Using Stereo Satellite Imagery for Topographic and Transportation Applications: An Accuracy Assessment

2010 ◽  
Vol 47 (3) ◽  
pp. 321-337 ◽  
Author(s):  
Ahmed Shaker ◽  
Wai Yeung Yan ◽  
Said Easa
Keyword(s):  
Satellite Imagery ◽  
Accuracy Assessment ◽  
Transportation Applications

GIS based analysis and accuracy assessment of low-resolution satellite imagery for coastline monitoring

2020 ◽  
Author(s):  
Dionysios Apostolopoulos ◽  
Konstantinos G. Nikolakopoulos ◽  
Vassilios Boumpoulis ◽  
Nikolaos Depountis
Keyword(s):  
Satellite Imagery ◽  
Accuracy Assessment ◽  
Low Resolution

scholarly journals ACCURACY ASSESSMENT OF GÖKTÜRK-1 SATELLITE IMAGERY

2020 ◽  
Author(s):  
Gökhan ARASAN ◽  
Altan YILMAZ ◽  
Orhan FIRAT ◽  
Ertuğrul AVŞAR ◽  
Hasan GÜNER ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Accuracy Assessment

scholarly journals MAPPING OF <i>SARGASSUM</i> DISTRIBUTION IN THE EASTERN COAST OF SOUTHERN LEYTE USING SENTINEL 2 SATELLITE IMAGERY

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 289-295
Author(s):  
J. J. Lasquites ◽  
A. C. Blanco ◽  
A. Tamondong
Keyword(s):  
Satellite Imagery ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accuracy Assessment ◽  
Marine Ecosystem ◽  
Brown Seaweed ◽  
The Philippines ◽  
Eastern Coast ◽  
Department Of Agriculture ◽  
Sentinel 2

Abstract. Sargassum is a brown seaweed distributed in the Philippines and recognized as an additional source of income for fishing communities. Due to uncontrolled harvesting of the seaweed, the Department of Agriculture regulated its collection and harvesting by imposing seasonal restrictions. Hence, the need to identify the locations and cover of healthy Sargassum is vital to address the demand in the market while maintaining ecological balance in the marine ecosystem. Two Sentinel-2 satellite imagery (10 m resolution) acquired on December 08, 2017 (peak growth) and May 27, 2018 (senescence stage) were used to map the presence of Sargassum in the eastern coast of Southern Leyte. Supervised classification using maximum likelihood algorithm and accuracy assessment were conducted before generating the map. Three classes were considered namely Sargassum, clouds and land. Furthermore, Anselin Local Moran’s I (cluster and outlier analysis) was conducted to determine which areas have significant clustering of “healthy” Sargassum using the normalized difference vegetation index (NDVI). For both image dates, high classification accuracies of Sargassum were obtained in the islands. However, there are misclassifications of Sargassum in Silago (UA = 78.72%) and Hinunangan (PA = 82.35%) using the May image. Furthermore, misclassification of Sargassum were obtained in Silago (PA = 93.6%) and Hinundayan (PA = 96.23%) using the December image. Clusters of high NDVI values are more evident in December. Healthy Sargassum are apparent in the coast of Silago and mostly found near shore and in rocky substrates.

scholarly journals Google Earth elevation data extraction and accuracy assessment for transportation applications

PLoS ONE ◽  
2017 ◽  
Vol 12 (4) ◽  
pp. e0175756 ◽  
Author(s):  
Yinsong Wang ◽  
Yajie Zou ◽  
Kristian Henrickson ◽  
Yinhai Wang ◽  
Jinjun Tang ◽  
...  
Keyword(s):  
Accuracy Assessment ◽  
Data Extraction ◽  
Google Earth ◽  
Elevation Data ◽  
Transportation Applications

scholarly journals OBJECT-BASED GREENHOUSE CLASSIFICATION FROM HIGH RESOLUTION SATELLITE IMAGERY: A CASE STUDY ANTALYA-TURKEY

2016 ◽  
Vol XLI-B7 ◽  
pp. 183-187 ◽  
Author(s):  
M. Coslu ◽  
N. K. Sonmez ◽  
D. Koc-San
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Accuracy Assessment ◽  
Digital Data ◽  
Classification Method ◽  
Classification Methods ◽  
Spectral Bands ◽  
Object Based ◽  
Rule Sets ◽  
High Resolution Satellite Imagery

Pixel-based classification method is widely used with the purpose of detecting land use and land cover with remote sensing technology. Recently, object-based classification methods have begun to be used as well as pixel-based classification method on high resolution satellite imagery. In the studies conducted, it is indicated that object-based classification method has more successful results than other classification methods. While pixel-based classification method is performed according to the grey value of pixels, object-based classification process is executed by generating imagery segmentation and updatable rule sets. In this study, it was aimed to detect and map the greenhouses from object-based classification method by using high resolution satellite imagery. The study was carried out in the Antalya province which includes greenhouse intensively. The study consists of three main stages including segmentation, classification and accuracy assessment. At the first stage, which was segmentation, the most important part of the object-based imagery analysis; imagery segmentation was generated by using basic spectral bands of high resolution Worldview-2 satellite imagery. At the second stage, applying the nearest neighbour classifier to these generated segments classification process was executed, and a result map of the study area was generated. Finally, accuracy assessments were performed using land studies and digital data of the area. According to the research results, object-based greenhouse classification using high resolution satellite imagery had over 80% accuracy.

scholarly journals Shallow Water Seabed Profile Changes in 2016 - 2018 Based on Landsat 8 Satellite Imagery (Case Study: Semak Daun Island, Karya Island and Gosong Balik Layar)

Omni-Akuatika ◽  
2020 ◽  
Vol 16 (3) ◽  
pp. 26
Author(s):  
Zahra Safira Aulia ◽  
Triguardi Tharik Ahmad ◽  
Ratih Rachma Ayustina ◽  
Fauzi Tri Hastono ◽  
Rizqi Rizaldi Hidayat ◽  
...  
Keyword(s):  
Shallow Water ◽  
Satellite Imagery ◽  
Accuracy Assessment ◽  
Satellite Image ◽  
Benthic Habitat ◽  
Landsat 8 ◽  
Marine Tourism ◽  
Profile Changes ◽  
High Level

Seribu Islands is one of the marine tourism destinations in  Jakarta. The high level of tourism in the Seribu Islands can be a threat to shallow water seabed profile habitat. Therefore, monitoring of changes in shallow water seabed profile habitat is needed so the sustainability can be monitored. This study aimed to determine changes in the shallow water seabed profile on Karya Island, Semak Daun Island, and Gosong Balik Layar in 2016 and 2018 based on Landsat 8 Satellite Imagery. Methods of this research used satellite image pre-processing, image classification, field survey, image reclassification, and accuracy assessment.  The results showed that the coral area had decrease trend, while the area of Seagrass mix Seaweed had increased. The result of this classification had an accuracy value of 71.52%. Keywords: remote sensing, multispectral imagery, Lyzenga, benthic habitat, Seribu Island

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