In this paper, we propose an analysis on the joint effect of hyperspectral and light detection and ranging (LIDAR) data for the classification of complex forest areas. In greater detail, we present: 1) an advanced system for the joint use of hyperspectral and LIDAR data in complex classification problems; 2) an investigation on the effectiveness of the very promising support vector machines (SVMs) and Gaussian maximum likelihood with leave-one-out-covariance algorithm classifiers for the analysis of complex forest scenarios characterized from a high number of species in a multisource framework; and 3) an analysis on the effectiveness of different LIDAR returns and channels (elevation and intensity) for increasing the classification accuracy obtained with hyperspectral images, particularly in relation to the discrimination of very similar classes. Several experiments carried out on a complex forest area in Italy provide interesting conclusions on the effectiveness and potentialities of the joint use of hyperspectral and LIDAR data and on the accuracy of the different classification techniques analyzed in the proposed system. In particular, the elevation channel of the first LIDAR return was very effective for the separation of species with similar spectral signatures but different mean heights, and the SVM classifier proved to be very robust and accurate in the exploitation of the considered multisource data.

Topography is a key driver of tropical forest structure and composition, as it constrains local nutrient and hydraulic conditions within which trees grow. Yet, we do not fully understand how changes in forest physiognomy driven by topography impact other emergent properties of forests, such as their aboveground carbon density (ACD). Working in Borneo – at a site where 70‐m‐tall forests in alluvial valleys rapidly transition to stunted heath forests on nutrient‐depleted dip slopes – we combined field data with airborne laser scanning and hyperspectral imaging to characterise how topography shapes the vertical structure, wood density, diversity and ACD of nearly 15 km² of old‐growth forest. We found that subtle differences in elevation – which control soil chemistry and hydrology – profoundly influenced the structure, composition and diversity of the canopy. Capturing these processes was critical to explaining landscape‐scale heterogeneity in ACD, highlighting how emerging remote sensing technologies can provide new insights into long‐standing ecological questions.

Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able – for the first time – to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed – specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the world's forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large‐scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models.

Summary: Forests are a major component of the global carbon cycle, and accurate estimation of forest carbon stocks and fluxes is important in the context of anthropogenic global change. Airborne laser scanning (ALS) data sets are increasingly recognized as outstanding data sources for high‐fidelity mapping of carbon stocks at regional scales. We develop a tree‐centric approach to carbon mapping, based on identifying individual tree crowns (ITCs) and species from airborne remote sensing data, from which individual tree carbon stocks are calculated. We identify ITCs from the laser scanning point cloud using a region‐growing algorithm and identifying species from airborne hyperspectral data by machine learning. For each detected tree, we predict stem diameter from its height and crown‐width estimate. From that point on, we use well‐established approaches developed for field‐based inventories: above‐ground biomasses of trees are estimated using published allometries and summed within plots to estimate carbon density. We show this approach is highly reliable: tests in the Italian Alps demonstrated a close relationship between field‐ and ALS‐based estimates of carbon stocks (r² = 0·98). Small trees are invisible from the air, and a correction factor is required to accommodate this effect. An advantage of the tree‐centric approach over existing area‐based methods is that it can produce maps at any scale and is fundamentally based on field‐based inventory methods, making it intuitive and transparent. Airborne laser scanning, hyperspectral sensing and computational power are all advancing rapidly, making it increasingly feasible to use ITC approaches for effective mapping of forest carbon density also inside wider carbon mapping programs like REDD++.