Poplars are one of the most widespread fast-growing tree species used for forest plantations. Owing to their distinct features (fast growth and short rotation) and the dependency on the timber price market, poplar plantations are characterized by large inter-annual fluctuations in their extent and distribution. Therefore, monitoring poplar plantations requires a frequent update of information–not feasible by National Forest Inventories due to their periodicity–achievable by remote sensing systems applications. In particular, the new Sentinel-2 mission, with a revisiting period of 5 days, represents a potentially efficient tool for meeting this need. In this paper, we present a deep learning approach for mapping poplar plantations using Sentinel-2 time series. A reference dataset of poplar plantations was available for a large study area of more than 46,000 km² in Northern Italy and served as training and testing data. Two classification methods were compared: (1) a fully connected neural network (also called multilayer perceptron), and (2) a traditional logistic regression. The performance of the two approaches was estimated through bootstrapping procedure with a confidence interval of 99%. Results indicated for deep learning an omission error rate of 2.77%±2.76%, showing improvements compared to logistic regression, omission error rate = 8.91%±4.79%. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

Accurate estimates of canopy cover (CC) are central for a wide range of forestry studies. As direct measurements are impractical, indirect optical methods have often been used to estimate CC from the complement of gap fraction measurements obtained with restricted-view sensors. In this short note we evaluated the influence of the image pixel resolution (ground sampling distance; GSD) on CC estimation in poplar plantations obtained from field (cover photography; GSD < 1 cm), unmanned aerial (UAV; GSD <10 cm) and satellite (Sentinel-2; GSD = 10 m) imagery. The trial was conducted in poplar tree plantations in Northern Italy, with varying age and canopy cover. Results indicated that the coarser resolution available from satellite data is suitable to obtain estimates of canopy cover, as compared with field measurements obtained from cover photography; therefore, S2 is recommended for larger scale monitoring and routine assessment of canopy cover in poplar plantations. The higher resolution of UAV compared with Sentinel-2 allows finer assessment of canopy structure, which could also be used for calibrating metrics obtained from coarser-scale remote sensing products, avoiding the need of ground measurements. © 2021 Centro di Ricerca per la Selvicoltura, Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria. All rights reserved.

Monitoring tree functional traits is essential for understanding forest ecosystems' capability to respond to climate change. Advancements in continuous proximal sensors and IoT technologies hold great potential for monitoring forest and tree ecosystem processes at the finest spatial and temporal scale. An example is the TreeTalker (TT) technology, which features sensors for measurements of the radial growth, sap flow, multispectral light transmission, air temperature, and humidity at tree level with an hourly frequency rate. Such information can be linked with remote sensing data acquired by the Sentinel-2 (S2) mission, allowing for scaling results over more spatially extensive areas. Firstly, we compared six TT with four S2 spectral bands with similar wavelengths. No correlation was found for blue, green and red channels (R² ranged between 0.04 and 0.09) while higher values were found for the near-infrared channel (R² = 0.9). To obtain an accurate prediction of TTs bands, also for those TTs bands which wavelengths are not similar to that of S2 bands, we implemented a Sentinel-2 to TreeTalker model (S2TT) by using an 8-layers fully connected deep neural network. The model was tested by using 23 Sentinel-2 imagery and data acquired by 40 TreeTalkers located in two different sites in Tuscany (a beech and a silver fir forest stand) in the period between 2020-07-15 and 2020-11-15. The R² ranged between 0.61 (B7, blue) and 0.96 (B6, near-infrared band). The S2TT model represents the first link between remote sensing and TreeTalkers, which might allow predicting tree functional traits using Sentinel-2 imagery. © 2021, Italian Society of Remote Sensing. All rights reserved.

The dataset reports data collected in 38 square (50 x 50m) 0.25 ha plots representative of poplar plantations in Lombardy Region (Northern Italy), which were used to calibrate optical information derived from unmanned aerial vehicle (UAV) and satellite (Sentinel-2) sensors. In each plot, the diameter at breast height was measured using a caliper; height, stem and crown volume of each tree were then derived from diameter using allometric equations developed in an independent study. Additional canopy attributes (foliage and crown cover, crown porosity, leaf area index) were derived in each plot from 12-20 optical images collected using digital cover photography (DCP). The collected data allows characterizing the assessment of structure of these plantations, along with their variation over the rotation time. Canopy and crown data also enable the evaluation of optimal rotation and tree spacing, as well as the relationship between stand and canopy structure. The raw datasets consist of 2,591 records (trees) associated with inventory measurements and 616 records (images) associated with optical canopy measurements. An R code was also provided to calculate plot-level attributes from raw data. Dataset and associated metadata are freely available at http://dx.doi.org/10.17632/ycr7w5pvkt.1. © 2021 Centro di Ricerca per la Selvicoltura, Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria. All rights reserved.