scholarly journals Evaluation of Satellite Imagery for Monitoring Pacific Walruses at a Large Coastal Haulout

2021 ◽  
Vol 13 (21) ◽  
pp. 4266
Author(s):  
Anthony S. Fischbach ◽  
David C. Douglas
Keyword(s):  
Satellite Imagery ◽  
Chukchi Sea ◽  
Late Summer ◽  
Visual Interpretation ◽  
Odobenus Rosmarus Divergens ◽  
Radar Imagery ◽  
Moderate Resolution ◽  
Satellite Sensors ◽  
Sar Imagery ◽  
Sentinel 2

Pacific walruses (Odobenus rosmarus divergens) are using coastal haulouts in the Chukchi Sea more often and in larger numbers to rest between foraging bouts in late summer and autumn in recent years, because climate warming has reduced availability of sea ice that historically had provided resting platforms near their preferred benthic feeding grounds. With greater numbers of walruses hauling out in large aggregations, new opportunities are presented for monitoring the population. Here we evaluate different types of satellite imagery for detecting and delineating the peripheries of walrus aggregations at a commonly used haulout near Point Lay, Alaska, in 2018–2020. We evaluated optical and radar imagery ranging in pixel resolutions from 40 m to ~1 m: specifically, optical imagery from Landsat, Sentinel-2, Planet Labs, and DigitalGlobe, and synthetic aperture radar (SAR) imagery from Sentinel-1 and TerraSAR-X. Three observers independently examined satellite images to detect walrus aggregations and digitized their peripheries using visual interpretation. We compared interpretations between observers and to high-resolution (~2 cm) ortho-corrected imagery collected by a small unoccupied aerial system (UAS). Roughly two-thirds of the time, clouds precluded clear optical views of the study area from satellite. SAR was unaffected by clouds (and darkness) and provided unambiguous signatures of walrus aggregations at the Point Lay haulout. Among imagery types with 4–10 m resolution, observers unanimously agreed on all detections of walruses, and attained an average 65% overlap (sd 12.0, n 100) in their delineations of aggregation boundaries. For imagery with ~1 m resolution, overlap agreement was higher (mean 85%, sd 3.0, n 11). We found that optical satellite sensors with moderate resolution and high revisitation rates, such as PlanetScope and Sentinel-2, demonstrated robust and repeatable qualities for monitoring walrus haulouts, but temporal gaps between observations due to clouds were common. SAR imagery also demonstrated robust capabilities for monitoring the Point Lay haulout, but more research is needed to evaluate SAR at haulouts with more complex local terrain and beach substrates.

scholarly journals A FUSION APPROACH FOR FLOOD MAPPING USING SENTINEL-1 AND SENTINEL-2 DATASETS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 641-648
Author(s):  
B. Tavus ◽  
S. Kocaman ◽  
H. A. Nefeslioglu ◽  
C. Gokceoglu
Keyword(s):  
Sample Selection ◽  
Training Sample ◽  
Optical Data ◽  
Flood Mapping ◽  
Visual Interpretation ◽  
Satellite Sensors ◽  
Flood Detection ◽  
High Level ◽  
Fusion Approach ◽  
Sentinel 2

Abstract. The frequency of flood events has increased in recent years most probably due to the climate change. Flood mapping is thus essential for flood modelling, hazard and risk analyses and can be performed by using the data of optical and microwave satellite sensors. Although optical imagery-based flood analysis methods have been often used for the flood assessments before, during and after the event; they have the limitation of cloud coverage. With the increasing temporal availability and spatial resolution of SAR (Synthetic Aperture Radar) satellite sensors, they became popular in data provision for flood detection. On the other hand, their processing may require high level of expertise and visual interpretation of the data is also difficult. In this study, a fusion approach for Sentinel-1 SAR and Sentinel-2 optical data for flood extent mapping was applied for the flood event occurred on August 8th, 2018, in Ordu Province of Turkey. The features obtained from Sentinel-1 and Sentinel-2 processing results were fused in random forest supervised classifier. The results show that Sentinel-2 optical data ease the training sample selection for the flooded areas. In addition, the settlement areas can be extracted from the optical data better. However, the Sentinel-2 data suffer from clouds which prevent from mapping of the full flood extent, which can be carried out with the Sentinel-1 data. Different feature combinations were evaluated and the results were assessed visually. The results are provided in this paper.

scholarly journals Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 312
Author(s):  
Miltiadis Iatrou ◽  
Christos Karydas ◽  
George Iatrou ◽  
Ioannis Pitsiorlas ◽  
Vassilis Aschonitis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nitrogen Concentration ◽  
Image Data ◽  
Leaf Nitrogen ◽  
Machine Learning Algorithms ◽  
Visual Interpretation ◽  
Demand Prediction ◽  
Nitrogen Levels ◽  
Radar Imagery ◽  
Sar Imagery

This research is an outcome of the R&D activities of Ecodevelopment S.A. (steadily supported by the Hellenic Agricultural Organization—Demeter) towards offering precision farming services to rice growers. Within this framework, a new methodology for topdressing nitrogen prediction was developed based on machine learning. Nitrogen is a key element in rice culture and its rational management can increase productivity, reduce costs, and prevent environmental impacts. A multi-source, multi-temporal, and multi-scale dataset was collected, including optical and radar imagery, soil data, and yield maps by monitoring a 110 ha pilot rice farm in Thessaloniki Plain, Greece, for four consecutive years. RapidEye imagery underwent image segmentation to delineate management zones (ancillary, visual interpretation of unmanned aerial system scenes was employed, too); Sentinel-1 (SAR) imagery was modelled with Computer Vision to detect inundated fields and (through this) indicate the exact growth stage of the crop; and Sentinel-2 image data were used to map leaf nitrogen concentration (LNC) exactly before topdressing applications. Several machine learning algorithms were configured to predict yield for various nitrogen levels, with the XGBoost model resulting in the highest accuracy. Finally, yield curves were used to select the nitrogen dose maximizing yield, which was thus recommended to the grower. Inundation mapping proved to be critical in the prediction process. Currently, Ecodevelopment S.A. is expanding the application of the new method in different study areas, with a view to further empower its generality and operationality.

Automatic extraction of land cover statistics from satellite imagery by deep learning1

2021 ◽  
pp. 1-17
Author(s):  
Eleonora Bernasconi ◽  
Fabrizio De Fausti ◽  
Francesco Pugliese ◽  
Monica Scannapieco ◽  
Diego Zardetto
Keyword(s):  
Land Cover ◽  
Satellite Imagery ◽  
Ground Truth ◽  
Careful Analysis ◽  
Production Costs ◽  
Moderate Resolution ◽  
Estimation System ◽  
Land Cover Maps ◽  
Sentinel 2 ◽  
Good Agreement

In this paper, we address the challenge of producing fully automated land cover estimates from satellite imagery through Deep Learning algorithms. We developed our system according to a tile-based, classify-and-count design. To implement the classification engine of the system, we adopted a cutting-edge Convolutional Neural Network model named Inception-V3, which we customized and trained for scene classification on the EuroSAT dataset. We tested and validated our system on two Sentinel-2 images representing quite different Italian territories (with an area of 751 km2 and 443 km2, respectively). Because no genuine ground-truth is available for the land cover of these sub-regional territories, we built a pseudo ground-truth by integrating land cover information from flagship European projects CORINE and LUCAS. A critical and careful analysis shows that our automatic land cover estimates are in good agreement with the pseudo ground-truth and offers extensive evidence of the remarkable segmentation ability of our system. The limits of our approach are also critically discussed in the paper and possible countermeasures are illustrated. When compared with traditional projects like CORINE and LUCAS, our automatic land cover estimation system exhibits three fundamental advantages: it can dramatically reduce production costs; it can allow delivering very timely and frequent land cover statistics; it can enable land cover estimation for very small territorial areas, well beyond the NUTS-2 level. As an additional outcome of land cover estimation, our system also automatically generates moderate resolution land cover maps that might be used in cartography projects as an agile first-level tool for map update or change detection purposes.

scholarly journals Status of Phenological Research Using Sentinel-2 Data: A Review

2020 ◽  
Vol 12 (17) ◽  
pp. 2760
Author(s):  
Gourav Misra ◽  
Fiona Cawkwell ◽  
Astrid Wingler
Keyword(s):  
Vegetation Index ◽  
Temporal Frequency ◽  
Short Wave ◽  
Current State ◽  
Red Edge ◽  
Natural Grasslands ◽  
Wide Range ◽  
Normalised Difference Vegetation Index ◽  
Satellite Sensors ◽  
Sentinel 2

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

scholarly journals The short life of the volcanic island New Late’iki (Tonga) analyzed by multi-sensor remote sensing data

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Simon Plank ◽  
Francesco Marchese ◽  
Nicola Genzano ◽  
Michael Nolde ◽  
Sandro Martinis
Keyword(s):  
Infrared Imaging ◽  
Rapid Change ◽  
Monitoring Network ◽  
Remote Sensing Data ◽  
Volcanic Island ◽  
Imaging Spectrometer ◽  
Remote Areas ◽  
Moderate Resolution ◽  
Satellite Sensors ◽  
Resolution Imaging

AbstractSatellite-based Earth observation plays a key role for monitoring volcanoes, especially those which are located in remote areas and which very often are not observed by a terrestrial monitoring network. In our study we jointly analyzed data from thermal (Moderate Resolution Imaging Spectrometer MODIS and Visible Infrared Imaging Radiometer Suite VIIRS), optical (Operational Land Imager and Multispectral Instrument) and synthetic aperture radar (SAR) (Sentinel-1 and TerraSAR-X) satellite sensors to investigate the mid-October 2019 surtseyan eruption at Late’iki Volcano, located on the Tonga Volcanic Arc. During the eruption, the remains of an older volcanic island formed in 1995 collapsed and a new volcanic island, called New Late’iki was formed. After the 12 days long lasting eruption, we observed a rapid change of the island’s shape and size, and an erosion of this newly formed volcanic island, which was reclaimed by the ocean two months after the eruption ceased. This fast erosion of New Late’iki Island is in strong contrast to the over 25 years long survival of the volcanic island formed in 1995.

scholarly journals Cloud Cover throughout All the Paddy Rice Fields in Guangdong, China: Impacts on Sentinel 2 MSI and Landsat 8 OLI Optical Observations

2021 ◽  
Vol 13 (15) ◽  
pp. 2961
Author(s):  
Rui Jiang ◽  
Arturo Sanchez-Azofeifa ◽  
Kati Laakso ◽  
Yan Xu ◽  
Zhiyan Zhou ◽  
...  
Keyword(s):  
Cloud Cover ◽  
Southern China ◽  
Vegetation Indices ◽  
Paddy Rice ◽  
Rice Fields ◽  
Optical Observations ◽  
Landsat 8 ◽  
Monthly Basis ◽  
Satellite Sensors ◽  
Sentinel 2

Cloud cover hinders the effective use of vegetation indices from optical satellite-acquired imagery in cloudy agricultural production areas, such as Guangdong, a subtropical province in southern China which supports two-season rice production. The number of cloud-free observations for the earth-orbiting optical satellite sensors must be determined to verify how much their observations are affected by clouds. This study determines the quantified wide-ranging impact of clouds on optical satellite observations by mapping the annual total observations (ATOs), annual cloud-free observations (ACFOs), monthly cloud-free observations (MCFOs) maps, and acquisition probability (AP) of ACFOs for the Sentinel 2 (2017–2019) and Landsat 8 (2014–2019) for all the paddy rice fields in Guangdong province (APRFG), China. The ATOs of Landsat 8 showed relatively stable observations compared to the Sentinel 2, and the per-field ACFOs of Sentinel 2 and Landsat 8 were unevenly distributed. The MCFOs varied on a monthly basis, but in general, the MCFOs were greater between August and December than between January and July. Additionally, the AP of usable ACFOs with 52.1% (Landsat 8) and 47.7% (Sentinel 2) indicated that these two satellite sensors provided markedly restricted observation capability for rice in the study area. Our findings are particularly important and useful in the tropics and subtropics, and the analysis has described cloud cover frequency and pervasiveness throughout different portions of the rice growing season, providing insight into how rice monitoring activities by using Sentinel 2 and Landsat 8 imagery in Guangdong would be impacted by cloud cover.

scholarly journals Application of Satellite Sentinel-2 Images to Study Alternate Sandbars Movement at Lower Vistula River (Poland)

2021 ◽  
Vol 13 (8) ◽  
pp. 1505
Author(s):  
Klaudia Kryniecka ◽  
Artur Magnuszewski
Keyword(s):  
Satellite Images ◽  
Average Distance ◽  
Low Flow ◽  
Visual Interpretation ◽  
Time Intervals ◽  
Manual Delineation ◽  
Vistula River ◽  
Interclass Correlation ◽  
Optical Satellite Images ◽  
Sentinel 2

The lower Vistula River was regulated in the years 1856–1878, at a distance of 718–939 km. The regulation plan did not take into consideration the large transport of the bed load. The channel was shaped using simplified geometry—too wide for the low flow and overly straight for the stabilization of the sandbar movement. The hydraulic parameters of the lower Vistula River show high velocities of flow and high shear stress. The movement of the alternate sandbars can be traced on the optical satellite images of Sentinel-2. In this study, a method of sandbar detection through the remote sensing indices, Sentinel Water Mask (SWM) and Automated Water Extraction Index no shadow (AWEInsh), and the manual delineation with visual interpretation (MD) was used on satellite images of the lower Vistula River, recorded at the time of low flows (20 August 2015, 4 September 2016, 30 July 2017, 20 September 2018, and 29 August 2019). The comparison of 32 alternate sandbar areas obtained by SWM, AWEInsh, and MD manual delineation methods on the Sentinel-2 images, recorded on 20 August 2015, was performed by the statistical analysis of the interclass correlation coefficient (ICC). The distance of the shift in the analyzed time intervals between the image registration dates depends on the value of the mean discharge (MQ). The period from 30 July 2017 to 20 September 2018 was wet (MQ = 1140 m3 × s−1) and created conditions for the largest average distance of the alternate sandbar shift, from 509 to 548 m. The velocity of movement, calculated as an average shift for one day, was between 1.2 and 1.3 m × day−1. The smallest shift of alternate sandbars was characteristic of the low flow period from 20 August 2015 to 4 September 2016 (MQ = 306 m3 × s−1), from 279 to 310 m, with an average velocity from 0.7 to 0.8 m × day−1.

Road Construction and Rainfall as Landslide Triggers in the Rio Lucma Catchment, Eastern Cordillera Blanca, Peru

2021 ◽  
Author(s):  
Lorena Abad ◽  
Daniel Hölbling ◽  
Adam Emmer
Keyword(s):  
Satellite Imagery ◽  
Southern Oscillation ◽  
Road Construction ◽  
Automated Analysis ◽  
Rainfall Data ◽  
Construction Work ◽  
Visual Interpretation ◽  
Wet Season ◽  
Cordillera Blanca ◽  
The Road

<p>Extensive road construction works were recently undertaken in the remote eastern part of the Peruvian Cordillera Blanca, aiming at better connecting isolated mountain communities with regional administrative centres. In the Río Lucma catchment, approximately 47 km of roads were constructed between 2015 and 2018, triggering several landslides that affected an approximate area of 32 ha. We identified and characterised these landslides by combining field mapping, visual interpretation and semi-automated analysis of satellite imagery (PlanetScope and RapidEye-2), and analysis of rainfall data from two stations of the Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI). We investigated in detail three specific areas of interest, where we identified, delineated, and described 56 landslides. We classified the landslides in relation to their position to the road as: landslides downslope the roads (48.2%), complex landslides crossing the roads (46.4 %), and landslides onto the road (5.3%). According to the type of movement, we found that the slide-type movement (60.7%) prevails over the flow-type movement (39.3%). Timewise, we found that 75% of landslides were observed on satellite imagery simultaneously with road construction work, while the remaining 25% were identified between one week and seven months after the roads had been constructed. We analysed lagged cumulative rainfall data against the occurrence of these subsequent landslides, determining that a two-week rainfall accumulation can act as triggering factor of landslides after road construction work. In general, 51% of the landslides were observed during the wet season (November to April) while 41.1% occurred during El Niño–Southern Oscillation (ENSO) strong cool phase or “La Niña” period. We observed that the majority of mapped landslides were directly (e.g., landslides resulting from slope undercutting) or indirectly associated with road constructions (e.g., rainfall-induced landslides resulting from a combination of extreme precipitation over slopes with decreased stability) and that the road constructions also may set preconditions for subsequent rainfall-triggered landslides.</p>

scholarly journals Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery in Google Earth and Microsoft Bing Maps as a Source of Reference Data

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 118 ◽  
Author(s):  
Myroslava Lesiv ◽  
Linda See ◽  
Juan Laso Bayas ◽  
Tobias Sturn ◽  
Dmitry Schepaschenko ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Reference Data ◽  
Temporal Distribution ◽  
Google Earth ◽  
Training Data ◽  
Visual Interpretation ◽  
The Usa ◽  
Very High

Very high resolution (VHR) satellite imagery from Google Earth and Microsoft Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, one use of this imagery is to create reference data sets through visual interpretation, e.g., to complement existing training data or to aid in the validation of land-cover products. Through new applications such as Collect Earth, this imagery is also being used for monitoring purposes in the form of statistical surveys obtained through visual interpretation. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global overview of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Microsoft Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India, and with clear discontinuities at political borders. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas using visual interpretation.

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