Pemetaan Sebaran dan Kerapatan Mangrove Menggunakan Citra Sentinel-2A di Desa Pantai Mekar, Muara Gembong

Abstract

Desa Pantai Mekar, Kecamatan Muara Gembong merupakan wilayah pesisir dengan risiko abrasi yang tinggi. Banyak upaya rehabilitasi mangrove telah dilakukan namun belum optimal. Salah satu faktor penyebabnya adalah belum meratanya kegiatan rehabilitasi dan penanaman mangrove. Penelitian ini bertujuan menghasilkan peta sebaran dan kerapatan kanopi mangrove melalui kombinasi indeks vegetasi. Penelitian ini memanfaatkan citra Sentinel-2A dengan indeks Mangrove Vegetation Index (MVI), Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), dan Automatic Mangrove Map and Index (AMMI) untuk menghasilkan peta sebaran dan kerapatan kanopi mangrove yang representatif. Hasil penentuan threshold mangrove menggunakan MVI berada di rentang 4,20–15,13. Hasil pengolahan persentase kerapatan kanopi bervariasi mulai dari 37–90%. Hasil analisis korelasi menunjukkan bahwa kombinasi NDVI dan AMMI menghasilkan model kerapatan kanopi terbaik dengan koefisien korelasi sebesar 0,87 dan RMSE sebesar 0,083. Uji akurasi menggunakan confusion matrix menghasilkan nilai overall accuracy sebesar 83,3% dan koefisien kappa sebesar 0,72. Hasil penelitian menunjukkan total luas mangrove sebesar 182,34 ha, yang terdiri atas kerapatan kanopi jarang (20,12 ha), sedang (119,25 ha), dan rapat (42,97 ha).

Pantai Mekar Village, Muara Gembong District, is a coastal area with a high risk of abrasion. Various mangrove rehabilitation efforts have been implemented, however, the outcomes have not yet been optimal. One contributing factor is the uneven distribution of rehabilitation and mangrove planting activities. This study aimed to produce mangrove distribution and canopy density maps through the combination of vegetation indices. Sentinel-2A imagery was utilized using the Mangrove Vegetation Index (MVI), Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), and Automatic Mangrove Map and Index (AMMI) to generate representative mangrove distribution and canopy density maps. The mangrove threshold determined using MVI ranged from 4.20 to 15.13. The processed canopy density percentages varied from 37% to 90%. Correlation analysis showed that the combination of NDVI and AMMI produced the best canopy density model, with a correlation coefficient of 0.87 and an RMSE of 0.083. Accuracy assessment using a confusion matrix resulted in an overall accuracy of 83.3% and a kappa coefficient of 0.72. The mapping results indicated a total mangrove area of 182.34 ha, consisting of sparse canopy density (20.12 ha), moderate canopy density (119.25 ha), and dense canopy density (42.97 ha).