Evidence of climate effects on the height-diameter relationships of tree species

International audienceAbstractKey messageThe mean temperature from March to September affects the height-diameter relationship of many tree species in France. For most of these species, the temperature effect is nonlinear, which makes the identification of an optimal temperature possible. Increases in mean temperature could impact the volume supply of commercial species by the end of the twenty-first century.ContextHeight-diameter (HD) relationships are central in forestry since they are essential to estimate tree volume and biomass. Since the late 1960s, efforts have been made to generalize models of HD relationships through the inclusion of plot- and tree-level explanatory variables. In some recent studies, climate variables such as mean annual temperature and precipitation have been found to have a significant effect on HD allometry. However, in these studies, the effects were all considered to be linear or almost linear, which supposes that there is no optimal temperature and no optimal precipitation.AimsIn this study, we tested the hypothesis that an optimum effect of temperature and precipitation exists on tree heights.MethodsWe fitted generalized models of HD relationships to 44 tree species distributed across France. To make sure that the climate variables would not hide some differences in terms of the local environment, the models included explanatory variables accounting for competition, tree social status and other plot-level factors such as slope inclination and the occurrence of harvesting in the last five years.ResultsIt turned out that the temperature effect was significant for 33 out of 44 species and an optimum was found in 26 cases. The precipitation effect was linear and was found to be significant for only seven species. Although the two climate variables did not contribute as much as the competition and the social status indices to the model fit, they were still important contributors. Under the representative concentration pathway (RCP) 2.6 and the assumptions of constant form factors and forest conditions in terms of competition and social statuses, it is expected that approximately two thirds of the species with climate-sensitive HD relationships will generally be shorter. This would induce a decrease in volume ranging from 1 to 5% for most of these species.ConclusionForest practitioners should be aware that the volume supply of some commercial species could decrease by the end of the twenty-first century. However, these losses could be partly compensated for by changes in the form factors and the species distributions

Effets des facteurs environnementaux sur la distribution et l'abondance des espèces végétales forestières aux échelles locales et régionales

The objective of this PhD is to analyse and to predict the presence and abundance of understorey species locally by factors linked with stand structure (canopy openness) and according to different environments (soil-pH). Also the aim is to assess whether soil-pH, canopy openness and climate do have similar effects on species distribution, derived on one hand from presence-absence data, and on the other hand from abundance measurements. For this we used floristic and forest dendrometry inventories performed five years after canopy opening (293 gaps - northeastern France) and under closed canopy conditions (10 996 sites - France). Different spatial scales were used to asses the effect of the factors: local, regional and national scale. First we modelled the presence and abundance distributions (i) first at regional scale in relation with local canopy openness and regional soil-pH factors and (ii) secondly at national scale in relation with soil-pH and climate. At regional scale, the results indicate that the combination of a local canopy openness factor and a regional soil-pH affect the distribution of understorey species five years after canopy opening. We succeeded to quantify the optimal requirements of the most common broadleaf tree seedlings and herbaceous species along canopy openness and soil pH gradients. At national scale, our results show that the species distribution, derived from presence-absence data, indicate the ecological optimum of abundance when using climate and soil-pH gradients, however give larger geographical habitats because of overestimation of the ecological amplitude.Cette thèse a pour objectif d'analyser et de prédire la présence et l'abondance des espèces de sous-bois localement aux facteurs liés à la structure du peuplement (ouverture de la canopée) et selon différents contextes de milieu (pH du sol). Ainsi, cette thèse vise à évaluer si l'impact du pH du sol, de l'ouverture de la canopée et du climat a un effet similaire sur la distribution de présence et d'abondance. A cet effet, nous avons utilisé des relevés floristiques et dendrométriques effectués cinq ans après l'ouverture de la canopée (293 trouées - Nord-est de la France) et sous peuplements fermés (10 996 sites - France). Nous avons modélisé les distributions de présence et d'abondance des espèces (i) premièrement, au niveau régional, en relation avec un facteur local d'ouverture de la canopée et un facteur régional de pH du sol et (ii) deuxièmement, au niveau national, en relation avec un facteur de pH du sol et le climat. Au niveau régional, les résultats indiquent que la prise en compte simultanée d'un facteur local d'ouverture de la canopée et d'un facteur régional de pH du sol permet de déterminer les distributions des espèces cinq ans après son ouverture. Nous avons pu quantifier les conditions optimales des principales essences feuillues au stade juvénile et des espèces herbacées associées à l'ouverture de la canopée et le pH du sol. Au niveau national, nos résultats montrent que les modèles de distribution d'espèces issus de données présence-absence indiquent l'optimum écologique de l'abondance selon les gradients climatiques et le pH du sol, mais désignent des habitats géographique trop importants à cause d'une surestimation de l'amplitude écologique

Automated recognition of habitat classes from overhead imagery

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Automated segmentation of land use from overhead imagery

International audienceReliable land cover or habitat maps are an important component of any long-term landscape planning initiatives relying on current and past land use. Particularly in regions where sustainable management of natural resources is a goal, high spatial resolution habitat maps over large areas will give guidance in land-use management. We propose a computational approach to identify habitats based on the automated analysis of overhead imagery. Ultimately, this approach could be used to assist experts, policy and decision makers who promote sustainable agroecology by evaluating habitat services and prioritizing land uses. The overall objective of our project is to classify the evolution of land usage since the advent of aerial imagery. In this paper, our goal is to bring automatic habitat classification to the level achieved by a human expert performing a high spatial resolution classification. This classification consists in identifying habitats such as hedges, lakes, fields, pastures or forests. Therefore, we train a machine vision algorithm to segment an overhead imagery into a dozen of expert-specified land use classes. Relying on the recent developments in machine learning, and in particular deep learning, the best machine vision model appears to be convolutional neural networks (e.g. SegNet, DeepLab). The training was performed using data from a hand-labelled high-resolution (0.5m/pixel) database around the Orne River (Moselle, France-2000km²). Aerial orthophoto are available for two time periods: 2015 and 1955. In addition, we also generated artificial 1955 data from 2015 imagery and used them as learning base for the 1955 imagery as the data available in 2015 provides more quantity and more diversity. The paper highlights the performances of these state-of-the-art machine learning algorithms for land use recognition and segmentation. It shows their potential in the context of studies in environment sciences and environmental decisions. The automatic approach presents an alternative for detailed and accurate land cover maps acquired manually, which are labor intensive and time consuming. The paper also illustrates the potential benefits of generating artificial imagery to pre-train the machine vision model and requires less annotated data. This approach may prove useful for time periods where there is few labeled data