Comparing 3D Point Cloud Data from Laser Scanning and Digital Aerial Photogrammetry for Height Estimation of Small Trees and Other Vegetation in a Boreal–Alpine Ecotone

Changes in vegetation height in the boreal-alpine ecotone are expected over the coming decades due to climate change. Previous studies have shown that subtle changes in vegetation height (<0.2 m) can be estimated with great precision over short time periods (~5 yrs) for small spatial units (~1 ha) utilizing bi-temporal airborne laser scanning (ALS) data, which is promising for operation vegetation monitoring. However, ALS data may not always be available for multi-temporal analysis and other tree-dimensional (3D) data such as those produced by digital aerial photogrammetry (DAP) using imagery acquired from aircrafts and unmanned aerial systems (UAS) may add flexibility to an operational monitoring program. There is little existing evidence on the performance of DAP for height estimation of alpine pioneer trees and vegetation in the boreal-alpine ecotone. The current study assessed and compared the performance of 3D data extracted from ALS and from UAS DAP for prediction of tree height of small pioneer trees and evaluated how tree size and tree species affected the predictive ability of data from the two 3D data sources. Further, precision of vegetation height estimates (trees and other vegetation) across a 12 ha study area using 3D data from ALS and from UAS DAP were compared. Major findings showed smaller regression model residuals for vegetation height when using ALS data and that small and solitary trees tended to be smoothed out in DAP data. Surprisingly, the overall vegetation height estimates using ALS (0.64 m) and DAP data (0.76 m), respectively, differed significantly, despite the use of the same ground observations for model calibration. It was concluded that more in-depth understanding of the behavior of DAP algorithms for small scattered trees and low ground vegetation in the boreal-alpine ecotone is needed as even small systematic effects of a particular technology on height estimates may compromise the validity of a monitoring system since change processes encountered in the boreal-alpine ecotone often are subtle and slow.

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Accuracy Assessment of GEDI Terrain Elevation and Canopy Height Estimates in European Temperate Forests: Influence of Environmental and Acquisition Parameters

Remote Sensing ◽

10.3390/rs12233948 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3948

Author(s):

Markus Adam ◽

Mikhail Urbazaev ◽

Clémence Dubois ◽

Christiane Schmullius

Keyword(s):

Laser Scanning ◽

Temperate Forests ◽

Accuracy Assessment ◽

Canopy Cover ◽

Global Scale ◽

Canopy Height ◽

Ecosystem Dynamics ◽

Vegetation Height ◽

Height Estimates ◽

Terrain Elevation

Lidar remote sensing has proven to be a powerful tool for estimating ground elevation, canopy height, and additional vegetation parameters, which in turn are valuable information for the investigation of ecosystems. Spaceborne lidar systems, like the Global Ecosystem Dynamics Investigation (GEDI), can deliver these height estimates on a near global scale. This paper analyzes the accuracy of the first version of GEDI ground elevation and canopy height estimates in two study areas with temperate forests in the Free State of Thuringia, central Germany. Digital terrain and canopy height models derived from airborne laser scanning data are used as reference heights. The influence of various environmental and acquisition parameters (e.g., canopy cover, terrain slope, beam type) on GEDI height metrics is assessed. The results show a consistently high accuracy of GEDI ground elevation estimates under most conditions, except for areas with steep slopes. GEDI canopy height estimates are less accurate and show a bigger influence of some of the included parameters, specifically slope, vegetation height, and beam sensitivity. A number of relatively high outliers (around 9–13% of the measurements) is present in both ground elevation and canopy height estimates, reducing the estimation precision. Still, it can be concluded that GEDI height metrics show promising results and have potential to be used as a basis for further investigations.

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Digital Aerial Photogrammetry (DAP) and Airborne Laser Scanning (ALS) as Sources of Information about Tree Height: Comparisons of the Accuracy of Remote Sensing Methods for Tree Height Estimation

Remote Sensing ◽

10.3390/rs12111808 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1808 ◽

Cited By ~ 1

Author(s):

Miłosz Mielcarek ◽

Agnieszka Kamińska ◽

Krzysztof Stereńczak

Keyword(s):

Laser Scanning ◽

Tree Height ◽

Airborne Laser Scanning ◽

Surface Model ◽

Sources Of Information ◽

Related Factors ◽

Height Estimation ◽

Aerial Photogrammetry ◽

Airborne Laser ◽

The Difference

The rapid developments in the field of digital aerial photogrammetry (DAP) in recent years have increased interest in the application of DAP data for extracting three-dimensional (3D) models of forest canopies. This technology, however, still requires further investigation to confirm its reliability in estimating forest attributes in complex forest conditions. The main purpose of this study was to evaluate the accuracy of tree height estimation based on a crown height model (CHM) generated from the difference between a DAP-derived digital surface model (DSM) and an airborne laser scanning (ALS)-derived digital terrain model (DTM). The tree heights determined based on the DAP-CHM were compared with ground-based measurements and heights obtained using ALS data only (ALS-CHM). Moreover, tree- and stand-related factors were examined to evaluate the potential influence on the obtained discrepancies between ALS- and DAP-derived heights. The obtained results indicate that the differences between the means of field-measured heights and DAP-derived heights were statistically significant. The root mean square error (RMSE) calculated in the comparison of field heights and DAP-derived heights was 1.68 m (7.34%). The results obtained for the CHM generated using only ALS data produced slightly lower errors, with RMSE = 1.25 m (5.46%) on average. Both ALS and DAP displayed the tendency to underestimate tree heights compared to those measured in the field; however, DAP produced a higher bias (1.26 m) than ALS (0.88 m). Nevertheless, DAP heights were highly correlated with the heights measured in the field (R2 = 0.95) and ALS-derived heights (R2 = 0.97). Tree species and height difference (the difference between the reference tree height and mean tree height in a sample plot) had the greatest influence on the differences between ALS- and DAP-derived heights. Our study confirms that a CHM computed based on the difference between a DAP-derived DSM and an ALS-derived DTM can be successfully used to measure the height of trees in the upper canopy layer.

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Applicability Assessments of Close-Range Photogrammetry for Slope Face 3D Modelling

Aceh International Journal of Science and Technology ◽

10.13170/aijst.8.3.14650 ◽

2020 ◽

Vol 8 (3) ◽

pp. 143-150

Author(s):

Haqul Baramsyah ◽

Less Rich

Keyword(s):

Laser Scanning ◽

3D Model ◽

Laser Scanner ◽

Cost Effective ◽

3D Models ◽

Digital Photogrammetry ◽

Aerial Photogrammetry ◽

3D Data ◽

Image Capturing ◽

Close Range

The digital single lens reflex (DSLR) cameras have been widely accepted to use in slope face photogrammetry rather than the expensive metric camera used for aerial photogrammetry. 3D models generated from digital photogrammetry can approach those generated from terrestrial laser scanning in term of scale and level of detail. It is cost effective and has equipment portability. This paper presents and discusses the applicability of close-range digital photogrammetry to produce 3D models of rock slope faces. Five experiments of image capturing method were conducted to capture the photographs as the input data for processing. As a consideration, the appropriate baseline lengths to capture the slope face to get better result are around 1/6 to 1/8 of target distance. A fine quality of 3D model from data processing is obtained using strip method and convergent method with 80% overlapping in each photograph. A random camera positions with different distances from the slope face can also generate a good 3D model, however the entire target should be captured in each photograph. The accuracy of the models is generated by comparing the 3D models produced from photogrammetry with the 3D data obtained from laser scanner. The accuracy of 3D models is quite satisfactory with the mean error range from 0.008 to 0.018 m.

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An Evaluation of the Effects of UAS Flight Parameters on Digital Aerial Photogrammetry Processing and Dense-Cloud Production Quality in a Scots Pine Forest

Remote Sensing ◽

10.3390/rs13061121 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1121

Author(s):

Raul Sampaio de Lima ◽

Mait Lang ◽

Niall G. Burnside ◽

Miguel Villoslada Peciña ◽

Tauri Arumäe ◽

...

Keyword(s):

Data Acquisition ◽

Laser Scanning ◽

Model Fitting ◽

Ground Level ◽

Point Clouds ◽

Production Quality ◽

Unmanned Aerial Systems ◽

Aerial Photogrammetry ◽

Wide Range ◽

Scots Pine Forest

The application of unmanned aerial systems (UAS) in forest research includes a wide range of equipment, systems, and flight settings, creating a need for enhancing data acquisition efficiency and quality. Thus, we assessed the effects of flying altitude and lateral and longitudinal overlaps on digital aerial photogrammetry (DAP) processing and the ability of its products to provide point clouds for forestry inventory. For this, we used 18 combinations of flight settings for data acquisition, and a nationwide airborne laser scanning (ALS) dataset as reference data. Linear regression was applied for modeling DAP quality indicators and model fitting quality as the function of flight settings; equivalence tests compared DAP- and ALS-products. Most of DAP-Digital Terrain Models (DTM) showed a moderate to high agreement (R2 > 0.70) when fitted to ALS-based models; nine models had a regression slope within the 1% region of equivalence. The best DAP-Canopy Height Model (CHM) was generated using ALS-DTM with an R2 = 0.42 when compared with ALS-CHM, indicating reduced similarity. Altogether, our results suggest that the optimal combination of flight settings should include a 90% lateral overlap, a 70% longitudinal overlap, and a minimum altitude of 120 m above ground level, independent of the availability of an ALS-derived DTM for height normalization. We also provided insights into the effects of flight settings on DAP outputs for future applications in similar forest stands, emphasizing the benefits of overlaps for comprehensive scene reconstruction and altitude for canopy surface detection.

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Vegetation Phenology Driving Error Variation in Digital Aerial Photogrammetrically Derived Terrain Models

Remote Sensing ◽

10.3390/rs10101554 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1554 ◽

Cited By ~ 11

Author(s):

Tristan Goodbody ◽

Nicholas Coops ◽

Txomin Hermosilla ◽

Piotr Tompalski ◽

Gaetan Pelletier

Keyword(s):

Vegetation Cover ◽

Laser Scanning ◽

Point Clouds ◽

Vegetation Coverage ◽

Unmanned Aerial Systems ◽

Management Activity ◽

Data Set ◽

Aerial Photogrammetry ◽

Terrain Models ◽

Additive Mixed Models

Digital aerial photogrammetry (DAP) and unmanned aerial systems (UAS) have emerged as synergistic technologies capable of enhancing forest inventory information. A known limitation of DAP technology is its ability to derive terrain surfaces in areas with moderate to high vegetation coverage. In this study, we sought to investigate the influence of flight acquisition timing on the accuracy and coverage of digital terrain models (DTM) in a low cover forest area in New Brunswick, Canada. To do so, a multi-temporal UAS-acquired DAP data set was used. Acquired imagery was photogrammetrically processed to produce high quality DAP point clouds, from which DTMs were derived. Individual DTMs were evaluated for error using an airborne laser scanning (ALS)-derived DTM as a reference. Unobstructed road areas were used to validate DAP DTM error. Generalized additive mixed models (GAMM) were generated to assess the significance of acquisition timing on mean vegetation cover, DTM error, and proportional DAP coverage. GAMM models for mean vegetation cover and DTM error were found to be significantly influenced by acquisition date. A best available terrain pixel (BATP) compositing exercise was conducted to generate a best possible UAS DAP-derived DTM and outline the importance of flight acquisition timing. The BATP DTM yielded a mean error of −0.01 m. This study helps to show that the timing of DAP acquisitions can influence the accuracy and coverage of DTMs in low cover vegetation areas. These findings provide insight to improve future data set quality and provide a means for managers to cost-effectively derive high accuracy terrain models post-management activity.

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UAS Identify and Monitor Unusual Small-Scale Rhythmic Features in the Bay of Cádiz (Spain)

Remote Sensing ◽

10.3390/rs13061188 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1188

Author(s):

Lara Talavera ◽

Javier Benavente ◽

Laura Del Río

Keyword(s):

Monitoring Program ◽

Wave Theory ◽

Circulation Pattern ◽

Low Energy ◽

Small Scale ◽

Unmanned Aerial Systems ◽

Spatiotemporal Resolution ◽

Novel Technologies ◽

Bay Of Cadiz ◽

Bay Of Cádiz

Unusual shore-normal and barred-like rhythmic features were found in Camposoto Beach (Bay of Cádiz, SW Spain) during a monitoring program using unmanned aerial systems (UAS). They appeared in the backshore and persisted for 6 months (October 2017–March 2018). Their characteristics and possible formation mechanism were investigated analyzing: (1) UAS-derived high-resolution digital elevation models (DEMs), (2) hydrodynamic conditions, and (3) sediment samples. The results revealed that the features did not migrate spatially, that their wavelength was well predicted by the edge wave theory, and that they shared characteristics with both small-scale low-energy finger bars (e.g., geometry/appearance and amplitude) and swash cusps (e.g., wavelength, seaward circulation pattern, and finer and better sorted material in the runnels with respect to the crests). Our findings pinpoint to highly organized swash able to reach the backshore during spring tides under low-energy and accretionary conditions as well as backwash enhanced by conditions of water-saturated sediment. This study demonstrates that rhythmic features can appear under different modalities and beach locations than the ones observed up to date, and that their unusual nature may be attributed to the low spatiotemporal resolution of the traditional coastal surveying methods in comparison with novel technologies such as UAS.

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Accuracy Assessment of Point Clouds from LiDAR and Dense Image Matching Acquired Using the UAV Platform for DTM Creation

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7090342 ◽

2018 ◽

Vol 7 (9) ◽

pp. 342 ◽

Cited By ~ 28

Author(s):

Adam Salach ◽

Krzysztof Bakuła ◽

Magdalena Pilarska ◽

Wojciech Ostrowski ◽

Konrad Górski ◽

...

Keyword(s):

Land Cover ◽

Laser Scanning ◽

Point Clouds ◽

Data Sources ◽

Digital Terrain Models ◽

Vegetation Height ◽

Digital Terrain ◽

Terrain Models ◽

Airborne Laser ◽

Vertical Accuracy

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data.

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Augmentation of Traditional Forest Inventory and Airborne Laser Scanning with Unmanned Aerial Systems and Photogrammetry for Forest Monitoring

Remote Sensing ◽

10.3390/rs10101562 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1562 ◽

Cited By ~ 17

Author(s):

Kathryn Fankhauser ◽

Nikolay Strigul ◽

Demetrios Gatziolis

Keyword(s):

Laser Scanning ◽

Canopy Cover ◽

Tree Height ◽

Forest Monitoring ◽

Unmanned Aerial Systems ◽

Aerial Laser Scanning ◽

Forest Inventories ◽

Airborne Laser ◽

Aerial Systems

Forest inventories are constrained by resource-intensive fieldwork, while unmanned aerial systems (UASs) offer rapid, reliable, and replicable data collection and processing. This research leverages advancements in photogrammetry and market sensors and platforms to incorporate a UAS-based approach into existing forestry monitoring schemes. Digital imagery from a UAS was collected, photogrammetrically processed, and compared to in situ and aerial laser scanning (ALS)-derived plot tree counts and heights on a subsample of national forest plots in Oregon. UAS- and ALS-estimated tree counts agreed with each other (r2 = 0.96) and with field data (ALS r2 = 0.93, UAS r2 = 0.84). UAS photogrammetry also reasonably approximated mean plot tree height achieved by the field inventory (r2 = 0.82, RMSE = 2.92 m) and by ALS (r2 = 0.97, RMSE = 1.04 m). The use of both nadir-oriented and oblique UAS imagery as well as the availability of ALS-derived terrain descriptions likely sustain a robust performance of our approach across classes of canopy cover and tree height. It is possible to draw similar conclusions from any of the methods, suggesting that the efficient and responsive UAS method can enhance field measurement and ALS in longitudinal inventories. Additionally, advancing UAS technology and photogrammetry allows diverse users access to forest data and integrates updated methodologies with traditional forest monitoring.

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Geomorphological mapping based on DEMs and GIS: A review

Abstracts of the ICA ◽

10.5194/ica-abs-1-275-2019 ◽

2019 ◽

Vol 1 ◽

pp. 1-1

Author(s):

Takashi Oguchi

Keyword(s):

Remote Sensing ◽

Laser Scanning ◽

Aerial Photographs ◽

Topographic Maps ◽

Formation Processes ◽

Geomorphological Mapping ◽

Topographic Data ◽

Aerial Photogrammetry ◽

Landform Classification ◽

Shape Characteristics

Abstract. Geomorphology is a scientific discipline dealing with the characteristics, origin, and evolution of landforms. It utilizes topographic data such as spot height information, contour lines on topographic maps, and DEMs (Digital Elevation Models). Topographic data were traditionally obtained by ground surveying, but introduction of aerial photogrammetry in the early 20th century enabled more efficient data acquisition based on remote sensing. In recent years, active remote sensing methods including airborne and terrestrial laser scanning and applications of satellite radar have also been employed, and aerial photogrammetry has become easier and popular thanks to drones and a new photogrammetric method, SfM (Structure from Motion). The resultant topographic data especially raster DEMs are combined with GIS (Geographic Information Systems) to obtain derivatives such as slope and aspect as well as to conduct efficient geomorphological mapping. Resultant maps can depict various topographic characteristics based on surface height and DEM derivatives, and applications of advanced algorithms and some heuristic reasoning permit semi-automated landform classification. This quantitative approach differs from traditional and more qualitative methods to produce landform classification maps using visual interpretation of analogue aerial photographs and topographic maps as well as field observations.For scientific purposes, landforms need to be classified based on not only shape characteristics but also formation processes and ages. Among them, DEMs only represent shape characteristics, and understanding formation processes and ages usually require other data such as properties of surficial deposits observed in the field. However, numerous geomorphological studies indicate relationships between shapes and forming-processes of landforms, and even ages of landforms affect shapes such as a wider distribution of dissected elements within older landforms. Recent introduction of artificial intelligence in geomorphology including machine learning and deep learning may permit us to better understand the relationships of shapes with processes and ages. Establishing such relationships, however, is still highly challenging, and at this moment most geomorphologists think landform classification maps based on the traditional methods are more usable than those from the DEM-based methods. Nevertheless, researchers of some other fields such as civil engineering more appreciate the DEM-based methods because they can be conducted without deep geomorphological knowledge. Therefore, the methods should be developed for interdisciplinary understanding. This paper reviews and discusses such complex situations of geomorphological mapping today in relation to historical development of methodology.

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Vegetation Height Estimation using Satellite Remote Sensing in Peat Land of Central Kalimantan

Journal of Environmental Analysis and Progress ◽

10.24221/jeap.6.1.2021.3001.024-034 ◽

2021 ◽

Vol 6 (1) ◽

pp. 024-034

Author(s):

Atriyon Julzarika ◽

Harintaka Harintaka ◽

Tatik Kartika

Keyword(s):

Remote Sensing ◽

Spatial Information ◽

Geoid Undulation ◽

Height Estimation ◽

Central Kalimantan ◽

Vegetation Height ◽

Accuracy Test ◽

Sensing Technology ◽

The Difference ◽

Difference Test

Vegetation height is an important parameter in monitoring peatlands. Vegetation height can be estimated using remote sensing. Vegetation height can be estimated by utilizing DSM and DTM. The data that can be used are LiDAR, X-SAR, and SRTM C. In this study, LiDAR data is used for DSM2018 and DTM2018 extraction. The purpose of this research is to detect the vegetation height in Central Kalimantan peatlands using remote sensing technology. The research location is in Bakengbongkei, Kalampangan, Central Kalimantan. The integration of X-SAR and SRTM C is used for DSM2000 and DTM2000 extraction. DSM2000, DTM2000, DSM2018, and DTM2018 performed height error correction with tolerance of 1.96? (95%). Then do the geoid undulation correction to EGM2008. The results obtained are DSM and DTM with a similar height reference field. If it meets these conditions it can be calculated the vegetation height estimation. Vegetation height can be obtained using the Differential DEM method. The Changing in vegetation height from 2000 to 2018 can be estimated from the difference in vegetation height from 2000 to vegetation height in 2018. Results of spatial information on vegetation height and its changes need to be tested for the accuracy. This accuracy-test includes a cross section test, height difference test, and comparison with measurements of vegetation height in the field. The results of this research can be used to monitor the changing the vegetation height in peatlands.

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