Object-Based Mapping of Plastic Greenhouses with Scattered Distribution in Complex Land Cover Using Landsat 8 OLI Images: A Case Study in Xuzhou, China

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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The effects of polynomial interpolation and resampling methods in geometric correction on the land-cover classification accuracy of Landsat-8 OLI imagery: a case study of Kulon Progo area, Yogyakarta

Sixth Geoinformation Science Symposium ◽

10.1117/12.2548423 ◽

2019 ◽

Author(s):

Muhammad Z. Afwani ◽

Projo Danoedoro

Keyword(s):

Land Cover ◽

Classification Accuracy ◽

Polynomial Interpolation ◽

Land Cover Classification ◽

Landsat 8 ◽

Geometric Correction ◽

Landsat 8 Oli ◽

Resampling Methods

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Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain)

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2016.07.011 ◽

2016 ◽

Vol 52 ◽

pp. 403-411 ◽

Cited By ~ 73

Author(s):

Antonio Novelli ◽

Manuel A. Aguilar ◽

Abderrahim Nemmaoui ◽

Fernando J. Aguilar ◽

Eufemia Tarantino

Keyword(s):

Performance Evaluation ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Object Based ◽

Sentinel 2

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Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

Remote Sensing ◽

10.3390/rs13122299 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2299

Author(s):

Andrea Tassi ◽

Daniela Gigante ◽

Giuseppe Modica ◽

Luciano Di Martino ◽

Marco Vizzari

Keyword(s):

Land Cover ◽

National Park ◽

Time Window ◽

Central Italy ◽

Google Earth ◽

Data Cube ◽

Landsat 8 ◽

Change Analysis ◽

Object Based ◽

Google Earth Engine

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

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Delineation of burned arid landscapes using Landsat 8 OLI data: a case study of Karaganda region in Kazakhstan

Arid Land Research and Management ◽

10.1080/15324982.2021.1887398 ◽

2021 ◽

pp. 1-16

Author(s):

Andrey Karpachevskiy ◽

Sergey Lednev ◽

Ivan Semenkov ◽

Anna Sharapova ◽

Sultan Nagiyev ◽

...

Keyword(s):

Landsat 8 ◽

Landsat 8 Oli ◽

Arid Landscapes ◽

Karaganda Region

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Object-based Wetland Characterization Using Radarsat-2 Quad-Polarimetric SAR Data, Landsat-8 OLI Imagery, and Airborne Lidar-Derived Geomorphometric Variables

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.83.1.27 ◽

2017 ◽

Vol 83 (1) ◽

pp. 27-36 ◽

Cited By ~ 14

Author(s):

StevenE. Franklin ◽

OumerS. Ahmed

Keyword(s):

Airborne Lidar ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Polarimetric Sar ◽

Object Based ◽

Sar Data

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Making Of Classification Land Cover Through Result Of Visual Data Satellite Image Analysis Landsat 8 OLI : Case Study in Tapaktuan District, South Aceh District

Jurnal Inotera ◽

10.31572/inotera.vol6.iss1.2021.id139 ◽

2021 ◽

Vol 6 (1) ◽

pp. 59-65

Author(s):

Safridatul Audah ◽

Muharratul Mina Rizky ◽

Lindawati

Keyword(s):

Land Use ◽

Land Cover ◽

Image Classification ◽

Satellite Image ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Sensing Technology ◽

Open Land ◽

Cover Class ◽

Satellite Image Analysis

Tapaktuan is the capital and administrative center of South Aceh Regency, which is a sub-district level city area known as Naga City. Tapaktuan is designated as a sub-district to be used for the expansion of the capital's land. Consideration of land suitability is needed so that the development of settlements in Tapaktuan District is directed. The purpose of this study is to determine the level of land use change from 2014 to 2018 by using remote sensing technology in the form of Landsat-8 OLI satellite data through image classification methods by determining the training area of the image which then automatically categorizes all pixels in the image into land cover class. The results obtained are the results of the two image classification tests stating the accuracy of the interpretation of more than 80% and the results of the classification of land cover divided into seven forms of land use, namely plantations, forests, settlements, open land, and clouds. From these classes, the area of land cover change in Tapaktuan is increasing in size from year to year.

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