Abstract
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.
Highlights
Sustainable forest management plays a central role in achieving the 2030 climate targets that were endorsed by the European Council in 2014
This study presents useful information for forest managers of sweet chestnut coppice by providing an approach for detecting and monitoring changes in this particular forest ecosystem
The forest change analyzed is directly related to forest management, i.e., thinning treatments
Summary
Sustainable forest management plays a central role in achieving the 2030 climate targets that were endorsed by the European Council in 2014. The indirect methods can be divided into two different categories: allometric equations and optical methods The former estimates LAI based on empirical regression with other forest variables, such as diameter at breast height (DBH) while the latter uses a logarithmic relationship based on gap fraction measurements or canopy transmittance [17]. While indirect methods can be used to cover a larger area than direct methods, the reality is that the results can only be applied to the scale at which they were measured This limits the utility of these methods in terms of spatial coverage, and the results cannot be applied at the landscape level [20]
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