Claudia Paris

A Three-Dimensional Model-Based Approach to the Estimation of the Tree Top Height by Fusing Low-Density LiDAR Data and Very High Resolution Optical Images

Light detection and ranging (LiDAR) technology has been extensively used for estimating forest attributes. Although high-spatial-density LiDAR data can be used to accurately derive attributes at single tree level, low-density LiDAR data are usually acquired for reducing the cost. However, a low density strongly affects the estimation accuracy due to the underestimation of the tree top and the possible loss of crowns that are not hit by any LiDAR point. In this paper, we propose a 3-D model-based approach to the estimation of the tree top height based on the fusion between low-density LiDAR data and high-resolution optical images. In the proposed approach, the integration of the two remotely sensed data sources is first exploited to accurately detect and delineate the single tree crowns. Then, the LiDAR vertical measures are associated to those crowns hit by at least one LiDAR point and used together with the radius of the crown and the tree apex location derived from the optical image for reconstructing the tree top height by a properly defined parametric model. For the remaining crowns detected only in the optical image, we reconstruct the tree top height by proposing a k-nearest neighbor trees technique that estimates the height of the missed trees as the average of the k reconstructed height values of the trees having most similar crown properties. The proposed technique has been tested on a coniferous forest located in the Italian Alps. The experimental results confirmed the effectiveness of the proposed method.

A Hierarchical Approach to Three-Dimensional Segmentation of LiDAR Data at Single-Tree Level in a Multilayered Forest

Small-footprint high-density LiDAR data provide information on both the dominant and the subdominant layers of the forest. However, tree detection is usually carried out in the Canopy Height Model (CHM) image domain, where not all the dominant trees are distinguishable and the understory vegetation is not visible. To address these issues, we propose a novel method that integrates the analysis of the CHM with that of the point cloud space (PCS) to 1) improve the accuracy in the detection and delineation of the dominant trees and 2) identify and delineate the subdominant trees. By means of a derivative analysis of the horizontal profile of the forest, the method detects the missed crowns and delineates the crown boundaries directly in the PCS. Then, for each segmented crown, the vertical profile is analyzed to identify the presence of subcanopies and extract them. The proposed method does not require any prior knowledge on the stand properties (e.g., crown size and forest density). Experimental results obtained on two LiDAR data sets characterized by different laser point density show that the proposed method always improved the detection rate compared to other state-of-the-art techniques. It correctly detected 97% and 92% of the dominant trees measured in situ in high- and low-density LiDAR data, respectively. Moreover, it automatically identified 77% of the subdominant trees manually extracted by an expert operator in the high-density LiDAR data.

Fusion of high and very high density LiDAR data for 3D forest change detection

Light Detection And Ranging (LiDAR) data have proven to be very effective in the estimation of parameters for forestry applications. However, little research has been done regarding the multitemporal analysis of these data. In this paper we propose a novel hierarchical change detection approach that first performs the detection of major changes (e.g., harvested trees) and then focuses on the detection of minor changes (e.g., single tree growth), using multitemporal LiDAR data having different point densities. Splitting the change detection problem allows us to analyze the different types of changes with different techniques. In particular, the detection of minor changes is carried out directly on the point clouds in order to exploit all the informative content of the LiDAR data. The approach has been tested on a dataset acquired in 2010 and 2014 on a complex forest area located in the Southern Italian Alps. The experimental results confirm the effectiveness of the proposed approach.

A Novel Automatic Method for the Fusion of ALS and TLS LiDAR Data for Robust Assessment of Tree Crown Structure

Tree crown structural parameters are key inputs to studies spanning forest fire propagation, invasive species dynamics, avian habitat provision, and so on, but these parameters consistently are difficult to measure. While airborne laser scanning (ALS) provides uniform data and a consistent nadir perspective necessary for crown segmentation, the data characteristics of terrestrial laser scanning (TLS) make such crown segmentation efforts much more challenging. We present a data fusion approach to extract crown structure from TLS, by exploiting the complementary perspective of ALS. Multiple TLS point clouds are automatically registered to a single ALS point cloud by maximizing the normalized cross correlation between the global ALS canopy height model (CHM) and each of the local TLS CHMs through parameter optimization of a planar Euclidean transform. Per-tree canopy segmentation boundaries, which are reliably obtained from ALS, can then be adapted onto the more irregular TLS data. This is repeated for each TLS scan; the combined segmentation results from each registered TLS scan and the ALS data are fused into a single per-tree point cloud, from which canopy-level structural parameters readily can be extracted.

A hierarchical approach to the segmentation of single dominant and dominated trees in forest areas by using high-density LiDAR data

In this paper we present a hierarchical approach to the segmentation of high-density LiDAR data which aims to automatically detect and delineate the single tree crowns of both the dominant and the dominated layers of the forest. First, we detect the dominant tree crowns by using both the image derived from the LiDAR data and the LiDAR point cloud. Hence, the detected crowns are delineated directly in the LiDAR point cloud by means of a radial angular analysis. Second, the dominated crowns are detected by analyzing the vertical profile of the dominant trees. Finally, we extract the dominated trees, thus reconstructing the structure of the forest. Experiments carried out in a forest area located in the Southern Italian Alps by using very high density LiDAR data (up to 50 points/m2) point out the effectiveness of the proposed approach.

Analysis of multitemporal Sentinel-2 images in the framework of the ESA Scientific Exploitation of Operational Missions

This paper focuses on the scientific preliminary results of the project “S2-4Sci Land and Water - Multitemporal Analysis” funded by the European Space Agency (ESA) in the framework of the Scientific Exploitation of Operational Missions (SEOM). The aim of the project is the development of advanced multitemporal methods tailored on the specific properties of S2 images. The Sentinel-2 (S2) constellation has a huge potential for multitemporal analysis, due to the increased geometrical resolution, the novel spectral capabilities, a swath width of 290Km and the short revisit time. Three main applications and methodological areas are investigated: i) land-cover maps updating, ii) land-cover change detection, and iii) time series analysis. The proposed approaches are briefly described and preliminary results obtained on S2 images are discussed.

A novel technique for tree stem height estimation by fusing low density LiDAR data and optical images

Light detection and ranging (LiDAR) is one of the most efficient remote sensing technologies for the estimation of forest parameters. However, when acquired with a low laser sampling density, LiDAR data fail in providing accurate tree height measures. In order to address this issue, in this paper we propose a novel technique for the reconstruction of tree-top height based on the joint use of low-density LiDAR data and high resolution optical images. The proposed method is based on the following steps: i) detection of all the tree crowns present in the scene by fusing the two remotely sensed data sources; ii) reconstruction of the tree-top height for those crown hit by at least one LiDAR point; iii) estimation of the tree-top height for those crowns without LiDAR points. The proposed technique has been tested on a coniferous forest located in the Italian Alps. The experimental results points out the effectiveness of the proposed method.

A novel data-driven approach to tree species classification using high density multireturn airborne lidar data

Tree species information is crucial for accurate forest parameter estimation. Small footprint high density multireturn Light Detection and Ranging (LiDAR) data contain a large amount of structural details for modelling and thus distinguishing individual tree species. To fully exploit the potential of these data, we propose a data-driven tree species classification approach based on a volumetric analysis of single-tree-point-cloud that extracts features that are able to characterize both the internal and the external crown structure. The method captures the spatial distribution of the LiDAR points within the crown by generating a feature vector representing the threedimensional (3D) crown information. Each element in the feature vector uniquely corresponds to an Elementary Quantization Volume (EQV) of the crown. Three strategies have been defined to generate unique EQVs that model different representations of the crown components. The classification is performed by using a Support Vector Machines (C-SVM) classifier using the histogram intersection kernel that has the enhanced ability to give maximum preference to the key features in high dimensional feature space. All the experiments were performed on a set of 200 trees belonging to Norway Spruce, European Larch, Swiss Pine, and Silver Fir (i.e., 50 trees per species). The classifier is trained using 120 trees and tested on an independent set of 80 trees. The proposed method outperforms the classification performance of the state-of-the-art method used for comparison.

A Sensor-Driven Hierarchical Method for Domain Adaptation in Classification of Remote Sensing Images

This paper presents a sensor-driven hierarchical domain adaptation method that aims at transferring the knowledge from a source domain (RS image where reference data are available) to a different but related target domain (RS image where no labeled reference data are available) for solving a classification problem. Due to the different acquisition conditions, a difference in the source and target distributions of the features representing the same class is generally expected. To solve this problem, the proposed method takes advantage from the availability of multisensor data to hierarchically detect features subspaces where for some classes data manifolds are partially (or completely) aligned. These feature subspaces are associated with invariant physical properties of classes measured by the sensors in the scene, i.e., measures having almost the same behavior in both domains. The detection of these invariant feature subspaces allows us to infer labels of the target samples that result more aligned to the source data for the considered subset of classes. Then, the labeled target samples are analyzed in the full feature space to classify the remaining target samples of the same classes. Finally, for those classes for which none of the sensors can measure invariant features, we perform the adaptation via a standard active learning technique. Experimental results obtained on two real multisensor data sets confirm the effectiveness of the proposed method.

A novel automatic approach to the update of land-cover maps by unsupervised classification of remote sensing images

This paper presents an approach to the update of land-cover maps by classifying Remote Sensing (RS) images in an unsupervised way. The proposed method assumes that: i) an old thematic map is available; ii) no ground truth data are available; iii) the source used to generate the available thematic map is unknown. To classify the most recent RS image available on the considered area, the method automatically extracts from the considered land-cover map a “pseudo” training set. First, a preprocessing phase adapts the map to the properties of the RS data. Then, we perform an automatic “pseudo” training set identification to select the most reliable samples from the existing thematic map. Finally, a consistency check is defined to determine whether the inconsistencies between the updated and the original maps are due to real changes on the ground or classification errors. Experimental results obtained by updating the 2012 Corine Land Cover Map (CLC) in Trentino, Italy, using Sentinel 2 (S2) images confirm the effectiveness of the proposed method.