scholarly journals Application of Remote Sensing Technology in Sediment Estimating Entering the Dam Reservoirs due to Floods

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Mohammad Hadian ◽  
Abolfazl Mosaedi
Keyword(s):  
Remote Sensing ◽  
Particulate Matter ◽  
Near Infrared ◽  
Spectral Response ◽  
Landsat 8 ◽  
Exponential Relationship ◽  
Particulate Materials ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Dam Reservoirs

The present study aimed to use remote sensing technology to estimate the concentration of particulate materials in the water entering the reservoirs of dams and consequently investigate the possibility of estimating the amount of sediment carried to the reservoir by flood during the life of the dam and its annual estimate. Using an advanced spectrometer device (ASD), the reflectance values of water containing different amounts of particulate sediments were measured in the range of 400–2500 nm; then, these reflectance values were represented for the Landsat 8 satellite OLI bands using their spectral response functions. In the study of interband correlation with the number of particulate materials, band 2 (blue) and band 5 (near-infrared) were identified to prepare a specific and appropriate model. The specificity of the reflectance difference in the two abovementioned bands was presented as an exponential relationship between the concentration of particulate materials and spectral reflectance. In this model, the RMSE parameter for the maximum possible sediment concentration was equal to 1.57 and the parameter R2 was equal to 0.91. In the second step, at the same time as the satellite passed, the area was visited and the sediments of the Ardak dam reservoir were sampled by recording their location. To complete this research, two measures were performed simultaneously, calculating the concentration of particulate materials sampled in the laboratory environment and their location on the image. Then, the number of particulate materials is estimated by taking into account the coordinates recorded from the images on which the relevant corrections have been made. According to the extracted exponential model, the results of estimating the concentration of particulate matter obtained from the model and Landsat satellite images with the concentration of particulate matter obtained from sampling showed its complete compatibility with field surveys to validate this research.

scholarly journals Analysis Comparison of Algorithms for Determination Concentration of Chlorophyll-a in Traditional and Intensive Milkfish Ponds Using LANDSAT 8 Images

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Nurhadi Bashit ◽  
Abdi Sukmono ◽  
Baskoro Agum Gumelar
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Coastal Area ◽  
Average Concentration ◽  
The Body ◽  
Landsat 8 ◽  
Northern Coast ◽  
Fish Production ◽  
Remote Sensing Technology ◽  
Sensing Technology

Indonesia is an Archipelago Country because the Country of Indonesia consists of many islands stretching from Sabang in the west to the island of Merauke on the east. The Archipelago Country also comes from the old name of the Indonesian Country called Nusantara, because Nusantara is a country that consists of many islands. Indonesia is an Archipelago Country which means it has potential resources in the coastal areas, one of which is found on the northern coast of Java. The coastal area is an important area to be reviewed, one of which is the use of coastal resources by paying attention to the condition of the ecosystem that remains stable. Opportunities for coastal area utilization in the field of fisheries are in the form of fishing activities or fish farming, especially pond cultivation activities. Based on data from the Department of Marine and Fisheries of the Province of Central Java in 2010, pond cultivation is one of the potential resources on the coast. This potential is supported by the government to increase fish production in order to increase the consumption of fish in the community. Therefore, it is necessary to choose the most effective method of pond cultivation between traditional methods and intensive methods to optimize fish production. One indicator of effectiveness between the two methods can be seen from the phytoplankton distribution. Phytoplankton contains chlorophyll-a in the body and is a natural food from fish. Phytoplankton provides important ecological functions for the aquatic life cycle by serving as the basis of food webs in water. Phytoplankton also functions as the main food item in freshwater fish culture and seawater fish cultivation. Therefore, it is necessary to know the chlorophyll-a concentration in the ponds of traditional and intensive methods to determine the concentration chlorophyll-a of the two pond methods. One method used to determine the concentration of chlorophyll-a using remote sensing technology. Remote sensing technology can be used to determine the concentration of chlorophyll-a using the Wouthuyzen, Wibowo, Pentury, Much Jisin Arief and Lestari Laksmi algorithms. The results showed that the Pentury algorithm was relatively better to determine the concentration of chlorophyll-a in shallow waters (ponds). The lowest concentration of chlorophyll-a in traditional ponds is 0.47068 mg/m3, the highest concentration is 1.95017 mg/m3 and the average concentration is 1.12893 mg/m3, while in intensive ponds the lowest concentration is 0.36713 mg/m3, the concentration the highest is 3.17063 mg/m3 and the average concentration is 1.53556 mg/m3.

scholarly journals Assessment of Land Use and Vegetative Cover in Kano Metropolis (from 1975-2015) Employing GIS and Remote Sensing Technology

2020 ◽  
Vol 27 (2) ◽  
pp. 1-7
Author(s):  
M. Haruna ◽  
M.K. Ibrahim ◽  
U.M. Shaibu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Agricultural Land ◽  
Landsat 8 ◽  
Vegetative Cover ◽  
Gis And Remote Sensing ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Agricultural Planning ◽  
Bare Land

This study applied GIS and remote sensing technology to assess agricultural land use and vegetative cover in Kano Metropolis. It specifically examined the intensity of land use for agricultural and non agricultural purpose from 1975 – 2015. Images (1975, 1995 and 2015), landsat MSS/TM, landsat 8, scene of path 188 and 052 were downloaded for the study. Bonds for these imported scenes were processed using ENVI 5.0 version. The result indicated five classified features-settlement, farmland, water body, vegetation and bare land. The finding revealed an increase in settlement, vegetation and bare land between 1995 and 2015, however, farmland decreased in 2015. Indicatively, higher percentage of land use for non agricultural purposes was observed in recent time. Conclusively, there is need to accord surveying the rightful place and priority in agricultural planning and development if Nigeria is to be self food sufficient. Keywords: Geographic Information System, Agriculture, Remote sensing, Land use, Land cover

Estimation of Sea Surface Temperature (SST) Using Split Window Methods for Monitoring Industrial Activity in Coastal Area

2017 ◽  
Vol 862 ◽  
pp. 90-95 ◽  
Author(s):  
Agung Budi Cahyono ◽  
Dian Saptarini ◽  
Cherie Bhekti Pribadi ◽  
Haryo Dwito Armono
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Brightness Temperature ◽  
Coastal Development ◽  
Sea Surface ◽  
Distribution Area ◽  
Landsat 8 ◽  
Remote Sensing Technology ◽  
Sensing Technology

The three drivers of environmental change: climate change, population growth and economic growth, result in a range of pressures on our coastal environment. Coastal development for industry and farming are a major pressure on terrestrial and environmental quality. In their process most of industry using sea water as cooling water. When water used as a coolant is returned to the natural environment at a higher temperature, the change in temperature decreases oxygen supply and affects marine ecosystem. This research is presents results from ongoing study on application of Landsat 8 for monitoring the intensity and distribution area of sea surface temperature changed by the heated effluent discharge from the power plant on Paiton coast, Probolinggo, East Java province. Remote sensing technology using a thermal band in Operational Land Imager (OLI) sensor of Landsat 8 sattelite imagery (band 10 and band 11) are used to determine the intensity and distribution of temperature changes. Estimation of sea surface temperature (SST) using remote sensing technology is applied to provide ease of marine temperature monitoring with a large area coverage. The method used in this research using the Split Window Algorithm (SWA) methods which is an algorithm with ability to perform extraction of sea surface temperature (SST) with brigthness temperature (BT) value calculation on the band 10 and band 11 of Landsat 8. Formula which was used in this area is Ts = BT10 + (2.946*(BT10 - BT11)) - 0.038 (Ts is the surface temperature value (°C), BT10 is the brightness temperature value (°C) Band 10, BT11 is the brightness temperature value (°C) Band 11. The result of this algorithm shows the good performance with Root Mean Square Error (RMSE) amount 0.406.

scholarly journals IDENTIFICATION OF MANGROVE FORESTS USING MULTI-RESOLUTION SATELLITE IMAGERY

Jurnal Segara ◽  
2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Anang Dwi Purwanto
Keyword(s):  
Remote Sensing ◽  
Mangrove Forest ◽  
Image Data ◽  
Field Measurements ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Remote Sensing Technology ◽  
Red Edge ◽  
Sensing Technology ◽  
Sentinel 2A

The development of remote sensing technology for identifying various of coastal and marine ecosystems which one of them is mangrove forest increasing rapidly. Identification of mangrove forests visually is constrained by much of combinations of RGB composite. The aims of this research is to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using Optimum Index Factor (OIF) method. The image data used represents 3 levels of intermediate to high resolution spatial resolution including Landsat 8 imagery (30 m) acquisition on 30 May 2013, Sentinel 2A image (10 m) acquisition on 18 March 2018 and SPOT 6 image (6 m) acquisition on 10 January 2015. Data of mangrove distributions used were the results of field measurements in the period 2013-2015. The results showed that the band composites of 564 (NIR+SWIR+Red) of Landsat 8 image and the band composites of 8a114 (Vegetation Red Edge+SWIR+Red) of Sentinel 2A are the best RGB composites for identifying mangrove forest, while the band composites of 341 (Red+NIR+Blue) of SPOT 6 image is  also the best colour composites (R-G-B) for identifying mangrove forest in Segara Anakan, Cilacap. The RGB composites of images developed from Landsat 8 and Sentinel 2A image are able to distinguish objects of mangrove forest from surrounding objects more clearly, but image composites from SPOT 6 image still require additional of association elements to identify mangrove objects.The development of remote sensing technology for identifying various of coastal and marine ecosystems which one of them is mangrove forest increasing rapidly. Identification of mangrove forests visually is constrained by much of combinations of RGB composite. The aims of this research is to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using Optimum Index Factor (OIF) method. The image data used represents 3 levels of intermediate to high resolution spatial resolution including Landsat 8 imagery (30 m) acquisition on 30 May 2013, Sentinel 2A image (10 m) acquisition on 18 March 2018 and SPOT 6 image (6 m) acquisition on 10 January 2015. Data of mangrove distributions used were the results of field measurements in the period 2013-2015.The results showed that the band composites of 564 (NIR+SWIR+Red) of Landsat 8 image and the band composites of 8a114 (Vegetation Red Edge+SWIR+Red) of Sentinel 2A are the best RGB composites for identifying mangrove forest, while the band composites of 341 (Red+NIR+Blue) of SPOT 6 image is  also the best colour composites(R-G-B) for identifying mangrove forest in Segara Anakan, Cilacap. The RGB composites of images developed from Landsat 8 and Sentinel 2A image are able to distinguish objects of mangrove forest from surrounding objects more clearly, but imagecomposites from SPOT 6 image still require additional of association elements to identify mangrove objects.

scholarly journals ANALYSING THE GROWTH CONDITION OF HAZEL MUSHROOM BASE ON THE DATE OF LANDSAT 8 AND THE PLATFORM OF ENVI

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 953-957
Author(s):  
J. T. Zhu ◽  
Y. Luo ◽  
M. X. Zhao ◽  
L. Wang ◽  
C. F. Gong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
The Body ◽  
Landsat 8 ◽  
Optimum Growth ◽  
Soil Moisture Condition ◽  
Growth Environment ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Armillariella Mellea

Abstract. Armillariella mellea mainly distributes in Changbai Mountain forest area, which is one of the few edible fungi that can be cultured artificially. It contains a variety of essential amino acids and vitamins for human body. Frequent consumption can strengthen the body immunity. It is of great significance to analyze the growth environment of Armillariella mellea by remote sensing technology for its growth prediction and artificial cultivation research. Based on ENVI software and Landsat 8 image, the surface temperature and soil moisture in Xiao Hinggan Mountains in August 2014 were retrieved by the method of atmospheric correction and TVDI, and the optimum growth environment of Armillariella mellea was analyzed. The results are as follows: 1) The optimum growth temperature of Armillariella mellea is 25–30 °C, and the soil moisture condition is 0.4–0.6. The Armillariella mellea mainly distributes in the north-central part of the study area. Combined with other growth environment information, the study area is generally suitable for the growth of Armillariella mellea; 2) we found the Armillariella mellea around the area of 83 samples of 100 samples which were choose to analyse. The accuracy is higher; 3) It is feasible to obtain the optimum growth environment of Hazelnut mushroom by remote sensing technology.

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.

Spectral Discrimination of Coral Reef Benthic Substance and Implications for Environmental Protection

2012 ◽  
Vol 610-613 ◽  
pp. 3646-3650
Author(s):  
Bing Xu ◽  
Ying Zhang
Keyword(s):  
Remote Sensing ◽  
Coral Reef ◽  
Environmental Protection ◽  
Spectral Reflectance ◽  
Spectral Response ◽  
Great Influence ◽  
Fundamental Parameter ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Coral Sand

Spectral reflectance of benthic substance is a fundamental parameter to coral reef environmental protection by remote sensing technology. It collected in situ a total of 44 spectral reflectance of healthy coral, fine sand, dead coral, sand mixed with coral rubble, and sea grass in Leizhou peninsula, China. Analyses of spectral reflectance revealed that there exist three distinct spectral zones, the high reflectance zone (400-580nm), the attenuation zone (580-720nm) and low reflectance zone (720-850nm). The reflectance between 400 to 580nm band range can be used to discriminate coral reefs and other substances efficiently. Meanwhile, water depth and quality has great influence on coral reef spectrum. By using spectral response function, analyses of simulated reflectance spectra for several remote sensors (ETM+, SPOT5, IKONOS, Quick bird) indicate that narrowband multispectral sensors have the ability to discriminate different benthic substances. The research demonstrates that basic reef benthic classes are spectrally identifiable and differentiable; it establishes the basis of utilizing remote sensing technology for environmental protection of coral reef ecosystem.

scholarly journals Multi-Sensor Approach to Improve Bathymetric Lidar Mapping of Semi-Arid Groundwater-Dependent Streams: Devils River, Texas

2020 ◽  
Vol 12 (15) ◽  
pp. 2491 ◽  
Author(s):  
Kutalmis Saylam ◽  
Aaron R. Averett ◽  
Lucie Costard ◽  
Brad D. Wolaver ◽  
Sarah Robertson
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Aquatic Vegetation ◽  
Survey Methods ◽  
Airborne Lidar ◽  
Active Sensors ◽  
Geomorphic Mapping ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Complex Settings

Remote sensing technology enables detecting, acquiring, and recording certain information about objects and locations from distances relative to their geographic locations. Airborne Lidar bathymetry (ALB) is an active, non-imaging, remote sensing technology for measuring the depths of shallow and relatively transparent water bodies using light beams from an airborne platform. In this study, we acquired Lidar datasets using near-infrared and visible (green) wavelength with the Leica Airborne Hydrography AB Chiroptera-I system over the Devils River basin of southwestern Texas. Devils River is a highly groundwater-dependent stream that flows 150 km from source springs to Lake Amistad on the lower Rio Grande. To improve spatially distributed stream bathymetry in aquatic habitats of species of state and federal conservation interest, we conducted supplementary water-depth observations using other remote sensing technologies integrated with the airborne Lidar datasets. Ground penetrating radar (GPR) mapped the river bottom where vegetation impeded other active sensors in attaining depth measurements. We confirmed the accuracy of bathymetric Lidar datasets with a differential global positioning system (GPS) and compared the findings to sonar and GPR measurements. The study revealed that seamless bathymetric and geomorphic mapping of karst environments in complex settings (e.g., aquatic vegetation, entrained air bubbles, riparian zone obstructions) require the integration of a variety of terrestrial and remotely operated survey methods. We apply this approach to Devils River of Texas. However, the methods are applicable to similar streams globally.

scholarly journals Analisis Perubahan Garis Pantai Kabupaten Jembrana dengan Menggunakan Citra Satelit Landsat 8

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Mega Fitria Istiqomah ◽  
Sutrisno Sutrisno ◽  
Adi Wijaya
Keyword(s):  
Remote Sensing ◽  
Survey Methods ◽  
Sea Breeze ◽  
Actual Condition ◽  
Landsat 8 ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Waves And Tides ◽  
Strategic Position ◽  
The Waves

 Abstrak. Kabupaten Jembrana mempunyai posisi yang strategis secara geografis, sebagai pintu gerbang Bali bagian barat yang merupakan kunci pertukaran dan percampuran budaya serta penduduk. Daerah ini berbatasan langsung dengan Selat Bali sehingga mendapat pengaruh angin laut yang membangkitkan gelombang dan pasang surut. Tujuan dari penelitian ini adalah untuk menganalisis perubahan garis pantai menggunakan teknologi penginderaan jauh pesisir Kabupaten Jembrana tahun 2013 hingga 2016. Metode yang digunakan dalam penelitian ini adalah metode penginderaan jauh menggunakan citra satelit Landsat 8 dan metode survei lapangan untuk mengetahui kondisi yang sebenarnya. Hasil penelitian ini menunjukkan bahwa perubahan garis pantai tahun 2013 sampai 2014 mengalami abrasi sebesar 801.717 m2 dan akresi sebesar 1348.564 m2, tahun 2014 sampai 2015 mengalami abrasi sebesar 4921.561 m2 dan akresi sebesar 388.969 m2, terakhir di tahun 2015 sampai 2016 perubahan akibat abrasi terjadi sebesar 384.637 m2 dan akresi sebesar 4431.331 m2.Abstract.Jembrana district has a strategic position geographically, as the gateway to west Bali which is the key exchange and the mixing of culture as well as residents. This area is directly adjacent to the Strait of Bali so it gets the sea breeze effect that evokes the waves and tides. The purpose of this study was to analyze the changes in the shoreline using remote sensing technology at Jembrana coast in 2013 to 2016. The method used in this research is the method of remote sensing using Landsat 8 satellite imagery and field survey methods to determine the actual condition. These results indicate that changes in the coastline of 2013 to 2014 experience abrasion of 801 717 m2 and accretion amounted to 1348,564 m2,2014 to 2015 experience abrasion of 4921,561 m2 and accretion amounted to 388 969 m2, the last in 2015 until 2016 the changes due abrasion occurs at 384 637 m2 and accretion amounted to 4431,331 m2.  

scholarly journals CLASSIFICATION OF LANDS OF REMOTE SENSITIVE DATA BY NDVI METHOD IN SMART AGRICULTURE

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 73-77
Author(s):  
İ. Avcı ◽  
E. Farzaliyev ◽  
E. Kabullar
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Threshold Function ◽  
Vegetation Density ◽  
Sensitive Data ◽  
Index Method ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Plant Groups

Abstract. A large share of the earth's surface is observed with remote sensing technology. Thanks to the data obtained from this process, information about the observed lands is obtained. In this study, NDVI (normalized difference), which is developed by applying mathematical operations on the reflection values of plants at different wavelengths from remote sensing technology and different application areas of this technology, electromagnetic rays, and spectral reflection values, and which is used as a method that provides a value expressing vegetation density. Vegetation index) method, NDVI value, and plant groups analyzed according to this value, sample MATLAB applications related to the NDVI method are mentioned. -Green-Blue) image of visible red and infrared regions, histogram graph showing the relationships between the intensities of values in NIR (near-infrared) and Red (visible Red) bands, NDVI image, and threshold function at the end. The NDVI image was obtained by using the direction (to detect areas that may have vegetation) is shown.

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