scholarly journals sUAS for 3D Tree Surveying: Comparative Experiments on a Closed-Canopy Earthen Dam

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 659
Author(s):  
Cuizhen Wang ◽  
Grayson Morgan ◽  
Michael E. Hodgson
Keyword(s):  
Remote Sensing ◽  
Low Cost ◽  
Tree Height ◽  
Unmanned Aircraft ◽  
Height Measurement ◽  
Earthen Dam ◽  
Local Maxima ◽  
Lower Accuracy ◽  
Closed Canopy ◽  
Good Potential

Defined as “personal remote sensing”, small unmanned aircraft systems (sUAS) have been increasingly utilized for landscape mapping. This study tests a sUAS procedure of 3D tree surveying of a closed-canopy woodland on an earthen dam. Three DJI drones—Mavic Pro, Phantom 4 Pro, and M100/RedEdge-M assembly—were used to collect imagery in six missions in 2019–2020. A canopy height model was built from the sUAS-extracted point cloud and LiDAR bare earth surface. Treetops were delineated in a variable-sized local maxima filter, and tree crowns were outlined via inverted watershed segmentation. The outputs include a tree inventory that contains 238 to 284 trees (location, tree height, crown polygon), varying among missions. The comparative analysis revealed that the M100/RedEdge-M at a higher flight altitude achieved the best performance in tree height measurement (RMSE = 1 m). However, despite lower accuracy, the Phantom 4 Pro is recommended as an optimal drone for operational tree surveying because of its low cost and easy deployment. This study reveals that sUAS have good potential for operational deployment to assess tree overgrowth toward dam remediation solutions. With 3D imaging, sUAS remote sensing can be counted as a reliable, consumer-oriented tool for monitoring our ever-changing environment.

Improved Architecture Designs for a Low Cost Personal Remote Sensing Platform: Flight Control and Safety

2011 ◽  
Author(s):  
Calvin Coopmans ◽  
Long Di ◽  
Austin Jensen ◽  
Aaron A. Dennis ◽  
YangQuan Chen
Keyword(s):  
Remote Sensing ◽  
Flight Control ◽  
Large Scale ◽  
Current System ◽  
Low Cost ◽  
Flight Test ◽  
Unmanned Aircraft ◽  
Personal Remote Sensing ◽  
Sensing Platform ◽  
Fail Safe

Remote sensing is a field traditionally dominated by expensive, large-scale operations. This paper presents our efforts to improve our unmanned aircraft (UA) platforms for low-cost personal remote sensing purposes. Safety concerns are first emphasized regarding the local airspace and multiple fail-safe features are shown in the current system. Then the AggieAir unmanned system architecture is briefly described including the Paparazzi UA autopilot, AggieAir JAUS implementation, AggieNav navigation unit and payload integration. Some preliminary flight test results and images acquired using an example thermal IR payload system are also shown. Finally Multi-UAV and heterogeneous platform capabilities are discussed with respect to their applications. Based on our approaches on the new architecture design, personal remote sensing on smaller-scale operations can be more beneficial and common.

scholarly journals Performance of Hyperspectral Imaging with Drone Swarms

2018 ◽  
Vol 2672 (45) ◽  
pp. 36-44
Author(s):  
Raj Bridgelall ◽  
James B. Rafert ◽  
Denver D. Tolliver
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Spatial Resolution ◽  
Low Cost ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
Aircraft Systems ◽  
Sensing Platforms ◽  
Spatial Coverage ◽  
Ground Coverage

The ongoing proliferation and diversification of remote sensing platforms offer greater flexibility to select from a range of hyperspectral imagers as payloads. The emergence of low-cost unmanned aircraft systems (drones) and their launch flexibility present an opportunity to maximize spectral resolution while scaling both daily spatial coverage and spatial resolution simultaneously by operating synchronized swarms. This article presents a model to compare the performance of hyperspectral-imaging platforms in their spatial coverage and spatial resolution envelope. The authors develop a data acquisition framework and use the model to compare the achievable performance among existing airborne and spaceborne hyperspectral imaging vehicles and drone swarms. The results show that, subject to cost and operational limitations, a platform implemented with drone swarms has the potential to provide greater spatial resolution for the same daily ground coverage compared with existing airborne platforms.

Meso- to macro-scale landscape modelling with SfM-MVS photogrammetry: a case study from the Urft Lake water reservoir (Eifel Mountains, western Germany)

2021 ◽  
Author(s):  
Lukas Dörwald ◽  
Alexander Esch ◽  
Georg Stauch ◽  
Janek Walk
Keyword(s):  
Remote Sensing ◽  
Structure From Motion ◽  
Low Cost ◽  
Water Reservoir ◽  
Unmanned Aircraft ◽  
United States Geological Survey ◽  
River Terraces ◽  
Fluvial Morphology ◽  
Eifel Mountains ◽  
Macro Scale

<p>3D landscape reconstruction derived from imagery acquired by unmanned aerial systems (UAS) is an increasingly applied method within the field of geosciences. Low-cost UAS and subsequent Structure from Motion (SfM) and multi-view stereo (MVS) processing provides the opportunity to study landforms and processes in high detail; for instance mapping of river terraces (Li et al. 2019) or landslide monitoring (Devoto et al. 2020).</p><p>Due to an almost complete drainage of the Urft Lake reservoir in the northern Eifel mountains (W-Germany) in the autumn of 2020, the lake’s entire ground could be surveyed using a low-cost UAS.</p><p>The lake stretches for 12 km and has a maximum impoundment volume of approximately 45 million m³. Its shape is characterized by multiple fluvial bends and steep slopes, which required an elaborated flight layout. A DJI Phantom 4 Pro V2.0 was used. Each flight was carried out in two parallel heights (90 and 120 m), 80° camera inclination, and in double-grid pattern. Five full days of surveying yielded over 6,000 aerial images. Despite the difficulty to access the drained reservoir, 154 evenly distributed ground control points were taken using a Leica RTK dGPS instrument (accuracy <5 cm). SfM-MVS photogrammetric processing was conducted with Agisoft Metashape Professional 1.6, using an optimized workflow based on USGS (2017) and James et al. (2020).</p><p>The resulting 3D model features high accuracy and precision making it suitable for further detailed stationary as well as multi-temporal geomorphologic analyses. The derived DEM features a spatial resolution of <6 cm and will be used to calculate geometric changes of the reservoir body since its construction in 1905; in particular, due to sedimentation and mass movements along the hillslopes. Moreover, the products can be used to study the anthropogenic influences of the water reservoir on the fluvial morphology of the Urft.</p><p> </p><p>References:</p><p>Devoto, S., Macovaz, V., Mantovani, M., Soldati, S., Furlani, S., 2020. Advantages of Using UAV Digital Photogrammetry in the Study of Slow-Moving Coastal Landslides.  Remote Sensing 2020, 12, 3566. https://doi.org/10.3390/rs12213566  </p><p>James, M.R., Antoniazza, G., Robson, S., Lane, S.N., 2020. Mitigating systematic error in topographic models for geomorphic change detection: accuracy, precision and considerations beyond off-nadir imagery. Earth Surface Processes and Landforms 45, 2251–2271. https://doi.org/10.1002/esp.4878</p><p>Li, H., Lin, C., Wang, Z., Yu, Z., 2019. Mapping of River Terraces with Low-Cost UAS Based Structure-from-Motion Photogrammetry in a Complex Terrain Setting. Remote Sensing 2019, 11, 464. https://doi.org/10.3390/rs11040464</p><p>United States Geological Survey (USGS), 2017. Unmanned Aircraft Systems Data Post Processing: Structure-from-Motion Photogrammtery. Section 2 – MicaSense 5-band MultiSpectral Imagery. USGS National UAS Project Office. https://uas.usgs.gov/nupo/pdf/PhotoScanProcessingMicaSenseMar2017.pdf (Retrieved: 24 July 2020).</p>

scholarly journals Evaluation of P-Band SAR Tomography for Mapping Tropical Forest Vertical Backscatter and Tree Height

2021 ◽  
Vol 13 (8) ◽  
pp. 1485
Author(s):  
Naveen Ramachandran ◽  
Sassan Saatchi ◽  
Stefano Tebaldini ◽  
Mauro Mariotti d’Alessandro ◽  
Onkar Dikshit
Keyword(s):  
Tropical Forest ◽  
Vertical Profile ◽  
Mean Squared Error ◽  
Low Frequency ◽  
Tree Height ◽  
Canopy Layer ◽  
Local Maxima ◽  
Slope Correction ◽  
Compensation Approach ◽  
The Impact

Low-frequency tomographic synthetic aperture radar (TomoSAR) techniques provide an opportunity for quantifying the dynamics of dense tropical forest vertical structures. Here, we compare the performance of different TomoSAR processing, Back-projection (BP), Capon beamforming (CB), and MUltiple SIgnal Classification (MUSIC), and compensation techniques for estimating forest height (FH) and forest vertical profile from the backscattered echoes. The study also examines how polarimetric measurements in linear, compact, hybrid, and dual circular modes influence parameter estimation. The tomographic analysis was carried out using P-band data acquired over the Paracou study site in French Guiana, and the quantitative evaluation was performed using LiDAR-based canopy height measurements taken during the 2009 TropiSAR campaign. Our results show that the relative root mean squared error (RMSE) of height was less than 10%, with negligible systematic errors across the range, with Capon and MUSIC performing better for height estimates. Radiometric compensation, such as slope correction, does not improve tree height estimation. Further, we compare and analyze the impact of the compensation approach on forest vertical profiles and tomographic metrics and the integrated backscattered power. It is observed that radiometric compensation increases the backscatter values of the vertical profile with a slight shift in local maxima of the canopy layer for both the Capon and the MUSIC estimators. Our results suggest that applying the proper processing and compensation techniques on P-band TomoSAR observations from space will allow the monitoring of forest vertical structure and biomass dynamics.

scholarly journals Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications

2020 ◽  
Vol 13 (1) ◽  
pp. 77
Author(s):  
Tianyu Hu ◽  
Xiliang Sun ◽  
Yanjun Su ◽  
Hongcan Guan ◽  
Qianhui Sun ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Low Cost ◽  
Canopy Cover ◽  
Tree Height ◽  
Flying Height ◽  
Tree Level ◽  
Forest Site ◽  
Lidar System ◽  
Individual Tree ◽  
Gap Fraction

Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.

scholarly journals Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1020
Author(s):  
Yanqi Dong ◽  
Guangpeng Fan ◽  
Zhiwu Zhou ◽  
Jincheng Liu ◽  
Yongguo Wang ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Low Cost ◽  
Reference Value ◽  
Tree Height ◽  
Point Clouds ◽  
Tree Structure ◽  
Mean Square ◽  
Tree Model

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

scholarly journals Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3338
Author(s):  
Ivan Vajs ◽  
Dejan Drajic ◽  
Nenad Gligoric ◽  
Ilija Radovanovic ◽  
Ivan Popovic
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Lower Accuracy ◽  
Wide Range ◽  
The Impact ◽  
Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

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