Development of a forest structural complexity index based on multispectral airborne remote sensing and topographic dataThis article is one of a selection of papers from Extending Forest Inventory and Monitoring over Space and Time.

Abstract

This paper presents development of a multivariate forest structural complexity index based on relationships between field-based structural variables and geospatial data. Remote sensing has been widely used to model individual forest structural attributes at many scales. As opposed to, or in addition to, individual structural parameters such as leaf area index or tree height, overall structural complexity information can enhance forest inventories and provide a variety of information to forest managers, including identifying damage and disturbance as well as indicators of habitat or biodiversity. In this study, a multivariate modelling technique, redundancy analysis, was implemented to derive a model incorporating both horizontal and vertical structural attributes as predicted by an ensemble of high-resolution multispectral airborne imagery and topographic variables. The first redundancy analysis axis of the final model explained 35% of the total variance of the field variables and was used as the complexity index. With a root mean squared error of 19.9%, the model was capable of differentiating four to five relative levels of complexity. This paper presents the forest ecological and modelling aspects of the research. A related paper presents the remote sensing aspects, including application of the model to map predicted structural complexity, map validation, and testing of the method at multiple scales.Résumé : Cet article présente le développement d'un indice multivarié de complexité de la structure de la forêt basé sur les relations entre des variables structurales mesurées sur le terrain et des données géospatiales. La télédétection a été largement utilisée pour modéliser les attributs structuraux de la forêt à plusieurs échelles. Contrairement à, ou en plus, des paramètres structuraux individuels comme l'indice de surface foliaire ou la hauteur des arbres, une information globale au sujet de la complexité structurale peut améliorer les inventaires forestiers et fournir une variété de renseignements aux aménagistes forestiers, incluant l'identification des dommages et des perturbations aussi bien que des indicateurs d'habitat ou de biodiversité. Dans cette étude, une technique de modélisation multivariée, l'analyse de redondance, a été appliquée pour dériver un modèle qui incorpore des attributs, tant horizontaux que verticaux de la structure, prédits par un ensemble d'images aériennes multispectrales à haute résolution et des variables topographiques. Le premier axe de l'analyse de redondance du modèle final expliquait 35 % de la variance totale des variables terrain et a été utilisé comme indice de complexité. Avec une erreur quadratique moyenne de 19,9 %, le modèle était capable de différencier quatre à cinq niveaux de complexité. Cet article présente les aspects de la recherche reliés à la modélisation et à l'écologie forestière. Un autre article présente les aspects reliés à la télédétection, incluant l'application du modèle pour cartographier la complexité structurale prédite, la validation de la cartogr...

Deriving Stand Structural Complexity from Airborne Laser Scanning Data—What Does It Tell Us about a Forest?

“…A relationship between structural complexity and basal area may be expected, since D b is dependent on stand density and material distribution in space [39]. Earlier studies found that basal area was related to Zenner and Hibbs' SCI [53]. In addition, previous research showed that structural complexity measured using SSCI was dependent on stand density as well, even though space-filling was used instead of basal area as a measure of density [12].…”

Forest Structure Mapping of Boreal Coniferous Forests Using Multi-Source Remote Sensing Data

“…The profiles obtained by the tomographic reflect certain changes in the vertical and horizontal directions of the forest stand and the variability of the reflectivity is regarded as the forest structure [3,[10][11][12]. However, the majority of current remote sensing-based research on forest structure ignores the impact that the overall complexity of the forest structure has on vegetation characteristics in favor of modeling individual horizontal or vertical structures [13].…”

Mapping Forest Canopy Height Across Large Areas by Upscaling ALS Estimates with Freely Available Satellite Data

“…Synoptic capture of large forested areas is provided by space borne passive optical remote sensing platforms and has proved useful for the attribution of forest structure [1,[4][5][6][7]. However, the inability of passive instruments to sense below the principal canopy limits their applicability for assessing three-dimensional forest structure attributes, such as canopy height [8][9][10]. Over the last two decades, Light Detection and Ranging (LiDAR) technologies, and in particular discrete return Airborne Laser Scanners (ALS), have become an operational alternative to traditional forest inventory [2,11,12].…”