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. This 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. The 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.
本研究旨在通过地理信息系统和遥感处理，利用Sentinel 2图像，了解西苏门答腊省巴东市的Teluk Bayur浅水深度。卫星图像的目的是以负担得起的费用获得深入的资料，并审查所使用的算法的使用差异。这项研究使用哨兵2号卫星数据。本研究中使用的算法是应用于Sentinel-2数字卫星图像的Bathymetric Empirical Modeling，它将经历几个分析过程，从水体的提取开始，这个过程将水体和非水体分开。水，之后的过程中，纠正水面的反射或太阳返辉。本研究的结果是Teluk Bayur浅水的经验图，使用波段1和波段2获得的最大深度为125 m，而使用波段2和波段3获得的最大深度为128 m，其中最大深度128 m也是测深仪PT. PELINDO II Teluk Bayur Branch的数据采集结果深度。
{"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}
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). The 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. The 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. High-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.
这些土地被转换成已建成的土地，如住房、商店和工业。Darmawan(2002)认为，引起土地变化的因素之一是与人类生活需要相关的社区社会经济因素。印度尼西亚西苏门答腊地区是稻田转化最多的省份之一。许多因素导致土地利用的变化，对土地本身产生影响，如社会和经济因素，以及增加居民数量的因素。土地利用变化是指土地利用的旧形式和位置向新的形式和位置的过渡。或农业用地(如建设用地)功能的变化(Adhiatma et al.， 2020)。之所以选择巴东城区作为研究地点，是因为巴东市的土地利用发生了明显的变化，这是由于巴东市的人口增长率(根据BPS数据，2015-2020年期间巴东市人口增长率每年都在增加)为1.52%，到2020年人口为90904万人等因素造成的。巴东市总体上由政府确定的空间格局是一个面积为4540.10公顷的稻田种植区。根据巴东市的BPS数据，稻田面积每年减少0.7%，这些稻田被转化为巴东市的住房和商店和工业。发生在巴东市的建成区的开发，慢慢地将稻田地区变成了一个建成区，这个建成区不受当地政府设定的空间格局的规定。由政府设定的空间格局，以便利用遥感数据维持稻田的面积。通过使用遥感数据，如图像。Spot图像是高分辨率遥感图像之一，它是法国拥有的一颗卫星，用于提供遥感数据。SPOT图像提供了一种成像工具，然后将其作为稻田地图和政府设定的稻田空间模式之间的叠加方法，以查看其适用性。高分辨率光学是全色(P)和多光谱(绿色，红色和近红外)的同义词。SPOT图像的空间分辨率为2.5米10米，具有宽视角，在双模仪器中覆盖60 x 60公里或60 x 120公里，轨道高度为822公里，SPOT提供了高分辨率和宽可见性的理想组合，可以满足足够精确的数据需求，以识别稻田。
{"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}
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. In 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. The 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.
本研究是为了确定庞卡兰科图巴鲁地区的洪水分布区域。采用归一化差分西格玛指数(NDSI)方法。利用这种遥感方法，可以确定2017年3月15日庞卡兰科图巴鲁地区的洪水分布区。在本研究中，利用Sentinel-1卫星图像数据识别洪水分布区。所需的sentinel-1图像数据为洪水发生前(2017年2月20日)和洪水发生时(2017年3月15日)。此外，Sentinel-1图像处理开始于一个子集，一些辐射校正和几何校正。采用归一化差分西格玛指数(NDSI)方法识别洪水分布，然后进行矢量化。研究结果认为，基于GIS技术的洪水分析结果，本研究确定的洪水面积为41561.172 Ha。在Nagari Tanjuang Pauah为±2454.301 Ha, Nagari Tanjuang Balik为±2076.138 Ha, Nagari Pangkalan为±14765.141 Ha, Nagari Mangilang为±917.724 Ha, Nagari Koto Alam为±8361.579 Ha, Nagari Gunuang Manggilang为±917.724 Ha。
{"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}
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.
{"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}
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.
索洛克市是西苏门答腊岛人口增长较快的城市之一，这导致了发展的增加和绿色开放土地或植被土地的减少。这会影响吸收和反射更多太阳热量的地表。这些条件对地表温度上升有影响。采用归一化植被指数(NDVI)和归一化建筑指数(NDVI)算法模型，利用2015年和2021年索洛克市Landsat-8影像，分析索洛克市植被用地、建设用地和地表温度的变化。(NDBI)和地表温度(LST)。研究结果表明，索洛市归一化植被差异指数(NDVI)呈下降趋势，2015年植被密度面积为2344 Ha, 2021年减少至1888 Ha。这与2015年建筑面积/标准化建筑差异指数(NDBI)的增长一致，即从921公顷增加到2021年的2295公顷。植被面积的减少和建筑面积的增加使区域的地表温度升高。研究发现，2015年地表温度约为32.9°C, 2021年地表温度上升至33.6°C，通过Pearson积差相关分析LST与NDVI和NDBI之间的关系水平。
{"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}
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.
{"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}
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.
{"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}
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%.
{"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}
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.
{"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}
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.
本研究的目的是(1)绘制2016年和2021年Bengkalis Regency西部林地面积的变化，(2)确定Bengkalis Regency西部森林被烧毁面积的分布，(3)确定Bengkalis West Bengkalis地区森林火灾的严重程度。本研究采用Huete et. Al对森林火灾前后利用ArcGIS软件处理的Landsat 8影像进行5波段(NIR)和4波段(Red)合成的归一化植被指数(NDVI)方法。以及Eidenshink等人在使用QGIS软件处理的Landsat 8图像上合成波段5 (NIR)和波段7 (SWIR)的NBR (Normalized Burn Ratio)和dNBR (Difference Normalized Burn Ratio)方法。采用随机抽样法进行抽样，采用总体精度、用户精度、生产者精度和卡帕分析法进行精度检验。研究结果表明:(1)Bengkalis县林地面积逐年减少，2016年林地面积为903,920 ha, 2021年森林总面积为463,441 ha。(2) Bengkalis县森林火灾烧毁林地面积最小，为267.43 ha, 1468.93 ha，面积最广，为2186.53 ha。(3)根据一张森林火灾分布图，将Bengkalis县划分为火后再生高、火后再生低、不燃烧、低、中高和极高7个火灾严重等级，其中最主要的森林火灾等级为低-高。
{"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}
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