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

Şekertekin, Aliihsan; Marangoz, Aycan Murat; Akçın, Hakan

doi:10.5194/isprs-archives-xlii-4-w6-91-2017

Cited by 60 publications

(39 citation statements)

References 10 publications

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“…Landsat-8 Operational Land Imager (OLI) data were acquired on 14 April 2013 for LULC classification and from November 2013 to May 2014 for LST retrieval, respectively (https://earthexplorer.usgs.gov/). Landsat images are constantly improving by the richness in spectral, spatial, radiometric, and temporal resolution [54] with the new generations of satellites with improved sensors. The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are instruments onboard the Landsat-8 satellite, which was launched in February 2013.…”

Section: Landsat-8 Oli/tirsmentioning

confidence: 99%

Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka

et al. 2020

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Spatial urban growth and its impact on land surface temperature (LST) is a high priority environmental issue for urban policy. Although the impact of horizontal spatial growth of cities on LST is well studied, the impact of the vertical spatial distribution of buildings on LST is under-investigated. This is particularly true for cities in sub-tropical developing countries. In this study, TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-XDEM), Advanced Spaceborne Thermal Emission and Reflection (ASTER)-Global Digital Elevation Model (GDEM), and ALOS World 3D-30m (AW3D30) based Digital Surface Model (DSM) data were used to investigate the vertical growth of the Dhaka Metropolitan Area (DMA) in Bangladesh. Thermal Infrared (TIR) data (10.6-11.2µm) of Landsat-8 were used to investigate the seasonal variations in LST. Thereafter, the impact of horizontal and vertical spatial growth on LST was studied. The result showed that: (a) TanDEM-X DSM derived building height had a higher accuracy as compared to other existing DSM that reveals mean building height of the Dhaka city is approximately 10 m, (b) built-up areas were estimated to cover approximately 94%, 88%, and 44% in Dhaka South City Corporation (DSCC), Dhaka North City Corporation (DNCC), and Fringe areas, respectively, of DMA using a Support Vector Machine (SVM) classification method, (c) the built-up showed a strong relationship with LST (Kendall tau coefficient of 0.625 in summer and 0.483 in winter) in comparison to vertical growth (Kendall tau coefficient of 0.156 in the summer and 0.059 in the winter), and (d) the ‘low height-high density’ areas showed high LST in both seasons. This study suggests that vertical development is better than horizontal development for providing enough open spaces, green spaces, and preserving natural features. This study provides city planners with a better understating of sustainable urban planning and can promote the formulation of action plans for appropriate urban development policies.

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Section: Landsat-8 Oli/tirsmentioning

confidence: 99%

Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka

et al. 2020

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“…The results of the Sentinel-2 images' classification in the study were satisfactory, indicating that it is reasonable to automate the process of the classification of land cover and for monitoring the increase in forested areas. According to papers using Sentinel images [31][32][33][34] and preparing terrain information for reclaimed areas using remote sensing data [7][8][9][65][66][67][68][69], research has confirmed the possibility of using Sentinel-2 images for LULC change detection and for the monitoring of vegetated areas in reclaimed areas.…”

Section: Discussionmentioning

confidence: 99%

“…The process of land cover mapping, especially in terms of reclaimed areas, is the subject Sentinel-2 satellite imageries (European Space Agency; ESA) consist of two satellites: Sentinel-2A (launched in 2015) and Sentinel-2B (launched in 2017). The satellites are equipped with modern multi-spectral high-resolution scanners − 13 spectral channels with spatial resolution of 10, 20, and 60 m. Using Sentinel-2 (ESA) imageries opens up new possibilities in environmental studies [11,[31][32][33][34], mainly as a result of the good spatial and spectral resolution and short revisit time (five days for two satellites).…”

Section: Introductionmentioning

confidence: 99%

Reclaimed Area Land Cover Mapping Using Sentinel-2 Imagery and LiDAR Point Clouds

2020

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This paper investigates the possibility of using fusion Sentinel-2 imageries (2016, ESA) and light detection and ranging (LiDAR) point clouds for the automation of land cover mapping with a primary focus on detecting and monitoring afforested areas and deriving precise information about the spatial (2D and 3D) characteristics of vegetation for reclaimed areas. The study was carried out for reclaimed areas – two former sulfur mines located in Southeast Poland, namely, Jeziórko, where 216.5 ha of afforested area was reclaimed after borehole exploitation, and Machów, where 871.7 ha of dump area was reclaimed after open cast strip mining. The current land use and land cover (LULC) classes at the Machów and Jeziórko former sulfur mines are derived based on Sentinel-2 image processing, and confirmed the applied type of reclamation for both analysed areas. The following LULC classes showed a significant spatial range: broad-leaved forest, coniferous forest, and transitional woodland shrub. The progress of afforested areas, not only in terms of the occupied area, but also in terms of the growth of trees and shrubs, was confirmed. The results of the study showed differences in vegetation parameters, namely, height and canopy cover. Various stages of vegetation growth were also observed. This indicates an ongoing process of vegetation development, as an effect of the reclamation treatment for these areas.

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“…Several studies have demonstrated the effectiveness of Sentinel-2 spectral bands in land-use classification (Gašparović & Jogun, 2017;Georgescu, Vaduva & Datcu, 2017;Sekertekin, Marangoz & Akein, 2017). Most have found that the Vegetation Red Edge bands are helpful in discriminating between vegetation types (Delegido, Verrelst, Alonso & Moreno, 2011;Immitzer, Vuolo & Atzberger, 2016).…”

Section: Input Featuresmentioning

confidence: 99%

Creating Models of Custom Image Classification Workflows Using Softmax Regression and Support Vector Machine

Wolfe¹,

Bähr²,

Jin³

2018

giforum

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This study demonstrates how complex image classification workflows can be built using a visual modelling tool. Models facilitate the comparison of different classifiers while allowing an analyst to experiment with different input features. The models include custom workflow steps for preparing input and training data, training the classifier, classifying images and evaluating the results. The example models presented here were used to classify Sentinel-2 imagery of eastern Texas, USA, into five land-use categories that consisted primarily of vegetation. Separate models were created for Softmax Regression and Support Vector Machine (SVM) classification, each using Sentinel-2 spectral bands and again with an additional entropy texture image as input. The results showed that SVM performed better than Softmax Regression and that the selected texture measure did not improve classification results. A discussion is provided of how the models could be extended further to provide different analysis options.

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

Cited by 60 publications

References 10 publications

Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka

Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka

Reclaimed Area Land Cover Mapping Using Sentinel-2 Imagery and LiDAR Point Clouds

Creating Models of Custom Image Classification Workflows Using Softmax Regression and Support Vector Machine

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