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Fernando Rossi
Faculty of Environment and Natural Resources, Forest Utilisation, University of Freiburg, Freiburg, Germany

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Articles
Published: 02 October 2019 in Australian Forestry
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The rehabilitation of degraded subtropical natural forests is a global concern. A detailed assessment of their structure is a challenging and costly prerequisite because diverse structures exist depending on the cause and degree of degradation. Recent remote sensing concepts and technologies provide a detailed picture of actual forest structure, even in difficult terrain. When it comes to planning and implementing rehabilitation measures on the ground, however, meaningful forest management units (FMUs) must be created that are large enough to allow technical implementation, but which are also homogenous in structure. To date, the delineation of FMUs has, in most cases, been achieved qualitatively based on expert knowledge. The aim of this contribution is to develop and demonstrate a method for creating and delineating meaningful FMUs based on quantitative information acquired from remote sensing and spatial statistics. Therefore, a case study was conducted in a 3940-ha fire-degraded forest area in the Argentinean cloud forest of Yungas Pedemontana. A plot-based field inventory and an aerial survey with an unmanned aerial vehicle were conducted. The Adjusted Canopy Coverage Index (ACCI), as a metric for stand structure, was formulated to predict basal area from canopy height models. A SPOT6 image of the area was object-based segmented and classified into four fire-severity strata by training it with the ACCI values. The resulting classification presented a mosaic pattern in which the stands are homogenous but far too small (average 3129 m2) for planning adaptive management. Therefore, features in close proximity with similar structure (i.e. ACCI values) were aggregated using the Hot Spot Analysis (Getis-Ord Gi*) tool from the Arc geographic information system environment to create FMUs. Clusters were calculated at four scales: 10, 20, 30 and 40 ha (resulting in threshold radii of 178, 252, 309 and 357 m, respectively), using ACCI values as the variable of aggregation. As a result, average cluster areas were obtained of 33.9 ha for the shortest threshold distance of analysis and 138.5 ha for the greatest threshold distance. The tool significantly aggregated between 30.7% and 60.8% of the area into either coldspots or hotspots of ACCI, facilitating the delineation of FMUs for the planning of adaptive rehabilitation measures. There is a trade-off, however, between the gain in area of the FMUs and the loss of homogeneity: for a 357 m distance threshold, 12% more of the area was misclassified, compared with a 178 m threshold.

ACS Style

F. Rossi; G. Becker. Creating forest management units with Hot Spot Analysis (Getis-Ord Gi*) over a forest affected by mixed-severity fires. Australian Forestry 2019, 82, 166 -175.

AMA Style

F. Rossi, G. Becker. Creating forest management units with Hot Spot Analysis (Getis-Ord Gi*) over a forest affected by mixed-severity fires. Australian Forestry. 2019; 82 (4):166-175.

Chicago/Turabian Style

F. Rossi; G. Becker. 2019. "Creating forest management units with Hot Spot Analysis (Getis-Ord Gi*) over a forest affected by mixed-severity fires." Australian Forestry 82, no. 4: 166-175.

Journal article
Published: 28 June 2018 in Sustainability
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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.

ACS Style

Fernando Rossi; Andreas Fritz; Gero Becker. Combining Satellite and UAV Imagery to Delineate Forest Cover and Basal Area after Mixed-Severity Fires. Sustainability 2018, 10, 2227 .

AMA Style

Fernando Rossi, Andreas Fritz, Gero Becker. Combining Satellite and UAV Imagery to Delineate Forest Cover and Basal Area after Mixed-Severity Fires. Sustainability. 2018; 10 (7):2227.

Chicago/Turabian Style

Fernando Rossi; Andreas Fritz; Gero Becker. 2018. "Combining Satellite and UAV Imagery to Delineate Forest Cover and Basal Area after Mixed-Severity Fires." Sustainability 10, no. 7: 2227.