This study uses two DEM images, namely ASTER GDEM and DEM SRTM to map the distribution of rivers and geomorphology located in The District of Pesisir Selatan. In this study a comparison of the two images was carried out with the same level of resolution of 30 meters to see the accuracy of the images used in the study of watersheds and geomorphology. The method used in this research is processing image data then identifying the river for each image used. Further carrying out a confusion matrix which is used to check or improve data from a quantitative approach. The results of the study in terms of comparison of ASTER and SRTM images for watershed identification show that SRTM imagery is more accurate in identifying watersheds compared to ASTER imagery. After taking samples with the number of sample points taken, namely 36 samples on each, and then testing for spatial accuracy, the results show that the SRTM imagery had an accuracy rate of 88% where out of 36 sample points only 5 were wrong or not on the river. Whereas in the ASTER image of 36 sample points, there were only 6 which were right on the river, show that the level of image accuracy is only 14% for river identification. The study also shows that after the research process and accuracy test, for geomorphologic identification on the two DEM images, namely DEM SRTM and ASTER GDEM, it found that both images have the same level of accuracy, therefore both images are equally good at identifying geomorphology.
{"title":"COMPARISON OF ASTER GDEM IMAGES AND SRTM IMAGES FOR RIVER WATERSHED AND GEOMORPHOLOGY STUDY","authors":"Naf’an Arifian, Kemal Rahman Denis, S. Putri","doi":"10.24036/irsaj.v3i2.38","DOIUrl":"https://doi.org/10.24036/irsaj.v3i2.38","url":null,"abstract":"This study uses two DEM images, namely ASTER GDEM and DEM SRTM to map the distribution of rivers and geomorphology located in The District of Pesisir Selatan. In this study a comparison of the two images was carried out with the same level of resolution of 30 meters to see the accuracy of the images used in the study of watersheds and geomorphology. \u0000The method used in this research is processing image data then identifying the river for each image used. Further carrying out a confusion matrix which is used to check or improve data from a quantitative approach. \u0000The results of the study in terms of comparison of ASTER and SRTM images for watershed identification show that SRTM imagery is more accurate in identifying watersheds compared to ASTER imagery. After taking samples with the number of sample points taken, namely 36 samples on each, and then testing for spatial accuracy, the results show that the SRTM imagery had an accuracy rate of 88% where out of 36 sample points only 5 were wrong or not on the river. Whereas in the ASTER image of 36 sample points, there were only 6 which were right on the river, show that the level of image accuracy is only 14% for river identification. The study also shows that after the research process and accuracy test, for geomorphologic identification on the two DEM images, namely DEM SRTM and ASTER GDEM, it found that both images have the same level of accuracy, therefore both images are equally good at identifying geomorphology.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121281272","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}
The availability of carrying capacity of food in an area is closely related to the availability of sufficient agricultural rice fields, from both sides they are very mutually supportive, so if the area of agricultural land is in an area, the availability of food in the area will also help reduce this problem. will have an impact on the food-carrying capacity of the people in the region. This study uses a quantitative descriptive approach using a supervised classification method using the SNI 7645 Classification. The data required are Landsat images from 2000, 2010 and 2020 . The data obtained from the results of image data processing is the occurrence of changes in the area of rice fields in Solok Regency in 2000, 2010 and 2020, where in 2000 the area of rice fields was 90,344, in 2010 the area of rice fields again was 80,452Ha, and in 2020 the area of rice fields continues to decrease to 75,750 Ha.
{"title":"DETERMINATION OF TH DYNAMICS OF THE FIELD AREA WITH FOOD SUPPORTUSING REMOTE SENSING IN SOLOK DISTRICT","authors":"Teguh Trivo Maulana, Fitriana Syahar","doi":"10.24036/irsaj.v3i2.34","DOIUrl":"https://doi.org/10.24036/irsaj.v3i2.34","url":null,"abstract":"The availability of carrying capacity of food in an area is closely related to the availability of sufficient agricultural rice fields, from both sides they are very mutually supportive, so if the area of agricultural land is in an area, the availability of food in the area will also help reduce this problem. will have an impact on the food-carrying capacity of the people in the region. This study uses a quantitative descriptive approach using a supervised classification method using the SNI 7645 Classification. The data required are Landsat images from 2000, 2010 and 2020 . The data obtained from the results of image data processing is the occurrence of changes in the area of rice fields in Solok Regency in 2000, 2010 and 2020, where in 2000 the area of rice fields was 90,344, in 2010 the area of rice fields again was 80,452Ha, and in 2020 the area of rice fields continues to decrease to 75,750 Ha.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132206048","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}
Mangroves are a part of the coastal ecosystem, mangroves play an important role in coastal ecosystems where the presence of mangroves can prevent abrasion. This study aims to identify the distribution of mangroves in the Mandeh Area using Sentinel 2A Imagery data assisted by geospatial technology tools. The methods used in this research are Normalized Difference Vegetation Index (NDVI), overlay, and maximum likelihood guided classification, these three methods are a combination of techniques from remote sensing and geographic information systems. The results of the study show that in 2015 the total area of mangrove land was 437 ha, in 2020 the area of mangrove forest with the most extensive mangrove forest density was a high density of (227 ha/ 68%).
红树林是沿海生态系统的一部分，红树林在沿海生态系统中发挥着重要作用，红树林的存在可以防止磨损。本研究旨在利用地理空间技术工具辅助的Sentinel 2A图像数据，确定曼德地区红树林的分布。本研究采用归一化植被指数(NDVI)、覆盖和最大似然分类方法，这三种方法是遥感技术和地理信息系统技术的结合。研究结果表明，2015年红树林总面积为437 ha, 2020年红树林密度最广的红树林面积为227 ha/ 68%。
{"title":"UTILIZATION OF SENTINEL-2A IMAGERY FOR MAPPING THE DISTRIBUTION OF MANGROVE FORESTS IN THE MANDEH AREA, WEST SUMATERA PROVINCE","authors":"Shahna Qintania Meron, Triyatno Triyatno","doi":"10.24036/irsaj.v3i2.35","DOIUrl":"https://doi.org/10.24036/irsaj.v3i2.35","url":null,"abstract":"Mangroves are a part of the coastal ecosystem, mangroves play an important role in coastal ecosystems where the presence of mangroves can prevent abrasion. This study aims to identify the distribution of mangroves in the Mandeh Area using Sentinel 2A Imagery data assisted by geospatial technology tools. The methods used in this research are Normalized Difference Vegetation Index (NDVI), overlay, and maximum likelihood guided classification, these three methods are a combination of techniques from remote sensing and geographic information systems. The results of the study show that in 2015 the total area of mangrove land was 437 ha, in 2020 the area of mangrove forest with the most extensive mangrove forest density was a high density of (227 ha/ 68%).","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"449 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125801391","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}
This study aims (1) to determine the distribution of Land Surface Temperature (LST) in the Baso District in 2022 (2) to determine the Normalized Burn Ratio (NBR) in Baso District in 2022 (3) to map areas prone to forest and land fires by utilizing the Land Surface Temperature (LST) and Normalized Burn Ratio (NBR) algorithms in Baso District in 2022. This study uses the Land Surface Temperature (LST) method to determine the distribution of land surface temperatures in the Baso District in 2022. The Normalized Burn Ratio (NBR) method is used to identify areas that are burned and then weighted overlay using Arcgis to obtain data on land and forest fire vulnerability. in Baso District. The results of this study are (1) showing a minimum temperature value of 13.6oC maximum temperature of 34.5oC and an average temperature of 26oC (2) showing the results of the distribution of areas with a value of -1 which are identified as burnt or those with bad vegetation of 2.5 and areas with a value of 0 indicating vegetation a good area of 7,636 Ha (3) on the mapping of areas prone to forest and land fires after the Weighted Overlay was carried out found 4 classes of vulnerability levels not prone to forest and land fires, moderately prone, prone and very prone to forest and land fires.
{"title":"MAPPING OF FOREST AND LAND FIRE HAZARDOUS USING LANDSAT 8 SATELLITE IMAGERY WITH LAND SURFACE TEMPERATURE (LST) AND NORMALIZED BURN RATIO (NBR) METHODS","authors":"Sri Mayang, Dilla Angraina","doi":"10.24036/irsaj.v3i2.37","DOIUrl":"https://doi.org/10.24036/irsaj.v3i2.37","url":null,"abstract":"This study aims (1) to determine the distribution of Land Surface Temperature (LST) in the Baso District in 2022 (2) to determine the Normalized Burn Ratio (NBR) in Baso District in 2022 (3) to map areas prone to forest and land fires by utilizing the Land Surface Temperature (LST) and Normalized Burn Ratio (NBR) algorithms in Baso District in 2022. \u0000This study uses the Land Surface Temperature (LST) method to determine the distribution of land surface temperatures in the Baso District in 2022. The Normalized Burn Ratio (NBR) method is used to identify areas that are burned and then weighted overlay using Arcgis to obtain data on land and forest fire vulnerability. in Baso District. \u0000The results of this study are (1) showing a minimum temperature value of 13.6oC maximum temperature of 34.5oC and an average temperature of 26oC (2) showing the results of the distribution of areas with a value of -1 which are identified as burnt or those with bad vegetation of 2.5 and areas with a value of 0 indicating vegetation a good area of 7,636 Ha (3) on the mapping of areas prone to forest and land fires after the Weighted Overlay was carried out found 4 classes of vulnerability levels not prone to forest and land fires, moderately prone, prone and very prone to forest and land fires.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115061491","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}
West Sumatra Province is one of the provinces in Indonesia which is the best rice producer in Indonesia, but a large number of conversions to paddy fields has resulted in food threats for the local population, data from the Ministry of Agriculture states that the decline in paddy fields in West Sumatra in 2008 was 228,176 ha. , in 2009 amounted to 229,693 ha, then in 2010 amounted to 231,463 ha, and in 2011 amounted to 229,368 ha, then decreased in 2012 amounted to 224,182 ha and in the area of West Sumatra Cities that experienced land conversion, namely Agam Regency, conversion of agricultural land to non-use Agriculture is a threat to national food security.
{"title":"DETERMINATION OF CHANGES IN FIELD AREA WITH FOOD-SUPPORTING CAPACITY USING REMOTE SENSING IN AGAM","authors":"S. Alhamda, Yudi Antomi","doi":"10.24036/irsaj.v3i1.33","DOIUrl":"https://doi.org/10.24036/irsaj.v3i1.33","url":null,"abstract":"West Sumatra Province is one of the provinces in Indonesia which is the best rice producer in Indonesia, but a large number of conversions to paddy fields has resulted in food threats for the local population, data from the Ministry of Agriculture states that the decline in paddy fields in West Sumatra in 2008 was 228,176 ha. , in 2009 amounted to 229,693 ha, then in 2010 amounted to 231,463 ha, and in 2011 amounted to 229,368 ha, then decreased in 2012 amounted to 224,182 ha and in the area of West Sumatra Cities that experienced land conversion, namely Agam Regency, conversion of agricultural land to non-use Agriculture is a threat to national food security.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123450759","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}
Limestone potential is important information that can be obtained from remote sensing data which has advantages and speed in processing results. Remote sensing is a technology that can overcome the problemof measuring data for fast and accurate information. This research was carried out in some areas of the Timpeh sub-district,andDharmasraya districtusing Landsat 8-OLI imagery with the aimof1) identifying the potential of limestone using the Band Ratio method. 2) How to apply remote sensing in mapping the potential of limestoneusing Landsat 8 Oli imagery. This research was carried out in several stages, namely Pre Processing which included radiometric correction and atmospheric correction, image cropping according to the research area, and processing whichincluded making geological maps, making landform maps, making maps of river flow patterns and vegetationindex maps and limestone identification using the RGB band ratio method (5/4;6/3;4/2). The results of field identification in potential limestone areas, where the RGB (Red Green Blue)composite of the band ratio 5/4;6/3;4/2 shows that the presence of limestone is characterized by the appearanceof greenish-brown colored objects. The average pixel value for limestone with a band ratio of 5/4 is 2.475, for a6/3 ratio is 1.275 and for a 4/3 ratio is 0.788. In this study, the potential area of limestone in the research areawasfound,whichwas approximately 2352,14564 ha.
{"title":"Mapping of Limestone Potential Using Landsat 8 Satellite Imageryin Some Areasof Timpeh","authors":"Sabrina Roselini, D. Arif, S. Putri","doi":"10.24036/irsaj.v3i2.36","DOIUrl":"https://doi.org/10.24036/irsaj.v3i2.36","url":null,"abstract":"Limestone potential is important information that can be obtained from remote sensing data which has advantages and speed in processing results. Remote sensing is a technology that can overcome the problemof measuring data for fast and accurate information. This research was carried out in some areas of the Timpeh sub-district,andDharmasraya districtusing Landsat 8-OLI imagery with the aimof1) identifying the potential of limestone using the Band Ratio method. 2) How to apply remote sensing in mapping the potential of limestoneusing Landsat 8 Oli imagery. \u0000 This research was carried out in several stages, namely Pre Processing which included radiometric correction and atmospheric correction, image cropping according to the research area, and processing whichincluded making geological maps, making landform maps, making maps of river flow patterns and vegetationindex maps and limestone identification using the RGB band ratio method (5/4;6/3;4/2). \u0000 The results of field identification in potential limestone areas, where the RGB (Red Green Blue)composite of the band ratio 5/4;6/3;4/2 shows that the presence of limestone is characterized by the appearanceof greenish-brown colored objects. The average pixel value for limestone with a band ratio of 5/4 is 2.475, for a6/3 ratio is 1.275 and for a 4/3 ratio is 0.788. In this study, the potential area of limestone in the research areawasfound,whichwas approximately 2352,14564 ha.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"20 31","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132747266","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}
The purpose of this study was to determine changes in the coastline and the extent of abrasion and accretion that occurred from 2002 to 2012 and 2012 to 2022. This study utilized geographic information systems and remote sensing techniques in the form of Landsat 7 imagery in 2002, 2012 and Landsat 8 imagery. in 2022. The research uses the Digital Shoreline Analysis System method 'DSAS' which Net Shoreline Movement (NSM) and Endpoint Rate (EPR). To calculate the area of abrasion and accretion use the Calculate Geometry menu. The results of this study are maps of shoreline changes from 2002 to 2012 and from 2012 to 2022. From 2002 to 2012 the rates and distances that occur are accretions 2012 to 2022, the change in the coastline, the rate and distance that will occur is abrasion. The coastline area due to abrasion increased by 57,702 m in 2002-2012 and 2012-2022, while the coastline area due to accretion in 2002-2012 and 2012-2022 decreased by 61,851 m.
本研究的目的是确定2002年至2012年和2012年至2022年期间海岸线的变化以及磨损和增生的程度。本研究利用地理信息系统和遥感技术，以2002年、2012年和Landsat 8影像的形式进行研究。在2022年。该研究使用数字海岸线分析系统方法“DSAS”，其中净海岸线移动(NSM)和终点率(EPR)。要计算磨损和吸积的面积，请使用计算几何菜单。这项研究的结果是2002年至2012年和2012年至2022年的海岸线变化地图。从2002年到2012年，发生的速度和距离是增生2012年到2022年，海岸线的变化，发生的速度和距离是磨损。2002-2012年和2012-2022年海岸带磨损面积增加了57702 m, 2002-2012年和2012-2022年海岸带增生面积减少了61851 m。
{"title":"COASTLINE MAPPING IN KOTO TANGAH DISTRICT USING MULTITEMPORAL REMOTE SENSING IMAGES, 2002, 2012 AND 2022","authors":"Rizka Nofriyanti, Febriandi Febriandi","doi":"10.24036/irsaj.v3i1.31","DOIUrl":"https://doi.org/10.24036/irsaj.v3i1.31","url":null,"abstract":"The purpose of this study was to determine changes in the coastline and the extent of abrasion and accretion that occurred from 2002 to 2012 and 2012 to 2022. This study utilized geographic information systems and remote sensing techniques in the form of Landsat 7 imagery in 2002, 2012 and Landsat 8 imagery. in 2022. The research uses the Digital Shoreline Analysis System method 'DSAS' which Net Shoreline Movement (NSM) and Endpoint Rate (EPR). To calculate the area of abrasion and accretion use the Calculate Geometry menu. The results of this study are maps of shoreline changes from 2002 to 2012 and from 2012 to 2022. From 2002 to 2012 the rates and distances that occur are accretions 2012 to 2022, the change in the coastline, the rate and distance that will occur is abrasion. The coastline area due to abrasion increased by 57,702 m in 2002-2012 and 2012-2022, while the coastline area due to accretion in 2002-2012 and 2012-2022 decreased by 61,851 m.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126083874","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}
This research was conducted to see the trend of the development of residential areas in the City of Bukittinggi using remote sensing methods. This technique is considered important and effective in providing spatial information on the earth's surface quickly, precisely and easily. This study aims to classify land use for residential areas using Landsat 8 OLI (Operational Land Imaginer) imagery. In this study, the maximum likelihood classification (MLC) method was used. The research used is descriptive with a quantitative approach, namely using numerical data, analysis, interpretation and presenting data in numerical form for sampling in identifying the results of land use for settlements in the City of Bukittinggi. The research results have changed in the last 5 years, it was found that there was an increase in residential areas of 7.92 ha in 2016 and 2021 using Landsat imagery. The results of the research in the form of a map are land use maps in the City of Bukittinggi to see the distribution of residential areas.
本研究是为了利用遥感方法了解武吉亭吉市住宅区的发展趋势。这种技术对于快速、精确、方便地提供地球表面的空间信息被认为是重要而有效的。本研究旨在利用Landsat 8 OLI (Operational land Imaginer)图像对住宅区的土地利用进行分类。本研究采用最大似然分类(MLC)方法。所使用的研究是描述性和定量方法，即使用数字数据、分析、解释和以数字形式提供数据进行抽样，以确定武吉亭吉市定居点土地使用的结果。在过去的5年里，研究结果发生了变化，使用Landsat图像发现，2016年和2021年，住宅面积增加了7.92公顷。研究的结果以地图的形式是武吉亭吉市的土地利用图，可以看到住宅区的分布情况。
{"title":"UTILIZATION OF SATELLITE IMEGERY FOR MAPPING SETTLEMENT DEVELOPMENT TRENDS IN THE CITY BUKITTINGGI","authors":"Sherena Aurelia Anwar, E. Ernawati","doi":"10.24036/irsaj.v3i1.32","DOIUrl":"https://doi.org/10.24036/irsaj.v3i1.32","url":null,"abstract":"This research was conducted to see the trend of the development of residential areas in the City of Bukittinggi using remote sensing methods. This technique is considered important and effective in providing spatial information on the earth's surface quickly, precisely and easily. This study aims to classify land use for residential areas using Landsat 8 OLI (Operational Land Imaginer) imagery. In this study, the maximum likelihood classification (MLC) method was used. The research used is descriptive with a quantitative approach, namely using numerical data, analysis, interpretation and presenting data in numerical form for sampling in identifying the results of land use for settlements in the City of Bukittinggi. \u0000The research results have changed in the last 5 years, it was found that there was an increase in residential areas of 7.92 ha in 2016 and 2021 using Landsat imagery. The results of the research in the form of a map are land use maps in the City of Bukittinggi to see the distribution of residential areas.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123064034","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}
This type of research is quantitative descriptive, with image interpretation through high-resolution images, and primary data as a source of data obtained through field surveys. The technique for determining informants is Total Sampling. The population in this study are villages in the Koto Tangah District, Padang City. This analysis uses quantitative analysis, namely on-screen digitization using the Arcgis application. Based on the results of research and discussion on High-Resolution Image Interpretation for Identification of Irrigation Channel Damage in Kasang and Kapalo Hilalang, Koto Tangah District, Padang City, the results obtained, namely the identification of irrigation canals using high-resolution imagery produces sufficient data in accordance with the conditions field. Based on the field survey, the condition of the network damage for the Hilalang Headquarters, starting from the weir building to BKH 1 was heavily damaged, BKH 2 to BKH 6 was moderately damaged. Starting from BKH 7 to BKH 8 still has good conditions. While the condition of the Kasang II irrigation canal from the weir to BKD 5 is still in good condition. BKD 6 to BKD 7 is moderately damaged. In contrast to BKD 4, it is in good condition, while parts of BAA 1 to 3 are in moderately damaged condition. The shape of the irrigation image in the city of Padang is tortuous, this is influenced by the topography of the area around the river which consists of community rice fields. The pattern shown in the image of the irrigation canal in the city of Padang is elongated, this shows the flow of the river from the upstream area to the downstream area of the river. The texture that is displayed in the image of the irrigation canal in the city of Padang has a smooth texture. The site shown in the image of the irrigation canal in the city of Padang is side by side with the rice fields belonging to the community in the Koto Tangah District.
{"title":"INTERPRETATION OF HIGH-RESOLUTION IMAGES FOR IDENTIFICATION OF DAMAGE TO RASANG AND LOST SHIP IRRIGATION CHANNEL KOTO TANGAH SUB-DISTRICT, PADANG CITY","authors":"Liza Septi Dhamara Asri, Triyatno Triyatno","doi":"10.24036/irsaj.v3i1.30","DOIUrl":"https://doi.org/10.24036/irsaj.v3i1.30","url":null,"abstract":"This type of research is quantitative descriptive, with image interpretation through high-resolution images, and primary data as a source of data obtained through field surveys. The technique for determining informants is Total Sampling. The population in this study are villages in the Koto Tangah District, Padang City. This analysis uses quantitative analysis, namely on-screen digitization using the Arcgis application. Based on the results of research and discussion on High-Resolution Image Interpretation for Identification of Irrigation Channel Damage in Kasang and Kapalo Hilalang, Koto Tangah District, Padang City, the results obtained, namely the identification of irrigation canals using high-resolution imagery produces sufficient data in accordance with the conditions field. Based on the field survey, the condition of the network damage for the Hilalang Headquarters, starting from the weir building to BKH 1 was heavily damaged, BKH 2 to BKH 6 was moderately damaged. Starting from BKH 7 to BKH 8 still has good conditions. While the condition of the Kasang II irrigation canal from the weir to BKD 5 is still in good condition. BKD 6 to BKD 7 is moderately damaged. In contrast to BKD 4, it is in good condition, while parts of BAA 1 to 3 are in moderately damaged condition. The shape of the irrigation image in the city of Padang is tortuous, this is influenced by the topography of the area around the river which consists of community rice fields. The pattern shown in the image of the irrigation canal in the city of Padang is elongated, this shows the flow of the river from the upstream area to the downstream area of the river. The texture that is displayed in the image of the irrigation canal in the city of Padang has a smooth texture. The site shown in the image of the irrigation canal in the city of Padang is side by side with the rice fields belonging to the community in the Koto Tangah District.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"607 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114049449","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}
This study aims to determine: (1) The level of vegetation density in Koto Tangah District, Padang City in 2019 using the NDVI, EVI, and SAVI methods, (2) The vegetation index method has the highest accuracy in predicting vegetation density in Koto Tangah District, Padang City. The type of research conducted is quantitative research, with research data in the form of Landsat 8 imagery data to identify the vegetation index NDVI, EVI, and SAVI. These indexes utilize a combination of bands on Landsat imagery. The value of the vegetation index can be calculated using the existing formula. carried out ArcGIS by using the raster calculator tool by entering the band values and calculations. In taking the accuracy test on the sample used a simple random sampling technique and using the Fitzpatricklens formula for each vegetation index method. Data collection techniques used are literature study, observation, and documentation. Meanwhile, the data analysis technique uses vegetation density analysis by looking at the accuracy of the NDVI, EVI, and SAVI methods. The results in this study indicate that each vegetation index is vulnerable, namely NDVI -1 -0.3 Very rare, -0.03- 0.15 Rare, 0.15 – 0.25 Medium, 0.25 – 0.35 Meeting, 0.35 – 1 Very Meeting, SAVI -1- -0.26 Very Rare, -0.26 – 0.29 Rare, 0.29-0.66 Moderate, 0.66-0.99 Meeting, 0.99-1 Very Meeting; EVI -0.99-0.1 Very Rare, 0.1-0.17 Rarely, 0.24-037 Moderate, 0.37-0.47 Meeting, 0.47-1 Very Meeting. the value results obtained that the area of the sub-district of Koto Tangah, the city of Padang, is dominated by high. Based on the research results of the three indices, the most dominating class is very dense vegetation density. The accuracy test results for the NDVI method were 86.95%, for the EVI method it was 86.95%, and for the SAVI method, it was 91.30%.
本研究旨在:(1)利用NDVI、EVI和SAVI方法确定2019年巴东市上唐加区植被密度水平;(2)植被指数法预测巴东市上唐加区植被密度精度最高。研究类型为定量研究，研究数据为Landsat 8影像数据，识别植被指数NDVI、EVI和SAVI。这些指数利用陆地卫星图像上的波段组合。植被指数的取值可以使用现有的公式进行计算。利用栅格计算器工具进行ArcGIS，通过输入波段值进行计算。在对样本进行精度检验时，采用了简单的随机抽样技术，并对每一种植被指数方法采用了Fitzpatricklens公式。使用的数据收集技术有文献研究、观察和记录。同时，数据分析技术采用植被密度分析，考察NDVI、EVI和SAVI方法的精度。研究结果表明，各植被指数均具有脆弱性，分别为NDVI -1- 0.3 Very rare、-0.03- 0.15 rare、0.15 - 0.25 Medium、0.25 - 0.35 Meeting、0.35 -1 Very Meeting、SAVI -1- -0.26 Very rare、-0.26 - 0.29 rare、0.29-0.66 Moderate、0.66-0.99 Meeting、0.99-1 Very Meeting;EVI -0.99-0.1非常罕见，0.1-0.17很少，0.24-037中等，0.37-0.47会议，0.47-1非常会议。结果表明，巴东市古东唐加街道面积以高为主。从三个指标的研究结果来看，最具优势的一类是极密植被密度。NDVI法、EVI法和SAVI法的准确率分别为86.95%、86.95%和91.30%。
{"title":"COMPARISON OF NDVI, EVI, AND SAVI METHODS TO KNOW VEGETATION DENSITY WITH LANDSAT 8 OIL IMAGES, 2019","authors":"Ilham Hasan Suardi, Dilla Anggraina","doi":"10.24036/irsaj.v2i2.28","DOIUrl":"https://doi.org/10.24036/irsaj.v2i2.28","url":null,"abstract":"This study aims to determine: (1) The level of vegetation density in Koto Tangah District, Padang City in 2019 using the NDVI, EVI, and SAVI methods, (2) The vegetation index method has the highest accuracy in predicting vegetation density in Koto Tangah District, Padang City. The type of research conducted is quantitative research, with research data in the form of Landsat 8 imagery data to identify the vegetation index NDVI, EVI, and SAVI. These indexes utilize a combination of bands on Landsat imagery. The value of the vegetation index can be calculated using the existing formula. carried out ArcGIS by using the raster calculator tool by entering the band values and calculations. In taking the accuracy test on the sample used a simple random sampling technique and using the Fitzpatricklens formula for each vegetation index method. Data collection techniques used are literature study, observation, and documentation. Meanwhile, the data analysis technique uses vegetation density analysis by looking at the accuracy of the NDVI, EVI, and SAVI methods. The results in this study indicate that each vegetation index is vulnerable, namely NDVI -1 -0.3 Very rare, -0.03- 0.15 Rare, 0.15 – 0.25 Medium, 0.25 – 0.35 Meeting, 0.35 – 1 Very Meeting, SAVI -1- -0.26 Very Rare, -0.26 – 0.29 Rare, 0.29-0.66 Moderate, 0.66-0.99 Meeting, 0.99-1 Very Meeting; EVI -0.99-0.1 Very Rare, 0.1-0.17 Rarely, 0.24-037 Moderate, 0.37-0.47 Meeting, 0.47-1 Very Meeting. the value results obtained that the area of the sub-district of Koto Tangah, the city of Padang, is dominated by high. Based on the research results of the three indices, the most dominating class is very dense vegetation density. The accuracy test results for the NDVI method were 86.95%, for the EVI method it was 86.95%, and for the SAVI method, it was 91.30%.","PeriodicalId":272417,"journal":{"name":"International Remote Sensing Applied Journal","volume":"118 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":"125004327","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}
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