Abstract
Abstract. Accurate and reliable assessment of above-ground biomass (AGB) is important for the sustainable forest management, especially in Zagros forests, in which a frangible forest ecosystem is being threatened by anthropogenic factors as well as climate change effects. This study presents a new method for AGB estimation and demonstrates the potential of Sentinel-2 Multi-Spectral Instrument (MSI) data as an alternative to other costly remotely sensed data, such as hyperspectral and LiDAR data in unapproachable regions. Sentinel-2 performance was evaluated for a forest in Kurdistan province, west of Iran, using in-situ measured AGB as a dependent variable and spectral band values and spectral-derived vegetation indices as independent variables in the Random Forest Regression (RFR) algorithm. The influence of the input variables number on AGB prediction was also investigated. The model using all spectral bands plus all derived spectral vegetation indices provided better AGB estimates (R2 = 0.87 and RMSE = 10.75 t ha−1). Including the optimal subset of key variables did not improve model variance but slightly reduced the error. This result is explained by the technically-advanced nature of Sentinel-2, which includes fine spatial resolution (10, 20 m) and strategically-positioned bands (red-edge), conducted in different topographical conditions with an advanced machine learning algorithm. However, assessing its transferability to other forest types with varying conditions would enable future performance and interpretability assessments of Sentinel-2.
Highlights
Accurate assessment of forest above-ground biomass (AGB) is important for the sustainable management of forests, for Zagros forest areas whose currently degraded through overgrazing and deforestation
The main aim of this study is, to 1) investigate the performance of spectrally-derived indices using Sentinel-2 multi-spectral instrument (MSI) combined with in-situ measurements for estimating AGB in the Zagros forests, West of Iran and 2) point the major spectral variable to generate the smallest subset of input variables in the Random Forest Regression (RFR) algorithm
This study investigates the performance of the RF algorithm in predicting forest AGB in Zagros forests, West of Iran, using fine spatial resolution Sentinel-2 MSI data
Summary
Accurate assessment of forest above-ground biomass (AGB) is important for the sustainable management of forests, for Zagros forest areas whose currently degraded through overgrazing and deforestation. Employing hyperspectral and LiDAR remote sensing technologies, confronts with some restrictions, e.g. high data accusation and processing costs and data redundancy, that have resulted in a shift towards the use of free and readily available broadband, including Landsat and Sentinel-2 (pandit et al, 2018), which offer a large swath width, letting timely AGB estimations from local to regional-scale (Hall et al, 2011; Laurin at al., 2014). Sentinel-2 equipped with a multi-spectral instrument (MSI) sensor, launched on 23 June 2015 by the European Space Agency (ESA), provides a significant improvement in spectral coverage, spatial resolution, and temporal frequency over the current generation of Landsat sensors (Gómez, 2017) It offers a multi-purpose design of 13 spectral bands ranging from visible and near-infrared (NIR) wavelengths to shortwave infra-red wavelengths at 10 m, 20 m and 60 m ground pixel size. Sentinel-2 was recently evaluated for forest AGB estimation in tropical forests (Chen at al., 2018; pandit at al., 2018), to the best of our knowledge, it
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
