Jean-François Prieur

Centre d’Enseignement et de Recherche en Foresterie de Sainte-Foy (CERFO) | CERFO · Remote Sensing

Establishing field inventories can be labor intensive, logistically challenging and expensive. Optimizing a sample to derive accurate forest attribute predictions is a key management-level inventory objective. Traditional sampling designs involving pre-defined, interpreted strata could result in poor selection of within-strata sampling intensities,...

Species identification is a critical factor for obtaining accurate forest inventories. This paper compares the same method of tree species identification (at the individual crown level) across three different types of airborne laser scanning systems (ALS): two linear lidar systems (monospectral and multispectral) and one single-photon lidar (SPL) s...

The objective of this study was to evaluate the effectiveness of three standardization approaches for airborne laser scanning (ALS) feature values used for individual tree species classification. This study is the first effort to assess the transferability of forest tree species classification models derived using monospectral and multispectral ALS...

Field studies have shown that dense tree canopies and regular tree arrangements reduce noise from a point source. In urban areas, noise sources are multiple and tree arrangements are rarely dense. There is a lack of data on the association between the urban tree canopy characteristics and noise in complex urban settings. Our aim was to investigate...

Le secteur forestier canadien a besoin d’information détaillée au sujet de la quantité et des caractéristiques des ressources forestières. Pour répondre à de tels besoins, des systèmes d’inventaire exacts, complets et opportuns qui quantifient spatialement le bois d’œuvre et les autres services écosystémiques liés aux forêts sont nécessaires. Le pr...

The Canadian forest sector requires detailed information regarding the amount and characteristics of the forest resource. To address these needs, inventory systems that spatially quantify timber and other forest related ecosystem services are required, that are accurate, comprehensive and timely. The Assessment of Wood properties using Remote Sensi...

Species classification is a cornerstone in decision-making for environmental conservation as well as for many scientific, and management activities for forest managers. The goal of this research was to explore standardization approaches for airborne laser scanning (ALS) feature values ("classification metrics"). Standardization approaches could hel...

The aim of this paper was to develop a generalized classification model using multispectral airborne laser scanning (ALS) data to provide species or genus level tree identification. We tested the robustness, transferability, and generalizability of the developed method across two sites in Ontario, Canada. We focused on the generalization of the app...

We propose a method for mapping above-ground biomass (AGB) (Mg ha⁻¹) in boreal forests based predominantly on Landsat 8 images and on canopy height models (CHM) generated using interferometric synthetic aperture radar (InSAR) from the Shuttle Radar Topographic Mission (SRTM) and the TanDEM-X mission. The original SRTM digital elevation model (DEM)...

Forest inventory attributes need to be updated regularly to accurately reflect continually changing forest conditions due to fire, harvesting, natural succession, insect infestation and climate change. A data fusion of multispectral satellite images, existing high-resolution Digital Surface Models (DSM) and Shuttle Radar Topographic Mission (SRTM)...

Species identification of individual trees using 3D and intensity features derived from ALS data requires numerous training crowns (e.g., 50/species). These training samples should be representative of within-­‐‑species variability caused by factors such as age. A single dataset of samples valid across a large region (several sites) would be desira...