Stand structure indices as tools to support forest management: an application in Trentino forests (Italy)

Pastorella, Fabio; Paletto, Alessandro

doi:10.17221/75/2012-jfs

Cited by 61 publications

(44 citation statements)

References 23 publications

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“…Examples of the non-spatial indices are the Simpson, Shannon and Weaver, Sorenson, A profile, and uniform angle. Examples of the spatial indices are spatial mingling species, differentiation, dominance, Clark and Evans and Pielou [44][45][46][47][48][49][50] as well as composite stand indices, for example S index [51].…”

Section: Pure Stands Vs Mixed Standsmentioning

confidence: 99%

Multi-Species Stand Classification: Definition and Perspectives

Gonçalves¹

2017

Forest Ecology and Conservation

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The increasing demands for products and services from forests enhanced new approaches to stand composition, structure, and management, which encompass multiple use systems, frequently mixed either even aged or uneven aged. Stand classification is frequently based on one density measure (number of trees, basal area, volume or crown cover). As no standard criteria exist, the direct comparison between the different stand classifications is difficult. This created a need for a stand classification that incorporates not only the forest species and composition but also their horizontal and vertical arrangements. The four criteria stand classification incorporates the number of species and their proportion, their horizontal and vertical distribution. The application of this methodology enables an integrated approach, bridging the gap between composition and stand structure. Its use in the National Forest Inventories and in research studies is simple, as shown in the two cases of study presented. It also allows the evaluation of stands in a certain moment in time and their dynamics.

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Section: Pure Stands Vs Mixed Standsmentioning

confidence: 99%

Multi-Species Stand Classification: Definition and Perspectives

Gonçalves¹

2017

Forest Ecology and Conservation

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“…The second data set refers to data collected during research activities whose aim was to investigate stand structure indices as tools to support forest management (Pastorella and Paletto 2013). Stratified random sampling was applied to place 24 plots in Norway spruce forests pure and mixed with silver fir, 12 plots in beech forests, 12 plots in Scots pine forests, and 6 plots in European larch forests for a total of 54 plots surveyed.…”

Section: Field Data Collection and Response Variablementioning

confidence: 99%

Using classification trees to predict forest structure types from LiDAR data

Torresan

Corona

Scrinzi³

et al. 2016

Ann. For. Res.

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Torresan C., Corona P., Scrinzi G., Marsal J.V., 2016. Using classification trees to predict forest structure types from LiDAR data. Ann. For. Res. 59(1): 281-298.Abstract. This study assesses whether metrics extracted from airborne Li-DAR (Light Detection and Ranging) raw point cloud can be exploited to predict different forest structure types by means of classification trees. Preliminarily, a bivariate analysis by means of Pearson statistical test was developed to find associations between LiDAR metrics and the proportion of basal area into three stem diameter classes (understory, mid-story, and over-story trees) of 243 random distributed plots surveyed from 2007 to 2012 in Trento Province (Northern Italy). An unsupervised clustering approach was adopted to determine forest structural patterns on the basis of basal area proportion in the three stem diameter classes, using a k-means procedure combined with a previous hierarchical classification algorithm. A comparison among the identified clusters centroids was performed by the Kruskall-Wallis test. A classification tree model to predict forest structural patterns originating from the cluster analysis was developed and validated. Between 18 potential LiDAR metrics, 11 were significantly correlated with the proportion of basal area of understory, mid-story, and overstory trees. The results coming from the agglomerative hierarchical clustering allowed identification of 5 clusters of forest structure: pole-stage (70% of the considered cases), young (15%), adult (24.3%), mature (24.3%), and old forests (30%). Five LiDAR metrics were selected by the classification tree to predict the forest structural types: standard deviation and mode of canopy heights, height at which 95% and 99% of canopy heights fall below, difference between height at which 90% and 10% of canopy heights fall below. The validation tree model process showed a misclassification error of 45.9% and a level of user's accuracy ranging between 100% and 33.3% in the validation data set. The highest level of user's accuracy was reached in the classification of pole-stage forests (100%), in which more than 82% of basal area is due to the understory-trees, follow by the classification of old forests types (63.5% of basal area due to the overstory-trees) achieved 76.5% of user's accuracy. The model has provided moderately satisfactory results in term of classification performance: substantial room for improvement might be established by multi-or hyperspectral imaging that allow detailed characterization of the spectral behaviour of the forest structure types.

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“…The complex diversity index is composed of three indices describing the spatial positioning of trees, species diversity and size differentiation, each of which receives a different weight (Pastorella & Paletto 2013). In the original form, tree diameter was the characteristic used in the size differentiation-index calculations.…”

Section: Complex Structural Diversity Index (Si)mentioning

confidence: 99%

Spatial diversity of forest regeneration after catastrophic wind in northeastern Poland

Szmyt

Dobrowolska

2016

iForest

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We examined the spatial diversity of young growth established after the catastrophic windthrow occurred in 2002 in the northeastern part of Poland. Our observations and measurements were conducted on permanent circular measurement plots located in the Szast Protected Forest (continental lowland temperate forests) that were established 3 years after the windstorm and left to natural succession. We evaluated the spatial indices characterizing the four main aspects of stand structure: the spatial arrangement of seedlings and saplings, species mingling, tree size diversity at the local spatial scale and the overall structural complexity index. The calculations were conducted in parts of the forest with differing severity of disturbance. The obtained results indicated the prevalence of a random arrangement of young growth. Clumps of regeneration were observed to a lesser degree in all parts of the forest. The species diversity was moderate and was the highest in the slightly or severely disturbed stands. Scots pine formed homogenous groups of regeneration and oaks were intermingled among other tree species. The height of the natural regeneration was moderately or highly differentiated in all stands. The overall structural diversity index showed that stand regeneration in the slightly or moderately disturbed stands was more differentiated than the young growth in the severely disturbed stands. The size differentiation of young growth is a long-lasting process and thus should be considered in practices aimed at the re-growing of areas after a natural disturbance. This process might be used to the replace monocultures with more diversified forests even in poor forest site types.

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Stand structure indices as tools to support forest management: an application in Trentino forests (Italy)

Cited by 61 publications

References 23 publications

Multi-Species Stand Classification: Definition and Perspectives

Multi-Species Stand Classification: Definition and Perspectives

Using classification trees to predict forest structure types from LiDAR data

Spatial diversity of forest regeneration after catastrophic wind in northeastern Poland

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