Forest Data Collection by UAV Lidar-Based 3D Mapping: Segmentation of Individual Tree Information from 3D Point Clouds

In this study, we develop a system for efficiently measuring detailed information of trees in a forest environment using a small unmanned aerial vehicle (UAV) equipped with light detection and ranging (lidar). The main purpose of forest measurement is to predict the volume of wood for harvesting and delineating forest boundaries by tree location. Herein, we propose a method for extracting the position, number of trees, and vertical height of trees from a set of three-dimensional (3D) point clouds acquired by a UAV lidar system. The point cloud obtained from a UAV is dense in the tree’s crown, and the trunk 3D points are sparse because the crown of the tree obstructs the laser beam. Therefore, it is difficult to extract single-tree information from 3D point clouds because the characteristics of 3D point clouds differ significantly from those of conventional 3D point clouds using ground-based laser scanners. In this study, we segment the forest point cloud into three regions with different densities of point clouds, i.e., canopy, trunk, and ground, and process each region individually to extract the target information. By comparing a ground laser survey and the proposed method in an actual forest environment, it is discovered that the number of trees in an area measuring 100 m × 100 m is 94.6% of the total number of trees. The root mean square error of the tree position is 0.3 m, whereas that of the vertical height is 2.3 m, indicating that single-tree information can be measured with sufficient accuracy for forest management.

Sensor Agnostic Semantic Segmentation of Structurally Diverse and Complex Forest Point Clouds Using Deep Learning

Forest inventories play an important role in enabling informed decisions to be made for the management and conservation of forest resources; however, the process of collecting inventory information is laborious. Despite advancements in mapping technologies allowing forests to be digitized in finer granularity than ever before, it is still common for forest measurements to be collected using simple tools such as calipers, measuring tapes, and hypsometers. Dense understory vegetation and complex forest structures can present substantial challenges to point cloud processing tools, often leading to erroneous measurements, and making them of less utility in complex forests. To address this challenge, this research demonstrates an effective deep learning approach for semantically segmenting high-resolution forest point clouds from multiple different sensing systems in diverse forest conditions. Seven diverse point cloud datasets were manually segmented to train and evaluate this model, resulting in per-class segmentation accuracies of Terrain: 95.92%, Vegetation: 96.02%, Coarse Woody Debris: 54.98%, and Stem: 96.09%. By exploiting the segmented point cloud, we also present a method of extracting a Digital Terrain Model (DTM) from such segmented point clouds. This approach was applied to a set of six point clouds that were made publicly available as part of a benchmarking study to evaluate the DTM performance. The mean DTM error was 0.04 m relative to the reference with 99.9% completeness. These approaches serve as useful steps toward a fully automated and reliable measurement extraction tool, agnostic to the sensing technology used or the complexity of the forest, provided that the point cloud has sufficient coverage and accuracy. Ongoing work will see these models incorporated into a fully automated forest measurement tool for the extraction of structural metrics for applications in forestry, conservation, and research.

MÉTODOS DE MEDIÇÃO DE ALTURA EM FRAGMENTO DE FLORESTA ESTACIONAL DECIDUAL

Dificuldades relativas à aquisição e à utilização de hipsômetros contribuem para o uso da estimativa visual da altura de árvores em florestas nativas. Diante disso, este trabalho objetivou comparar o método da estimativa visual com balizamento com o hipsômetro digital Haglof na medição de altura de árvores em um fragmento de Floresta Estacional Decidual. A coleta de dados compreendeu 187 árvores contidas em quatro parcelas permanentes. Para as análises comparativas, os dados foram distribuídos em classes de altura e de diâmetro. A estimativa visual com balizamento apresentou confiabilidade na medição da altura de árvores de até 11 m, não diferindo estatisticamente do hipsômetro digital. Entretanto, observou-se uma tendência de subestimação da altura de árvores maiores. Para árvores com altura superior a 11 m, o hipsômetro digital mostrou-se mais confiável. Palavras-chave: inventário florestal; mensuração florestal; hipsômetro. METHODS OF MEASUREMENT OF HEIGHT IN FRAGMENT OF DECIDUAL STATE FOREST ABSTRACT: Difficulties related to the acquisition and use of hypsometers contribute to the use of the visual estimate of the height of trees in native forests. Therefore, this study aimed to compare the method of visual estimation with beaconing with the Haglof digital hypsometer to measure the height of trees in a fragment of Seasonal Deciduous Forest. The data collection comprised 187 trees contained in four permanent plots. For comparative analysis, the data were distributed in height and diameter classes. The visual estimation with beacon showed reliability in measuring the height of trees up to 11 m, not differing statistically from the digital hypsometer. However, there was a tendency to underestimate the height of larger trees. For trees higher than 11 m, the digital hypsometer was more reliable. Keywords: forest inventory; forest measurement; hypsometer.

CANOPY COVER ESTIMATION FROM SATELLITE DATA FOR <i>ACACIA MANGIUM</i> PLANTATION BASAY, NEGROS ORIENTAL

Forest assessment and measurement can be costly, laborious and time-consuming when done manually. Remote Sensing aids by providing data of sufficient accuracy for large tracts of forest lands in the form of maps. These data can then assist in decision- making for better forest management. This study estimated canopy cover, a primary forest measurement parameter, using remotely- sensed data. Satellite images such as Planetscope and WorldView were used to estimate canopy cover. The results were then compared to measurements obtained from a manual inventory – in this case, of an Acacia mangium plantation. The manual inventory was conducted in a National Greening Program (NGP) site in Basay, Negros Oriental. Field inventory involved a Static Global Navigation Satellite System (GNSS) survey and a Total Station survey to get the accurate location of trees present in the plot. Diameter- at- breast was measured for all trees. Tree height and crown diameter were measured for at least 10 percent of all trees in the plot.