scholarly journals Automatic Flood Duration Estimation Based on Multi-Sensor Satellite Data

2020 ◽  
Vol 12 (4) ◽  
pp. 643 ◽  
Author(s):  
Michaela Rättich ◽  
Sandro Martinis ◽  
Marc Wieland
Keyword(s):  
Land Use Planning ◽  
Satellite Image ◽  
Integrated System ◽  
Economic Losses ◽  
Landsat 8 ◽  
Flood Duration ◽  
Multi Temporal ◽  
Satellite Image Processing ◽  
Spatio Temporal ◽  
Duration Estimation

Flood duration is a crucial parameter for disaster impact assessment as it can directly influence the degree of economic losses and damage to structures. It also provides an indication of the spatio-temporal persistence and the evolution of inundation events. Thus, it helps gain a better understanding of hydrological conditions and surface water availability and provides valuable insights for land-use planning. The objective of this work is to develop an automatic procedure to estimate flood duration and the uncertainty associated with the use of multi-temporal flood extent masks upon which the procedure is based. To ensure sufficiently high observation frequencies, data from multiple satellites, namely Sentinel-1, Sentinel-2, Landsat-8 and TerraSAR-X, are analyzed. Satellite image processing and analysis is carried out in near real-time with an integrated system of dedicated processing chains for the delineation of flood extents from the range of aforementioned sensors. The skill of the proposed method to support satellite-based emergency mapping activities is demonstrated on two cases, namely the 2019 flood in Sofala, Mozambique and the 2017 flood in Bihar, India.

scholarly journals Analysis of Changes in Area and Density of Mangroves through Landsat 8 Satellite Image Processing

2019 ◽  
Vol 9 (2) ◽  
pp. 16-22
Author(s):  
Nadya Fiqi Nurcahyani
Keyword(s):  
Image Processing ◽  
Satellite Imagery ◽  
Good Accuracy ◽  
Social Values ◽  
Satellite Image ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Android Application ◽  
Mangrove Area ◽  
Satellite Image Processing

Mangrove forests have high ecological, economic and social values ??which function to maintain shoreline stability, protect beaches and riverbanks, filter and remediate waste, and to withstand floods and waves. The facts show that mangrove damage is everywhere, even the intensity of damage and its area tends to increase significantly. Many roles of mangroves require proper management to maintain the existence of mangroves. One way to determine the area of ??mangroves is by processing Landsat 8 satellite imagery. The stages of mangrove identification are carried out by using 564 RGB band merger, then separating the mangrove and non-mangrove objects. Next step is to analyze the density of mangroves using NDVI formula. To maximize monitoring of mangrove area, an android application was created that provides information on the area and density of mangroves at several locations, namely Clungup, Bangsong Teluk Asmara and Cengkrong from 2015 to 2018.The results showed that Landsat 8 satellite imagery can be used to identify changes in the area of ??mangrove forests with good accuracy, namely in the Clungup area of ??90% and Cengkrong of 86.67%. From processing results, the mangrove area in the Clungup area has also decreased from 2015 to 2017 but has increased in 2018 so that the application provides recommendations for embroidering mangroves in 2016 to 2017 and mangrove recommendations are maintained in 2018. As for Bangsong Teluk area Asmara and Cengkrong have increased the area of ??mangroves every year so that the application provides recommendations to be maintained from 2016 to 2018.

scholarly journals CLOUD AND CLOUD SHADOW MASKING USING MULTI-TEMPORAL CLOUD MASKING ALGORITHM IN TROPICAL ENVIRONMENTAL

2016 ◽  
Vol XLI-B2 ◽  
pp. 95-100 ◽  
Author(s):  
D. S. Candra ◽  
S. Phinn ◽  
P. Scarth
Keyword(s):  
Satellite Images ◽  
Confusion Matrix ◽  
Satellite Image ◽  
Landsat 8 ◽  
Qualitative And Quantitative ◽  
Algorithm Comparison ◽  
Multi Temporal ◽  
The Difference ◽  
Very High ◽  
Better Than

A cloud masking approach based on multi-temporal satellite images is proposed. The basic idea of this approach is to detect cloud and cloud shadow by using the difference reflectance values between clear pixels and cloud and cloud shadow contaminated pixels. Several bands of satellite image which have big difference values are selected for developing Multi-temporal Cloud Masking (MCM) algorithm. Some experimental analyses are conducted by using Landsat-8 images. Band 3 and band 4 are selected because they can distinguish between cloud and non cloud. Afterwards, band 5 and band 6 are used to distinguish between cloud shadow and clear. The results show that the MCM algorithm can detect cloud and cloud shadow appropriately. Moreover, qualitative and quantitative assessments are conducted using visual inspections and confusion matrix, respectively, to evaluate the reliability of this algorithm. Comparison between this algorithm and QA band are conducted to prove the reliability of the approach. The results show that MCM better than QA band and the accuracy of the results are very high.

scholarly journals Satellite image processing of the Buxus hyrcana Pojark dieback in the Northern Forests of Iran

2021 ◽  
Vol 67 (No. 2) ◽  
pp. 71-79
Author(s):  
Marzieh Ghavidel ◽  
Peyman Bayat ◽  
Mohammad Ebrahim Farashiani
Keyword(s):  
Image Processing ◽  
Disease Progression ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Leaf Size ◽  
Disease Spread ◽  
Landsat 8 ◽  
Distribution Maps ◽  
Satellite Image Processing

Pests and diseases can cause a variety of reactions in plants. In recent years, the boxwood dieback has become one of the essential concerns of practitioners and natural resources managers in Iran. To control the boxwood dieback spread, the early detection and disease distribution maps are required. The boxwood dieback causes a range of changes in colour, shape and leaf size with respect to photosynthesis and transpiration. Through remote sensing techniques, e.g. satellite image processing data, the variation of thermal and visual characteristics of the plant could be used to measure and illustrate the symptoms of the disease. In this study, five common vegetation indices like difference vegetation index (DVI), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), simple ratio (SR), and plant health index (PHI) were extracted and calculated from Landsat 8 satellite image data from six regions in the Gilan province, located in the northern part of Iran out of 150 maps over the time period 2014‒2018. It turned out that among the aforementioned indices, based upon the results of the models, SR and NDVI indices were more useful for the disease spread, respectively. Our disease progression model fitting criteria showed that this technique could probably be used to assess the extent of the affected areas and also the disease progression in the investigated regions in future.

scholarly journals CLOUD AND CLOUD SHADOW MASKING USING MULTI-TEMPORAL CLOUD MASKING ALGORITHM IN TROPICAL ENVIRONMENTAL

2016 ◽  
Vol XLI-B2 ◽  
pp. 95-100 ◽  
Author(s):  
D. S. Candra ◽  
S. Phinn ◽  
P. Scarth
Keyword(s):  
Satellite Images ◽  
Confusion Matrix ◽  
Satellite Image ◽  
Landsat 8 ◽  
Qualitative And Quantitative ◽  
Algorithm Comparison ◽  
Multi Temporal ◽  
The Difference ◽  
Very High ◽  
Better Than

A cloud masking approach based on multi-temporal satellite images is proposed. The basic idea of this approach is to detect cloud and cloud shadow by using the difference reflectance values between clear pixels and cloud and cloud shadow contaminated pixels. Several bands of satellite image which have big difference values are selected for developing Multi-temporal Cloud Masking (MCM) algorithm. Some experimental analyses are conducted by using Landsat-8 images. Band 3 and band 4 are selected because they can distinguish between cloud and non cloud. Afterwards, band 5 and band 6 are used to distinguish between cloud shadow and clear. The results show that the MCM algorithm can detect cloud and cloud shadow appropriately. Moreover, qualitative and quantitative assessments are conducted using visual inspections and confusion matrix, respectively, to evaluate the reliability of this algorithm. Comparison between this algorithm and QA band are conducted to prove the reliability of the approach. The results show that MCM better than QA band and the accuracy of the results are very high.

scholarly journals Measuring land losses caused by water erosion using the SWAT model in the Ourika watershed in the High Atlas of Morocco

2021 ◽  
Vol 234 ◽  
pp. 00083
Author(s):  
Meysara Elmalki ◽  
Fouad Mounir ◽  
Abdellah Ichen ◽  
Taoufiq Qaini ◽  
Thami Khai ◽  
...  
Keyword(s):  
Assessment Tool ◽  
Thermal Emission ◽  
Swat Model ◽  
Satellite Image ◽  
Water Erosion ◽  
Economic Losses ◽  
Soil Productivity ◽  
Landsat 8 ◽  
Digital Elevation ◽  
Ourika Watershed

In Morocco, the phenomena of water erosion cause significant economic losses mainly linked to the silting up of dams, the degradation of equipment and socio-economic infrastructures, the loss of soil productivity and the insecurity of the population. The SWAT (Soil and Water Assessment Tool) model was used to estimate the quantities of sediments generated by the various erosive processes at the level of the Ourika watershed. The SWAT modeling, which is done with daily time steps, used as basic data; a Digital Elevation Model GDEM-ASTER (Global Digital Elevation-Advanced Space borne Thermal Emission and Reflection Radiometer) with 30 m of resolution, a land cover map developed from the Landsat 8 OLI (Operational Land Imager) satellite image of 2017 with 30 m of resolution and a soil map published by FAO (Harmonized World Soil Database). Also, daily meteorological data from the Tensift Water Basin Agency over a period from 1992 to 2001 were used. The results obtained showed that soil losses due to water erosion in the Ourika watershed reached an average of 9.18 t.ha-1.year-1. The model was calibrated and validated using the SWAT-CUP (SWAT Calibration and Uncertainty Procedures) software SUFI-2 (Sequential Uncertainty Fitting) and after several simulations and iterations a determination coefficient R2 of 0.76 was obtained.

scholarly journals Improved Consistency of an Automated Multisatellite Method for Extracting Temporal Changes in Flood Extent

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Husniyah Binti Mahmud ◽  
Vaibhav Katiyar ◽  
Masahiko Nagai
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Water Area ◽  
Temporal Changes ◽  
Landsat 8 ◽  
Water Index ◽  
Flood Duration ◽  
Optical Images ◽  
Landsat 7 ◽  
Moderate Resolution Imaging Spectroradiometer

Malaysia is affected by floods almost every year. In this situation, high-frequency flood monitoring is crucial so that timely measures can be taken. However, the low revisit time of the satellites, as well as occlusion cast by clouds in optical images, limits the frequency of flood observation of the focused area. Therefore, this study proposes utilising multisatellite data from optical satellites such as Landsat 7, Landsat 8, and Moderate Resolution Imaging Spectroradiometer (MODIS), as well as Synthetic Aperture Radar (SAR) images from Advanced Land Observation Satellite (ALOS-2) and Sentinel-1, to increase observation of flood. The main objective was to utilize Otsu image segmentation over both optical and SAR satellite images to distinguish water and nonwater areas in each image separately. For this, modified normalized difference water index (MNDWI) for the optical satellite and total dual-polarization backscatter for SAR satellite images were estimated. The focused area has been divided into Universal Transverse Mercator (UTM) square-size grids of 30 pixels, and each satellite image was reprojected and resampled with a pixel size of 0.001° to standardize the flood map resolution. The second objective was to assess the potential of image fusion for increasing the consistency of water area extraction. Two pairs of satellite images with the same observation period covering a flood event in September 2017 in Perlis, Malaysia, were processed using 2D wavelet transform. Lastly, the temporal changes of the integrated surface water extent were evaluated by comparing the output from both multisatellite and fused images with the observed water level data from the Department of Drainage and Irrigation. The results showed that the proposed model can be used to estimate flood duration as well as to estimate the flood-related losses, especially in ungauged or data-poor regions.

scholarly journals Análise Multitemporal dos Lençóis Maranhenses Entre 1984 a 2014 Utilizando Sensoriamento Remoto Orbital (Spatio-Temporal Analysis of Lençóis Maranhenses Between 1984 and 2014 Using Orbital Remote Sensing)

2016 ◽  
Vol 9 (1) ◽  
pp. 280
Author(s):  
Thiago Diniz Araujo ◽  
Eliana Lima da Fonseca
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Temporal Dynamics ◽  
Coastal Dunes ◽  
Temporal Analysis ◽  
Landsat 8 ◽  
Infrared Band ◽  
Trade Winds ◽  
Multi Temporal ◽  
Spatio Temporal

A análise multitemporal possibilita comparar uma mesma paisagem entre dois ou mais períodos, auxiliando no monitoramento das suas dinâmicas. O objetivo deste artigo foi analisar a dinâmica de caráter espaço-temporal do Parque Nacional dos Lençóis Maranhenses, mapeando as mudanças do sistema dunário a partir de imagens de satélite, no período de 1984 a 2014. Para esta análise foram utilizadas imagens de satélite adquiridas pelos sensores TM-Landsat 5 e OLI-Landsat 8. A borda limite do parque, na parte interior do continente, foi vetorizada para o ano inicial e final da análise, gerando mapas com o deslocamento das dunas no período de 31 anos o que permitiu identificar as áreas de avanço e retração do sistema dunário. A área total de avanço das dunas foi de 23,69 km² enquanto que a retração apresentou 14,95 km². Identificou-se expansão das dunas do litoral em direção ao interior do continente no sentido nordeste - sudoeste, seguindo a circulação dos ventos alísios. Foram selecionados quatro pontos de observações onde foram monitoradas as mudanças na cobertura do solo a partir da variação anual dos valores de reflectância da superfície na banda do infravermelho próximo, permitindo identificar o tipo de mudança quanto o tempo de ocorrência das mesmas.  A B S T R A C T The multi-temporal analysis allows comparing the same landscape between two or more time periods, assisting in the monitoring of its dynamics. The objective of this study were to analyze the spatio-temporal dynamics of the Parque Nacional dos Lençóis Maranhenses, mapping the changes in the dunes system using satellite imagery, from 1984 to 2014. For this analysis were used satellite images acquired by TM-Landsat 5 and OLI-Landsat 8 sensors. The park boundary, in the inner part of the continent, was vectored for the initial and final year of analysis, generating maps with the changes in the dunes locations in the 31 years period identified the forward areas and retraction and areas. The total area of advancement of dunes was 23.69 km² while the downturn area was 14.95 km². It was identified expansion of coastal dunes toward the interior of the continent towards northeast - southwest, following the movement of trade winds. Were selected four points of observations which were monitored changes in land cover from the annual change of the surface reflectance values in the near infrared band, allowing identify both the type of change and its time of occurrence.Keywords: remote sensing, environmental monitoring, migration of sediment. 

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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.

scholarly journals Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

2020 ◽  
Vol 12 (11) ◽  
pp. 1876 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Hideki Saito
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Series Data ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

Sign in / Sign up

Export Citation Format

Share Document