A Novel Approach to 3-D Change Detection in Multitemporal LiDAR Data Acquired in Forest Areas

Abstract

LiDAR data have been widely used to characterize the three-dimensional structure of the forest. However, their use in a multitemporal framework has been quite limited due to the relevant challenges introduced by the comparison of pairs of point clouds. Because of the irregular sampling of the laser scanner and the complex structure of forest areas, it is not possible to perform a point-to-point comparison between the two data. To overcome these challenges, a novel hierarchical approach to the detection of 3-D changes in forest areas is proposed. The method first detects the large changes (e.g., cut trees) by comparing the Canopy Height Models (CHMs) derived from the two LiDAR data. Then, according to an object-based Change Detection (CD) approach, it identifies the single-tree changes by monitoring both the tree-top and the crown volume growth. The proposed approach can compare LiDAR data with significantly different pulse densities, thus allowing the use of many data available in real applications. Experimental results pointed out that the method can accurately detect large changes, exhibiting a low rate of false and missed alarms. Moreover, it can detect changes in terms of single-tree growth which are consistent with the expected growth rates of the considered areas.

Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

“…Despite the great potential of this technology, multi-temporal ALS data have been utilized less, as the availability of two or more surveys in the same area has been limited by acquisition costs as well as by the need of temporal-concomitant field data (e.g., [3,6,9,10]). Recently, organizations, companies, and countries have made an effort to gather multi-temporal datasets in different years (e.g., [11][12][13]) allowing the estimation of biophysical properties in forested areas over time. As a result, height growth has been estimated using the single tree or the area-based approach [14][15][16][17][18][19][20].…”

Monitoring Selective Logging in a Pine-Dominated Forest in Central Germany with Repeated Drone Flights Utilizing A Low Cost RTK Quadcopter

“…Limitations exist due to non-regular acquisitions and the enduring evolution of LiDAR sensors, resulting in increasing point densities and thus complications when comparing two dissimilar datasets. Nevertheless, in the work of Marinelli et al [41,42] a new approach for selective logging using multitemporal LiDAR data in forest areas was proposed and tested in the Trento province of the southern Alps. The two test sites were covered by needle-leaved forest, and the point density of the LiDAR data ranged from 10 to 50 pls/m 2 , with four returns for each pulse.…”

“…The two test sites were covered by needle-leaved forest, and the point density of the LiDAR data ranged from 10 to 50 pls/m 2 , with four returns for each pulse. One benefit of the used LiDAR dataset in [41,42] is the high point density, and thus the high probability to receive ground returns even for small gaps in the canopy.…”

Dynamics of a human‐modified tropical peat swamp forest revealed by repeat lidar surveys

“…For instance , Tompalski et al (2018) developed yield curves by combining lidar and field data acquired at two points in time, and then used a curve matching approach to estimate forest growth. Taking these ideas one step further, Marinelli, Paris, and Bruzzone (2018) and Paris and Bruzzone (2018) attempted to capture dynamics at the tree level using automated crown segmentation approaches to first detect individual trees in the lidar point clouds and then estimate their growth rate based on changes in lidar metrics through time.…”