Stand Delineation of Pinus sylvestris L. Plantations Suffering Decline Processes Based on Biophysical Tree Crown Variables: A Necessary Tool for Adaptive Silviculture

Abstract

Many planted Pinus forests are severely affected by defoliation and mortality processes caused by pests and droughts. The mapping of forest tree crown variables (e.g., leaf area index and pigments) is particularly useful in stand delineation for the management of declining forests. This work explores the potential of integrating multispectral WorldView-2 (WV-2) and Airborne Laser Scanning (ALS) data for stand delineation based on selected tree crown variables in Pinus sylvestris plantations in southern Spain. Needle pigments (chlorophyll and carotenes) and leaf area index (LAI) were quantified. Eight vegetation indices and ALS-derived metrics were produced, and seven predictors were selected to estimate and map tree crown variables using a Random Forest method and Gini index. Chlorophylls a and b (Chla and Chlb) were significantly higher in the non-defoliated and moderately defoliated trees than in severely defoliated trees (F = 14.02, p < 0.001 for Chla; F = 13.09, p < 0.001 for Chlb). A similar response was observed for carotenoids (Car) (F = 14.13, p < 0.001). The LAI also showed significant differences among the defoliation levels (F = 26.5, p < 0.001). The model for the chlorophyll a pigment used two vegetation indices, Plant Senescence Reflectance Index (PSRI) and Carotenoid Reflectance Index (CRI); three WV-2 band metrics, and three ALS metrics. The model built to describe the tree Chlb content used similar variables. The defoliation classification model was established with a single vegetation index, Green Normalized Difference Vegetation Index (GNDVI); two metrics of the blue band, and two ALS metrics. The pigment contents models provided R2 values of 0.87 (Chla, RMSE = 12.98%), 0.74 (Chlb, RMSE = 10.39%), and 0.88 (Car, RMSE = 10.05%). The cross-validated confusion matrix achieved a high overall classification accuracy (84.05%) and Kappa index (0.76). Defoliation and Chla showed the validation values for segmentations and, therefore, in the generation of the stand delineation. A total of 104 stands were delineated, ranging from 6.96 to 54.62 ha (average stand area = 16.26 ha). The distribution map of the predicted severity values in the P. sylvestris plantations showed a mosaic of severity patterns at the stand and individual tree scales. Overall, the findings of this work underscore the potential of WV-2 and ALS data integration for the assessment of stand delineation based on tree health status. The derived cartography is a relevant tool for developing adaptive silvicultural practices to reduce Pinus sylvestris mortality in planted forests at risk due to climate change.

Contribution to the Study of the Health State of Holm Oak in the Chettaba Forest (Algeria)

“…Tree vitality is a crucial indicator of forest condition, indicating a tree's overall appearance and its health, which also reflect its disease state [Cherubini et al, 2021]. Vitality cannot be directly measured, but various indicators can be Each individual tree's social class corresponds to its ability to mobilize resources in its specific environment [Henry et al, 2010;Varo-Martínez et al, 2021]. In the Chettaba forest, codominant and dominant trees are best represented.…”

Site Index Estimation Using Airborne Laser Scanner Data in Eucalyptus dunnii Maide Stands in Uruguay

“…The parameters corresponded to spatial radius (SR), range radius (RR) and minimum region size (MRS). The value of SR was used to define the neighborhood, while the value of RR refers to the digital value threshold that the algorithm considers to be delimiting segments, and the value of MRS defines the smallest size of the segment [39,40]. These parameters define the homogeneity within each segment and the heterogeneity between different segments.…”

Integrating Dendrochronological and LiDAR Data to Improve Management of Pinus canariensis Forests under Different Thinning and Climatic Scenarios

“…Using forest attributes as a basis, stand segmentation was implemented using Orpheo ToolBox software for QGIS 3.28 [48] in the study area, as is commonly done when modeling forest attributes using ALS data in forest management inventories [49]. The models were ultimately used to produce predictions for tree density (N, tress ha −1 ) at stand scale covering the entire P. canariensis plantations in the study area, which included the current tree density in a particular stand that could potentially be thinned at a particular time.…”