ANALISIS PERUBAHAN GARIS PANTAI UJUNG PANGKAH DENGAN MENGGUNAKAN METODE EDGE DETECTION DAN NORMALIZED DIFFERENCE WATER INDEX (UJUNG PANGKAH SHORELINE CHANGE ANALYSIS USING EDGE DETECTION METHOD AND NORMALIZED DIFFERENCE WATER INDEX)

Besides to the effects from tidal, coastline position changed due to abrasion and accretion. Therefore, it is necessary to detect the position of coastline, one of them by utilizing Landsat data by using edge detection and NDWI filter. Edge detection is a mathematical method that aims to identify a point on a digital image based on the brightness level. Edge detection is used because it is very good to present the appearance of a very varied object on the image so it can be distinguished easily. NDWI is able to separate land and water clearly, making it easier for coastline analysis. This study aimed to detect coastline changes in Ujung Pangkah of Gresik Regency caused by accretion and abrasion using edge detection and NDWI filters on temporal Landsat data (2000 and 2015). The data used in this research was Landsat 7 in 2000 and Landsat 8 in 2015. The results showed that the coastline of Ujung Pangkah Gresik underwent many changes due to accretion and abrasion. The accretion area reached 11,35 km2 and abrasion 5,19 km2 within 15 year period. Abstrak Selain akibat adanya pasang surut, posisi garis pantai berubah akibat adanya abrasi dan akresi. Oleh karena itu diperlukan adanya deteksi posisi garis pantai, salah satunya dengan memanfaatkan data Landsat dengan menggunakan filter edge detection dan NDWI. Edge detection adalah suatu metode matematika yang bertujuan untuk mengidentifikasi suatu titik pada gambar digital berdasarkan tingkat kecerahan. Filter edge detection digunakan karena sangat baik untuk menyajikan penampakan obyek yang sangat bervariasi pada citra sehingga dapat dibedakan dengan mudah. NDWI mampu memisahkan antara daratan dan perairan dengan jelas sehingga memudahkan untuk analisis garis pantai. Penelitian ini bertujuan untuk deteksi perubahan garis pantai di Ujung Pangkah Kabupaten Gresik yang disebabkan oleh adanya akresi dan abrasi dengan menggunakan filter edge detection dan NDWI pada data Landsat temporal (tahun 2000 dan 2015). Data yang digunakan pada penelitian ini adalah citra Landsat 7 tahun 2000 dan Landsat 8 tahun 2015. Hasil penelitian menunjukkan bahwa garis pantai di Ujung Pangkah Gresik banyak mengalami perubahan akibat adanya akresi dan abrasi. Luas akresi mencapai 11,35 km2 dan abrasi 5,19 km2 dalam periode waktu 15 tahun

PEMETAAN MANGROVE MENGGUNAKAN ALGORITMA MULTIVARIATE RANDOM FOREST: Studi Kasus di Segara Anakan, Cilacap

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

Detection of True Mangroves in Indonesia Using Satellite Remote Sensing

Mangrove existence is necessary to protect coastal. One method that can be used to keep mangrove existence were using satellite imagery monitoring. The number of bands in the imagery led to the selection for the RGB composite bands was difficult because a lot of combinations to try. One technique that can be done to get the best RGB combination of an object is to use Optimum Index Factor (OIF). OIF is a statistical technique for selecting three combinations of imagery bands to visualize the image display to the fullest. It is based on the value of total variance and the correlation coefficient between the bands. Landsat 8 has 7 bands with 30 m resolution, one panchromatic band with  15 m resolution, and two bands with 100 m resolution. The purpose of this study was to detect true mangrove using three bands from  OIF value of  Landsat 8. The results of the processing from 6 bands (2-7), obtained 20 bands combinations  with the highest value of OIF is 0,168, ie, bands 2-56 (Blue, NIR, SWIR-1). Based on the combination, the next step was unsupervised classification process for true mangrove identification (Rizhopora, Brugueira, Avicennia, Soneratia). The best classification using band combination 2-7 with true mangrove reached 4.041 ha

Land changes detection on Rote Island using harmonic modelling method

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

MANGROVE ABOVE GROUND BIOMASS ESTIMATION USING COMBINATION OF LANDSAT 8 AND ALOS PALSAR DATA

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

ANALISIS SPASIAL KESESUAIAN BUDIDAYA KERAPU BERBASIS DATA PENGINDERAAN JAUH (STUDI KASUS: PULAU AMBON MALUKU)

Perairan Indonesia memiliki potensi budidaya laut yang melimpah. kegiatan ini perlu dimaksimalkan dengan pendekatan teknologi penginderaan jauh untuk menentukan lokasi yang memiliki potensi area akuakultur. Lokasi penelitian adalah Teluk Ambon, Provinsi Maluku. Metode yang digunakan untuk kesesuaian lokasi adalah overlay antara hasil pembobotan dalam parameter total padatan tersuspensi (TSS), suhu permukaan laut (SST), klorofil, dan batimetri. Selain itu, data mangrove dan terumbu karang digunakan sebagai faktor pembatas untuk lokasi kesesuaian. Berdasarkan hasil pengolahan data, kelas-kelas cukup cocok didominasi di Teluk Piru, Teluk Banguala, dan Teluk Ambon; kelas yang sesuai terdeteksi di Teluk Ambon Dalam; dan kelas yang sangat cocok terdeteksi di Teluk Piru dan Teluk Ambon. Hasil verifikasi pengukuran lapangan menunjukkan bahwa suhu data gambar dengan data insitu berkorelasi dengan nilai R2 0,74 dan gambar TSS dengan data insitu menunjukkan R2 sebesar 0,63.

The Examination of The Satellite Image-Based Growth Curve Model Within Mangrove Forest

Developing growth curve for forest and environmental management is a crucial activity in forestry planning. This paper describes a proposed technique for developing a growth curve based on the SPOT 6 satellite imageries. The most critical step in developing a model is on pre-processing the images, particularly during performing the radiometric correction such as reducing the thin cloud. The pre-processing includes geometric correction, radiometric correction with image regression, and index calculation, while the processing technique include training area selection, growth curve development, and selection. The study found that the image regression offered good correction to the haze-distorted digital number.  The corrected digital number was successfully implemented to evaluate the most accurate growth-curve for predicting mangrove.  Of the four growth curve models, i.e., Standard classical, Richards, Gompertz, and Weibull models, it was found that the Richards is the most accurate model in predicting the mean annual increment and current annual increment.  The study concluded that the growth curve model developed using high-resolution satellite image provides comparable accuracy compared to the terrestrial method.  The model derived using remote sensing has about 9.16% standard of error, better than those from terrestrial data with 15.45% standard of error

BIOMASS ESTIMATION MODEL AND CARBON DIOXIDE SEQUESTRATION FOR MANGROVE FOREST USING SENTINEL-2 IN BENOA BAY, BALI

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

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

ESTIMASI BATIMETRI DARI DATA SPOT 7 STUDI KASUS PERAIRAN GILI MATRA NUSA TENGGARA BARAT

Indonesia merupakan negara kepulauan dengan ribuan pulau besar dan kecil yang memliki perairan laut dangkal. Salah satu informasi yang dibutuhkan dari pulau-pulau tersebut adalah peta batimetri khususnya diperairan laut dangkal. Informasi tersebut masih sangat terbatas pada skala yang besar untuk skala yang lebih detil masih sangat terbatas. Untuk menyelesaikan permasalahan tersebut dibutuhkan teknogi penginderaan jauh. Salah satu pemanfaatan teknologi penginderaan jauh adalah untuk menghasilkan informasi batimetri. Banyak metode yang dapat digunakan untuk menghasilkan informasi batimetri dengan teknologi tersebut. Metode yang digunakan dalam penelitian ini adalah metode regresi linier berganda (MLR) yang dikembangkan oleh Lyzenga, 2006. Data yang akan di gunakan adalah citra satelit SPOT 7 di Perairan Laut Dangkal Gili Trawangan, Gili Meno dan Gili Air Pulau Lombok Provinsi Nusa Tenggara Barat. Metode penentuan batimetri tersebut dilakukan pada data kedalaman insitu dengan melakukan dua modifikasi yaitu yang pertama dengan tidak memperhatikan jenis objek habitat dasar dan yang kedua memperhatikan objek habitat dasar karang, lamun, makroalga dan substrat.Hasil dari penelitian ini memberikan korelasi R2 yang meningkat dari 0,721 menjadi 0,786 serta penuruanan nilai kesalahan RMSE dari 3,3 meter menjadi 2,9 meter