Modelling reservoir turbidity from medium resolution Sentinel-2A/MSI and Landsat-8/OLI satellite imagery

2020 ◽  
Author(s):  
Yashon O. Ouma
Keyword(s):  
Satellite Imagery ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Medium Resolution ◽  
Sentinel 2A

scholarly journals A Novel GIS-Based Approach for Automated Detection of Nearshore Sandbar Morphological Characteristics in Optical Satellite Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2233
Author(s):  
Rasa Janušaitė ◽  
Laurynas Jukna ◽  
Darius Jarmalavičius ◽  
Donatas Pupienis ◽  
Gintautas Žilinskas
Keyword(s):  
Satellite Imagery ◽  
Morphological Characteristics ◽  
Unsupervised Classification ◽  
Landsat 8 ◽  
Multi Scale ◽  
Medium Resolution ◽  
Wide Range ◽  
Position Index ◽  
Local Relief ◽  
Length Width

Satellite remote sensing is a valuable tool for coastal management, enabling the possibility to repeatedly observe nearshore sandbars. However, a lack of methodological approaches for sandbar detection prevents the wider use of satellite data in sandbar studies. In this paper, a novel fully automated approach to extract nearshore sandbars in high–medium-resolution satellite imagery using a GIS-based algorithm is proposed. The method is composed of a multi-step workflow providing a wide range of data with morphological nearshore characteristics, which include nearshore local relief, extracted sandbars, their crests and shoreline. The proposed processing chain involves a combination of spectral indices, ISODATA unsupervised classification, multi-scale Relative Bathymetric Position Index (RBPI), criteria-based selection operations, spatial statistics and filtering. The algorithm has been tested with 145 dates of PlanetScope and RapidEye imagery using a case study of the complex multiple sandbar system on the Curonian Spit coast, Baltic Sea. The comparison of results against 4 years of in situ bathymetric surveys shows a strong agreement between measured and derived sandbar crest positions (R2 = 0.999 and 0.997) with an average RMSE of 5.8 and 7 m for PlanetScope and RapidEye sensors, respectively. The accuracy of the proposed approach implies its feasibility to study inter-annual and seasonal sandbar behaviour and short-term changes related to high-impact events. Algorithm-provided outputs enable the possibility to evaluate a range of sandbar characteristics such as distance from shoreline, length, width, count or shape at a relevant spatiotemporal scale. The design of the method determines its compatibility with most sandbar morphologies and suitability to other sandy nearshores. Tests of the described technique with Sentinel-2 MSI and Landsat-8 OLI data show that it can be applied to publicly available medium resolution satellite imagery of other sensors.

Detecting Burn Severity across Mediterranean Forest Types by Coupling Medium-Spatial Resolution Satellite Imagery and Field Data

2020 ◽  
Vol 12 (4) ◽  
pp. 741 ◽  
Author(s):  
Luigi Saulino ◽  
Angelo Rita ◽  
Antonello Migliozzi ◽  
Carmine Maffei ◽  
Emilia Allevato ◽  
...  
Keyword(s):  
Fire Effects ◽  
Mediterranean Forest ◽  
Burn Severity ◽  
Landsat 8 ◽  
Mediterranean Countries ◽  
Medium Resolution ◽  
Severity Levels ◽  
The Mediterranean ◽  
Sentinel 2A ◽  
Normalized Burn Ratio

In Mediterranean countries, in the year 2017, extensive surfaces of forests were damaged by wildfires. In the Vesuvius National Park, multiple summer wildfires burned 88% of the Mediterranean forest. This unprecedented event in an environmentally vulnerable area suggests conducting spatial assessment of the mixed-severity fire effects for identifying priority areas and support decision-making in post-fire restoration. The main objective of this study was to compare the ability of the delta Normalized Burn Ratio (dNBR) spectral index obtained from Landsat-8 and Sentinel-2A satellites in retrieving burn severity levels. Burn severity levels experienced by the Mediterranean forest communities were defined by using two quali-quantitative field-based composite burn indices (FBIs), namely the Composite Burn Index (CBI), its geometrically modified version CBI (GeoCBI), and the dNBR derived from the two medium-resolution multispectral remote sensors. The accuracy of the burn severity map produced by using the dNBR thresholds developed by Key and Benson (2006) was first evaluated. We found very low agreement (0.15 < K < 0.21) between the burn severity class obtained from field-based indices (CBI and GeoCBI) and satellite-derived metrics (dNBR) from both Landsat-8 and Sentinel-2A. Therefore, the most appropriate dNBR thresholds were rebuilt by analyzing the relationships between two field-based (CBI and GeoCBI) and dNBR from Landsat-8 and Sentinel-2A. By regressing alternatively FBIs and dNBRs, a slightly stronger relationship between GeoCBI and dNBR metrics obtained from the Sentinel-2A remote sensor (R2 = 0.69) was found. The regressed dNBR thresholds showed moderately high classification accuracy (K = 0.77, OA = 83%) for Sentinel-2A, suggesting the appropriateness of dNBR-Sentinel 2A in assessing mixed-severity Mediterranean wildfires. Our results suggest that there is no single set of dNBR thresholds that are appropriate for all burnt biomes, especially for the low levels of burn severity, as biotic factors could affect satellite observations.

scholarly journals Urban thermal micro-mapping using satellite imagery and ground-truth measurements: Kyiv city area case study

2019 ◽  
pp. 40-48
Author(s):  
Iryna Piestova ◽  
Mykola Lubskyi ◽  
Mykhailo Svideniuk ◽  
Stanislav Golubov ◽  
Oleksandr Laptiev
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Thermal Conditions ◽  
Ground Truth ◽  
Bare Soil ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
City Area ◽  
Thermal Emissivity ◽  
Sentinel 2A

The aim of this research is to enhance approaches existing for the assessment of cities thermal conditions under climate change impact by using multispectral satellite data for Kyiv city area. This paper describes the method and results of the Earth’s surface temperature (LST) and thermal emissivity calculation. Particularly, the thermal distribution was estimated based on spectral densities according to Planck’s law for “grey bodies” by using the Landsat-8 TIRS and Sentinel-2 MSI satellite imagery. Furthermore, the result was calibrated by ground data collected during the ground-truth measurements of the typical city surfaces temperature and thermal emissivity. The spatial resolution of the LST images obtained was enhanced by using the approach of subpixel processing, that is the pairs of invariant images shifted with subpixel accuracy. As a result, such an approach allowed to enhance the spatial resolution of the image up 46%, which is much higher than the potential performance of the thermal imaging sensors existing. The interrelation between the Earth’s surface type and the temperature was revealed by the results of the Sentinel-2A MSI image of 21 August 2017 supervised classification. Thus, the image was divided into the six major classes of the urban environment: building’s rooftops, roads surface, bare soil, grass, wood, and water. As a result, surfaces with vegetation much more cool next to artificial ones. The time-series analysis of 18 thermal images (Landsat TM and Landsat-8 TIRS) of Kyiv for the period from 6 Jun 1985 till 1 June 2018 was done for spatiotemporal changes investigation. Therefore, the sites of the LST thermal anomalies caused by landscape changes were developed. Among them are the sites of increased LST where thw “Olimpiyskiy” national sport center and adjacent parking was built and the site of decreased LST where the tram depot was liquidated and the territory was flooded.

scholarly journals The estimation model of mangrove forest biomass using a medium resolution satellite imagery in the concession area of forest consession company in West Kalimantan

2016 ◽  
Vol 6 (2) ◽  
pp. 69-81
Author(s):  
SENDI YUSANDI ◽  
I NENGAH SURATI JAYA
Keyword(s):  
Satellite Imagery ◽  
Mangrove Forest ◽  
Forest Biomass ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Mean Deviation ◽  
Landsat 8 ◽  
Estimation Model ◽  
West Kalimantan ◽  
Medium Resolution

Yusandi S, Jaya INS. 2016. The estimation model of mangrove forest biomass using a medium resolution satellite imagery in the concession area of forest consession company in West Kalimantan. Bonorowo Wetlands 6: 69-81. Mangrove forest is one of forest ecosystem types having the highest carbon stock in the tropics. Mangrove forests have a good assimilation capability with their environmental elements as well as have a high capability on carbon sequestration. Up to now, however, the availability of data and information on carbon storage, especially on tree biomass content of mangrove is still limited. Conventionally, an accurate estimation of biomass could be obtained from terrestrial measurements, but those methods costly and time-consuming. This study offered an alternative solution to overcome these limitations by using remote sensing technology, i.e., by using the moderate resolution imageries Landsat 8. The objective of this study is to formulate the biomass estimation model using medium resolution satellite imagery, as well as to develop a biomass distribution map based on the selected model. The study found that the NDVI has a considerably high correlation coefficient of larger than > 0.7071 with the stand biomass. On the basis of the values of aggregation deviation, mean deviation, bias, RMSE, χ², R², and s, the best model for estimating the mangrove stand biomass is B=0.00023404 with the R² value of 77.1%. In general, the concession area of BSN Group (PT Kandelia Alam Semesta and PT Bina Ovivipari) have the potential of biomass ranging from 45 to 100 ton per ha.

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