Potentiality and Limitations of Open Satellite Data for Flood Mapping

Mapping Intimacies ◽

10.20944/preprints201807.0624.v1 ◽

2018 ◽

Author(s):

Davide Notti ◽

Daniele Giordan ◽

Fabiana Calò ◽

Antonio Pepe ◽

Francesco Zucca ◽

...

Keyword(s):

Satellite Data ◽

Low Cost ◽

Ground Truth ◽

Flood Mapping ◽

Ancillary Data ◽

Ground Truth Data ◽

Flood Peak ◽

Sar Data ◽

Flooded Areas ◽

Flood Maps

Satellite remote sensing is a powerful tool to map flooded areas. In the last years, the availability of free satellite data sensibly increased in terms of type and frequency, allowing producing flood maps at low cost around the World. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. As case studies, we selected three flood events recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, Sentinel-2 and SAR data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., MNDWI; SAR backscattering variation; Supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data like DEM based water depth model and available ground truth data. For the areas affected by major floods, we also validated and compared the produced flood maps with official maps made by river authorities. We calculated flood detection performance (flood ratio) for the different datasets we used. The results show that it is necessary to take into account different factors for the choice of best satellite data, among these, the time of satellite pass with respect to the flood peak is the most important one. SAR data showed good results only for co-flood acquisitions, whereas multispectral images allowed detecting flooded areas also with the post-flood acquisition. With the support of ancillary data, it was possible to produce reliable geomorphological based flood maps in the study areas.

Potential and Limitations of Open Satellite Data for Flood Mapping

Remote Sensing ◽

10.3390/rs10111673 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1673 ◽

Cited By ~ 27

Author(s):

Davide Notti ◽

Daniele Giordan ◽

Fabiana Caló ◽

Antonio Pepe ◽

Francesco Zucca ◽

...

Keyword(s):

Remote Sensing ◽

Satellite Data ◽

Flood Hazard ◽

Flood Mapping ◽

Ancillary Data ◽

Po River ◽

Ground Truth Data ◽

Hazard Management ◽

Flooded Areas ◽

Flood Maps

Satellite remote sensing is a powerful tool to map flooded areas. In recent years, the availability of free satellite data significantly increased in terms of type and frequency, allowing the production of flood maps at low cost around the world. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. The proposed methods are suitable to be applied by the community involved in flood hazard management, not necessarily experts in remote sensing processing. As case studies, we selected three flood events that recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, and Sentinel-2 and synthetic aperture radar (SAR) data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., Modified Normalized Difference Water Index, SAR backscattering variation, and supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data such as the digital elevation model-based water depth model and available ground truth data. We calculated flood detection performance (flood ratio) for the different datasets by comparing with flood maps made by official river authorities. The results show that it is necessary to consider different factors when selecting the best satellite data. Among these factors, the time of the satellite pass with respect to the flood peak is the most important. With co-flood multispectral images, more than 90% of the flooded area was detected in the 2015 Ebro flood (Spain) case study. With post-flood multispectral data, the flood ratio showed values under 50% a few weeks after the 2016 flood in Po and Tanaro plains (Italy), but it remained useful to map the inundated pattern. The SAR could detect flooding only at the co-flood stage, and the flood ratio showed values below 5% only a few days after the 2016 Po River inundation. Another result of the research was the creation of geomorphology-based inundation maps that matched up to 95% with official flood maps.

SENTINEL-1 BASED FLOOD MAPPING USING INTERFEROMETRIC COHERENCE AND INTENSITY CHANGE DETECTION APPROACH

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

10.5194/isprs-archives-xliii-b3-2020-1697-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 1697-1703

Author(s):

I. Papila ◽

U. Alganci ◽

E. Sertel

Keyword(s):

Change Detection ◽

Urban Areas ◽

Satellite Image ◽

Region Growing ◽

Ground Truth ◽

Intensity Change ◽

Flood Mapping ◽

Satellite Mission ◽

Ground Truth Data ◽

Sar Data

Abstract. This study presents a semi-automatic algorithm for mapping floods. Both Optical and Synthetic Aperture Radar (SAR) data are used to observe the flood that hit the Cukurova region of Adana (Turkey) in 2019. The performance of the interferometric coherence in complementing intensity component of SAR data is investigated for mapping the floods occurred in agricultural and urban environments. There was no ground truth data available from the flooded area, thus classification result of optical satellite image is used as a seed for the region growing algorithm that defines the classes according to a threshold value. The advantage of using both intensity and coherence change detection is verified with the results. The results have been evaluated through very high-resolution SPOT-6 optical image which acquired simultaneously with Sentinel-1B SAR image. The comparison with the SPOT-6 data results shows that the proposed approach can map flooded areas with acceptable accuracy using the SAR data from Sentinel-1 satellite mission. Highly affected agricultural areas along with the river line could be mapped both by optical and SAR analysis. Comparison of results from VV and VH polarization provided that cross-polarization VH has a very little effect on flood mapping. The proposed algorithm successfully distinguishes the classes among the affected region, especially in urban areas.

Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery

Remote Sensing ◽

10.3390/rs10121907 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1907 ◽

Cited By ~ 18

Author(s):

Luís Pádua ◽

Pedro Marques ◽

Jonáš Hruška ◽

Telmo Adão ◽

Emanuel Peres ◽

...

Keyword(s):

Accuracy Assessment ◽

Low Cost ◽

Ground Truth ◽

Volume Estimation ◽

Height Range ◽

Ground Truth Data ◽

Operational Technique ◽

Digital Elevation ◽

Multi Temporal ◽

Aerial Vehicle

This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to compute crop surface models. By filtering vegetation within a given height-range, it was possible to separate grapevine vegetation from other vegetation present in a specific vineyard plot, enabling the estimation of grapevine area and volume. The results showed high accuracy in grapevine detection (94.40%) and low error in grapevine volume estimation (root mean square error of 0.13 m and correlation coefficient of 0.78 for height estimation). The accuracy assessment showed that the proposed method based on UAV-based RGB imagery is effective and has potential to become an operational technique. The proposed method also allows the estimation of grapevine areas that can potentially benefit from canopy management operations.

Automatic Flood Monitoring based on SAR Intensity and Interferometric Coherence using Machine Learning

10.5194/egusphere-egu2020-12954 ◽

2020 ◽

Author(s):

Binayak Ghosh ◽

Mahdi Motagh ◽

Mahmud Haghshenas Haghighi ◽

Setareh Maghsudi

Keyword(s):

Machine Learning ◽

Open Water ◽

Semantic Segmentation ◽

Flood Mapping ◽

Assistance Programs ◽

Amplitude Data ◽

Flood Monitoring ◽

Sar Data ◽

Flood Detection ◽

Flood Maps

Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. Emergency responders frequently request satellite-based crisis information for flood monitoring to target the often-limited resources and to prioritize response actions throughout a disaster situation. Flood mapping algorithms are usually based on automatic thresholding algorithms for the initialization of the classification process in SAR amplitude data. These thresholding processes like Otsu thresholding, histogram leveling etc., are followed by clustering techniques like K-means, ISODATA for segmentation of water and non-water areas. These methods are capable of extracting the flood extent if there is a significant contrast between water and non-water areas in the SAR data. However, the classification result may be related to overestimations if non-water areas have a similar low backscatter as open water surfaces and also, these backscatter values differentiate from VV and VH polarizations. Our method aims at improving existing satellite-based emergency mapping methods by incorporating systematically acquired Sentinel-1A/B SAR data at high spatial (20m) and temporal (3-5 days) resolution. Our method involves a supervised learning method for flood detection by leveraging SAR intensity and interferometric coherence as well as polarimetry information.&#160;It uses multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. By incorporating multitemporal satellite imagery, our method allows for rapid and accurate post-disaster damage assessment and can be used for better coordination of medium- and long-term financial assistance programs for affected areas. In this paper, we present a strategy using machine learning for semantic segmentation of the flood map, which extracts the&#160;spatio-temporal information from the SAR images having both&#160;intensity&#160;as well coherence bands. The flood maps produced by the fusion of intensity and coherence are validated against state-of-the art methods for producing flood maps.&#160;

EXPLORING MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR CROP CLASSIFICATION USING SENTINEL 2 DATA

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

10.5194/isprs-archives-xlii-3-w6-573-2019 ◽

2019 ◽

Vol XLII-3/W6 ◽

pp. 573-578 ◽

Cited By ~ 3

Author(s):

◽

S. S. Ray

Keyword(s):

Machine Learning ◽

Satellite Data ◽

Classification Accuracy ◽

Ground Truth ◽

Kappa Coefficient ◽

Ground Truth Data ◽

Classification Techniques ◽

Machine Learning Classification ◽

Crop Classification ◽

Sentinel 2

Abstract. Crop Classification and recognition is a very important application of Remote Sensing. In the last few years, Machine learning classification techniques have been emerging for crop classification. Google Earth Engine (GEE) is a platform to explore the multiple satellite data with different advanced classification techniques without even downloading the satellite data. The main objective of this study is to explore the ability of different machine learning classification techniques like, Random Forest (RF), Classification And Regression Trees (CART) and Support Vector Machine (SVM) for crop classification. High Resolution optical data, Sentinel-2, MSI (10&thinsp;m) was used for crop classification in the Indian Agricultural Research Institute (IARI) farm for the Rabi season 2016 for major crops. Around 100 crop fields (~400 Hectare) in IARI were analysed. Smart phone-based ground truth data were collected. The best cloud free image of Sentinel 2 MSI data (5 Feb 2016) was used for classification using automatic filtering by percentage cloud cover property using the GEE. Polygons as feature space was used as training data sets based on the ground truth data for crop classification using machine learning techniques. Post classification, accuracy assessment analysis was done through the generation of the confusion matrix (producer and user accuracy), kappa coefficient and F value. In this study it was found that using GEE through cloud platform, satellite data accessing, filtering and pre-processing of satellite data could be done very efficiently. In terms of overall classification accuracy and kappa coefficient, Random Forest (93.3%, 0.9178) and CART (73.4%, 0.6755) classifiers performed better than SVM (74.3%, 0.6867) classifier. For validation, Field Operation Service Unit (FOSU) division of IARI, data was used and encouraging results were obtained.

A study on landuse and landcover classification using microwave data in Joida taluk of Uttara Kannada district, Karnataka

Journal of Applied and Natural Science ◽

10.31018/jans.v12i1.2209 ◽

2020 ◽

Vol 12 (1) ◽

pp. 9-12

Author(s):

Arjun G. Koppad ◽

Syeda Sarfin ◽

Anup Kumar Das

Keyword(s):

Land Use ◽

Land Cover ◽

Accuracy Assessment ◽

Ground Truth ◽

Land Cover Classification ◽

Data Sets ◽

Radiometric Calibration ◽

Ground Truth Data ◽

Dense Forest ◽

Sar Data

The study has been conducted for land use and land cover classification by using SAR data. The study included examining of ALOS 2 PALSAR L- band quad pol (HH, HV, VH and VV) SAR data for LULC classification. The SAR data was pre-processed first which included multilook, radiometric calibration, geometric correction, speckle filtering, SAR Polarimetry and decomposition. For land use land cover classification of ALOS-2-PALSAR data sets, the supervised Random forest classifier was used. Training samples were selected with the help of ground truth data. The area was classified under 7 different classes such as dense forest, moderate dense forest, scrub/sparse forest, plantation, agriculture, water body, and settlements. Among them the highest area was covered by dense forest (108647ha) followed by horticulture plantation (57822 ha) and scrub/Sparse forest (49238 ha) and lowest area was covered by moderate dense forest (11589 ha). Accuracy assessment was performed after classification. The overall accuracy of SAR data was 80.36% and Kappa Coefficient was 0.76. Based on SAR backscatter reflectance such as single, double, and volumetric scattering mechanism different land use classes were identified.

A Data-Driven Approach to SAR Data-Focusing

Sensors ◽

10.3390/s19071649 ◽

2019 ◽

Vol 19 (7) ◽

pp. 1649 ◽

Cited By ~ 2

Author(s):

Cataldo Guaragnella ◽

Tiziana D’Orazio

Keyword(s):

Imaging Technique ◽

Low Cost ◽

Data Matrix ◽

Point Scatterer ◽

Ancillary Data ◽

Raw Data ◽

Strong Point ◽

Sar Data ◽

Application Fields ◽

Reference Functions

Synthetic Aperture RADAR (SAR) is a radar imaging technique in which the relative motion of the sensor is used to synthesize a very long antenna and obtain high spatial resolution. Several algorithms for SAR data-focusing are well established and used by space agencies. Such algorithms are model-based, i.e., the radiometric and geometric information about the specific sensor must be well known, together with the ancillary data information acquired on board the platform. In the development of low-cost and lightweight SAR sensors, to be used in several application fields, the precise mission parameters and the knowledge of all the specific geometric and radiometric information about the sensor might complicate the hardware and software requirements. Despite SAR data processing being a well-established imaging technique, the proposed algorithm aims to exploit the SAR coherent illumination, demonstrating the possibility of extracting the reference functions, both in range and azimuth directions, when a strong point scatterer (either natural or manmade) is present in the scene. The Singular Value Decomposition is used to exploit the inherent redundancy present in the raw data matrix, and phase unwrapping and polynomial fitting are used to reconstruct clean versions of the reference functions. Fairly focused images on both synthetic and real raw data matrices without the knowledge of mission parameters and ancillary data information can be obtained; as a byproduct, azimuth beam pattern and estimates of a few other parameters have been extracted from the raw data itself. In a previous paper, authors introduced a preliminary work dealing with this problem and able to obtain good-quality images, if compared to the standard processing techniques. In this work, the proposed technique is described, and performance parameters are extracted to compare the proposed approach to RD, showing good adherence of the focused images and pulse responses.

Simple-Yet-Effective SRTM DEM Improvement Scheme for Dense Urban Cities Using ANN and Remote Sensing Data: Application to Flood Modeling

Water ◽

10.3390/w12030816 ◽

2020 ◽

Vol 12 (3) ◽

pp. 816

Author(s):

Dong Eon Kim ◽

Shie-Yui Liong ◽

Philippe Gourbesville ◽

Ludovic Andres ◽

Jiandong Liu

Keyword(s):

Low Cost ◽

Remote Sensing Data ◽

Ground Truth ◽

Shuttle Radar Topography Mission ◽

Performance Criteria ◽

Srtm Dem ◽

Flood Modeling ◽

High Quality ◽

Improvement Scheme ◽

Flood Maps

Digital elevation models (DEMs) are crucial in flood modeling as DEM data reflects the actual topographic characteristics where water can flow in the model. However, a high-quality DEM is very difficult to acquire as it is very time consuming, costly, and, often restricted. DEM data from a publicly accessible satellite, Shuttle Radar Topography Mission (SRTM), and Sentinel 2 multispectral imagery are selected and used to train the artificial neural network (ANN) to improve the quality of SRTM’s DEM. High-quality DEM is used as target data in the training of ANN. The trained ANN will then be ready to efficiently and effectively generate a high-quality DEM, at low cost, for places where ground truth DEM data is not available. In this paper, the performance of the DEM improvement scheme is evaluated over two dense urban cities, Nice (France) and Singapore; with the performance criteria using various matrices, e.g., visual clarity, scatter plots, root mean square error (RMSE) and flood maps. The DEM resulting from the improved SRTM (iSRTM) showed significantly better results than the original SRTM DEM, with about 38% RMSE reduction. Flood maps from iSRTM DEM show much more reasonable flood patterns than SRTM DEM’s flood map.

Sentinel 1A SAR Backscattering Signature of Maize and Cotton Crops

Madras Agricultural Journal ◽

10.29321/maj.01.000398 ◽

2017 ◽

Vol 104 (.1-.4) ◽

Author(s):

Kumaraperumal R ◽

◽

Shama M ◽

Balaji Kannan ◽

Ragunath K P ◽

...

Keyword(s):

Tamil Nadu ◽

Ground Truth ◽

Backscattering Coefficient ◽

Ground Truth Data ◽

Crop Monitoring ◽

Multi Temporal ◽

The Mean ◽

Sar Data ◽

Remote Sensing Techniques ◽

Crop Discrimination

Crop discrimination is a key issue for agricultural monitoring using remote sensing techniques. Synthetic Aperture Radar (SAR) data are advantageous for crop monitoring and classification because of their all-weather imaging capabilities. The multi-temporal Sentinel 1A SAR data was acquired from 08th August, 2015 to 23rd January, 2016 at 12 days interval covering the extent of Perambalur district of Tamil Nadu. Both the Vertical - Vertical (VV) and Vertical-Horizontal (VH) polarized data are compared. The ground truth data collection was performed for cotton and maize during the vegetative, flowering and harvesting stages. The temporal backscattering coefficient (σ0 ) for cotton and maize are extracted using the training datasets. The mean backscattering values for cotton during the entire cropping period ranges from -10.58 dB to -6.28 dB and -20.59 dB to -14.53 dB for VV and VH polarized data respectively, and for maize it ranges from -11.08 dB to -7.07 dB and -19.85 dB to -14.14 dB for VV and VH polarized data respectively.

LOW COST MULTI-SENSOR ROBOT LASER SCANNING SYSTEM AND ITS ACCURACY INVESTIGATIONS FOR INDOOR MAPPING APPLICATION

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

10.5194/isprs-archives-xlii-2-w8-83-2017 ◽

2017 ◽

Vol XLII-2/W8 ◽

pp. 83-85 ◽

Cited By ~ 3

Author(s):

C. Chen ◽

X. Zou ◽

M. Tian ◽

J. Li ◽

W. Wu ◽

...

Keyword(s):

Inertial Measurement Unit ◽

Laser Scanning ◽

Low Cost ◽

Laser Scanner ◽

Ground Truth ◽

Measurement Unit ◽

Scanning System ◽

Ground Truth Data ◽

Multiple Sensor ◽

Laser Scanning System

In order to solve the automation of 3D indoor mapping task, a low cost multi-sensor robot laser scanning system is proposed in this paper. The multiple-sensor robot laser scanning system includes a panorama camera, a laser scanner, and an inertial measurement unit and etc., which are calibrated and synchronized together to achieve simultaneously collection of 3D indoor data. Experiments are undertaken in a typical indoor scene and the data generated by the proposed system are compared with ground truth data collected by a TLS scanner showing an accuracy of 99.2% below 0.25 meter, which explains the applicability and precision of the system in indoor mapping applications.