scholarly journals PIXEL-BASED CLASSIFICATION ANALYSIS OF LAND USE LAND COVER USING SENTINEL-2 AND LANDSAT-8 DATA

2017 ◽  
Vol XLII-4/W6 ◽  
pp. 91-93 ◽  
Author(s):  
A. Sekertekin ◽  
A. M. Marangoz ◽  
H. Akcin
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Accuracy Assessment ◽  
Kappa Statistics ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Landsat 8 Oli ◽  
Sentinel 2

The aim of this study is to conduct accuracy analyses of Land Use Land Cover (LULC) classifications derived from Sentinel-2 and Landsat-8 data, and to reveal which dataset present better accuracy results. Zonguldak city and its near surrounding was selected as study area for this case study. Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 the Operational Land Imager (OLI) data, acquired on 6 April 2016 and 3 April 2016 respectively, were utilized as satellite imagery in the study. The RGB and NIR bands of Sentinel-2 and Landsat-8 were used for classification and comparison. Pan-sharpening process was carried out for Landsat-8 data before classification because the spatial resolution of Landsat-8 (30m) is far from Sentinel-2 RGB and NIR bands (10m). LULC images were generated using pixel-based Maximum Likelihood (MLC) supervised classification method. As a result of the accuracy assessment, kappa statistics for Sentinel-2 and Landsat-8 data were 0.78 and 0.85 respectively. The obtained results showed that Sentinel-2 MSI presents more satisfying LULC images than Landsat-8 OLI data. However, in some areas of Sea class Landsat-8 presented better results than Sentinel-2.

scholarly journals CLASSIFICATION AND ASSESSMENT OF THE LAND USE – LAND COVER CHANGES IN JODHPUR CITY USING REMOTE SENSING TECHNOLOGIES

2018 ◽  
Vol XLII-5 ◽  
pp. 767-771 ◽  
Author(s):  
M. A. Saharan ◽  
N. Vyas ◽  
S. L. Borana ◽  
S. K. Yadav
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Assessment Method ◽  
Land Cover Changes ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Rock Outcrops ◽  
Barren Land

<p><strong>Abstract.</strong> Land Use – Land Cover (LULC) classification mapping is an important tool for management of natural resources of an area. The remote sensing technology in recent times has been used in monitoring the changing patterns of land use-land cover. The aim of the study is to monitor the LULC changes in Jodhpur city over the period 1990–2018. Satellite imagery of Landsat 8 OLI (June, 2018) &amp;amp; Landsat TM (Oct, 1990) were used for classification analysis. Supervised classification-maximum likelihood algorithm is used in ENVI software to detect land use land cover changes. Five LULC categories were used, namely- urban area, mining area, vegetation, water bodies and other area (Rock outcrops and barren land). The LULC classified maps of two different periods i.e. 2018 and 1990 were generated on 1<span class="thinspace"></span>:<span class="thinspace"></span>50,000 scale. The accuracy assessment method was used to measure the accuracy of classified maps. This study shall be of good assistance to the town planners of Jodhpur city for the purpose of the sustainable development as per the master plan 2031.</p>

scholarly journals LAND USE AND LAND COVER CLASSIFICATION OF SENTINEL 2-A: ST PETERSBURG CASE STUDY

2019 ◽  
Vol XLII-1/W2 ◽  
pp. 13-16 ◽  
Author(s):  
M. Cavur ◽  
H. S. Duzgun ◽  
S. Kemec ◽  
D. C. Demirkan
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Ground Truth ◽  
Remotely Sensed Data ◽  
Vector Machines ◽  
Kappa Value ◽  
Sentinel 2

<p><strong>Abstract.</strong> Land use and land cover (LULC) maps in many areas have been used by companies, government offices, municipalities, and ministries. Accurate classification for LULC using remotely sensed data requires State of Art classification methods. The SNAP free software and ArcGIS Desktop were used for analysis and report. In this study, the optical Sentinel-2 images were used. In order to analyze the data, an object-oriented method was applied: Supported Vector Machines (SVM). An accuracy assessment is also applied to the classified results based on the ground truth points or known reference pixels. The overall classification accuracy of 83,64% with the kappa value of 0.802 was achieved using SVM. The study indicated that of SVM algorithms, the proposed framework on Sentinel-2 imagery results is satisfactory for LULC maps.</p>

scholarly journals Assessment on the Potential of Multispectral and Hyperspectral Datasets for Land Use/Land Cover Classification

Proceedings ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 12
Author(s):  
K. Nivedita Priyadarshini ◽  
V. Sivashankari ◽  
Sulochana Shekhar ◽  
K. Balasubramani
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Urban Ecosystem ◽  
Vital Role ◽  
Land Use Land Cover ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Spectral Responses ◽  
Sentinel 2

Land use/land cover (LULC) is a significant factor which plays a vital role in defining an urban ecosystem. Interpretations of LULC are eased in recent times by utilizing hyperspectral and multispectral datasets obtained from various platforms. An attempt is made to comparatively assess the potentiality of AVIRIS NG with Sentinel 2 data through applied classification techniques for Kalaburagi urban sphere. Spectral responses of both datasets were analyzed to derive reflectance spectra. A standard supervised classification algorithm associated with dimensionality reduction techniques is applied. For performance evaluation, results are validated to check which dataset outperforms well and provides better accuracy.

scholarly journals APPLYING RANDOM FOREST CLASSIFICATION TO MAP LAND USE/LAND COVER USING LANDSAT 8 OLI

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 363-367 ◽  
Author(s):  
H. T. T. Nguyen ◽  
T. M. Doan ◽  
V. Radeloff
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Land Cover ◽  
Deciduous Forest ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Landsat 8 Oli ◽  
Plantation Forest ◽  
Barren Land ◽  
Random Forest Classification

<p><strong>Abstract.</strong> This study used the Random Forest classifier (RF) running in R environment to map Land use/Land cover (LULC) of Dak Lak province in Vietnam based on the Landsat 8 OLI. The values of two RF parameters of ntree (number of tree) and mtry (the number of variables used to split at each node) were tested and compared. In current study the best results indicate the number of suitable decision trees involved in the classification process is 300 (ntree), and the suitable number of variables used to split at each node is 4 variables (mtry). These parameters were used to classify 7 bands multi-spectral resolution from 1&amp;ndash;7 of Landsat 8 into ten classes of LULC including natural broad-leaved evergreen, semi-evergreen, dipterocarp deciduous forest, plantation forest, rubber, coffee land, crop land, barren land, residential area and water surface. The overall accuracy of 90.32&amp;thinsp;% with Kappa coefficient of 0.8434 was found in this case.</p>

scholarly journals Assessing the Accuracy of Different Supervised Classification Methods of Satellite Image

2020 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Ibochi Andrew Abah ◽  
Richard jeremiah Uriah
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Accuracy Assessment ◽  
Satellite Image ◽  
Image Data ◽  
Image Process ◽  
Land Use Land Cover ◽  
Error Matrix

Assessing the accuracy of the classification map is an essential area in remote sensing digital image process. This is because a poorly classified map will result in inestimable errors of spatial analysis and modeling arising from the use of such data. This study was designed to evaluate different supervised classification algorithms in terms of accuracy assessment with a view of recommending an appropriate algorithm for image processing. The analysis was carried out using Andoni L.G.A. Rivers State, Nigeria as the study area. Supervised classification of ETM+ 2014 Landsat image of the study area was carried out using ENVI 5.0 software. Seven land use/land cover categories were identified on the image data and appropriate information classes were also assigned using region of interest. The classifiers adopted for the study include SAM, SVM, and MDC and each classifier was set using appropriate thresholds and parameters. The output error matrix of the classified map produced overall accuracy and kappa coefficient for MDC as 94.00% and 0.91, SAM as 64.45% and 0.53, and SVM as 98.92% and 0.98 respectively. The overall accuracy obtained from SVM indicates that a perfect classification map will be produced from the algorithm. The advanced supervised classification should be utilized for classification of land use/ land cover for both high and medium resolution images for improved classification accuracy.

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