scholarly journals Selecting the Best Image Pairs to Measure Slope Deformation

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4721
Author(s):  
Wentao Yang
Keyword(s):  
Optical Sensors ◽  
Low Cost ◽  
Slope Deformation ◽  
Optical Remote Sensing ◽  
Optical Images ◽  
Large Numbers ◽  
Image Pairs ◽  
Slope Displacement ◽  
Selection Of ◽  
Sentinel 2

Optical remote sensing images can be used to monitor slope deformation in mountain regions. Abundant optical sensors onboard various platforms were designed to provide increasingly high spatial–temporal resolution images at low cost; however, finding the best image pairs to derive slope deformation remains difficult. By selecting a location in the east Tibetan Plateau, this work used the co-registration of optically sensed images and correlation (COSI-Corr) method to analyze 402 Sentinel-2 images from August 2015 to February 2020, to quantify temporal patterns of uncertainty in deriving slope deformation. By excluding 66% of the Sentinel-2 images that were contaminated by unfavorable weather, uncertainties were found to fluctuate annually, with the least uncertainty achieved in image pairs of similar dates in different years. Six image pairs with the least uncertainties were selected to derive ground displacement for a moving slope in the study area. Cross-checks among these image pairs showed consistent results, with uncertainties less than 1/10 pixels in length. The findings from this work could help in the selection of the best image pairs to derive reliable slope displacement from large numbers of optical images.

scholarly journals Integration of Sentinel-1 and Sentinel-2 for Classification and LULC Mapping in the Urban Area of Belém, Eastern Brazilian Amazon

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1140 ◽  
Author(s):  
Paulo Tavares ◽  
Norma Beltrão ◽  
Ulisses Guimarães ◽  
Ana Teodoro
Keyword(s):  
Optical Sensors ◽  
Brazilian Amazon ◽  
Synthetic Aperture ◽  
Tropical Regions ◽  
Cloud Coverage ◽  
Optical Images ◽  
Vegetation Indexes ◽  
Tropical Areas ◽  
The City ◽  
Sentinel 2

In tropical regions, such as in the Amazon, the use of optical sensors is limited by high cloud coverage throughout the year. As an alternative, Synthetic Aperture Radar (SAR) products could be used, alone or in combination with optical images, to monitor tropical areas. In this sense, we aimed to select the best Land Use and Land Cover (LULC) classification approach for tropical regions using Sentinel family products. We choose the city of Belém, Brazil, as the study area. Images of close dates from Sentinel-1 (S-1) and Sentinel-2 (S-2) were selected, preprocessed, segmented, and integrated to develop a machine learning LULC classification through a Random Forest (RF) classifier. We also combined textural image analysis (S-1) and vegetation indexes (S-2). A total of six LULC classifications were made. Results showed that the best overall accuracy (OA) was found for the integration of S-1 and S-2 (91.07%) data, followed by S-2 only (89.53%), and S-2 with radiometric indexes (89.45%). The worse result was for S-1 data only (56.01). For our analysis the integration of optical products in the stacking increased de OA in all classifications. However, we suggest the development of more investigations with S-1 products due to its importance for tropical regions.

scholarly journals Design Considerations and Selection of Cost-Effective Switched Reluctance Drive for Radiator Cooling Fans

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 917
Author(s):  
Ickjin Son ◽  
Grace Firsta Lukman ◽  
Mazahir Hussain Shah ◽  
Kwang-Il Jeong ◽  
Jin-Woo Ahn
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
Magnetic Characteristics ◽  
Switched Reluctance Motors ◽  
Switched Reluctance ◽  
Design Considerations ◽  
Cooling Fan ◽  
Cooling Fans ◽  
Power Switches ◽  
Selection Of

Switched reluctance motors (SRMs) are simple in structure, easy to manufacture, magnet-less, brushless, and highly robust compared to other AC motors which makes them a good option for applications that operate in harsh environment. However, the motor has non-linear magnetic characteristics, and it comes with various pole-phase combinations and circuit topologies that causes many difficulties in deciding on which type to choose. In this paper, the viability of SRM as a low-cost, rugged machine for vehicle radiator cooling fan is considered. First, necessary design considerations are presented, then three commonly use types of SRM are analyzed: A 3-phase 6/4, 3-phase 12/8, and a 4-phase 8/6 to find their static and dynamic characteristics so the most suitable type can be selected. Simulation results show that the 8/6 SRM produces the highest efficiency with less phase current which reduces the converter burden. However, with asymmetric half bridge converter, eight power switches are required for 8/6 SRM and thus put a burden on the overall drive cost. As a solution, the Miller converter with only six switches for four phase SRM. To verify the proposed idea, the 8/6 SRM was manufactured and tested. The results show that Miller converter can be used for the proposed SRM with slightly reduced efficiency at 80.4%.

scholarly journals An IoT-Based Participatory Antitheft System for Public Safety Enhancement in Smart Cities

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 919-937
Author(s):  
Nikos Papadakis ◽  
Nikos Koukoulas ◽  
Ioannis Christakis ◽  
Ilias Stavrakas ◽  
Dionisis Kandris
Keyword(s):  
Smart Cities ◽  
Low Cost ◽  
Negative Influence ◽  
Asset Tracking ◽  
Tracking Tasks ◽  
Tracking Process ◽  
The Impact ◽  
Operation Center ◽  
Safety Enhancement ◽  
Selection Of

The risk of theft of goods is certainly an important source of negative influence in human psychology. This article focuses on the development of a scheme that, despite its low cost, acts as a smart antitheft system that achieves small property detection. Specifically, an Internet of Things (IoT)-based participatory platform was developed in order to allow asset-tracking tasks to be crowd-sourced to a community. Stolen objects are traced by using a prototype Bluetooth Low Energy (BLE)-based system, which sends signals, thus becoming a beacon. Once such an item (e.g., a bicycle) is stolen, the owner informs the authorities, which, in turn, broadcast an alert signal to activate the BLE sensor. To trace the asset with the antitheft tag, participants use their GPS-enabled smart phones to scan BLE tags through a specific smartphone client application and report the location of the asset to an operation center so that owners can locate their assets. A stolen item tracking simulator was created to support and optimize the aforementioned tracking process and to produce the best possible outcome, evaluating the impact of different parameters and strategies regarding the selection of how many and which users to activate when searching for a stolen item within a given area.

scholarly journals Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2313
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Reflectance ◽  
Remote Sensing Data ◽  
Soil Surface ◽  
Optical Remote Sensing ◽  
Test Field ◽  
Sensing Data ◽  
Sentinel 2

Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance.

scholarly journals Presenting a Semi-Automatic, Statistically-Based Approach to Assess the Sharpness Level of Optical Images from Natural Targets via the Edge Method. Case Study: The Landsat 8 OLI–L1T Data

2021 ◽  
Vol 13 (8) ◽  
pp. 1593
Author(s):  
Luca Cenci ◽  
Valerio Pampanoni ◽  
Giovanni Laneve ◽  
Carla Santella ◽  
Valentina Boccia
Keyword(s):  
Landsat 8 ◽  
Image Sharpness ◽  
Optical Images ◽  
Data Quality Assessment ◽  
Relevant Role ◽  
Edge Method ◽  
Selection Of ◽  
Statistical Criteria

Developing reliable methodologies of data quality assessment is of paramount importance for maximizing the exploitation of Earth observation (EO) products. Among the different factors influencing EO optical image quality, sharpness has a relevant role. When implementing on-orbit approaches of sharpness assessment, such as the edge method, a crucial step that strongly affects the final results is the selection of suitable edges to use for the analysis. Within this context, this paper aims at proposing a semi-automatic, statistically-based edge method (SaSbEM) that exploits edges extracted from natural targets easily and largely available on Earth: agricultural fields. For each image that is analyzed, SaSbEM detects numerous suitable edges (e.g., dozens-hundreds) characterized by specific geometrical and statistical criteria. This guarantees the repeatability and reliability of the analysis. Then, it implements a standard edge method to assess the sharpness level of each edge. Finally, it performs a statistical analysis of the results to have a robust characterization of the image sharpness level and its uncertainty. The method was validated by using Landsat 8 L1T products. Results proved that: SaSbEM is capable of performing a reliable and repeatable sharpness assessment; Landsat 8 L1T data are characterized by very good sharpness performance.

scholarly journals Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

2021 ◽  
Vol 13 (5) ◽  
pp. 956
Author(s):  
Florian Mouret ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Denis Kouamé ◽  
Guillaume Rieu ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Experimental Validation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Images ◽  
Vegetation Indexes ◽  
Crop Development ◽  
Different Types ◽  
Selection Of ◽  
Sentinel 2

This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.

scholarly journals Theoretical and Empirical Characterization of Water as a Factor: Examples and Related Issues with the World Trade Model

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 459
Author(s):  
Ignacio Cazcarro ◽  
Albert E. Steenge
Keyword(s):  
World Trade ◽  
Low Cost ◽  
Virtual Water ◽  
Considerable Change ◽  
Water Research ◽  
Trade Model ◽  
The World ◽  
Main Model ◽  
Selection Of

This article originates from the theoretical and empirical characterization of factors in the World Trade Model (WTM). It first illustrates the usefulness of this type of model for water research to address policy questions related to virtual water trade, water constraints and water scarcity. It also illustrates the importance of certain key decisions regarding the heterogeneity of water and its relation to the technologies being employed and the prices obtained. With regard to WTM, the global economic input–output model in which multiple technologies can produce a “homogeneous output”, it was recently shown that two different mechanisms should be distinguished by which multiple technologies can arise, i.e., from “technology-specific” or from “shared” factors, which implies a mechanism-specific set of prices, quantities and rents. We discuss and extend these characterizations, notably in relation to the real-world characterization of water as a factor (for which we use the terms technology specific, fully shared and “mixed”). We propose that the presence of these separate mechanisms results in the models being sensitive to relatively small variations in specific numerical values. To address this sensitivity, we suggest a specific role for specific (sub)models or key choices to counter unrealistic model outcomes. To support our proposal we present a selection of simulations for aggregated world regions, and show how key results concerning quantities, prices and rents can be subject to considerable change depending on the precise definitions of resource endowments and the technology-specificity of the factors. For instance, depending on the adopted water heterogeneity level, outcomes can vary from relatively low-cost solutions to higher cost ones and can even reach infeasibility. In the main model discussed here (WTM) factor prices are exogenous, which also contributes to the overall numerical sensitivity of the model. All this affects to a large extent our interpretation of the water challenges, which preferably need to be assessed in integrated frameworks, to account for the main socioeconomic variables, technologies and resources.

scholarly journals The Quality of Remote Sensing Optical Images from Acquisition to Users

2021 ◽  
Vol 13 (7) ◽  
pp. 1295
Author(s):  
Massimo Selva
Keyword(s):  
Remote Sensing ◽  
Optical Sensors ◽  
Constant Increase ◽  
Optical Images

The need to observe and characterize the environment leads to a constant increase of the spatial, spectral, and radiometric resolution of new optical sensors [...]

scholarly journals Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

2020 ◽  
Vol 12 (12) ◽  
pp. 2015 ◽  
Author(s):  
Manuel Ángel Aguilar ◽  
Rafael Jiménez-Lao ◽  
Abderrahim Nemmaoui ◽  
Fernando José Aguilar ◽  
Dilek Koc-San ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Remote Sensing Techniques ◽  
Image Pairs ◽  
Study Sites ◽  
High Level ◽  
Sentinel 2

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

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