scholarly journals Monitoring Perubahan Pola Alur Sungai Menggunakan Citra Satelit Resolusi Spasial Menengah Berbasis Spectral Classification

2020 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Agung Kurniawan
Keyword(s):  
Remote Sensing ◽  
Maximum Likelihood ◽  
Flow Pattern ◽  
Satellite Imagery ◽  
River Flow ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Process Data ◽  
Sensing Technology ◽  
Image Appearance

The development of remote sensing technology allows humans to acquire and process data remotely and temporally. Changes in the flow of the Progo river from the last few years are significant, this can be caused by natural factors and human factors. The influence of the intensity of flow and the level of sedimentation in the Progo river causes a massive flow pattern change in the Progo river body. The data used in this research is Medium Spatial Resolution Satellite Imagery, Landsat 5 satellite imagery acquired in 1995 and Landsat 8 acquired in 2017. Monitoring of changes in river flow pattern is generally done by using the method of terrestrial or conventional measurement, which takes a long time, for that the use of methods and remote sensing data can be used to save time. The method used is multispectral classification approach with maximum likelihood algorithm. The results of extraction using digital classification method (maximum likelihood) resulted in the appearance of flow pattern quickly and representative, so this method is suitable for the purpose of rapid detection of changes in flow pattern. The results obtained from the extraction of the Progo river flow pattern show an intricate river flow pattern with many river rubbers on the image appearance of 1995, whereas in the image extraction results in 2017 the river banks and turns do not look dominant, it shows that erosion and sedimentation activities continue to occur massively.

scholarly journals Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China

2020 ◽  
Vol 12 (21) ◽  
pp. 3539
Author(s):  
Haifeng Tian ◽  
Jie Pei ◽  
Jianxi Huang ◽  
Xuecao Li ◽  
Jian Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Satellite Imagery ◽  
Remote Sensing Data ◽  
Northern China ◽  
Google Earth ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Winter Crops ◽  
Sentinel 2

Garlic and winter wheat are major economic and grain crops in China, and their boundaries have increased substantially in recent decades. Updated and accurate garlic and winter wheat maps are critical for assessing their impacts on society and the environment. Remote sensing imagery can be used to monitor spatial and temporal changes in croplands such as winter wheat and maize. However, to our knowledge, few studies are focusing on garlic area mapping. Here, we proposed a method for coupling active and passive satellite imagery for the identification of both garlic and winter wheat in Northern China. First, we used passive satellite imagery (Sentinel-2 and Landsat-8 images) to extract winter crops (garlic and winter wheat) with high accuracy. Second, we applied active satellite imagery (Sentinel-1 images) to distinguish garlic from winter wheat. Third, we generated a map of the garlic and winter wheat by coupling the above two classification results. For the evaluation of classification, the overall accuracy was 95.97%, with a kappa coefficient of 0.94 by eighteen validation quadrats (3 km by 3 km). The user’s and producer’s accuracies of garlic are 95.83% and 95.85%, respectively; and for the winter wheat, these two accuracies are 97.20% and 97.45%, respectively. This study provides a practical exploration of targeted crop identification in mixed planting areas using multisource remote sensing data.

scholarly journals Monitoring of specially protected natural territories of forest-steppe landscapes of the Stavropol upland by means of remote sensing data

2021 ◽  
Vol 27 (1) ◽  
pp. 364-376
Author(s):  
Tigran Shahbazyan
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Anthropogenic Factors ◽  
Forest Steppe ◽  
Landsat 8 ◽  
Sensing Data ◽  
Ndvi Index ◽  
The Impact

The article considers the methodology of monitoring specially protected natural areas using remote sensing data. The research materials are satellite images of the Landsat 5 and Landsat 8 satellites, obtained from the resource of the US Geological Survey. The key areas of the study were 3 specially protected areas located within the boundaries of the forest-steppe landscapes of the Stavropol upland, the reserves «Alexandrovskiy», «Russkiy Les», «Strizhament». The space survey materials were selected for the period 1991–2020, and the data from the summer seasons were used. The NDVI index is chosen as the method of processing the spectral channels of satellite imagery. To integrate long-term satellite imagery into a single raster image, the method of variance of the variation series for the NDVI index was used. The article describes an algorithm for processing satellite images, which allows us to identify the features of the dynamics of the vegetation state of the studied territory for the period 1991–2020. The bitmap image constructed by means of the variance of the NDVI index was classified by the quantile method, to translate numerical values into classes with qualitative characteristics. There were 4 classes of the territory according to the degree of dynamism of the vegetation state: “stable”, “slightly variable”, “moderately variable”, “highly variable”. The paper highlights the factors of landscape transformation, including natural and anthropogenic ones. In the course of the study, the determining influence of anthropogenic factors of transformation was noted. The greatest impact is on the reserve «Alexandrovskiy», the least on the reserve «Russkiy Les», in the reserve «Strizhament» the impact is expressed locally. The paper identifies the leading anthropogenic factors of vegetation transformation, based on their influence on vegetation.

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 Estimation of Total Suspended Sediment Solid in Porong River Waters Using Multitemporal Satellite Imagery

2021 ◽  
Vol 936 (1) ◽  
pp. 012006
Author(s):  
Z N Ghuvita Hadi ◽  
T Hariyanto ◽  
N Hayati
Keyword(s):  
Remote Sensing ◽  
Suspended Sediment ◽  
Satellite Imagery ◽  
River Estuary ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Sample Point ◽  
Sensing Data ◽  
Google Earth Engine

Abstract Monitoring the concentration of Total Suspended Solid (TSS) is one method to determine water quality, because a high TSS value indicates a high level of pollution. Remote sensing data can be used effectively in generating suspended sediment concentrations. Nowdays, Google Earth Engine platform has provided a large collection of remote sensing data. Therefore, this study uses Google Earth Engine which is processed for free and aims to calculate the TSS value in the Kali Porong area. This research was conducted multitemporal in the last ten years, namely from 2013-2021 using multitemporal satellite imagery landsat-8 and sentinel-2 by applying empirical algorithms for calculating TSS. The results of this study are the value of TSS concentration at each sample point and a multitemporal TSS concentration distribution map. The year 2016, 2017, and 2021, the distribution of TSS concentration values was higher than in other years. At the sample point, the lowest TSS concentration value was 16.55 mg/L in 2013. Meanwhile, the highest TSS concentration value of 266.33 mg/L occurred in 2014 precisely in the Porong River estuary area which is the border area between land and water. the sea so that a lot of TSS material is concentrated in the area due to waves and ocean currents.

scholarly journals WATERMARKING METHOD OF REMOTE SENSING DATA USING STEGANOGRAPHY TECHNIQUE BASED ON LEAST SIGNIFICANT BIT HIDING

2018 ◽  
Vol 15 (1) ◽  
pp. 63
Author(s):  
Destri Yanti Hutapea ◽  
Octaviani Hutapea
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Original Image ◽  
Least Significant Bit ◽  
Confidential Information ◽  
Sensing Data ◽  
Remote Sensing Satellite

Remote sensing satellite imagery is currently needed to support the needs of information in various fields. Distribution of remote sensing data to users is done through electronic media. Therefore, it is necessary to make security and identity on remote sensing satellite images so that its function is not misused. This paper describes a method of adding confidential information to medium resolution remote sensing satellite images to identify the image using steganography technique. Steganography with the Least Significant Bit (LSB) method is chosen because the insertion of confidential information on the image is performed on the rightmost bits in each byte of data, where the rightmost bit has the smallest value. The experiment was performed on three Landsat 8 images with different area on each composite band 4,3,2 (true color) and 6,5,3 (false color). Visually the data that has been inserted information does not change with the original data. Visually, the image that has been inserted with confidential information (or stego image) is the same as the original image. Both images cannot be distinguished on histogram analysis.  The Mean Squared Error value of stego images of  all three data less than 0.053 compared with the original image.  This means that information security with steganographic techniques using the ideal LSB method is used on remote sensing satellite imagery.

scholarly journals Farmland productivity estimation based on vegetation indexes from remote sensing data

2020 ◽  
Vol 206 ◽  
pp. 02015
Author(s):  
Shaoshuai Li ◽  
Baipeng Li ◽  
Wenjing Cao
Keyword(s):  
Remote Sensing ◽  
Production Capacity ◽  
Remote Sensing Data ◽  
High Standard ◽  
Estimation Accuracy ◽  
Landsat 8 ◽  
Yield Estimation ◽  
Sensing Technology ◽  
Vegetation Indexes ◽  
Estimation Models

Ensuring food security is a long-term and arduous task. Timely and accurate grasp of grain production capacity information can provide favourable data support for the nation to formulate macroeconomic plans and food policies. With the development of remote sensing technology, it has been widely used in crop yield estimation models. In this paper, the yield of spring maize in Da’an of Jilin province was estimated based on vegetation indexes calculated from Landsat-8 images. The results have shown that the fitting degree and estimation accuracy of yield estimation models at tasselling stage are significantly better than those at milk stage. Among these vegetation indexes, the model based on GNDVI has better fitting degree and estimation accuracy. This paper can provide reference for the post construction evaluation of high standard farmland in China.

scholarly journals Indices Extraction from Multitemporal Remote Sensing Data for Mapping Urban Built-Up

2021 ◽  
Vol 887 (1) ◽  
pp. 012004
Author(s):  
A. K. Hayati ◽  
Y.F. Hestrio ◽  
N. Cendiana ◽  
K. Kustiyo
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Sensing Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Multitemporal Remote Sensing ◽  
Multitemporal Data

Abstract Remote sensing data analysis in the cloudy area is still a challenging process. Fortunately, remote sensing technology is fast growing. As a result, multitemporal data could be used to overcome the problem of the cloudy area. Using multitemporal data is a common approach to address the cloud problem. However, most methods only use two data, one as the main data and the other as complementary of the cloudy area. In this paper, a method to harness multitemporal remote sensing data for automatically extracting some indices is proposed. In this method, the process of extracting the indices is done without having to mask the cloud. Those indices could be further used for many applications such as the classification of urban built-up. Landsat-8 data that is acquired during 2019 are stacked, therefore each pixel at the same position creates a list. From each list, indices are extracted. In this study, NDVI, NDBI, and NDWI are used to mapping built-up areas. Furthermore, extracted indices are divided into four categories by their value (maximum, quantile 75, median, and mean). Those indices are then combined into a simple formula to mapping built-up to see which produces better accuracy. The Pleiades as high-resolution remote sensing data is used to assist supervised classification for assessment. In this study, the combination of mean NDBI, maximum NDVI, and mean NDWI result highest Kappa coefficient of 0.771.

scholarly journals Sugarcane Productivity Mapping through C-Band and L-Band SAR and Optical Satellite Imagery

2019 ◽  
Vol 11 (9) ◽  
pp. 1109 ◽  
Author(s):  
Ramses Molijn ◽  
Lorenzo Iannini ◽  
Jansle Vieira Rocha ◽  
Ramon Hanssen
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Optical Sensors ◽  
Time Windows ◽  
Remote Sensing Data ◽  
Crop Productivity ◽  
Landsat 8 ◽  
L Band ◽  
Sar Imagery ◽  
Sugarcane Productivity

Space-based remote sensing imagery can provide a valuable and cost-effective set of observations for mapping crop-productivity differences. The effectiveness of such signals is dependent on several conditions that are related to crop and sensor characteristics. In this paper, we present the dynamic behavior of signals from five Synthetic Aperture Radar (SAR) sensors and optical sensors with growing sugarcane, focusing on saturation effects and the influence of precipitation events. In addition, we analyzed the level of agreement within and between these spaceborne datasets over space and time. As a result, we produced a list of conditions during which the acquisition of satellite imagery is most effective for sugarcane productivity monitoring. For this, we analyzed remote sensing data from two C-band SAR (Sentinel-1 and Radarsat-2), one L-band SAR (ALOS-2), and two optical sensors (Landsat-8 and WorldView-2), in conjunction with detailed ground-reference data acquired over several sugarcane fields in the state of São Paulo, Brazil. We conclude that satellite imagery from L-band SAR and optical sensors is preferred for monitoring sugarcane biomass growth in time and space. Additionally, C-band SAR imagery offers the potential for mapping spatial variations during specific time windows and may be further exploited for its precipitation sensitivity.

scholarly journals Assessment of Gap-Filling Interpolation Methods for Identifying Mangrove Trends at Segara Anakan in 2015 by using Landsat 8 OLI and Proba-V

2020 ◽  
Vol 52 (3) ◽  
pp. 341
Author(s):  
Sanjiwana Arjasakusuma ◽  
Abimanyu Putra Pratama ◽  
Intan Lestari
Keyword(s):  
Remote Sensing ◽  
Missing Data ◽  
Remote Sensing Data ◽  
Kendall Test ◽  
Linear Interpolation ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Gap Filling ◽  
Interpolation Methods ◽  
Sensing Technology

The existence and services of mangrove ecosystems in Segara Anakan are threatened by changes in land use on land and global warming, which requires proper and intensive monitoring. The monitoring of mangrove and its trend over large areas can be done using multi-temporal remote sensing technology. However, remote sensing data is often contaminated by cloud cover, and its corresponding shadow resulted in missing data. This study aims to assess the performance of the existed gap-filling techniques, such as, linear, spline, stineman,  data interpolation Empirical Orthogonal Function (dineof) and spatial downscaling strategy employing the Proba-V imagery in 100 m, when being used for estimating the missing data and depicting the trend in NDVI from Landsat 8 OLI by using Mann-Kendall test. Our result suggested that EOF-based interpolation gave better prediction results and more accurate in predicting longer missing data. Linear interpolation, on the other hand, was accurate to predict shorter missing data. Overall, all interpolation results can reconstruct 64 (spline) to 72 % (dineof) of missing data in NDVI with the RMSE of 0.10 (dineof) – 0.13 (spline). A consistent decreasing trend was also found from the four interpolation methods, which showed the consistency of the interpolated values when used for deriving trends with similar patterns of overall decreasing trend and magnitude of changes of -0.0095 to -0.0099 (NDVI unit) over the mangrove areas in 2015. The result demonstrated the potential ability of gap-filling methods for simulating the value of missing data and for deriving trends.

scholarly journals TINGKAT KERAWANAN KEBAKARAN HUTAN DAN LAHAN MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS (SIG) DI KOTA AMBON (STUDI KASUS DI JAZIRAH LEITIMUR SELATAN)

2021 ◽  
Vol 5 (1) ◽  
pp. 1-13
Author(s):  
Aldi Herdian ◽  
Aryanto Boreel ◽  
Ronny Loppies
Keyword(s):  
Remote Sensing ◽  
Geographic Information Systems ◽  
Satellite Imagery ◽  
Landsat 8 ◽  
Limited Data ◽  
Landsat 8 Oli ◽  
Digital Map ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Level 1

Limited data and the lack of use of Remote Sensing Technology and Geographic Information Systems (GIS) to map areas that are potentially prone to forest and land fires in Ambon City are one of the obstacles in handling forest and land fires. This study aims to identify the factors that cause forest and land fires, determine the level of vulnerability to forest and land fires and produce a digital map of forest and land fires in Jazirah Leitimur Selatan, Ambon City. The data used are Landsat 8 OLI/TIRS C1 Level-1 path/row 109/62 satellite imagery acquired on October 28, 2017. Hotspot data was obtained from FIRMS and Lapan Fire Hotspot. Data processing is done by using the method of overlaying variables that trigger the occurrence of forest and land fires. The results showed that the Jazirah Leitimur Selatan has the potential to be prone to forest and land fires with 76.6% of the area included in the vulnerable to very vulnerable category, while 23.4% is in the non-prone category.

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