AMMI Automatic Mangrove Map and Index: Novelty for Efficiently Monitoring Mangrove Changes with the Case Study in Musi Delta, South Sumatra, Indonesia

Abstract

Mapping mangroves using satellite imagery has been done for decades. It helps reduce obstacles in inaccessible places caused by the mangroves’ intricate root system, thick mud, and loss of position signals. There is an urgent need to produce a mangrove map that automatically and accurately covers the mangroves with the density index of the canopy as visually represented in satellite imagery. The research was conducted through an analytical desk study of the mangrove features from space. The study aims to develop a simple formula for automatically tracing, capturing, and mapping mangroves and determining the canopy density index from open access of satellite data to eliminate manual digitization work, make it easy to use, and save cost and time. The goal is to monitor, assess, and manage the condition of mangroves for anyone interested in mangroves, including the central government, local authorities, and local communities. As a result, the authors proposed an algorithm: (ρNIR − ρRed)/(ρRed + ρSWIR1) ∗ (ρNIR − ρSWIR1)/(ρSWIR1 − 0.65 ∗ ρRed). Experimental results in many mangrove forests using Landsat 5 TM, Landsat 7 ETM, Landsat 8 OLI, and Sentinel 2 imageries show satisfactory performance. The maps capture the spatial extent of the mangroves automatically and match the satellite imagery visually. The index correlates significantly with the Normalized Difference Water Index (NDWI), with R2 reaching 0.99. The research will apply the formula of the Musi Delta mangrove complex in South Sumatra, Indonesia. The advantage of the algorithm is that it works well, is easy to use, produces mangrove maps faster, informs the index, and efficiently monitors the change in mangrove conditions from time to time.

Enhanced mangrove index: A spectral index for discrimination understorey, nypa, and mangrove trees

“…However, this index has not been applied for discerning nypa from the matrix of mangrove trees. Several additional spectral indices have been created to distinguish mangrove pixels from non-mangrove pixels, such as Automatic Mangrove Map and Index (AMMI), Mangrove Vegetation Index (MVI), and Combined Mangrove Recognition Index (CMRI) [4 , 14 , 34] . However, these indices have only been used to determine the extent and distribution of mangroves and have not been applied for any other purpose, such as identifying understorey or nypa palm.…”

Pemetaan Kerapatan Vegetasi Mangrove Menggunakan Metode Normalized Difference Vegetation Index (Ndvi) Di Kecamatan Jawai, Kabupaten Sambas

“…Pemetaan mangrove telah dilakukan pada beberapa dekade sebelumnya sebagai bentuk solusi terkait tantangan dalam melakukan pemetaan mangrove. Dimana tantangan tersebut diantaranya yaitu akses lokasi yang sulit yang diakibatkan sistem perakaran pada mangrove, serta kehilangan sinyal di lokasi (Suyarso & Avianto, 2022). Dan dari kegiatan pemetaan dengan menggunakan teknologi penginderaan jauh dan sistem informasi geografis ini, pemantauan ekosistem mangrove bisa dilakukan sepanjang waktu dengan skala wilayah yang luas serta lebih menghemat biaya.…”

Vegetation Indices for Spatio-Temporal Analysis of the Quality of Can Gio Mangrove for Biodiversity and Conservation

“…Therefore, NDWI is a useful indicator to monitor the changes of mangrove forest, even the small disturbance or tiny cuts. This index can seize changes by the shortwave infrared bands [13].…”

“…Other indices such as PTC (percentage tree cover), SAVI (soil adjusted vegetation index), MSAVI (modified soil adjusted vegetation index), OSAVI (optimized soil adjusted vegetation index), TSAVI (transformed soil adjusted vegetation index), FAPAR (fraction of absorbed photosynthetically active radiation), etc., can well support analyses of vegetation quality and health [2,7,10,12]. In the mangrove forest management, the Normalized Difference Water Index (NDWI) can be used for monitoring water bodies and water content in vegetation canopies [7,13]. There are direct and indirect methods to monitor vegetation.…”