LiDAR for Forestry

The trees in pastures are recognized for the benefits they provide to livestock, farmers, and the environment; nevertheless, their study has been restricted to small areas, making it difficult to upscale this information to national levels. For tropical developing countries, it is particularly important to understand the contribution of these systems to national carbon budgets. However, the costs associated with performing field measurements might limit the acquisition of this information. The use of unoccupied aerial systems (UAS) for ecological surveys has proved useful for collecting information at larger scales and with significantly lower costs. This study proposes a methodology that integrates field and UAS surveys to study trees on pasture areas across different terrain conditions. Our overall objective was to test the suitability of UAS surveys to the estimation of aboveground biomass (AGB), relying mainly on open-source software. The tree heights and crown diameters were measured on 0.1-hectare circular plots installed on pasture areas on livestock farms in the Amazon and Coastal regions in Ecuador. An UAS survey was performed on 1-hectare plots containing the circular plots. Field measurements were compared against canopy-height model values and biomass estimates using the two sources of information. Our results demonstrate that UAS surveys can be useful for identifying tree spatial arrangements and provide good estimates of tree height (RMSE values ranged from 0.01 to 3.53 m), crown diameter (RMSE values ranged from 0.04 to 4.47 m), and tree density (density differences ranging from 21.5 to 64.3%), which have a direct impact on biomass estimates. The differences in biomass estimates between the UAS and the field-measured values ranged from 25 to 75%, depending on site characteristics, such as slope and tree coverage. The results suggest that UASs are reliable and feasible tools with which to study tree characteristics on pastures, covering larger areas than field methods only.

Logging causes damage on residual trees, with differing characteristics and severities. The causal agent, as well as the size and type of injury, is influenced by the type of machines, the harvesting technology adopted, and the machine operator. This study descriptively documents residual tree damage observed in two sledge-yarding operations conducted under contrasting stand and operational conditions: a beech stand managed with a fulltree system and a Scots pine stand managed with a cut-to-length system. Two stands were selected: the harvesting intensity was 50% in the coniferous stand (salvage logging) and 20% in the deciduous stand (thinning). In each stand, six 20 × 20 m plots (0.04 ha) were delineated to assess residual tree damage. In the two observed cases, the beech operation showed a higher proportion of damaged residual trees, 32.2%, than the Scots pine operation, 5.3%. In the deciduous stand, bark injuries were mainly slight wood exposure (75%), whereas in the coniferous stand, crushed bark (42.9%) was most frequent, followed by slight wood exposure (35.7%). No concerning damage to seedlings was detected. In general, the number of damaged trees and the severity of injuries were considerably lower than those typically observed when extracting with a cable skidder, and especially with an adapted farm tractor. To reduce mechanical damage to residual trees, protective devices can be deployed around trees at risk of root and stem injury. Another effective measure is to financially motivate workers to implement environmentally sound forest operations.

A exploração madeireira na Amazônia é importante à economia regional, mas enfrenta entraves de gestão, identificação taxonômica e qualidade de dados. Este estudo quantifica e analisa a exploração madeireira no Acre (2009Acre ( -2023) ) com base em 457 AUTEX (volumes autorizados) e DOF (volumes efetivamente transportados), padronizando nomenclatura científica, segmentando espécies por Pareto (Grupos A-C) e estimando a Intensidade de Exploração. A variação espacial foi avaliada por mapas de densidade (KDE) no QGIS; análises descritivas e classificações foram conduzidas no SAS 9.4. De 2009 a 2023, totalizaram-se 5.810,595 m³ autorizados dos quais 3.232,424 m³ foram efetivamente explorados na floresta gerando uma intensidade de exploração (IE) média do período de 62,3%. Do total de volume explorado, 78,9% esteve concentrado e, 16 espécies florestais com destaque para Dipteryx spp., Apuleia leiocarpa e Ceiba spp.. Observou-se deslocamento geográfico dos polos de manejo, antes concentrados em Bujari, Sena Madureira e Rio Branco, em direção a Feijó e Tarauacá, com Rio Branco, Porto Acre, Sena Madureira e Feijó como principais destinos/logísticos. A IE apresentou forte heterogeneidade: medianas acima de 80% para Dipteryx spp. e Couratari spp., e patamares entre 30-50% para Hura crepitans, Eschweilera spp. e Castilla ulei. Resultados indicam gargalos de identificação, limitações operacionais/mercadológicas e potenciais riscos de superexploração em táxons de alto valor. Recomenda-se (i) estudos para a diversificação o uso madeireiro ampliando o leque de espécies; (ii) integrar a IE como métrica de alerta em Sinaflor/DOF+ e sistemas estaduais acima de 90%.

The airborne lidar system (ALS) provides a means to efficiently monitor the status of remote tropical forests and continues to be the subject of intense evaluation. However, the cost of ALS acquisition can vary significantly depending on the acquisition parameters, particularly the return density (i.e., spatial resolution) of the lidar point cloud. This study assessed the effect of lidar return density on the accuracy of lidar metrics and regression models for estimating aboveground biomass (AGB) and basal area (BA) in tropical peat swamp forests (PSF) in Kalimantan, Indonesia. A large dataset of ALS covering an area of 123,000 ha was used in this study. This study found that cumulative return proportion (CRP) variables represent a better accumulation of AGB over tree heights than height-related variables. The CRP variables in power models explained 80.9% and 90.9% of the BA and AGB variations, respectively. Further, it was found that low-density (and low-cost) lidar should be considered as a feasible option for assessing AGB and BA in vast areas of flat, lowland PSF. The performance of the models generated using reduced return densities as low as 1/9 returns per m 2 also yielded strong agreement with the original high-density data. The use model-based statistical inferences enabled relatively precise estimates of the mean AGB at the landscape scale to be obtained with a fairly low-density of 1/4 returns per m 2 , with less than 10% standard error (SE). Further, even when very low-density lidar data was used (i.e., 1/49 returns per m 2 ) the bias of the mean AGB estimates were still less than 10% with a SE of approximately 15%. This study also investigated the influence of different DTM resolutions for normalizing the elevation during the generation of forest-related lidar metrics using various return densities point cloud. We found that the high-resolution digital terrain model (DTM) had little effect on the accuracy of lidar metrics calculation in PSF. The accuracy of low-density lidar metrics in PSF was more influenced by the density of aboveground returns, rather than the last return. This is due to the flat topography of the study area. The results of this study will be valuable for future economical and feasible assessments of forest metrics over large areas of tropical peat swamp ecosystems.

The airborne lidar system (ALS) provides a means to efficiently monitor the status of remote tropical forests and continues to be the subject of intense evaluation. However, the cost of ALS acquisition can vary significantly depending on the acquisition parameters, particularly the return density (i.e., spatial resolution) of the lidar point cloud. This study assessed the effect of lidar return density on the accuracy of lidar metrics and regression models for estimating aboveground biomass (AGB) and basal area (BA) in tropical peat swamp forests (PSF) in Kalimantan, Indonesia. A large dataset of ALS covering an area of 123,000 ha was used in this study. This study found that cumulative return proportion (CRP) variables represent a better accumulation of AGB over tree heights than height-related variables. The CRP variables in power models explained 80.9% and 90.9% of the BA and AGB variations, respectively. Further, it was found that low-density (and low-cost) lidar should be considered as a feasible option for assessing AGB and BA in vast areas of flat, lowland PSF. The performance of the models generated using reduced return densities as low as 1/9 returns per m 2 also yielded strong agreement with the original high-density data. The use model-based statistical inferences enabled relatively precise estimates of the mean AGB at the landscape scale to be obtained with a fairly low-density of 1/4 returns per m 2 , with less than 10% standard error (SE). Further, even when very low-density lidar data was used (i.e., 1/49 returns per m 2 ) the bias of the mean AGB estimates were still less than 10% with a SE of approximately 15%. This study also investigated the influence of different DTM resolutions for normalizing the elevation during the generation of forest-related lidar metrics using various return densities point cloud. We found that the high-resolution digital terrain model (DTM) had little effect on the accuracy of lidar metrics calculation in PSF. The accuracy of low-density lidar metrics in PSF was more influenced by the density of aboveground returns, rather than the last return. This is due to the flat topography of the study area. The results of this study will be valuable for future economical and feasible assessments of forest metrics over large areas of tropical peat swamp ecosystems.

The airborne lidar system (ALS) provides a means to efficiently monitor the status of remote tropical forests and continues to be the subject of intense evaluation. However, the cost of ALS acquisition can vary significantly depending on the acquisition parameters, particularly the return density (i.e., spatial resolution) of the lidar point cloud. This study assessed the effect of lidar return density on the accuracy of lidar metrics and regression models for estimating aboveground biomass (AGB) and basal area (BA) in tropical peat swamp forests (PSF) in Kalimantan, Indonesia. A large dataset of ALS covering an area of 123,000 ha was used in this study. This study found that cumulative return proportion (CRP) variables represent a better accumulation of AGB over tree heights than height-related variables. The CRP variables in power models explained 80.9% and 90.9% of the BA and AGB variations, respectively. Further, it was found that low-density (and low-cost) lidar should be consi...

Modeling the 3D canopy structure of trees provides the structural mapping capability on which to assign distributed values of light-driven physiological processes in tree canopies. We evaluate the potential of automatically extracted skeletons from terrestrial lidar data as a basis for modeling canopy structure. The automatic and species independent evaluation method for lidar data of trees is based on the SKELTRE algorithm. The SKELTRE skeleton is a graphical representation of the branch hierarchy. The extraction of the branch hierarchy utilizes a graph splitting procedure to extract the branches from the skeleton. Analyzing the distance between the point cloud points and the skeleton is the key to the branch diameter. Frequency distributions of branch length and diameter were chosen to test the algorithm performance in comparison to manually measured data and resulted in a correlation of up to 0.78 for the branch length and up to 0.99 for the branch diameter.

This paper presents a new method for individual tree measurement from Airborne LiDAR data. This method involves 3 steps; 1) individual tree crown delineation based on density of high points (DHP), 2) tree filtering, and 3) measurement of tree trunk diameter at breast height (DBH). In the second step, a special tree filtering algorithm is introduced which combines a histogram analysis and region growing (RG) segmentation method. In forest area, undergrowth vegetation is considered as noise and it should be removed to ease the DBH measurement process of trees. The DBH measurement on point cloud is done based on two steps; 1) three-dimensional line fitted on points of tree trunk, and 2) histogram analysis of distances between points and the line. It shows that more than 60% trees are successfully filtered and compared to the actual DBH measurement in the field the DBH estimations on point cloud have the root mean square error of 0.18 m.

High-density airborne light detection and ranging (LiDAR) data with point densities over 50 points/m 2 provide new opportunities, because previously inaccessible quantities of an individual tree can be derived directly from the data. We introduce a skeleton measurement methodology to extract the diameter at breast height (DBH) from airborne point clouds of trees. The estimates for the DBH are derived by analyzing the point distances to a suitable tree skeleton. The method is validated in three scenarios: 1) on a synthetic point cloud, simulating the point cloud acquisition over a forest; 2) on examples of free-standing and partly occluded trees; and 3) on automatically extracted trees from a sampled forest. The proposed diameter estimation performed well in all three scenarios, although influences of the tree extraction method and the field validation could not be fully excluded.

Canopy metrics from ICESat/GLAS lidar waveforms were applied to estimate stemwood volume for a mixed temperate forest. Gaussian decomposition from product GLA14 was used to infer a ground surface return from within the waveform. The region of the waveform returned from the vegetation was subsequently taken to be between this position and the beginning of the waveform signal. The heights of vegetation return cumulative energy percentiles were then calculated and used for the estimation of stemwood volume. For the tallest species within footprints, stemwood volume estimates for conifers produced R of 0.59, RMSE 98.3 m/ha and for broadleaf species, R of 0.75, RMSE 59.1 m/ha were found. Stemwood volume estimates taking account of the mixed species composition within stands were also calculated. For mixed stand estimates, R of 0.66, RMSE 82.5 m/ha was found for stands dominated by conifers whilst stands with greatest percentage cover provided by broadleaf species produced R of 0.47, RMSE...

A Digital Terrain Model (DTM) is a digital model representing the earth 's surface topography in three dimensions (3D) while a Digital Surface Model (DSM) represents the whole terrain including the objects on it such as trees and buildings. DTMs can be created through stereo-plotting. The advantage of this method is that the 3D data can be obtained with a high level of accuracy, but the data is limited in cloudy areas. This problem can be solved by using DSM, which can be created using TerraSAR-X and TanDEM-X Satellites with Synthetic Aperture Radar (SAR) system. This research aims to analyze the accuracy of DSMs from TerraSAR-X alongside DTMs extracted from aerial photogrammetry. The accuracy assessment of vertical height was completed by choosing 121 check points spread systematically on a paddy field in Karawang Regency, West Java. As paddy field was chosen to minimize errors between the DSM and DTM. The average, minimum, and maximum value of height differences between the DSM and DTM was calculated to obtain the standard deviation. The result showed that the average height difference between the DSM and DTM was 3.4 m with a minimum and maximum difference as 0.2 m and 10.808 m respectively. The standard deviation obtained was 4.9 m.