Detecting Change in Forest Structure with Simulated GEDI Lidar Waveforms: A Case Study of the Hemlock Woolly Adelgid (HWA; Adelges tsugae) Infestation

Abstract

The hemlock woolly adelgid (HWA; Adelges tsugae) is an invasive insect infestation that is spreading into the forests of the northeastern United States, driven by the warmer winter temperatures associated with climate change. The initial stages of this disturbance are difficult to detect with passive optical remote sensing, since the insect often causes its host species, eastern hemlock trees (Tsuga canadensis), to defoliate in the midstory and understory before showing impacts in the overstory. New active remote sensing technologies-such as the recently launched NASA Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar-can address this limitation by penetrating canopy gaps and recording lower canopy structural changes. This study explores new opportunities for monitoring the HWA infestation with airborne lidar scanning (ALS) and GEDI spaceborne lidar data. GEDI waveforms were simulated using airborne lidar datasets from an HWA-infested forest plot at the Harvard Forest ForestGEO site in central Massachusetts. Two airborne lidar instruments, the NASA G-LiHT and the NEON AOP, overflew the site in 2012 and 2016. GEDI waveforms were simulated from each airborne lidar dataset, and the change in waveform metrics from 2012 to 2016 was compared to field-derived hemlock mortality at the ForestGEO site. Hemlock plots were shown to be undergoing dynamic changes as a result of the HWA infestation, losing substantial plant area in the middle canopy, while still growing in the upper canopy. Changes in midstory plant area (PAI 11-12 m above ground) and overall canopy permeability (indicated by RH10) accounted for 60% of the variation in hemlock mortality in a logistic regression model. The robustness of these structure-condition relationships held even when simulated waveforms were treated as real GEDI data with added noise and sparse spatial coverage. These results show promise for future disturbance monitoring studies with ALS and GEDI lidar data.

Development and Validation of LiDAR Sensor Simulators Based on Parallel Raycasting

“…It is necessary to have a platform where the user can build a scene based on the desired target. As seen earlier in this paper, most of these platforms are long-established simulators, especially in the automobilistic area, wherein the sensor simulator is created [ 4 , 18 , 23 , 24 , 25 , 26 , 27 , 28 ]. Here we propose a different approach: to develop or adapt a virtual scene builder so the user can build a scene and choose the sensor scanning locations.…”

“…The sensor behavior model is the heart of any sensor simulator, as it is how the simulator will gather data from the digital targets [ 4 , 6 , 15 , 18 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. This research sought to create a hybrid model, i.e., joining a model based on measurements [ 4 ] with a model based on errors [ 4 ] as an attempt to increase accuracy on the raw data acquisition of the virtual scanning in comparison with ground truth (here a real LiDAR sensor) [ 29 ].…”

“…Tallavajhula [ 15 ] shows in his thesis a LiDAR simulator for robotic navigation and justifies the development of these simulators as an aid in the development and improvement of robot navigation. Boucher et al [ 27 ] simulate GEDI (NASA Global Ecosystem Dynamics Investigation) spaceborne LiDAR waveforms using airborne LiDAR datasets to analyze the mortality rate of hemlock plots by an insect infestation of hemlock wooly adelgid. Yun et al [ 28 ] developed a new multiple-scan methodology to evaluate how much occlusion affects the computation of tree leaf area using simulated LiDAR point clouds from different trees 3D models.…”

“…During the period of this research, it was evident that the sensor simulators in the current literature were built from the perspective of the target problem, and this forces new research projects to develop the simulator from scratch [ 4 , 6 , 15 , 16 , 18 , 23 , 24 , 25 , 26 , 27 , 28 ]. So, the main collaborations of the present work to the field are: A guideline for LiDAR simulator development for generating synthetic dataset with semiautomatic annotation to easily adapt for different fields of research; The development of a sensor simulator based on both sensor models mentioned in [ 4 ], therefore called in this paper as sensor hybrid model [ 29 ]; and A method for calibration of the sensor simulator by comparison with a real sensor.…”

“…During the period of this research, it was evident that the sensor simulators in the current literature were built from the perspective of the target problem, and this forces new research projects to develop the simulator from scratch [4,6,15,16,18,[23][24][25][26][27][28]. So, the main collaborations of the present work to the field are:…”