scholarly journals Mapping scrub vegetation cover from photogrammetric point clouds: A useful tool in reserve management

2021 ◽  
Author(s):  
Jim Vafidis ◽  
Isaac Lucksted ◽  
Moyrah Gall ◽  
Pete Maxfield ◽  
Kathy Meakin ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Point Clouds ◽  
Reserve Management

scholarly journals Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain

2021 ◽  
Vol 58 (1) ◽  
pp. 120-137
Author(s):  
R. Niederheiser ◽  
M. Winkler ◽  
V. Di Cecco ◽  
B. Erschbamer ◽  
R. Fernández ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Point Clouds ◽  
Mountain Terrain ◽  
Close Range

scholarly journals Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation

2020 ◽  
Vol 12 (2) ◽  
pp. 298 ◽  
Author(s):  
Linyuan Li ◽  
Jun Chen ◽  
Xihan Mu ◽  
Weihua Li ◽  
Guangjian Yan ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Forest Floor ◽  
Low Cost ◽  
3D Structure ◽  
Point Clouds ◽  
Northern China ◽  
Understory Vegetation ◽  
Coefficient Of Determination ◽  
Observation System ◽  
Validation Data

Vegetation cover estimation for overstory and understory layers provides valuable information for modeling forest carbon and water cycles and refining forest ecosystem function assessment. Although previous studies demonstrated the capability of light detection and ranging (LiDAR) in the three-dimensional (3D) characterization of forest overstory and understory communities, the high cost inhibits its application in frequent and successive survey tasks. Low-cost commercial red–green–blue (RGB) cameras mounted on unmanned aerial vehicles (UAVs), as LiDAR alternatives, provide operational systems for simultaneously quantifying overstory crown cover (OCC) and understory vegetation cover (UVC). We developed an effective method named back-projection of 3D point cloud onto superpixel-segmented image (BAPS) to extract overstory and forest floor pixels using 3D structure-from-motion (SfM) point clouds and two-dimensional (2D) superpixel segmentation. The OCC was estimated from the extracted overstory crown pixels. A reported method, called half-Gaussian fitting (HAGFVC), was used to segement green vegetation and non-vegetation pixels from the extracted forest floor pixels and derive UVC. The UAV-based RGB imagery and field validation data were collected from eight forest plots in Saihanba National Forest Park (SNFP) plantation in northern China. The consistency of the OCC estimates between BAPS and canopy height model (CHM)-based methods (coefficient of determination: 0.7171) demonstrated the capability of the BAPS method in the estimation of OCC. The segmentation of understory vegetation was verified by the supervised classification (SC) method. The validation results showed that the OCC and UVC estimates were in good agreement with reference values, where the root-mean-square error (RMSE) of OCC (unitless) and UVC (unitless) reached 0.0704 and 0.1144, respectively. The low-cost UAV-based observation system and the newly developed method are expected to improve the understanding of ecosystem functioning and facilitate ecological process modeling.

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

2018 ◽  
Vol 10 (10) ◽  
pp. 1554 ◽  
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.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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