Forest management treatments often translate into changes in forest structure. Understanding and assessing how forests react to these changes is key for forest managers to develop and follow sustainable practices. A strategy to remotely monitor the development of the canopy after thinning using satellite imagery time-series data is presented. The aim was to identify optimal remote sensing Vegetation Indices (VIs) to use as time-sensitive indicators of the early response of vegetation after the thinning of sweet chestnut (Castanea Sativa Mill.) coppice. For this, the changes produced at the canopy level by different thinning treatments and their evolution over time (2014–2019) were extracted from VI values corresponding to two trials involving 33 circular plots (r = 10 m). Plots were subjected to one of the following forest management treatments: Control with no intervention (2800–3300 stems ha⁻¹), Treatment 1, one thinning leaving a living stock density of 900–600 stems ha⁻¹ and Treatment 2, a more intensive thinning, leaving 400 stems ha⁻¹. Time series data from Landsat-8 and Sentinel-2 were collected to calculate values for different VIs. Canopy development was computed by comparing the area under curves (AUCs) of different VI time-series annually throughout the study period. Soil-Line VIs were compared to the Normalized Vegetation Index (NDVI) revealing that the Second Modified Chlorophyll Absorption Ratio Index (MCARI2) more clearly demonstrated canopy evolution tendencies over time than the NDVI. MCARI2 data from both L8 and S2 reflected how the influence of treatment on the canopy cover decreases over the years, providing significant differences in the thinning year and the year after. Metrics derived from the MCARI2 time-series also demonstrated the capacity of the canopy to recovery to pretreatment coverage levels. The AUC method generates a specific V-shaped time-signature, the vertex of which coincides with the thinning event and, as such, provides forest managers with another tool to assist decision making in the development of sustainable forest management strategies.

The Leaf Area Index (LAI) is a key parameter that helps to understand the connection between canopy structure and ecosystem functions. In this study, the main aims were to examine the impact of forest management on canopy structure using LiDAR data to characterize the canopy vertical profile, as well as to develop LAI models and an LAI mapping tool for sweet chestnut (Castanea Sativa Mill.) coppice. Twenty-one circular plots (r = 10 m) were established, each of which was submitted to one of the following forest management treatments: Control, with no intervention (3300–3700 stems ha⁻¹); Treatment 1, one thinning to leave a living stock density of 900–600 stems ha⁻¹; or Treatment 2, a more intensive thinning, leaving 400 stems ha⁻¹. A LAI field measurement was made in all plots and the study area was recorded by LiDAR. With the LiDAR, two types of metrics were calculated: standard elevation metrics and canopy metrics. The results showed the different canopy layers of the study area, highlighting how the resprout layer influences the canopy structure of sweet chestnut coppice. By combining the LiDAR data and the LAI field estimates, various linear and nonlinear models were developed and tested, the linear model being found to have the best performance (R² = 0.79) for the study area. With the selected linear model and other LiDAR data of interest such as the 95th percentile, an automatic mapping tool was designed. This tool allows spatially information to be generated that can be used to implement management strategies.

One important part of a forest stand that is impacted by forest management is the canopy structure. Canopy structure is closely related to ecosystem functions as it plays an important role in the relationships between structural complexity, biodiversity and stand productivity. The Leaf Area Index (LAI) is a key parameter that helps to understand the connection between canopy structure and ecosystem functions. In this study, the main aims were to examine the impact of forest management on canopy structure using LiDAR data to characterize the canopy vertical profile, the development of LAI models and a descriptive mapping tool for sweet chestnut (Castanea Sativa Mill.) coppice. Twenty-one circular plots (r=10m) were established, each of which was submitted to one of the following forest management treatments: Control, with no intervention (3300-3700 stems ha^-1); Treatment 1, one thinning to leave a living stock density of 900–600 stems ha^-1; or Treatment 2, a more intensive thinning, leaving 400 stems ha^-1. An LAI field measurement was made in all plots and the study area was recorded by LiDAR. With the LiDAR two types of metrics were calculated: standard elevation metrics and canopy metrics. The latter allowed the complete characterization of the canopy from ground to maximum height. The results showed the different canopy layers of the study area, highlighting how the resprout layer influences the canopy structure of sweet chestnut coppice. By combining the LiDAR data and the LAI field estimates various linear and non-linear models were developed and tested, the linear model being found to have the best accuracy for the study area (Cross-validation: R² (0.79) and RMSE (0.20)). With the selected linear model and other LiDAR data of interest such as the 95^th percentile, an automatic mapping tool was designed. This tool allows spatially information to be generated that can be used to implement management strategies to improve productivity, ecosystem functions, forest management planning and/or fuel management.