Pemanfaatan Citra Sentinel-2 untuk Mendeteksi Kesehatan Mangrove di Resort Bama, Taman Nasional Baluran

Ridho, Ghulam Muhammad and Ir. Bambang Semedi,, M.Sc., Ph.D and Ir. Aida Sartimbul,, M.Sc., Ph.D (2023) Pemanfaatan Citra Sentinel-2 untuk Mendeteksi Kesehatan Mangrove di Resort Bama, Taman Nasional Baluran. Sarjana thesis, Universitas Brawijaya.

Abstract

Hutan mangrove merupakan komunitas tumbuhan yang berada di daerah intertidal, dengan kemampuannya untuk beradaptasi, hutan ini mampu hidup di wilayah yang ekstrem. Hutan mangrove merupakan salah satu ekosistem yang paling produktif, dengan memiliki fungsi ekologi, ekonomi, dan budaya yang tinggi. Namun, dari tahun ke tahun luasan hutan mangrove semakin berkurang, hal ini dikarenakan kegiatan antropogenik yang semakin tinggi. Pemantauan hutan mangrove bertujuan untuk memberikan informasi kepada pengelola kawasan untuk mengembangkan kebijakan dan mengatur pengelolaan berkelanjutan. Dengan adanya penginderaan jarak jauh, memudahkan kita untuk melakukan pemantauan dan analisis kesehatan hutan mangrove. Penelitian dilakukan di Resort Bama, Taman Nasional Baluran, Kecamatan Banyuputih, Kabupaten Situbondo, Jawa Timur pada bulan Mei 2022. Penelitian ini bertujuan untuk menganalisis kemampuan citra sentinel-2 untuk analisis kesehatan mangrove di suatu wilayah. Pada penelitian ini, metode yang digunakan adalah Mangrove Health Index (MHI). MHI berfungsi untuk menentukan kesehatan mangrove disuatu wilayah dengan parameter penting tutupan mangrove (C), Diameter (DBH), dan Kerapatan pancang (Nsp). Dengan struktur komunitas yang baik akan mendukung kehidupan biotik maupun abiotik di sekitarnya. Kategori MHI antara lain kategori MHI Poor MHI 0 ≤ 33.33%, Moderate 33.33 < MHI ≤ 66.67, Excellent MHI > 66.67. Formulai MHI ini merupakan gabungan dari beberapa indeks vegetasi antara lain Normalized Burn Ratio (NBR), Green Chlorophyll Index (GCI), Structure Insensitive Pigment Index (SIPI), dan Atmospherically Resistant Vegetation Index (ARVI). Rumus MHI yang digunakan pada penelitian ini MHI = 102.12*NBR - 4.64*GCI + 178.15*SIPI + 159.53*ARVI - 252.39. Sampling data lapang menggunakan metode stratisfied random sampling dengan transek 10m x 10m. Pengolahan data citra dilakukan di Google Earth Engine (GEE) melalui proses pengunduhan citra, pemotongan citra, memunculkan false color untuk membedakan area mangrove dan non-mangrove, dan memasukkan formula pada GEE. Dari pengolahan data citra diperoleh luasan hutan mangrove sebesar 111,72 Ha. Dari total luasan hutan mangrove yang ada di Resort Bama kemudian dikelompokkan menjadi 3 kelas, yaitu: poor, moderate, dan excellent. Pengolahan data citra menghasilkan kategori poor sebesar 9,01 Ha, moderate sebesar 37,78 Ha, dan excellent sebesar 64,93 Ha. Berdasarkan pengolahan data, didapatkan nilai R² sebesar 87,7% yang berarti bahwa kemampuan nilai MHI citra dalam menjelaskan variasi nilai MHI lapang sebesar 87,7%. Didapatkan nilai R² > 0,67 yang berarti MHI citra dan MHI lapang memiliki hubungan yang kuat. 1. Asumsi normalitas data terpenuhi di mana nilai Asymp. Sig. (2-tailed) 0,2 > 0,05. Over atau under estimated nilai MHI bisa terjadi karena adanya perbedaan waktu dari pengambilan data citra dan pengambilan data lapang. Selain itu, faktor-faktor seperti kualitas citra, atmosfer, dan tutupan awan dapat mempengaruhi keakuratan hasil penginderaan.

English Abstract

The mangrove forest is a plant community that thrives in the intertidal area, demonstrating its adaptability to extreme environments. As one of the most productive ecosystems, the mangrove forest holds significant ecological, economic, and cultural value. Unfortunately, the area of mangrove forests has been declining over the years, largely due to increasing anthropogenic activities. Considering the long-term benefits in terms of ecology, economy, and culture, it is crucial to prioritize the health of mangrove forests. Monitoring efforts are aimed at providing valuable information to area managers for policy development and sustainable management practices. Remote sensing technology has greatly facilitated the monitoring and analysis of mangrove forest health. This study was conducted in May 2022 at the Bama Resort, Baluran National Park, Banyuputih District, Situbondo Regency, East Java. The objective of the study was to analyze the efficacy of Sentinel-2 imagery in assessing the health of mangroves in the area. The Mangrove Health Index (MHI) methodology was employed for this purpose. MHI utilizes important parameters such as mangrove cover (C), diameter (DBH), and sapling density (Nsp) to determine the health status of mangroves in a given area. A well-structured community will support both biotic and abiotic life in its surroundings. The MHI categories include Poor (MHI ≤ 33.33%), Moderate (33.33% < MHI ≤ 66.67%), and Excellent (MHI > 66.67%). The MHI formula incorporates several vegetation indices including Normalized Burn Ratio (NBR), Green Chlorophyll Index (GCI), Structure Insensitive Pigment Index (SIPI), and Atmospherically Resistant Vegetation Index (ARVI). In this study, the MHI formula used was MHI = 102.12NBR - 4.64GCI + 178.15SIPI + 159.53ARVI - 252.39. Field data sampling was conducted using the stratified random sampling method along a 10m x 10m transect. The image data was processed using the Google Earth Engine (GEE), involving steps such as image downloading, cropping, false color generation to distinguish mangrove and non-mangrove areas, and inputting formulas into GEE. The image data processing revealed a total mangrove forest area of 111.72 hectares within the Bama Resort. These mangrove forests were categorized into three classes: poor, moderate, and excellent. The image data processing indicated 9.01 hectares in the poor category, 37.78 hectares in the moderate category, and 64.93 hectares in the excellent category. The data processing yielded an R² value of 87.7%, signifying that the image-derived MHI value effectively explained 87.7% of the variations observed in the field-derived MHI values. The R² value exceeding 0.67 indicated a strong relationship between the image-derived MHI and the field-derived MHI. The normality assumption was validated as Asymp. Sig. (2-tailed) 0.2 > 0.05, signifying the data's normal distribution. The potential for over or underestimation of MHI values can arise from the time discrepancy between image data collection and field data collection. Additionally, factors such as image quality, atmospheric conditions, and cloud cover can influence the accuracy of sensing results.

Item Type:	Thesis (Sarjana)
Identification Number:	0523080444
Subjects:	500 Natural sciences and mathematics > 551 Geology, hydrology, meteorology > 551.4 Geomorphology and hydrosphere > 551.46 Oceanography and submarine geology
Divisions:	Fakultas Perikanan dan Ilmu Kelautan > Ilmu Kelautan
Depositing User:	Sugeng Moelyono
Date Deposited:	11 Dec 2023 03:54
Last Modified:	11 Dec 2023 03:54
URI:	http://repository.ub.ac.id/id/eprint/205027

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