Spatial Scaling of Forest Aboveground Biomass Using Multi-Source Remote Sensing Data

Abstract

Accurate estimation of aboveground forest biomass (AGB) at a large scale is important in global carbon cycle, forest productivity, and climate change. Coarse resolution remote sensing data of long time series are often used to estimate large scale AGB, but the result is inaccurate due to the scaling effect caused by nonlinearity in data representation and the existence of mixed pixels containing different forest types and land uses. Improvement in the accuracy of AGB estimated from coarse resolution remote sensing data is urgently needed. Research on spatial scaling of AGB is still lacking, therefore, this paper proposed an approach based on structural analysis of mixed pixels and the Random Forest model (SMPRF) to increase the accuracy of AGB estimated from coarse resolution data. MODIS and SPOT 5 data were used to create forest biomass distribution maps of the study area at two scales. The scaling effect on estimating forest biomass based on remote sensing was analyzed by comparing data from these two datasets. SMPRF, which included a correction factor for the scaling effect on AGB estimated from coarse resolution MODIS data, was used to create a model that scaled from the fine resolution data (SPOT 5) to the coarse resolution data (MODIS). The results showed that the accuracy of AGB estimated from MODIS data was increased using this method. The Pearson correlation coefficient (r) for data verification increased from 0.63 to 0.89 and the root mean squared error decreased from 51.6 Mg•ha-1 to 26.8 Mg•ha-1. The difference tests showed that the changes were extremely significant (p = 0). Thus, SMPRF can significantly improve the accuracy of large scale AGB estimation based on coarse resolution remote sensing data and the feasibility of applying the method proposed in this study to related fields is verified.

Data mining applied to feature selection methods for aboveground carbon stock modelling

“…(Lu et al, 2016). An approach based on structural analysis of mixed pixels and the random forest model was proposed by Wang & Jiao (2020), in order to increase the accuracy of AGB estimated from coarse resolution data in broadleaf forest, mixed forest, and some coniferous forests. The results showed that the accuracy of AGB estimated from MODIS data was increased using this method, and RMSE decreased from 51.6 Mg ha -1 to 26.8 Mg ha -1 .…”

Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

“…Multilayer perceptron neural networks were used to identify eight different types of forests based on the integrated dataset. The research in [30] proposed a novel method for aboveground biomass (AGB) estimation of forest based on the structural analysis of mixed pixels and the RF model. Specifically, a correction factor estimated from MODIS data was used to create a model that scales from fine-resolution data (SPOT 5) to coarse-resolution data (MODIS).…”

Estimating Crown Biomass in a Multilayered Fir Forest Using Airborne LiDAR Data

“…Remote sensing techniques have been widely used to estimate forest biomass and other forest inventory attributes, due to their ability to provide low-cost, accurate and timely results over large and impassable areas [11,12]. Various remote sensing data have been tested for forest biomass and carbon quantification, such as optical (e.g., multi-and hyperspectral) [13][14][15][16], synthetic aperture radar (SAR) [17][18][19], light detection and ranging (LiDAR) [20][21][22][23][24], and integrations between them [25][26][27][28][29].…”

“…Multispectral and SAR image analyse are the most common approaches for large-scale biomass estimation. However, signal saturation is the main limitation for both of them, especially over forested areas with complex topographies [12,30]. Unlike optical sensors, LiDAR signals can penetrate through the forest canopy, providing valuable vertical information about the forest structure [31].…”