scholarly journals Assessment of Coral Reef Life-Form Classification Scheme using Multiresolution Images on Parang Island, Indonesia

2021 ◽  
Vol 6 (3) ◽  
pp. 377
Author(s):  
Wahyu Lazuardi ◽  
Pramaditya Wicaksono
Keyword(s):  
Coral Reef ◽  
Life Form ◽  
Classification Scheme ◽  
Spatial Information ◽  
Satellite Image ◽  
Creative Commons ◽  
Central Java ◽  
Object Based ◽  
Efficient Data ◽  
Sentinel 2A

Spatial information on the varying composition of coral reefs is beneficial for the management and preservation of natural resources in coastal areas. Its availability is inseparable from environmental management goals; however, it can also be used as a means of supporting tourism activities and predicting the emergence of certain living species. A satellite image is one of the effective and efficient data sources that provide spatial information on coral reef variations. This study aimed to evaluate the classification scheme of coral reef life-form using images with different spatial resolutions on Parang Island, Karimunjawa Islands, Central Java. These images were from PlanetScope (3m), PlanetScope resampling (6m), and Sentinel-2A MSI (10m), whose spatial resolutions functioned as the base for building the 3m, 6m, and 10m classification schemes producing 12, 11, and 9 classes, respectively. As for the classification method, it integrated both object-based and pixel-based approaches. The results showed that the highest overall accuracy (60%) was obtained using Sentinel-2A MSI image (10m), followed by PlanetScope (3m) with 48% accuracy, and PlanetScope resampling (6m) with 40% accuracy. This finding indicates that multiresolution images can be used to produce complex coral reef life-form maps with different levels of information details. Keywords: Coral reef; Life-form; Planetscope; Spatial resolution; Classification scheme   Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

scholarly journals THE USE OF SPECTRAL AND TEXTURAL FEATURES IN CROP TYPE MAPPING USING SENTINEL-2A IMAGES: A CASE STUDY, ÇUKUROVA REGION, TURKEY

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 117-122
Author(s):  
A. Tuzcu Kokal ◽  
A. F. Sunar ◽  
A. Dervisoglu ◽  
S. Berberoglu
Keyword(s):  
Satellite Image ◽  
Support Vector ◽  
Textural Features ◽  
Factors Affecting ◽  
Object Based ◽  
Crop Types ◽  
Sentinel 2A ◽  
Occurrence Matrix ◽  
Agriculture Management ◽  
Sentinel 2

Abstract. Turkey has favorable agricultural conditions (i.e. fertile soils, climate and rainfall) and can grow almost any type of crop in many regions, making it one of the leading sectors of the economy. For sustainable agriculture management, all factors affecting the agricultural products should be analyzed on a spatial-temporal basis. Therefore, nowadays space technologies such as remote sensing are important tools in providing an accurate mapping of the agricultural fields with timely monitoring and higher repetition frequency and accuracy. In this study, object based classification method was applied to 2017 Sentinel 2 Level 2A satellite image in order to map crop types in the Adana, Çukurova region in Turkey. Support Vector Machine (SVM) was used as a classifier. Texture information were incorporated to spectral wavebands of Sentinel-2 image, to increase the classification accuracy. In this context, all of the textural features of Gray-Level Co-occurrence Matrix (GLCM) were tested and Entropy, Standard deviation, and Mean textural features were found to be the most suitable among them. Multi-spectral and textural features were used as an input separately and/or in combination to evaluate the potential of texture in differentiating crop types and the accuracy of output thematic maps. As a result, with the addition of textural features, it was observed that the Overall Accuracy and Kappa coefficient increased by 7% and 8%, respectively.

scholarly journals Object Based and Pixel Based Classification Using Rapideye Satellite Imager of ETI-OSA, Lagos, Nigeria

2016 ◽  
Vol 15 (2) ◽  
pp. 59-70 ◽  
Author(s):  
Esther Oluwafunmilayo Makinde ◽  
Ayobami Taofeek Salami ◽  
James Bolarinwa Olaleye ◽  
Oluwapelumi Comfort Okewusi
Keyword(s):  
Spatial Information ◽  
Accuracy Assessment ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Bare Soil ◽  
Spectral Angle Mapper ◽  
Object Based ◽  
Object Oriented Approach ◽  
Nearest Neighbour Classifier ◽  
Source Of Information

Several studies have been carried out to find an appropriate method to classify the remote sensing data. Traditional classification approaches are all pixel-based, and do not utilize the spatial information within an object which is an important source of information to image classification. Thus, this study compared the pixel based and object based classification algorithms using RapidEye satellite image of Eti-Osa LGA, Lagos. In the object-oriented approach, the image was segmented to homogenous area by suitable parameters such as scale parameter, compactness, shape etc. Classification based on segments was done by a nearest neighbour classifier. In the pixel-based classification, the spectral angle mapper was used to classify the images. The user accuracy for each class using object based classification were 98.31% for waterbody, 92.31% for vegetation, 86.67% for bare soil and 90.57% for Built up while the user accuracy for the pixel based classification were 98.28% for waterbody, 84.06% for Vegetation 86.36% and 79.41% for Built up. These classification techniques were subjected to accuracy assessment and the overall accuracy of the Object based classification was 94.47%, while that of Pixel based classification yielded 86.64%. The result of classification and accuracy assessment show that the object-based approach gave more accurate and satisfying results

scholarly journals COMPARAÇÃO DA CLASSIFICAÇÃO DE OCUPAÇÃO DO SOLO DO MUNICÍPIO DE FREDERICO WESTPHALEN-RS, UTILIZANDO OS MÉTODOS ISODATA E DISTÂNCIA MÍNIMA

Nativa ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 727
Author(s):  
Ana Claudia Guedes Silva ◽  
Gabriel De Menezes Trevisan
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Minimum Distance ◽  
Spatial Information ◽  
Satellite Image ◽  
Kappa Index ◽  
Classification Techniques ◽  
Sentinel 2A ◽  
Global Accuracy

O uso dos Sistemas de Informações Geográficas (SIG), em produtos de sensoriamento remoto, tem sido cada vez mais utilizadas no mapeamento terrestre, facilitando na obtenção de informações espaciais. O objetivo deste trabalho foram avaliar duas diferentes técnicas de classificação digital, para o mapeamento de uso do solo do município de Frederico Westphalen - RS. Para este fim, utilizou-se o software Quantum Gis 2.18.13 (QGis) para primeiramente realizar a composição de bandas, realce de contraste e recorte da imagem de satélite Sentinel-2A (10m de resolução espacial) aos limites do município em estudo. Aplicaram-se diferentes técnicas de classificação digital: 1) Mínima Distância (supervisionada) e 2) ISODATA (não supervisionada); sendo o 1 realizado no QGis e o 2 no software ArcGIS 10.5. Foram obtidos mapas com diferentes informações, dos quais a acurácia foi avaliada pelos Índice Kappa, Exatidão Global, Erros de Omissão e Comissão. Constatou-se, pela análise dos valores das classes temáticas, em km², que os melhores resultados foram obtidos para a classificação supervisionada, a qual apresentou mais concordância com o mapa visual considerado a verdade de campo. Já para a validação a mesma classificação se destacou com maiores valores de Exatidão Global e Índice Kappa, (63,41% e 45%) diferente do encontrado para a classificação ISODATA (48,17% e 31%).Palavras-chave: geoprocessamento; sensoriamento remoto; classificação digital; mapeamento. COMPARISON OF THE CLASSIFICATION OF LAND USE OF THE MUNICIPALITY OF FREDERICO WESTPHALEN - RS, USING THE ISODATA AND MINIMUM DISTANCE ABSTRACT: The use of Geographic Information Systems (GIS) in remote sensing products has been increasingly used in terrestrial mapping, making it easier to obtain spatial information. The objective of this work was to evaluate different digital classification techniques for the land use mapping of the municipality of Frederico Westphalen - RS. For this purpose, the software Quantum Gis 2.18.13 (QGis) was used to first perform band composition, contrast enhancement and cut-off of the Sentinel-2A satellite image (10m spatial resolution) at the boundaries of the studied municipality. Different digital classification techniques were applied: 1) Minimum Distance (supervised) and 2) ISODATA (unsupervised); 1 being done in QGis and 2 in ArcGIS 10.5 software. We obtained maps with different information, of which the accuracy was evaluated by the Kappa Index, Global Accuracy, Errors of Omission and Commission. The best results were obtained for the supervised classification, which presented more agreement with the visual map considered the truth of the field, by analyzing the values of the thematic classes in km². For the validation, the same classification stood out with higher values of Global Accuracy and Kappa Index, (63.41% and 45%) than that found for the ISODATA classification (48.17% and 31%). However, the thematic classification should be adjusted as well as changing the RGB band composition to improve the statistical parameters.Keywords: geoprocessing; remote sensing; digital classification; mapping.

scholarly journals Shallow-Water Benthic Habitat Mapping Using Drone with Object Based Image Analyses

2021 ◽  
Vol 13 (21) ◽  
pp. 4452
Author(s):  
Bisman Nababan ◽  
La Ode Khairum Mastu ◽  
Nurul Hazrina Idris ◽  
James P. Panjaitan
Keyword(s):  
Coral Reef ◽  
Shallow Water ◽  
Spatial Information ◽  
Aerial Photographs ◽  
Support Vector ◽  
Benthic Habitat ◽  
Benthic Habitats ◽  
Object Based ◽  
Svm Algorithm ◽  
Level 2

Spatial information on benthic habitats in Wangiwangi island waters, Wakatobi District, Indonesia was very limited in recent years. However, this area is one of the marine tourism destinations and one of the Indonesia’s triangle coral reef regions with a very complex coral reef ecosystem. The drone technology that has rapidly developed in this decade, can be used to map benthic habitats in this area. This study aimed to map shallow-water benthic habitats using drone technology in the region of Wangiwangi island waters, Wakatobi District, Indonesia. The field data were collected using a 50 × 50 cm squared transect of 434 observation points in March–April 2017. The DJI Phantom 3 Pro drone with a spatial resolution of 5.2 × 5.2 cm was used to acquire aerial photographs. Image classifications were processed using object-based image analysis (OBIA) method with contextual editing classification at level 1 (reef level) with 200 segmentation scale and several segmentation scales at level 2 (benthic habitat). For level 2 classification, we found that the best algorithm to map benthic habitat was the support vector machine (SVM) algorithm with a segmentation scale of 50. Based on field observations, we produced 12 and 9 benthic habitat classes. Using the OBIA method with a segmentation value of 50 and the SVM algorithm, we obtained the overall accuracy of 77.4% and 81.1% for 12 and 9 object classes, respectively. This result improved overall accuracy up to 17% in mapping benthic habitats using Sentinel-2 satellite data within the similar region, similar classes, and similar method of classification analyses.

scholarly journals Testing a detailed classification scheme for land-cover/ land-use mapping of typical Indonesian landscapes: case study of Sarolangun, Jambi and Salatiga, Central Java

2020 ◽  
Vol 52 (3) ◽  
pp. 327
Author(s):  
Projo Danoedoro ◽  
Irvan Nurrahman Ananda ◽  
Candra Sari Djati Kartika ◽  
Assyria F Umela ◽  
Alvidita Beatrix Indayani
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Development ◽  
Classification Scheme ◽  
Satellite Image ◽  
Digital Processing ◽  
Disaster Mitigation ◽  
Landsat 8 ◽  
Landscape Characteristics ◽  
Central Java

Land-cover/land-use (LCLU) mapping is an important activity to produce very useful information to support  various sectors, such as land supply, spatial planning, disaster mitigation, and agricultural development.  In Indonesia, a LCLU classification scheme has been developed at a scale of 1: 50,000, but it still requires an evaluation due to its advantages and limitations. This study tried to apply a classification scheme for LCLU-based on SNI 7645-1 2014 for two regions in Indonesia with different landscape characteristics, i.e.  Sarolangun in Jambi and Salatiga and surroundings in Central Java.. The trial was conducted by developing methods of Landsat-8 satellite image analysis and interpretation combining digital processing and manual delineation. Based on this research, a number of 52 LCLU classes were identified  in Sarolanguni and 32 classes were found in Salatiga and surrounding areas. The validation showed that the LCLU map of Jambi region reached 80.75.% of total accuracy, while that of Salatiga and surroundings reached 88.7%.  Different accuracies found related to the number of classes produced, the pattern of relationship between LCLU with the existing landform characteristics, and the quality of images due to cloud cover. 

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 Change Detection in Coral Reef Environment Using High-Resolution Images: Comparison of Object-Based and Pixel-Based Paradigms

2018 ◽  
Vol 7 (11) ◽  
pp. 441 ◽  
Author(s):  
Zhenjin Zhou ◽  
Lei Ma ◽  
Tengyu Fu ◽  
Ge Zhang ◽  
Mengru Yao ◽  
...  
Keyword(s):  
Coral Reef ◽  
Change Detection ◽  
Accuracy Assessment ◽  
Detection Accuracy ◽  
Reef Environment ◽  
Object Based ◽  
Coral Reef Environment ◽  
Study Sites ◽  
Object Area ◽  
Segmentation Image

Despite increases in the spatial resolution of satellite imagery prompting interest in object-based image analysis, few studies have used object-based methods for monitoring changes in coral reefs. This study proposes a high accuracy object-based change detection (OBCD) method intended for coral reef environment, which uses QuickBird and WorldView-2 images. The proposed methodological framework includes image fusion, multi-temporal image segmentation, image differencing, random forests models, and object-area-based accuracy assessment. For validation, we applied the method to images of four coral reef study sites in the South China Sea. We compared the proposed OBCD method with a conventional pixel-based change detection (PBCD) method by implementing both methods under the same conditions. The average overall accuracy of OBCD exceeded 90%, which was approximately 20% higher than PBCD. The OBCD method was free from salt-and-pepper effects and was less prone to images misregistration in terms of change detection accuracy and mapping results. The object-area-based accuracy assessment reached a higher overall accuracy and per-class accuracy than the object-number-based and pixel-number-based accuracy assessment.

scholarly journals Identifikasi Gosong Karang Mengggunakan Citra Satelit Sentinel 2A (Studi Kasus: Perairan Pesisir Nias Utara)

2020 ◽  
Vol 21 (1) ◽  
pp. 95-108
Author(s):  
Anang Dwi Purwanto ◽  
Teguh Prayogo ◽  
Sartono Marpaung
Keyword(s):  
Coral Reefs ◽  
Image Classification ◽  
Classification Method ◽  
Fishing Ground ◽  
Field Observations ◽  
Geometric Correction ◽  
Radiometric Correction ◽  
Object Based ◽  
Sentinel 2A ◽  
North Sumatra

ABSTRACTThe waters of Northern Nias, North Sumatra Province have a great potential for natural resources, one of which is the reef which is often used as a fishing ground. This study aims to identify and monitor the distribution of coral reefs around the waters of Northern Nias. The location of study is limited by coordinates 97° 0'31'' - 97° 16'54'' E and 1° 29'2'' LU - 1° 6'24'' N. The study locations were grouped in 6 (six) areas including Mardika reef, Wunga reef, Mausi1 reef, Mausi2 reef, Tureloto reef and Senau reef. The data used were Sentinel 2A imagery acquisition on 19 September 2018 and field observations made on 6-12 September 2018. Data processing includes geometric correction, radiometric correction, water column correction and classification using pixel-based and object-based methods as well as by delineating on the image. One classification method will be chosen that is most suitable for the location of the reef. The results show Sentinel 2A was very helpful in mapping the distribution of coral reefs compared to direct observation in the field. The use of image classification method rightly is very helpful in distinguishing coral reef objects from surrounding objects. The estimated area of coral reefs was 1,793.20 ha with details of the Mardika reef 143.27 ha, Wunga reef 627.06 ha, Mausi1 reef 299.84 ha, Mausi2 reef 141.873 ha, Tureloto reef 244.73 ha, Senau reef 336.44 ha. The existence of coral reefs have a high potential as a fishing ground and a natural tourist attraction.Keywords: coral reefs, sentinel 2A, lyzenga 1978, image classification, Northern NiasABSTRAKPerairan Nias Utara yang terletak di Provinsi Sumatra Utara memiliki potensi kekayaan alam yang besar dimana salah satunya adalah gosong karang yang sering dijadikan lokasi penangkapan ikan oleh nelayan. Penelitian ini bertujuan untuk mengidentifikasi dan monitoring sebaran gosong karang di sekitar perairan Nias Utara. Lokasi penelitian dibatasi dengan koordinat 97°0’31’’ - 97°16’54’’ BT dan 1°29’2’’LU – 1°6’24’’  LU. Untuk mempermudah dalam pengolahan data maka lokasi kajian dikelompokkan dalam 6 (enam) kawasan diantaranya gosong Mardika, gosong Wunga, gosong Mausi1, gosong Mausi2, gosong Tureloto dan gosong Senau. Data yang digunakan adalah citra satelit Sentinel 2A hasil perekaman tanggal 19 September 2018 dan hasil pengamatan lapangan yang telah dilakukan pada tanggal 6 - 12 September 2018. Pengolahan data meliputi koreksi geometrik, koreksi radiometrik, koreksi kolom air dan klasifikasi menggunakan metode klasifikasi berbasis piksel dan berbasis objek serta deliniasi citra. Dari ketiga metode klasifikasi tersebut akan dipilih satu metode klasifikasi yang sesuai dengan lokasi gosong karang. Hasil penelitian menunjukkan citra Sentinel 2A sangat membantu dalam memetakan sebaran gosong karang dibandingkan dengan pengamatan langsung di lapangan. Pemilihan metode klasifikasi citra satelit yang tepat sangat membantu dalam membedakan objek gosong karang dengan objek di sekitarnya. Estimasi total luasan gosong karang di perairan Nias Utara adalah 1,793.20 ha dengan rincian luasan gosong karang Mardika 143.27 ha, gosong Wunga 627.06 ha, gosong Mausi1 299.84 ha, gosong Mausi2 141.873 ha, gosong Tureloto 244.73 ha, gosong Senau 336.44 ha. Keberadaan gosong karang memiliki potensi yang tinggi sebagai lokasi penangkapan ikan dan memiliki daya tarik sebagai tempat wisata alam.Kata kunci: gosong karang, sentinel 2A, lyzenga 1978, klasifikasi citra, Nias Utara

scholarly journals Verification of multispectral data processing for the Sentinel-2A bands, field ASD FieldSpec® 3FR and UAV with the DJI STS-VIS

2019 ◽  
pp. 29-39 ◽  
Author(s):  
Stanislav Dugin ◽  
Oksana Sybirtseva ◽  
Stanislav Golubov ◽  
Yelizaveta Dorofey
Keyword(s):  
Pearson Correlation ◽  
Plant Cover ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Image Data ◽  
Multispectral Data ◽  
Maximum Value ◽  
Substantial Correlation ◽  
Satellite Image Data ◽  
Sentinel 2A

The study of plant cover have been performed by the hyperspectral remote sensing method using ASD FieldSpec® 3FR and DJI STS-VIS measurements. The orthophotoplans are compiled for the test plots of interest at the spatial resolution of 2.5 cm. The substantial correlation for the results of terrestrial verification for the satellite image data in the range of Sentinel-2A bands are confirmed. 15 vegetation indices for the Sentinel-2А wavelength bands were drawn at the Pearson correlation coefficient r > 0.97, with a maximum value of the correlation error of 0.07.

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