Remote Sensing Data Integration of Landsat 8 and SRTM for Geomorphological Characteristics Identification in Karst Pringkuku, Pacitan, East Java

2020 ◽  
Vol 10 (1) ◽  
pp. 212 ◽  
Author(s):  
Silmi Afina Aliyan ◽  
Ayi Syaeful Bahri ◽  
Amien Widodo ◽  
Widya Utama
Keyword(s):  
Remote Sensing ◽  
Data Integration ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Sensing Data

scholarly journals Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

2021 ◽  
Vol 10 (2) ◽  
pp. 58
Author(s):  
Muhammad Fawad Akbar Khan ◽  
Khan Muhammad ◽  
Shahid Bashir ◽  
Shahab Ud Din ◽  
Muhammad Hanif
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Kappa Coefficient ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Sensing Data ◽  
Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

scholarly journals Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Clement Kwang ◽  
Edward Matthew Osei Jnr ◽  
Adwoa Sarpong Amoah
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Water Bodies ◽  
Water Extraction ◽  
Landsat 8 ◽  
Sensing Data ◽  
Optimal Classifier ◽  
Sentinel 2A

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

scholarly journals A Minimum Cloud Cover Mosaic Image Model of the Operational Land Imager Landsat-8 Multitemporal Data using Tile based

2018 ◽  
Vol 8 (1) ◽  
pp. 360
Author(s):  
Ratih Dewanti Dimyati ◽  
Projo Danoedoro ◽  
Hartono Hartono ◽  
Kustiyo Kustiyo
Keyword(s):  
Remote Sensing ◽  
Cloud Cover ◽  
National Development ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Sensing Data ◽  
Tile Size ◽  
Mosaic Image ◽  
Clear Area ◽  
Mosaic Images

<p>The need for remote sensing minimum cloud cover or cloud free mosaic images is now increasing in line with the increased of national development activities based on one map policy. However, the continuity and availability of cloud and haze free remote sensing data for the purpose of monitoring the natural resources are still low. This paper presents a model of medium resolution remote sensing data processing of Landsat-8 uses a new approach called mosaic tile based model (MTB), which is developed from the mosaic pixel based model (MPB) algorithm, to obtain an annual multitemporal mosaic image with minimum cloud cover mosaic imageries. The MTB model is an approach constructed from a set of pixels (called tiles) considering the image quality that is extracted from cloud and haze free areas, vegetation coverage, and open land coverage of multitemporal imageries. The data used in the model are from Landsat-8 Operational Land Imager (OLI) covering 10 scenes area, with 2.5 years recording period from June 2015 to June 2017; covered Riau, West Sumatra and North Sumatra Provinces. The MTB model is examined with tile size of 0.1 degrees (11x11 km2), 0.05 degrees (5.5x5.5 km2), and 0.02 degrees (2.2x2.2 km2). The result of the analysis shows that the smallest tile size 0.02 gives the best result in terms of minimum cloud cover and haze (or named clear area). The comparison of clear area values to cloud cover and haze for three years (2015, 2016 and 2017) for the three mosaic images of MTB are 68.2%, 78.8%, and 86.4%, respectively.</p>

scholarly journals Mapping Paleohydrologic Features in the Arid Areas of Saudi Arabia Using Remote-Sensing Data

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 417 ◽  
Author(s):  
Mohamed Abdelkareem ◽  
Fathy Abdalla ◽  
Samar Y. Mohamed ◽  
Farouk El-Baz
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Volcanic Activity ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Alos Palsar ◽  
Sensing Data ◽  
The Past ◽  
Aeolian Deposits

At present, the Arabian Peninsula is one of the driest regions on Earth; however, this area experienced heavy rainfall in the past thousand years. During this period, catchments received substantial amounts of surface water and sustained vast networks of streams and paleolakes, which are currently inactive. The Advanced Land Observing Satellite (ALOS) Phased Array Type L-band Synthetic Aperture Radar (PALSAR) data reveal paleohydrologic features buried under shallow aeolian deposits in many areas of the ad-Dawasir, Sahba, Rimah/Batin, and as-Sirhan wadis. Optical remote-sensing data support that the middle of the trans-peninsula Wadi Rimah/Batin, which extends for ~1200 km from the Arabian Shield to Kuwait and covers ~200,000 km2, is dammed by linear sand dunes formed by changes in climate conditions. Integrating Landsat 8 Operational Land Imager (OLI), Geo-Eye, Shuttle Radar Topography Mission (SRTM) digital elevation model, and ALOS/PALSAR data allowed for the characterization of paleodrainage reversals and diversions shaped by structural and volcanic activity. Evidence of streams abruptly shifting from one catchment to another is preserved in Wadi ad-Dawasir along the fault trace. Volcanic activity in the past few thousand years in northern Saudi Arabia has also changed the slope of the land and reversed drainage systems. Relics of earlier drainage directions are well maintained as paleoslopes and wide upstream patterns. This study found that paleohydrologic activity in Saudi Arabia is impacted by changes in climate and by structural and volcanic activity, resulting in changes to stream direction and activity. Overall, the integration of radar and optical remote-sensing data is significant for deciphering past hydrologic activity and for predicting potential water resource areas.

Detecting drought effects on tree mortality in forests of Franconia (Germany)

2020 ◽  
Author(s):  
Johannes Heisig ◽  
Cyrus Samimi
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Water Levels ◽  
Landsat 8 ◽  
Sensing Data ◽  
Dead Trees ◽  
Spatial Coverage ◽  
Data Products ◽  
Dead Vegetation

&lt;p&gt;Central European forests face challenges with climate changing much faster than they can adapt. Extremely hot and dry summers like in 2018 deprive forests of soil moisture, leaving them with low ground water levels. While individuals with deep and well-established root systems survive, young individuals and shallow-rooted species perish.&lt;/p&gt;&lt;p&gt;In southern Germany, die-off of single trees or small groups got noticeable recently. Such effects of harsher conditions rarely occur over large areas, but more in a spotted, irregular manner. This makes the phenomenon difficult to detect and to estimate its extent. The share of trees lately deteriorated may be larger than expected and represent a considerable portion of forests. Therefore, we see the great need for monitoring. Remote sensing data is suitable to examine inaccessible areas at a large scale. To quantify mortality of individual trees among a majority of vital ones, sensor platforms and respective data have to fulfill certain criteria regarding spatial, temporal and spectral resolution. Dead trees can be distinguished from others due to discoloration and defoliation. This change in appearance affects the spectral response, even in pixels larger than the tree&amp;#8217;s extent.&lt;/p&gt;&lt;p&gt;This study aims at recommending a suitable spatial scale for space-borne multispectral imagery products to achieve this task. We evaluate commercial and free remote sensing data products and their ability to estimate fractional cover of dead vegetation. Satellite data employed in this study comes from Landsat 8 (30 m), Sentinel-2 (10 m), RapidEye (6.5 m) and PlanetScope (3 m). Classification performance is tested against high-resolution multispectral aerial imagery (17 cm) acquired with a Micasense RedEdge-M camera.&lt;/p&gt;&lt;p&gt;High-resolution Micasense images are capable of detecting single dead trees, even after downgrading the resolution from 17 cm to 3 m. For all data products tested, fraction of dead trees per pixel did not differ significantly among land cover types (dead vegetation, vital vegetation, pavement, open soil). This indicates that individual dead trees may not be detectable in vital forest stands. The finding even seems to be valid for a resolution of 3 m (PlanetScope), which is identical to the downgraded Micasense data. In the near future the detection of this phenomenon might profit from technical developments towards even higher spatial detail of space-borne sensors. Alternatively, high resolution images from aerial campaigns, manned or unmanned, could bridge this gap when flight time and spatial coverage are increased significantly and facilitating policies are in place.&lt;/p&gt;

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