Integrating forest inventory data and MODIS data to map species-level biomass in Chinese boreal forests
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Abstract
Timely and accurate knowledge of species-level biomass is essential for forest managers to sustain forest resources and respond to various forest disturbance regimes. In this study, maps of species-level biomass in Chinese boreal forests were generated by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) images with forest inventory data using k nearest neighbor (kNN) methods and evaluated at different scales. The performance of 630 kNN models based on different distance metrics, k values, and temporal MODIS predictor variables were compared. Random Forest (RF) showed the best performance among the six distance metrics: RF, Euclidean distance, Mahalanobis distance, most similar neighbor in canonical correlation space, most similar neighbor computed using projection pursuit, and gradient nearest neighbor. No appreciable improvement was observed using multi-month MODIS data compared with using single-month MODIS data. At the pixel scale, species-level biomass for larch and white birch had relatively good accuracy (root mean square deviation < 62.1%), while the other species had poorer accuracy. The accuracy of most species except for willow and spruce was improved up to the ecoregion scale. The maps of species-level biomass captured the effects of disturbances including fire and harvest and can provide useful information for broad-scale forest monitoring over time.
Suggested Citation
Zhang, Q., He, H.S., Liang, Y., Hawbaker, T., Henne, P., Liu, J., Huang, S., Wu, Z., and Huang, C., 2018, Integrating forest inventory data and MODIS data to map species-level biomass in Chinese boreal forests: Canadian Journal of Forest Research, v. 48, no. 5, p. 461-479, https://doi.org/10.1139/cjfr-2017-0346.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Integrating forest inventory data and MODIS data to map species-level biomass in Chinese boreal forests |
| Series title | Canadian Journal of Forest Research |
| DOI | 10.1139/cjfr-2017-0346 |
| Volume | 48 |
| Issue | 5 |
| Year Published | 2018 |
| Language | English |
| Publisher | Canadian Science Publishing |
| Contributing office(s) | Geosciences and Environmental Change Science Center |
| Description | 19 p. |
| First page | 461 |
| Last page | 479 |
| Country | China |