A Validated and Accurate Method for Quantifying and Extrapolating Mangrove Above-Ground Biomass Using LiDAR Data

LiDAR data and derived canopy height models can provide useful information about mangrove tree heights that assist with quantifying mangrove above-ground biomass. This study presents a validated method for quantifying mangrove heights using LiDAR data and calibrating this against plot-based estimates of above-ground biomass. This approach was initially validated for the mangroves of Darwin Harbour, in Northern Australia, which are structurally complex and have high species diversity. Established relationships were then extrapolated to the nearby West Alligator River, which provided the opportunity to quantify biomass at a remote location where intensive fieldwork was limited. Relationships between LiDAR-derived mangrove heights and mean tree height per plot were highly robust for Ceriops tagal, Rhizophora stylosa and Sonneratia alba (r2 = 0.84–0.94, RMSE = 0.03–0.91 m; RMSE% = 0.07%–11.27%), and validated well against an independent dataset. Additionally, relationships between the derived canopy height model and field-based estimates of above-ground biomass were also robust and validated (r2 = 0.73–0.90, RMSE = 141.4 kg–1098.58 kg, RMSE% of 22.94–39.31%). Species-specific estimates of tree density per plot were applied in order to align biomass of individual trees with the resolution of the canopy height model. The total above-ground biomass at Darwin Harbour was estimated at 120 t ha−1 and comparisons with prior estimates of mangrove above-ground biomass confirmed the accuracy of this assessment. To establish whether accurate and validated relationships could be extrapolated elsewhere, the established relationships were applied to a LiDAR-derived canopy height model at nearby West Alligator River. Above-ground biomass derived from extrapolated relationships was estimated at 206 t ha−1, which compared well with prior biomass estimates, confirming that this approach can be extrapolated to remote locations, providing the mangrove forests are biogeographically similar. The validated method presented in this study can be used for reporting mangrove carbon storage under national obligations, and is useful for quantifying carbon within various markets.

Comparison and Evaluation of Different Pit-Filling Methods for Generating High Resolution Canopy Height Model Using UAV Laser Scanning Data

As a common form of light detection and ranging (LiDAR) in forestry applications, the canopy height model (CHM) provides the elevation distribution of aboveground vegetation. A CHM is traditionally generated by interpolating all the first LiDAR echoes. However, the first echo cannot accurately represent the canopy surface, and the resulting large amount of noise (data pits) also reduce the CHM quality. Although previous studies concentrate on many pit-filling methods, the applicability of these methods in high-resolution unmanned aerial vehicle laser scanning (UAVLS)-derived CHMs has not been revealed. This study selected eight widely used, recently developed, representative pit-filling methods, namely first-echo interpolation, smooth filtering (mean, medium and Gaussian), highest point interpolation, pit-free algorithm, spike-free algorithm and graph-based progressive morphological filtering (GPMF). A comprehensive evaluation framework was implemented, including a quantitative evaluation using simulation data and an additional application evaluation using UAVLS data. The results indicated that the spike-free algorithm and GPMF had excellent visual performances and were closest to the real canopy surface (root mean square error (RMSE) of simulated data were 0.1578 m and 0.1093 m, respectively; RMSE of UAVLS data were 0.3179 m and 0.4379 m, respectively). Compared with the first-echo method, the accuracies of the spike-free algorithm and GPMF improved by approximately 23% and 22%, respectively. The pit-free algorithm and highest point interpolation method also have advantages in high-resolution CHM generation. The global smooth filter method based on the first-echo CHM reduced the average canopy height by approximately 7.73%. Coniferous forests require more pit-filling than broad-leaved forests and mixed forests. Although the results of individual tree applications indicated that there was no significant difference between these methods except the median filter method, pit-filling is still of great significance for generating high-resolution CHMs. This study provides guidance for using high-resolution UAVLS in forestry applications.

Use of an Unmanned Aerial Vehicle and Sound Localization to Determine Bird Microhabitat

Study of bird microhabitat use is time consuming and labour intensive. Our objective was to present a proof of concept of how emerging, high-resolution bird survey methods can be combined with vegetation data collected via unmanned aerial vehicles to accurately and efficiently quantify bird microhabitat. We used sound localization to determine Mourning Warbler (Geothlypis philadelphia) songposts, and a hybrid light detection and ranging/digital aerial photogrammetry canopy height model to demonstrate how Mourning Warblers use regenerating vegetation on reclaimed wellsites. We identified differences in vegetation heights at locations used by Mourning Warblers versus random background locations on a reclaimed wellsite, with sound localization and the canopy height model both providing measurements with 1-metre resolution (t = -3.45, p-value = 0.002). These technologies have the potential to provide large numbers of accurate bird locations that can be associated with high-resolution, spatially explicit vegetation metrics, and be used in different ecological niche modeling frameworks.

THE EFFECTS OF COHERENCE CALCULATION ON FOREST HEIGHT ESTIMATION USING SINC MODEL

In this paper, the effects of coherence on forest height estimation by SINC model based on Tandem-X InSAR data were explored. First, different coherence calculation methods and different window sizes were used to obtain interferometric coherence images. Then, the forest heights were obtained based on SINC model. Finally, the estimated forest heights were validated against reference data from airborne LIDAR CHM (Canopy height model, CHM). The results showed that the coherence calculation algorithm affect the forest height inversion results with SINC model. The algorithm using only phase information for coherence calculation show better performance than the other one using both magnitude and phase information. Meanwhile, window size selecting for coherence calculation also affect the forest height estimation results. In this study, window size with 9 × 9 shows best agreement with the forest height extracted from LiDAR CHM. The R2 and RMSE are 0.656 and 3.54 m, respectively.

OPTIMIZED POST-PROCESSING OF MULTIPLE UAV IMAGES FOR FORESTRY INSPECTIONS

The following paper discusses possible optimized post-processing and data tracking of UAV imagery for forestry inspection. The survey took place in the National Natural Reserve Božídarské rašeliniště – The Wetland of Božídar from 2015 till now. The purpose of this study is to provide with a suitable post-processing method of UAV images in a protected area with no necessity of human interaction with the species. The authors used UAV imagery from RGB and multispectral sensors. The focus of the paper is the post-processing which relies solely on open-source tools. The results of the paper are a script for automatic computation of vegetation indices, a script for canopy height model in a certain part of the mapped area a possible GIS solution for storing and tracking the data.