scholarly journals Assessment of Remote Sensing Techniques Applicability for Beach Morphology Mapping: A Case Study of Hvar Island, Central Adriatic, Croatia

2021 ◽  
Vol 9 (12) ◽  
pp. 1407
Author(s):  
Marin Mićunović ◽  
Sanja Faivre ◽  
Mateo Gašparović
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Average Deviation ◽  
Linear Feature ◽  
Significant Difference ◽  
Data Source ◽  
Remote Sensing Techniques ◽  
Definition Of

This study investigates the quality and accuracy of remote sensing data in beach surveys based on three different data sources covering a 10-year period (2011–2021). Orthophotos from State Geodetic Administration Geoportal and satellite imagery from Google Earth were compared with orthophotos generated from UAV using ArcGIS Pro and Drone2Map. The beach area and length of 20 beaches on the island of Hvar were measured using each data source from different years. The average deviation for beach area (−2.3 to 5.6%) and length (−1 to 2.7%) was determined (without outliers). This study confirms that linear feature measurement is more accurate than polygon-based measurement. Hence, smaller beach areas were associated with higher errors. Furthermore, it was observed that morphological complexity of the beach may also affect the measurement accuracy. This work showed that different remote sensing sources could be used for relatively accurate beach surveys, as there is no statistically significant difference between the calculated errors. However, special care should always be addressed to the definition of errors.

Effect of Locust Invasion and Mitigation Using Remote Sensing Techniques: A Case Study of North Sindh Pakistan

2022 ◽  
Vol 88 (1) ◽  
pp. 47-53
Author(s):  
Muhammad Nasar Ahmad ◽  
Zhenfeng Shao ◽  
Orhan Altan
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Abrupt Decrease ◽  
Computing Platform ◽  
Remote Sensing Techniques ◽  
Processing Techniques

This study comprises the identification of the locust outbreak that happened in February 2020. It is not possible to conduct ground-based surveys to monitor such huge disasters in a timely and adequate manner. Therefore, we used a combination of automatic and manual remote sensing data processing techniques to find out the aftereffects of locust attack effectively. We processed MODIS -normalized difference vegetation index (NDVI ) manually on ENVI and Landsat 8 NDVI using the Google Earth Engine (GEE ) cloud computing platform. We found from the results that, (a) NDVI computation on GEE is more effective, prompt, and reliable compared with the results of manual NDVI computations; (b) there is a high effect of locust disasters in the northern part of Sindh, Thul, Ghari Khairo, Garhi Yaseen, Jacobabad, and Ubauro, which are more vulnerable; and (c) NDVI value suddenly decreased to 0.68 from 0.92 in 2020 using Landsat NDVI and from 0.81 to 0.65 using MODIS satellite imagery. Results clearly indicate an abrupt decrease in vegetation in 2020 due to a locust disaster. That is a big threat to crop yield and food production because it provides a major portion of food chain and gross domestic product for Sindh, Pakistan.

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

scholarly journals Remote Sensing Applications for Landslide Monitoring and Investigation in Western Canada

2021 ◽  
Vol 13 (3) ◽  
pp. 366
Author(s):  
Renato Macciotta ◽  
Michael T. Hendry
Keyword(s):  
Remote Sensing ◽  
Case Studies ◽  
Slope Failure ◽  
Landslide Monitoring ◽  
Western Canada ◽  
Extensive Literature ◽  
Sensing Applications ◽  
Remote Sensing Techniques ◽  
Definition Of ◽  
River Valleys

Transportation infrastructure in mountainous terrain and through river valleys is exposed to a variety of landslide phenomena. This is particularly the case for highway and railway corridors in Western Canada that connect towns and industries through prairie valleys and the Canadian cordillera. The fluidity of these corridors is important for the economy of the country and the safety of workers, and users of this infrastructure is paramount. Stabilization of all active slopes is financially challenging given the extensive area where landslides are a possibility, and monitoring and minimization of slope failure consequences becomes an attractive risk management strategy. In this regard, remote sensing techniques provide a means for enhancing the monitoring toolbox of the geotechnical engineer. This includes an improved identification of active landslides in large areas, robust complement to in-place instrumentation for enhanced landslide investigation, and an improved definition of landslide extents and deformation mechanisms. This paper builds upon the extensive literature on the application of remote sensing techniques and discusses practical insights gained from a suite of case studies from the authors’ experience in Western Canada. The review of the case studies presents a variety of landslide mechanisms and remote sensing technologies. The aim of the paper is to transfer some of the insights gained through these case studies to the reader.

Comparison of aerosol vertical profiles derived by passive and active remote sensing techniques—a case study

2004 ◽  
Vol 38 (39) ◽  
pp. 6679-6685 ◽  
Author(s):  
B. Padma Kumari ◽  
A.L. Londhe ◽  
H.K. Trimbake ◽  
D.B. Jadhav
Keyword(s):  
Remote Sensing ◽  
Vertical Profiles ◽  
Remote Sensing Techniques
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