In this paper, we present a two-stage approach for characterizing the structure of Pinus sylvestris L. stands in forests of central Spain. The first stage was to delimit forest stands using eCognition and a digital canopy height model (DCHM) derived from lidar data. The polygons were then clustered (fc-means algorithm) into forest structure types based on the DCHM data within forest stands. Hypsographs of each polygon and field data validated the separability of structure types. In the study area, 112 polygons of Pinus sylvestris were segmented and classified into five forest structure types, ranging from high dense forest canopy (850 trees ha 1 and Loreys height of 17.4 m) to scarce tree coverage (60 tree ha 1 and Loreys height of 9.7 m). Our results indicate that the best variables for the definition and characterization of forest structure in these forests are the median and standard deviation (S.D.), both derived from lidar data. In these forest types, lidar median height and standard deviation (S.D.) varied from 15.8 m (S.D. of 5.6 m) to 2.6 m (S.D. of 4.5 m). The present approach could have an operational application in the inventory procedure and forest management plans.

Vegetation indices (VI) describe vegetation structure and functioning but they are affected by illumination conditions (IC). Moreover, the fact that the effect of the IC on VI can be stronger than other biophysical or seasonal processes is under debate. Using Google Earth Engine and the latest Landsat Surface Reflectance level 1 data, we evaluated the temporal patterns of IC and two VI, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) in a mountainous tropical forest during the years 1984–2017. We evaluated IC and VI at different times, their relationship with the topography and the correlations between them. We show that IC is useful for understanding the patterns of variation between VI and IC at the pixel level using Landsat sensors. Our findings confirmed a strong correlation between EVI and IC and less between NDVI and IC. We found a significant increase in IC, EVI, and NDVI throughout time due to an improvement in the position of all Landsat sensors. Our results reinforce the need to consider IC to interpret VI over long periods using Landsat data in order to increase the precision of monitoring VI in irregular topography.

Although soil is one of the factors in the biology of Tuber melanosporum (black truffle of Périgord), which has been the subject of much study, there are few quantitative analyses relating to its effects on carpophore production. The aim of this work is to establish the statistical relationship between T. melanosporum productivity in 20 soil surface horizons and their conventional soil properties (granulometric texture, pH, calcareous fractions, organic carbon, total nitrogen, exchangeable cations). The study area is situated in the centre of the Iberian Peninsula under a Mediterranean climate. Statistical analysis shows that the collective influence of these conventional soil features is low, as principal component analysis explains only 27% of the variance in T. melanosporum production. However, a high percentage of active limestone (calcium carbonate extractable with ammonium oxalate) accounts for 40% of the variance in T. melanosporum production. Active limestone is very important to T. melanosporum because it regulates soil pH and exchangeable Ca2+ availability and participates in the organisation and maintenance of the soil structure; it also plays a role in the truffle’s nutrition. These results obtained for active limestone may be of use in truffle cultivation.