| Title | Assessment of convolution neural networks for wetland mapping with landsat in the central Canadian boreal forest region |
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| Author | Pouliot, D; Latifovic, R; Pasher, J; Duffe, J |
| Source | Remote Sensing vol. 11, 7, 772, 2019., https://doi.org/10.3390/rs11070772  Open Access |
| Image |  |
| Year | 2019 |
| Alt Series | Natural Resources Canada, Contribution Series 20190631 |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Subjects | geophysics; remote sensing; vegetation |
| Program | Canada Centre for Remote Sensing Divsion |
| Released | 2019 03 31 |
| Abstract | The Environment and Sustainable Development Indicators (ESDI) Initiative was introduced to track Canada's overall wealth in the form of natural and human capital, in additionto familiar economic data
such as the gross domestic product (GDP). One of the six ESDIs is the Forest Cover Indicator (FCI). In this paper we define FCI, outline the overall method for deriving FCI, and report results for addressing four key technical issues in carrying out
this overall method. The FCI is defined as interannual variations of Canada's forest area with the middle-summer crown closure (CC) ? 10%. Crown closure is the percentage of the ground surface covered by a downward vertical projection of the tree
crowns. Theoverall monitoring method is mainly based on coarse resolution remote sensing data because of the need to cover Canada's extensive landmass during the middle-summer months and toupdate the results annually. Medium resolution satellite
data, field measurements, and modeling approaches were used for calibration, correction, validation, and down-scaling, with a focus on the following 4 key technical issues: (1) correcting understory non-tree vegetation effect on CC, (2) downscaling
forest cover area from 1-km to 100-m spatial resolution as required by the FCI definition, (3) detecting the changes of CC caused by disturbances, and (4) detecting changes in CC caused by forest regrowth. Methods and results for addressing these
technical issues are described in the paper. While these results indicate that the key technical issues can be solved by integrating satellite remote sensing data/products and other data, there are clear needs for further development, especially
testing against field measurements. © 2006 Rocky Mountain Mathematics Consortium. |
| GEOSCAN ID | 321960 |
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