Predictive Modelling of Mangrove Above Ground Biomass through the Integration of Spectral Indices and Field-Based Allometric Data
Aldea Noor Alina1,2*, Lalu Muhamad Jaelani1, and Noorlaila Hayati1

1Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia.
2Department of Geography Education, Universitas Negeri Surabaya, Surabaya 60231, Indonesia.

Abstract

This study aims to develop a predictive model for estimating Above Ground Biomass (AGB) of mangroves by integrating various vegetation spectral indices. The research addresses the challenge of accurately estimating AGB by combining high resolution satellite imagery with field-based allometric data obtained through non-destructive surveys in the mangrove area of East Surabaya Coast. Allometric data sampling was conducted using purposive random sampling with dominantly 30 x 30 meters plot area according to SNI 7724:2019. The data retrieved was circumference or Girth Breast Height (GBH) with standard at 1.3 meters from the ground surface, measurement was conducted for all sapling, poles, and stands of mangrove trees in each plot. The data acquired also height, soil moisture, salinity, and pH level of the plot area The methodology involves spectral indices calculation such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), and Combined Mangrove Recognition Index (CMRI) derived from Worldview-2 Satellite Imagery. These spectral indices are integrated with field derived biomass values to develop multiple predictive models. Allometric equation that incorporate DBH and Height displayed better relation value between Aboveground Biomass with its parameter compared to Aboveground Biomass that only incorporate DBH. The AGB model with a DBH^2H parameter has higher R square value at 0.61 compared to the AGB model with a DBH parameter of 0.39. Stock carbon modeling developed using vegetation indices showed the relationship between field carbon stock data and pixel values in the vegetation index transformation. The results of the correlation test on NDVI and CMRI were positive, meanwhile NDWI showed negative correlation. According to R2 value, NDWI displayed best relation value compared to NDVI and CMRI, even thought the relation were negative with 0.543.

Keywords: Above Ground Biomass, Allometric, Mangrove, Spectral Indices, Predictive Model

Topic: Topic B: Applications of Remote Sensing