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.

scholarly journals Simplified Evaluation of Cotton Water Stress Using High Resolution Unmanned Aerial Vehicle Thermal Imagery

2019 ◽  
Vol 11 (3) ◽  
pp. 267 ◽  
Author(s):  
Jiang Bian ◽  
Zhitao Zhang ◽  
Junying Chen ◽  
Haiying Chen ◽  
Chenfeng Cui ◽  
...  
Keyword(s):  
Water Stress ◽  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Canopy Temperature ◽  
Reference Temperature ◽  
Mild Stress ◽  
Crop Water ◽  
Aerial Vehicle ◽  
Irrigation Treatments ◽  
Crop Water Stress

Irrigation water management and real-time monitoring of crop water stress status can enhance agricultural water use efficiency, crop yield, and crop quality. The aim of this study was to simplify the calculation of the crop water stress index (CWSI) and improve its diagnostic accuracy. Simplified CWSI (CWSIsi) was used to diagnose water stress for cotton that has received four different irrigation treatments (no stress, mild stress, moderate stress, and severe stress) at the flowering and boll stage. High resolution thermal infrared and multispectral images were taken using an Unmanned Aerial Vehicle remote sensing platform at midday (local time 13:00), and stomatal conductance (gs), transpiration rate (tr), and cotton root zone soil volumetric water content (θ) were concurrently measured. The soil background pixels of thermal images were eliminated using the Canny edge detection to obtain a unimodal histogram of pure canopy temperatures. Then the wet reference temperature (Twet), dry reference temperature (Tdry), and mean canopy temperature (Tl) were obtained from the canopy temperature histogram to calculate CWSIsi. The other two methods of CWSI evaluation were empirical CWSI (CWSIe), in which the temperature parameters were determined by measuring natural reference cotton leaves, and statistical CWSI (CWSIs), in which Twet was the mean of the lowest 5% of canopy temperatures and Tdry was the air temperature (Tair) + 5 °C. Compared with CWSIe, CWSIs and spectral indices (NDVI, TCARI, OSAVI, TCARI/OSAVI), CWSIsi has higher correlation with gs (R2 = 0.660) and tr (R2 = 0.592). The correlation coefficient (R) for θ (0–45 cm) and CWSIsi is also high (0.812). The plotted high-resolution map of CWSIsi shows the different distribution of cotton water stress in different irrigation treatments. These findings demonstrate that CWSIsi, which only requires parameters from a canopy temperature histogram, may potentially be applied to precision irrigation management.

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.

scholarly journals Unmanned Aerial Vehicle for Remote Sensing Applications—A Review

2019 ◽  
Vol 11 (12) ◽  
pp. 1443 ◽  
Author(s):  
Huang Yao ◽  
Rongjun Qin ◽  
Xiaoyu Chen
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Unmanned Aerial Vehicle ◽  
Land Cover Classification ◽  
Sensor Data ◽  
Future Research ◽  
Systematic Analysis ◽  
Sensing Applications ◽  
Aerial Vehicle

The unmanned aerial vehicle (UAV) sensors and platforms nowadays are being used in almost every application (e.g., agriculture, forestry, and mining) that needs observed information from the top or oblique views. While they intend to be a general remote sensing (RS) tool, the relevant RS data processing and analysis methods are still largely ad-hoc to applications. Although the obvious advantages of UAV data are their high spatial resolution and flexibility in acquisition and sensor integration, there is in general a lack of systematic analysis on how these characteristics alter solutions for typical RS tasks such as land-cover classification, change detection, and thematic mapping. For instance, the ultra-high-resolution data (less than 10 cm of Ground Sampling Distance (GSD)) bring more unwanted classes of objects (e.g., pedestrian and cars) in land-cover classification; the often available 3D data generated from photogrammetric images call for more advanced techniques for geometric and spectral analysis. In this paper, we perform a critical review on RS tasks that involve UAV data and their derived products as their main sources including raw perspective images, digital surface models, and orthophotos. In particular, we focus on solutions that address the “new” aspects of the UAV data including (1) ultra-high resolution; (2) availability of coherent geometric and spectral data; and (3) capability of simultaneously using multi-sensor data for fusion. Based on these solutions, we provide a brief summary of existing examples of UAV-based RS in agricultural, environmental, urban, and hazards assessment applications, etc., and by discussing their practical potentials, we share our views in their future research directions and draw conclusive remarks.

scholarly journals Mapping Plastic Mulched Farmland for High Resolution Images of Unmanned Aerial Vehicle Using Deep Semantic Segmentation

2019 ◽  
Vol 11 (17) ◽  
pp. 2008 ◽  
Author(s):  
Qinchen Yang ◽  
Man Liu ◽  
Zhitao Zhang ◽  
Shuqin Yang ◽  
Jifeng Ning ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Classification Method ◽  
Remote Sensing Images ◽  
Segmentation Methods ◽  
Traditional Classification ◽  
Aerial Vehicle ◽  
Segmentation Models

With increasing consumption, plastic mulch benefits agriculture by promoting crop quality and yield, but the environmental and soil pollution is becoming increasingly serious. Therefore, research on the monitoring of plastic mulched farmland (PMF) has received increasing attention. Plastic mulched farmland in unmanned aerial vehicle (UAV) remote images due to the high resolution, shows a prominent spatial pattern, which brings difficulties to the task of monitoring PMF. In this paper, through a comparison between two deep semantic segmentation methods, SegNet and fully convolutional networks (FCN), and a traditional classification method, Support Vector Machine (SVM), we propose an end-to-end deep-learning method aimed at accurately recognizing PMF for UAV remote sensing images from Hetao Irrigation District, Inner Mongolia, China. After experiments with single-band, three-band and six-band image data, we found that deep semantic segmentation models built via single-band data which only use the texture pattern of PMF can identify it well; for example, SegNet reaching the highest accuracy of 88.68% in a 900 nm band. Furthermore, with three visual bands and six-band data (3 visible bands and 3 near-infrared bands), deep semantic segmentation models combining the texture and spectral features further improve the accuracy of PMF identification, whereas six-band data obtains an optimal performance for FCN and SegNet. In addition, deep semantic segmentation methods, FCN and SegNet, due to their strong feature extraction capability and direct pixel classification, clearly outperform the traditional SVM method in precision and speed. Among three classification methods, SegNet model built on three-band and six-band data obtains the optimal average accuracy of 89.62% and 90.6%, respectively. Therefore, the proposed deep semantic segmentation model, when tested against the traditional classification method, provides a promising path for mapping PMF in UAV remote sensing images.

scholarly journals Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV)

2017 ◽  
Vol 9 (8) ◽  
pp. 828 ◽  
Author(s):  
Suyoung Park ◽  
Dongryeol Ryu ◽  
Sigfredo Fuentes ◽  
Hoam Chung ◽  
Esther Hernández-Montes ◽  
...  
Keyword(s):  
Water Stress ◽  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Adaptive Estimation ◽  
Crop Water ◽  
High Resolution Imagery ◽  
Aerial Vehicle ◽  
Crop Water Stress
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