Estimation de la diversité taxonomique de la canopée supérieure d'une forêt tropicale humide à partir de données de hyperspectrales

Tropical forests are the largest reservoir of terrestrial biodiversity. Today, this biodiversity is rapidly eroding due to climate change, land use change and human pressure. Management and monitoring of tropical forests are difficult and costly in terms of financial and human resources. Forest inventories are generally conducted at a limited spatial scale and our knowledge of floristic composition and species distribution in tropical forests is still incomplete. Remote sensing data are a promising tool for the development of biodiversity monitoring systems and the development of hyperspectral sensors has greatly contributed to advances in remote sensing vegetation analysis. Spectral diversity, here considered as the variation in space of the spectral information, can be calculated as the total variance of the reflectance table. The structure of spectral variance itself and its relationship to the compositional structure of tropical forest communities has not been thoroughly! studied yet, mainly due to lack of sufficient field data. However, in an operational framework of biodiversity estimation without prior identification of species, it is essential to address this issue in order to understand precisely what the spectral signal is able to measure of taxonomic diversity, but also to establish the potential and limitations of current space hyperspectral imaging sensors (EnMAP, DESIS, PRISMA), and the instrumental needs in terms of spectral information and spatial resolution for future satellite missions (SBG, CHIME, HYSP).Thus, our objective is to explore the relationship between taxonomic and spectral diversity derived from airborne hyperspectral imaging acquisitions. Specifically, we want to assess the strength and sensitivity of the relationship between taxonomic and alpha and beta spectral diversity at the plot scale of our study site: is it possible, on a highly diverse landscape, to measure subtle compositional differences using hyperspectral imagery?We used georeferenced forest inventory data Abstract 165 collected at the Paracou experimental rainforest station in French Guiana, complemented by airborne acquisitions including very high spatial resolution hyperspectral imagery from visible to near infrared, high spatial resolution orthophotos and LiDAR data. We analysed the influence of different data preprocessing and evaluated the limitations of the spectral variance approach.We finally applied spectral variance partitioning to Paracou plots to evaluate the ability of the method to estimate alpha and beta diversity of canopy species in an operational context. This thesis work confirms the potential of hyperspectral remote sensing for vegetation analysis, but also highlights the fact that the ability of these data to estimate biodiversity directly at a global scale should not be overestimated. While remote sensing data are a powerful tool for monitoring vegetation, over less contrasting and biologically diverse landscapes, spectral diversity is not a reliable indicator of local biodiversity. Large-scale mapping of biodiversity on different ecosystem types using spectral data is not yet within reach: the methods used have proven to be not robust enough and, above all, not generalizable enough to succeed without abundant, high-quality floristic surveys to calibrate the estimation models.L'objectif principal du projet de recherche proposé est d'explorer les relations entre la diversité spectrale et la diversité biologique (taxonomique et fonctionnelle) dans les forêts néotropicales et leur lien avec la résolution spectrale et spatiale afin de clarifier la pertinence et la portée d'une future mission satellite hyperspectrale pour l'observation et la surveillance de la biodiversité forestièr

Mapping tropical forest diversity from multi- and hyper-spectral imagery

International audienceTropical forests are receiving increasing attention given the rapid loss of biodiversity due to the combined action of global warming and increasing anthropogenic pressure. There is a need to go beyond monitoring deforestation and to set up monitoring systems capable of assessing tropical forest degradation in terms of biodiversity.We use a botanical inventory where each trunk is identified on the ground as well as a partial inventory of the crowns segmented by LiDAR data on very high resolution images to explore the relationships between spectral and biological diversity (taxonomic and functional) in neotropical forests and its sensitivity to spectral and spatial resolution to help clarify the relevance and scope of a future hyperspectral satellite mission heralding an observation system to monitor the evolution of forest biodiversity.Hyperspectral data, because of their high dimensionality, are complex: methodological needs are mainly related to the quality of the estimators and their resistance to noise, due to spectral and spatial variability of the observed elements. Therefore, we need to find the right approach to be able to make the link with the diversity measured on the ground and to determine what can be measured via spectral diversity.The choice of a diversity metric requires a preliminary analysis in order to have a robust interpretation of the signal: knowing what is being measured when measuring pixel scale spectral diversity and what are the factors and components of spectral diversity. In a first analysis, we aim to estimate the intraspecific variability of species and delineated crowns and characterize the contribution of SWIR data on species inter/intra variability and separability

Exploring the link between spectral variance and upper canopy taxonomic diversity in a tropical forest: influence of spectral processing and feature selection

International audienceThe rapid loss of biodiversity in tropical rainforests calls for new remote sensing approaches capable of providing rapid estimates of biodiversity over large areas. Imaging spectroscopy has shown potential for the estimation of taxonomic diversity, but the link with spectral diversity has not been investigated extensively with experimental data so far. We explored the relationship between taxonomic diversity and visible to near infrared spectral variance derived from various spectral processing techniques by means of a labeled dataset comprising 2000 individual tree crowns from 200 species from an experimental tropical forest station in French Guiana. We generated a set of artificially assembled communities covering a broad range of taxonomic diversity from this experimental dataset. We analyzed the impact of various processing steps: spectral normalization, spectral transformation through principal component analysis, and feature selection. Correlation between taxonomic diversity and inter-specific spectral variance was strong. Correlation was lower with total spectral variance, with or without normalization and transformation. Dimensionality reduction through feature selection resulted in dramatic improvement of the correlation between Shannon index and spectral variance. While airborne diversity mapping of tropical forest may not be at hand yet, our results confirm that spectral diversity metrics, when computed on properly preprocessed and selected spectral information can predict taxonomic diversity in tropical ecosystems