scholarly journals Monitoring the Invasion ofSpartina alternifloraUsing Very High Resolution Unmanned Aerial Vehicle Imagery in Beihai, Guangxi (China)

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Huawei Wan ◽  
Qiao Wang ◽  
Dong Jiang ◽  
Jingying Fu ◽  
Yipeng Yang ◽  
...  
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Spartina Alterniflora ◽  
Native Species ◽  
Field Survey ◽  
Negative Impact ◽  
Ecological Engineering ◽  
Control Measures ◽  
Aerial Vehicle ◽  
Very High

Spartina alterniflorawas introduced to Beihai, Guangxi (China), for ecological engineering purposes in 1979. However, the exceptional adaptability and reproductive ability of this species have led to its extensive dispersal into other habitats, where it has had a negative impact on native species and threatens the local mangrove and mudflat ecosystems. To obtain the distribution and spread ofSpartina alterniflora, we collected HJ-1 CCD imagery from 2009 and 2011 and very high resolution (VHR) imagery from the unmanned aerial vehicle (UAV). The invasion area ofSpartina alterniflorawas 357.2 ha in 2011, which increased by 19.07% compared with the area in 2009. A field survey was conducted for verification and the total accuracy was 94.0%. The results of this paper show that VHR imagery can provide details on distribution, progress, and early detection ofSpartina alterniflorainvasion. OBIA, object based image analysis for remote sensing (RS) detection method, can enable control measures to be more effective, accurate, and less expensive than a field survey of the invasive population.

scholarly journals AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV)

2019 ◽  
Vol 11 (12) ◽  
pp. 1413 ◽  
Author(s):  
Víctor González-Jaramillo ◽  
Andreas Fries ◽  
Jörg Bendix
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Tree Level ◽  
Mountain Forest ◽  
Multispectral Images ◽  
Individual Tree ◽  
Tropical Mountain ◽  
Aerial Vehicle ◽  
Rgb Images ◽  
Very High

The present investigation evaluates the accuracy of estimating above-ground biomass (AGB) by means of two different sensors installed onboard an unmanned aerial vehicle (UAV) platform (DJI Inspire I) because the high costs of very high-resolution imagery provided by satellites or light detection and ranging (LiDAR) sensors often impede AGB estimation and the determination of other vegetation parameters. The sensors utilized included an RGB camera (ZENMUSE X3) and a multispectral camera (Parrot Sequoia), whose images were used for AGB estimation in a natural tropical mountain forest (TMF) in Southern Ecuador. The total area covered by the sensors included 80 ha at lower elevations characterized by a fast-changing topography and different vegetation covers. From the total area, a core study site of 24 ha was selected for AGB calculation, applying two different methods. The first method used the RGB images and applied the structure for motion (SfM) process to generate point clouds for a subsequent individual tree classification. Per the classification at tree level, tree height (H) and diameter at breast height (DBH) could be determined, which are necessary input parameters to calculate AGB (Mg ha−1) by means of a specific allometric equation for wet forests. The second method used the multispectral images to calculate the normalized difference vegetation index (NDVI), which is the basis for AGB estimation applying an equation for tropical evergreen forests. The obtained results were validated against a previous AGB estimation for the same area using LiDAR data. The study found two major results: (i) The NDVI-based AGB estimates obtained by multispectral drone imagery were less accurate due to the saturation effect in dense tropical forests, (ii) the photogrammetric approach using RGB images provided reliable AGB estimates comparable to expensive LiDAR surveys (R2: 0.85). However, the latter is only possible if an auxiliary digital terrain model (DTM) in very high resolution is available because in dense natural forests the terrain surface (DTM) is hardly detectable by passive sensors due to the canopy layer, which impedes ground detection.

Archaeological documentation of wood caribou fences using unmanned aerial vehicle and very high-resolution satellite imagery in the Mackenzie Mountains, Northwest Territories

2020 ◽  
Vol 8 (3) ◽  
pp. 186-206
Author(s):  
Jurjen van der Sluijs ◽  
Glen MacKay ◽  
Leon Andrew ◽  
Naomi Smethurst ◽  
Thomas D. Andrews
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Satellite Imagery ◽  
Woody Debris ◽  
Forest Products ◽  
Data Sets ◽  
Aerial Vehicle ◽  
High Resolution Satellite Imagery ◽  
Hunting Technology ◽  
Very High

Indigenous peoples of Canada’s North have long made use of boreal forest products, with wooden drift fences to direct caribou movement towards kill sites as unique examples. Caribou fences are of archaeological and ecological significance, yet sparsely distributed and increasingly at risk to wildfire. Costly remote field logistics requires efficient prior fence verification and rapid on-site documentation of structure and landscape context. Unmanned aerial vehicle (UAV) and very high-resolution (VHR) satellite imagery were used for detailed site recording and detection of coarse woody debris (CWD) objects under challenging Subarctic alpine woodlands conditions. UAVs enabled discovery of previously unknown wooden structures and revealed extensive use of CWD (n = 1745, total length = 2682 m, total volume = 16.7 m3). The methodology detected CWD objects much smaller than previously reported in remote sensing literature (mean 1.5 m long, 0.09 m wide), substantiating a high spatial resolution requirement for detection. Structurally, the fences were not uniformly left on the landscape. Permafrost patterned ground combined with small CWD contributions at the pixel level complicated identification through VHR data sets. UAV outputs significantly enriched field techniques and supported a deeper understanding of caribou fences as a hunting technology, and they will aid ongoing archaeological interpretation and time-series comparisons of change agents.

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