Analisa Citra satelit dan Machine Learning untuk Prediksi Abrasi Pantai

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Analisa Citra satelit dan Machine Learning untuk Prediksi Abrasi Pantai

Metode

Research Location

Pantai Utara Tangerang

Comparison Data Classification Using ML methods

MARS (Multivariate Adaptive Regression Splines Algorithms)

K-NN

CART (Classification and Regression Trees Aplines Algorithms)

Random Forest

Testing Results of Accuracy and Validation

MNDWI Prediction Abration

NDVI and MSAVI Prediction

MPE

ME

MAPE

MSE

RMSE

Pendahuluan

Tingkat keparahan abrasi pantai

Kondisi abrasi pantai

Indonesia sebagai negara kepulauan memiliki potensi terhadap abrasi pantai

Dataset

Radio Metric Correction

MSAVI

Vegetasi (NDVI)

Kerapatan RTH (SAVI)

MNDWI

VHI

Satelite Imagery Data

Satelit Landsat 8 OLI/TIRS memiliki sensor Onboard Operational Land Imager (OLI)

Ekstraksi Data dari Citra Landat 8 OLI

Band 9

Band 8

Band 7

Band 6

Band 5

Band 4

Band 3

Band 2

Band 1

Data Preprocessing

www.earthexplorer.usgs.gov

Studi Literatur

Compare and Contrast

Conference Publications

National Journal Publications

MANGROVE MONITORING USING NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI): CASE STUDY IN NORTH HALMAHERA, INDONESIA

Perubahan Konversi Lahan Menggunakan NDVI, EVI, SAVI dan PCA pada Citra Landsat 8 (Studi Kasus : Kota Salatiga)

Tsunami Vulnerability and Risk Assessment Using Machine Learning and Landsat 8

International Journal Publications

Drought Analysis and Forecast Using Landsat-8 Sattelite Imagery, Standardized Precipitation Index and Time Series

Computer model for tsunami vulnerability using sentinel 2A and SRTM images optimized by machine learning