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All Journal Indonesian Journal of Geography Ecotrophic, Journal of Environmental Science Jurnal Pembangunan Wilayah dan Kota Geoplanning : Journal of Geomatics and Planning Majalah Geografi Indonesia AGRIVITA, Journal of Agricultural Science Forum Geografi Jurnal Wilayah dan Lingkungan IJEMS (Indonesian Journal of Environmental Management and Sustainability) Jurnal Serambi Engineering Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital International Journal of Remote Sensing and Earth Sciences (IJReSES) Social, Humanities, and Educational Studies (SHEs): Conference Series Geodika: Jurnal Kajian Ilmu dan Pendidikan Geografi Journal of Applied Engineering and Technological Science (JAETS) International Journal of Disaster Management Journal of Fisheries & Marine The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Masita Dwi Mandini Manessa
Department Of Geography, Faculty Of Mathematics And Natural Sciences, Universitas Indonesia
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences

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VEGETATION INDICES FROM LANDSAT-8 DATA IN PALABUHANRATU Hermawan Setiawan; Masita Dwi Mandini Manessa; Hafid Setiadi
International Journal of Remote Sensing and Earth Sciences Vol. 20 No. 1 (2023)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3829

Abstract

Land cover will change due to population pressure, resource use, and human interest in space. Measuring the land area is important to determine how much-converted land is positive and negative. The vegetation on land was determined by how densely the plants were spread out. This study is conducted in Palabuhanratu, Sukabumi Regency. Aims to test and compare how accurate EVI and SAVI are at seeing vegetation density. The images used are from Landsat 8 in 2018 and 2022. Calibration is performed using high-resolution images, followed by field surveys with 98 points from polygon sampling. The average accuracy of the results from EVI is 49%, while the average accuracy of the results from SAVI is 45%. So, we can say that the EVI or SAVI based-input gives a similar result on observing the vegetation density in Palabuhanratu.
FUTURE SUITABILITY OF TEA PLANTS -CLIMATE ANALYSIS USING REMOTE ANALYSIS IN JAVA, INDONESIA Pramudhian Firdaus; Masita Dwi Mandini Manessa; Mangapul P. Tambunan; Rudy P. Tambunan
International Journal of Remote Sensing and Earth Sciences Vol. 20 No. 1 (2023)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3833

Abstract

Tea production is highly dependent on the geographical and climatic conditions of the environment where the plants are grown and on the crisis of climate change from time to time. Therefore, an analysis is needed to determine the impact of climatic conditions on the tea production industry, especially in Indonesia. Precipitation and temperature are the contributing factors to the productivity of tea. This phenomenon can be understood through analysis and projection of climate. This analysis can be utilized for mitigation and adaptation to applied climate in Indonesia's agriculture sector, especially in the industrial production of tea. By comparing the analysis of climate for tea in the past 1991 – 2020 period and the projection of future climate in the period 2051 – 2070, this study explains climate analysis to the production of tea, especially in Gunung Mas and Java Island, Indonesia. The result shows that climate analysis in the past in period 1991 – 2020, obtained existence influence and trend change to bulk available rain and temperature for the region Gunung Mas and its surroundings. Projection suitability land industry plant tea based on scenario future climate seen the impact with decrease suitable area as land growth plant tea. Climate scenarios RCP 4.5 and RCP 8.5 for 2070 show the influence of climate impact on the suitability of the tea plantation land industry.
UTILIZING REMOTE SENSING AND MACHINE LEARNING FOR ECOSYSTEM SERVICES MAPPING AT GUNUNG MAS TEA PLANTATION Annisa Fitria; Masita Dwi Mandini Manessa; Rudy Parluhutan Tambunan
International Journal of Remote Sensing and Earth Sciences Vol. 20 No. 2 (2023)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3880

Abstract

Land use and land cover changes are one of the main factors affecting ecosystems and the services they provide. Conversion from natural vegetation to agricultural and urban land can lead to the degradation of ecosystem services and loss of biodiversity. Puncak area, Bogor, which is a highland area, has become an area that is synonymous with tea plantations because it has an ecosystem that is suitable for being a tea plantation area. Gunung Mas tea plantation managed by PTPN VIII is one of the largest tea plantations and a contributor to foreign exchange in Indonesia. The tourism potential in the plantation and agricultural business sectors has a high selling value as a tourist object and attraction. The purpose of this study is to find out the distribution of ecosystem services for climate regulation, water flow and flood regulation, and ecotourism and cultural recreation services at Gunung Mas tea plantation which is displayed in the form of an Ecosystem Service Map. The land cover classification was extracted from the Sentinel 2A image, which was then scored based on expert judgment. The scoring results are then processed using the AHP Pairwise Comparison method. The results of the study show that the research area has very high climate regulation ecosystem services, very high water flow and flood regulation, and high cultural recreation and ecotourism ecosystem services.
SPATIAL ANALYSIS OF LAND USE AND LAND COVER VARIATIONS AFFECTING TEA PRODUCTION IN GUNUNGMAS PLANTATION THROUGH REMOTE SENSING TECHNIQUES Elok Lestari Paramita; Masita Dwi Mandini Manessa; Mangapul Parlindungan Tambunan
International Journal of Remote Sensing and Earth Sciences Vol. 20 No. 2 (2023)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3888

Abstract

Tea is a manufactured beverage that is popular around the world. In value chain analysis to increase efficiency, remote sensing technology can be developed to monitor the phenomenon of land use land cover (LULC) change and vegetation health conditions. This study aims to identify LULC in tea plantations, identify the health condition of tea plantations, then analyze spatial trends of changes in tea productivity in Gunungmas Afdeling-1 due to changes in tea area or tea vegetation health condition. Identification of changes in LULC in tea plantations can be carried out using remote sensing technology and machine learning, in this study, Google Earth Engine (GEE) LULC identification was generated using a supervised classification with the random forest algorithm on the GEE. Tea productivity trends decreased from 2019 to 2020, but increased from 2020 to 2021. They show that the trend of changes in the area of tea plantation classification is decreasing. According to the NDVI result, most of the reduced area of tea plantations is in areas with healthy vegetation. The trends in tea productivity changes are not in line with changes in the LULC area of tea plantation classification class and tea vegetation health condition.
BATHYMETRY EXTRACTION FROM SPOT 7 SATELLITE IMAGERY USING RANDOM FOREST METHODS Kuncoro Teguh Setiawan; Nana Suwargana; Devica Natalia BR Ginting; Masita Dwi Mandini Manessa; Nanin Anggraini; Syifa Wismayati Adawiah; Atriyon Julzarika; Surahman; Syamsu Rosid; Agustinus Harsono Supardjo
International Journal of Remote Sensing and Earth Sciences Vol. 16 No. 1 (2019)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/ijreses.v16i1.13834

Abstract

The scope of this research is the application of the random forest method to SPOT 7 data to produce bathymetry information for shallow waters in Indonesia. The study aimed to analyze the effect of base objects in shallow marine habitats on estimating bathymetry from SPOT 7 satellite imagery. SPOT 7 satellite imagery of the shallow sea waters of Gili Matra, West Nusa Tenggara Province was used in this research. The estimation of bathymetry was carried out using two in-situ depth-data modifications, in the form of a random forest algorithm used both without and with benthic habitats (coral reefs, seagrass, macroalgae, and substrates). For bathymetry estimation from SPOT 7 data, the first modification (without benthic habitats) resulted in a 90.2% coefficient of determination (R2) and 1.57 RMSE, while the second modification (with benthic habitats) resulted in an 85.3% coefficient of determination (R2) and 2.48 RMSE. This research showed that the first modification achieved slightly better results than the second modification; thus, the benthic habitat did not significantly influence bathymetry estimation from SPOT 7 imagery
DETERMINATION OF THE BEST METHODOLOGY FOR BATHYMETRY MAPPING USING SPOT 6 IMAGERY: A STUDY OF 12 EMPIRICAL ALGORITHMS Masita Dwi Mandini Manessa; Muhammad Haidar; Maryani Hastuti; Diah Kirana Kresnawati
International Journal of Remote Sensing and Earth Sciences Vol. 14 No. 2 (2017)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2017.v14.a2827

Abstract

For the past four decades, many researchers have published a novel empirical methodology for bathymetry extraction using remote sensing data. However, a comparative analysis of each method has not yet been done. Which is important to determine the best method that gives a good accuracy prediction. This study focuses on empirical bathymetry extraction methodology for multispectral data with three visible band, specifically SPOT 6 Image. Twelve algorithms have been chosen intentionally, namely, 1) Ratio transform (RT); 2) Multiple linear regression (MLR); 3) Multiple nonlinear regression (RF); 4) Second-order polynomial of ratio transform (SPR); 5) Principle component (PC); 6) Multiple linear regression using relaxing uniformity assumption on water and atmosphere (KNW); 7) Semiparametric regression using depth-independent variables (SMP); 8) Semiparametric regression using spatial coordinates (STR); 9) Semiparametric regression using depth-independent variables and spatial coordinates (TNP), 10) bagging fitting ensemble (BAG); 11) least squares boosting fitting ensemble (LSB); and 12) support vector regression (SVR). This study assesses the performance of 12 empirical models for bathymetry calculations in two different areas: Gili Mantra Islands, West Nusa Tenggara and Menjangan Island, Bali. The estimated depth from each method was compared with echosounder data; RF, STR, and TNP results demonstrate higher accuracy ranges from 0.02 to 0.63 m more than other nine methods. The TNP algorithm, producing the most accurate results (Gili Mantra Island RMSE = 1.01 m and R2=0.82, Menjangan Island RMSE = 1.09 m and R2=0.45), proved to be the preferred algorithm for bathymetry mapping.
Co-Authors A. Harsono Supardjo Adisty Pratamasari Agustinus Harsono Supardjo Agustinus Harsono Supardjo Angga Kurniawansyah Angga Kurniawansyah Anisya Feby Efriana Annisa Fitria Aris Poniman Aris Poniman K Ariyo Kanno Atriyon Julzarika Aulia Puji Hartati Ayu Mardalena Devica Natalia BR Ginting Devica Natalia Br. Ginting Dewi Susiloningtyas Diah Kirana Kresnawati Dini Nuraeni Dini Nuraeni Dony Kushardono Dwi Hastuti DWI HASTUTI Eghbert Elvan Ampou Elok Lestari Paramita Faisal Hamzah Farida Ayu Fathia Hashilah Gathot Winarso Gigih Girrastowo Glendy Somae Haeropan Daniko Putra Heinrich Rakuasa Herianto Herianto Hermawan Setiawan Hermawan Setiawan Indira Indira Iqbal Putut Ash Sidik Kartika Kusuma Wardani Kartika Pratiwi Koichi Yamamoto Kuncoro Teguh Setiawan Kuncoro Teguh Setiawan Kustiyo Kustiyo Mangapul P. Tambunan Mangapul P. Tambunan Mangapul Parlindungan Marwah Noer Maryani Hastuti Masahiko Sekine Muhammad Haidar Muhammad Haidar Muhammad Haidar Muhammad Rafi Andhika Pratama Mukhoriyah Mukhoriyah Mutia Kamalia Mukhtar Nana Suwargana Nana Suwargana Nanin Anggraini Nanin Anggraini Nanin Anggraini Ni Ketut Feny Permatasari Niken Anissa Putri Niken Anissa Putri Nurina Rachmita Nurina Rachmita Nurwita Mustika Sari Nurwita Mustika Sari Nurwita Mustika Sari Nuryani Widagti Pramudhian Firdaus Rahmadi Rahmatia Susanti Rokhmatulloh Rokhmatulloh Rokhmatuloh Rokhmatuloh Rudy P. Tambunan Rudy Parluhutan Tambunan Rudy Parluhutan Tambunan S Supriatna S Supriatna S. Supriatna S. Supriatna Setiadi, Hafid Sri Fauza Pratiwi Sri Fauza Pratiwi Supriatna Supriatna Supriatna Supriatna Supriatna Supriatna Supriatna Supriatna Supriatna Supriatna Supriyadi, Asep Adang Surahman Surahman Surahman Syamsu Rosid Syamsu Rosid Syamsu Rosid Syifa Wismayati Adawiah Takaya Higuchi Tambunan, Mangapul Parlindungan Tia Pramudiyasari Tsuyoshi Imai Wikanti Astriningrum Yoniar Hufan Ramadhani Yulia Indri Astuty