Species-level classification of mangrove forest using AVIRIS-NG hyperspectral imagery

Paramanik, Somnath; Deep, Nikhil Raj; Bhattacharya, Bimal K.; Dash, Jadunandan

doi:10.1080/2150704x.2023.2215945

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…28,41 Behera et al 38 identified the mangrove species Heritiera fomes, Excoecaria agallocha, and Avicennia officinalis using reflectance/backscatter bands and vegetation indices derived from Sentinel-2, AVIRIS-NG, and Sentinel-1. Meanwhile, Paramanik et al 42 only used AVIRIS-NG hyperspectral imagery, which successfully identified the species H. fomes, E. agallocha, and A. officinalis and two of their combinations (H. fomes-E. agallocha and A. officinalis-E. agallocha), and Ghorbanian et al 22 were limited to mapping mangrove ecosystems using Sentinel-1 and Sentinel-2 images using RF. For developing the classification model, the parameter used was vegetation index; 38 this is in contrast to the present study, which not only used the vegetation index but also the wetness and moisture indices.…”

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Mangrove ecosystem species mapping from integrated Sentinel-2 imagery and field spectral data using random forest algorithm

Simarmata,

Wikantika,

Darmawan

et al. 2024

J. Appl. Rem. Sens.

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Mangroves maintain coastal balance and have the greatest potential for carbon sequestration. Most mapping studies on mangroves have focused on their extent and distribution and rarely featured mangrove species. Therefore, the objective of our study is to investigate mangrove species mapping from integrated Sentinel-2 imagery and field spectral data using a random forest (RF) algorithm. Study areas are located in East and South Lampung, Indonesia. The field samples used represented 144 points of mangrove species. The classification method used an RF algorithm and four models with varying parameters: model 1 with Sentinel-2; model 2 with both Sentinel-2 and field spectral data; model 3 with Sentinel-2, field spectral data, and spectrally transformed data; and model 4 only with spectrally transformed data. The results showed that Rhizophora mucronata, Sonneratia alba, Avicennia lanata, and Avicennia marina were the most common mangrove species in these areas, with reflectance values in the range of 0.002 to 0.493, 0.006 to 0.833, 0.014 to 0.768, and 0.002 to 0.758. Permutation importance (PI) that affects the classification model is the red band, near-infrared, and green normalized difference vegetation index, where the most PI in model 3 is 0.283. The highest level of agreement for mangrove species is found in model 3. Model 3 is the best parameter for RF classification that showed the best mapping accuracy, with the overall accuracy, user accuracy, producer accuracy, and kappa value being 81.25%, 81.68%, 81.25%, and 0.80, respectively.

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