{"title":"在选择优先级和基于植被分类数据的替代区域时,采用简单的多元素评级方法(SMART)","authors":"Yerik Afrianto Singgalen","doi":"10.47065/bits.v5i2.4085","DOIUrl":null,"url":null,"abstract":"Vegetation index analysis using the Normalized Difference Vegetation Index (NDVI) model needs to be processed using a decision support model to follow up on the Landsat 8/9 Operational Land Imaginer (OLI) satellite image data interpretation results. However, studies using the Simple Multi-Attribute Rating Technique (SMART) method to determine priority zones based on vegetation index classification data are still limited. This study uses the SMART decision support model to process NDVI classification data in mangrove areas. The stages in this study consist of four parts: the data collection stage, the data processing stage; the data analysis stage; and the data interpretation stage. At the data collection stage, the raster data used was sourced from the United States Geology Survey (USGS) platform, namely Landsat 8/9 OLI with coordinate raster data (Lat 01o43'18\" N, Lon: 128o04'15\" E) in 2013, 2018, and 2023. In addition, video and aerial photographs at the study site were taken using drones (Phantom 4 Version 2). At the data processing stage, the model used in calculating raster data is NDVI using the QGIS 3.30.1 application. This research data analysis and interpretation stage uses the SMART decision support model. The SMART decision support model is used to produce recommendations for priority zones for mangrove ecotourism development based on the results of the NDVI classification (minimum value, average value, maximum value) adjusted to the Decree of the State Minister of Environment Number 201 of 2004 concerning standard criteria and guidelines for mangrove forest destruction (rare, medium, and dense). Based on the calculation of the utility value of criterion C1 as a cost with a weight of 0.50 in the NDVI classification data for 2023, the second observation station is recommended as a priority zone with a total value of 0.50. Meanwhile, the calculation of the utility value of criterion C3 as a cost with a weight of 0.50 in the NDVI classification data in 2023 recommended the third observation station as a priority zone with a total value of 0.88. This means that the SMART method can be used to identify and analyze priority and alternative zones for the sustainable development of mangrove ecotourism areas.","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Implementasi Metode Simple Multi Attribute Rating Technique (SMART) dalam Pemilihan Zona Prioritas dan Alternatif Berbasis Data Klasifikasi Indeks Vegetasi\",\"authors\":\"Yerik Afrianto Singgalen\",\"doi\":\"10.47065/bits.v5i2.4085\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Vegetation index analysis using the Normalized Difference Vegetation Index (NDVI) model needs to be processed using a decision support model to follow up on the Landsat 8/9 Operational Land Imaginer (OLI) satellite image data interpretation results. However, studies using the Simple Multi-Attribute Rating Technique (SMART) method to determine priority zones based on vegetation index classification data are still limited. This study uses the SMART decision support model to process NDVI classification data in mangrove areas. The stages in this study consist of four parts: the data collection stage, the data processing stage; the data analysis stage; and the data interpretation stage. At the data collection stage, the raster data used was sourced from the United States Geology Survey (USGS) platform, namely Landsat 8/9 OLI with coordinate raster data (Lat 01o43'18\\\" N, Lon: 128o04'15\\\" E) in 2013, 2018, and 2023. In addition, video and aerial photographs at the study site were taken using drones (Phantom 4 Version 2). At the data processing stage, the model used in calculating raster data is NDVI using the QGIS 3.30.1 application. This research data analysis and interpretation stage uses the SMART decision support model. The SMART decision support model is used to produce recommendations for priority zones for mangrove ecotourism development based on the results of the NDVI classification (minimum value, average value, maximum value) adjusted to the Decree of the State Minister of Environment Number 201 of 2004 concerning standard criteria and guidelines for mangrove forest destruction (rare, medium, and dense). Based on the calculation of the utility value of criterion C1 as a cost with a weight of 0.50 in the NDVI classification data for 2023, the second observation station is recommended as a priority zone with a total value of 0.50. Meanwhile, the calculation of the utility value of criterion C3 as a cost with a weight of 0.50 in the NDVI classification data in 2023 recommended the third observation station as a priority zone with a total value of 0.88. This means that the SMART method can be used to identify and analyze priority and alternative zones for the sustainable development of mangrove ecotourism areas.\",\"PeriodicalId\":474248,\"journal\":{\"name\":\"Building of Informatics, Technology and Science (BITS)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Building of Informatics, Technology and Science (BITS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.47065/bits.v5i2.4085\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Building of Informatics, Technology and Science (BITS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47065/bits.v5i2.4085","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
使用归一化植被指数(NDVI)模型的植被指数分析需要使用决策支持模型进行处理,以跟进Landsat 8/9 Operational Land Imaginer (OLI)卫星图像数据解译结果。然而,基于植被指数分类数据,利用简单多属性评级技术(Simple Multi-Attribute Rating Technique, SMART)方法确定优先区划的研究仍然有限。本研究采用SMART决策支持模型对红树林地区NDVI分类数据进行处理。本研究的阶段包括四个部分:数据收集阶段、数据处理阶段;数据分析阶段;以及数据解释阶段。在数据采集阶段,使用的栅格数据来源于2013年、2018年和2023年美国地质调查局(USGS)平台Landsat 8/9 OLI坐标栅格数据(Lat 01o43’18”N, Lon 128o04’15”E)。此外,使用无人机(Phantom 4 Version 2)拍摄研究地点的视频和航拍照片。在数据处理阶段,使用QGIS 3.30.1应用程序计算栅格数据时使用的模型为NDVI。本研究的数据分析和解释阶段采用SMART决策支持模型。SMART决策支持模型用于根据NDVI分类(最小值、平均值、最大值)的结果,对红树林生态旅游开发的优先区域提出建议,该分类根据2004年国家环境部长第201号法令进行调整,该法令涉及红树林破坏(稀有、中等和茂密)的标准标准和指南。根据2023年NDVI分类数据中C1准则的效用值作为权重为0.50的成本计算,推荐第二观测站作为优先区,其总价值为0.50。同时,通过计算2023年NDVI分类数据中C3准则作为成本的效用值,其权重为0.50,推荐第3观测站作为优先区,其总价值为0.88。这意味着SMART方法可用于识别和分析红树林生态旅游区可持续发展的优先区域和备选区域。
Implementasi Metode Simple Multi Attribute Rating Technique (SMART) dalam Pemilihan Zona Prioritas dan Alternatif Berbasis Data Klasifikasi Indeks Vegetasi
Vegetation index analysis using the Normalized Difference Vegetation Index (NDVI) model needs to be processed using a decision support model to follow up on the Landsat 8/9 Operational Land Imaginer (OLI) satellite image data interpretation results. However, studies using the Simple Multi-Attribute Rating Technique (SMART) method to determine priority zones based on vegetation index classification data are still limited. This study uses the SMART decision support model to process NDVI classification data in mangrove areas. The stages in this study consist of four parts: the data collection stage, the data processing stage; the data analysis stage; and the data interpretation stage. At the data collection stage, the raster data used was sourced from the United States Geology Survey (USGS) platform, namely Landsat 8/9 OLI with coordinate raster data (Lat 01o43'18" N, Lon: 128o04'15" E) in 2013, 2018, and 2023. In addition, video and aerial photographs at the study site were taken using drones (Phantom 4 Version 2). At the data processing stage, the model used in calculating raster data is NDVI using the QGIS 3.30.1 application. This research data analysis and interpretation stage uses the SMART decision support model. The SMART decision support model is used to produce recommendations for priority zones for mangrove ecotourism development based on the results of the NDVI classification (minimum value, average value, maximum value) adjusted to the Decree of the State Minister of Environment Number 201 of 2004 concerning standard criteria and guidelines for mangrove forest destruction (rare, medium, and dense). Based on the calculation of the utility value of criterion C1 as a cost with a weight of 0.50 in the NDVI classification data for 2023, the second observation station is recommended as a priority zone with a total value of 0.50. Meanwhile, the calculation of the utility value of criterion C3 as a cost with a weight of 0.50 in the NDVI classification data in 2023 recommended the third observation station as a priority zone with a total value of 0.88. This means that the SMART method can be used to identify and analyze priority and alternative zones for the sustainable development of mangrove ecotourism areas.