scholarly journals High-resolution mapping of upland swamp vegetation using an unmanned aerial vehicle-hyperspectral system

2017 ◽  
Author(s):  
Bikram Banerjee ◽  
Simit Raval ◽  
Patrick Cullen
Keyword(s):  
Remote Sensing ◽  
Health Status ◽  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Anthropogenic Impacts ◽  
Remote Sensing Data ◽  
Imaging Data ◽  
Lower Accuracy ◽  
Aerial Vehicle ◽  
Resolution Mapping

Mapping of vegetation species and communities in sensitive ecosystems is essential for identification and management of anthropogenic impacts. Unmanned aerial vehicle (UAV)-hyperspectral systems are among the latest technologies in remote sensing that hold a potential for obtaining unprecedented quality of remote sensing data for vegetation mapping and health status monitoring applications. In this study, high-resolution (1–1.5 cm) spectral imaging data (15 bands) from a tunable spectrometer is used to map five species of vegetation in a complex upland swamp environment. The overall accuracy of classification was found to be 88.9% with a kappa coefficient of 0.83. Three classes (bare earth, sedgeland grass and black sheoak) have achieved higher accuracy (above 78%) and one class (bracken fern) has lower accuracy (58%). UAV-hyperspectral technology is, therefore, an effective tool to identify and map sensitive swamp vegetation. The technology can be potentially applied to determine the health status of the species.

Launching an unmanned aerial vehicle remote sensing data carrier: concept, key components and prospects

2019 ◽  
Vol 13 (10) ◽  
pp. 1172-1185
Author(s):  
Xiaohan Liao ◽  
Huanyin Yue ◽  
Ronggao Liu ◽  
Xiangyong Luo ◽  
Bin Luo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Data Carrier ◽  
Aerial Vehicle

scholarly journals Measurement of nitrogen content in rice by inversion of hyperspectral reflectance data from an unmanned aerial vehicle

2018 ◽  
Vol 48 (6) ◽  
Author(s):  
Du Wen ◽  
Xu Tongyu ◽  
Yu Fenghua ◽  
Chen Chunling
Keyword(s):  
Remote Sensing ◽  
Nitrogen Content ◽  
Unmanned Aerial Vehicle ◽  
Gaussian Process Regression ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Ground Truth ◽  
Hyperspectral Remote Sensing ◽  
Inversion Algorithm ◽  
Aerial Vehicle

ABSTRACT: The Nitrogen content of rice leaves has a significant effect on growth quality and crop yield. We proposed and demonstrated a non-invasive method for the quantitative inversion of rice nitrogen content based on hyperspectral remote sensing data collected by an unmanned aerial vehicle (UAV). Rice canopy albedo images were acquired by a hyperspectral imager onboard an M600-UAV platform. The radiation calibration method was then used to process these data and the reflectance of canopy leaves was acquired. Experimental validation was conducted using the rice field of Shenyang Agricultural University, which was classified into 4 fertilizer levels: zero nitrogen, low nitrogen, normal nitrogen, and high nitrogen. Gaussian process regression (GPR) was then used to train the inversion algorithm to identify specific spectral bands with the highest contribution. This led to a reduction in noise and a higher inversion accuracy. Principal component analysis (PCA) was also used for dimensionality reduction, thereby reducing redundant information and significantly increasing efficiency. A comparison with ground truth measurements demonstrated that the proposed technique was successful in establishing a nitrogen inversion model, the accuracy of which was quantified using a linear fit (R2=0.8525) and the root mean square error (RMSE=0.9507). These results support the use of GPR and provide a theoretical basis for the inversion of rice nitrogen by UAV hyperspectral remote sensing.

A review on the architecture construction of remote sensing data from unmanned aerial vehicle networking

2021 ◽  
Vol 40 (9) ◽  
pp. 1467-1479
Author(s):  
Yong WANG ◽  
Yusen YANG ◽  
Shibo WANG ◽  
Yu YANG ◽  
Rui ZHANG ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Aerial Vehicle ◽  
Vehicle Networking

scholarly journals Monitoring vineyards with UAV and multi-sensors for the assessment of water stress and grape maturity

2017 ◽  
Vol 5 (2) ◽  
pp. 37-50 ◽  
Author(s):  
I. Soubry ◽  
P. Patias ◽  
V. Tsioukas
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Efficient Solution ◽  
Grape Variety ◽  
Agricultural Applications ◽  
Aerial Vehicle ◽  
Remote Sensing Indices

This paper deals with the monitoring of vineyards for the assessment of water stress and grape maturity using an unmanned aerial vehicle (UAV) equipped with multispectral/infrared and red-green-blue (RGB) cameras. The study area is the Gerovassiliou winery in the region of Epanomi, Greece, cultivated with the local grape variety of Malagouzia. Fifteen flights were conducted with a fixed-wing UAV during the months of April to August 2015 with a mean interval of 2 weeks. The flight images were photogrammetrically processed for the production of orthoimages and then used to extract indices for the detection of water stress. Grape samples were collected 2 days before harvest and then analyzed and correlated with remote sensing indices. The TCARI/OSAVI index showed the best correlation with the grape samples with regards to maturity and the likelihood of water stress. Furthermore, the final results were of high resolution as far as farm purposes are concerned (a scale of 1:500 for all three sensors). These facts suggest that the instruments used in this study represent a fast, reliable, and efficient solution to the evaluation of crops for agricultural applications.

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