Improving Aboveground Biomass Estimation of Pinus densata Forests in Yunnan Using Landsat 8 Imagery by Incorporating Age Dummy Variable and Method Comparison

Abstract

Optical remote sensing data have been widely used for estimating forest aboveground biomass (AGB). However, the use of optical images is often restricted by the saturation of spectral reflectance for forests that have multilayered and complex canopy structures and high AGB values and by the effect of spectral reflectance from underlayer shrub, grass, and bare soil for young stands. This usually leads to overestimations and underestimations for smaller and larger values, respectively, and makes it very challenging to improve the estimation accuracy of forest AGB. In this study, a novel methodology was proposed by incorporating stand age as a dummy variable into four models to improve the estimation accuracy of the Pinus densata forest AGB in Yunnan of Southwestern China. A total of eight models, including two parametric models (LM: linear regression model and LMC: LM with combined variables), two nonparametric models (RF: random forest and ANN: artificial neural network) without the age dummy variable, and four corresponding models with the age dummy variable (DLM, DLMC, DRF, and DANN), were compared to estimate AGB. Landsat 8 Operational Land Imager (OLI) images and 147 sample plots were acquired and utilized. The results showed that (1) compared with the two parametric models, the two nonparametric algorithms resulted in significantly greater estimation accuracies of Pinus densata forest AGB, and the increases of accuracy varied from 8% to 32% for 100 modeling plots and from 12% to 35% for 47 test plots based on root mean square error (RMSE); (2) compared with the models without the age dummy variable, the models with the age dummy variable greatly reduced the overestimations for the plots with AGB values smaller than 70 Mg/ha and the underestimations for the plots with AGB values larger than 180 Mg/ha and, thus, significantly improved the overall estimation accuracy by 14% to 42% for the modeling plots and by 32% to 44% for the test plots based on RMSE; and (3) the texture measures derived from the Landsat 8 OLI images contributed more to improving the estimation accuracy than the original spectral bands and other transformations. This implied that two nonparametric models, coupled with the use of the age dummy variable and texture measures, offered a great potential for improving the estimation accuracy of Pinus densata forest AGB.

Improving Forest Aboveground Biomass Estimation of Pinus densata Forest in Yunnan of Southwest China by Spatial Regression using Landsat 8 Images

“…The bands consisted of Band1-coastal aerosol, band2-blue (BLU), band3-green (GRN), band4-red (RED), band5-near infrared (NIR), band6-shortwave infrared 1 (SWIR1), and band7-shortwave infrared 2 (SWIR2). The 22 vegetation indices were consistent with those in the study by Ou et al [26], including normalized difference vegetation index (NDVI), simple ratio index (RVI), difference vegetation index (DVI), perpendicular vegetation index (PVI), two soil adjusted vegetation indices, brightness, greenness and temperature vegetation indices, atmospherically resistant vegetation index (ARVI), modified soil adjusted vegetation index (MSAVI), etc. In addition, a total of 224 grey-level co-occurrence matrix-based texture measures including mean, angular second moment, contrast, correlation, dissimilarity, entropy, homogeneity, and variance were derived using moving windows of 3 × 3, 5 × 5, 7 × 7 and 9 × 9 pixels, respectively.…”

“…The relative humidity is 70%, and the evaporation is 1671 mm. Dark brown forest soil and cold temperate coniferous forests are commonly found with dominant tree species of Abies, Pinus, Larix, and Picea [26].…”

“…A total of one hundred and forty-seven 30 m square sample plots were measured in 2016 ( Figure 2). The plots were sampled with a distance of about 1 km based on the different stand ages, elevations, slopes, and aspects [26]. These factors were determined using a spatial distribution map of Pinus densata forests.…”

“…In the present study, three Landsat 8 OLI images were downloaded from the website of the United States Geological Survey, and their image IDs are LC81310412016005LGN00, LC18320402016012LGN00, and LC81320412016012LGN00. There was a Universal Transverse Mercator coordinate system with zone 47 north in the images [26]. The images were mosaicked into one image and clipped using the shape file of the study area ( Figure 2).…”

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

“…The high FSV areas often have complex canopy structures, which may affect the reflectance values. A similar study by Ou et al [106] also found this was a common problem in the estimation of the FSV or biomass using multispectral remote sensing data. However, if the forest area with a low FSV and the forest area with a high FSV reach a certain ratio, the underestimation and overestimation problems of the RF will reach a certain balance.…”