Abstract
In northern Argentina, the assessment of degraded forests is a big challenge for both science and practice, due to their heterogeneous structure. However, new technologies could contribute to mapping post-disturbance canopy cover and basal area in detail. Therefore, this research assesses whether or not the inclusion of partial cover unmanned aerial vehicle imagery could reduce the classification error of a SPOT6 image used in an area-based inventory. BA was calculated from 77 ground inventory plots over 3944 ha of a forest affected by mixed-severity fires in the Argentinian Yungas. In total, 74% of the area was covered with UAV flights, and canopy height models were calculated to estimate partial canopy cover at three tree height classes. Basal area and partial canopy cover were used to formulate the adjusted canopy cover index, and it was calculated for 70 ground plots and an additional 20 image plots. Four classes of fire severity were created based on basal area and adjusted canopy cover index, and were used to run two supervised classifications over a segmented (algorithm multiresolution) wall-to-wall SPOT6 image. The comparison of the Cohan’s Kappa coefficient of both classifications shows that they are not significantly different (p-value: 0.43). However, the approach based on the adjusted canopy cover index achieved more homogeneous strata (Welch t-test with 95% of confidence). Additionally, UAV-derived canopy height model estimates of tree height were compared with field measurements of 71 alive trees. The canopy height models underestimated tree height with an RMSE ranging from 2.8 to 8.3 m. The best accuracy of the canopy height model was achieved using a larger pixel size (10 m), and for lower stocked plots due to high fire severity.
Highlights
The assessment and recuperation of degraded forest is a great challenge for science and practice [1,2]
18 GeoTIFF orthomosaics were created according to the groups of flight planning, with a ground resolution of 11.78 cm/pixel, flights had been planned with a ground sampling distance of 10 cm/pixel
The results reported in the current study showed a remarkable contribution of partial cover UAV imagery-derived metrics to reduce stratified estimation error in an area-based inventory, in comparison with the traditional approach of using only ground plots and satellite imagery
Summary
The assessment and recuperation of degraded forest is a great challenge for science and practice [1,2]. An intuitive approach is to consider the definition of a forest by the Argentinian Federal Council of Environment (COFEMA—2012) [18], which is used for the implementation of the Argentinian forest law 26331 [19]. It has set minimum thresholds of extension (0.5 ha) and canopy cover (20%) of a certain tree height (3 m) for a land to be considered a forest. Forest canopy cover and tree height are, variables of great interest for the prediction of live basal area (BA), which has already been used to assess forests affected by mixed-severity fires [15]. In order to delineate homogeneously degraded forest, this manuscript addresses fire severity based on the losses of forest stocks and productivity
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