Aboveground Biomass Estimation in Amazonian Tropical Forests: a Comparison of Aircraft- and GatorEye UAV-borne LiDAR Data in the Chico Mendes Extractive Reserve in Acre, Brazil

Abstract

Tropical forests are often located in difficult-to-access areas, which make high-quality forest structure information difficult and expensive to obtain by traditional field-based approaches. LiDAR (acronym for Light Detection And Ranging) data have been used throughout the world to produce time-efficient and wall-to-wall structural parameter estimates for monitoring in native and commercial forests. In this study, we compare products and aboveground biomass (AGB) estimations from LiDAR data acquired using an aircraft-borne system in 2015 and data collected by the unmanned aerial vehicle (UAV)-based GatorEye Unmanned Flying Laboratory in 2017 for ten forest inventory plots located in the Chico Mendes Extractive Reserve in Acre state, southwestern Brazilian Amazon. The LiDAR products were similar and comparable among the two platforms and sensors. Principal differences between derived products resulted from the GatorEye system flying lower and slower and having increased returns per second than the aircraft, resulting in a much higher point density overall (11.3 ± 1.8 vs. 381.2 ± 58 pts/m2). Differences in ground point density, however, were much smaller among the systems, due to the larger pulse area and increased number of returns per pulse of the aircraft system, with the GatorEye showing an approximately 50% higher ground point density (0.27 ± 0.09 vs. 0.42 ± 0.09). The LiDAR models produced by both sensors presented similar results for digital elevation models and estimated AGB. Our results validate the ability for UAV-borne LiDAR sensors to accurately quantify AGB in dense high-leaf-area tropical forests in the Amazon. We also highlight new possibilities using the dense point clouds of UAV-borne systems for analyses of detailed crown structure and leaf area density distribution of the forest interior.

Precision and Digital Agriculture: Adoption of Technologies and Perception of Brazilian Farmers

“…The developing new remote sensors in satellites, nanosatellites, and UAVs for agriculture have significantly enhance the potential of the applications in last year's [8][9][10], e.g., soil assessment, field mapping and monitoring-variable rate application, fertility, irrigation and drainage, harvest planning, and livestock monitoring. Further, new applications are developed with customized remote sensors with machine learning algorithms and computer vision to gather more accurate data and imagery.…”

“…Proximal and Field Sensors [5][6][7] Medium Remote Sensors [8][9][10] Embedded Electronics, Telemetry, and Automation [12,37] Deep Learning and Internet of Things [17][18][19][20] High Cloud Computing, Big Data, Blockchain and Cryptography [3,15,16] Artificial Intelligence [23] The questionnaire was prepared, organized, and made available to the public through the online platform LimeSurvey under the registration number 25889/2020 [38]. The system was available from 17 April to 2 June 2020, allowing answers to be collected based on specific questions previously defined ( Table 2).…”

Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery

“…In situ measurements of these parameters can be a time-consuming, expensive, and biased task. To assist in plant breeding programs [ 2 ] as well as in precision agriculture practices [ 3 ], remote sensing technologies have been used in multiple approaches [ 4 , 5 , 6 ], and, lately, this has expanded with the implementation of UAV (Unmanned Aerial Vehicle) based-data. In recent years, UAV-based images, in conjunction with robust and intelligent data processing methods, are being used as an alternative to the human-visual inspection of agricultural landscapes [ 7 ].…”

Lidar-Based Aboveground Biomass Estimations for the Maya Archaeological Site of Yaxnohcah, Campeche, Mexico

“…Besides the traditional use of aerial and satellite images, lidar (light detection and ranging) imaging technology produces time-efficient and wall-to-wall estimates [5]. Foresters initially used lidar to assess AGB and carbon and to monitor trees and forests at a finegrained level that previously could only be done with forest surveys over a limited area [6].…”