scholarly journals DETECTING THE LAVA FLOW DEPOSITS FROM 2018 ANAK KRAKATAU ERUPTION USING DATA FUSION LANDSAT-8 OPTIC AND SENTINEL-1 SAR

2019 ◽  
Vol 15 (2) ◽  
pp. 157
Author(s):  
NFn Suwarsono ◽  
Indah Prasasti ◽  
Jalu Tejo Nugroho ◽  
Jansen Sitorus ◽  
Djoko Triyono
Keyword(s):  
Data Fusion ◽  
Lava Flow ◽  
Remote Sensing Data ◽  
Weather Conditions ◽  
Landsat 8 ◽  
Major Disaster ◽  
Sar Data ◽  
Using Data ◽  
Display Quality ◽  
Krakatau Volcano

The increasing volcanic activity of Anak Krakatau volcano has raised concerns about a major disaster in the area around the Sunda Strait. The objective of the research is to fuse Landsat-8 OLI (Operational Land Imager) and Sentinel-1 TOPS (Terrain Observation with Progressive Scans), an integration of SAR and optic remote sensing data, in observing the lava flow deposits resulted from Anak Krakatau eruption during the middle 2018 eruption. RGBI and the Brovey transformation were conducted to merge (fuse) the optical and SAR data.  The results showed that optical and SAR data fusion sharpened the appearance of volcano morphology and lava flow deposits. The regions are often constrained by cloud cover and volcanic ash, which occurs at the time of the volcanic eruption.  The RGBI-VV and Brovey RGB-VV methods provide better display quality results in revealing the morphology of volcanic cone and lava deposits. The entire slopes of Anak Krakatau Volcano, with a radius of about 1 km from the crater is an area prone to incandescent lava and pyroclastic falls. The direction of the lava flow has the potential to spread in all directions. The fusion method of optical Landsat-8 and Sentinel-1 SAR data can be used continuously in monitoring the activity of Anak Krakatau volcano and other volcanoes in Indonesia both in cloudy and clear weather conditions.

scholarly journals MANGROVE ABOVE GROUND BIOMASS ESTIMATION USING COMBINATION OF LANDSAT 8 AND ALOS PALSAR DATA

2017 ◽  
Vol 12 (2) ◽  
pp. 85 ◽  
Author(s):  
Gathot Winarso ◽  
Yenni Vetrita ◽  
Anang D. Purwanto ◽  
Nanin Anggraini ◽  
Soni Darmawan ◽  
...  
Keyword(s):  
Carbon Stock ◽  
Vegetation Indices ◽  
Emission Trading ◽  
Remote Sensing Data ◽  
Tree Height ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Alos Palsar ◽  
Sar Data ◽  
Estimation Models

Mangrove ecosystem is important coastal ecosystem, both ecologically and economically. Mangrove provides rich-carbon stock, most carbon-rich forest among ecosystems of tropical forest. It is very important for the country to have a large mangrove area in the context of global community of climate change policy related to emission trading in the Kyoto Protocol. Estimation of mangrove carbon-stock using remote sensing data plays an important role in emission trading in the future. Estimation models of above ground mangrove biomass are still limited and based on common forest biomass estimation models that already have been developed. Vegetation indices are commonly used in the biomass estimation models, but they have low correlation results according to several studies. Synthetic Aperture Radar (SAR) data with capability in detecting volume scattering has potential applications for biomass estimation with better correlation. This paper describes a new model which was developed using a combination of optical and SAR data. Biomass is volume dimension related to canopy and height of the trees. Vegetation indices could provide two dimensional information on biomass by recording the vegetation canopy density and could be well estimated using optical remote sensing data. One more dimension to be 3 dimensional feature is height of three which could be provided from SAR data. Vegetation Indices used in this research was NDVI extracted from Landsat 8 data and height of tree estimated from ALOS PALSAR data. Calculation of field biomass data was done using non-decstructive allometric based on biomass estimation at 2 different locations that are Segara Anakan Cilacap and Alas Purwo Banyuwangi, Indonesia. Correlation between vegetation indices and field biomass with ALOS PALSAR-based biomass estimation was low. However, multiplication of NDVI and tree height with field biomass correlation resulted R2 0.815 at Alas Purwo and R2 0.081 at Segara Anakan.  Low correlation at Segara anakan was due to failed estimation of tree height. It seems that ALOS PALSAR height was not accurate for determination of areas dominated by relative short trees as we found at Segara Anakan Cilacap, but the result was quite good for areas dominated by high trees. To improve the accuracy of tree height estimation, this method still needs validation using more data.

scholarly journals Multiple Flights or Single Flight Instrument Fusion of Hyperspectral and ALS Data? A Comparison of their Performance for Vegetation Mapping

2019 ◽  
Vol 11 (8) ◽  
pp. 970 ◽  
Author(s):  
Łukasz Sławik ◽  
Jan Niedzielko ◽  
Adam Kania ◽  
Hubert Piórkowski ◽  
Dominik Kopeć
Keyword(s):  
Data Collection ◽  
Data Fusion ◽  
Laser System ◽  
Remote Sensing Data ◽  
Natura 2000 ◽  
Vegetation Mapping ◽  
Sensor Data ◽  
The Difference ◽  
Using Data ◽  
Instrument Fusion

Fusion of remote sensing data often improves vegetation mapping, compared to using data from only a single source. The effectiveness of this fusion is subject to many factors, including the type of data, collection method, and purpose of the analysis. In this study, we compare the usefulness of hyperspectral (HS) and Airborne Laser System (ALS) data fusion acquired in separate flights, Multiple Flights Data Fusion (MFDF), and during a single flight through Instrument Fusion (IF) for the classification of non-forest vegetation. An area of 6.75 km2 was selected, where hyperspectral and ALS data was collected during two flights in 2015 and one flight in 2017. This data was used to classify three non-forest Natura 2000 habitats i.e., Xeric sand calcareous grasslands (code 6120), alluvial meadows of river valleys of the Cnidion dubii (code 6440), species-rich Nardus grasslands (code 6230) using a Random Forest classifier. Our findings show that it is not possible to determine which sensor, HS, or ALS used independently leads to a higher classification accuracy for investigated Natura 2000 habitats. Concurrently, increased stability and consistency of classification results was confirmed, regardless of the type of fusion used; IF, MFDF and varied information relevance of single sensor data. The research shows that the manner of data collection, using MFDF or IF, does not determine the level of relevance of ALS or HS data. The analysis of fusion effectiveness, gauged as the accuracy of the classification result and time consumed for data collection, has shown a superiority of IF over MFDF. IF delivered classification results that are more accurate compared to MFDF. IF is always cheaper than MFDF and the difference in effectiveness of both methods becomes more pronounced when the area of aerial data collection becomes larger.

scholarly journals MULTI-MODAL REMOTE SENSING DATA FUSION FRAMEWORK

2017 ◽  
Vol XLII-4/W2 ◽  
pp. 85-89
Author(s):  
M. A. A. Ghaffar ◽  
T. T. Vu ◽  
T. H. Maul
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Machine Learning Techniques ◽  
Landsat 8 ◽  
Classification Problems ◽  
Learning Techniques ◽  
Level Of Details ◽  
Mixed Pixels

The inconsistency between the freely available remote sensing datasets and crowd-sourced data from the resolution perspective forms a big challenge in the context of data fusion. In classical classification problems, crowd-sourced data are represented as points that may or not be located within the same pixel. This discrepancy can result in having mixed pixels that could be unjustly classified. Moreover, it leads to failure in retaining sufficient level of details from data inferences. In this paper we propose a method that can preserve detailed inferences from remote sensing datasets accompanied with crowd-sourced data. We show that advanced machine learning techniques can be utilized towards this objective. The proposed method relies on two steps, firstly we enhance the spatial resolution of the satellite image using Convolutional Neural Networks and secondly we fuse the crowd-sourced data with the upscaled version of the satellite image. However, the covered scope in this paper is concerning the first step. Results show that CNN can enhance Landsat 8 scenes resolution visually and quantitatively.

scholarly journals SYNERGETIC USE OF SENTINEL-1 AND SENTINEL-2 DATA FOR EXTRACTION OF BUILT-UP AREA IN A ROCKY DESERT OASIS, EXAMPLE FOR DRAA TAFILALT, SOUTH-EAST OF MOROCCO

2019 ◽  
Vol XLII-4/W12 ◽  
pp. 65-68 ◽  
Author(s):  
L. Eddahby ◽  
A. A. Kozlova ◽  
M. A. Popov ◽  
N. S. Lubskiy ◽  
D. Mezzane ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Weather Conditions ◽  
Processing Technique ◽  
Remote Sensing Technique ◽  
Additional Information ◽  
Signal Features ◽  
High Resolution Imagery ◽  
Urbanized Areas ◽  
Sar Data

<p><strong>Abstract.</strong> Synthetic Aperture Radar (SAR) is an active remote sensing technique capable of providing high-resolution imagery independent from daytime and to great extent unimpaired by weather conditions. Unlike the passive remote sensing active radars receive its' own reflected signal. Features of received signal make able to obtain additional information about surface objects and covers. Because of signal, double reflecting upon vertical surfaces like walls, it become common to study urbanized areas using SAR data. Because of mostly similar spectral characteristic of the typical buildings' roofs and sandy soils, that are distinguishing for Morocco, classification using visible and NIR multispectral remote sensing data is complicated. Thus, SAR data processing technique is rather useful while application to deserted area studying and urbanized areas identification.</p>

scholarly journals Newly Built Construction Detection in SAR Images Using Deep Learning

2019 ◽  
Vol 11 (12) ◽  
pp. 1444 ◽  
Author(s):  
Raveerat Jaturapitpornchai ◽  
Masashi Matsuoka ◽  
Naruo Kanemoto ◽  
Shigeki Kuzuoka ◽  
Riho Ito ◽  
...  
Keyword(s):  
Deep Learning ◽  
Remote Sensing Data ◽  
Ground Truth ◽  
Weather Conditions ◽  
Google Earth ◽  
Sar Images ◽  
Convolutional Network ◽  
Optical Images ◽  
Sar Data ◽  
Deep Learning Network

Remote sensing data can be utilized to help developing countries monitor the use of land. However, the problem of constant cloud coverage prevents us from taking full advantage of satellite optical images. Therefore, we instead opt to use data from synthetic-aperture radar (SAR), which can capture images of the Earth’s surface regardless of the weather conditions. In this study, we use SAR data to identify newly built constructions. Most studies on change detection tend to detect all of the changes that have a similar temporal change characteristic occurring on two occasions, while we want to identify only the constructions and avoid detecting other changes such as the seasonal change of vegetation. To do so, we study various deep learning network techniques and have decided to propose the fully convolutional network with a skip connection. We train this network with pairs of SAR data acquired on two different occasions from Bangkok and the ground truth, which we manually create from optical images available from Google Earth for all of the SAR pairs. Experiments to assign the most suitable patch size, loss weighting, and epoch number to the network are discussed in this paper. The trained model can be used to generate a binary map that indicates the position of these newly built constructions precisely with the Bangkok dataset, as well as with the Hanoi and Xiamen datasets with acceptable results. The proposed model can even be used with SAR images of the same specific satellite from another orbit direction and still give promising results.

scholarly journals Development of the Statistical Model for Monitoring Salinization in the Mekong Delta of Vietnam Using Remote Sensing Data and In-Situ Measurements

Proceedings ◽  
2018 ◽  
Vol 2 (10) ◽  
pp. 565
Author(s):  
Nguyen Nguyen Vu ◽  
Le Van Trung ◽  
Tran Thi Van
Keyword(s):  
Statistical Model ◽  
Mekong Delta ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Measured Data ◽  
In Situ Measurements ◽  
Landsat 8 ◽  
Spectral Bands ◽  
Component Band

This article presents the methodology for developing a statistical model for monitoring salinity intrusion in the Mekong Delta based on the integration of satellite imagery and in-situ measurements. We used Landsat-8 Operational Land Imager and Thermal Infrared Sensor (Landsat- 8 OLI and TIRS) satellite data to establish the relationship between the planetary reflectance and the ground measured data in the dry season during 2014. The three spectral bands (blue, green, red) and the principal component band were used to obtain the most suitable models. The selected model showed a good correlation with the exponential function of the principal component band and the ground measured data (R2 > 0.8). Simulation of the salinity distribution along the river shows the intrusion of a 4 g/L salt boundary from the estuary to the inner field of more than 50 km. The developed model will be an active contribution, providing managers with adaptation and response solutions suitable for intrusion in the estuary as well as the inner field of the Mekong Delta.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

Enhancing Athlete Tracking Using Data Fusion in Wearable Technologies

2021 ◽  
Vol 70 ◽  
pp. 1-13
Author(s):  
Adnan Waqar ◽  
Iftekhar Ahmad ◽  
Daryoush Habibi ◽  
Nicolas Hart ◽  
Quoc Viet Phung
Keyword(s):  
Data Fusion ◽  
Wearable Technologies ◽  
Using Data
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