Estimating Mangrove Biophysical Variables Using WorldView-2 Satellite Data: Rapid Creek, Northern Territory, Australia

Abstract

Abstract:Mangroves are one of the most productive coastal communities in the world. Although we acknowledge the significance of ecosystems, mangroves are under natural and anthropogenic pressures at various scales. Therefore, understanding biophysical variations of mangrove forests is important. An extensive field survey is impossible within mangroves. WorldView-2 multi-spectral images having a 2-m spatial resolution were used to quantify above ground biomass (AGB) and leaf area index (LAI) in the Rapid Creek mangroves, Darwin, Australia. Field measurements, vegetation indices derived from WorldView-2 images and a partial least squares regression algorithm were incorporated to produce LAI and AGB maps. LAI maps with 2-m and 5-m spatial resolutions showed root mean square errors (RMSEs) of 0.75 and 0.78, respectively, compared to validation samples. Correlation coefficients between field samples and predicted maps were 0.7 and 0.8, respectively. RMSEs obtained for AGB maps were 2.2 kg/m 2 and 2.0 kg/m 2 for a 2-m and a 5-m spatial resolution, and the correlation coefficients were 0.4 and 0.8, respectively. We would suggest implementing the transects method for field sampling and establishing end points of these transects with a highly accurate positioning system. The study demonstrated the possibility of assessing biophysical variations of mangroves using remotely-sensed data.

Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data

“…The relevance of rededge information for AGB prediction has been previously proved; red edge bands have been used as additional data in LIDAR-based tropical forest biomass predictions 61 and for wetland biomass prediction. 74,75 The two produced AGB maps, one based on SAR data and the other on SAR plus optical data, showed reasonable agreement with each other at the per-pixel level, except in areas with high AGB values in the S1-S2 map. When considering the total AGB for the province, the prediction from S1-ALOS2 seems in line with what would be expected if the INFC regional estimates are scaled over the provincial territory and the large occurrence of mature forests in Viterbo area is taken into account.…”

Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

“…A wide range of studies have been conducted using different remote sensing approaches to estimate the biophysical parameters of mangroves, which can be grouped into two main categories: radiative transfer models and empirical-statistical regression models [85]. The former are developed to inverse forest biophysical parameters [86,87], while the latter are commonly utilized to retrieve these parameters based on parametric statistical and non-parametric machine learning algorithms [53,56,60,63]. Among the various biophysical parameters, leaf area index (LAI) is considered an important biophysical parameter for understanding mangrove forest conditions.…”

Mangrove Vegetation Health Assessment Based on Remote Sensing Indices for Tanjung Piai, Malay Peninsular

“…This is because lowland tropical forests consist of multiple layers, multiple species of forest trees, and a rich forest floor making visualizing forest images in remote sensing more difficult. The application of satellite remote sensing has been emphasized by (Heenkenda et al, 2016) in a study for extracting biophysical variables for the Northern Territory of Australia.…”

“…The index has been tested in forest biomes, including deciduous and evergreen broadleaf, tropical rainforest, herbaceous savannah, and in the succession of crops (Hmimina et al, 2013). Additionally, studies that applied indices for mangrove include (Heenkenda et al, 2016;Kongwongjan et al, 2012;Kovacs et al, 2005), whereas Tasselled Cap transformation is index producing three data structure axes defining the vegetation information content: brightness-a weighted sum of all bands, as determined by the phonological variation in soil reflectance; greenness, which is orthogonal to brightness and measures the contrast between the near-infrared and visible bands; and wetness, which relates to canopy and soil moisture. In a comprehensive statement (Crist & Cicone, 1984) defined TC as an orientation data plane such that the two features which define it are directly related to physical scene characteristics.…”