Comparison of Sentinel-2 and Landsat-8 OLI satellite images vs. high spatial resolution images (MIVIS and WorldView-2) for mapping Posidonia oceanica meadows

2018 ◽  
Author(s):  
Pasquale Lanera ◽  
Luigi Dattola ◽  
Francesco Sante Rende ◽  
Rossella Di Mento ◽  
Rocco Dominici ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Posidonia Oceanica ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sentinel 2

scholarly journals Modeling spatial structure of thermokarst lake fields in permafrost of Western Siberia based on satellite images

2019 ◽  
Vol 19 (1) ◽  
pp. 1-10
Author(s):  
Vladimir Yu. Polishchuk ◽  
Ildar N. Muratov ◽  
Yury M. Polishchuk
Keyword(s):  
Spatial Structure ◽  
Size Distribution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Western Siberia ◽  
High Spatial Resolution ◽  
The Arctic ◽  
Landsat 8 ◽  
Small Lakes ◽  
Thermokarst Lakes

Deciphering the satellite images of medium and high spatial resolution of the northern territories of Western Siberia has been carried out using geoinformation system ArcGIS 10.3. Images of medium resolution Landsat-8 and high resolution Kanopus-V were used. Kanopus-V images alluded to determine the number and areas of small lakes, which are considered as intensive sources of methane emission into the atmosphere from thermokarst lakes. Data on the spatial characteristics of thermokarst lakes were obtained. Based on the integration of images of medium and high spatial resolution, a synthesized histogram of the distribution of lakes in a wide range of sizes was constructed, taking into account small lakes. The obtained histogram was approximated by a lognormal distribution law by the Pearson criterion with a probability of 0.99. Based on the geo-simulation approach, a new model of the spatial structure of the fields of thermokarst lakes is presented, taking into account the lognormal law of the lake size-distribution. Algorithms for modeling the spatial structure of the fields of thermokarst lakes are described. An example of modeling the field of thermokarst lakes with a lognormal law of their size-distribution is given. The practical applicability of the previously developed model with an exponential distribution of lakes in size, based on data from Landsat images, has been experimentally confirmed. The results can be used to obtain predictions of the dynamics of methane emissions from the thermokarst lakes of the Arctic zone of Northern Eurasia for the coming decades in the context of climate changes.

scholarly journals Evaluation of Landsat 8 OLI/TIRS Level-2 and Sentinel 2 Level-1C Fusion Techniques Intended for Image Segmentation of Archaeological Landscapes and Proxies

2020 ◽  
Vol 12 (3) ◽  
pp. 579
Author(s):  
Athos Agapiou
Keyword(s):  
Spatial Resolution ◽  
Near Infrared ◽  
Archaeological Site ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Object Based Image Analysis ◽  
Segmentation Process ◽  
Object Based ◽  
Level 2 ◽  
Sentinel 2

The use of medium resolution, open access, and freely distributed satellite images, such as those of Landsat, is still understudied in the domain of archaeological research, mainly due to restrictions of spatial resolution. This investigation aims to showcase how the synergistic use of Landsat and Sentinel optical sensors can efficiently support archaeological research through object-based image analysis (OBIA), a relatively new scientific trend, as highlighted in the relevant literature, in the domain of remote sensing archaeology. Initially, the fusion of a 30 m spatial resolution Landsat 8 OLI/TIRS Level-2 and a 10 m spatial resolution Sentinel 2 Level-1C optical images, over the archaeological site of “Nea Paphos” in Cyprus, are evaluated in order to improve the spatial resolution of the Landsat image. At this step, various known fusion models are implemented and evaluated, namely Gram–Schmidt, Brovey, principal component analysis (PCA), and hue-saturation-value (HSV) algorithms. In addition, all four 10 m available spectral bands of the Sentinel 2 sensor, namely the blue, green, red, and near-infrared bands (Bands 2 to 4 and Band 8, respectively) were assessed for each of the different fusion models. On the basis of these findings, the next step of the study, focused on the image segmentation process, through the evaluation of different scale factors. The segmentation process is an important step moving from pixel-based to object-based image analysis. The overall results show that the Gram–Schmidt fusion method based on the near-infrared band of the Sentinel 2 (Band 8) at a range of scale factor segmentation to 70 are the optimum parameters for the detection of standing visible monuments, monitoring excavated areas, and detecting buried archaeological remains, without any significant spectral distortion of the original Landsat image. The new 10 m fused Landsat 8 image provides further spatial details of the archaeological site and depicts, through the segmentation process, important details within the landscape under examination.

scholarly journals Modeling spatial structure of thermokarst lake fields in permafrost of Western Siberia based on satellite images

2019 ◽  
Vol 19 (1) ◽  
pp. 1-10
Author(s):  
Vladimir Yu Polishchuk ◽  
Ildar N Muratov ◽  
Yury M Polishchuk
Keyword(s):  
Spatial Structure ◽  
Size Distribution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Western Siberia ◽  
High Spatial Resolution ◽  
The Arctic ◽  
Landsat 8 ◽  
Small Lakes ◽  
Thermokarst Lakes

Deciphering the satellite images of medium and high spatial resolution of the northern territories of Western Siberia has been carried out using geoinformation system ArcGIS 10.3. Images of medium resolution Landsat-8 and high resolution Kanopus-V were used. Kanopus-V images alluded to determine the number and areas of small lakes, which are considered as intensive sources of methane emission into the atmosphere from thermokarst lakes. Data on the spatial characteristics of thermokarst lakes were obtained. Based on the integration of images of medium and high spatial resolution, a synthesized histogram of the distribution of lakes in a wide range of sizes was constructed, taking into account small lakes. The obtained histogram was approximated by a lognormal distribution law by the Pearson criterion with a probability of 0.99. Based on the geo-simulation approach, a new model of the spatial structure of the fields of thermokarst lakes is presented, taking into account the lognormal law of the lake size-distribution. Algorithms for modeling the spatial structure of the fields of thermokarst lakes are described. An example of modeling the field of thermokarst lakes with a lognormal law of their size-distribution is given. The practical applicability of the previously developed model with an exponential distribution of lakes in size, based on data from Landsat images, has been experimentally confirmed. The results can be used to obtain predictions of the dynamics of methane emissions from the thermokarst lakes of the Arctic zone of Northern Eurasia for the coming decades in the context of climate changes.

scholarly journals Dual-satellite (Sentinel-2 and Landsat 8) remote sensing of supraglacial lakes in Greenland

2018 ◽  
Author(s):  
Andrew G. Williamson ◽  
Alison F. Banwell ◽  
Ian C. Willis ◽  
Neil S. Arnold
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
Greenland Ice Sheet ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Supraglacial Lakes ◽  
Dual Sensor ◽  
Sentinel 2

Abstract. Although remote sensing is commonly used to monitor supraglacial lakes on the Greenland Ice Sheet, most satellite records must trade-off high spatial resolution for high temporal resolution (e.g. MODIS) or vice versa (e.g. Landsat). Here, we overcome this issue by developing and applying a dual-sensor method that can monitor changes to lake areas and volumes at high spatial resolution (10–30 m) with a frequent revisit time (~ 3 days). We achieve this by mosaicking imagery from the Landsat 8 OLI with imagery from the recently launched Sentinel-2 MSI for a ~ 12 000 km2 area of West Greenland in summer 2016. First, we validate a physically based method for calculating lake depths with Sentinel-2 by comparing measurements against those derived from the available contemporaneous Landsat 8 imagery; we find close correspondence between the two sets of values (R2 = 0.841; RMSE = 0.555 m). This provides us with the methodological basis for automatically calculating lake areas, depths and volumes from all available Landsat 8 and Sentinel-2 images. These automatic methods are incorporated into an algorithm for Fully Automated Supraglacial lake Tracking at Enhanced Resolution (FASTER). The FASTER algorithm produces time series showing lake evolution during the 2016 melt season, including automated rapid (≤ 4 day) lake-drainage identification. With the dual Sentinel-2-Landsat 8 record, we identify 184 rapidly draining lakes, many more than identified with either imagery collection alone (93 with Sentinel-2; 66 with Landsat 8), due to their inferior temporal resolution, or would be possible with MODIS, due to its omission of small lakes 

scholarly journals Tidal Correction Effects Analysis on Shoreline Mapping in Jepara Regency

2018 ◽  
Vol 2 (2) ◽  
pp. 145-151
Author(s):  
Arief Wicaksono ◽  
Pramaditya Wicaksono ◽  
Nurul Khakhim ◽  
Nur Mohammad Farda ◽  
Muh Aris Marfai
Keyword(s):  
Spatial Resolution ◽  
Reference Data ◽  
High Spatial Resolution ◽  
Acquisition Time ◽  
Spatial And Temporal Variability ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Shoreline Mapping ◽  
Tidal Correction ◽  
High Spatial Resolution Imagery

The existence of high-spatial resolution imagery that are now available free by Planet Labs opens up opportunities in detailed scale mapping research, both as basic data and as reference data for geometry accuracy assessment. However, the use of several satellite sensors types with different recording times is the biggest obstacle in the use of high spatial resolution imagery as reference data because the shoreline instantaneous imaging at the data acquisition time does not consider the spatial and temporal variability of the shoreline boundaries. The purpose of this study was to analyze the effect of tidal correction on shoreline mapping in Jepara Regency using Landsat 8 OLI imagery in 2018.The effect of tidal correction analysis is done by comparing the position of the shoreline corrected by tides with the shoreline that is not corrected for tides. The influence of tidal correction is marked by differences in the position of the two shorelines. Shoreline shift calculation when there is a difference in tidal conditions between the test shoreline and the reference shoreline is carried out using the theory of right triangle (also called as one-line shift method).Based on the analysis of tidal correction effects, it is known that the shift in shoreline position after tidal correction varies from 0.21 m to 1.8 m, the value does not exceed one pixel of the PlanetScope image (3 m) so that tidal correction does not needs to be done because the effect is insignificant and undetectable on PlanetScope imagery. Keywords: tidal correction, shoreline, Planetscope, Landsat 8 OLI, Jepara

scholarly journals The Utility of the Upcoming HyspIRI’s Simulated Spectral Settings in Detecting Maize Gray Leafy Spot in Relation to Sentinel-2 MSI, VENµS, and Landsat 8 OLI Sensors

Agronomy ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 846
Author(s):  
Mbulisi Sibanda ◽  
Onisimo Mutanga ◽  
Timothy Dube ◽  
John Odindi ◽  
Paramu L. Mafongoya
Keyword(s):  
Food Security ◽  
Precision Agriculture ◽  
Leaf Spot ◽  
Gray Leaf Spot ◽  
Economic Losses ◽  
Leaf Spot Disease ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Maize Crop ◽  
Sentinel 2

Considering the high maize yield loses caused by incidences of disease, as well as incomprehensive monitoring initiatives in crop farming, there is a need for spatially explicit, cost-effective, and consistent approaches for monitoring, as well as for forecasting, food-crop diseases, such as maize Gray Leaf Spot. Such approaches are valuable in reducing the associated economic losses while fostering food security. In this study, we sought to investigate the utility of the forthcoming HyspIRI sensor in detecting disease progression of Maize Gray Leaf Spot infestation in relation to the Sentinel-2 MSI and Landsat 8 OLI spectral configurations simulated using proximally sensed data. Healthy, intermediate, and severe categories of maize crop infections by the Gray Leaf Spot disease were discriminated based on partial least squares–discriminant analysis (PLS-DA) algorithm. Comparatively, the results show that the HyspIRI’s simulated spectral settings slightly performed better than those of Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor. HyspIRI exhibited an overall accuracy of 0.98 compared to 0.95, 0.93, and 0.89, which were exhibited by Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor sensors, respectively. Furthermore, the results showed that the visible section, red-edge, and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray Leaf Spot infections. These findings underscore the potential value of the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop-disease epidemics, which are necessary to ensure food security.

Sign in / Sign up

Export Citation Format

Share Document