Landscape-Scale Aboveground Biomass Estimation in Buffer Zone Community Forests of Central Nepal: Coupling In Situ Measurements with Landsat 8 Satellite Data

Abstract

Knowledge of forest productivity status is an important indicator of the amount of biomass accumulated and the role of terrestrial ecosystems in the carbon cycle. However, accurate and up-to-date information on forest biomass and forest succession remain rudimentary within natural forests. This study sought to understand and establish the potential of a new-generation sensor in estimating aboveground biomass (AGB) stored in the natural forest, also known as ‘community forest’ or buffer zone community forest (BZCF), in the Parsa National Park, Nepal. The utility of the 30-m resolution Landsat 8 Operational Land Imager (OLI) and in situ data was tested using two statistical approaches, namely multiple linear regression (MLR) and random forest (RF). The analysis was done based on four computational procedures. These included spectral bands, vegetation indices and pooled dataset (spectral bands + vegetation indices), and model selected important variables. AGB estimation based on pooled data showed that the RF algorithm produced better results when compared to the use of the MLR model. For instance, the RF model estimated AGB with an R2 value of 0.87 and a root mean square error of 20.50 t ha−1, as well as an R2 value of 0.95 and a RMSE of 13.3 t ha−1 when using selected important variables. Comparatively, the MLR using pooled data produced an R2 value of 0.56 and RMSE value of 37.01 t ha−1. The RF model selected Optimized Soil Adjusted Vegetation index (OSAVI), Simple ratio (SR), Modified simple ratio (MSR), and Normalized difference Vegetation index (NDVI) as the most important variables for estimating AGB, whereas MLR selected band 5 and SR. These findings demonstrate the relevance of the relatively new Landsat 8 sensor in the estimation of AGB in community buffer zones.

Remote Estimation of Mangrove Aboveground Carbon Stock at the Species Level Using a Low-Cost Unmanned Aerial Vehicle System

“…The cross-validation approach was based on the entire reference dataset, rather than using separate training and validation data subsets, which is a useful approach when only limited reference data is available [74]. Five validation measures of model performance were calculated from the 10-fold cross-validation including R 2 , RMSE, rRMSE, MAE, and rMAE [37][38][39][40][41][42][43]. Models with higher R 2 , smaller rRMSE and smaller rMAE values indicate a higher prediction accuracy:…”

“…However, linear regression methods are based on the assumption of linear relationships between biomass and predictors or independent variables, and thus, they may not provide satisfactory results due to the complex relationships between remote-sensing variables and biomass [41][42][43]. In this case, nonparametric and machine-learning algorithms (MLAs), such as artificial neural network (ANN), support vector regression (SVR), and random forest (RF), can deal with nonlinear relationships, learn from limited training data, and successfully solve classification problems that are difficult to distinguish; such approaches have been widely employed in forest biomass estimation [41,42,[44][45][46][47]. Nevertheless, there is no single MLA that performs best for every study object and area [41,[44][45][46][47], and a comparison of MLAs is highly desired, which will help us select the most appropriate model.…”

Inversion of Winter Wheat Growth Parameters and Yield Under Different Water Treatments Based on UAV Multispectral Remote Sensing

“…Mathematically, a higher R 2 corresponds to a smaller RMSE and thus represents better model accuracy. The following equations were used to calculate R 2 and RMSE ( Pandit et al, 2018 ; Li et al, 2019 ), respectively:…”

“…In recent decades, remote sensing technology has been successfully applied to crop growth monitoring through satellite platforms, manned airborne platforms, and ground spectral equipment (Michele et al, 2015;Maimaitijiang et al, 2017;Ansar and Muhammad, 2020;Dehkordi et al, 2020). There are two kinds of satellite remote sensing data for crop parameters, namely, optical image and synthetic aperture radar data (Cougo et al, 2015;Castillo et al, 2017;Du et al, 2017;Pham and Yoshino, 2017;Pandit et al, 2018;Li et al, 2019), providing different spatial resolutions, such as SPOT (20 m), MODIS (250 m), Sentinel 1A (10 m), and ALOS-2 PLASAR2 (6 m) (Niu et al, 2019). However, several limitations such as deficient spatiotemporal resolution and cloud cover contamination restrain the application of satellite-based platforms.…”

Estimating Structure and Biomass of a Secondary Atlantic Forest in Brazil Using Fourier Transforms of Vertical Profiles Derived from UAV Photogrammetry Point Clouds

“…Remote sensing data associated with field inventory campaigns and modeling techniques have enabled increasingly reliable estimates of AGB [10][11][12]. Even where repeated inventory data are available, they have limited spatial coverage, which is insufficient for continuous spatial predictions of forest biomass [13].…”