Land cover classification in Romanian Carpathians and Subcarpathians using multi-date Sentinel-2 remote sensing imagery

Rujoiu-Mare, Marina-Ramona; Olariu, Bogdan; Mihai, Bogdan; Nistor, Constantin; Săvulescu, Ionuț

doi:10.1080/22797254.2017.1365570

Cited by 44 publications

(31 citation statements)

References 24 publications

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“…The pixel-based land cover/use classification is one of the most common classification approaches applied to Sentinel-2 [31,81,82]. The literature shows that RF is the most common classifiers used for pixel-based approach (Table 3).…”

Section: Pixel-based Image Analysismentioning

confidence: 99%

Sentinel-2 Data for Land Cover/Use Mapping: A Review

et al. 2020

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The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

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Section: Pixel-based Image Analysismentioning

confidence: 99%

Sentinel-2 Data for Land Cover/Use Mapping: A Review

et al. 2020

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“…The spatial resolution was 10 m in both cases of the TCW and TCG. The spectral bands with 20-m resolution were resampled to 10 m with the nearest neighbor method (the resampled pixel grid was identical to the original 10-m bands) [54,55]. The tasseled cap transformation is the conversion of the values in a set of bands; thus, it transforms the image data to a special coordinate system with a set of orthogonal axes.…”

Section: Ts Analysismentioning

confidence: 99%

Sentinel-2 Data in an Evaluation of the Impact of the Disturbances on Forest Vegetation

et al. 2020

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In this article, we investigated the detection of forest vegetation changes during the period of 2017 to 2019 in the Low Tatras National Park (Slovakia) and the Sumava National Park (Czechia) using Sentinel-2 data. The evaluation was based on a time-series analysis using selected vegetation indices. The case studies represented five different areas according to the type of the forest vegetation degradation (one with bark beetle calamity, two areas with forest recovery mode after a bark beetle calamity, and two areas without significant disturbances). The values of the trajectories of the vegetation indices (normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI)) and the orthogonal indices (tasseled cap greenness (TCG) and tasseled cap wetness (TCW)) were analyzed and validated by in situ data and aerial photographs. The results confirm the abilities of the NDVI, the NDMI and the TCW to distinguish disturbed and undisturbed areas. The NDMI vegetation index was particularly useful for the detection of the disturbed forest and forest recovery after bark beetle outbreaks and provided relevant information regarding the health of the forest (the individual stages of the disturbances and recovery mode). On the contrary, the TCG index demonstrated only limited abilities. The TCG could distinguish healthy forest and the gray-attack disturbance phase; however, it was difficult to use this index for detecting different recovery phases and to distinguish recovery phases from healthy forest. The areas affected by the disturbances had lower values of NDVI and NDMI indices (NDVI quartile range Q2–Q3: 0.63–0.71; NDMI Q2–Q3: 0.10–0.19) and the TCW index had negative values (Q2–Q3: −0.06–−0.05)). The analysis was performed with a cloud-based tool—Sentinel Hub. Cloud-based technologies have brought a new dimension in the processing and analysis of satellite data and allowed satellite data to be brought to end-users in the forestry sector. The Copernicus program and its data from Sentinel missions have evoked new opportunities in the application of satellite data. The usage of Sentinel-2 data in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability, distribution, and well-designed spectral, temporal, and spatial resolution of the Sentinel-2 data for monitoring forest ecosystems.

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“…• Compare classification results from other supervised methods, such as Maximum Likelihood, Mahalanobis Distance and Minimum Distance, that do not require training. • Run SVM classification on a time-series of images, as suggested by Rujoiu-Mare et al (2017). This has the potential to improve the accuracy of the classification.…”

Section: Discussionmentioning

confidence: 99%

“…Qiu, He, Yin and Liao (2017) showed that Vegetation Red Edge bands 5 (703.9 µm) and 6 (740 µm) were the most beneficial in improving the classification of vegetation land-cover classes, particularly agriculture. Rujoiu-Mare, Olariu, Mihai, Nistor and Săvulescu (2017) found that increasing the number of spectral bands as input to image classification improved the separability of land-cover classes.…”

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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Land cover classification in Romanian Carpathians and Subcarpathians using multi-date Sentinel-2 remote sensing imagery

Cited by 44 publications

References 24 publications

Sentinel-2 Data for Land Cover/Use Mapping: A Review

Sentinel-2 Data for Land Cover/Use Mapping: A Review

Sentinel-2 Data in an Evaluation of the Impact of the Disturbances on Forest Vegetation

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

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