A comparison of open-source LiDAR filtering algorithms in a mediterranean forest environment

Light detection and ranging (LiDAR) is an emerging remote-sensing technology with potential to assist in mapping, monitoring, and assessment of forest resources. Despite a growing body of peer-reviewed literature documenting the filtering methods of LiDAR data, there seems to be little information about qualitative and quantitative assessment of filtering methods to select the most appropriate to create digital elevation models with the final objective of normalizing the point cloud in forestry applications. Furthermore, most algorithms are proprietary and have high purchase costs, while a few are openly available and supported by published results. This paper compares the accuracy of seven discrete return LiDAR filtering methods, implemented in nonproprietary tools and software in classification of the point clouds provided by the Spanish National Plan for Aerial Orthophotography (PNOA). Two test sites in moderate to steep slopes and various land cover types were selected. The classification accuracy of each algorithm was assessed using 424 points classified by hand and located in different terrain slopes, cover types, point cloud densities, and scan angles. MCC filter presented the best overall performance with an 83.3% of success rate and a Kappa index of 0.67. Compared to other filters, MCC and LAStools balanced quite well the error rates. Sprouted scrub with abandoned logs, stumps, and woody debris and terrain slopes over 15° were the most problematic cover types in filtering. However, the influence of point density and scan-angle variables in filtering is lower, as morphological methods are less sensitive to them

DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning

In this paper, we investigate estimating emergence and biomass traits from color images and elevation maps of wheat field plots. We employ a state-of-the-art deconvolutional network for segmentation and convolutional architectures, with residual and Inception-like layers, to estimate traits via high dimensional nonlinear regression. Evaluation was performed on two different species of wheat, grown in field plots for an experimental plant breeding study. Our framework achieves satisfactory performance with mean and standard deviation of absolute difference of 1.05 and 1.40 counts for emergence and 1.45 and 2.05 for biomass estimation. Our results for counting wheat plants from field images are better than the accuracy reported for the similar, but arguably less difficult, task of counting leaves from indoor images of rosette plants. Our results for biomass estimation, even with a very small dataset, improve upon all previously proposed approaches in the literature.Comment: WACV 2018 (Code repository: https://github.com/p2irc/deepwheat_WACV-2018

lidR : an R package for analysis of Airborne Laser Scanning (ALS) data

Airborne laser scanning (ALS) is a remote sensing technology known for its applicability in natural resources management. By quantifying the three-dimensional structure of vegetation and underlying terrain using laser technology, ALS has been used extensively for enhancing geospatial knowledge in the fields of forestry and ecology. Structural descriptions of vegetation provide a means of estimating a range of ecologically pertinent attributes, such as height, volume, and above-ground biomass. The efficient processing of large, often technically complex datasets requires dedicated algorithms and software. The continued promise of ALS as a tool for improving ecological understanding is often dependent on user-created tools, methods, and approaches. Due to the proliferation of ALS among academic, governmental, and private-sector communities, paired with requirements to address a growing demand for open and accessible data, the ALS community is recognising the importance of free and open-source software (FOSS) and the importance of user-defined workflows. Herein, we describe the philosophy behind the development of the lidR package. Implemented in the R environment with a C/C++ backend, lidR is free, open-source and cross-platform software created to enable simple and creative processing workflows for forestry and ecology communities using ALS data. We review current algorithms used by the research community, and in doing so raise awareness of current successes and challenges associated with parameterisation and common implementation approaches. Through a detailed description of the package, we address the key considerations and the design philosophy that enables users to implement user-defined tools. We also discuss algorithm choices that make the package representative of the ‘state-of-the-art' and we highlight some internal limitations through examples of processing time discrepancies. We conclude that the development of applications like lidR are of fundamental importance for developing transparent, flexible and open ALS tools to ensure not only reproducible workflows, but also to offer researchers the creative space required for the progress and development of the discipline

International Benchmarking of the Individual Tree Detection Methods for Modeling 3-D Canopy Structure for Silviculture and Forest Ecology Using Airborne Laser Scanning

Canopy structure plays an essential role in biophysical activities in forest environments. However, quantitative descriptions of a 3-D canopy structure are extremely difficult because of the complexity and heterogeneity of forest systems. Airborne laser scanning (ALS) provides an opportunity to automatically measure a 3-D canopy structure in large areas. Compared with other point cloud technologies such as the image-based Structure from Motion, the power of ALS lies in its ability to penetrate canopies and depict subordinate trees. However, such capabilities have been poorly explored so far. In this paper, the potential of ALS-based approaches in depicting a 3-D canopy structure is explored in detail through an international benchmarking of five recently developed ALS-based individual tree detection (ITD) methods. For the first time, the results of the ITD methods are evaluated for each of four crown classes, i.e., dominant, codominant, intermediate, and suppressed trees, which provides insight toward understanding the current status of depicting a 3-D canopy structure using ITD methods, particularly with respect to their performances, potential, and challenges. This benchmarking study revealed that the canopy structure plays a considerable role in the detection accuracy of ITD methods, and its influence is even greater than that of the tree species as well as the species composition in a stand. The study also reveals the importance of utilizing the point cloud data for the detection of intermediate and suppressed trees. Different from what has been reported in previous studies, point density was found to be a highly influential factor in the performance of the methods that use point cloud data. Greater efforts should be invested in the point-based or hybrid ITD approaches to model the 3-D canopy structure and to further explore the potential of high-density and multiwavelengths ALS data

Individual Tree Crown Delineation Using Multispectral LiDAR Data

In this study, an improved treetop detection and a region-based segmentation algorithm were developed to delineate Individual Tree Crowns (ITCs) using multispectral Light Detection and Ranging (LiDAR) data. The dataset used for this research was acquired from Teledyne Optechs Titan LiDAR sensor which was operated at three wavelengths: 1550 nm, 1064 nm, and 532 nm. An improved multi-scale method was developed to identify treetops for different crown sizes and merge them via Gaussian fitting. With the improved region growing segmentation method, neutrosophic logic was extensively used to incorporate contextual intensity information in the region merging decision heuristics. The LiDAR positional data was uniquely exploited, in this research, to generate refine crown boundary approximations. The results from the proposed method were compared with manually delineated ITCs to highlight the performance improvements. A 12% increase in the accuracy was observed with the proposed method over the popular Marker Controlled Watershed segmentation technique

Mapping change of functional forest traits and diversity using airborne laser scanning in the canton Aargau 2014-2019

Forests contribute substantially to ecosystem functions and services making their ecological quality valuable. Due to climate change, monitoring diversity is becoming increasingly important to record a possible decline. High functional diversity has been related to a decreasing vulnerability to disturbances like diseases, storms and insect attacks. Remote sensing and especially LiDAR are promising methods to assess functional traits and diversity in forests and have been linked to plant diversity and ecosystem functioning. However, large-scale and multitemporal analyses using LiDAR datasets are just at the beginning. This thesis aims to assess functional forest traits and diversity metrics out of ALS data and to compare them between the years 2014 and 2019. Three morphological traits, namely canopy height, foliage height diversity and plant area index were estimated for the entire forest area of the canton Aargau under defoliated conditions. Then, functional richness and divergence were computed out of the traits. For three subregions of the canton, occlusion in the lower canopy was computed to assess if traits and diversity metrics are influenced. More complex derivations of ALS point clouds, e.g. plant area index, richness or divergence, were found to be more sensitive to external influences like different sensor and flight settings and occluded fractions of the canopy volume. Various spatial patterns of the derived traits and diversity metrics were mapped, e.g. a decrease or smaller increase in steep and high altitude regions. Richness values showed a very large global increase of 123%, which cannot solely be attributed to biotic changes, but is rather caused by the sensitivity to sensor-related factors. The results demonstrate how the development of robust methods for trait and diversity estimations is important. The incorporation of sensor and flight parameters into the estimation methods is crucial for improved performance in multitemporal analyses using ALS point clouds

Consumer‐grade UAV solid‐state LiDAR accurately quantifies topography in a vegetated fluvial environment

To accompany a journal article in Earth Surface Processes & Landforms (ESPL) which details an investigation into the data quality and limitations of using the DJI L1 solid-state LiDAR system at both a controlled test location (Garscube Sports Fields, Glasgow, Scotland) and also in a geomorphic environment (River Feshie, Highlands, Scotland). Included are the following datasets (further detailed in readme file): • Point Clouds o Garscube – 4 x raw, unthinned o Feshie – 6 x thinned to 15cm density o Terrestrial Laser Scanning – 7 x gravel comparison patches • Digital Elevation Model o River Feshie only (see Figure 10 of article) • GNSS points o Garscube – GCPs & Football pitch markings o Feshie – GCPs, check points and vegetatio

Methods for tree cover extraction from high resolution orthophotos and airborne LiDAR scanning in Spanish dehesas

[EN] Dehesas are high value agroecosystems that benefit from the effect tree cover has on pastures. Such effect occurs when tree cover is incomplete and homogeneous. Tree cover may be characterized from field data or through visual interpretation of remote sensing data, both time-consuming tasks. An alternative is the extraction of tree cover from aerial imagery using automated methods, on spectral derivate products (i.e. NDVI) or LiDAR point clouds. This study focuses on assessing and comparing methods for tree cover estimation from high resolution orthophotos and airborne laser scanning (ALS). RGB image processing based on thresholding of the ‘Excess Green minus Excess Red’ index with the Otsu method produced acceptable results (80%), lower than that obtained by thresholding the digital canopy model obtained from the ALS data (87%) or when combining RGB and LiDAR data (87.5%). The RGB information was found to be useful for tree delineation, although very vulnerable to confusion with the grass or shrubs. The ALS based extraction suffered for less confusion as it differentiated between trees and the remaining vegetation using the height. These results show that analysis of historical orthophotographs may be successfully used to evaluate the effects of management changes while LiDAR data may provide a substantial increase in the accuracy for the latter period. Combining RGB and Lidar data did not result in significant improvements over using LIDAR data alone.[ES] Las dehesas son agroecosistemas de alto valor que se benefician del efecto de la cobertura arbórea sobre el pasto. Este efecto facilitador aparece cuando la cobertura arbolada es incompleta y homogénea. La cobertura arbórea puede caracterizarse con datos de campo o mediante fotointerpretación de datos de teledetección, ambas tareas que requieren mucho tiempo. Una alternativa es extraer la cobertura arbórea a partir de imagen aérea, derivados espectrales (i.e. NDVI) o nubes de puntos LiDAR. Este estudio se centra en evaluar y comparar métodos para la estimación de cobertura arbolada a partir de ortofotografías de alta resolución y LiDAR aeroportado (ALS). El procesado de imagen RGB basado en la umbralización del índice ‘Excess green minus excess red’ con el método de Otsu produjo resultados aceptables, algo peores que los obtenidos mediante umbralización del modelo digital de copa obtenido con datos ALS (87%) o al combinar datos RGB y LiDAR (87.5%). La información RGB resultó ser útil para la delineación de copas, aunque muy vulnerable a la confusión con pastos o arbustos. La extracción basada en ALS sufrió menos confusión, ya que diferencia entre el arbolado y otros tipos de vegetación usando la altura. Estos resultados muestran que el análisis de ortofotografías históricas podría usarse para evaluar el efecto en los cambios en la gestión, mientras que los datos LiDAR pueden permitir un aumento sustancial en la precisión en períodos posteriores. Combinar LiDAR y RGB no produjo una mejora sustancial sobre el uso de datos LiDAR.IGN and the Andalusian government are acknowledged for providing the airborne datasets. The study was carried out under the projects LIFE+ bioDehesa (LIFE11/BIO/ES/000726) and FUNDIVER (MINECO, Spain; CGL2015-69186-C2-2-R projects), funded through the LIFE+ program.Borlaf-Mena, I.; Tanase, MA.; Gómez-Sal, A. (2019). Métodos para la estimación de la cabida cubierta a partir de ortofotografías de alta resolución y LiDAR aeroportado en dehesas españolas. Revista de Teledetección. (53):17-32. https://doi.org/10.4995/raet.2019.11320SWORD17325