scholarly journals Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques

2019 ◽  
Vol 11 (15) ◽  
pp. 1835 ◽  
Author(s):  
Mohammad Sadegh Askari ◽  
Timothy McCarthy ◽  
Aidan Magee ◽  
Darren J. Murphy
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Quality Indicators ◽  
Prediction Models ◽  
Unmanned Aircraft ◽  
Multispectral Imagery ◽  
European Climate ◽  
Grass Biomass ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Hyperspectral and multispectral imagery have been demonstrated to have a considerable potential for near real-time monitoring and mapping of grass quality indicators. The objective of this study was to evaluate the efficiency of remote sensing techniques for quantification of aboveground grass biomass (BM) and crude protein (CP) in a temperate European climate such as Ireland. The experiment was conducted on 64 plots and 53 paddocks with varying quantities of nitrogen applied. Hyperspectral imagery (HSI) and multispectral imagery (MSI) were analyzed to develop the prediction models. The MSI data used in this study were captured using an unmanned aircraft vehicle (UAV) and the satellite Sentinel-2, while the HSI data were obtained using a handheld hyperspectral camera. The prediction models were developed using partial least squares regression (PLSR) and stepwise multi-linear regression (MLR). Eventually, the spatial distribution of grass biomass over plots and paddocks was mapped to assess the within-field variability of grass quality metrics. An excellent accuracy was achieved for the prediction of BM and CP using HSI (RPD > 2.5 and R2 > 0.8), and a good accuracy was obtained via MSI-UAV (2 < RPD < 2.5 and R2 > 0.7) for the grass quality indicators. The accuracy of the models calculated using MSI-Sentinel-2 was reasonable for BM prediction and insufficient for CP estimation. The red-edge range of the wavelengths showed the maximum impact on the predictability of grass BM, and the NIR range had the greatest influence on the estimation of grass CP. Both the PLSR and MLR techniques were found to be sufficiently robust for spectral modelling of aboveground BM and CP. The PLSR yielded a slightly better model than MLR. This study suggested that remote sensing techniques can be used as a rapid and reliable approach for near real-time quantitative assessment of fresh grass quality under a temperate European climate.

scholarly journals Exploring the Suitability of UAS-Based Multispectral Images for Estimating Soil Organic Carbon: Comparison with Proximal Soil Sensing and Spaceborne Imagery

2021 ◽  
Vol 13 (2) ◽  
pp. 308
Author(s):  
James Kobina Mensah Biney ◽  
Mohammadmehdi Saberioon ◽  
Luboš Borůvka ◽  
Jakub Houška ◽  
Radim Vašát ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Prediction Models ◽  
Unmanned Aircraft ◽  
Support Vector ◽  
Spectral Indices ◽  
Entire Study ◽  
Proximal Soil Sensing ◽  
Sentinel 2

Soil organic carbon (SOC) is a variable of vital environmental significance in terms of soil quality and function, global food security, and climate change mitigation. Estimation of its content and prediction accuracy on a broader scale remain crucial. Although, spectroscopy under proximal sensing remains one of the best approaches to accurately predict SOC, however, spectroscopy limitation to estimate SOC on a larger spatial scale remains a concern. Therefore, for an efficient quantification of SOC content, faster and less costly techniques are needed, recent studies have suggested the use of remote sensing approaches. The primary aim of this research was to evaluate and compare the capabilities of small Unmanned Aircraft Systems (UAS) for monitoring and estimation of SOC with those obtained from spaceborne (Sentinel-2) and proximal soil sensing (field spectroscopy measurements) on an agricultural field low in SOC content. Nine calculated spectral indices were added to the remote sensing approaches (UAS and Sentinel-2) to enhance their predictive accuracy. Modeling was carried out using various bands/wavelength (UAS (6), Sentinel-2 (9)) and the calculated spectral indices were used as independent variables to generate soil prediction models using five-fold cross-validation built using random forest (RF) and support vector machine regression (SVMR). The correlation regarding SOC and the selected indices and bands/wavelengths was determined prior to the prediction. Our results revealed that the selected spectral indices slightly influenced the output of UAS compared to Sentinel-2 dataset as the latter had only one index correlated with SOC. For prediction, the models built on UAS data had a better accuracy with RF than the two other data used. However, using SVMR, the field spectral prediction models achieved a better overall result for the entire study (log(1/R), RPD = 1.40; R2CV = 0.48; RPIQ = 1.65; RMSEPCV = 0.24), followed by UAS and then Sentinel-2, respectively. This study has shown that UAS imagery can be exploited efficiently using spectral indices.

scholarly journals Possibilities of milpa identification in Yucatan through remote sensing techniques and Sentinel-2 data

10.29007/hbs2 ◽  
2019 ◽  
Author(s):  
Juan Carlos Valdiviezo-Navarro ◽  
Adan Salazar-Garibay ◽  
Karla Juliana Rodríguez-Robayo ◽  
Lilián Juárez ◽  
María Elena Méndez-López ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Food Sovereignty ◽  
Region Of Interest ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Multispectral Images ◽  
Crop Fields ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Maya milpa is one of the most important agrifood systems in Mesoamerica, not only because its ancient origin but also due to lead an increase in landscape diversity and to be a relevant source of families food security and food sovereignty. Nowadays, satellite remote sensing data, as the multispectral images of Sentinel-2 platforms, permit us the monitor- ing of different kinds of structures such as water bodies, urban areas, and particularly agricultural fields. Through its multispectral signatures, mono-crop fields or homogeneous vegetation zones like corn fields, barley fields, or other ones, have been successfully detected by using classification techniques with multispectral images. However, Maya milpa is a complex field which is conformed by different kinds of vegetables species and fragments of natural vegetation that in conjunction cannot be considered as a mono-crop field. In this work, we show some preliminary studies on the availability of monitoring this complex system in a region of interest in Yucatan, through a support vector machine (SVM) approach.

scholarly journals Geomorphological evolution of landslides near an active normal fault in northern Taiwan, as revealed by lidar and unmanned aircraft system data

2018 ◽  
Vol 18 (3) ◽  
pp. 709-727 ◽  
Author(s):  
Kuo-Jen Chang ◽  
Yu-Chang Chan ◽  
Rou-Fei Chen ◽  
Yu-Chung Hsieh
Keyword(s):  
Remote Sensing ◽  
Normal Fault ◽  
Unmanned Aircraft ◽  
Airborne Lidar ◽  
Reconstruction Method ◽  
Remote Sensing Techniques ◽  
Aircraft System ◽  
Geomorphological Evolution ◽  
Northern Taiwan ◽  
Landslide Morphology

Abstract. Several remote sensing techniques, namely traditional aerial photographs, an unmanned aircraft system (UAS), and airborne lidar, were used in this study to decipher the morphological features of obscure landslides in volcanic regions and how the observed features may be used for understanding landslide occurrence and potential hazard. A morphological reconstruction method was proposed to assess landslide morphology based on the dome-shaped topography of the volcanic edifice and the nature of its morphological evolution. Two large-scale landslides in the Tatun volcano group in northern Taiwan were targeted to more accurately characterize the landslide morphology through airborne lidar and UAS-derived digital terrain models and images. With the proposed reconstruction method, the depleted volume of the two landslides was estimated to be at least 820 ± 20  ×  106 m3. Normal faulting in the region likely played a role in triggering the two landslides, because there are extensive geological and historical records of an active normal fault in this region. The subsequent geomorphological evolution of the two landslides is thus inferred to account for the observed morphological and tectonic features that are indicative of resulting in large and life-threatening landslides, as characterized using the recent remote sensing techniques.

scholarly journals Influencia del ángulo de observación en la estimación del índice de área foliar (LAI) mediante imágenes PROBA/CHRIS

2016 ◽  
pp. 45 ◽  
Author(s):  
J. Delegido ◽  
C. M. Meza ◽  
N. Pasqualotto ◽  
J. Moreno
Keyword(s):  
Remote Sensing ◽  
Viewing Angle ◽  
Area Index ◽  
Red Edge ◽  
The Subject ◽  
Remote Sensing Techniques ◽  
Normalized Difference Index ◽  
The Relationship ◽  
Chlorophyll Absorption ◽  
Sentinel 2

<p>The estimation of biophysical variables, such as the Leaf Area Index (LAI), using remote sensing techniques, is still the subject of numerous studies, since these variables allow obtaining valuable information on the vegetation status. In this work, we estimate LAI from multiangular PROBA/CHRIS images, by analyzing the reflectance measured in its 5 observation angles, for the bands centered in 665 and 705 nm. These wavelengths correspond to the chlorophyll absorption band and the Red-Edge region, respectively. The Normalized Difference Index (NDI) calculated from this wavelengths, shows good correlation with LAI and allows its remote sensing estimation and its applicability to the recently launched ESA Sentinel 2, thanks to its new bands in the Red-Edge. This research analyzed the influence on the acquisition geometry in the NDI, calibrating the relationship between this index and the LAI for each of the five observation angles in the PROBA / CHRIS images. As a result, we have obtained a relationship capable of providing LAI from the viewing angle and the NDI index.</p>

scholarly journals Automatic Mapping of Rice Growth Stages Using the Integration of SENTINEL-2, MOD13Q1, and SENTINEL-1

2020 ◽  
Vol 12 (21) ◽  
pp. 3613 ◽  
Author(s):  
Fadhlullah Ramadhani ◽  
Reddy Pullanagari ◽  
Gabor Kereszturi ◽  
Jonathan Procter
Keyword(s):  
Remote Sensing ◽  
Food Security ◽  
Prediction Models ◽  
Spatial Information ◽  
Rice Production ◽  
Growth Stages ◽  
Time Lags ◽  
Cropping Pattern ◽  
Rice Growth ◽  
Sentinel 2

Rice (Oryza sativa L.) is a staple food crop for more than half of the world’s population. Rice production is facing a myriad of problems, including water shortage, climate, and land-use change. Accurate maps of rice growth stages are critical for monitoring rice production and assessing its impacts on national and global food security. Rice growth stages are typically monitored by coarse-resolution satellite imagery. However, it is difficult to accurately map due to the occurrence of mixed pixels in fragmented and patchy rice fields, as well as cloud cover, particularly in tropical countries. To solve these problems, we developed an automated mapping workflow to produce near real-time multi-temporal maps of rice growth stages at a 10-m spatial resolution using multisource remote sensing data (Sentinel-2, MOD13Q1, and Sentinel-1). This study was investigated between 1 June and 29 September 2018 in two (wet and dry) areas of Java Island in Indonesia. First, we built prediction models based on Sentinel-2, and fusion of MOD13Q1/Sentinel-1 using the ground truth information. Second, we applied the prediction models on all images in area and time and separation between the non-rice planting class and rice planting class over the cropping pattern. Moreover, the model’s consistency on the multitemporal map with a 5–30-day lag was investigated. The result indicates that the Sentinel-2 based model classification gives a high overall accuracy of 90.6% and the fusion model MOD13Q1/Sentinel-1 shows 78.3%. The performance of multitemporal maps was consistent between time lags with an accuracy of 83.27–90.39% for Sentinel-2 and 84.15% for the integration of Sentinel-2/MOD13Q1/Sentinel-1. The results from this study show that it is possible to integrate multisource remote sensing for regular monitoring of rice phenology, thereby generating spatial information to support local-, national-, and regional-scale food security applications.

DIAGNÓSTICO DO USO E COBERTURA DA TERRA NOS RESERVATÓRIOS PÚBLICOS DO MUNICÍPIO DE PAU DOS FERROS, RN, BRASIL

2020 ◽  
Vol 11 (38) ◽  
pp. 146-161
Author(s):  
Aluizio Bezerra Júnior ◽  
◽  
Agassiel Medeiros Alves ◽  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Natural Resources ◽  
Spatial Resolution ◽  
Sustainable Use ◽  
Rio Grande ◽  
Spatial Dimensions ◽  
Methodological Procedures ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Research objective is to classify, measure and map the spatial dimensions of land use and land cover classes in public reservoirs 25 de Março and Dr. Pedro Diógenes Fernandes, both belonging to the municipality of Pau dos Ferros, state of Rio Grande do Norte. For the methodological procedures, remote sensing techniques (SIG Qgis version Lyon 2.12.3) were used, of the medium spatial resolution images of the SENTINEL 2 satellite, MSI sensor (Multispectral Instrument), accompanied by the interpretation key. The results showed that there is a possibility of sustainable use, since the exploration and conservation remains in balance, therefore, this research can subsidize the conservation of the use of natural resources around the reservoirs.

Smart Technologies for Emergency Response and Disaster Management

2019 ◽  
pp. 939-979
Author(s):  
Kavitha T ◽  
Saraswathi S
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Urban Areas ◽  
Earth Observation ◽  
Extreme Temperatures ◽  
Sensing Technology ◽  
Sensor Information ◽  
Remote Sensing Techniques ◽  
Smart Technologies ◽  
Timely Warning

Disasters are the convergence of hazards that strikes a vulnerable community which is insufficient to withstand with its adverse effects and impact. Completely avoiding natural or anthropogenic disaster is not possible but its impact can be minimized by generating timely warning. The real-time earth observation is very important for generating such early warning. The earth observation is improving through the advancement in remote sensing technologies. Sensing technology provides real time monitoring and risk assessment. It helps in fast communication of an event occurrence. Disaster detection in urban areas is greatly improved by using remote sensing techniques. This chapter discus various devices used for real time earth monitoring of disaster events like Flood, Tsunami, Tornadoes, Droughts, Extreme Temperatures, Avalanches and Landslide. These devices gather information by continuous monitoring in their deployed location. The sensor information thus gathered must be communicated and processed to extract the disaster information.

Smart Technologies for Emergency Response and Disaster Management

2018 ◽  
pp. 1-40
Author(s):  
Kavitha T ◽  
Saraswathi S
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Urban Areas ◽  
Earth Observation ◽  
Extreme Temperatures ◽  
Sensing Technology ◽  
Sensor Information ◽  
Remote Sensing Techniques ◽  
Smart Technologies ◽  
Timely Warning

Disasters are the convergence of hazards that strikes a vulnerable community which is insufficient to withstand with its adverse effects and impact. Completely avoiding natural or anthropogenic disaster is not possible but its impact can be minimized by generating timely warning. The real-time earth observation is very important for generating such early warning. The earth observation is improving through the advancement in remote sensing technologies. Sensing technology provides real time monitoring and risk assessment. It helps in fast communication of an event occurrence. Disaster detection in urban areas is greatly improved by using remote sensing techniques. This chapter discus various devices used for real time earth monitoring of disaster events like Flood, Tsunami, Tornadoes, Droughts, Extreme Temperatures, Avalanches and Landslide. These devices gather information by continuous monitoring in their deployed location. The sensor information thus gathered must be communicated and processed to extract the disaster information.

Ulyssys Water Quality Viewer: a satellite-based global near real time water quality visualization

2020 ◽  
Author(s):  
András Zlinszky ◽  
Gergely Padányi-Gulyás
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Real Time ◽  
Suspended Sediment ◽  
Satellite Imagery ◽  
Quality Information ◽  
Custom Script ◽  
Chlorophyll Concentrations ◽  
Sentinel 2

&lt;p&gt;Sampling-based water quality monitoring networks are inherently spatially sparse. In locations or times where no in-situ water quality data are available, satellite imagery is an essential source of information. Satellite remote sensing can provide high spatial or temporal resolution imagery and has provided a breakthrough for oceanography, but so far, applications for coastal and inland water were limited by data resolution. Recently established satellite systems provide significant advances: Sentinel-2 delivers imagery with 20 m resolution, suitable for viewing even small rivers and ponds. Sentinel-3 delivers daily imagery with 300 m pixel size, which for lakes and coastal seas allows tracking water quality processes at the speed they happen. Information on suspended sediment and chlorophyll concentrations in water can be derived from optical images using simple calculations. The accuracy of these operations will vary across locations and can only be assessed through calibration and validation with in situ data. In absence of such data for all lakes globally, UWQV is based on a small set of algorithms that have been verified on several optically complex water systems to have a close to linear correlation with chlorophyll or suspended sediment concentration. Suspended sediment visualization is based on radiances observed in the 620 or 700 nm spectral bands, while chlorophyll visualization uses fluorescence-based indicators: Fluorescence Line Height, Reflectance Line Height and Maximum Chlorophyll Index. Since remote sensing based chlorophyll retrieval in sediment-laden waters with low transparency is hardly possible, for such cases chlorophyll concentrations are not visualized. The viewer runs as a Custom Script in the Sentinel-Hub EO Browser, which is a global, near real-time satellite data viewing and algorithm testing framework. The Javascript code is open source and enables users to easily tune visualization parameters and select different algorithms for cloud and water masking and chlorophyll and suspended sediment visualization.&lt;br&gt;Wherever in-situ water quality measurements are available, UWQV contributes significant added value by complementing water sample or instrument-based data, providing a map view or even a timelapse of maps; by providing an early warning system for water quality deterioration; by supporting optimization of sampling times and locations based on spatially and temporally explicit information, and &amp;#160;enabling cross-validating water quality information from different sources to reduce uncertainty or identify implausible measurements. Additionally, data-driven spatially explicit models can be verified and tuned based on similarity of their output to situations observed on satellite imagery.&lt;br&gt;UWQV is has all the advantages and drawbacks of a global solution: it will never be more accurate than a locally tuned water quality remote sensing algorithm; however, we hope that it will encourage water quality authorities and stakeholders to initiate the development of locally optimized satellite-based monitoring. By providing easy to read visualizations in a framework accessible to the general public, UWQV can democratize water quality information and raise public awareness of water quality processes and problems.&lt;/p&gt;&lt;p&gt;The first version of the algorithm is available in the Sentinel-Hub Custom Script Repository under the following link: https://github.com/sentinel-hub/custom-scripts/tree/master/sentinel-2/ulyssys_water_quality_viewer&lt;/p&gt;&lt;p&gt;An interactive test example of the visualization can be accessed here: tinyurl.com/UWQV-example&lt;/p&gt;

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