and their integration have been used to estimate the AGB. It is worth mentioning that the airborne light detection and ranging (LiDAR) AGB raster has been used as a reference data for training/testing purposes. The results demonstrate that the OBIA approach enhanced the RMSE of AGB estimation about 5.32 Mg/ha, 8.9 Mg/ha, and 5.29 Mg/ha for optical, SAR, and optical + SAR data, respectively. Moreover, optical + SAR data with the RMSE of 42.63 Mg/ha and $R^{2}$ of 0.72 for pixel-based and RMSE of 37.31 Mg/ha and $R^{2}$ of 0.77 for object-based approach provided the best results. |
| Ending Page | 196 |
| Page Count | 6 |
| Starting Page | 191 |
| e-ISSN | 21949034 |
| DOI | 10.5194/isprs-archives-xlvi-m-2-2022-191-2022 |
| Journal | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume Number | XLVI-M-2-2 |
| Language | English |
| Publisher | Copernicus GmbH |
| Publisher Date | 2022-07-25 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Object Based Mg/ha Pixel Based |
| Content Type | Text |
| Resource Type | Article |
