Quantifying the contribution of spectral metrics derived from digital aerial photogrammetry to area-based models of forest inventory attributes

REMOTE SENSING OF ENVIRONMENT

卷:234

Article

Tompalski, Piotr¹  White, Joanne C.²  Coops, Nicholas C.¹  Wulder, Michael A.² 

[1] Univ British Columbia, Fac Forestry, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada

[2] Nat Resources Canada, Canadian Forest Serv, Pacific Forestry Ctr, 506 West Burnside Rd, Victoria, BC V8Z 1M5, Canada

关键词: Digital aerial photogrammetry;    Forestry;    Inventory;    Remote sensing;    Point cloud;    Lidar;    Airborne laser scanning;   

DOI  :  10.1016/j.rse.2019.111434

来源: Elsevier

【 摘 要 】

Digital aerial photogrammetry (DAP) has demonstrated utility across a range of forest environments as an alternative data source to airborne laser scanning (ALS) for estimating forest inventory attributes in an area-based approach. In this context, metrics are typically derived from the DAP point cloud in a manner analogous to that of ALS data. However, image matching algorithms also allow for spectral information from the image data to be transferred to the point cloud. Herein, we quantify the contribution of this spectral information to the area-based prediction of five forest inventory attributes: Lorey's mean height, quadratic mean diameter, basal area, gross volume per ha, and stems per ha in a highly productive coastal temperate rainforest on Vancouver Island, British Columbia, Canada. Using ground plots and ALS-derived area-based estimates as reference, we compare plot-level predictions generated using (i) DAP point cloud metrics, (ii) DAP spectral metrics, and (iii) combinations of DAP point cloud and spectral metrics. In addition to prediction accuracy, we assessed variable importance to identify those metrics that were most informative for the developed models. Our results indicated that for models generated using DAP data, prediction accuracy was greatest when the point cloud-based metrics were incorporated. Models that incorporated both point cloud and spectral information were only slightly more accurate than models based on point cloud metrics only. We found that the improvement in accuracy was not observed for all stand attributes. The highest increase in accuracy for models combining point cloud and spectral metrics was observed for quadratic mean diameter, basal area per hectare, and stem volume per hectare, with change in relative root mean square error of -1.3%, -1.75%, and -0.23%, respectively. Models derived with spectral metrics only had the lowest accuracy with R-2 values never exceeding 0.25. Analysis of the variable importance indicated that point cloud metrics are markedly more important than spectral metrics. We conclude that the benefit of the additional spectral information in this forest environment is negligible, and the effort to derive the spectral information cannot be justified for operational applications. Our results confirm those of other studies in other environments that have likewise found minimal benefit to the incorporation of DAP spectral information in area-based estimation.

【 授权许可】

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