scholarly journals Coral Reef and Shallow Water Benthic Identification Using Landsat 7 ETM+ Satellite Data in Nusa Penida District

2018 ◽  
Vol 2 (1) ◽  
pp. 17
Author(s):  
Arief Reza Fahlevi ◽  
Takahiro Osawa ◽  
I Wayan Arthana
Keyword(s):  
Coral Reefs ◽  
Shallow Water ◽  
Health Monitoring ◽  
Confusion Matrix ◽  
Satellite Image ◽  
Kappa Coefficient ◽  
Landsat 7 ◽  
Triangle Center ◽  
Reef Health

This study aims to investigate the feasibility of Landsat 7 ETM+ to identify coral reefs and shallow water benthic at Nusa Penida district in 2009 and 2012, comparison with the Reef Health Monitoring (RHM) results conducted by the Coral Triangle Center (CTC)  using the Point Intercept Transect (PIT) method in the same periods. This study also aims to determine the changes of coral reefs and shallow water benthic cover during this period and the distribution at Nusa Penida districk. Shallow water benthic classification refers to English et al. (1997), with a modified by the addition of vegetation classes (seagrass and seaweed). The classification result using confusion matrix between the Reef Health Monitoring (RHM) with the classification of satellite image in 2009 obtained an accuracy rate of 65.85% with a kappa coefficient value of 0.525, while in 2012 the accuracy value obtained of 74.81% with kappa coefficient value of 0.650, which shows the results of that classification of satellite images of Landsat 7 ETM+ with the results of in-situ research is in a moderate level.

scholarly journals Spatial analysis of coral reefs and its degradation patterns in Bunaken National Park

2019 ◽  
Vol 76 ◽  
pp. 01001
Author(s):  
Nafil Rabbani Attamimi ◽  
Ratna Saraswati
Keyword(s):  
Coral Reefs ◽  
Spatial Pattern ◽  
Shallow Water ◽  
National Park ◽  
Sea Water ◽  
Satellite Image ◽  
Landsat 8 ◽  
Marine Habitats ◽  
Observation Process ◽  
Healthy Coral

This article will analyze the spatial pattern as well as the degradation pattern of the coral reefs in the Bunaken National Park. Bunaken National Park is a marine national park located in the Province of North Sulawesi; the park was built as means of conservation as well as providing a region for tourism. The national park contains a different type of marine and land ecosystem, one of the many types of the ecosystem that are in the national park is coral reefs. Coral reefs in Bunaken National Park provides different kinds of function and benefits whether for the marine habitats that live around the ecosystem, as well as for the local people who live in the islands of the national park. Remote sensing could be used as a tool to identify the spatial pattern and the type of ecosystem that habits inside shallow sea water. The main issue with this method is that the research cannot be conduct directly to identify which type of ecosystem specifically (such as coral reefs, seagrass, etc.), as well as its condition. Therefore, data collecting is necessary to observe and identify the ecosystem and its condition specifically. This study uses satellite image from Landsat 8 OLI as the main secondary data to be processed. The satellite image will be processed by using an algorithm of shallow water analysis that was introduced by Lyzenga in 1981. Since data verification and data observation is needed for this study, the research observes the pattern of the different type of ecosystem and its condition that spreads around Bunaken National Park. The verification and observation process was done by GPS, there were 250 different samples from the data that were collected around the Bunaken National Park. The sample that was collected in the study area will be used to classify the satellite image that has been processed by shallow water algorithm, on which could identify: seagrass, bleached coral reefs, deceased coral reefs, and healthy coral reefs around the national park. The results of this study show the spatial pattern of the coral reefs is located usually around the islands in the Bunaken National Park. The results show that the coral reefs are mostly located around the islands in the National Park. The map results show that the healthy coral reefs are usually located in the outermost layer around the shallow water ecosystem. The bleached reefs are usually located in the middle section of the shallow water, between the healthy coral and the islands itself. Most of the reefs that died and bleached are in the southwest of Bunaken Island, and the northwest of Nain Island.

scholarly journals ANALISIS PERUBAHAN LUASAN TERUMBU KARANG DENGAN MENGGUNAKAN DATA PENGINDERAAN JAUH CITRA LANDSAT 7 ETM+ DAN 8 OLI DI PERAIRAN PULAU MENJANGAN KECIL KARIMUNJAWA (JEPARA)

2018 ◽  
Vol 7 (1) ◽  
pp. 157-163
Author(s):  
Muhammad Ilham ◽  
Supriharyono Supriharyono ◽  
Niniek Widyorini
Keyword(s):  
Coral Reef ◽  
Coral Cover ◽  
Confusion Matrix ◽  
Satellite Image ◽  
Line Transect ◽  
Tourist Destinations ◽  
Wide Range ◽  
Landsat 7 ◽  
Coral Reef Ecosystems ◽  
Coral Reef Area

Ekosistem terumbu karang menjadi salah satu potensi sumberdaya pesisir yang memiliki banyak manfaat bagi lingkungan sekitar. Pulau Menjangan Kecil merupakan salah satu pulau di Karimunjawa yang memiliki ekosistem terumbu karang. Beraneka ragam ekosistem terumbu karang yang ada, menjadikan pulau ini sebagai salah satu destinasi wisata yang menarik untuk wisatawan. Penelitian dilaksanakan pada Bulan September 2017. Penelitian bertujuan untuk mengetahui kondisi terumbu karang ditinjau dari nilai persentase luasam penutupan terumbu karang, tingkat akurasi penggunaan citra Landsat 7 ETM+ dan 8 OLI tahun 2013, 2015, dan 2017. Sampling dilakukan pada empat titik. Metode yang digunakan Line Transect, Lyzenga Transformation dan Confusion Matrix. Hasil penelitian menunjukkan bahwa persentase tutupan luasan terumbu karang sebesar 54,31%, perubahan luasan terumbu karang berkurang sebesar 14,1 Ha (3,2%), uji akurasi citra satelit yang dihasilkan sebesar 86,95%. Coral reef ecosystems become one of the potential of coastal resources that have many benefits for the surrounding environment. Menjangan Kecil Island is one of the islands in Karimunjawa which has coral reef ecosystem. A wide range of coral reef ecosystems, making this island as one of the tourist destinations to attract tourists. The research was conducted in September 2017. The objectives of this study were to determine the condition of coral reefs from the percentage of coral cover coverage, the accuracy of Landsat 7 ETM + and 8 OLI imagery in 2013, 2015 and 2017. Sampling was conducted on four stations. The method used is Line Transect, Lyzenga Transformation and Confusion Matrix. The results showed that the percentage of coral cover cover was 54,31%, the coral reef area decreased by 14,1 Ha (3,2%), the test of satellite image accuracy was 86,95%. 

Optimization of K Value in KNN Algorithm for Spam and Ham Email Classification

2020 ◽  
Vol 4 (2) ◽  
pp. 377-383
Author(s):  
Eko Laksono ◽  
Achmad Basuki ◽  
Fitra Bachtiar
Keyword(s):  
Frequency Distribution ◽  
Confusion Matrix ◽  
High Accuracy ◽  
K Value ◽  
A Value ◽  
Optimal Value ◽  
Classification Evaluation ◽  
Email Spam ◽  
Email Classification

There are many cases of email abuse that have the potential to harm others. This email abuse is commonly known as spam, which contains advertisements, phishing scams, and even malware. This study purpose to know the classification of email spam with ham using the KNN method as an effort to reduce the amount of spam. KNN can classify spam or ham in an email by checking it using a different K value approach. The results of the classification evaluation using confusion matrix resulted in the KNN method with a value of K = 1 having the highest accuracy value of 91.4%. From the results of the study, it is known that the optimization of the K value in KNN using frequency distribution clustering can produce high accuracy of 100%, while k-means clustering produces an accuracy of 99%. So based on the results of the existing accuracy values, the frequency distribution clustering and k-means clustering can be used to optimize the K-optimal value of the KNN in the classification of existing spam emails.

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 Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

2019 ◽  
Vol 12 (1) ◽  
pp. 96 ◽  
Author(s):  
James Brinkhoff ◽  
Justin Vardanega ◽  
Andrew J. Robson
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Satellite Image ◽  
Resource Planning ◽  
Support Vector ◽  
Perennial Crop ◽  
Perennial Crops ◽  
Object Based ◽  
Rbf Kernel

Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently.

scholarly journals Artificial Neural Networks for Satellite Image Classification of Shoreline Extraction for Land and Water Classes of the North West Coast of Peninsular Malaysia

2018 ◽  
Vol 24 (2) ◽  
pp. 1382-1387 ◽  
Author(s):  
Syaifulnizam Abd Manaf ◽  
Norwati Mustapha ◽  
Md. Nasir Sulaiman ◽  
Nor Azura Husin ◽  
Mohd Radzi Abdul Hamid
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Image Classification ◽  
West Coast ◽  
Satellite Image ◽  
Peninsular Malaysia ◽  
North West ◽  
The North ◽  
Artificial Neural

scholarly journals Status of Coral Reefs in the Water of Spermonde, Makassar, South Sulawesi

2021 ◽  
Vol 324 ◽  
pp. 03007
Author(s):  
Ni Wayan Purnama Sari ◽  
Rikoh Manogar Siringoringo ◽  
Muhammad Abrar ◽  
Risandi Dwirama Putra ◽  
Raden Sutiadi ◽  
...  
Keyword(s):  
Coral Reef ◽  
Coral Reefs ◽  
Coral Cover ◽  
Public Awareness ◽  
Live Coral ◽  
Health Index ◽  
Point Count ◽  
Live Coral Cover ◽  
Reef Health ◽  
Index Value

Observations of the condition of coral reefs have been carried out in Spermonde waters from 2015 to 2018. The method used in this observation uses Underwater Photo Transect (UPT), and the data obtained is analyzed using CPCe (Coral Point Count with Excel Extensions) software. The results show that the percentage of coral cover has increased from year to year. The percentage of live coral cover in 2015 was 19.64%, 23.60 in 2016, 23.72% in 2017, and 27.83% in 2018. The increase in live coral cover from year to year is thought to occur due to the availability of nutrients. or increasing public awareness, considering this location is one of the most famous tourist attractions in Makassar. Coral reef health index values can be used to classify coral reef health. Through the analysis of the coral reef health index, an index value of 4 was obtained, which means that the condition of the coral reefs is in the “moderate” category.

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