Land cover classification using Landsat 8 Operational Land Imager data in Beijing, China

Jia, Kun; Wei, Xiangqin; Gu, Xingfa; Yao, Yunjun; Xie, Xianhong; Li, Bin

doi:10.1080/10106049.2014.894586

Cited by 197 publications

(115 citation statements)

References 31 publications

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“…Sentinel-2 MSI and Landsat-8 OLI are recently operational new generation Earth observation satellites, and thus in this case study these satellites were selected as data sources. Many studies have been conducted using only Sentinel-2 data, only Landsat-8 data and both together, and so many methods have been applied to investigate which method gives better the accuracy results (Elhag & Boteva, 2016;Liu et al, 2015;Jia et al, 2014;Pirotti et al, 2016;Topaloglu et al, 2016;Marangoz et al, 2017). The aim of this study is to generate LULC images from Sentinel-2 MSI and Landsat-8 OLI data using pixel-based MLC supervised classification method, and to reveal which LULC image presents better accuracy results.…”

Section: Introductionmentioning

confidence: 99%

Pixel-Based Classification Analysis of Land Use Land Cover Using Sentinel-2 and Landsat-8 Data

Şekertekin

Marangoz

Akçın

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT: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.

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Section: Introductionmentioning

confidence: 99%

Pixel-Based Classification Analysis of Land Use Land Cover Using Sentinel-2 and Landsat-8 Data

Şekertekin

Marangoz

Akçın

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The WGS-84 coordinate system was used for the Lambert Conic Conformal Projection [9]. Ground truth data was collected via field trips of AOI and gathered information such as Crop Sowing date, water delivery status, amount of fertilizers required and crop Health [5] from concerned persons(farmers). Accordingly fieldwork was undertaken covering kharif crop seasons.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

“…Due to similar values of reflectance of many similar crops, there is likely to be spectral confusion. So it is very difficult to solve problem of spectral confusion using traditional parametric classifier like maximum likelihood [5]. With the help of Knowledge Classifier we can reduce this confusion using extra knowledge gathered from ancillary data or other data such as DEM, Toposheet vegetation map [6].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Crop and Forest Areas using Expert System based Knowledge Classifier Approach for Aurangabad District

Thorat¹,

Rajendra²,

Kale³

et al. 2015

IJCA

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The present study demonstrates the application of remote sensing for the estimation of areas corresponding to crop and forest lands covered in the district of Aurangabad, (Maharashtra), India. The data acquired by IRS-P6 Advanced Wide Field Sensors (AWiFS) having 56m spatial resolution for the months of October & December 2012 which covered complete study areas with its swath of 740Km has been used for the study. The Maximum Likelihood Classification (MLC) and Knowledge Classification (KC) techniques based on Decision Tree approach were applied. It has basically two elements, knowledge engineering and knowledge classifier. Knowledge engineering provides an interface to build up decision tree which defines the rules and variables represented by three parameters, i.e. Normalized Difference Vegetation Index (NDVI), Soil Adjust Vegetation Index (SAVI), and Normalized Difference Water Index (NDWI) threshold value of each class. Knowledge classifier generates the required output classification. The objective of this research work is to perform classification of crop and forest acreage estimation from the AWiFS data and comparing it to the supervised classification techniques, MLC and KC. The result shows that values of overall classification accuracy were 82% and 84% for the months of December and October 2012 respectively using MLC, whereas corresponding values of accuracy were found to be 85% and 87% based on KC. Thus the classification results based on KC provide better results than corresponding results based on the MLC.

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“…Land cover information is important for climate change studies and understanding complex interactions between human activities and global change [1][2][3][4][5][6]. Remote sensing has long been an effective means for land cover mapping with its ability to quickly collect information on a large regional scale, and many land cover maps on global and regional scales have been produced in recent years using remote sensing data [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Land Cover Classification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data

et al. 2014

Self Cite

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Temporal-related features are important for improving land cover classification accuracy using remote sensing data. This study investigated the efficacy of phenological features extracted from time series MODIS Normalized Difference Vegetation Index (NDVI) data in improving the land cover classification accuracy of Landsat data. The MODIS NDVI data were first fused with Landsat data via the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to obtain NDVI data at the Landsat spatial resolution. Next, phenological features, including the beginning and ending dates of the growing season, the length of the growing season, seasonal amplitude, and the maximum fitted NDVI value, were extracted from the fused time series NDVI data using the TIMESAT tool. The extracted data were integrated with the spectral data of the Landsat data to improve classification accuracy using a maximum likelihood classifier (MLC) and support vector machine (SVM) classifier. The results indicated that phenological features had a statistically OPEN ACCESSRemote Sens. 2014, 6 11519 significant effect on improving the land cover classification accuracy of single Landsat data (an approximately 3% increase in overall classification accuracy), especially for vegetation type discrimination. However, the phenological features did not improve on statistical measures including the maximum, the minimum, the mean, and the standard deviation values of the time series NDVI dataset, especially for human-managed vegetation types. Regarding different classifiers, SVM could achieve better classification accuracy than the traditional MLC classifier, but the improvement in accuracy obtained using advanced classifiers was inferior to that achieved by involving the temporally derived features for land cover classification.

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Land cover classification using Landsat 8 Operational Land Imager data in Beijing, China

Cited by 197 publications

References 31 publications

Pixel-Based Classification Analysis of Land Use Land Cover Using Sentinel-2 and Landsat-8 Data

Pixel-Based Classification Analysis of Land Use Land Cover Using Sentinel-2 and Landsat-8 Data

Estimation of Crop and Forest Areas using Expert System based Knowledge Classifier Approach for Aurangabad District

Land Cover Classification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data

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