Modelling Aboveground Biomass of Miombo Woodlands in Niassa Special Reserve, Northern Mozambique

Abstract

Aboveground biomass (AGB) estimation plays a crucial role in forest management and carbon emission reporting, especially for developing countries wishing to address REDD+ projects. Both passive and active remote-sensing technologies can provide spatially explicit information of AGB by using a limited number of field samples, thus reducing the substantial budgetary cost of field inventories. The aim of the current study was to estimate AGB in the Niassa Special Reserve (NSR) using fusion of optical (Landsat 8/OLI and Sentinel 2A/MSI) and radar (Sentinel 1B and ALOS/PALSAR-2) data. The performance of multiple linear regression models to relate ground biomass with different combinations of sensor data was assessed using root-mean-square error (RMSE), and the Akaike and Bayesian information criteria (AIC and BIC). The mean AGB and carbon stock (CS) estimated from field data were estimated at 56 Mg ha−1 (ranging from 11 to 95 Mg ha−1) and 28 MgC ha−1, respectively. The best model estimated AGB at 63 ± 20.3 Mg ha−1 for NSR, ranging from 0.6 to 200 Mg ha−1 (r2 = 87.5%, AIC = 123, and BIC = 51.93). We obtained an RMSE % of 20.46 of the mean field estimate of 56 Mg ha−1. The estimation of AGB in this study was within the range that was reported in the existing literature for the miombo woodlands. The fusion of vegetation indices derived from Landsat/OLI and Sentinel 2A/MSI, and backscatter from ALOS/PALSAR-2 is a good predictor of AGB.

Woody biomass increases across three contrasting land uses in Hurungwe, mid-Zambezi valley, Zimbabwe

“…Novel solutions to some of the field data sampling challenges are being addressed by remote sensing (RS) technologies through increased precision of inventory estimates and reduced costs of forest resource inventory and monitoring at landscape scales (McRoberts et al 2014;Naesset et al 2016;Esteban et al 2020). Thus, RS products including optical (e.g., Landsat, Sentinel 2A, Lidar), synthetic aperture radar (SAR, e.g., Sentinel 1) data, or their combination are now commonly used in vegetation assessment and monitoring (Ribeiro et al 2008b;Saatchi et al 2011;Harris et al 2012;Vibrans et al 2013;Hansen et al 2015;Macave et al 2022). Optical images are dependent on atmospheric conditions at the time of data acquisition (Lu et al 2016).…”

“…However, SAR are active sensors that emit radiation at wavelengths that are less susceptible to atmospheric backscattering and thus have high transmissivity through clouds (Lu et al 2016;Urbazaev et al 2018). The relationship between both types of RS data (optical and SAR) and field data is used to develop models that can predict AGB at the landscape level (McNicol et al 2018;Macave et al 2022). However, both optical and SAR are affected by data saturation at high AGB (greater than 80 Mg/ha (Ribeiro et al 2008b;Lu et al 2016;Urbazaev et al 2018).…”

Assessing Trends in Tree Cover, Wildfire and Population Growth in Zimbabwe since 2000

“…Provisioning services such as fuelwood, construction materials, charcoal, and medicines, are estimated to support 100 million rural people and 50 million urban residents, contributing $9 billion a year to rural and urban populations [7,8]. Regulating services provided by Zimbabwe's woodlands are also notable, including carbon sequestration and storage [9], with significantly higher soil organic carbon content under relatively undisturbed woodlands than on cultivated lands. Sediment retention and regulation of water have also been documented to be better under woodland versus cleared woodland in southern Africa [7].…”

Identification of Urban Green Space Types and Estimation of Above-Ground Biomass Using Sentinel-1 and Sentinel-2 Data

“…Its basic idea is to establish a correlation between remote sensing image features and sample point-based measured data, and then use parametric models or machine learning methods to calculate AGB [25][26][27]. Multiple linear regression is an important method for inferring vegetation AGB because it is highly interpretable and understandable [28,29].…”