Application of J48 Decision Tree for the Identification of Water Bodies using Landsat 8 OLI Sensor

2015 ◽  
Author(s):  
Dong-Ha Lee ◽  
Tri Acharya ◽  
In-Tae Yang
Keyword(s):  
Decision Tree ◽  
Water Bodies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
J48 Decision Tree

scholarly journals Identification of Water Bodies in a Landsat 8 OLI Image Using a J48 Decision Tree

Sensors ◽  
2016 ◽  
Vol 16 (7) ◽  
pp. 1075 ◽  
Author(s):  
Tri Acharya ◽  
Dong Lee ◽  
In Yang ◽  
Jae Lee
Keyword(s):  
Decision Tree ◽  
Water Bodies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
J48 Decision Tree

Combining Water Indices to Detect Water Bodies using Landsat 8 OLI

2019 ◽  
Vol 25 (5) ◽  
pp. 470-475
Author(s):  
Michelle V. Japitana ◽  
Chul-Soo Ye ◽  
Marlowe Edgar C. Burce
Keyword(s):  
Water Bodies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Indices

scholarly journals Comparative Analysis of Machine Learning Algorithms in Automatic Identification and Extraction of Water Boundaries

2021 ◽  
Vol 11 (21) ◽  
pp. 10062
Author(s):  
Aimin Li ◽  
Meng Fan ◽  
Guangduo Qin ◽  
Youcheng Xu ◽  
Hailong Wang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Water Bodies ◽  
Support Vector ◽  
Landsat 8 ◽  
Transfer Performance ◽  
Remote Sensing Images

Monitoring open water bodies accurately is important for assessing the role of ecosystem services in the context of human survival and climate change. There are many methods available for water body extraction based on remote sensing images, such as the normalized difference water index (NDWI), modified NDWI (MNDWI), and machine learning algorithms. Based on Landsat-8 remote sensing images, this study focuses on the effects of six machine learning algorithms and three threshold methods used to extract water bodies, evaluates the transfer performance of models applied to remote sensing images in different periods, and compares the differences among these models. The results are as follows. (1) Various algorithms require different numbers of samples to reach their optimal consequence. The logistic regression algorithm requires a minimum of 110 samples. As the number of samples increases, the order of the optimal model is support vector machine, neural network, random forest, decision tree, and XGBoost. (2) The accuracy evaluation performance of each machine learning on the test set cannot represent the local area performance. (3) When these models are directly applied to remote sensing images in different periods, the AUC indicators of each machine learning algorithm for three regions all show a significant decline, with a decrease range of 0.33–66.52%, and the differences among the different algorithm performances in the three areas are obvious. Generally, the decision tree algorithm has good transfer performance among the machine learning algorithms with area under curve (AUC) indexes of 0.790, 0.518, and 0.697 in the three areas, respectively, and the average value is 0.668. The Otsu threshold algorithm is the optimal among threshold methods, with AUC indexes of 0.970, 0.617, and 0.908 in the three regions respectively and an average AUC of 0.832.

scholarly journals EKSTRAKSI INFORMASI PENUTUP LAHAN AREA LUAS DENGAN METODE EXPERT KNOWLEDGE OBJECT-BASED IMAGE ANALYSIS (OBIA) PADA CITRA LANDSAT 8 OLI PULAU KALIMANTAN

2016 ◽  
Vol 18 (1) ◽  
pp. 09
Author(s):  
Zylshal Zylshal ◽  
Heri Susanto ◽  
Sarip Hidayat
Keyword(s):  
Decision Tree ◽  
Expert Knowledge ◽  
Contextual Information ◽  
Acquisition Time ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Visual Interpretation ◽  
Large Area ◽  
Spectral Difference ◽  
Multiresolution Segmentation

<p class="abstrak">Ekstraksi informasi penutup/penggunaan lahan area luas seperti di Pulau Kalimantan umumnya terkendala oleh variasi nilai spektral di beberapa area yang berbeda, serta sulitnya mendapatkan hasil perekaman yang bebas dari awan. Klasifikasi visual, meski memberikan hasil yang baik, merupakan pekerjaan yang membutuhkan waktu dan tenaga yang relatif banyak, belum lagi potensi pengaruh subjektifitas interpreter. OBIA yang sudah mulai diterima dan banyak digunakan dalam klasifikasi digital bisa menjadi alternatif tambahan selain interpretasi visual maupun analisis digital berbasis piksel konvensional. Penelitian ini menggunakan data Landsat 8 OLI <em>orthorectified </em>yang telah melalui proses <em>mosaicking</em> dan <em>cloud masking</em> untuk mendapatkan citra satu Pulau Kalimantan yang bebas awan. <em>Layer </em>NDVI, MNDWI, NDBI, BSI, SAVI, dan <em>Built-up Index</em> kemudian diturunkan dari data Citra Landsat untuk dimasukkan ke dalam tahap segmentasi dan klasifikasi. Segmentasi dilakukan dengan menggunakan algoritma <em>Multiresolution Segmentation</em> dan <em>Spectral Difference Segmentation</em>. Klasifikasi dilakukan dengan menggunakan serangkaian <em>multilevel threshold</em> yang disusun dalam bentuk <em>decision tree</em>. Empat belas kelas penutup/penggunaan lahan kemudian berhasil diekstrak, dengan nilai <em>overall accuracy</em> 77,65%. Metode yang digunakan juga menunjukkan akurasi yang tinggi untuk kelas hutan lahan kering, perkebunan, kebun campur dan semak belukar dengan nilai akurasi di atas 80%. Hasil ini menunjukkan bahwa metode ini bisa dijadikan sebagai alternatif dalam mengidentifikasi dan mengekstrak informasi tutupan vegetasi untuk kegiatan pemetaan area luas.</p><p class="katakunci"><strong>Kata kunci: </strong>OBIA, area luas, perubahan penutupan/penggunaan lahan, citra landsat, <em>decision tree</em></p><p class="judulABS"><strong><em>ABSTRACT</em></strong></p><p class="Abstrakeng"><em>Large area landuse/landcover extraction such as on the island of Borneo using Landsat 8 data are generally constrained by the great variations in the spectral values, due to the vast use of different scenes with different acquisition time, as well as the fact that it almost impossible to get a completely cloud-free image of the whole island. Visual classification, despite the good results, is a labour-intensif job that requires a huge time and effort, not to mention the potential influence of interpreter’s subjectivity. While the pixel based digital classification suffer from“salt pepper” effect as well as almost exclusively relied on spectral information, OBIA has been accepted and widely used in digital classification as an alternative for the visual interpretation and conventional pixel-based classification, with its ability to use additional contextual information. This study aimed to used OBIA method on Landsat 8 OLI cloudfree mosaic dataset for the whole Borneo region to create a landuse/landcover map using both spectral and contextual information, as well as ancilarry DEM data. Additional layers of NDVI, MNDWI, NDBI, BSI, SAVI, and Built-up Index were then derived from Landsat data to be used in the segmentation and classification process. Multiresolution Segmentation algorithm and Spectral Difference Segmentation were then conducted respectively. The classification wasdone by using a series of multilevel crisp classification using thresholds in the form of a decision tree. Fourteen of landuse/landcover classes were then successfully extracted, with a value of 77.65% on overall accuracy. The proposed method showed reasonable high accuracy for the forest, plantation, mixed garden and shrub classes with the accuracy all above 80%. These results indicate that the proposed method can be used as an alternative to identify and extract information related to vegetation cover for large areamapping activities.</em></p><em><strong>Keywords: </strong>OBIA, large area, land use cover change (LULC), landsat image, decision tree</em>

scholarly journals A NOVEL METHOD FOR WATER AND WATER CANAL EXTRACTION FROM LANDSAT-8 OLI IMAGERY

2018 ◽  
Vol XLII-5 ◽  
pp. 323-328 ◽  
Author(s):  
S. L. K. Reddy ◽  
C. V. Rao ◽  
P. R. Kumar ◽  
R. V. G. Anjaneyulu ◽  
B. G. Krishna
Keyword(s):  
Remote Sensing ◽  
Water Body ◽  
Remote Sensing Data ◽  
Water Bodies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Additional Information ◽  
Water Canal ◽  
Water Canals

<p><strong>Abstract.</strong> Constituents of hydrologic network, River and water canals play a key role in Agriculture for cultivation, Industrial activities and urban planning. Remote sensing images can be effectively used for water canal extraction, which significantly improves the accuracy and reduces the cost involved in mapping using conventional means. Using remote sensing data, the water Index (WI), Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) are used in extracting the water bodies. These techniques are aimed at water body detection and need to be complemented with additional information for the extraction of complete water canal networks. The proposed index MNDWI-2 is able to find the water bodies and water canals as well from the Landsat-8 OLI imagery and is based on the SWIR2 band. In this paper, we use Level-1 precision terrain corrected OLI imagery at 30 meter spatial resolution. The proposed MNDWI-2 index is derived using SWIR2 (B7) band and Green (B3) band. The usage of SWIR2 band over SWIR1 results in very low reflectance values for water features, detection of shallow water and delineation of water features with rest of the features in the image. The computed MNDWI-2 index values are threshold by making the values greater than zero as 1 and less than zero as zero. The binarised values of 1 represent the water bodies and 0 represent the non-water body. This normalized index detects the water bodies and canals as well as vegetation which appears in the form of noise. The vegetation from the MNDWI-2 image is removed by using the NDVI index, which is calculated using the Top of Atmosphere (TOA) corrected images. The paper presents the results of water canal extraction in comparison with the major available indexes. The proposed index can be used for water and water canal extraction from L8 OLI imagery, and can be extended for other high resolution sensors.</p>

scholarly journals Mapping chlorophyll-a concentrations in a cyanobacteria- and algae-impacted Vaal Dam using Landsat 8 OLI data

2018 ◽  
Vol 114 (9/10) ◽  
Author(s):  
Oupa E. Malahlela ◽  
Thando Oliphant ◽  
Lesiba T. Tsoeleng ◽  
Paidamwoyo Mhangara
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Chlorophyll A ◽  
Water Quality Assessment ◽  
Water Bodies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Inland Water ◽  
Chl A ◽  
Vaal Dam

Mapping chlorophyll-a (chl-a) is crucial for water quality management in turbid and productive case II water bodies, which are largely influenced by suspended sediment and phytoplankton. Recent developments in remote sensing technology offer new avenues for water quality assessment and chl-a detection for inland water bodies. In this study, the red to near-infrared (NIR-red) bands were tested for the Vaal Dam in South Africa to classify chl-a concentrations using Landsat 8 Operational Land Imager (OLI) data for 2014–2016 by means of stepwise logistic regression (SLR). The moderate-resolution imaging spectroradiometer (MODIS) data were also used for validating chl-a concentration classes. The chl-a concentrations were classified into low and high concentrations. The SLR applied on 2014 images yielded an overall accuracy of 80% and kappa coefficient (κ) of 0.74 on April 2014 data, while an overall accuracy of 65% and κ=0.30 were obtained for the May 2015 Landsat data. There was a significant (p less than 0.05) negative correlation between chl-a classes and red band in all analyses, while the NIR band showed a positive correlation (0.0001; p less than 0.89) for April 2014 data set. The 2015 image classification yielded an overall accuracy of 83% and κ=0.43. The difference vegetation index showed a significant (p less than 0.003) positive correlation with chl-a concentrations for May 2015 and July 2016, with chl-a ranges of between 2.5 μg/L and 1219 μg/L. These correlations show that a class increase in chl-a (from low to high) is in response to an increase in greenness within the Vaal Dam. We have demonstrated the applicability of Landsat 8 OLI data for inland water quality assessment.

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