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

Abstract

Abstract:The data saturation problem in Landsat imagery is well recognized and is regarded as an important factor resulting in inaccurate forest aboveground biomass (AGB) estimation. However, no study has examined the saturation values for different vegetation types such as coniferous and broadleaf forests. The objective of this study is to estimate the saturation values in Landsat imagery for different vegetation types in a subtropical region and to explore approaches to improving forest AGB estimation. Landsat Thematic Mapper imagery, digital elevation model data, and field measurements in Zhejiang province of Eastern China were used. Correlation analysis and scatterplots were first used to examine specific spectral bands and their relationships with AGB. A spherical model was then used to quantitatively estimate the saturation value of AGB for each vegetation type. A stratification of vegetation types and/or slope aspects was used to determine the potential to improve AGB estimation performance by developing a specific AGB estimation model for each category. Stepwise regression analysis based on Landsat spectral signatures and textures using grey-level co-occurrence matrix (GLCM) was used to develop AGB estimation models for different scenarios: non-stratification, stratification based on either vegetation types, slope aspects, or the combination of vegetation types and slope aspects. The results indicate that pine forest and mixed forest have the highest AGB saturation values (159 and 152 Mg/ha, respectively), Chinese fir and broadleaf forest have lower saturation values (143 and 123 Mg/ha, respectively), and bamboo forest and shrub have the lowest saturation values (75 and 55 Mg/ha, respectively). The stratification based on either vegetation types or slope aspects provided smaller root mean squared errors (RMSEs) than non-stratification. The AGB estimation models based on stratification of both vegetation types and slope aspects provided the most accurate estimation with the smallest RMSE of 24.5 Mg/ha. Relatively low AGB (e.g., less than 40 Mg/ha) sites resulted in overestimation and higher AGB (e.g., greater than 140 Mg/ha) sites resulted in underestimation. The smallest RMSE was obtained when AGB was 80-120 Mg/ha. This research indicates the importance of stratification in mitigating the data saturation problem, thus improving AGB estimation.

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

“…For example, previous research on forest disturbance studies in North America has proven that NBR is more sensitive than traditional indices such as NDVI [23,24]. However, vegetation indices have a data saturation problem; that is, when forest canopy density reaches a certain value, NDVI values become the same even if forest biomass continued to increase [66]. The fraction image from the decomposing multispectral imagery can improve the performance for forest disturbance, as shown in a detection of drought-induced disturbance of hickory plantations [31].…”

Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods

“…On the other hand, the underestimations for the sample plots and areas with AGB values larger than 100 Mg/ha for the MLR model or 120 Mg/ha for the LR model were also due to reflectance saturation of multi-layer canopy and high biomass forest stands [79]. More importantly, this was mainly because both regression models were global methods that modeled and used global trends of AGB to generate estimates of local areas or pixels and in contrast, both CW-kNN and g-kNN were local methods that modeled and utilized local variability of AGB to create the estimates and thus improved the underestimations.…”

“…Although Landsat TM images have been widely employed for AGB estimation of forest ecosystems [4,52,[72][73][74][75][76][77][78], extracting and selecting spectral variables to accurately derive spatial distribution of AGB is still challenging mainly due to the saturation of spectral reflectance and the presence of mixed pixels [4,58,79]. The image data and spectral bands from different sensors have their own characteristics in reflecting land surfaces [4].…”

Atmospheric and Radiometric Correction Algorithms for the Multitemporal Assessment of Grasslands Productivity

“…Even though these methods are accurate, they are costly, as well as time-and labor-consuming, when large pieces of land have to be covered [9]. With the aim of developing more effective monitoring methods, there have been numerous studies on indirect methods to estimate the biomass of grasslands using remote sensing information [10][11][12]. In this endeavor, optical sensors, radar, and Lidar systems have been used [13].…”