Evaluation of Floods and Landslides Triggered by a Meteorological Catastrophe (Ordu, Turkey, August 2018) Using Optical and Radar Data

This study explores the potential of photogrammetric datasets and remote sensing methods for the assessment of a meteorological catastrophe that occurred in Ordu, Turkey in August 2018. During the event, flash floods and several landslides caused losses of lives, evacuation of people from their homes, collapses of infrastructure and large constructions, destruction of agricultural fields, and many other economic losses. The meteorological conditions before and during the flood were analyzed here and compared with long-term records. The flood extent and the landslide susceptibility were investigated by using multisensor and multitemporal data. Sentinel-1 SAR (Synthetic Aperture Radar), Sentinel-2 optical data, and aerial photogrammetric datasets were employed for the investigations using machine learning techniques. The changes were assessed both at a local and regional level and evaluated together with available damage reports. The analysis of the rainfall data recorded during the two weeks before the floods and landslides in heavily affected regions shows that the rainfall continued for consecutive hours with an amount of up to 68 mm/hour. The regional level classification results obtained from Sentinel-1 and Sentinel-2 data by using the random forest (RF) method exhibit 97% accuracy for the flood class. The landslide susceptibility prediction performance from aerial photogrammetric datasets was 92% represented by the Area Under Curve (AUC) value provided by the RF method. The results presented here show that considering the occurrence frequency and immense damages after such events, the use of novel remote sensing technologies and spatial analysis methods is unavoidable for disaster mitigation efforts and for the monitoring of environmental effects. Although the increasing number of earth observation satellites complemented with airborne imaging sensors is capable of ensuring data collection requirement with diverse spectral, spatial, and temporal resolutions, further studies are required to automate the data processing, efficient information extraction, and data fusion and also to increase the accuracy of the results.

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Monitoring Bark Beetle Forest Damage in Central Europe. A Remote Sensing Approach Validated with Field Data

Remote Sensing ◽

10.3390/rs12213634 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3634 ◽

Cited By ~ 1

Author(s):

Angel Fernandez-Carrillo ◽

Zdeněk Patočka ◽

Lumír Dobrovolný ◽

Antonio Franco-Nieto ◽

Beatriz Revilla-Romero

Keyword(s):

Remote Sensing ◽

Central Europe ◽

Bark Beetle ◽

Forest Damage ◽

Economic Losses ◽

In Situ Data ◽

Damage Mapping ◽

Beetle Damage ◽

Forest Vitality ◽

Sentinel 2

Over the last decades, climate change has triggered an increase in the frequency of spruce bark beetle (Ips typographus L.) in Central Europe. More than 50% of forests in the Czech Republic are seriously threatened by this pest, leading to high ecological and economic losses. The exponential increase of bark beetle infestation hinders the implementation of costly field campaigns to prevent and mitigate its effects. Remote sensing may help to overcome such limitations as it provides frequent and spatially continuous data on vegetation condition. Using Sentinel-2 images as main input, two models have been developed to test the ability of this data source to map bark beetle damage and severity. All models were based on a change detection approach, and required the generation of previous forest mask and dominant species maps. The first damage mapping model was developed for 2019 and 2020, and it was based on bi-temporal regressions in spruce areas to estimate forest vitality and bark beetle damage. A second model was developed for 2020 considering all forest area, but excluding clear-cuts and completely dead areas, in order to map only changes in stands dominated by alive trees. The three products were validated with in situ data. All the maps showed high accuracies (acc > 0.80). Accuracy was higher than 0.95 and F1-score was higher than 0.88 for areas with high severity, with omission errors under 0.09 in all cases. This confirmed the ability of all the models to detect bark beetle attack at the last phases. Areas with no damage or low severity showed more complex results. The no damage category yielded greater commission errors and relative bias (CEs = 0.30–0.42, relB = 0.42–0.51). The similar results obtained for 2020 leaving out clear-cuts and dead trees proved that the proposed methods could be used to help forest managers fight bark beetle pests. These biotic damage products based on Sentinel-2 can be set up for any location to derive regular forest vitality maps and inform of early damage.

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Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium

Remote Sensing ◽

10.3390/rs10101642 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1642 ◽

Cited By ~ 56

Author(s):

Kristof Van Tricht ◽

Anne Gobin ◽

Sven Gilliams ◽

Isabelle Piccard

Keyword(s):

Remote Sensing ◽

Random Forest ◽

Satellite Remote Sensing ◽

Random Forest Classifier ◽

Optical Observations ◽

Optical Data ◽

Crop Monitoring ◽

Crop Types ◽

Optical Imagery ◽

Sentinel 2

A timely inventory of agricultural areas and crop types is an essential requirement for ensuring global food security and allowing early crop monitoring practices. Satellite remote sensing has proven to be an increasingly more reliable tool to identify crop types. With the Copernicus program and its Sentinel satellites, a growing source of satellite remote sensing data is publicly available at no charge. Here, we used joint Sentinel-1 radar and Sentinel-2 optical imagery to create a crop map for Belgium. To ensure homogenous radar and optical inputs across the country, Sentinel-1 12-day backscatter mosaics were created after incidence angle normalization, and Sentinel-2 normalized difference vegetation index (NDVI) images were smoothed to yield 10-daily cloud-free mosaics. An optimized random forest classifier predicted the eight crop types with a maximum accuracy of 82% and a kappa coefficient of 0.77. We found that a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs, and that classification performance increased throughout the season until July, when differences between crop types were largest. Furthermore, we showed that the concept of classification confidence derived from the random forest classifier provided insight into the reliability of the predicted class for each pixel, clearly showing that parcel borders have a lower classification confidence. We concluded that the synergistic use of radar and optical data for crop classification led to richer information increasing classification accuracies compared to optical-only classification. Further work should focus on object-level classification and crop monitoring to exploit the rich potential of combined radar and optical observations.

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A Precipitation Nowcasting Mechanism for Real-World Data Based on Machine Learning

Mathematical Problems in Engineering ◽

10.1155/2020/8408931 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Yanfei Xiang ◽

Jianbing Ma ◽

Xi Wu

Keyword(s):

Machine Learning ◽

Optical Flow ◽

Weather Prediction ◽

Radar Data ◽

Machine Learning Techniques ◽

Economic Losses ◽

Model Parameters ◽

Real World Data ◽

Flow Method ◽

Optical Flow Method

Unpredicted precipitations, even mild, may cause severe economic losses to many businesses. Precipitation nowcasting is hence significant for people to make correct decisions timely. For traditional methods, such as numerical weather prediction (NWP), the accuracy is limited because the smaller scale of strong convective weather must be smaller than the minimum scale that the model can capture. And it often requires a supercomputer. Furthermore, the optical flow method has been proved to be available for precipitation nowcasting. However, it is difficult to determine the model parameters because the two steps of tracking and extrapolation are separate. In contrast, current machine learning applications are based on well-selected full datasets, ignoring the fact that real datasets quite often contain missing data requiring extra consideration. In this paper, we used a real Hubei dataset in which a few radar echo data are missing and proposed a proper mechanism to deal with the situation. Furthermore, we proposed a novel mechanism for radar reflectivity data with single altitudes or cumulative altitudes using machine learning techniques. From the experimental results, we conclude that our method can predict future precipitation with a high accuracy when a few data are missing, and it outperforms the traditional optical flow method. In addition, our model can be used for various types of radar data with a type-specific feature extraction, which makes the method more flexible and suitable for most situations.

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Extracting multilayer networks from Sentinel-2 satellite image time series

Network Science ◽

10.1017/nws.2019.58 ◽

2020 ◽

Vol 8 (S1) ◽

pp. S26-S42 ◽

Cited By ~ 1

Author(s):

Roberto Interdonato ◽

Raffaele Gaetano ◽

Danny Lo Seen ◽

Mathieu Roche ◽

Giuseppe Scarpa

Keyword(s):

Remote Sensing ◽

Time Series ◽

Satellite Image ◽

European Space Agency ◽

Image Data ◽

Earth Observation ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Case Scenario ◽

Sentinel 2

AbstractNowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is the Sentinel-2 Earth Observation mission, developed by the European Space Agency as part of the Copernicus Programme, which supplies images from the whole planet at high spatial resolution (up to 10 m) with unprecedented revisit time (every 5 days at the equator). In this data-rich scenario, the remote sensing community is showing a growing interest toward modern supervised machine learning techniques (e.g., deep learning) to perform information extraction, often underestimating the need for reference data that this framework implies. Conversely, few attention is being devoted to the use of network analysis techniques, which can provide a set of powerful tools for unsupervised information discovery, subject to the definition of a suitable strategy to build a network-like representation of image data. The aim of this work is to provide clues on how Satellite Image Time Series can be profitably represented using complex network models, by proposing a methodology to build a multilayer network from such data. This is the first work to explore the possibility to exploit this model in the remote sensing domain. An example of community detection over the provided network in a real-case scenario for the mapping of complex land use systems is also presented, to assess the potential of this approach.

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Investigating Sentinel 2 Multispectral Imagery Efficiency in Describing Spectral Response of Vineyards Covered with Plastic Sheets

Agronomy ◽

10.3390/agronomy10121909 ◽

2020 ◽

Vol 10 (12) ◽

pp. 1909

Author(s):

Enrico Borgogno-Mondino ◽

Laura de Palma ◽

Vittorino Novello

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spectral Response ◽

Table Grape ◽

Optical Data ◽

Strong Impact ◽

Spectral Indices ◽

Intrinsic Nature ◽

Sentinel 2

The protection of vineyards with overhead plastic covers is a technique largely applied in table grape growing. As with other crops, remote sensing of vegetation spectral reflectance is a useful tool for improving management even for table grape viticulture. The remote sensing of the spectral signals emitted by vegetation of covered vineyards is currently an open field of investigation, given the intrinsic nature of plastic sheets that can have a strong impact on the reflection from the underlying vegetation. Baring these premises in mind, the aim of the present work was to run preliminary tests on table grape vineyards covered with polyethylene sheets, using Copernicus Sentinel 2 (Level 2A product) free optical data, and compare their spectral response with that of similar uncovered vineyards to assess if a reliable spectral signal is detectable through the plastic cover. Vine phenology, air temperature and shoot growth, were monitored during the 2016 growing cycle. Twenty-four Copernicus Sentinel 2 (S2, Level 2A product) images were used to investigate if, in spite of plastic sheets, vine phenology can be similarly described with and without plastic covers. For this purpose, time series of S2 at-the-ground reflectance calibrated bands and correspondent normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index, version two (MSAVI2) and normalized difference water index (NDWI) spectral indices were obtained and analyzed, comparing the responses of two covered vineyards with different plastic sheets in respect of two uncovered ones. Results demonstrated that no significant limitation (for both bands and spectral indices) was introduced by plastic sheets while monitoring spectral behavior of covered vineyards.

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Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data

Remote Sensing ◽

10.3390/rs13020243 ◽

2021 ◽

Vol 13 (2) ◽

pp. 243

Author(s):

Amal Chakhar ◽

David Hernández-López ◽

Rocío Ballesteros ◽

Miguel A. Moreno

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Dynamics ◽

Remote Sensing Data ◽

Radar Data ◽

Optical Data ◽

Support Vector ◽

Sensing Data ◽

Crop Classification

The availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes. To select the best SAR input feature, we tried two approaches, one based on classification with only SAR features and one based on integrating SAR with optical data. In total, nine scenarios were tested. Furthermore, we evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data. The results revealed that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) (the kernel function of the classifier is cubic) as the classifier with the highest accuracy among all those tested.

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COMBINED ANALYSIS OF RADARSAT-2 SAR AND SENTINEL-2 OPTICAL DATA FOR IMPROVED MONITORING OF TUBER INITIATION STAGE OF POTATO

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-275-2018 ◽

2018 ◽

Vol XLII-5 ◽

pp. 275-279 ◽

Cited By ~ 1

Author(s):

D. Mandal ◽

V. Kumar ◽

Y. S. Rao ◽

A. Bhattacharya ◽

S. Bera ◽

...

Keyword(s):

Remote Sensing ◽

Temporal Dynamics ◽

Potato Production ◽

Tuber Initiation ◽

Optical Data ◽

Growth Monitoring ◽

Tuber Bulking ◽

Potato Tuberization ◽

Sar Data ◽

Sentinel 2

Abstract. Tuber initiation and tuber bulking stages are critical part of various phenological phases for potato production. Tuber initiation covers the period from the formation of spherical rhizome ends, the flowering and the start of tuber bulking. In general, the tuberization spans from 3 to 5 weeks after emergence and ends with the row closer i.e. canopies in adjacent rows touch each other across the furrow. Hence, this rapid growth seeks critical agronomic management practices such as irrigation and fertilization. It majorly influences the growth of stems, foliar area, dry weight and number of tubers particularly at the phase of tuber initiation. During these phenological stages, potato crops are susceptible to the infestation of late blight diseases caused by Phytophthora infestans and largely affects the potato production. Thus identifying the crop risk using remote sensing approaches can provide an efficient potato growth monitoring framework. In the context of monitoring crop dynamics, quad-pol Synthetic Aperture Radar (SAR) data has proven to be effective due to its sensitivity towards dielectric and geometric properties. In addition to SAR data, optical remote sensing data derived vegetation information can provide an improved insight into crop growth when combined with SAR data. In this research, quad-pol RADARSAT-2 and Sentinel-2 optical data are analyzed to monitor potato tuberization phase over Bardhaman district in the state ofWest Bengal, which is one of the major potato growing regions in India. The proposed approach uses polarimetric parameters such as backscatter intensities, ratio (HH/VV, VH/VV, linear depolarization ratio), and co-pol correlation (&rho;HH–VV) along with the vegetation indices derived from the Sentinel-2 data for understanding the spatio-temporal dynamics. The initial results show a promising accuracy in monitoring the dynamics of potato tuberization. Integration of such earth observation (EO) data, in conjunction with in-situ field measurements, might significantly enhance the current capabilities for crop monitoring.

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Combined usage of the optical and radar remote sensing data in territory monitoring tasks

Information Technology and Nanotechnology ◽

10.18287/1613-0073-2019-2391-334-341 ◽

2019 ◽

pp. 334-341

Author(s):

V N Kopenkov

Keyword(s):

Remote Sensing ◽

Information Technology ◽

Satellite Images ◽

Integrated Approach ◽

Radar Data ◽

Optical Data ◽

Joint Analysis ◽

Data Types ◽

Joint Processing ◽

Radar Satellite

At the present time, a lot of problems in a sphere of fundamental sciences as well as technical and applied tasks can be solved only with the use of satellite images, since their usage reduces material, financial and time costs significantly in comparison with traditional methods. One of the modern integrated approach remote sensing processing is to join the measurements obtained from the various sources, such as optical and radar sensors, allowing to achieve a gain in comparison with independent processing due to the extension of the information volume and the opportunities of data acquisition (weather conditions, spectral ranges, etc.). However, methods of digital processing and interpretation of radar data, as well as qualitative and proven methods and algorithms for joint processing of optical and radar satellite images, has not sufficiently been well developed yet. Therefore, the development of new methods and information technology of joint analysis and interpretation of optical and radar data which are a major issue of the current paper, are certainly relevant. The paper presents an information technology for joint processing of optical and radar satellite imagery, based on training the processing procedure based on the reference values of data from sensors of the one type (optical data), followed by applying to both data types: optical and SAR data.

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An attempt to understand corn and winter wheat temporal behavior by means of Sentinel-1 and Sentinel-2 data and its relation with phonological stages.

10.5194/egusphere-egu21-12307 ◽

2021 ◽

Author(s):

Marta Pasternak ◽

Kamila Pawluszek-Filipiak

Keyword(s):

Remote Sensing ◽

Winter Wheat ◽

Strong Correlation ◽

Vegetation Index ◽

Radar Data ◽

Temporal Behavior ◽

Spectral Indices ◽

Chlorophyll Index ◽

Sar Data ◽

Sentinel 2

Crops are of the fundamental food sources for humanity. Due to the population growth as well as climate change, monitoring of the crops is important to sustain agriculture and conserve natural resources. Development of the remote sensing techniques especially in terms of revisiting time opens new avenues to study crops temporal behaviors from space. Moreover, thanks to the Copernicus program, which guarantees optical as well as radar data to be freely available, there are opportunities to utilize them in an operative way. Additionally, utilization of spectral as well as radar data allows for the synergetic application of both datasets. However, to utilize this data in the operational crop monitoring, it is very important to understand the temporal variations of the remote sensing signal. Therefore, we make an attempt to understand spectral as well as radar remote sensing temporal behavior and its relation with phonological stages.For the analysis, 14 cloud-free Sentinel-2 (S-2) acquisitions as well as 34 Sentinel-1 (S-1) acquisitions are utilized. S-2 data were collected with 2A-level while S-1 data was captured in the format of Single Look Complex (SLC) in the Interferometric Wide (IW) swath mode. SLC products consist of complex SAR data preserving phase information which allows studying polarimetric indicators. All remote sensing (spectral as well as SAR) data cover the time period from 04/05/2020 to 07/11/2020. During this time, also 14 field visits were carried out to capture information about phonological stages of corn and wheat according to the BBCH scale (Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie). Additionally, to better understand the temporal behavior of S-1/S-2 signal, weather information from the Institute of Meteorology and Water Management (IMGW) was captured.Based on various spectral bands of S-2 data, 12 spectral indices were calculated e.g., GNDVI (Green Normalized Vegetation Index), IRECI (Inverted Red-Edge Chlorophyll Index), MCARI (Modified Chlorophyll Absorption in Reflectance Index), MSAVI (Modified Soil-Adjusted Vegetation Index), MTCI (MERIS Terrestrial Chlorophyll Index), NDVI (Normalized Difference Vegetation Index), PSSRa (Pigment Specific Simple Ratio) and others. After radiometric calibration and the Lee speckle filtering, backscattering coefficients (&#963;VVo ,&#963;VHo) of S-1 images were calculated as well as its backscattering ratio (&#963;VHo/ &#963;VVo).&#160; All images were then converted from linear to decibel (dB). Additionally, 2 &#215; 2 covariance matrix delivered from S-1 was extracted from the scattering matrix of each SLC image using PolSARpro version 6.0.2 software. After speckle filtration, total scattered power was derived which allows calculating the Shannon Entropy. This value measures the randomness of the scattering within a pixel.Time series of many S-2 indices reveal the strong correlation between the development of phenology stages of corn and wheat and the increase of S2 delivered values of spectral indices. However, such a strong correlation cannot be observed within many of S-1 indices. Some of them very poorly indicate the correlation between the development of phenology stages of corn and wheat and increase of S-1 indices values. Additionally, it was observed that values of S1/S2 indices for the same phenology stage very between corn and winter wheat.&#160;

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Detection of Seasonal Inundations by Satellite Data at Shkoder Urban Area, North Albania for Sustainable Management

Sustainability ◽

10.3390/su11164454 ◽

2019 ◽

Vol 11 (16) ◽

pp. 4454 ◽

Cited By ~ 1

Author(s):

Stefano Morelli ◽

Matteo Del Soldato ◽

Silvia Bianchini ◽

Veronica Pazzi ◽

Ervis Krymbi ◽

...

Keyword(s):

European Space Agency ◽

Temporal Analysis ◽

Radar Data ◽

Optical Data ◽

Future Perspective ◽

Management Service ◽

Surface Mapping ◽

Water Index ◽

Space Agency ◽

Sentinel 2

The European Space Agency satellites Sentinel-1 radar and Sentinel-2 optical data are widely used in water surface mapping and management. In this work, we exploit the potentials of both radar and optical images for satellite-based quick detection and extent mapping of inundations/water raising events over Shkodër area, which occurred in the two last years (2017–2018). For instance, in March 2018 the Shkodër district (North Albania) was affected twice by the overflow of the Drin and Buna (Bojana) Rivers and by the Shkodër lake plain inundation. Sentinel-1 radar data allowed a rapid mapping of seasonal fluctuations and provided flood extent maps by discriminating water surfaces (permanent water and flood areas) from land/non-flood areas over all the informal zones of Shkodër city. By means of Sentinel-2 data, two color composites maps were produced and the Normalized Difference Water Index was estimated, in order to further distinguish water/moisturized soil surfaces from built-up and vegetated areas. The obtained remote sensing-based maps were combined and discussed with the urban planning framework in order to support a sustainable urban and environmental management. The provided multi-temporal analysis could be easily exploited by the local authorities for flood prevention and management purposes in the inherited territorial context. The proposed approach outputs were validated by comparing them with official Copernicus EMS (Emergency Management Service) maps available for one of the chosen events. The comparison shows good accordance results. As for a further enhancement in the future perspective, it is worth to highlight that a more accurate result could be obtained by performing a post-processing edit to further refine the flooded areas, such as water mask application and supervised classification to filter out isolated flood elements, to remove possible water-lookalikes and weed out false positives.

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