Potential of Landsat-8 and Sentinel-2A composite for land use land cover analysis (original) (raw)
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The 2nd International Electronic Conference on Geosciences, 2019
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.
Computer Optics, 2018
The main aim of this research work is to compare k-nearest neighbor algorithm (KNN) supervised classification with migrating means clustering unsupervised classification (MMC) method on the performance of hyperspectral and multispectral data for spectral land cover classes and develop their spectral library in Samara, Russia. Accuracy assessment of the derived thematic maps was based on the analysis of the classification confusion matrix statistics computed for each classified map, using for consistency the same set of validation points. We were analyzed and compared Earth Observing-1 (EO-1) Hyperion hyperspectral data to Landsat 8 Operational Land Imager (OLI) and Advance Land Imager (ALI) multispectral data. Hyperspectral imagers, currently available on airborne platforms, provide increased spectral resolution over existing space based sensors that can document detailed information on the distribution of land cover classes, sometimes species level. Results indicate that KNN (95, 94, 88 overall accuracy and .91, .89, .85 kappa coefficient for Hyp, ALI, OLI respectively) shows better results than unsupervised classification (93, 90, 84 overall accuracy and .89, .87, .81 kappa coefficient for Hyp, ALI, OLI respectively). Development of spectral library for land cover classes is a key component needed to facilitate advance analytical techniques to monitor land cover changes. Different land cover classes in Samara were sampled to create a common spectral library for mapping landscape from remotely sensed data. The development of these libraries provides a physical basis for interpretation that is less subject to conditions of specific data sets, to facilitate a global approach to the application of hyperspectral imagers to mapping landscape. In addition, it is demonstrated that the hyperspectral satellite image provides more accurate classification results than those extracted from the multispectral satellite image. The higher classification accuracy by KNN supervised was attributed principally to the ability of this classifier to identify optimal separating classes with low generalization error, thus producing the best possible classes' separation.
Evaluation of Algorithms for Land Cover Analysis using Hyperspectral Data
Land cover is important for many planning and management activities and is considered an essential element for modelling and understanding the earth as a system. Land cover analysis relates to identifying the type of feature present on the surface of the earth. It deals with the identification of land cover features on the ground, whether vegetation, geologic, urban infrastructure, water, bare soil or others. Variations in land cover and its other physical characteristics influence weather and climate of our earth. Therefore the study of land cover plays an important role at the local/regional as well as global level for monitoring the dynamics associated with the earth. Land cover analysis has been done most effectively through satellite images of various spatial, spectral and temporal resolutions. Due to the spectral resolution limitations of conventional multispectral imageries, sensors that could collect numerous bands in precisely defined spectral regions were developed, leading to Hyperspectral Remote Sensing.
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences
The automated analysis of large areas with respect to land-cover and land-use is nowadays typically performed based on the use of hyperspectral or multispectral data acquired from airborne or spaceborne platforms. While hyperspectral data offer a more detailed description of the spectral properties of the Earth's surface and thus a great potential for a variety of applications, multispectral data are less expensive and available in shorter time intervals which allows for time series analyses. Particularly with the recent availability of multispectral Sentinel-2 data, it seems desirable to have a comparative assessment of the potential of both types of data for land-cover and land-use classification. In this paper, we focus on such a comparison and therefore involve both types of data. On the one hand, we focus on the potential of hyperspectral data and the commonly applied techniques for data-driven dimensionality reduction or feature selection based on these hyperspectral data. On the other hand, we aim to reason about the potential of Sentinel-2 data and therefore transform the acquired hyperspectral data to Sentinel-2-like data. For performance evaluation, we provide classification results achieved with the different types of data for two standard benchmark datasets representing an urban area and an agricultural area, respectively.
Development of Spectral Indexes in Hyperspectral Imagery for Land Cover Assessment
IETE Technical Review, 2018
Spectral indexes (SI) are widely used for land cover characterization and also in several physical models for the study of land surface processes. For example, the normalized differenced vegetation index (NDVI) is used in the characterization of soil moisture along with shortwave infrared reflectance. However, for hyperspectral imagery (HSI) comprising many bands within a single spectrum, it is significant to identify the optimal bands for the development of SI. In this paper, we study the potential of band selection in specific bandwidths for the determination of SI. The proposed methodology includes two strategies for development of SI: direct SI determined by the best band within specific spectrums and fused SI determined by fusion of two best bands within specific spectrums. The experiments are conducted using three datasets, two corresponding to snow-covered areas, studied using the normalized differenced snow index (NDSI) and one comprising the agricultural area, studied using NDVI. The developed SI are evaluated through a comparison with the supervised classification maps from the corresponding HSI. A kappa coefficient of 0.693, 0.726 and 0.803 was observed between the results obtained from histogram slicing of SI with respect to the classification maps for the three datasets, respectively.
Pixel-Based Classification Analysis of Land Use Land Cover Using SENTINEL-2 and LANDSAT-8 Data
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017
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.
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016
This study aims to compare classification accuracies of land cover/use maps created from Sentinel-2 and Landsat-8 data. Istanbul metropolitan city of Turkey, with a population of around 14 million, having different landscape characteristics was selected as study area. Water, forest, agricultural areas, grasslands, transport network, urban, airport- industrial units and barren land- mine land cover/use classes adapted from CORINE nomenclature were used as main land cover/use classes to identify. To fulfil the aims of this research, recently acquired dated 08/02/2016 Sentinel-2 and dated 22/02/2016 Landsat-8 images of Istanbul were obtained and image pre-processing steps like atmospheric and geometric correction were employed. Both Sentinel-2 and Landsat-8 images were resampled to 30m pixel size after geometric correction and similar spectral bands for both satellites were selected to create a similar base for these multi-sensor data. Maximum Likelihood (MLC) and Support Vector Machin...
2018
The purpose of this research work is to compare hyperspectral and multispectral imagery to discriminating land-cover classes by k-nearest neighbor algorithm (KNN) supervised classification with migrating means clustering unsupervised classification (MMC) method and in last develop spectral library. We used Earth Observing-1 (EO-1) Hyperion hyperspectral data to Landsat 8 Operational Land Imager (OLI) and Advance Land Imager (ALI) multispectral data. Results indicate that KNN (95, 94, 88 overall accuracy and .91, .89, .85 kappa coefficient for Hyp, ALI, OLI respectively) shows better results than unsupervised classification (93, 90, 84 overall accuracy and .89, .87, .81 kappa coefficient for Hyp, ALI, OLI respectively). In addition, it is demonstrated that the hyperspectral satellite image provides more accurate classification results than those extracted from the multispectral satellite image. The higher classification accuracy by KNN supervised was attributed principally to the abi...
IOP Conference Series: Earth and Environmental Science, 2019
Land cover in urban areas can be detected through surveys or using high-resolution imagery with a better accuracy. Especially if the need related to land cover information regionally in the period of the nineties that require the availability of data and unavailability of high resolution images. Therefore, images with intermediate spatial resolution are still required. However, the use of medium-resolution images such as Landsat is constrained by the presence of mixed pixels that cause land cover in urban areas to vary. The mixed pixel will be the source of error in the multispectral classification process, so it takes analysis up to the subpixel level. The need for information up to the subpixel level for ground cover detection can be obtained through the Linear Spectral Mixture Analysis method, where one pixel in the Landsat image in this study will be separated into four endmember, ie vegetation, impervious surface, bare soil, and water. These four endmembers are assumed to repre...