Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates

Abstract

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM's shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data.

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

“…Estimating and mapping forest biomass/carbon stocks become essential for greenhouse gas inventories, global carbon cycle, and climate change modeling [1][2][3]. Various methods such as process models and remote sensing-based approaches have been developed and used [4][5][6][7]. The process models-based methods do not generate spatially explicit predictions and often lead to a large amount of uncertainty for specific sites, partly because too many variables and input parameters are required to run the models and partly because different source data such as climate and soil data have very coarse spatial resolutions [8][9][10].…”

“…The process models-based methods do not generate spatially explicit predictions and often lead to a large amount of uncertainty for specific sites, partly because too many variables and input parameters are required to run the models and partly because different source data such as climate and soil data have very coarse spatial resolutions [8][9][10]. In contrast, remote sensing-based approaches have become popular due to their unique characteristics in data collection and presentation; that is, multitemporal remote sensing images not only reveal spatial variability, spatial distributions, and patterns of forests but also provide the potential to estimate their changes over time [4][5][6][7]. A large number of research papers on biomass estimation using remote sensing data have been published in the past three decades, as summarized in previous literature review papers (e.g., [4,6,[11][12][13][14][15][16][17]).…”

Mapping forests with Lidar provides flexible, accurate data with many uses

“…Hyde et al (2006) compared the biomass estimation from Lidar, SAR/ InSAR (a radar satellite), and ETM+ and Quickbird (optical sensors of moderate and high spatial resolution, respectively) and found that Lidar was the best single sensor for estimating biomass. Its higher accuracy in estimating biomass compared with Landsat TM and high-resolution Quickbird is also supported by Lu et al (2012) and Gonzalez et al (2010). Other studies focused on identifying methods to integrate Lidar with optical remote sensing (aerial and satellite images) to improve biomass estimation, given that biomass is related not only to tree structure, but also to factors strictly dependent on vegetation type , and optical remote sensing can provide information on vegetation type.…”

“…Accurate measures and predictions of biomass are critical for estimating carbon storage on a stand and forest scale and also a global scale. Vegetation height metrics derived from Lidar are often used to predict biomass and have been found to provide accurate estimates of biomass even when forest density is high, because Lidar is not affected by the saturation problem associated with optical sensors, which can make moderate-density forests appear similar to high-density forests (Lu et al 2012). Hyde et al (2006) compared the biomass estimation from Lidar, SAR/ InSAR (a radar satellite), and ETM+ and Quickbird (optical sensors of moderate and high spatial resolution, respectively) and found that Lidar was the best single sensor for estimating biomass.…”

Sensitivity Analysis of 3D Individual Tree Detection from LiDAR Point Clouds of Temperate Forests

“…In the past several years, a few authors have been dealing with similar topics. In [21,22], an uncertainty analysis is performed to estimate forest carbon stock and biomass from satellite imagery and LiDAR data. The dominate sources of uncertainty are the variation of input sample plot data and data saturation problems related to optical sensors.…”