scholarly journals ASSESSMENT OF RGB AND HYPERSPECTRAL UAV REMOTE SENSING FOR GRASS QUANTITY AND QUALITY ESTIMATION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 489-494 ◽  
Author(s):  
R. A. Oliveira ◽  
R. Näsi ◽  
O. Niemeläinen ◽  
L. Nyholm ◽  
K. Alhonoja ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Quality Parameters ◽  
Flying Height ◽  
Harvesting Time ◽  
Spectral Bands ◽  
Primary Growth ◽  
Dry Biomass ◽  
Reference Measurements ◽  
Orientation Parameters ◽  
D Value

<p><strong>Abstract.</strong> The information on the grass quantity and quality is needed for several times in a growing season for making optimal decisions about the harvesting time and the fertiliser rate, especially in northern countries, where grass swards quality declines and yield increases rapidly in the primary growth. We studied the potential of UAV-based photogrammetry and spectral imaging in grass quality and quantity estimation. To study this, a trial site with large variation in the quantity and quality parameters was established by using different nitrogen fertilizer application rates and harvesting dates. UAV-based remote sensing datasets were captured four times during the primary growth season in June 2017 and agricultural reference measurements including dry biomass and quality parameters, such as the digestibility (D-value) were collected simultaneously. The datasets were captured using a flying height of 50&amp;thinsp;m which provided a GSD of 0.7&amp;thinsp;cm for the photogrammetric imagery and 5&amp;thinsp;cm for the hyperspectral imagery. A rigorous photogrammetric workflow was carried out for all data sets aiming to determine the image exterior orientation parameters, camera interior orientation parameters, 3D point clouds and orthomosaics. The quantitative radiometric calibration included sensor corrections, atmospheric correction, and correction for the radiometric non-uniformities caused by illumination variations, BRDF correction and the absolute reflectance transformation. Random forest (RF) and multilinear regression (MLR) estimators were trained using spectral bands, vegetation indices and 3D features, extracted from the remote sensing datasets, and insitu reference measurements. From the FPI hyperspectral data, the 35 spectral bands and 11 spectral indices were used. The 3D features were extracted from the canopy height model (CHM) generated using RGB data. The most accurate results were obtained in the second measurement day (15th June) which was near to the optimal harvesting time and generally RF outperformed MLR slightly. When assessed with the leave-one-out-estimation, the best root mean squared error (RMSE%) were 8.9% for the dry biomass using 3D features. The best D-value estimation using RF algorithm (RMSE%&amp;thinsp;=&amp;thinsp;0.87%) was obtained using spectral features. Using the estimators, we then calculated grass quality and quantity maps covering the entire test site to compare different techniques and to evaluate the variability in the field. The results showed that the low-cost drone remote sensing gave excellent precision both for biomass and quality parameter estimation if accurately calibrated, offering an excellent tool for efficient and accurate management of silage grass production.</p>

Yield and Seed quality of barley (Hordeum vulgare L.) as affected by variety, nitrogen level and harvesting time

2014 ◽  
Vol 2 (2) ◽  
pp. 93-100
Author(s):  
Shahnaj Yesmina ◽  
Moushumi Akhtarb ◽  
Belal Hossain
Keyword(s):  
Grain Yield ◽  
Seed Weight ◽  
Seed Quality ◽  
Quality Parameters ◽  
Nitrogen Level ◽  
Yield Attributes ◽  
Harvesting Time ◽  
Nitrogen Levels ◽  
Vigour Index

The experiment was conducted to find out the effect of variety, nitrogen level and harvesting time on yield and seed quality of barley. The treatments used in the experiment consisted of two varieties viz. BARI Barley 4 and BARI Barley 5, three harvesting time viz. 35, 40 and 45 Days after Anthesis (DAA) and nitrogen levels viz. 0, 70, 85 and 100 kg N ha-1 . The experiment was laid out in a spilt- spilt-plot design with three replications assigning the variety to the main plot, harvesting time to the sub-plots and nitrogen level to the sub-sub plots. Variety had significant effects on the all yield attributes except fertile seeds spike-1 . Seed quality parameters viz. normal seeds spike-1 , deformed seeds spike-1 , germination (%) and vigour index were statistically significant. The variety BARI Barley 5 produced higher grain yield and seed quality than BARI Barley 4. Grain yield from BARI Barley 5 and BARI Barley 4 were 4.59 t ha-1 and 4.24 t ha-1 , respectively. Significantly, the highest 1000-seed weight (46.90 g) was produced by BARI Barley 5 than (37.90 g) BARI Barley 4. The result revealed that harvesting time had significant effect on yield and yield attributes and seed quality parameters. Seed yield was highest (4.65 t ha-1 ) when the crop harvested at 40 DAA and it was increased linearly from 35 DAA. Maximum quality seed and 1000-seed weight (43.20 g) was obtained when the crop harvested at 40 DAA. All the yields, yield attributes and seed quality parameters were significantly influenced by nitrogen levels. The highest grain yield (5.14 t ha-1 ) was obtained when BARI Barley 5 variety was fertilized by 100 kg N ha-1 and the lowest (3.14 t ha-1 ) was obtained from control treatments. Normal seeds spike-1 , vigour index, germination (%) were better at 85 kg N ha-1 in variety of BARI Barley 5 than BARI Barley 4. So it can be concluded that BARI Barley 5 showed better result when fertilized with 100 kg N ha-1 and harvested at 40 DAA for getting maximum yield and 85 kg N ha-1 and harvested at 40 DAA for getting better quality seed.

scholarly journals How can remote sensing techniques help monitoring the vine and maximize the terroir potential?

2018 ◽  
Vol 50 ◽  
pp. 02007
Author(s):  
Cecile Tondriaux ◽  
Anne Costard ◽  
Corinne Bertin ◽  
Sylvie Duthoit ◽  
Jérôme Hourdel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Sampling Strategy ◽  
Wine Quality ◽  
Wine Production ◽  
Spectral Bands ◽  
High Resolution Satellite Images ◽  
Remote Sensing Techniques ◽  
Set Up

In each winegrowing region, the winegrower tries to value its terroir and the oenologists do their best to produce the best wine. Thanks to new remote sensing techniques, it is possible to implement a segmentation of the vineyard according to the qualitative potential of the vine stocks and make the most of each terroir to improve wine quality. High resolution satellite images are processed in several spectral bands and algorithms set-up specifically for the Oenoview service allow to estimate vine vigour and a heterogeneity index that, used together, directly reflect the vineyard oenological potential. This service is used in different terroirs in France (Burgundy, Languedoc, Bordeaux, Anjou) and in other countries (Chile, Spain, Hungary and China). From this experience, we will show how remote sensing can help managing vine and wine production in all covered terroirs. Depending on the winegrowing region and its specificities, its use and results present some differences and similarities that we will highlight. We will give an overview of the method used, the advantage of implementing field intra-or inter-selection and how to optimize the use of amendment and sampling strategy as well as how to anticipate the whole vineyard management.

Specific spectral bands for different land cover contexts to improve the efficiency of remote sensing archaeological prospection: The Arpi case study

2009 ◽  
Vol 10 ◽  
pp. e41-e48 ◽  
Author(s):  
Cristiana Bassani ◽  
Rosa Maria Cavalli ◽  
Roberto Goffredo ◽  
Angelo Palombo ◽  
Simone Pascucci ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Archaeological Prospection ◽  
Spectral Bands

The Effect of Harvesting Time on Olive Fruits and Oils Quality Parameters of Tortiglione and Dritta Olive Cultivars

2021 ◽  
pp. 2000382
Author(s):  
Flamminii Federica ◽  
Marone Elettra ◽  
Neri Lilia ◽  
Pollastri Luciano ◽  
Cichelli Angelo ◽  
...  
Keyword(s):  
Quality Parameters ◽  
Harvesting Time ◽  
Olive Cultivars

LAI Retrieval Based on PROSPECT-SAILH Model from Multi-Angular Data of WiDAS Imaging System

2012 ◽  
Vol 518-523 ◽  
pp. 5697-5703
Author(s):  
Zhao Yan Liu ◽  
Ling Ling Ma ◽  
Ling Li Tang ◽  
Yong Gang Qian
Keyword(s):  
Remote Sensing ◽  
Imaging System ◽  
Remote Sensing Data ◽  
Canopy Reflectance ◽  
Area Index ◽  
Sensing Data ◽  
Spectral Bands ◽  
Sensing Technology ◽  
Leaf Optical Properties ◽  
Angular Data

The aim of this study is to assess the capability of estimating Leaf Area Index (LAI) from high spatial resolution multi-angular Vis-NIR remote sensing data of WiDAS (Wide-Angle Infrared Dual-mode Line/Area Array Scanner) imaging system by inverting the coupled radiative transfer models PROSPECT-SAILH. Based on simulations from SAILH canopy reflectance model and PROSPECT leaf optical properties model, a Look-up Table (LUT) which describes the relationship between multi-angular canopy reflectance and LAI has been produced. Then the LAI can be retrieved from LUT by directly matching canopy reflectance of six view directions and four spectral bands with LAI. The inversion results are validated by field data, and by comparing the retrieval results of single-angular remote sensing data with multi-angular remote sensing data, we can found that the view angle takes the obvious impact on the LAI retrieval of single-angular data and that high accurate LAI can be obtained from the high resolution multi-angular remote sensing technology.

Remote sensing inversion of water quality in coastal sea area based on machine learning: a case study of Shenzhen bay, China

2021 ◽  
Author(s):  
Xiaotong Zhu ◽  
Jinhui Jeanne Huang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Predictive Accuracy ◽  
Water Environment ◽  
Quality Parameters ◽  
Machine Learning Algorithms ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Seawater Quality

&lt;p&gt;Remote sensing monitoring has the characteristics of wide monitoring range, celerity, low cost for long-term dynamic monitoring of water environment. With the flourish of artificial intelligence, machine learning has enabled remote sensing inversion of seawater quality to achieve higher prediction accuracy. However, due to the physicochemical property of the water quality parameters, the performance of algorithms differs a lot. In order to improve the predictive accuracy of seawater quality parameters, we proposed a technical framework to identify the optimal machine learning algorithms using Sentinel-2 satellite and in-situ seawater sample data. In the study, we select three algorithms, i.e. support vector regression (SVR), XGBoost and deep learning (DL), and four seawater quality parameters, i.e. dissolved oxygen (DO), total dissolved solids (TDS), turbidity(TUR) and chlorophyll-a (Chla). The results show that SVR is a more precise algorithm to inverse DO (R&lt;sup&gt;2&lt;/sup&gt; = 0.81). XGBoost has the best accuracy for Chla and Tur inversion (R&lt;sup&gt;2&lt;/sup&gt; = 0.75 and 0.78 respectively) while DL performs better in TDS (R&lt;sup&gt;2&lt;/sup&gt; =0.789). Overall, this research provides a theoretical support for high precision remote sensing inversion of offshore seawater quality parameters based on machine learning.&lt;/p&gt;

scholarly journals Towards HD Maps from Aerial Imagery: Robust Lane Marking Segmentation Using Country-Scale Imagery

2018 ◽  
Vol 7 (12) ◽  
pp. 458 ◽  
Author(s):  
Peter Fischer ◽  
Seyed Majid Azimi ◽  
Robert Roschlaub ◽  
Thomas Krauß
Keyword(s):  
Remote Sensing ◽  
Autonomous Driving ◽  
Quality Parameters ◽  
Random Forest Classifier ◽  
Data Sources ◽  
Aerial Imagery ◽  
Remote Sensing Technology ◽  
Current State ◽  
Lane Marking ◽  
Sensing Technology

The upraise of autonomous driving technologies asks for maps characterized bya broad range of features and quality parameters, in contrast to traditional navigation maps which in most cases are enriched graph-based models. This paper tackles several uncertainties within the domain of HD Maps. The authors give an overview about the current state in extracting road features from aerial imagery for creating HD maps, before shifting the focus of the paper towards remote sensing technology. Possible data sources and their relevant parameters are listed. A random forest classifier is used, showing how these data can deliver HD Maps on a country-scale, meeting specific quality parameters.

scholarly journals Assessment of the Single-Mixture Gas Assumption for the Correlated K-Distribution Fictitious Gas Method in H2O–CO2–CO Mixture at High Temperature

2008 ◽  
Vol 130 (10) ◽  
Author(s):  
C. Caliot ◽  
G. Flamant ◽  
M. El Hafi ◽  
Y. Le Maoult
Keyword(s):  
Remote Sensing ◽  
Path Length ◽  
Narrow Band ◽  
Specific Area ◽  
Sensitivity Study ◽  
Computational Time ◽  
Line Of Sight ◽  
Spectral Bands ◽  
Integrated Intensity ◽  
Atmospheric Path

This paper deals with the comparison of spectral narrow band models based on the correlated-K (CK) approach in the specific area of remote sensing of plume signatures. The CK models chosen may or may not include the fictitious gas (FG) idea and the single-mixture-gas assumption (SMG). The accuracy of the CK and the CK-SMG as well as the CKFG and CKFG-SMG models are compared, and the influence of the SMG assumption is inferred. The errors induced by each model are compared in a sensitivity study involving the plume thickness and the atmospheric path length as parameters. This study is conducted in two remote-sensing situations with different absolute pressures at sea level (105Pa) and at high altitude (16.6km, 104Pa). The comparisons are done on the basis of the error obtained for the integrated intensity while leaving a line of sight that is computed in three common spectral bands: 2000–2500cm−1, 3450–3850cm−1, and 3850–4150cm−1. In most situations, the SMG assumption induces negligible differences. Furthermore, compared to the CKFG model, the CKFG-SMG model results in a reduction of the computational time by a factor of 2.

scholarly journals Payload Configurations for Efficient Image Acquisition – Indian Perspective

2014 ◽  
Vol XL-8 ◽  
pp. 1201-1206 ◽  
Author(s):  
D. R. M. Samudraiah ◽  
M. Saxena ◽  
S. Paul ◽  
P. Narayanababu ◽  
S. Kuriakose ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Acquisition ◽  
High Efficiency ◽  
Image Acquisition ◽  
Image Data ◽  
Space Research ◽  
Remotely Sensed Data ◽  
Data Utilization ◽  
Spectral Bands ◽  
The World

The world is increasingly depending on remotely sensed data. The data is regularly used for monitoring the earth resources and also for solving problems of the world like disasters, climate degradation, etc. Remotely sensed data has changed our perspective of understanding of other planets. With innovative approaches in data utilization, the demands of remote sensing data are ever increasing. More and more research and developments are taken up for data utilization. The satellite resources are scarce and each launch costs heavily. Each launch is also associated with large effort for developing the hardware prior to launch. It is also associated with large number of software elements and mathematical algorithms post-launch. The proliferation of low-earth and geostationary satellites has led to increased scarcity in the available orbital slots for the newer satellites. Indian Space Research Organization has always tried to maximize the utility of satellites. Multiple sensors are flown on each satellite. In each of the satellites, sensors are designed to cater to various spectral bands/frequencies, spatial and temporal resolutions. Bhaskara-1, the first experimental satellite started with 2 bands in electro-optical spectrum and 3 bands in microwave spectrum. The recent Resourcesat-2 incorporates very efficient image acquisition approach with multi-resolution (3 types of spatial resolution) multi-band (4 spectral bands) electro-optical sensors (LISS-4, LISS-3* and AWiFS). The system has been designed to provide data globally with various data reception stations and onboard data storage capabilities. Oceansat-2 satellite has unique sensor combination with 8 band electro-optical high sensitive ocean colour monitor (catering to ocean and land) along with Ku band scatterometer to acquire information on ocean winds. INSAT- 3D launched recently provides high resolution 6 band image data in visible, short-wave, mid-wave and long-wave infrared spectrum. It also has 19 band sounder for providing vertical profile of water vapour, temperature, etc. The same system has data relay transponders for acquiring data from weather stations. The payload configurations have gone through significant changes over the years to increase data rate per kilogram of payload. Future Indian remote sensing systems are planned with very high efficient ways of image acquisition. <br><br> This paper analyses the strides taken by ISRO (Indian Space research Organisation) in achieving high efficiency in remote sensing image data acquisition. Parameters related to efficiency of image data acquisition are defined and a methodology is worked out to compute the same. Some of the Indian payloads are analysed with respect to some of the system/ subsystem parameters that decide the configuration of payload. Based on the analysis, possible configuration approaches that can provide high efficiency are identified. A case study is carried out with improved configuration and the results of efficiency improvements are reported. This methodology may be used for assessing other electro-optical payloads or missions and can be extended to other types of payloads and missions.

scholarly journals ESTIMATING EXTERIOR ORIENTATION PARAMETERS OF HYPERSPECTRAL BANDS BASED ON POLYNOMIAL MODELS

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 19-25 ◽  
Author(s):  
A. Berveglieri ◽  
A. M. G. Tommaselli ◽  
E. Honkavaara
Keyword(s):  
Real Data ◽  
Bundle Adjustment ◽  
Time Dependent ◽  
Camera Model ◽  
Exterior Orientation ◽  
Spectral Bands ◽  
Acquisition Mode ◽  
Polynomial Models ◽  
Orientation Parameters ◽  
Sequential Acquisition

Hyperspectral camera operating in sequential acquisition mode produces spectral bands that are not recorded at the same instant, thus having different exterior orientation parameters (EOPs) for each band. The study presents experiments on bundle adjustment with time-dependent polynomial models for band orientation of hyperspectral cubes sequentially collected. The technique was applied to a Rikola camera model. The purpose was to investigate the behaviour of the estimated polynomial parameters and the feasibility of using a minimum of bands to estimate EOPs. Simulated and real data were produced for the analysis of parameters and accuracy in ground points. The tests considered conventional bundle adjustment and the polynomial models. The results showed that both techniques were comparable, indicating that the time-dependent polynomial model can be used to estimate the EOPs of all spectral bands, without requiring a bundle adjustment of each band. The accuracy of the block adjustment was analysed based on the discrepancy obtained from checkpoints. The root mean square error (RMSE) indicated an accuracy of 1&amp;thinsp;GSD in planimetry and 1.5&amp;thinsp;GSD in altimetry, when using a minimum of four bands per cube.

Sign in / Sign up

Export Citation Format

Share Document