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

Mapping mangroves using satellite imagery has been done for decades. It helps reduce obstacles in inaccessible places caused bythe mangroves’ intricate root system, thick mud, and loss of position signals. Tere is an urgent need to produce a mangrove mapthat automatically and accurately covers the mangroves with the density index of the canopy as visually represented in satelliteimagery. Te research was conducted through an analytical desk study of the mangrove features from space. Te study aims todevelop a simple formula for automatically tracing, capturing, and mapping mangroves and determining the canopy density indexfrom open access of satellite data to eliminate manual digitization work, make it easy to use, and save cost and time. Te goal is tomonitor, 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 − ρ Red)/(ρ Red + ρSWIR1) ∗ (ρNIR − ρSWIR1)/(ρ SWIR1 − 0.65 ∗ ρ Red). Experimental results in many mangrove forests using Landsat 5 TM,Landsat 7 ETM, Landsat 8 OLI, and Sentinel 2 imageries show satisfactory performance. Te maps capture the spatial extent of themangroves automatically and match the satellite imagery visually. Te index correlates signifcantly with the NormalizedDiference Water Index (NDWI), with R2 reaching 0.99. Te research will apply the formula of the Musi Delta mangrove complexin South Sumatra, Indonesia. Te advantage of the algorithm is that it works well, is easy to use, produces mangrove maps faster,informs the index, and efciently monitors the change in mangrove conditions from time to time