scholarly journals Micro hydropower plant potential study based on Landsat 8 operational land imager satellite data

2021 ◽  
Vol 10 (6) ◽  
pp. 3167-3177
Author(s):  
Yuliana Susilowati ◽  
Pudji Irasari ◽  
Yugo Kumoro ◽  
Wawan Hendriawan Nur ◽  
Yunarto Yunarto
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Satellite Data ◽  
Hydropower Plant ◽  
Landsat 8 ◽  
Remote Sensing Technology ◽  
Drainage Patterns ◽  
Potential Study ◽  
Sensing Technology ◽  
Head Height

Remote sensing technology has been widely applied in various fields, including oil, gas, and mineral exploration, spatial planning, and environmental monitoring. This paper describes the application of remote sensing technology for the potential study of a renewable micro hydropower plant (MHP) using Landsat 8 satellite data. The Sukaati Watershed, West Java, Indonesia, was selected as the case study area. Landsat 8 satellite data, acquired on August 21, 2020, was applied to extract information on land use, geology, and potential landslides. Drainage patterns, watershed boundaries, and head height were obtained from topographic map data. Drainage patterns, watershed boundaries, and land use are used to calculate flow rates. Geological map and landslide are the basis of layout of MHP components, such as water intake, dam, waterway, settling tank, penstock, and powerhouse. A field survey to acquire actual flow rate and head height was conducted to validate the results of the remote sensing data interpretation. Two potential sites of MHP were selected with a hydropower design of 129 kW and 5.18 MW. This study showed that remote sensing technology is beneficial for studying the potential of MHP because fieldwork can be done more quickly and efficiently.

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

Based on IDRISI Remote Sensing Images Land-Use Types of Supervised Classification Techniques

2013 ◽  
Vol 415 ◽  
pp. 305-308
Author(s):  
Kun Zhang ◽  
Hai Feng Wang ◽  
Zhuang Li
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Classification Techniques ◽  
Analysis And Evaluation ◽  
Remote Sensing Classification ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Remote Sensing Image Classification ◽  
Land Use Types ◽  
Area Distribution

With remote sensing technology and computer technology, remote sensing classification technology has been rapid progress. In the traditional classification of remote sensing technology, based on the combination of today's technology in the field of remote sensing image classification, some new developments and applications for land cover classification techniques to make more comprehensive elaboration. Using the minimum distance classifier extracts of the study area land use types. Ultimately extracted land use study area distribution image and make its analysis and evaluation.

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.

The Quantitative Evaluation of Ecological Assets in Sanjiang Plain Based on Remote Sensing Technology

2013 ◽  
Vol 316-317 ◽  
pp. 245-249
Author(s):  
Fan Ying Meng ◽  
Zheng Hong Miao
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Quantitative Measurement ◽  
Sanjiang Plain ◽  
Land Use Type ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Land Use Types ◽  
Landsat 7 ◽  
Ecological Assets

on the basis of the measurement of ecological parameters by using remote sensing technology, combined with ecological method and Landsat–7 ETM +, we made a quantitative measurement of ecological assets of Sanjiang Plain in 2010, got the spatial distribution of ecological assets. The results show that: the value of ecological assets in Sanjiang Plain was 2.35 × 1012 yuan. The value was generally higher in the southern and lower in the northern parts of the study area. Baoqing has the highest sum value and Youyi has the lowest value than other areas. Forestlands have higher value of ecological assets than other land use types. Land use type may be the mainly reason for the spatial difference of ecological assets in Sanjiang Plain.

scholarly journals Perbandingan Quadratic Discriminant Analysis dan Support Vector Machine untuk Klasifikasi Tutupan Lahan di DKI Jakarta

2020 ◽  
Vol 9 (1) ◽  
pp. 12-20
Author(s):  
Kamaluddin Junianto Dimas ◽  
Rahma Anisa ◽  
Itasia Dina Sulvianti
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Land Cover ◽  
Training Data ◽  
Support Vector ◽  
Quadratic Discriminant Analysis ◽  
Remote Sensing Technology ◽  
Sensing Technology

DKI Jakarta is a center of government as well as economy and business of Indonesia, thus development projects in Jakarta continue every year. Therefore, monitoring for land use has to be improved in accordance to DKI Jakarta Spatial Planning. The attempt needs to be supported by continuous data availability regarding land cover condition in Jakarta. The aforementioned data collecting process become easier due to remote sensing technology development. Remote sensing technology can be utilized for analyzing the size of land use area by using classification analysis. It has been found that the level of accuracy depends on the type of classification method and number of training data. This research evaluated the level of overall accuracy, sensitivity, and specificity of Quadratic Discriminant Analysis (QDA) and Support Vector Machine (SVM) along with number of data training used in classifying Jakarta land cover in 2017. The results showed that in both methods, the variance of all the aforementioned criteria were getting smaller along with the increasing number of training data. QDA and SVM had similar performance based on overall accuracy and specificity. However, SVM was better than QDA on sensitivity.

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.

Sign in / Sign up

Export Citation Format

Share Document