scholarly journals A Collaborative Change Detection Approach on Multi-Sensor Spatial Imagery for Desert Wetland Monitoring after a Flash Flood in Southern Morocco

2019 ◽  
Vol 11 (9) ◽  
pp. 1042 ◽  
Author(s):  
Sofia Hakdaoui ◽  
Anas Emran ◽  
Biswajeet Pradhan ◽  
Chang-Wook Lee ◽  
Salomon Cesar Nguemhe Fils
Keyword(s):  
Spatial Data ◽  
Flash Flood ◽  
Optical Data ◽  
Support Vector ◽  
Landsat 8 ◽  
Spectral Processing ◽  
Linear Classifiers ◽  
Gaussian Kernels ◽  
Before And After ◽  
Efficient Information

This study aims to present a technique that combines multi-sensor spatial data to monitor wetland areas after a flash-flood event in a Saharan arid region. To extract the most efficient information, seven satellite images (radar and optical) taken before and after the event were used. To achieve the objectives, this study used Sentinel-1 data to discriminate water body and soil roughness, and optical data to monitor the soil moisture after the event. The proposed method combines two approaches: one based on spectral processing, and the other based on categorical processing. The first step was to extract four spectral indices and utilize change vector analysis on multispectral diachronic images from three MSI Sentinel-2 images and two Landsat-8 OLI images acquired before and after the event. The second step was performed using pattern classification techniques, namely, linear classifiers based on support vector machines (SVM) with Gaussian kernels. The results of these two approaches were fused to generate a collaborative wetland change map. The application of co-registration and supervised classification based on textural and intensity information from Radar Sentinel-1 images taken before and after the event completes this work. The results obtained demonstrate the importance of the complementarity of multi-sensor images and a multi-approach methodology to better monitor changes to a wetland area after a flash-flood disaster.

scholarly journals Mapping Mangrove Forests of Dongzhaigang Nature Reserve in China Using Landsat 8 and Radarsat-2 Polarimetric SAR Data

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4012 ◽  
Author(s):  
Jianing Zhen ◽  
Jingjuan Liao ◽  
Guozhuang Shen
Keyword(s):  
Classification Accuracy ◽  
Mangrove Forest ◽  
Nature Reserve ◽  
Radar Data ◽  
Optical Data ◽  
Support Vector ◽  
Accurate Information ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Sar Data

Mangrove forests are distributed in intertidal regions that act as a “natural barrier” to the coast. They have enormous ecological, economic, and social value. However, the world’s mangrove forests are declining under immense pressure from anthropogenic and natural disturbances. Accurate information regarding mangrove forests is essential for their protection and restoration. The main objective of this study was to develop a method to improve the classification of mangrove forests using C-band quad-pol Synthetic Aperture Radar (SAR) data (Radarsat-2) and optical data (Landsat 8), and to analyze the spectral and backscattering signatures of mangrove forests. We used a support vector machine (SVM) classification method to classify the land use in Hainan Dongzhaigang National Nature Reserve (HDNNR). The results showed that the overall accuracy using only optical information was 83.5%. Classification accuracy was improved to a varying extent by the addition of different radar data. The highest overall accuracy was 95.0% based on a combination of SAR and optical data. The area of mangrove forest in the reserve was found to be 1981.7 ha, as determined from the group with the highest classification accuracy. Combining optical data with SAR data could improve the classification accuracy and be significant for mangrove forest conservation.

scholarly journals POST-FIRE HAZARD DETECTION USING ALOS-2 RADAR AND LANDSAT-8 OPTICAL IMAGERY

2020 ◽  
Vol VI-3/W1-2020 ◽  
pp. 75-82
Author(s):  
S. Mutai ◽  
L. Chang
Keyword(s):  
Forest Fires ◽  
Incidence Angle ◽  
Fire Hazard ◽  
Image Feature ◽  
Support Vector ◽  
Landsat 8 ◽  
Burnt Area ◽  
Detection Techniques ◽  
Before And After ◽  
Normalized Burn Ratio

Abstract. This study investigates the use of Advanced Land Observing Satellite 2 (ALOS-2) equipped with an enhanced L-band SAR sensor imagery alongside with Landsat-8 optical sensor in detection and mapping of burnt and unburnt scars occurring after a bushfire in Victoria, Australia. The bushfires had recently occurred in the period of 2018–2019. The analysis was explored using a contextual classifier Support Vector Machine (SVM), as SVM allows us to integrate spectral information and spatial context through the optimal smoothing parameter without degrading image quality. The training and test set datasets consisting of burnt and unburnt pixels were created from Landsat-8 scenes used as reference data. The backscatter intensity maps (acquired before and after the forest fires) from ALOS-2 data were compared and investigated, with a special concern on topographic influence removal. The dual polarizations (HH and HV) have been used to improve the forest fire mapping capability. These change detection techniques were based on image feature differences, index calculation such as normalized burn ratio. The burnt area and unburnt area were then classified via a threshold given by the pre- and post- disaster differences. The classification result achieved an accuracy of 80% Landsat-8 and 89% ALOS-2. This result shows the limitations of burnt area mapping with ALOS-2 due to effect local incidence angle and topography were of greater impact resulting in shadows. Nevertheless, the results in both areas verify the use of satellite SAR sensors and optical in forestry application.

scholarly journals A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping

2020 ◽  
Vol 12 (17) ◽  
pp. 2688 ◽  
Author(s):  
Viet-Ha Nhu ◽  
Phuong-Thao Thi Ngo ◽  
Tien Dat Pham ◽  
Jie Dou ◽  
Xuan Song ◽  
...  
Keyword(s):  
Soil Type ◽  
Flash Flood ◽  
Predictive Performance ◽  
Slope Aspect ◽  
Support Vector ◽  
Landsat 8 ◽  
Mountainous Area ◽  
Random Subspace ◽  
Topographic Wetness Index ◽  
Inventory Map

Flash flood is one of the most dangerous natural phenomena because of its high magnitudes and sudden occurrence, resulting in huge damages for people and properties. Our work aims to propose a state-of-the-art model for susceptibility mapping of the flash flood using the decision tree random subspace ensemble optimized by hybrid firefly–particle swarm optimization (HFPS), namely the HFPS-RSTree model. In this work, we used data from a flood inventory map consisting of 1866 polygons derived from Sentinel-1 C-band synthetic aperture radar (SAR) data and a field survey conducted in the northwest mountainous area of the Van Ban district, Lao Cai Province in Vietnam. A total of eleven flooding conditioning factors (soil type, geology, rainfall, river density, elevation, slope, aspect, topographic wetness index (TWI), normalized difference vegetation index (NDVI), plant curvature, and profile curvature) were used as explanatory variables. These indicators were compiled from a geological and mineral resources map, soil type map, and topographic map, ALOS PALSAR DEM 30 m, and Landsat-8 imagery. The HFPS-RSTree model was trained and verified using the inventory map and the eleven conditioning variables and then compared with four machine learning algorithms, i.e., the support vector machine (SVM), the random forests (RF), the C4.5 decision trees (C4.5 DT), and the logistic model trees (LMT) models. We employed a range of statistical standard metrics to assess the predictive performance of the proposed model. The results show that the HFPS-RSTree model had the best predictive performance and achieved better results than those of other benchmarks with the ability to predict flash flood, reaching an overall accuracy of over 90%. It can be concluded that the proposed approach provides new insights into flash flood prediction in mountainous regions.

scholarly journals TESTING A COMBINED MULTISPECTRAL-MULTITEMPORAL APPROACH FOR GETTING CLOUDLESS IMAGERY FOR SENTINEL-2

2020 ◽  
Vol V-3-2020 ◽  
pp. 293-300
Author(s):  
N. Colaninno ◽  
A. Marambio ◽  
J. Roca
Keyword(s):  
Seismic Event ◽  
Optical Data ◽  
Data Reconstruction ◽  
Landsat 8 ◽  
Thermal Data ◽  
Before And After ◽  
Optical Imagery ◽  
Post Disaster ◽  
Selection Of ◽  
Sentinel 2

Abstract. Earth observation and land cover monitoring are among major applications for satellite data. However, the use of primary satellite information is often limited by clouds, cloud shadows, and haze, which generally contaminate optical imagery. For purposes of hazard assessment, for instance, such as flooding, drought, or seismic events, the availability of uncontaminated optical data is required. Different approaches exist for masking and replacing cloud/haze related contamination. However, most common algorithms take advantage by employing thermal data. Hence, we tested an algorithm suitable for optical imagery only. The approach combines a multispectral-multitemporal strategy to retrieve daytime cloudless and shadow-free imagery. While the approach has been explored for Landsat information, namely Landsat 5 TM and Landsat 8 OLI, here we aim at testing the suitability of the method for Sentinel-2 Multi-Spectral Instrument. A multitemporal stack, for the same image scene, is employed to retrieve a composite uncontaminated image over a temporal period of few months. Besides, in order to emphasize the effectiveness of optical imagery for monitoring post-disaster events, two temporal stages have been processed, before and after a critical seismic event occurred in Lombok Island, Indonesia, in summer 2018. The approach relies on a clouds and cloud shadows masking algorithm, based on spectral features, and a data reconstruction phase based on automatic selection of the most suitable pixels from a multitemporal stack. Results have been tested with uncontaminated image samples for the same scene. High accuracy is achieved.

scholarly journals Accelerate disaster risk mapping using an automated zonal change detection model for loss estimation and recovery: a flood case study

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Ali Amasha
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Flash Flood ◽  
Disaster Risk ◽  
Loss Estimation ◽  
Risk Mapping ◽  
Detection Model ◽  
Applied Model ◽  
High Resolution Satellite Images ◽  
Before And After

Abstract Background The flash flood still constitutes one of the major natural meteorological disasters harmfully threatening local communities, that creates life losses and destroying infrastructures. The severity and magnitude of disasters always reflected from the size of impacts. Most of the conventional research models related to flooding vulnerability are focusing on hydro-meteorological and morphometric measurements. It, however, requires quick estimate of the flood losses and assess the severity using reliable information. An automated zonal change detection model applied, using two high-resolution satellite images dated 2009 and 2011 coupled with LU/LC GIS layer, on western El-Arish City, downstream of Wadi El-Arish basin. The model enabled to estimate the severity of a past flood incident in 2010. Results The model calculated the total changes based on the before and after satellite images based on pixel-by-pixel comparison. The estimated direct-damages nearly 32,951 m2 of the total mapped LU/LC classes; (e.g., 11,407 m2 as 3.17% of the cultivated lands; 6031 m2 as 7.22% of the built-up areas and 4040 m2 as 3.62% of the paved roads network). The estimated cost of losses, in 2010 economic prices for the selected three LU/LC classes, is nearly 25 million USD, for the cultivation fruits and olives trees, ~ 4 million USD for built-up areas and ~ 1 million USD for paved roads network. Conclusion The disasters’ damage and loss estimation process takes many detailed data, longtime, and costed as well. The applied model accelerates the disaster risk mapping that provides an informative support for loss estimation. Therefore, decision-makers and professionals need to apply this model for quick the disaster risks management and recovery.

scholarly journals Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

2018 ◽  
Vol 10 (8) ◽  
pp. 1285 ◽  
Author(s):  
Reza Attarzadeh ◽  
Jalal Amini ◽  
Claudia Notarnicola ◽  
Felix Greifeneder
Keyword(s):  
Soil Moisture ◽  
Optical Data ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Practical Applications ◽  
Initial Stage ◽  
Object Based ◽  
Single Polarization ◽  
Selection Of

This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel- and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R2), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

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