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

2019 ◽  
Vol 11 (7) ◽  
pp. 738 ◽  
Author(s):  
Guanglong Ou ◽  
Chao Li ◽  
Yanyu Lv ◽  
Anchao Wei ◽  
Hexian Xiong ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Spectral Reflectance ◽  
Parametric Models ◽  
Estimation Accuracy ◽  
Stand Age ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Nonparametric Models ◽  
Dummy Variable ◽  
Pinus Densata

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.

scholarly journals Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

2021 ◽  
Vol 13 (4) ◽  
pp. 581 ◽  
Author(s):  
Yuanyuan Fu ◽  
Guijun Yang ◽  
Xiaoyu Song ◽  
Zhenhong Li ◽  
Xingang Xu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Least Squares ◽  
Aboveground Biomass ◽  
Regression Models ◽  
Digital Images ◽  
Predictive Performance ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Biomass Estimation ◽  
High Definition

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

scholarly journals Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1073 ◽  
Author(s):  
Li ◽  
Li ◽  
Li ◽  
Liu
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Variable Selection ◽  
Aboveground Biomass ◽  
Forest Type ◽  
Learning Algorithms ◽  
Forest Biomass ◽  
Machine Learning Algorithms ◽  
Biomass Estimation ◽  
Landsat 8

Forest biomass is a major store of carbon and plays a crucial role in the regional and global carbon cycle. Accurate forest biomass assessment is important for monitoring and mapping the status of and changes in forests. However, while remote sensing-based forest biomass estimation in general is well developed and extensively used, improving the accuracy of biomass estimation remains challenging. In this paper, we used China’s National Forest Continuous Inventory data and Landsat 8 Operational Land Imager data in combination with three algorithms, either the linear regression (LR), random forest (RF), or extreme gradient boosting (XGBoost), to establish biomass estimation models based on forest type. In the modeling process, two methods of variable selection, e.g., stepwise regression and variable importance-base method, were used to select optimal variable subsets for LR and machine learning algorithms (e.g., RF and XGBoost), respectively. Comfortingly, the accuracy of models was significantly improved, and thus the following conclusions were drawn: (1) Variable selection is very important for improving the performance of models, especially for machine learning algorithms, and the influence of variable selection on XGBoost is significantly greater than that of RF. (2) Machine learning algorithms have advantages in aboveground biomass (AGB) estimation, and the XGBoost and RF models significantly improved the estimation accuracy compared with the LR models. Despite that the problems of overestimation and underestimation were not fully eliminated, the XGBoost algorithm worked well and reduced these problems to a certain extent. (3) The approach of AGB modeling based on forest type is a very advantageous method for improving the performance at the lower and higher values of AGB. Some conclusions in this paper were probably different as the study area changed. The methods used in this paper provide an optional and useful approach for improving the accuracy of AGB estimation based on remote sensing data, and the estimation of AGB was a reference basis for monitoring the forest ecosystem of the study area.

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

2019 ◽  
Vol 11 (23) ◽  
pp. 2750
Author(s):  
Ou ◽  
Lv ◽  
Xu ◽  
Wang
Keyword(s):  
Aboveground Biomass ◽  
Regression Models ◽  
Spatial Regression ◽  
Southwestern China ◽  
Landsat 8 ◽  
Spatial Effects ◽  
Pinus Densata ◽  
Spatial Regression Models ◽  
The Impact ◽  
Forest Aboveground Biomass

Uncertainties in forest aboveground biomass (AGB) estimates resulting from over- and underestimations using remote sensing data have been widely studied. The uncertainties may occur due to the spatial effects of the plot data. In this study, we collected AGB data from a total of 147 Pinus densata forest sample plots in Yunnan of southwestern China and analyzed the spatial effects on the estimation of AGB. An ordinary least squares (OLS) and four spatial regression methods were compared for the estimation using Landsat 8-OLI images. Through the spatial analysis of AGB and residuals of model predictions, it was found that the spatial autocorrelation and heterogeneity of the plot data could not be ignored. Compared with the OLS, the impact of the spatial effects on AGB estimation could be reduced slightly by the spatial lag model (SLM) and the spatial error model (SEM) and greatly reduced by the linear mixed effects model (LMM) and geographically weighted regression (GWR) based on the distributions of prediction residuals, global Moran’s I, and Z score. The spatial regression models had better performance for model fitting and prediction because of the reduction in overestimations and underestimations for the forests with small and large AGB values, respectively. However, the reductions in the overestimations and underestimations varied depending on the spatial regression models. The GWR provided the most accurate predictions with the largest R2 (0.665), the smallest root mean square error (34.507), and mean relative error (−9.070%) by greatly reducing the AGB interval for overestimations occurring and significantly increasing the threshold of AGB from 150 Mg/ha to 200 Mg/ha for underestimations. Thus, GWR offered the greatest potential of improving the estimation of Pinus densata forest AGB in Yunnan of southwestern China.

scholarly journals Tree- and Stand-Level Biomass Estimation in a Larix decidua Mill. Chronosequence

Forests ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 587 ◽  
Author(s):  
Andrzej Jagodziński ◽  
Marcin Dyderski ◽  
Kamil Gęsikiewicz ◽  
Paweł Horodecki
Keyword(s):  
Aboveground Biomass ◽  
Biomass Conversion ◽  
Basal Area ◽  
Biomass Accumulation ◽  
Larix Decidua ◽  
Forest Stand ◽  
Stem Volume ◽  
Stand Age ◽  
Biomass Estimation ◽  
Forest Stands

Carbon pool assessments in forests is one of the most important tasks of forest ecology. Despite the wide cultivation range, and economical and traditional importance, the aboveground biomass of European larch (Larix decidua Mill.) stands is poorly characterized. To increase knowledge about forest biomass accumulation and to provide a set of tools for aboveground biomass estimation, we studied a chronosequence of 12 larch forest stands (7–120 years old). From these stands, we measured the biomass of 96 sample trees ranging from 1.9 to 57.9 cm in diameter at breast height. We provided age-specific and generalized allometric equations, biomass conversion and expansion factors (BCEFs) and biomass models based on forest stand characteristics. Aboveground biomass of stands ranged from 4.46 (7-year-old forest stand) to 445.76 Mg ha−1 (106-year-old). Stand biomass increased with increasing stand age, basal area, mean diameter, height and total stem volume and decreased with increasing density. BCEFs of the aboveground biomass and stem were almost constant (mean BCEFs of 0.4688 and 0.3833 Mg m−3, respectively). Our generalized models at the tree and stand level had lower bias in predicting the biomass of the forest stands studied, than other published models. The set of tools provided fills the gap in biomass estimation caused by the low number of studies on larch biomass, which allows for better estimation of forest carbon pools.

scholarly journals Estimation of Forest Aboveground Biomass in Karst Areas Using Multi-Source Remote Sensing Data and the K-DBN Algorithm

2021 ◽  
Vol 13 (24) ◽  
pp. 5030
Author(s):  
Chunhua Qian ◽  
Hequn Qiang ◽  
Feng Wang ◽  
Mingyang Li
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Forest Canopy ◽  
Learning Algorithm ◽  
Carbon Sink ◽  
Estimation Accuracy ◽  
Karst Region ◽  
Accurate Estimation ◽  
Landsat 8 ◽  
Karst Areas

Accurate estimation of forest biomass is the basis for monitoring forest productivity and carbon sink function, which is of great significance for the formulation of forest carbon neutralization strategy and forest quality improvement measures. Taking Guizhou, a typical karst region in China, as the research area, this study used Landsat 8 OLI, Sentinel-1A, and China national forest resources continuous inventory data (NFCI) in 2015 to build a deep belief network (DBN) model for aboveground biomass (AGB) estimation. Based on the introduction of forest canopy density (FCD), we improved the DBN model to design the K-DBN model with the highest estimation accuracy is selected for AGB inversion and spatial mapping. The results showed that: (1) The determination coefficients R2 of DBN is 0.602, which are 0.208, 0.101 higher than that of linear regression (LR) and random forest (RF) model. (2) The K-DBN algorithm was designed based on FCD to optimize the DBN model, which can alleviate the common problems of low-value overestimation and high-value underestimation in AGB estimation to a certain extent to improve the estimation accuracy. The maximum R2 of the model reached 0.848, and we mapped the forest AGB using the K-DBN model in the study area in 2015. The conclusion of this study: Based on multi-source optical and radar data, the retrieval accuracy of forest AGB can be improved by considering the FCD, and the deep learning algorithm K-DBN is excellent in forest AGB remote sensing estimation. These research results provide a new method and data support for the spatio-temporal dynamic remote sensing monitoring of forest AGB in karst areas.

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

2016 ◽  
Vol 8 (6) ◽  
pp. 469 ◽  
Author(s):  
Panpan Zhao ◽  
Dengsheng Lu ◽  
Guangxing Wang ◽  
Chuping Wu ◽  
Yujie Huang ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Spectral Reflectance ◽  
Landsat Imagery ◽  
Biomass Estimation ◽  
Forest Aboveground Biomass

Improving estimation of forest aboveground biomass using Landsat 8 imagery by incorporating forest crown density as a dummy variable

2020 ◽  
Vol 50 (4) ◽  
pp. 390-398
Author(s):  
Chao Li ◽  
Mingyang Li ◽  
Yingchang Li
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Vegetation Type ◽  
Coniferous Forest ◽  
Mixed Forest ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Dummy Variable ◽  
Crown Density ◽  
Forest Aboveground Biomass

Optical remote sensing data are widely used in estimation of forest aboveground biomass (AGB), and the accuracy of AGB estimations has drawn wide attention. A method to improve the accuracy of remote sensing-based AGB models was developed by combining Landsat 8’s Operational Land Imager (OLI) and forest crown density (FCD). Remote sensing-based AGB models with and without an FCD dummy variable were developed using linear regression based on vegetation type (coniferous forest, broadleaf forest, mixed forest, and total vegetation). The differences between the models with and without an FCD dummy variable were analysed and compared. The models involving stratification of vegetation types provided more accurate estimations than the models of total vegetation. The models with an FCD dummy variable performed better than the models without an FCD dummy variable for each vegetation type. In each FCD class, the models with an FCD dummy variable provided more accurate estimations than the models without an FCD dummy variable, and the over- and underestimation problems associated with the models without an FCD dummy variable in thin and dense stands were significantly alleviated by the models with an FCD dummy variable. Therefore, introducing FCD into remote sensing-based AGB models has great potential to improve AGB estimation.

scholarly journals Comparative Analysis of Seasonal Landsat 8 Images for Forest Aboveground Biomass Estimation in a Subtropical Forest

Forests ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 45 ◽  
Author(s):  
Chao Li ◽  
Mingyang Li ◽  
Jie Liu ◽  
Yingchang Li ◽  
Qianshi Dai
Keyword(s):  
Aboveground Biomass ◽  
Regional Scale ◽  
Coniferous Forest ◽  
Mixed Forest ◽  
Subtropical Forest ◽  
Acquisition Time ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Forest Types ◽  
Forest Aboveground Biomass

To effectively further research the regional carbon sink, it is important to estimate forest aboveground biomass (AGB). Based on optical images, the AGB can be estimated and mapped on a regional scale. The Landsat 8 Operational Land Imager (OLI) has, therefore, been widely used for regional scale AGB estimation; however, most studies have been based solely on peak season images without performance comparison of other seasons; this may ultimately affect the accuracy of AGB estimation. To explore the effects of utilizing various seasonal images for AGB estimation, we analyzed seasonal images collected using Landsat 8 OLI for a subtropical forest in northern Hunan, China. We then performed stepwise regression to estimate AGB of different forest types (coniferous forest, broadleaf forest, mixed forest and total vegetation). The model performances using seasonal images of different forest types were then compared. The results showed that textural information played an important role in AGB estimation of each forest type. Stratification based on forest types resulted in better AGB estimation model performances than those of total vegetation. The most accurate AGB estimations were achieved using the autumn (October) image, and the least accurate AGB estimations were achieved using the peak season (August) image. In addition, the uncertainties associated with the peak season image were largest in terms of AGB values < 25 Mg/ha and >75 Mg/ha, and the quality of the AGB map depicting the peak season was poorer than the maps depicting other seasons. This study suggests that the acquisition time of forest images can affect AGB estimations in subtropical forest. Therefore, future research should consider and incorporate seasonal time-series images to improve AGB estimation.

scholarly journals The Effect of Synergistic Approaches of Features and Ensemble Learning Algorith on Aboveground Biomass Estimation of Natural Secondary Forests Based on ALS and Landsat 8

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5974
Author(s):  
Chunyu Du ◽  
Wenyi Fan ◽  
Ye Ma ◽  
Hung-Il Jin ◽  
Zhen Zhen
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Aboveground Biomass ◽  
Laser Scanning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Secondary Forests ◽  
Two Factors

Although the combination of Airborne Laser Scanning (ALS) data and optical imagery and machine learning algorithms were proved to improve the estimation of aboveground biomass (AGB), the synergistic approaches of different data and ensemble learning algorithms have not been fully investigated, especially for natural secondary forests (NSFs) with complex structures. This study aimed to explore the effects of the two factors on AGB estimation of NSFs based on ALS data and Landsat 8 imagery. The synergistic method of extracting novel features (i.e., COLI1 and COLI2) using optimal Landsat 8 features and the best-performing ALS feature (i.e., elevation mean) yielded higher accuracy of AGB estimation than either optical-only or ALS-only features. However, both of them failed to improve the accuracy compared to the simple combination of the untransformed features that generated them. The convolutional neural networks (CNN) model was much superior to other classic machine learning algorithms no matter of features. The stacked generalization (SG) algorithms, a kind of ensemble learning algorithms, greatly improved the accuracies compared to the corresponding base model, and the SG with the CNN meta-model performed best. This study provides technical support for a wall-to-wall AGB mapping of NSFs of northeastern China using efficient features and algorithms.

Sign in / Sign up

Export Citation Format

Share Document