Non-destructive estimation of individual tree biomass: Allometric models, terrestrial and UAV laser scanning

Remote Sensing of Environment, Journal Year: 2022, Volume and Issue: 280, P. 113180 - 113180

Published: Aug. 5, 2022

Calibration and validation of aboveground biomass (AGB) products retrieved from satellite-borne sensors require accurate AGB estimates across hectare scales (1 to 100 ha). Recent studies recommend making use non-destructive terrestrial laser scanning (TLS) based techniques for individual tree estimation that provide unbiased predictors. However, applying these large sites landscapes remains logistically challenging. Unoccupied aerial vehicle (UAV-LS) has the potential address this through collection high density point clouds many hectares, but on data been challenging so far, especially in dense tropical canopies. In study, we investigated how TLS UAV-LS can be used purpose by testing different modelling strategies with availability framework requirements. The study included four forested three biomes: temperate, wet tropical, savanna. At each site, coincident campaigns were conducted. Diameter at breast height (DBH) estimated clouds. Individual was ≥170 trees per site quantitative structure (QSM), treated as best available, estimate absence direct, destructive measurements. automatically segmented using a shortest-path algorithm full 3D cloud. Predictions evaluated terms root mean square error (RMSE) population bias, latter being absolute difference between total sample QSM predicted AGB. application global allometric scaling models (ASM) local scale modalities, i.e., field-inventory light detection ranging LiDAR metrics, resulted prediction errors range reported studies, relatively bias. adjustment factors should considered translate modalities. When calibrating models, DBH confirmed strong predictor AGB, useful when field inventories. combination derived metrics non-parametric generally produced RMSE, very low bias ≤5% starting 55 training samples. hectares reduced fieldwork time. Overall, contributes exploitation scale, relevant calibration space-borne missions targeting estimation.

Language: Английский

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LiDAR Data Fusion to Improve Forest Attribute Estimates: A Review

Current Forestry Reports, Journal Year: 2024, Volume and Issue: 10(4), P. 281 - 297

Published: June 21, 2024

Abstract Purpose of the Review Many LiDAR remote sensing studies over past decade promised data fusion as a potential avenue to increase accuracy, spatial-temporal resolution, and information extraction in final products. Here, we performed structured literature review analyze relevant on these topics published last main motivations applications for fusion, methods used. We discuss findings with panel experts report important lessons, challenges, future directions. Recent Findings other datasets, including multispectral, hyperspectral, radar, is found be useful variety literature, both at individual tree level area level, tree/crown segmentation, aboveground biomass assessments, canopy height, species identification, structural parameters, fuel load assessments etc. In most cases, gains are achieved improving accuracy (e.g. better classifications), resolution height). However, questions remain regarding whether marginal improvements reported range worth extra investment, specifically from an operational point view. also provide clear definition “data fusion” inform scientific community combination, integration. Summary This provides positive outlook come, while raising about trade-off between benefits versus time effort needed collecting combining multiple datasets.

Language: Английский

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3D point cloud fusion from UAV and TLS to assess temperate managed forest structures

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2022, Volume and Issue: 112, P. 102917 - 102917

Published: July 16, 2022

Light detection and ranging (LiDAR) technology has become one of the most dominant acquisition methods for modeling forest attributes, such as very accurate tree structure information, which is necessary qualitative management research activities. In this study, we evaluated efficacy standalone unmanned aerial vehicle-laser scanning (UAV-LS) terrestrial laser (TLS) data to accurately estimate metrics under differing types. Furthermore, combined UAV-LS TLS test whether fusion can improve mapping three-dimensional (3D) individual trees favor estimates metrics. We initially calculated percentage point density per square meter aboveground in each height class at intervals 1 m UAV-LS, TLS, datasets. This helped illustrate vertical distribution that reflects structural complexity between broadleaf conifer trees. then used tree-level clouds assess several metrics, diameter breast (DBH), total (HT), crown projection area (PAC), width (WC), length (LC), 3D surface (SC), volume (VC). Our results indicated LiDAR increase estimation accuracy DBH HT, especially broadleaves (97.8% accuracy). addition, significantly reshaped modeled structures both plots, led improved all The show empirical evidence also have a determining role supporting ecosystem services. For example, detailed crowns be mitigation rainfall`s kinetic energy by concerning soil erosion sedimentation near habitable zones.

Language: Английский

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Ground-based/UAV-LiDAR data fusion for quantitative structure modeling and tree parameter retrieval in subtropical planted forest

Forest Ecosystems, Journal Year: 2022, Volume and Issue: 9, P. 100065 - 100065

Published: Jan. 1, 2022

Light detection and ranging (LiDAR) has contributed immensely to forest mapping 3D tree modelling. From the perspective of data acquisition, integration LiDAR from different platforms would enrich information at plot levels. This research develops a general framework integrate ground-based UAV-LiDAR (ULS) better estimate parameters based on quantitative structure modelling (QSM). is accomplished in three sequential steps. First, ground-based/ULS were co-registered local density peaks clustered canopy. Next, redundancy noise removed for fusion. Finally, modeling biophysical parameter retrieval QSM. Experiments performed Backpack/Handheld/UAV-based multi-platform mobile subtropical forest, including poplar dawn redwood species. Generally, fusion outperforms with respect estimation compared field data. The fusion-derived height, volume, crown volume significantly improved by up 9.01%, 5.28%, 18.61%, respectively, terms rRMSE. By contrast, diameter breast height (DBH) that least benefits fusion, rRMSE remains approximately same, because stems are already well sampled ground Additionally, particularly dense forests, those derived LiDAR. Ground-based can potentially be used low-stand-density whereby improvement owing not significant.

Language: Английский

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Latest Trends on Tree Classification and Segmentation Using UAV Data—A Review of Agroforestry Applications

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(9), P. 2263 - 2263

Published: April 25, 2023

When it comes to forest management and protection, knowledge is key. Therefore, mapping crucial obtain the required towards profitable resource exploitation increased resilience against wildfires. Within this context, paper presents a literature review on tree classification segmentation using data acquired by unmanned aerial vehicles, with special focus last decade (2013–2023). The latest research trends in field are presented analyzed two main vectors, namely: (1) data, where used sensors structures resumed; (2) methods, remote sensing analysis methods described, particular machine learning approaches. study methodology filtered 979 papers, which were then screened, resulting 144 works included paper. These systematically organized year, keywords, purpose, sensors, used, easily allowing readers have wide, but at same time detailed, view of automatic vehicles. This shows that image processing techniques applied forestry tasks focused improving accuracy interpretability results multi-modal 3D information, AI methods. Most use RGB or multispectral cameras, LiDAR scanners, individually. Classification mostly carried out supervised while uses unsupervised techniques.

Language: Английский

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Flying high: Sampling savanna vegetation with UAV ‐lidar

Methods in Ecology and Evolution, Journal Year: 2023, Volume and Issue: 14(7), P. 1668 - 1686

Published: March 16, 2023

Abstract The flexibility of UAV‐lidar remote sensing offers a myriad new opportunities for savanna ecology, enabling researchers to measure vegetation structure at variety temporal and spatial scales. However, this also increases the number customizable variables, such as flight altitude, pattern, sensor parameters, that, when adjusted, can impact data quality well applicability dataset specific research interest. To better understand impacts that UAV patterns parameters have on metrics, we compared 7 lidar point clouds collected with Riegl VUX − 1LR over 300 × m area in Kruger National Park, South Africa. We varied altitude (60 above ground, 100 m, 180 m) sampling pattern (slowing speed, increasing overlap between flightlines flying crosshatch pattern), vertical metrics related height fractional cover. Comparing from acquisitions different found both had significant derived variation causing largest impacts. Flying higher resulted lower cloud heights, leading consistent downward trend percentile magnitude direction these trends depending type sampled (trees, shrubs or grasses), showing composition interact signal alter metrics. While there were statistically differences among acquisitions, average often order few centimetres less, which shows great promise future comparison studies. discuss how results apply practice, explaining potential trade‐offs altitudes alternate patterns. highlight be geared toward ecological applications types, explore optimizing designs savannas.

Language: Английский

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A clustering-based automatic registration of UAV and terrestrial LiDAR forest point clouds

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 217, P. 108648 - 108648

Published: Jan. 19, 2024

Language: Английский

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Branch architecture quantification of large-scale coniferous forest plots using UAV-LiDAR data

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 306, P. 114121 - 114121

Published: April 2, 2024

Language: Английский

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Individual Tree Species Identification for Complex Coniferous and Broad-Leaved Mixed Forests Based on Deep Learning Combined with UAV LiDAR Data and RGB Images

Forests, Journal Year: 2024, Volume and Issue: 15(2), P. 293 - 293

Published: Feb. 3, 2024

Automatic and accurate individual tree species identification is essential for the realization of smart forestry. Although existing studies have used unmanned aerial vehicle (UAV) remote sensing data identification, effects different spatial resolutions combining multi-source automatic using deep learning methods still require further exploration, especially in complex forest conditions. Therefore, this study proposed an improved YOLOv8 model multisource under stand Firstly, RGB LiDAR natural coniferous broad-leaved mixed forests conditions Northeast China were acquired via a UAV. Then, resolutions, scales, band combinations explored, based on identification. Subsequently, Attention Multi-level Fusion (AMF) Gather-and-Distribute (GD) was proposed, according to characteristics data, which two branches AMF Net backbone able extract fuse features from sources separately. Meanwhile, GD mechanism introduced into neck model, order fully utilize extracted main trunk complete eight area. The results showed that YOLOv8x images combined with current mainstream object detection algorithms achieved highest mAP 75.3%. When resolution within 8 cm, accuracy exhibited only slight variation. However, decreased significantly decrease when greater than 15 cm. scales x, l, m could exhibit higher compared other scales. DGB PCA-D superior 75.5% 76.2%, respectively. had more significant improvement single 81.0%. clarified impact demonstrated excellent performance provides new solution technical reference forestry resource investigation data.

Language: Английский

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Security of target recognition for UAV forestry remote sensing based on multi-source data fusion transformer framework

Information Fusion, Journal Year: 2024, Volume and Issue: 112, P. 102555 - 102555

Published: July 2, 2024

Language: Английский

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