Machine Learning Algorithms to Predict Tree-Related Microhabitats using Airborne Laser Scanning

Abstract

<jats:p>In the last few years, the occurrence and abundance of tree-related microhabitats and habitat trees have gained great attention across Europe as indicators of forest biodiversity. Nevertheless, observing microhabitats in the field requires time and well-trained staff. For this reason, new efficient semiautomatic systems for their identification and mapping on a large scale are necessary. This study aims at predicting microhabitats in a mixed and multi-layered Mediterranean forest using Airborne Laser Scanning data through the implementation of a Machine Learning algorithm. The study focuses on the identification of LiDAR metrics useful for detecting microhabitats according to the recent hierarchical classification system for Tree-related Microhabitats, from single microhabitats to the habitat trees. The results demonstrate that Airborne Laser Scanning point clouds support the prediction of microhabitat abundance. Better prediction capabilities were obtained at a higher hierarchical level and for some of the single microhabitats, such as epiphytic bryophytes, root buttress cavities, and branch holes. Metrics concerned with tree height distribution and crown density are the most important predictors of microhabitats in a multi-layered forest.</jats:p>

Climate Change Alters Temperate Forest Canopies and Indirectly Reshapes Arthropod Communities

“…To circumvent these issues, either ground-based or aerial surveys should be selected for canopy access. New processes combining drone imagery, airborne LiDAR, deep learning and modeling should be developed to better assess habitat diversity and abundance in the canopy (Müller et al, 2018;Frey et al, 2020;Santopuoli et al, 2020), and microclimates (Duffy et al, 2021). This would also allow to investigate the outcomes of crown diebacks at larger spatial scales, i.e., from tree to the landscape level, which could be more relevant for stakeholders and conservation managers, but also for the study of local biodiversity (Jackson and Fahrig, 2015;Percel et al, 2019).…”

Mapping Aboveground Woody Biomass on Abandoned Agricultural Land Based on Airborne Laser Scanning Data

“…This software includes only original sets of algorithms scripted in C# programming language and ALS metrics can be computed from first, last, or all returns considering the height thresholds defined by user. Regarding the development of final predictive models, the nonparametric machine learning techniques, such as random forest (RF), have the ability to identify complex relationships between predictor and response variables, therefore showing their superiority or promising level of performance over parametric methods for the estimation of AGB [40][41][42].…”

“…Many studies have used different RS data and modelling methods to predict AGB in forest, shrub, or grassland ecosystems (e.g., [10,[32][33][34][35][38][39][40][41][42]). However, as far as we know, relatively few studies (e.g., [22,23,26]) have dealt with the spatial identification of AAL and prediction of AGB on AAL using ALS data.…”

3D Point Clouds in Forest Remote Sensing

“…Among these, only one paper exclusively uses passive RS data [21], while 29 papers use at least one LiDAR dataset in the analysis [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][22][23][24][25][26][27][28][29][30]. Ten papers exclusively use airborne laser scanning (ALS) data [4,6,7,10,11,13,18,23,26,27], nine papers exclusively use terrestrial laser scanning (TLS) data in the analysis [3,9,15,16,20,22,24,25,30], two papers exclusively use mobile laser scanning (MLS) data …”

“…Finally, five papers use combined active and passive remote sensing data sets [2,14,17,19,28]. Regarding the scale of the analysis, 18 of the studies perform individual tree level (ITL) analysis [1][2][3][8][9][10][11][12][14][15][16]19,20,[23][24][25][26]30], eight papers report stand level (SL) analysis [6,7,17,18,21,22,27,29] and four report a combination of ITL and SL [4,5,13,28]. Tree position, diameter at breast height (DBH) and individual tree height (h) are the most common variables of interest, analyzed in nine, six and six papers, respectively, while the most commonly used methods are 3D reconstruction, point filtering and statistical modelling, which are used in eight, five and five papers, respectively (see Table 1).…”