Creating forest management units with Hot Spot Analysis (Getis-Ord Gi*) over a forest affected by mixed-severity fires

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 m(2)) 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.