Mangrove Above Ground Biomass Estimation Using Combination of Landsat 8 and Alos Palsar Data

Abstract

Mangrove ecosystem is important coastal ecosystem, both ecologically and economically. Mangrove provides rich-carbon stock, most carbon-rich forest among ecosystems of tropical forest. It is very important for the country to have a large mangrove area in the context of global community of climate change policy related to emission trading in the Kyoto Protocol. Estimation of mangrove carbon-stock using remote sensing data plays an important role in emission trading in the future. Estimation models of above ground mangrove biomass are still limited and based on common forest biomass estimation models that already have been developed. Vegetation indices are commonly used in the biomass estimation models, but they have low correlation results according to several studies. Synthetic Aperture Radar (SAR) data with capability in detecting volume scattering has potential applications for biomass estimation with better correlation. This paper describes a new model which was developed using a combination of optical and SAR data. Biomass is volume dimension related to canopy and height of the trees. Vegetation indices could provide two dimensional information on biomass by recording the vegetation canopy density and could be well estimated using optical remote sensing data. One more dimension to be 3 dimensional feature is height of three which could be provided from SAR data. Vegetation Indices used in this research was NDVI extracted from Landsat 8 data and height of tree estimated from ALOS PALSAR data. Calculation of field biomass data was done using non-decstructive allometric based on biomass estimation at 2 different locations that are Segara Anakan Cilacap and Alas Purwo Banyuwangi, Indonesia. Correlation between vegetation indices and field biomass with ALOS PALSAR-based biomass estimation was low. However, multiplication of NDVI and tree height with field biomass correlation resulted R2 0.815 at Alas Purwo and R2 0.081 at Segara Anakan. Low correlation at Segara anakan was due to failed estimation of tree height. It seems that ALOS PALSAR height was not accurate for determination of areas dominated by relative short trees as we found at Segara Anakan Cilacap, but the result was quite good for areas dominated by high trees. To improve the accuracy of tree height estimation, this method still needs validation using more data.

Mangrove Mapping and Above-Ground Biomass Change Detection using Satellite Images in Coastal Areas of Thai Binh Province, Vietnam

“…This result was in line with (Hamdan et al, 2013;Li et al, 2007) that generated good correlations between vegetation indices and field biomass. Our result was in contrary to (Wicaksono, Danoedoro, Hartono, Nehren, & Ribbe, 2011) and (Winarso et al, 2017) that NDVI values and other vegetation indices had low correlation with field biomass.…”

“…Several methods, which confirmed that remote sensing is possible to estimate mangrove biomass, have been already developed and published. Remote sensing is a great data source facilitating field surveys by avoiding the destructive method and reducing time and cost for field surveys (Anaya et al, 2009;Muhd-Ekhzarizal, Mohd-Hasmadi, Hamdan, Mohamad-Roslan, & Noor-Shaila, 2018;Winarso et al, 2017).…”

Development of aboveground mangrove forests’ biomass dataset for Southeast Asia based on ALOS-PALSAR 25-m mosaic

“…We also have compared our result with Landsat data, where the actual optical images of the Landsat data with textural and spectral characteristics of the canopy and leaves are the main features used to distinguish among mangrove communities. 35,50 According to Fig. 6 the results of the classification of mangroves and nonmangroves on ALOS PALSAR imagery are almost the same as those produced by Landsat imagery.…”

Carbon Sequestration Service of a Ramsar Site: A Conservation-Role Model for Defying Developmental Pressure in the Middle of a Rapidly Expanding City

“…In estimating above-ground biomass using remote sensing, NDVI was used following the earlier studies (Hamdan et al, 2013;Manna et al, 2014;Patil et al, 2013;Winarso et al, 2017). A scatter plot between NDVI estimate and AGB calculated in the field was compared.…”

“…Accurate estimation of carbon stocks is necessary before taking any measures towards conservation and restoration (Liu et al, 2014). Over the years, there have been several studies on carbon sequestration of mangrove wetlands in China (Li et al, 2006), Malaysia (Hamdan et al, 2013), India (Manna et al, 2014;Patil et al, 2013;Sahu et al, 2016), Indonesia (Winarso et al, 2017), Australia (Tran, 2014). However, little is known about the importance of mangrove wetlands in hyper-arid regions.…”