Background and Aims Analysis of anatomical sections of wood provides important information for understanding the secondary growth and development of plants. This study reports on a new method for the automatic detection and characterization of cell files in wood images obtained by light microscopy. To facilitate interpretation of the results, reliability coefficients have been determined, which characterize the files, their cells and their respective measurements. Methods Histological sections and blocks of the gymnosperms Pinus canariensis, P. nigra and Abies alba were used, together with histological sections of the angiosperm mahogany (Swietenia spp.). Samples were scanned microscopically and mosaic images were built up. After initial processing to reduce noise and enhance contrast, cells were identified using a ‘watershed’ algorithm and then cell files were built up by the successive aggregation of cells taken from progressively enlarged neighbouring regions. Cell characteristics such as thickness and size were calculated, and a method was developed to determine the reliability of the measurements relative to manual methods. Key Results Image analysis using this method can be performed in less than 20 s, which compares with a time of approx. 40 min to produce the same results manually. The results are accompanied by a reliability indicator that can highlight specific configurations of cells and also potentially erroneous data. Conclusions The method provides a fast, economical and reliable tool for the identification of cell files. The reliability indicator characterizing the files permits quick filtering of data for statistical analysis while also highlighting particular biological configurations present in the wood sections.

Recent literature reflects the substantial progress in combining spatial, temporal and spectral capacities for remote sensing applications. As a result, new issues are arising, such as the need for methodologies that can process simultaneously the different dimensions of satellite information. This paper presents PLS regression extended to three-way data in order to integrate multiwavelengths as variables measured at several dates (time-series) and locations with Sentinel-2 at a regional scale. Considering that the multi-collinearity problem is present in remote sensing time-series to estimate one response variable and that the dataset is multidimensional, a multiway partial least squares (N-PLS) regression approach may be relevant to relate image information to ground variables of interest. N-PLS is an extension of the ordinary PLS regression algorithm where the bilinear model of predictors is replaced by a multilinear model. This paper presents a case study within the context of agriculture, conducted on a time-series of Sentinel-2 images covering regional scale scenes of southern France impacted by the heat wave episode that occurred on 28 June 2019. The model has been developed based on available heat wave impact data for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August 2019. The results validated the effectiveness of the proposed N-PLS method in estimating yield loss from spectral and temporal attributes. The performance of the model was evaluated by the R² obtained on the prediction set (0.661), and the root mean square of error (RMSE), which was 10.7%. Limitations of the approach when dealing with time-series of large-scale images which represent a source of challenges are discussed; however, the N–PLS regression seems to be a suitable choice for analysing complex multispectral imagery data with different spectral domains and with a clear temporal evolution, such as an extreme weather event.