Monitoring Total Suspended Sediment Concentration in Spatiotemporal Domain over Teluk Lipat Utilizing Landsat 8 (OLI)

Total suspended sediment (TSS) is a water quality parameter that is used to understand sediment transport, aquatic ecosystem health, and engineering problems. The majority of TSS in water bodies is due to natural and human factors such as brought by river runoff, coastal erosion, dredging activities, and waves. It is an important parameter that should be monitored periodically, particularly over the dynamic coastal region. This study aims to monitor spatiotemporal TSS concentration over Teluk Lipat, Malaysia. To date, there are two commonly used methods to monitor TSS concentration over wide water regions. Firstly, field sampling is known very expensive and time-consuming method. Secondly, the remote sensing technology that can monitor spatiotemporal TSS concentration freely. Although remote sensing technology could overcome these problems, universal empirical or semiempirical algorithms are still not available. Most of the developed algorithms are on a regional basis. To measure TSS concentration over the different regions, a new regional algorithm needs to develop. To do so, two field trip was conducted in the study area concurrent with the passing of Landsat 8. A total of 30 field samples were collected from 30 sampling points during the first field trip and 30 samples from 30 samplings from the second field trip. The samples were then analyzed using an established method to develop the TSS algorithm. The data obtained from the first field trip were then used to develop a regional TSS algorithm using the regression analysis technique. The developed algorithm was then validated by using data obtained from the second field trip. The results demonstrated that TSS in the study area is highly correlated with three Landsat 8 bands, namely green, near-infrared (NIR), and short-wavelength (SWIR) bands, with R2 = 0.79. The TSS map is constructed using the algorithm. Analyses of the image suggest that the highest TSSs are mainly observed along the coastal line and over the river mouth. It suggested that the main contributing factors over the study area are river runoff and wave splash.

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

JGISE Journal of Geospatial Information Science and Engineering ◽

10.22146/jgise.40276 ◽

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.

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

Nigerian Journal of Basic and Applied Sciences ◽

10.4314/njbas.v27i2.1 ◽

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

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.862.90 ◽

2017 ◽

Vol 862 ◽

pp. 90-95 ◽

Cited By ~ 5

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.

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

Shock and Vibration ◽

10.1155/2021/4469744 ◽

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.

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Retrieval and Analysis on the Sediment Concentration of the South Branch of the Yangtze Estuary Based on TM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.353-356.2763 ◽

2013 ◽

Vol 353-356 ◽

pp. 2763-2768 ◽

Cited By ~ 1

Author(s):

Jia Ling Hao ◽

Tong Cao ◽

Zhu Jun Zhang ◽

Li Ping Yin

Keyword(s):

Remote Sensing ◽

Suspended Sediment ◽

Suspended Sediment Concentration ◽

Sediment Concentration ◽

Yangtze Estuary ◽

The South ◽

The Yangtze Estuary ◽

Remote Sensing Technology ◽

Sensing Technology ◽

Quantitative Remote Sensing

Suspended sediment concentration is important index of water quality, and assessment coefficient of water environment. Remote sensing technology can overcome the shortcomings of conventional methods, such as low speed, long period, and scarce temporal and spatial data distribution. Thus it is meaningful to introduce remote sensing technology to monitoring suspended sediment. In this paper, two TM/ETM+ images of the Yangtze estuary were utilized, and based on review of available domestic and overseas remote sensing data of suspended sediment, also combined with analysis on the 21 samples of synchronizing collection on April 28, 2009 and 3 samples of synchronizing collection on March 26, 2000 at the same time of satellite passing through respectively, the inversion model of satellite quantitative data was setup correlated to suspended sediment concentration. Then the classification diagram of sediment concentration in the surface water at the South Branch of the Yangtze Estuary was drawn. This study gets the following conclusions:(1) TM4 band reflection coefficient is more related to surface sediment concentration, the correlation coefficient is 0.884. (2)Through the regression analysis, the quantitative remote sensing model is established. By the mode, using satellite picture, sediment concentration distribution map in study area is obtained. (3)The diffusion law of suspended sediment, the range of high turbid water region and the estuarine sediment transportation were further discussed from monitoring data, and its characteristic phenomenon were observed and the cause was also explained.

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IDENTIFICATION OF MANGROVE FORESTS USING MULTI-RESOLUTION SATELLITE IMAGERY

Jurnal Segara ◽

10.15578/segara.v16i2.7512 ◽

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.

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w10-953-2020 ◽

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.

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

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/887/1/012004 ◽

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.

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