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.

Assessment of mangrove forest change in Ca Mau province using satellite images in the period of 1988 - 2018

2021 ◽  
Vol 66 (1) ◽  
pp. 175-187
Author(s):  
Duong Phung Thai ◽  
Son Ton
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Image Interpretation ◽  
Image Data ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Forest Change ◽  
Shrimp Ponds ◽  
Area Classification

On the basis of using practical methods, satellite image processing methods, the vegetation coverage classification system of the study area, interpretation key for the study area, classification and post-classification pro cessing, this research introduces how to exploit and process multi-temporal satellite images in evaluating the changes of forest area. Landsat 4, 5 TM and Landsat 8 OLI remote sensing image data were used to evaluate the changes in the area of mangrove forests (RNM) in Ca Mau province in the periods of 1988 - 1998, 1998 - 2013, 2013 - 2018, and 1988 - 2018. The results of the image interpretation in 1988, 1998, 2013, 2018 and the overlapping of the above maps show: In the 30-year period from 1988 to 2018, the total area of mangroves in Ca Mau province was decreased by 28% compared to the beginning, from 71,093.3 ha in 1988 reduced to 51,363.5 ha in 2018, decreasing by 19,729.8 ha. The recovery speed of mangroves is 2 times lower than their disappearance speed. Specifically, from 1988 to 2018, mangroves disappeared on an area of 42,534.9 hectares and appeared on the new area of 22,805 hectares, only 12,154.5 hectares of mangroves remained unchanged. The fluctuation of mangrove area in Ca Mau province is related to the process of deforestation to dig shrimp ponds, coastal erosion, the formation of mangroves on new coastal alluvial lands and soil dunes in estuaries, as well as planting new mangroves in inefficient shrimp ponds.

scholarly journals Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Water ◽  
2017 ◽  
Vol 9 (4) ◽  
pp. 256 ◽  
Author(s):  
Yan Zhou ◽  
Jinwei Dong ◽  
Xiangming Xiao ◽  
Tong Xiao ◽  
Zhiqi Yang ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Landsat 8 ◽  
Open Surface ◽  
Body Mapping ◽  
Water Index ◽  
Landsat 7 ◽  
Surface Water Bodies ◽  
Water Indices ◽  
Sentinel 2

Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index- and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

scholarly journals Estimation of PM10 Distribution using Landsat5 and Landsat8 Remote Sensing

Proceedings ◽  
2018 ◽  
Vol 2 (23) ◽  
pp. 1430
Author(s):  
V. M. Fernández-Pacheco ◽  
C. A. López-Sánchez ◽  
E. Álvarez-Álvarez ◽  
M. J. Suárez López ◽  
L. García-Expósito ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Pollutants ◽  
Satellite Images ◽  
Satellite Image ◽  
Remote Sensing Image ◽  
Regional Studies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm ◽  
Source Of Information

Air pollution is one of the major environmental problems, especially in industrial and highly populated areas. Remote sensing image is a rich source of information with many uses. This paper is focused on estimation of air pollutants using Landsat-5 TM and Landsat-8 OLI satellite images. Particulate Matter with particle size less than 10 microns (PM10) is estimated for the study area of Principado de Asturias (Spain). When a satellite records the radiance of the surface received at sensor, does not represent the true radiance of the surface. A noise caused by Aerosol and Particulate Matters attenuate that radiance. In many applications of remote sensing, that noise called path radiance is removed during pre-processing. Instead, path radiance was used to estimate the PM10 concentration in the air. A relationship between the path radiance and PM10 measurements from ground stations has been established using Random Forest (RF) algorithm and a PM10 map was generated for the study area. The results show that PM10 estimation through satellite image is an efficient technique and it is suitable for local and regional studies.

scholarly journals Using Window Regression to Gap-Fill Landsat ETM+ Post SLC-Off Data

2018 ◽  
Vol 10 (10) ◽  
pp. 1502 ◽  
Author(s):  
Evan Brooks ◽  
Randolph Wynne ◽  
Valerie Thomas
Keyword(s):  
Computation Time ◽  
Window Size ◽  
Landsat 8 ◽  
Ancillary Data ◽  
Data Intensive ◽  
Landsat 7 ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Temporal Dimensions ◽  
Data Layers

The continued development of algorithms using multitemporal Landsat data creates opportunities to develop and adapt imputation algorithms to improve the quality of that data as part of preprocessing. One example is de-striping Enhanced Thematic Mapper Plus (ETM+, Landsat 7) images acquired after the Scan Line Corrector failure in 2003. In this study, we apply window regression, an algorithm that was originally designed to impute low-quality Moderate Resolution Imaging Spectroradiometer (MODIS) data, to Landsat Analysis Ready Data from 2014–2016. We mask Operational Land Imager (OLI; Landsat 8) image stacks from five study areas with corresponding ETM+ missing data layers, using these modified OLI stacks as inputs. We explored the algorithm’s parameter space, particularly window size in the spatial and temporal dimensions. Window regression yielded the best accuracy (and moderately long computation time) with a large spatial radius (a 7 × 7 pixel window) and a moderate temporal radius (here, five layers). In this case, root mean square error for deviations from the observed reflectance ranged from 3.7–7.6% over all study areas, depending on the band. Second-order response surface analysis suggested that a 15 × 15 pixel window, in conjunction with a 9-layer temporal window, may produce the best accuracy. Compared to the neighborhood similar pixel interpolator gap-filling algorithm, window regression yielded slightly better accuracy on average. Because it relies on no ancillary data, window regression may be used to conveniently preprocess stacks for other data-intensive algorithms.

scholarly journals PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.0)

2015 ◽  
Vol 8 (10) ◽  
pp. 8481-8518
Author(s):  
S. Härer ◽  
M. Bernhardt ◽  
K. Schulz
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Threshold Value ◽  
Landsat 8 ◽  
Snow Covered Area ◽  
Covered Area ◽  
Independent Information ◽  
Landsat 7 ◽  
Definition Of

Abstract. Terrestrial photography combined with the recently presented Photo Rectification And ClassificaTIon SoftwarE (PRACTISE V.1.0) has proven to be a valuable source to derive snow cover maps in a high temporal and spatial resolution. The areal coverage of the used digital photographs is however strongly limited. Satellite images on the other hand can cover larger areas but do show uncertainties with respect to the accurate detection of the snow covered area. This is especially the fact if user defined thresholds are needed e.g. in case of the frequently used Normalised-Difference Snow Index (NDSI). The definition of this value is often not adequately defined by either a general value from literature or over the impression of the user but not by reproducible independent information. PRACTISE V.2.0 addresses this important aspect and does show additional improvements. The Matlab based software is now able to automatically process and detect snow cover in satellite images. A simultaneously captured camera-derived snow cover map is in this case utilised as in-situ information for calibrating the NDSI threshold value. Moreover, an additional automatic snow cover classification, specifically developed to classify shadow-affected photographs was included. The improved software was tested for photographs and Landsat 7 Enhanced Thematic Mapper (ETM+) as well as Landsat 8 Operational Land Imager (OLI) scenes in the Zugspitze massif (Germany). The results have shown that using terrestrial photography in combination with satellite imagery can lead to an objective, reproducible and user-independent derivation of the NDSI threshold and the resulting snow cover map. The presented method is not limited to the sensor system or the threshold used in here but offers manifold application options for other scientific branches.

scholarly journals Comparison between Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) Assessment of Vegetation Indices

2017 ◽  
Vol 1 (2) ◽  
pp. 355-366
Author(s):  
E. O. Makinde ◽  
A. D. Obigha
Keyword(s):  
Statistical Analysis ◽  
Land Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Thematic Mapper ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Landsat 7 ◽  
Index 2

The Landsat system has contributed significantly to the understanding of the Earth observation for over forty years. Since May 2013, data from Landsat 8 has been available online for download, with substantial differences from its predecessors, having an extended number of spectral bands and narrower bandwidths. The objectives of this research were majorly to carry out a cross comparison analysis between vegetation indices derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) and also performed statistical analysis on the results derived from the vegetation indices. Also, this research carried out a change detection on four land cover classes present within the study area, as well as projected the land cover for year 2030. The methods applied in this research include, carrying out image classification on the Landsat imageries acquired between 1984 – 2016 to ascertain the changes in the land cover types, calculated the mean values of differenced vegetation indices derived from the four land covers between Landsat 7 ETM+ and Landsat 8 OLI. Statistical analysis involving regression and correlation analysis were also carried out on the vegetation indices derived between the two sensors, as well as scatter plot diagrams with linear regression equation and coefficients of determination (R2). The results showed no noticeable differences between Landsat 7 and Landsat 8 sensors, which demonstrates high similarities. This was observed because Global Environmental Monitoring Index (GEMI), Improved Modified Triangular Vegetation Index 2 (MTVI2), Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Leaf Area Index (LAI) and Land Surface Water Index (LSWI) had smaller standard deviations. However, Renormalized Difference Vegetation Index (RDVI), Anthocyanin Reflectance Index 1 (ARI1) and Anthocyanin Reflectance Index 2 (ARI2) performed relatively poorly because their standard deviations were high. the correlation analysis of the vegetation indices that both sensors had a very high linear correlation coefficient with R2 greater than 0.99. It was concluded from this research that Landsat 7 ETM+ and Landsat 8 OLI can be used as complimentary data.

scholarly journals Structural interpretation of lineaments using satellite image processing: A case study in the vicinity of the Charvak reservoir

2020 ◽  
Vol 26 (2) ◽  
pp. 436-442
Author(s):  
Lola Sichugova ◽  
Dilbarkhon Fazilova
Keyword(s):  
Water Level ◽  
Satellite Images ◽  
Satellite Image ◽  
Water Reservoir ◽  
Water Level Fluctuations ◽  
Landsat 8 ◽  
Base Line ◽  
The North ◽  
Automated Method ◽  
Density Maps

This work presents the results of lineaments interpretation using the automated method of the satellite images in the territory of the Charvak water reservoir in Uzbekistan. Tectonic and local (water impoundment in Charvak reservoir) features of the region deformation were determined on base LINE algorithm in software PCI Geomatica. The thematic map with the geospatial arrangement of lineaments was constructed on base of satellite images LANDSAT-8 processing. We concluded that water level fluctuations have a greater influence on the appearance of the lineaments structure than periods of water filling and downstream in the reservoir. Lineament density maps showed dominantly increased density towards the north-southern direction is due to tectonic features of the region and the west-eastern direction is due to water level fluctuations in the reservoir. The lineaments density maps for summer-autumn periods showed the faults arising from water level fluctuations only. Winter-spring period affected with high influence of the seasonal (snow pack, rainfall) processes as well.

scholarly journals AUTOMATIC DETECTION OF CLOUDS AND SHADOWS USING HIGH RESOLUTION SATELLITE IMAGE TIME SERIES

2016 ◽  
Vol XLI-B3 ◽  
pp. 475-479 ◽  
Author(s):  
Nicolas Champion
Keyword(s):  
Time Series ◽  
Satellite Images ◽  
Satellite Image ◽  
Region Growing ◽  
Shadow Detection ◽  
The Other ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Large Area ◽  
The Difference

Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled <i>seeds</i> if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled <i>shadows</i> if the difference of reflectance (in the NIR channel) with the <i>synthetic</i> ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled <i>clouds</i> during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pléiades-HR images and our first experiments show the feasibility to automate the detection of shadows and clouds in satellite image sequences.

scholarly journals ANALISIS PERUBAHAN GARIS PANTAI UJUNG PANGKAH DENGAN MENGGUNAKAN METODE EDGE DETECTION DAN NORMALIZED DIFFERENCE WATER INDEX (UJUNG PANGKAH SHORELINE CHANGE ANALYSIS USING EDGE DETECTION METHOD AND NORMALIZED DIFFERENCE WATER INDEX)

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Nanin Anggraini ◽  
Sartono Marpaung ◽  
Maryani Hartuti
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Shoreline Change ◽  
Landsat 8 ◽  
Landsat Data ◽  
Change Analysis ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Landsat 7 ◽  
Edge Detection Method

Besides to the effects from tidal, coastline position changed due to abrasion and accretion. Therefore, it is necessary to detect the position of coastline, one of them by utilizing Landsat data by using edge detection and NDWI filter. Edge detection is a mathematical method that aims to identify a point on a digital image based on the brightness level. Edge detection is used because it is very good to present the appearance of a very varied object on the image so it can be distinguished easily. NDWI is able to separate land and water clearly, making it easier for coastline analysis. This study aimed to detect coastline changes in Ujung Pangkah of Gresik Regency caused by accretion and abrasion using edge detection and NDWI filters on temporal Landsat data (2000 and 2015). The data used in this research was Landsat 7 in 2000 and Landsat 8 in 2015. The results showed that the coastline of Ujung Pangkah Gresik underwent many changes due to accretion and abrasion. The accretion area reached 11,35 km2 and abrasion 5,19 km2 within 15 year period. Abstrak Selain akibat adanya pasang surut, posisi garis pantai berubah akibat adanya abrasi dan akresi. Oleh karena itu diperlukan adanya deteksi posisi garis pantai, salah satunya dengan memanfaatkan data Landsat dengan menggunakan filter edge detection dan NDWI. Edge detection adalah suatu metode matematika yang bertujuan untuk mengidentifikasi suatu titik pada gambar digital berdasarkan tingkat kecerahan. Filter edge detection digunakan karena sangat baik untuk menyajikan penampakan obyek yang sangat bervariasi pada citra sehingga dapat dibedakan dengan mudah. NDWI mampu memisahkan antara daratan dan perairan dengan jelas sehingga memudahkan untuk analisis garis pantai. Penelitian ini bertujuan untuk deteksi perubahan garis pantai di Ujung Pangkah Kabupaten Gresik yang disebabkan oleh adanya akresi dan abrasi dengan menggunakan filter edge detection dan NDWI pada data Landsat temporal (tahun 2000 dan 2015). Data yang digunakan pada penelitian ini adalah citra Landsat 7 tahun 2000 dan Landsat 8 tahun 2015. Hasil penelitian menunjukkan bahwa garis pantai di Ujung Pangkah Gresik banyak mengalami perubahan akibat adanya akresi dan abrasi. Luas akresi mencapai 11,35 km2 dan abrasi 5,19 km2 dalam periode waktu 15 tahun.

scholarly journals Fractional Land Cover Classification and Temporal Change Evaluation of Deh Akro-II Wetland Complex and Chotiari Wetland Complex, Sindh, Pakistan, Using Google Earth Engine

2021 ◽  
Vol 11 (4) ◽  
pp. 4258-4277
Author(s):  
Samawia Rizwan ◽  
Dr. Khalid Mahmood ◽  
Dr. Sajid Rashid Ahmad ◽  
Dr. Shafiq Ur Rehman
Keyword(s):  
Anthropogenic Activities ◽  
Temporal Analysis ◽  
Water Area ◽  
Google Earth ◽  
Natural Vegetation ◽  
Landsat 8 ◽  
Landsat 7 ◽  
Wetland Complex ◽  
Surrounding Areas ◽  
Year 2000

Wetlands are one of the most important and rich eco system. Deh akro II wetland complex is unique inland type of wetlands comprise of 35 wetlands in middle of Nara desert on bank of Nara Canal. They face a lot of degradation because of anthropogenic activities in the surrounding areas and lack of rainfall in last 2 decades. Chotiari wetland complex located in south east of Deh akro II wetland complex, it comprises of several fresh water lakes converted into reservoir in year 2003 for better irrigation purposes. This conversion of wetlands into reservoir does not did very well for surrounding agricultural lands and natural vegetation. So in this study two technique of Fractional cover mapping were used to classify three types of land covers in both study areas. Temporal analysis was performed using the Landsat 7 ETM+ image of year 2000 and Landsat 8 OLI image of year 2018. For better results NDVI, EVI and NDWI were also calculated. For Deh akro II wetland complex Kappa accuracy statistics for year 2000 is 84% and for year 2018 its 87%. Several changes were recorded in this time span of 18 years as 42% of water bodies area has been decreased, 48% of Agriculture area has been increased and 68% of natural vegetation area has been increased. Increase in amount of vegetation and agriculture indicates that with better management and planning, effects of climate change over the area can be minimized. Kappa accuracy statistics for Chotiari Wetland complex for year 2000 is 71% and for year 2018 it’s 73%. Enormous changes were noted in 18 years as Agriculture area has been decreased up to 91%, water area has been increased up to 15% and vegetation has unluckily decreased up to 98% in reservoir area. This huge decrease in Agriculture and natural vegetation is an alarming situation for the wildlife and native population as well as authorities of Chotiari wetland complex.

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