International Remote Sensing Applied Journal最新文献

{"title":"MAPPING ESTIMATION OF SHALLOW WATER DEPTH USING BATHYMETRIC EMPIRICAL MODELING ECHOSOUNDER DATA AND SENTINEL-2 SATELLITE IMAGE DATA (CASE STUDY: SHALLOW WATERS OF BAYUR BAY, PADANG CITY)","authors":"Altha Nurzafira Melin, D. Arief","doi":"10.24036/irsaj.v2i2.24","DOIUrl":"https://doi.org/10.24036/irsaj.v2i2.24","url":null,"abstract":"This study aims to see the depth of the shallow waters of Teluk Bayur, Padang City, West Sumatra Province using Sentinel 2 imagery through the processing of Geographic Information Systems and Remote Sensing. Satellite imagery is intended to obtain in-depth information at an affordable cost and to examine differences in the use of the algorithms used. \u0000This study uses Sentinel 2 satellite data. The algorithm used in this research is Bathymetric Empirical Modeling which is applied to Sentinel-2 digital satellite imagery, it will go through several analytical processes, starting from the extraction of water bodies where this process separates between water bodies and non-water bodies. waters, after that the process of correcting the reflection of the water surface or Sunglint. \u0000The results of this study are empirical maps of the shallow waters of Teluk Bayur which get a maximum depth of 125 m using band 1 and band 2, while the maximum depth that is more accurate is 128 m using band 2 and band 3 where the maximum depth of 128 m is also the depth of data acquisition results echosounder PT. PELINDO II Teluk Bayur Branch.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115034047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UTILIZATION OF SPOT IMAGERY TO EVALUATE THE SUITABILITY OF RICE FIELD SPACE PATTERNS IN PADANG CITY","authors":"Ero Anelka Efendi, Dilla Angraina","doi":"10.24036/irsaj.v2i2.25","DOIUrl":"https://doi.org/10.24036/irsaj.v2i2.25","url":null,"abstract":"that are converted into built-up lands such as housing, shops and industry. According to Darmawan (2002), one of the factors that cause land change is the socioeconomic factors of the community related to the needs of human life. One of the provinces that experienced the largest paddy land conversion in Indonesia is the West Sumatra region. Many factors result in land use changes that have an impact on the land itself, such as social, and economic factors and also factors of increasing the number of inhabitants. Land use change is the transition of an old form and location of land use to a new one. Or the change in the function of agricultural land such as built-up land (Adhiatma et al., 2020). \u0000The selection of the Padang City Area as a research site was based on significant land use changes in Padang City, this was caused by several factors such as the rate of population growth in Padang City which increased every year based on BPS data in 2015-2020 period was 1.52% with a population of 909.04 thousand people in 2020. \u0000The spatial pattern that has been set by the government in general in the city of Padang is an area developed for the cultivation of rice fields covering an area of 4540.10 ha. Based on BPS data from Padang City, the area of paddy fields decreases by 0.7% every year which is converted into housing and shops and industries in Padang City. The development of built-up land that occurred in the city of Padang slowly changed the rice field area into a built-up area that was not by the provisions of the spatial pattern that had been set by the local government. The spatial pattern that has been set by the government so that the area of paddy fields can be maintained by utilizing remote sensing data. By using remote sensing data such as imagery. Spot imagery is one of the high-resolution remote sensing images that is a French-owned satellite that operates to provide remote sensing data. SPOT imagery provides an imaging instrument that is then carried out as an overlay method between the rice field map and the rice field space pattern that has been set by the government to see its suitability. \u0000High-resolution optics are synonymous with panchromatic (P) and Multispectral (Green, Red, and Near Infrared). SPOT imagery has a spatial resolution of 2.5meter 10meters with a wide viewing angle that covers 60 x 60 km or 60 x 120 km in twin mode instruments, and an orbital altitude of 822 km, SPOT provides an ideal combination of high resolution and also wide visibility that can meet the needs of data that is accurate enough for identification of rice fields.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122127596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UTILIZATION OF IMAGE SENTINEL-1 SAR FOR IDENTIFICATION OF FLOOD DISTRIBUTION AREA In PANGKALAN KOTO BARU SUMATERA DISTRICT","authors":"M. Mardalena, S. Putri","doi":"10.24036/irsaj.v2i2.27","DOIUrl":"https://doi.org/10.24036/irsaj.v2i2.27","url":null,"abstract":"This research was conducted to determine the flood distribution area in Pangkalan Koto Baru District. Using the Normalized Difference Sigma Index (NDSI) method. By using this remote sensing method, it is possible to identify the flood distribution areas in Pangkalan Koto Baru District on March 15 2017. \u0000In this study, the identification of flood distribution areas using Sentinel-1 satellite imagery data. The sentinel-1 image data needed is before the flood (20 February 2017) and at the time of the flood (15 March 2017). Furthermore, Sentinel-1 Image processing begins with a subset, some radiometric corrections and geometric corrections. The Normalized Difference Sigma Index (NDSI) method is used to identify the flood distribution which is then vectorized. \u0000The results of the study have taken that based on the results of flood analysis using the GIS technique the area identified as flooding in this study is 41561.172 Ha. In Nagari Tanjuang Pauah it is ± 2454.301 Ha, Nagari Tanjuang Balik is ± 2076.138 Ha, Nagari Pangkalan is ± 14765.141 Ha, Nagari Mangilang is ± 917.724 Ha, Nagari Koto Alam is ± 8361.579 Ha, and Nagari Gunuang Manggilang is ± 917.724 Ha.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"264 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123477867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DYNAMIC OF CHANGING AREA OF SUSPENDED SOLID BY UTILIZING LANDSAT 8 OIL IMAGES IN LAKE SINGKARAK, WEST SUMATRA PROVINCE, 2017 and 2022","authors":"I. Kurniawan, D. Arief","doi":"10.24036/irsaj.v2i2.26","DOIUrl":"https://doi.org/10.24036/irsaj.v2i2.26","url":null,"abstract":"TSS is suspended materials (diameter > 1 µm) retained on a millipore filter with a pore diameter of 0.45 µm. TSS consists of silt and fine sand and micro-organisms. The main cause of TSS in waterways is soil erosion or soil erosion that is carried into water bodies. If the TSS concentration is too high, it will inhibit the penetration of light into the water and result in disruption of the photosynthesis process (Effendi in Lestari, 2009:4). Many activities cause turbidity that affects the penetration of sunlight into water bodies, so it can hinder the process of photosynthesis and primary production of waters. Turbidity usually consists of an organic particle originating from watershed erosion and resuspension from the lake bottom. Keywords : Normalized Difference Vegetation Index, Normalized Burn Ratio, Landsat 8, Severity Level of Forest and Land Fires. Based on the results of the study, researchers have obtained TSS values ​​in 2017 and 2022 at Lake Singkarak with the Landsat 8 image data processing method using the Syarif Budiman algorithm with several stages, namely first combining image data bands from band 1 to band 7 then cropping which serves to determine the area to examine then performs masking which functions to separate land from water and then enter the Syarif Budiman algorithm formula then classify the TSS values ​​in Lake Singkarak. It can be seen that the predicted TSS concentration has not been too much different from the TSS concentration in the field. researchers have obtained TSS values ​​in 2017 and 2022 at Lake Singkarak with the Landsat 8 image data processing method using the Syarif Budiman algorithm with several stages, namely first combining image data bands from band 1 to band 7 then cropping which functions to determine the area which will be examined then do masking which functions to separate land from water and then enter the Syarif Budiman algorithm formula then classify the TSS values ​​in Lake Singkarak. It can be seen that the predicted TSS concentration has not had too much difference in the concentration in the field. researchers have obtained TSS values ​​in 2017 and 2022 at Lake Singkarak with the Landsat 8 image data processing method using the Syarif Budiman algorithm with several stages, namely first combining image data bands from band 1 to band 7 then cropping which functions to determine the area which will be examined then do masking which functions to separate land from water and then enter the Syarif Budiman algorithm formula then classify the TSS values ​​in Lake Singkarak. It can be seen that the predicted TSS concentration has not to have o much difference with wififrameS concentration in the field.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122308567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UTILIZATION OF REMOTE SENSING FOR LAND SURFACE TEMPERATURE (LST) DISTRIBUTION MAPPING IN SOLOK CITY IN 2021","authors":"M. Fitri, Triyatno Triyatno","doi":"10.24036/irsaj.v2i1.22","DOIUrl":"https://doi.org/10.24036/irsaj.v2i1.22","url":null,"abstract":"Solok City is one of the cities in West Sumatra which has a fairly rapid population growth, this has led to an increase in development and a decrease in green open land or vegetation land. This affects the ground surface which absorbs and reflects more of the sun's heat. These conditions have an impact on rising surface temperatures. This research was conducted to analyze changes in vegetation land, built-up land and changes in surface temperature in Solok City using Landsat-8 Imagery of Solok City in 2015 and 2021 using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDVI) algorithm models. (NDBI) and Land Surface Temperature (LST). The results of the study explain that the normalized difference vegetation index (NDVI) in Solok City has decreased, in 2015 the area of ​​vegetation density was 2344 Ha and in 2021 it was reduced to 1888 Ha. This is in line with the increase in building area / Normalized Difference Built-up Index (NDBI) in 2015, namely 1 from 921 Ha to 2295 Ha in 2021. Reduced vegetation area and increased built-up area increased Land Surface Temperature (LST) in the area. research, the temperature in 2015 was around 32.9° C and in 2021 there was an increase in surface temperature to 33.6° C. Pearson product-moment correlation was carried out to see the level of relationship between LST and NDVI and NDBI.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127836545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COMPARISON OF SOIL ADJUSTED VEGETATION INDEX (SAVI) AND MODIFIED SOIL ADJUSTED VEGETATION INDEX (MSAVI) METHODS TO VIEW VEGETATION DENSITY IN PADANG CITY USING LANDSAT 8 IMAGE","authors":"Gilang Novando, D. Arif","doi":"10.24036/irsaj.v2i1.23","DOIUrl":"https://doi.org/10.24036/irsaj.v2i1.23","url":null,"abstract":"This study aims to see how the shape of the vegetation density map uses the SAVI (Soil Adjusted Vegetation Index) and MSAVI (Modified Soil Adjusted Vegetation Index) methods in Padang City using remote sensing data in the form of Landsat 8 imagery. This type of research is quantitative using numerical data. and analysis, as well as presenting data in the form of a numerical table to see a comparison of the accuracy of the SAVI (Soil Adjusted Vegetation Index) and MSAVI (Modified Soil Adjusted Vegetation Index) methods in Padang City. In this study, it was found that the results of the accuracy test of the SAVI (Soil Adjusted Vegetation Index) method were 86.95% while the MSAVI (Modified Soil Adjusted Vegetation Index) method was 91.30%. This research uses the SAVI (Soil Adjusted Vegetation Index) and MSAVI (Modified Soil Adjusted Vegetation Index) vegetation index methods by entering the formula that has been determined for each index to find out how the vegetation density forms in the city of Padang. The results of this study are maps of vegetation density using the SAVI (Soil Adjusted Vegetation Index) and MSAVI (Modified Soil Adjusted Vegetation Index) methods and tables of SAVI (Soil Adjusted Vegetation Index) and MSAVI (Modified Soil Adjusted Vegetation Index) accuracy test results.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":" 44","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132159138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UTILIZATION OF WORLDVIEW-3 SATELLITE IMAGES FOR 3-DIMENSIONAL (3D) MAPPING AS VISUALIZATION OF TOURISM AREA, KAYU ARO SUB-DISTRICT","authors":"A. Fahri, Dilla Angraina","doi":"10.24036/irsaj.v2i1.21","DOIUrl":"https://doi.org/10.24036/irsaj.v2i1.21","url":null,"abstract":"One of the efforts to develop and improve the implementation of tourism is through the construction of objects and attractions, either in the form of working on existing tourist objects or creating new objects as tourist attractions. This study aims to map the Tourism Object Area of ​​Kayu Aro District for the tourism sector in the Kayu Aro District, Kerinci Regency, Jambi Province. The method used is descriptive with a quantitative approach. Quantitative research uses image data of description information about tourist objects found in the Tourism Object Area of ​​Kayu Aro District. The final result of this study is a 2-Dimensional Map and 3-Dimensional Visualization of the Tourism Object Area of ​​Kayu Aro District in the tourism sector, Kayu Aro District, Kerinci Regency, Jambi Province.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130622998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UTILIZATION OF REMOTE SENSING IMAGES IN MAPPING SUSPENDED SOLID IN LAKE MANINJAU WEST SUMATRA PROVINCE","authors":"Ilham Ridho, D. Arief, S. Putri","doi":"10.24036/irsaj.v2i1.20","DOIUrl":"https://doi.org/10.24036/irsaj.v2i1.20","url":null,"abstract":"Remote sensing is generally defined as the technical art of obtaining information or data regarding the physical condition of an object or object, target, target or area and phenomenon without touching or direct contact with the object or target (Soenarmo, 2009). With remote sensing data, this research can easily see how the condition of the lake water. Based on these factors, efforts are needed to monitor the distribution of TSS in Lake Maninjau considering the importance of water potential to support various needs. In this study the classification was divided into 5 for the first class with concentration values of tss- 0 – 15 mg/L, 15 – 25 mg/L, 25 – 35 mg/L, TSS 35 – 80 mg/L, TSS > 80 mg/L. The result of in situ data processing is the lowest value is 8.2 mg/L and the highest is 72.2 mg/L. The Syarif Budhiman algorithm has the lowest at 8.14 mg/L and the highest at 40.04 mg/L. The lowest Parwati algorithm is 3.32 mg/L and the highest is 32.86 mg/L. The Guzman - Santaella algorithm has the lowest at 3.15 mg/L and the highest at 164.38 mg/L. The TSS concentrations in the alleged party and budhiman algorithms tend to have the same pattern as the TSS concentrations in the field, but there are several points with significant differences. The validation test shows that the Budhiman Algorithm (2004) has the smallest NMAE value between in situ data and image processing with a value of 14.4%.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130131179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ESTIMATION OF LAND SURFACE TEMPERATURE IN BUNGO DISTRICT USING THERMAL CHANNELS OF LANDSAT 8 IMAGES","authors":"Annisa Firstyandina, Febriandi Febriandi","doi":"10.24036/irsaj.v1i2.14","DOIUrl":"https://doi.org/10.24036/irsaj.v1i2.14","url":null,"abstract":"The purpose of this study was to determine the land surface temperature in Bungo Regency using the Landsat 8 image thermal channel by carrying out three stages: (1) Mapping the comparison of vegetation density in 2016 and 2021 using the NDVI (Normalized Difference Vegetation Index) method. (2) Mapping land surface temperatures in 2016 and 2021 using the Land Surface Temperature method. (3) Knowing the relationship between LST and NDVI using the Correlation Person test. The results of the study explain the comparison of vegetation density using the Normalized Difference Vegetation Index (NDVI) method in 2016 and 2021 in Bungo Regency. In 2016 the classification is very dense with an area of ​​124,871 Ha, the classification dense with an area of ​​115,732 Ha, the classification medium with an area of ​​98,536 Ha, the classification is rare with an area of ​​71,920 Ha, and very rare classification with an area of ​​54,839 Ha. Whereas in 2021 the very dense classification will decrease to 117,216 Ha, the dense classification will decrease to 112,365 Ha, the moderate classification will decrease to 95,892 Ha, the rare classification will increase to 79,310 Ha, and the very rare classification will increase to 61,084.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116461392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UTILIZING LANDSAT 8 IMAGERY FOR MAPPING OF BURNED AREAS USING THE NORMALIZE DIFFERENCE VEGETATION INDEX (NDVI) AND NORMALIZE BURN RATIO (NBR) METHODS","authors":"Rizka Fadil, D. Arief, S. Putri","doi":"10.24036/irsaj.v1i2.17","DOIUrl":"https://doi.org/10.24036/irsaj.v1i2.17","url":null,"abstract":"This study aims (1) to map changes in the area of ​​forest land in the western part of Bengkalis Regency in 2016 and 2021, (2) to determine the distribution of the area of ​​forest burned in the western part of Bengkalis Regency, (3) to determine the severity of forest fires in the District of Bengkalis West Bengkalis.This study used the NDVI (Normalized Difference Vegetation Index) method by Huete et. Al by compositing band 5 (NIR) and band 4 (Red) on Landsat 8 imagery which was processed using ArcGIS software before and after a forest fire. As well as the NBR (Normalized Burn Ratio) and dNBR (Difference Normalized Burn Ratio) methods by Eidenshink et al by compositing band 5 (NIR) and band 7 (SWIR) on Landsat 8 images processed using QGIS software. For sampling using random sampling method and accuracy test using overall accuracy, user's accuracy, producer's accuracy, and kappa analysis. The results of this study are (1) the area of ​​forest land in Bengkalis Regency continues to decrease every year, in 2016 the area of ​​forest land903,920 ha and 2021 the total forest area is463,441 ha. (2)The area of ​​forest land burned due to forest fires in Bengkalis Regency, which burned the least was 267.43 ha, while it was 1468.93 ha and the most extensive was 2186.53 ha.(3) Based on one forest fire distribution map, it is divided into 7 fire severity classes, namely high post-fire regrowth, low post-fire regrowth, no burning, low, medium-high and very high and the most dominant forest fire level is low-high.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125358462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}