ESTIMATION OF MANGROVE FOREST ABOVEGROUND BIOMASS USING MULTISPECTRAL BANDS, VEGETATION INDICES AND BIOPHYSICAL VARIABLES DERIVED FROM OPTICAL SATELLITE IMAGERIES: RAPIDEYE, PLANETSCOPE AND SENTINEL-2

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-3, 2018 ISPRS TC III Mid-term Symposium “Developments, Technologies and Applications in Remote Sensing”, 7–10 May, Beijing, China ESTIMATION OF MANGROVE FOREST ABOVEGROUND BIOMASS USING MULTISPECTRAL BANDS, VEGETATION INDICES AND BIOPHYSICAL VARIABLES DERIVED FROM OPTICAL SATELLITE IMAGERIES: RAPIDEYE, PLANETSCOPE AND SENTINEL-2 A. B. Baloloy a*, A. C. Blanco a,b, C. G. Candido a, R. J. L. Argamosa a, J. B. L. C. Dumalag a, L. L. C. Dimapilis a, E. C. Paringit b a Training Center for Applied Geodesy and Photogrammetry, University of the Philippines, Diliman, Quezon City, 1001, Philippines - , b Department of Geodetic Engineering, University of the Philippines, Diliman, Quezon City, 1001, Philippines Commission III, WG III/10 KEY WORDS: Aboveground Biomass, RapidEye, Sentinel 2, PlanetScope, Mangroves ABSTRACT: Aboveground biomass estimation (AGB) is essential in determining the environmental and economic values of mangrove forests. Biomass prediction models can be developed through integration of remote sensing, field data and statistical models. This study aims to assess and compare the biomass predictor potential of multispectral bands, vegetation indices and biophysical variables that can be derived from three optical satellite systems: the Sentinel-2 with 10m, 20m and 60m resolution; RapidEye with 5m resolution and PlanetScope with 3m ground resolution. Field data for biomass were collected from a Rhizophoraceae-dominated mangrove forest in Masinloc, Zambales, Philippines where 30 test plots (1.2 ha) and 5 validation plots (0.2 ha) were established. Prior to the generation of indices, images from the three satellite systems were pre-processed using atmospheric correction tools in SNAP (Sentinel-2), ENVI (RapidEye) and python (PlanetScope). The major predictor bands tested are Blue, Green and Red, which are present in the three systems; and Red-edge band from Sentinel-2 and Rapideye. The tested vegetation index predictors are Normalized Differenced Vegetation Index (NDVI), Soil-adjusted Vegetation Index (SAVI), Green-NDVI (GNDVI), Simple Ratio (SR), and Red-edge Simple Ratio (SRre). The study generated prediction models through conventional linear regression and multivariate regression. Higher coefficient of determination (r2) values were obtained using multispectral band predictors for Sentinel-2 (r2 = 0.89) and Planetscope (r2 = 0.80); and vegetation indices for RapidEye (r2 = 0.92). Multivariate Adaptive Regression Spline (MARS) models performed better than the linear regression models with r2 ranging from 0.62 to 0.92. Based on the r2 and root-mean-square errors (RMSE’s), the best biomass prediction model per satellite were chosen and maps were generated. The accuracy of predicted biomass maps were high for both Sentinel-2 (r2 = 0.92) and RapidEye data (r2 = 0.91). 1. INTRODUCTION Mangroves have a wide range of economic, social and environmental benefits often referred to as ecosystem services. Like other vegetated coastal ecosystems, mangroves are important blue carbon sinks with a storage capacity between 990 and 1074 t C ha-1 (Donato et al., 2011). In the tropics, mangroves are among the carbon-rich forests with an average storage of 1023 t C ha-1 (Laffoley & Grimsditch, 2009). The greatest carbon pool in a tree is the aboveground biomass which refers to the living biomass above the soil including the stems, bark, branches, foliage, and seeds. It is usually measured for carbon flux monitoring (Vashum & Jayakumar, 2012), carbon stock quantification (Kumar and Mutanga, 2017) and for developing carbon policies and forest management protocols. Traditional approach to field biomass estimation of mangroves is limited to the spatial constraints of data collection and inaccessibility of mangroves stands. A common non-destructive approach is the use of allometric equations derived from parameters such as diameter at breast height (DBH). Remote sensing served as a non-destructive alternative for a more robust, continuous and spatially explicit biomass assessment (Herold and Johns, 2007). The availability of different remote sensing systems led to increased capability for biomass estimation. Optical remote sensing systems offers global coverage which is _________________________________ often cost effective. For regional scale, aboveground biomass estimation is usually carried using optical platforms such as Landsat (Shao & Zhang, 2016; Gleason & Im, 2011), IKONOS and MODIS (Yin et al, 2015). With newer moderate resolution satellite systems, plot-level biomass estimate can also be achieved through improved imaging sensors with shorter revisit time. Among these new platforms are RapidEye (2008), Sentinel2 (2015, 2017) and PlanetScope (2014). Sentinel-2 is a landmonitoring constellation of two identical satellite with novel spectral capabilities with a swath width of 290 km and a frequent revisit time of 5 days. The optical payload it carries has visible, near-infrared and infrared sensors, which provide a total of 13 spectral bands with 10m, 30m and 60m ground spatial resolution (ESA). Compared to Sentinel-2, RapidEye has higher resampled spatial resolution of 5 meters with revisit time of just one day. It is known as the first commercial satellite with a red-edge band in addition to the blue, green, red, and NIR bands. Prediction models using RapidEye bands were found to explain biomass variation better than Landsat (Ramoelo and Cho, 2014). PlanetScope has the least number of bands (blue, green, red, and NIR) but it has the highest spatial resolution of 3m. Fewer studies on biomass estimation were conducted using PlanetScope data compared to the other satellite imageries. No previous studies have compared the performance of these three satellite data using prediction models developed from the same field data, with focus on the common bands, indices, and biophysical factors that can be derived from these systems. * Corresponding author This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-IV-3-29-2018 | © Authors 2018. CC BY 4.0 License. 29 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-3, 2018 ISPRS TC III Mid-term Symposium “Developments, Technologies and Applications in Remote Sensing”, 7–10 May, Beijing, China This study aimed to evaluate the biomass prediction efficiency of multispectral bands, vegetation indices and biophysical variables derived from RapidEye, PlanetScope and Sentinel-2. Specifically, different biomass prediction models using linear regression and non-linear multivariate regression algorithms were developed in this study. Furthermore, the accuracy of each prediction model as well as the accuracy of the predicted aboveground biomass maps were assessed using field validation plots. bands were stac (...truncated)