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All Journal Journal of Degraded and Mining Lands Management MAJALAH ILMIAH GLOBE The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Nanin Anggraini
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Earth & Planetary Sciences Education

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Land changes detection on Rote Island using harmonic modelling method Atriyon Julzarika; Nanin Anggraini; K Kayat; Mutiaraning Pertiwi
Journal of Degraded and Mining Lands Management Vol 6, No 3 (2019)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2019.063.1719

Abstract

Rote Island is one of the islands in East Nusa Tenggara. In this island, land changes occur significantly. This land changes can be detected by Landsat images. These images are obtained from the big data engine. The big data engine used is the Google Earth Engine. This study aimed to detect land changes with harmonic modelling using multitemporal Landsat images from the big data engine. Harmonic modelling is used in monitoring changes in Normalized Difference Vegetation Index values in a multitemporal manner from Landsat images. Processing is done using the Geomatics approach. Land changes on Rote Island generally occur on coastal and savanna. Land changes on land generally have vertical deformation on its movement and horizontal on the savanna. The land changes accuracy result is 95% in 1,96σ. This method can be used for rapid mapping of land changes monitoring.
PEMETAAN MANGROVE MENGGUNAKAN ALGORITMA MULTIVARIATE RANDOM FOREST: Studi Kasus di Segara Anakan, Cilacap Muhammad Rizki Nandika; A.A. Md. Ananda Putra Suardana; Nanin Anggraini
Majalah Ilmiah Globe Vol. 25 No. 1 (2023): GLOBE VOL 25 NO 1 TAHUN 2023
Publisher : Badan Informasi Geospasial

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Abstract

Potensi pengembangan dan pemanfaatan Artificial Intelligence (AI) dan Machine Learning (ML) terus meningkat untuk dimanfaatkan dalam pemrosesan data penginderaan jauh pada periode waktu terakhir. Teknologi penginderaan jauh telah terbukti dapat diandalkan untuk mendeteksi sebaran tutupan mangrove. Salah satu metode berbasis ML yang digunakan untuk melakukan deteksi sebaran tutupan mangrove adalah metode Random Forest. Penelitian ini berfokus pada pengujian akurasi klasifikasi Random Forest dalam mengidentifikasi mangrove di Segara Anakan, Cilacap. Seluruh pemrosesan data dan analisis dilakukan menggunakan platform berbasis cloud, Google Earth Engine. Data yang digunakan yaitu citra satelit Sentinel-2A akuisisi tanggal 1 Januari - 31 Desember 2020. Metode klasifikasi menggunakan algoritma RF dengan 12 kombinasi band dan indeks yang berbeda: biru, hijau, merah, red edge, NIR, SWIR-1, SWIR-2, NDVI, MNDWI, SR, GCVI, MMRI. Hasil penelitian menunjukkan bahwa hasil klasifikasi menggunakan 12 parameter mampu mengidentifikasi mangrove dengan nilai akurasi yang tinggi (OA = 0,892; kappa = 0,782). Hasil penelitian ini menunjukkan bahwa MMRI menjadi parameter yang diketahui memiliki kemampuan yang paling baik dalam memisahkan objek mangrove dan non-mangrove, diikuti selanjutnya oleh SWIR-2.
BIOMASS ESTIMATION MODEL AND CARBON DIOXIDE SEQUESTRATION FOR MANGROVE FOREST USING SENTINEL-2 IN BENOA BAY, BALI A. A. Md. Ananda Putra Suardana; Nanin Anggraini; Kholifatul Aziz; Muhammad Rizki Nandika; Azura Ulfa; Agung Dwi Wijaya; Abd. Rahman As-syakur; Gathot Winarso; Wiji Prasetio; Ratih Dewanti
International Journal of Remote Sensing and Earth Sciences Vol. 19 No. 1 (2022)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3797

Abstract

Remote sensing technology can be used to find out the potential of mangrove forests information. One of the potentials is to be able to absorb three times more CO2 than other forests. CO2 absorbed during the photosynthesis process, produces organic compounds that are stored in the mangrove forest biomass. Utilization of remote sensing technology is able to detect mangrove forest biomass using the density level of the vegetation index. This study focuses on determining the best AGB model based on the vegetation index and the ability of mangrove forests to absorb CO2. This research was conducted in Benoa Bay, Bali Province, Indonesia. The satellite image used is Sentinel-2. Classification of mangroves and non-mangroves using a multivariate random forest algorithm. Furthermore, the mangrove forest biomass model using a semi-empirical approach, while the estimation of CO2 sequestration using allometric equations. Mean Absolute Error (MAE) is used to evaluate the validation of the model results. The classification results showed that the detected area of Benoa Bay mangrove forest reached 1134 ha (OA: 0.98, kappa: 0.95). The best AGB estimation result is the DVI-based AGB model (MAE: 23,525) with a value range of 0 to 468.38 Mg/ha. DVI-based AGB derivatives are BGB with a value range of 0 to 79.425 Mg/ha, TAB with a value range of 0 to 547.8 Mg/ha, TCS with a value range of 0 to 257.47 Mg/ha, and ACS with a value range of 0 to 944.912 Mg/ha.
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
MANGROVE ABOVE GROUND BIOMASS ESTIMATION USING COMBINATION OF LANDSAT 8 AND ALOS PALSAR DATA Gathot Winarso; Yenni Vetrita; Anang D Purwanto; Nanin Anggraini; Soni Darmawan; Doddy M. Yuwono
International Journal of Remote Sensing and Earth Sciences Vol. 12 No. 2 (2015)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2015.v12.a2687

Abstract

Mangrove ecosystem is important coastal ecosystem, both ecologically and economically. Mangrove provides rich-carbon stock, most carbon-rich forest among ecosystems of tropical forest. It is very important for the country to have a large mangrove area in the context of global community of climate change policy related to emission trading in the Kyoto Protocol. Estimation of mangrove carbon-stock using remote sensing data plays an important role in emission trading in the future. Estimation models of above ground mangrove biomass are still limited and based on common forest biomass estimation models that already have been developed. Vegetation indices are commonly used in the biomass estimation models, but they have low correlation results according to several studies. Synthetic Aperture Radar (SAR) data with capability in detecting volume scattering has potential applications for biomass estimation with better correlation. This paper describes a new model which was developed using a combination of optical and SAR data. Biomass is volume dimension related to canopy and height of the trees. Vegetation indices could provide two dimensional information on biomass by recording the vegetation canopy density and could be well estimated using optical remote sensing data. One more dimension to be 3 dimensional feature is height of three which could be provided from SAR data. Vegetation Indices used in this research was NDVI extracted from Landsat 8 data and height of tree estimated from ALOS PALSAR data. Calculation of field biomass data was done using non-decstructive allometric based on biomass estimation at 2 different locations that are Segara Anakan Cilacap and Alas Purwo Banyuwangi, Indonesia. Correlation between vegetation indices and field biomass with ALOS PALSAR-based biomass estimation was low. However, multiplication of NDVI and tree height with field biomass correlation resulted R2 0.815 at Alas Purwo and R2 0.081 at Segara Anakan. Low correlation at Segara anakan was due to failed estimation of tree height. It seems that ALOS PALSAR height was not accurate for determination of areas dominated by relative short trees as we found at Segara Anakan Cilacap, but the result was quite good for areas dominated by high trees. To improve the accuracy of tree height estimation, this method still needs validation using more data.
Co-Authors A. A. Md. Ananda Putra Suardana A.A. Md. Ananda Putra Suardana Abd. Rahman As-syakur Agung Dwi Wijaya Agustinus Harsono Supardjo Anang D Purwanto Atriyon Julzarika Atriyon Julzarika Azura Ulfa Devica Natalia BR Ginting Doddy M. Yuwono Gathot Winarso K Kayat Kholifatul Aziz Kuncoro Teguh Setiawan Masita Dwi Mandini Manessa Muhammad Rizki Nandika Mutiaraning Pertiwi Nana Suwargana Ratih Dewanti Soni Darmawan Surahman Syamsu Rosid Syifa Wismayati Adawiah Wiji Prasetio Yenni Vetrita