scholarly journals Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review

2020 ◽  
Vol 12 (18) ◽  
pp. 3062 ◽  
Author(s):  
Michel E. D. Chaves ◽  
Michelle C. A. Picoli ◽  
Ieda D. Sanches
Keyword(s):  
Systematic Review ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Near Infrared ◽  
Data Cube ◽  
Optical Data ◽  
Landsat 8 ◽  
Detailed Knowledge ◽  
Sentinel 2

Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land cover (LULC) provide a new perspective in remote sensing data analysis. Jointly, these sources permit researchers to improve operational classification and change detection, guiding better reasoning about landscape and intrinsic processes, as deforestation and agricultural expansion. However, the results of their applications have not yet been synthesized in order to provide coherent guidance on the effect of their applications in different classification processes, as well as to identify promising approaches and issues which affect classification performance. In this systematic review, we present trends, potentialities, challenges, actual gaps, and future possibilities for the use of L8/OLI and S2/MSI for LULC mapping and change detection. In particular, we highlight the possibility of using medium-resolution (Landsat-like, 10–30 m) time series and multispectral optical data provided by the harmonization between these sensors and data cube architectures for analysis-ready data that are permeated by publicizations, open data policies, and open science principles. We also reinforce the potential for exploring more spectral bands combinations, especially by using the three Red-edge and the two Near Infrared and Shortwave Infrared bands of S2/MSI, to calculate vegetation indices more sensitive to phenological variations that were less frequently applied for a long time, but have turned on since the S2/MSI mission. Summarizing peer-reviewed papers can guide the scientific community to the use of L8/OLI and S2/MSI data, which enable detailed knowledge on LULC mapping and change detection in different landscapes, especially in agricultural and natural vegetation scenarios.

scholarly journals Transboundary analysis of the land cover in Razdolnaya river basin

2021 ◽  
pp. 70-77
Author(s):  
Т.К. МУЗЫЧЕНКО ◽  
М.Н. МАСЛОВА
Keyword(s):  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Rice Fields ◽  
Landsat 8 ◽  
Agricultural Lands ◽  
Transboundary River ◽  
Russian Part ◽  
Land Use Structure ◽  
Sentinel 2

В статье рассмотрено пространственное распределение типов земель в пределах трансграничного бассейна р. Раздольная. На основе дешифрирования космических снимков Sentinel-2 и Landsat 8 составлена карта пространственного распределения типов земель по состоянию на 2019 г. Исходя из геоэкологической классификации ландшафтов В.А. Николаева в данной работе было выделено 12 типов земель: используемые и неиспользуемые сельскохозяйственные земли, используемые и неиспользуемые рисовые поля, карьеры, леса, лесопосадки, рубки, луга, застроенные земли, водные объекты, а также кустарники и редколесья. Представлены абсолютные и относительные площади для каждого типа земель по трансграничному бассейну в целом, а также отдельно для его российской и китайской частей. По результатам дешифрирования данных дистанционного зондирования установлено, что российская и китайская части бассейна р. Раздольная имеют существенные трансграничные различия в структуре земель. На российской части бассейна лесами покрыто чуть более половины площади, но при этом значительные площади занимают сельскохозяйственные земли и луга. В некоторых местах луга и сельскохозяйственные земли преобладают в большей степени, чем леса. На китайской части лесные территории доминируют над другими типами земель. Сельскохозяйственные земли и луга образуют узкие и длинные полосы и имеют более мозаичное распространение, чем на российской части. Здесь заметно меньше площади застроенных земель, а площади рубок и лесопосадок больше, чем на российской части. Площади карьеров примерно равны в обеих частях бассейна. The transboundary Razdolnaya river basin is nearly evenly split up between Primorsky Krai of Russian Federation and Heilongjiang and Jilin provinces of People’s Republic of China. The Chinese and the Russian parts of the transboundary river have developed independently of each other. Therefore, the two have a different land cover and land use structure. The analysis of land cover and land use structure is of utmost importance for the understanding the modern state of land development and the possibilities of its future development. Using the remote sensing data, such as Sentinel-2 and Landsat 8 satellite imagery, the land cover and land use map of the Razdolnaya transboundary river basin for 2019 has been composed by means of the ArcMap 10.5 software package. According to V.A. Nikolaev’s geoecological classification of landscapes, we have identified 12 land types: forests, meadows, shrubs and woodlands, agricultural lands, unused agricultural lands, rice fields, unused rice fields, built-up areas, reforestation lands, logging, quarries, and bodies of water. We have provided area coverage for each type of land of the whole transboundary basin, and for the Russian and Chinese parts. According to the results of computer-aided visual deciphering and automatic deciphering, forests are the most common land use type in the basin. In the Chinese part of the basin, forests dominate over the other types of land. Agricultural lands and meadows have assumed narrow and linear shapes. Built-up areas have less coverage here than in the Russian part of the basin. However, the coverage of logging and reforestation lands is considerably larger than in the Russian part of the basin. In the Russian part of the basin, forests co-dominate with the agricultural lands and meadows. In some areas of this part of the basin forests disappear almost completely. The Russian part of the basin also has the larger coverage of shrubs and woodlands, unused agricultural lands, rice fields and unused rice fields. The coverage of quarries is roughly equal in both parts of the basin.

scholarly journals Urban Bare Land Classification Using NDBaI Index Based on Combination of Sentinel 2 MSI and Landsat 8 Multiresolution Images

2020 ◽  
Vol 36 (2) ◽  
Author(s):  
Trinh Le Hung
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Image ◽  
Urban Land ◽  
Landsat 8 ◽  
Land Classification ◽  
Infrared Band ◽  
Urban Land Cover ◽  
Bare Land ◽  
Sentinel 2

The classification of urban land cover/land use is a difficult task due to the complexity in the structure of the urban surface. This paper presents the method of combining of Sentinel 2 MSI and Landsat 8 multi-resolution satellite image data for urban bare land classification based on NDBaI index. Two images of Sentinel 2 and Landsat 8 acquired closely together, were used to calculate the NDBaI index, in which sortware infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) of Landsat 8 image were used to improve the spatial resolution of NDBaI index. The results obtained from two experimental areas showed that, the total accuracy of classifying bare land from the NDBaI index which calculated by the proposed method increased by about 6% compared to the method using the NDBaI index, which is calculated using only Landsat 8 data. The results obtained in this study contribute to improving the efficiency of using free remote sensing data in urban land cover/land use classification.

scholarly journals Land cover classification using landsat-8 optical data and supervised classifiers

2018 ◽  
Vol 7 (2.17) ◽  
pp. 101
Author(s):  
K V. Ramana Rao ◽  
Prof P. Rajesh Kumar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Classification ◽  
Optical Data ◽  
Landsat 8 ◽  
Barren Land ◽  
Detection Techniques ◽  
Time Periods ◽  
Classification Information ◽  
Landsat Satellite

Land use and land cover information of an area has got importance in various aspects mainly because of various development activities that are taking place in every part of the world. Various satellite sensors are providing the required data collected by remote sensing techniques in the form of images using which the land use land cover information can be analyzed.  Constistency of Landsat satellite is illustrated with two time periods such as Operational Land Imager (OLI) of 2013 and consecutive 2014 procured by earth explorer with quantified changes for the same period in visakhapatnam of hudhud cyclone. Since this city is consisting of mainly urban, vegetation, few water bodies, some area of agriculture and barren,five classes have been chosen from the study area. The results indicate that due to the hudhud event some changes took place.  vegetation and built-up land have been increased by An increase of 19.1% (6.3 km2) and 11% (5.36 km2) has been observed in the case of vegetation and built up area  where as a decrease of 1.2% (4.06 km2), 6.1% (1.70 km2) and 1.2% (0.72 km2) has been observed in the case of  agriculture, barren land, and water body respectively. With the help of available satellite imagery belonging to the same area and of different time periods along with the  change detection techniques landscape dynamics have been analyzed. Using various classification algorithms along with the data available from the satellite sensor the land use and land cover classification information of the study area has been obtained. The maximum likelihood algorithm provided better results compared to other classification techniques and the accuracy achieved with this algorithm is 99.930% (overall accuracy) and 0.999 (Kappa coefficient).  

scholarly journals A development of the Enhanced Built-up and Bareness Index (EBBI) based on combination of multi-resolution Landsat 8 and Sentinel 2 MSI images

2021 ◽  
Vol 62 (1) ◽  
pp. 1-9
Author(s):  
Hung Le Trinh ◽  
Ha Thu Thi Le ◽  
Loc Duc Le ◽  
Long Thanh Nguyen ◽  
Keyword(s):  
Land Cover ◽  
Satellite Imagery ◽  
Near Infrared ◽  
Urban Land ◽  
Urban Land Use ◽  
Landsat 8 ◽  
Infrared Band ◽  
Bare Land ◽  
Sentinel 2

Classification of built-up land and bare land on remote sensing images is a very difficult problem due to the complexity of the urban land cover. Several urban indices have been proposed to improve the accuracy in classifying urban land use/land cover from optical satellite imagery. This paper presents an development of the EBBI (Enhanced Built-up and Bareness Index) index based on the combination of Landsat 8 and Sentinel 2 multi-resolution satellite imagery. Near infrared band (band 8a), short wave infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) Landsat 8 image were used to calculate EBBI index. The results obtained show that the combination of Landsat 8 and Sentinel 2 satellite images improves the spatial resolution of EBBI index image, thereby improving the accuracy of classification of bare land and built-up land by about 5% compared with the case using only Landsat 8 images.

scholarly journals Monitoring Land Use and Land Cover Change of Forest Ecosystems of Shendurney Wildlife Sanctuary, Western Ghats, India

2021 ◽  
pp. 20-27
Author(s):  
H. Bilyaminu ◽  
P. Radhakrishnan ◽  
K. Vidyasagaran ◽  
K. Srinivasan
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Forest Ecosystem ◽  
Forest Ecosystems ◽  
Evergreen Forest ◽  
Land Cover Changes ◽  
Landsat 8 ◽  
Wildlife Sanctuary

Understanding forest degradation due to human and natural phenomena is crucial to conserving and managing remnant forest resources. However, forest ecosystem assessment over a large and remote area is usually complex and arduous. The present study on land use and land cover change detection of the Shendurney Wildlife Sanctuary forest ecosystems was carried out to utilize the potential application of remote sensing (RS) and geographic information system (GIS). Moreover, to understand the trend in the forest ecosystem changes. The supervised classification with Maximum Likelihood Algorithm and change detection comparison approach was employed to study the land use and land cover changes, using the Landsat Enhanced Thematic Mapper (ETM±) and Landsat 8 OLI-TIRS using data captured on July 01, 2001, and January 14, 2018. The study indicated the rigorous land cover changes. It showed a significant increase in the proportion of degraded forest with negligible gain in the proportion of evergreen forest from 21.31% in 2001 to 22.97% in 2018.  A substantial loss was also observed in moist deciduous from 27.11 % in 2001 to 17.23 % in 2018. The result of the current study indicated the degree of impacts on forests from the various activities of their surroundings. This study provides baseline information for planning and sustainable management decisions.

The urban areas classification methodology according to multi-zone images of Sentinel 2 and Landsat 8 (on the example of the city of Thanh Hoa, Vietnam)

2020 ◽  
Vol 956 (2) ◽  
pp. 40-49
Author(s):  
Le Hung Trinh ◽  
Dinh Sinh Mai ◽  
V.R. Zablotskii
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Satellite Images ◽  
Near Infrared ◽  
Landsat 8 ◽  
Land Use Classification ◽  
Infrared Band ◽  
The Status ◽  
The Impact ◽  
Sentinel 2

In recent years, land cover changes very quickly in urban areas due to the impact of population growth and socio-economic development. The authors present the method of land cover/land use classification based on the combination of Sentinel 2 and Landsat 8 multi-resolution satellite images. A middle infrared band (band 11), a near infrared (band 8) of Sentinel 2 image and a thermal infrared one (band 10) of Landsat 8 image were used to calculate EBBI (Enhanced Built-up and Barreness Index). The EBBI index and Sentinel 2 spectral bands with spatial resolution 10 m (band 2, 3, 4, 8) were used to classify the land cover. The obtained results showed that, the method of land cover classification based on combination of Sentinel 2 and Landsat 8 satellite images improves the overall accuracy by about 5 % compared with the one using only Sentinel 2 data. The results obtained at the study can be used for the management, assessment and monitoring the status and dynamics of land cover in urban areas.

scholarly journals PIXEL-BASED CLASSIFICATION ANALYSIS OF LAND USE LAND COVER USING SENTINEL-2 AND LANDSAT-8 DATA

2017 ◽  
Vol XLII-4/W6 ◽  
pp. 91-93 ◽  
Author(s):  
A. Sekertekin ◽  
A. M. Marangoz ◽  
H. Akcin
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Accuracy Assessment ◽  
Kappa Statistics ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Landsat 8 Oli ◽  
Sentinel 2

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.

scholarly journals SPATIOTEMPORAL CHANGE DETECTION USING LANDSAT IMAGERY: THE CASE STUDY OF KARACABEY FLOODED FOREST, BURSA, TURKEY

2017 ◽  
Vol IV-4/W4 ◽  
pp. 31-35 ◽  
Author(s):  
A. E. Akay ◽  
B. Gencal ◽  
İ. Taş
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Short Paper ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Spatiotemporal Change ◽  
Change Detection Analysis ◽  
Flooded Forest ◽  
Detection Analysis

This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant land use/land cover types, including forest, flooded forest, swamp, water, and other lands (i.e. agriculture, sand, roads, settlement, and open areas). The results indicated that there was increase in flooded forest, water, and other lands, while the cover of forest and swamp decreased.

scholarly journals Rapid Flood Mapping and Evaluation with a Supervised Classifier and Change Detection in Shouguang Using Sentinel-1 SAR and Sentinel-2 Optical Data

2020 ◽  
Vol 12 (13) ◽  
pp. 2073 ◽  
Author(s):  
Minmin Huang ◽  
Shuanggen Jin
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Early Stage ◽  
Optical Data ◽  
Hazard Evaluation ◽  
Flood Mapping ◽  
Special Importance ◽  
Sar Images ◽  
Water Index ◽  
Sentinel 2

Rapid flood mapping is crucial in hazard evaluation and forecasting, especially in the early stage of hazards. Synthetic aperture radar (SAR) images are able to penetrate clouds and heavy rainfall, which is of special importance for flood mapping. However, change detection is a key part and the threshold selection is very complex in flood mapping with SAR. In this paper, a novel approach is proposed to rapidly map flood regions and estimate the flood degree, avoiding the critical step of thresholding. It converts the change detection of thresholds to land cover backscatter classifications. Sentinel-1 SAR images are used to get the land cover backscatter classifications with the help of Sentinel-2 optical images using a supervised classifier. A pixel-based change detection is used for change detection. Backscatter characteristics and variation rules of different ground objects are essential prior knowledge for flood analysis. SAR image classifications of pre-flood and flooding periods both take the same input to make sense of the change detection between them. This method avoids the inaccuracy caused by a single threshold. A case study in Shouguang is tested by this new method, which is compared with the flood map extracted by Otsu thresholding and normalized difference water index (NDWI) methods. The results show that our approach can identify the flood beneath vegetation well. Moreover, all required data and data processing are simple, so it can be popularized in rapid flooding mapping in early disaster relief.

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