scholarly journals Analysis of riverbank changes in Ho Chi Minh city in the period 1989 – 2015

2019 ◽  
Vol 2 (2) ◽  
pp. 80-88
Author(s):  
Thu Trang Hoang ◽  
Khoi Nguyen Dao ◽  
Loi Thi Pham ◽  
Hong Van Nguyen
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Ho Chi Minh City ◽  
Landsat 8 ◽  
River Bank ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Multi Temporal ◽  
Erosion And Accretion ◽  
Landsat Satellite

The objective of this study was to analyze the changes of riverbanks in Ho Chi Minh City for the period 1989-2015 using remote sensing and GIS. Combination of Modified Normalized Difference Water Index (MNDWI) and thresholding method was used to extract the river bank based on the multi-temporal Landsat satellite images, including 12 Landsat 4-5 (TM) images and 2 Landsat 8 images in the period 1989-2015. Then, DSAS tool was used to calculate the change rates of river bank. The results showed that, the processes of erosion and accretion intertwined but most of the main riverbanks had erosion trend in the period 1989-2015. Specifically, the Long Tau River, Sai Gon River, Soai Rap River had erosion trends with a rate of about 10.44 m/year. The accretion process mainly occurred in Can Gio area, such as Dong Tranh river and Soai Rap river with a rate of 8.34 m/year. Evaluating the riverbank changes using multi-temporal remote sensing data may contribute an important reference to managing and protecting the riverbanks.

scholarly journals Dynamic Threshold Selection for the Classification of Large Water Bodies within Landsat-8 OLI Water Index Images

2016 ◽  
Author(s):  
Fangfang Zhang ◽  
Junsheng Li ◽  
Qian Shen ◽  
Bing Zhang ◽  
Huping Ye ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Water Bodies ◽  
Landsat 8 ◽  
Threshold Selection ◽  
Water Index ◽  
Sensing Data ◽  
Twin Peaks ◽  
Large Water ◽  
Threshold Range

Surface water distribution extracted from remote sensing data has been used in water resource assessment, coastal management, and environmental change studies. Traditional manual methods for extracting water bodies cannot satisfy the requirements for mass processing of remote sensing data; therefore, accurate automated extraction of such water bodies has remained a challenge. The histogram bimodal method (HBM) is a frequently used objective tool for threshold selection in image segmentation. The threshold is determined by seeking twin peaks, and the valley values between them; however, automatically calculating the threshold is difficult because complex surfaces and image noise which lead to not perfect twin peaks (single or multiple peaks). We developed an operational automated water extraction method, the modified histogram bimodal method (MHBM). The MHBM defines the threshold range of water extraction through mass static data; therefore, it does not require the identification of twin histogram peaks. It then seeks the minimum values in the threshold range to achieve automated threshold. We calibrated the MHBM for many lakes in China using Landsat 8 Operational Land Imager (OLI) images, for which the relative error (RE) and squared correlation coefficient (R2) for threshold accuracy were found to be 2.1% and 0.96, respectively. The RE and root-mean-square error (RMSE) for the area accuracy of MHBM were 0.59% and 7.4 km2. The results show that the MHBM could easily be applied to mass time-series remote sensing data to calculate water thresholds within water index images and successfully extract the spatial distribution of large water bodies automatically.

scholarly journals THE EFFECT OF DIFFERENT ATMOSPHERIC CORRECTIONS ON BATHYMETRY EXTRACTION USING LANDSAT 8 SATELLITE IMAGERY

2017 ◽  
Vol 12 (1) ◽  
pp. 13 ◽  
Author(s):  
Kuncoro Teguh Setiawan ◽  
Yennie Marini ◽  
Johannes Manalu ◽  
Syarif Budhiman
Keyword(s):  
Remote Sensing ◽  
Satellite Image ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Image Data ◽  
Landsat 8 ◽  
Calculation Error ◽  
Sensing Technology ◽  
The Difference ◽  
Landsat Satellite

Remote sensing technology can be used to obtain information bathymetry. Bathymetric information plays an important role for fisheries, hydrographic and navigation safety. Bathymetric information derived from remote sensing data is highly dependent on the quality of satellite data use and processing. One of the processing to be done is the atmospheric correction process. The data used in this study is Landsat 8 image obtained on June 19, 2013. The purpose of this study was to determine the effect of different atmospheric correction on bathymetric information extraction from Landsat satellite image data 8. The atmospheric correction methods applied were the minimum radiant, Dark Pixels and ATCOR. Bathymetry extraction result of Landsat 8 uses a third method of atmospheric correction is difficult to distinguish which one is best. The calculation of the difference extraction results was determined from regression models and correlation coefficient value calculation error is generated.

scholarly journals Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2401 ◽  
Author(s):  
Chuanliang Sun ◽  
Yan Bian ◽  
Tao Zhou ◽  
Jianjun Pan
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Landsat 8 ◽  
Growth Status ◽  
Sensing Data ◽  
Crop Type ◽  
Multi Temporal ◽  
Crop Types ◽  
Sentinel 2

Crop-type identification is very important in agricultural regions. Most researchers in this area have focused on exploring the ability of synthetic-aperture radar (SAR) sensors to identify crops. This paper uses multi-source (Sentinel-1, Sentinel-2, and Landsat-8) and multi-temporal data to identify crop types. The change detection method was used to analyze spectral and indices information in time series. Significant differences in crop growth status during the growing season were found. Then, three obviously differentiated time features were extracted. Three advanced machine learning algorithms (Support Vector Machine, Artificial Neural Network, and Random Forest, RF) were used to identify the crop types. The results showed that the detection of (Vertical-vertical) VV, (Vertical-horizontal) VH, and Cross Ratio (CR) changes was effective for identifying land cover. Moreover, the red-edge changes were obviously different according to crop growth periods. Sentinel-2 and Landsat-8 showed different normalized difference vegetation index (NDVI) changes also. By using single remote sensing data to classify crops, Sentinel-2 produced the highest overall accuracy (0.91) and Kappa coefficient (0.89). The combination of Sentinel-1, Sentinel-2, and Landsat-8 data provided the best overall accuracy (0.93) and Kappa coefficient (0.91). The RF method had the best performance in terms of identity classification. In addition, the indices feature dominated the classification results. The combination of phenological period information with multi-source remote sensing data can be used to explore a crop area and its status in the growing season. The results of crop classification can be used to analyze the density and distribution of crops. This study can also allow to determine crop growth status, improve crop yield estimation accuracy, and provide a basis for crop management.

scholarly journals Investigation on Perceptron Learning for Water Region Estimation Using Large-Scale Multispectral Images

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4333 ◽  
Author(s):  
Poliyapram Vinayaraj ◽  
Nevrez Imamoglu ◽  
Ryosuke Nakamura ◽  
Atsushi Oda
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Landsat 8 ◽  
Multispectral Images ◽  
Water Index ◽  
Tuning Parameters ◽  
Water Classification ◽  
Water Region ◽  
Normalized Difference Water Index ◽  
Perceptron Learning

Land cover classification and investigation of temporal changes are considered to be common applications of remote sensing. Water/non-water region estimation is one of the most fundamental classification tasks, analyzing the occurrence of water on the Earth’s surface. However, common remote sensing practices such as thresholding, spectral analysis, and statistical approaches are not sufficient to produce a globally adaptable water classification. The aim of this study is to develop a formula with automatically derived tuning parameters using perceptron neural networks for water/non-water region estimation, which we call the Perceptron-Derived Water Formula (PDWF), using Landsat-8 images. Water/non-water region estimates derived from PDWF were compared with three different approaches—Modified Normalized Difference Water Index (MNDWI), Automatic Water Extraction Index (AWEI), and Deep Convolutional Neural Network—using various case studies. Our proposed method outperforms all three approaches, showing a significant improvement in water/non-water region estimation. PDWF performance is consistently better even in cases of challenging conditions such as low reflectance due to hill shadows, building-shadows, and dark soils. Moreover, our study implemented a sunglint correction to adapt water/non-water region estimation over sunglint-affected pixels.

scholarly journals Application of Deep Learning Algorithm to Build an Automated Cloud Segmentation Model Based on Open Data Cube Framework

2020 ◽  
Vol 36 (4) ◽  
Author(s):  
Pham Vu Dong ◽  
Bui Quang Thanh ◽  
Nguyen Quoc Huy ◽  
Vo Hong Anh ◽  
Pham Van Manh
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Rapid Development ◽  
Open Data ◽  
Remote Sensing Data ◽  
Data Cube ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Multi Temporal

Cloud detection is a significant task in optical remote sensing to reconstruct the contaminated cloud area from multi-temporal satellite images. Besides, the rapid development of machine learning techniques, especially deep learning algorithms, can detect clouds over a large area in optical remote sensing data. In this study, the method based on the proposed deep-learning method called ODC-Cloud, which was built on convolutional blocks and integrating with the Open Data Cube (ODC) platform. The results showed that our proposed model achieved an overall 90% accuracy in detecting cloud in Landsat 8 OLI imagery and successfully integrated with the ODC to perform multi-scale and multi-temporal analysis. This is a pioneer study in techniques of storing and analyzing big optical remote sensing data.

scholarly journals DETECTION AGRICULTURE DEGRADATION FOR THE SOUTH OF BAGHDAD CITY USING REMOTE SENSING DATA FOR YEARS 2010-2019

2020 ◽  
pp. 57-67
Author(s):  
Taif Adil DHAMIN ◽  
Ebtesam F. KHANJER ◽  
Fouad K. MASHEE
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Military Operations ◽  
The South ◽  
Water Index ◽  
Rs And Gis ◽  
Landsat Satellite

Recently, the develop of the science of remote sensing enabled humanity to achieve the accuracy and wide coverage for different natural phenomena, disasters and applications (such as desertification, rainstorms, floods, fires, sweeping torrents, urban planning, and even in military). The main aim of this study is monitoring, highlighting and assessing maps for the degradation of agriculture in the south areas of Baghdad governorate (Al-Rasheed, Al-Yusufiyah, Al-Mahmudiyah, Al-Latifiyah, and Al-Madaen). Based to several factors, including the economic, social and military operations, the area had suffer the lands degradation which led to agriculture retreating. Remote sensing and Geographic information system (GIS) was applied, using ArcGIS 10.4.1 to process, manage, and analysis datasets, beside field verification to estimate the severity assessment of a computerized land degradation. Two satellites were adapted Landsat5 TM+ and Landsat8 OLI/TIRS imageries to assess the extent of land degradation for the study area during the years (5th May 2010 and 2nd May 2019). Two indices used in this research are: The Normalized Difference Vegetation Index “NDVI”, and The Normalized Differential Water Index “NDWI”. The results showed that there is a clear spatial reduction in both NDVI and NDWI, where the NDVI reduced from 2461082400 m2 to 1552698000 m2, accounting for 89.67 and 56.57 percent, respectively, while the NDWI reduced from 14166000 m2 to 12053700 m2, accounting for 0.52, and 0.44 percent, respectively. Keywords: Agriculture Degradation, RS And GIS Techniques, Landsat Satellite Imagery, NDVI And NDWI.

scholarly journals A NOVEL METHOD FOR WATER AND WATER CANAL EXTRACTION FROM LANDSAT-8 OLI IMAGERY

2018 ◽  
Vol XLII-5 ◽  
pp. 323-328 ◽  
Author(s):  
S. L. K. Reddy ◽  
C. V. Rao ◽  
P. R. Kumar ◽  
R. V. G. Anjaneyulu ◽  
B. G. Krishna
Keyword(s):  
Remote Sensing ◽  
Water Body ◽  
Remote Sensing Data ◽  
Water Bodies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Additional Information ◽  
Water Canal ◽  
Water Canals

<p><strong>Abstract.</strong> Constituents of hydrologic network, River and water canals play a key role in Agriculture for cultivation, Industrial activities and urban planning. Remote sensing images can be effectively used for water canal extraction, which significantly improves the accuracy and reduces the cost involved in mapping using conventional means. Using remote sensing data, the water Index (WI), Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) are used in extracting the water bodies. These techniques are aimed at water body detection and need to be complemented with additional information for the extraction of complete water canal networks. The proposed index MNDWI-2 is able to find the water bodies and water canals as well from the Landsat-8 OLI imagery and is based on the SWIR2 band. In this paper, we use Level-1 precision terrain corrected OLI imagery at 30 meter spatial resolution. The proposed MNDWI-2 index is derived using SWIR2 (B7) band and Green (B3) band. The usage of SWIR2 band over SWIR1 results in very low reflectance values for water features, detection of shallow water and delineation of water features with rest of the features in the image. The computed MNDWI-2 index values are threshold by making the values greater than zero as 1 and less than zero as zero. The binarised values of 1 represent the water bodies and 0 represent the non-water body. This normalized index detects the water bodies and canals as well as vegetation which appears in the form of noise. The vegetation from the MNDWI-2 image is removed by using the NDVI index, which is calculated using the Top of Atmosphere (TOA) corrected images. The paper presents the results of water canal extraction in comparison with the major available indexes. The proposed index can be used for water and water canal extraction from L8 OLI imagery, and can be extended for other high resolution sensors.</p>

scholarly journals Contributions of remote sensing in the diachronic study of the spatial and temporal evolution of the Ahmed El Hansali dam water reservoir from 2002-03 to 2018-19

2020 ◽  
Vol 183 ◽  
pp. 02004
Author(s):  
Tarik El Orfi ◽  
Mohamed El Ghachi ◽  
Sébastien Lebaut
Keyword(s):  
Remote Sensing ◽  
Negative Impact ◽  
Water Reservoir ◽  
Water Levels ◽  
Landsat 8 ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Landsat 7 ◽  
Diachronic Study ◽  
Flooded Area

The OumErRbiabasin is one of the watersheds with the largest number of hydraulic infrastructures in Morocco. These hydraulic structuressupply water for drinking, industrial and agricultural uses. The Ahmed El Hansali dam is a 740 Mm³ reservoir located near Zaouyat Cheikh andhave an active storage of473 Mm³. The succession of dry years in the OumErRbiabasin has had a negative impact on the water resource and has caused a remarkable decrease in the reservoir of the Ahmed el Hansali dam. In this paper, the MNDWI (Modified Normalized Difference Water Index) from Landsat 5-TM, Landsat 7-ETM, and Landsat 8-OLI satellite images was used to estimate the spatial and temporal fluctuations of the volumes of water stored in the reservoir between hydrological years 2002-03 and 2018-19. Results show that the volumes estimated by remote sensing reasonably match the volumes estimated by the OumErRbia Hydraulic Basin Agency (OERHBA)using recorded water levels and reservoir storage curve for years 2002-03 and 2013-14; the determination coefficient R² exceeds 0.90. The mapping of the extent of the dam’s impoundment has shown a very significant decreasein the flooded area level during dry years.

scholarly journals Analysis on Thermal Islands Effectors in Ramadi City, Iraq Using Multi-temporal Landsat Images

2020 ◽  
Vol 1 (135) ◽  
pp. 67-78
Author(s):  
Ismael Abbas Hurat
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Landsat Images ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Landsat 7 ◽  
Multi Temporal

This paper analyzes the effects of urban density, vegetation cover, and water body on thermal islands measured by land surface temperature in Al Anbar province, Iraq using multi-temporal Landsat images. Images from Landsat 7 ETM and Landsat 8 OLI for the years 2000, 2014, and 2018 were collected, pre-processed, and anal yzed. The results suggested that the strongest correlation was found between the Normalized Difference Built-up Index (NDBI) and the surface temperature. The correlation between the Normalized Difference Vegetation Index (NDVI) and the surface temperature was slightly weaker compared to that of NDBI. However, the weakest correlation was found between the Normalized Difference Water Index (NDWI) and the temperature. The results obtained in this research may help the decision makers to take actions to reduce the effects of thermal islands by looking at the details in the produced maps and the analyzed values of these spectral indices.

scholarly journals CLOUD-DESKTOP REMOTE SENSING DATA MANAGEMENT TO ENSURE TIME SERIES ANALYSIS, INTEGRATION OF QGIS AND GOOGLE EARTH ENGINE

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 553-557
Author(s):  
E. Panidi ◽  
I. Rykin ◽  
P. Kikin ◽  
A. Kolesnikov
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Data Storage ◽  
Remote Sensing Data ◽  
Growing Season ◽  
Data Representation ◽  
Google Earth ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Google Earth Engine

Abstract. Our context research is conducted to investigate the possibility of common application of the remote sensing and ground-based monitoring data to detection and observation of the dynamics and change in climate and vegetation cover parameters. We applied the analysis of the annual graphs of Normalized Difference Water Index to estimate the length and time frames of growing seasons. Basing on previously gained results, we concluded that we can use the Index-based monitoring of growing season parameters as a relevant technique. We are working on automation of computations that can be applied to processing satellite imagery, computing Normalized Difference Water Index time series (in the forms of maps and annual graphs), and estimation of growing season parameters. As currently used data amounts are big (or up-to-big) geospatial data, we use the Google Earth Engine platform to process initial datasets. Our currently described experimental work incorporates investigation of the possibilities for integration of cloud computing data storage and processing with client-side data representation in universal desktop GISs. To ensure our study needs we developed a prototype of a QGIS plugin capable to run processing in GEE and represent results in QGIS.

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