scholarly journals A Novel GIS-Based Approach for Automated Detection of Nearshore Sandbar Morphological Characteristics in Optical Satellite Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2233
Author(s):  
Rasa Janušaitė ◽  
Laurynas Jukna ◽  
Darius Jarmalavičius ◽  
Donatas Pupienis ◽  
Gintautas Žilinskas
Keyword(s):  
Satellite Imagery ◽  
Morphological Characteristics ◽  
Unsupervised Classification ◽  
Landsat 8 ◽  
Multi Scale ◽  
Medium Resolution ◽  
Wide Range ◽  
Position Index ◽  
Local Relief ◽  
Length Width

Satellite remote sensing is a valuable tool for coastal management, enabling the possibility to repeatedly observe nearshore sandbars. However, a lack of methodological approaches for sandbar detection prevents the wider use of satellite data in sandbar studies. In this paper, a novel fully automated approach to extract nearshore sandbars in high–medium-resolution satellite imagery using a GIS-based algorithm is proposed. The method is composed of a multi-step workflow providing a wide range of data with morphological nearshore characteristics, which include nearshore local relief, extracted sandbars, their crests and shoreline. The proposed processing chain involves a combination of spectral indices, ISODATA unsupervised classification, multi-scale Relative Bathymetric Position Index (RBPI), criteria-based selection operations, spatial statistics and filtering. The algorithm has been tested with 145 dates of PlanetScope and RapidEye imagery using a case study of the complex multiple sandbar system on the Curonian Spit coast, Baltic Sea. The comparison of results against 4 years of in situ bathymetric surveys shows a strong agreement between measured and derived sandbar crest positions (R2 = 0.999 and 0.997) with an average RMSE of 5.8 and 7 m for PlanetScope and RapidEye sensors, respectively. The accuracy of the proposed approach implies its feasibility to study inter-annual and seasonal sandbar behaviour and short-term changes related to high-impact events. Algorithm-provided outputs enable the possibility to evaluate a range of sandbar characteristics such as distance from shoreline, length, width, count or shape at a relevant spatiotemporal scale. The design of the method determines its compatibility with most sandbar morphologies and suitability to other sandy nearshores. Tests of the described technique with Sentinel-2 MSI and Landsat-8 OLI data show that it can be applied to publicly available medium resolution satellite imagery of other sensors.

scholarly journals The estimation model of mangrove forest biomass using a medium resolution satellite imagery in the concession area of forest consession company in West Kalimantan

2016 ◽  
Vol 6 (2) ◽  
pp. 69-81
Author(s):  
SENDI YUSANDI ◽  
I NENGAH SURATI JAYA
Keyword(s):  
Satellite Imagery ◽  
Mangrove Forest ◽  
Forest Biomass ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Mean Deviation ◽  
Landsat 8 ◽  
Estimation Model ◽  
West Kalimantan ◽  
Medium Resolution

Yusandi S, Jaya INS. 2016. The estimation model of mangrove forest biomass using a medium resolution satellite imagery in the concession area of forest consession company in West Kalimantan. Bonorowo Wetlands 6: 69-81. Mangrove forest is one of forest ecosystem types having the highest carbon stock in the tropics. Mangrove forests have a good assimilation capability with their environmental elements as well as have a high capability on carbon sequestration. Up to now, however, the availability of data and information on carbon storage, especially on tree biomass content of mangrove is still limited. Conventionally, an accurate estimation of biomass could be obtained from terrestrial measurements, but those methods costly and time-consuming. This study offered an alternative solution to overcome these limitations by using remote sensing technology, i.e., by using the moderate resolution imageries Landsat 8. The objective of this study is to formulate the biomass estimation model using medium resolution satellite imagery, as well as to develop a biomass distribution map based on the selected model. The study found that the NDVI has a considerably high correlation coefficient of larger than > 0.7071 with the stand biomass. On the basis of the values of aggregation deviation, mean deviation, bias, RMSE, χ², R², and s, the best model for estimating the mangrove stand biomass is B=0.00023404 with the R² value of 77.1%. In general, the concession area of BSN Group (PT Kandelia Alam Semesta and PT Bina Ovivipari) have the potential of biomass ranging from 45 to 100 ton per ha.

scholarly journals Deep Learning with Multiband Synthesis from Landsat-8 Satellite Imagery using Machine Learning

2019 ◽  
Vol 8 (6S2) ◽  
pp. 163-165
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Information Extraction ◽  
Satellite Imagery ◽  
Landsat 8 ◽  
Learning Method ◽  
Multi Scale ◽  
Viable System ◽  
Bear Witness

This examination article proposes a novel profound learning portrayal and division approach for moderate goals remote detecting picture investigation. An information extraction approach utilizing profound various leveled understanding for remote detecting picture is embraced as a proving ground for further increment in spatial goals symbolism. The thought is the way that we can receive a speedy filtering picture division in a profound learning highlight portrayal structure utilizing a profound learning method to deliver sensible measured bunches in portioned locales until it frames a super-object. Our commitment is to actualize a viable system for multi-scale picture investigation to address the issue of estimating vulnerability by and by. We at that point propose to test our strategy on two high goals remote detecting picture datasets that will yield brings about the type of multi-layered scenes that bear witness to the proficiency and unwavering quality of our proposed framework.

scholarly journals COMPARISON BETWEEN SPECTRAL, SPATIAL AND POLARIMETRIC CLASSIFICATION OF URBAN AND PERIURBAN LANDCOVER USING TEMPORAL SENTINEL – 1 IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 789-796 ◽  
Author(s):  
K. Roychowdhury
Keyword(s):  
Classification Accuracy ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Unsupervised Classification ◽  
Landsat 8 ◽  
Open Spaces ◽  
Suburban Areas ◽  
Medium Resolution

Landcover is the easiest detectable indicator of human interventions on land. Urban and peri-urban areas present a complex combination of landcover, which makes classification challenging. This paper assesses the different methods of classifying landcover using dual polarimetric Sentinel-1 data collected during monsoon (July) and winter (December) months of 2015. Four broad landcover classes such as built up areas, water bodies and wetlands, vegetation and open spaces of Kolkata and its surrounding regions were identified. Polarimetric analyses were conducted on Single Look Complex (SLC) data of the region while ground range detected (GRD) data were used for spectral and spatial classification. Unsupervised classification by means of K-Means clustering used backscatter values and was able to identify homogenous landcovers over the study area. The results produced an overall accuracy of less than 50% for both the seasons. Higher classification accuracy (around 70%) was achieved by adding texture variables as inputs along with the backscatter values. However, the accuracy of classification increased significantly with polarimetric analyses. The overall accuracy was around 80% in Wishart H-A-Alpha unsupervised classification. The method was useful in identifying urban areas due to their double-bounce scattering and vegetated areas, which have more random scattering. Normalized Difference Built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) obtained from Landsat 8 data over the study area were used to verify vegetation and urban classes. The study compares the accuracies of different methods of classifying landcover using medium resolution SAR data in a complex urban area and suggests that polarimetric analyses present the most accurate results for urban and suburban areas.

scholarly journals Combining Camera Relascope-Measured Field Plots and Multi-Seasonal Landsat 8 Imagery for Enhancing the Forest Inventory of Boreal Forests in Central Russia

2018 ◽  
Vol 10 (11) ◽  
pp. 1796 ◽  
Author(s):  
Georgy Rybakov ◽  
Jussi Peuhkurinen ◽  
Petri Latva-Käyrä ◽  
Maria Villikka ◽  
Sanna Sirparanta ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Satellite Data ◽  
Forest Inventory ◽  
Basal Area ◽  
Bayesian Regression ◽  
Estimation Accuracy ◽  
Landsat 8 ◽  
Medium Resolution ◽  
The Mean ◽  
Field Plots

The study considers a forest inventory for the mean volume, basal area, and coniferous/deciduous mapping of a large territory in central Siberia (Russia), employing a camera relascope at arbitrary sized sample plots and medium resolution satellite imagery Landsat 8 from the leaf-on and leaf-off seasons. The research bases are on field plots and satellite data that are acquired for the real operational forest inventory, performed for industrial purposes during summer–fall 2015. Sparse Bayesian regression was used to estimate linear regression models between field-measured variables and features derived from satellite data. Coniferous/deciduous mapping was done, applying maximum likelihood classification. The study reported the root mean square error for the mean volume and basal area under 25% for both the plot level and compartment level. The overall accuracy of the forest-type classification in coniferous, mixed coniferous/deciduous, and deciduous classes was 71.6%. The features of Landsat 8 images from both seasons were selected in almost every model, indicating that the use of satellite imagery from different seasons improved the estimation accuracy. It has been shown that the combination of camera relascope-based field data and medium-resolution satellite imagery gives accurate enough results that compare well with previous studies in that field, and provide fast and solid data about forests of large areas for efficient investment decision making.

scholarly journals A Novel Classification Extension-Based Cloud Detection Method for Medium-Resolution Optical Images

2020 ◽  
Vol 12 (15) ◽  
pp. 2365
Author(s):  
Xidong Chen ◽  
Liangyun Liu ◽  
Yuan Gao ◽  
Xiao Zhang ◽  
Shuai Xie
Keyword(s):  
Satellite Imagery ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Low Resolution ◽  
Spectral Bands ◽  
Optical Images ◽  
Medium Resolution ◽  
Training Samples ◽  
Land Surfaces ◽  
Sentinel 2

Accurate cloud detection using medium-resolution multispectral satellite imagery (such as Landsat and Sentinel data) is always difficult due to the complex land surfaces, diverse cloud types, and limited number of available spectral bands, especially in the case of images without thermal bands. In this paper, a novel classification extension-based cloud detection (CECD) method was proposed for masking clouds in the medium-resolution images. The new method does not rely on thermal bands and can be used for masking clouds in different types of medium-resolution satellite imagery. First, with the support of low-resolution satellite imagery with short revisit periods, cloud and non-cloud pixels were identified in the resampled low-resolution version of the medium-resolution cloudy image. Then, based on the identified cloud and non-cloud pixels and the resampled cloudy image, training samples were automatically collected to develop a random forest (RF) classifier. Finally, the developed RF classifier was extended to the corresponding medium-resolution cloudy image to generate an accurate cloud mask. The CECD method was applied to Landsat-8 and Sentinel-2 imagery to test the performance for different satellite images, and the well-known function of mask (FMASK) method was employed for comparison with our method. The results indicate that CECD is more accurate at detecting clouds in Landsat-8 and Sentinel-2 imagery, giving an average F-measure value of 97.65% and 97.11% for Landsat-8 and Sentinel-2 imagery, respectively, as against corresponding results of 90.80% and 88.47% for FMASK. It is concluded, therefore, that the proposed CECD algorithm is an effective cloud-classification algorithm that can be applied to the medium-resolution optical satellite imagery.

scholarly journals Detection of True Mangroves in Indonesia Using Satellite Remote Sensing

2019 ◽  
pp. 157-167
Author(s):  
Atriyon Julzarika ◽  
Nanin Anggraini ◽  
Syifa Wismayati Adawiah
Keyword(s):  
Remote Sensing ◽  
Correlation Coefficient ◽  
Satellite Imagery ◽  
Satellite Remote Sensing ◽  
Unsupervised Classification ◽  
Statistical Technique ◽  
Image Display ◽  
Landsat 8 ◽  
Band Combination ◽  
Selection For

Mangrove existence is necessary to protect coastal. One method that can be used to keep mangrove existence were using satellite imagery monitoring. The number of bands in the imagery led to the selection for the RGB composite bands was difficult because a lot of combinations to try. One technique that can be done to get the best RGB combination of an object is to use Optimum Index Factor (OIF). OIF is a statistical technique for selecting three combinations of imagery bands to visualize the image display to the fullest. It is based on the value of total variance and the correlation coefficient between the bands. Landsat 8 has 7 bands with 30 m resolution, one panchromatic band with  15 m resolution, and two bands with 100 m resolution. The purpose of this study was to detect true mangrove using three bands from  OIF value of  Landsat 8. The results of the processing from 6 bands (2-7), obtained 20 bands combinations  with the highest value of OIF is 0,168, ie, bands 2-56 (Blue, NIR, SWIR-1). Based on the combination, the next step was unsupervised classification process for true mangrove identification (Rizhopora, Brugueira, Avicennia, Soneratia). The best classification using band combination 2-7 with true mangrove reached 4.041 ha.

scholarly journals COMPARISON BETWEEN SPECTRAL, SPATIAL AND POLARIMETRIC CLASSIFICATION OF URBAN AND PERIURBAN LANDCOVER USING TEMPORAL SENTINEL – 1 IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 789-796 ◽  
Author(s):  
K. Roychowdhury
Keyword(s):  
Classification Accuracy ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Unsupervised Classification ◽  
Landsat 8 ◽  
Open Spaces ◽  
Suburban Areas ◽  
Medium Resolution

Landcover is the easiest detectable indicator of human interventions on land. Urban and peri-urban areas present a complex combination of landcover, which makes classification challenging. This paper assesses the different methods of classifying landcover using dual polarimetric Sentinel-1 data collected during monsoon (July) and winter (December) months of 2015. Four broad landcover classes such as built up areas, water bodies and wetlands, vegetation and open spaces of Kolkata and its surrounding regions were identified. Polarimetric analyses were conducted on Single Look Complex (SLC) data of the region while ground range detected (GRD) data were used for spectral and spatial classification. Unsupervised classification by means of K-Means clustering used backscatter values and was able to identify homogenous landcovers over the study area. The results produced an overall accuracy of less than 50% for both the seasons. Higher classification accuracy (around 70%) was achieved by adding texture variables as inputs along with the backscatter values. However, the accuracy of classification increased significantly with polarimetric analyses. The overall accuracy was around 80% in Wishart H-A-Alpha unsupervised classification. The method was useful in identifying urban areas due to their double-bounce scattering and vegetated areas, which have more random scattering. Normalized Difference Built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) obtained from Landsat 8 data over the study area were used to verify vegetation and urban classes. The study compares the accuracies of different methods of classifying landcover using medium resolution SAR data in a complex urban area and suggests that polarimetric analyses present the most accurate results for urban and suburban areas.

scholarly journals COASTLINE ZONE EXTRACTION USING LANDSAT-8 OLI IMAGERY, CASE STUDY: BODRUM PENINSULA, TURKEY

2019 ◽  
Vol XLII-4/W12 ◽  
pp. 101-104 ◽  
Author(s):  
T. Isiacik Colak ◽  
G. Senel ◽  
C. Goksel
Keyword(s):  
Satellite Imagery ◽  
Satellite Image ◽  
Threshold Value ◽  
Unsupervised Classification ◽  
Boundary Line ◽  
Free Access ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Coast Line ◽  
Line Extraction

<p><strong>Abstract.</strong> Coastline extraction is a fundamental work for coastal resource management and coastal environmental protection. Today, by using digital image processing techniques, coastline extraction can be done with remote sensing imagery systems. In this study, Landsat 8 Operational Land Imagery (OLI) data have been the main data source due to free access and sufficient spatial resolution for coast line extraction. This research is focused on determining the coastline length and measuring land area by using Landsat 8 OLI satellite image for Bodrum Peninsula, Turkey. Three commonly used methods have been applied in order to determine sea-land boundary line and its length, and area of the study area. The Automatic Water Extraction Index (AWEI), Iterative Self-Organizing Data Analysis Technique (ISODATA) unsupervised classification technique and on screen digitizing method was chosen for identification of coastal boundaries. Results of coastline length and land areas of Bodrum by using AWEI, ISODATA and on-screen digitizing are compared with each other. This study shows that with using optimal threshold value, AWEI can be used for coast line extraction method with coherently for Landsat 8 OLI satellite imagery. The overall results show that coastline extraction from satellite imagery can be done with sufficient accuracy using spectral water indices instead of time consuming on-screen digitizing.</p>

Sign in / Sign up

Export Citation Format

Share Document