Analisis Sebaran dan Kerapatan Mangrove dengan menggunakan Citra Sentinel 2A di Pantai Indah Kapuk, Jakarta Utara
Date
2024Author
Sentosa, Bayu
Agus, Syamsul Bahri
Arhatin, Risti Endriani
Metadata
Show full item recordAbstract
Tahun 2013 telah berlangsung kegiatan reklamasi teluk jakarta yang berdampak pada salah satu lokasi yaitu Pantai Indah Kapuk, Jakarta Utara. Kegiatan tersebut menyebabkan perlunya pemantauan kondisi hutan mangrove melalui tingkat kerapatannya untuk menjaga stabilitas dan kelestarian hutan mangrove di Pantai Indah Kapuk. Penelitian ini bertujuan untuk menganalisis sebaran dan kerapatan mangrove serta mengetahui tingkat akurasi dengan menggunakan Citra Sentinel-2A di Pantai Indah Kapuk. Tingkat akurasi citra dilakukan dengan membandingkan klasifikasi Maximum Likelihood (MLH) berbasis piksel dan Support Vector Machine (SVM) berbasis objek. Uji akurasi juga dilakukan terhadap algoritma Mangrove Vegetation Index (MVI). Algoritma MVI akan di gabungkan dengan algoritma Normalized Difference Vegetation Index (NDVI) untuk menganalisis tingkat sebaran dan kerapatan mangrove. Hasil luasan sebaran mangrove dengan MLH sebesar 46,88 ha, SVM sebesar 47,43 ha dan algoritma MVI sebesar 49,75 ha. Akurasi keseluruhan yang dihasilkan untuk klasifikasi MLH dan SVM yaitu 73,23% dan 81,89% serta algoritma MVI sebesar 92,13%. Luasan sebaran dan kerapatan mangrove menghasilkan tiga kategori dengan mangrove jarang (0,94 ha), mangrove sedang (0,25 ha) dan didominasi oleh mangrove rapat (48,56 ha). Hasil pengujian hubungan antara data tutupan kanopi mangrove dengan NDVI didapatkan nilai koefisiesn korelasi (r) sebesar 0,8830 dan koefisien determinasi (R^2) sebesar 0,7842. In 2013, reclamation activities took place in Jakarta Bay, which affected one of the locations, Pantai Indah Kapuk, North Jakarta. This activity causes the need to monitor the condition of mangrove forests through the level of density to maintain the stability and sustainability of mangrove forests in Pantai Indah Kapuk. This study aims to analyze the distribution and density of mangroves and determine the level of accuracy using Sentinel-2A imagery at Pantai Indah Kapuk. Image accuracy tests were conducted by comparing pixel-based Maximum Likelihood (MLH) classification and object-based Support Vector Machine (SVM). Accuracy tests were also conducted on the Mangrove Vegetation Index (MVI) algorithm. The MVI algorithm will be combined with the Normalized Difference Vegetation Index (NDVI) algorithm to analyze the level of mangrove distribution and density. The results of mangrove distribution area with MLH amounted to 46.88 ha, SVM amounted to 47.43 ha and MVI algorithm amounted to 49.75 ha. The overall accuracy produced for MLH and SVM classification is 73.23% and 81.89% and the MVI algorithm is 92.13%. Mangrove distribution and density resulted in three categories with sparse mangroves (0.94 ha), moderate mangroves (0.25 ha) and dominated by dense mangroves (48.56 ha). The results of testing the relationship between mangrove canopy cover data with NDVI obtained a correlation coefficient (r) of 0.8830 and the coefficient of determination (R^2) of 0.7842.