Model uncertainty in forest biomass estimation

Lihou, 秦立厚 Qin; Maozhen, 张茂震 Zhang; Shihong, 钟世红 Zhong; Xiaohui, 于晓辉 Yu

doi:10.5846/stxb201609281973

Cited by 3 publications

(3 citation statements)

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“…To ensure the accuracy of sampling inventory results, it is necessary to increase the sample units. At present, the most common approach to estimating provincial forest biomass is by using data from continuous forest resource inventory plots data (Qin et al, 2017). However, the biomass estimation of this method is summarized from the level of individual trees to the level of plot, and then estimated the total biomass of the region.…”

Section: Random Samplingmentioning

confidence: 99%

“…Because the measurement of biomass at stand level rarely uses clear-cutting to obtain measured data, mostly obtains the biomass data based on the calculation of biomass model of individual trees or the measurement of standard trees (Dong & Li, 2016). Uncertainty analysis in the process of conversion from individual tree to stand level is also an important aspect of stand biomass research (Qin et al, 2017).…”

Section: Stand Levelmentioning

confidence: 99%

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A Review of Sampling and Modeling Techniques for Forest Biomass Inventory

Wu¹,

2023

A&R

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Forest biomass is the energy base and material source of forest ecosystem cycle, which is expressed by the dry matter weight or energy accumulated per unit area and time. It is also an important index to study the structure and function of forest ecosystem, and is the premise and basis of scientific management of forest ecosystem. In this paper, the concept, development history, and research status of forest biomass were reviewed. The sampling methods, model construction methods of forest biomass survey were analyzed. Finally, the research prospects and summaries of key technologies of forest biomass inventory and monitoring were put forward.

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Section: Random Samplingmentioning

confidence: 99%

Section: Stand Levelmentioning

confidence: 99%

A Review of Sampling and Modeling Techniques for Forest Biomass Inventory

Wu¹,

2023

A&R

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“…Among the factors mentioned above, model uncertainty is a major problem to be solved. The number of samples used for remote sensing modeling has a significant effect on the uncertainty caused by model parameters, and the uncertainty gradually decreases as the number of samples increases [48]. Wu et al [47] selected 1/4 samples, 1/2 samples, 3/4 samples, and all samples from different biomass interval samples in a recoverable form to compare the effects of different samples on modeling accuracy.…”

Section: The Influence Of Sample Size On Model Accuracymentioning

confidence: 99%

Estimation of Quercus Biomass in Shangri-La Based on GEDI Spaceborne Lidar Data

Shu

et al. 2023

Forests

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Accurately estimating forest biomass based on spaceborne lidar on a county scale is challenging due to the incomplete coverage of spaceborne lidar data. Therefore, this research aims to interpolate GEDI spots and explore the feasibility of approaches to improving Quercus forest biomass estimation accuracy in the alpine mountains of Yunnan Province, China. This paper uses GEDI data as the main information source and a typical mountainous area in Shangri-La, northwestern Yunnan Province, China, as the study area. Based on the pre-processing of light spots. A total of 38 parameters were extracted from the canopy and vertical profiles of 1307 light spots in the study area, and the polygon data of the whole study area were obtained from the light spot data through Kriging interpolation. Multiple linear regression, support vector regression, and random forest were used to establish biomass models. The results showed that the optimal model is selected using the semi-variance function for the Kriging interpolation of each parameter of GEDI spot, the optimal model of modis_nonvegetated is a linear model, and the optimal model for rv, sensitivity, and modis_treecover is the exponential model. Analysis of the correlation between 39 parameters extracted from GEDI L2B and three topographic factors with oak biomass showed that sensitivity had a highly significant positive correlation (p < 0.01) with Quercus biomass, followed by a significant negative correlation (p < 0.05) with aspect and modis_nonvegation. After variable selection, the estimation model of Quercus biomass established using random forest had R2 = 0.91, RMSE = 19.76 t/hm2, and the estimation accuracy was better than that of multiple linear regression and support vector regression. The estimated total biomass of Quercus in the study area was mainly distributed between 26.48 and 257.63 t/hm2, with an average value of 114.33 t/hm2 and a total biomass of about 1.26 × 107 t/hm2. This study obtained spatial consecutive information using Kriging interpolation. It provided a new research direction for estimating other forest structural parameters using GEDI data.

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Optimization of Samples for Remote Sensing Estimation of Forest Aboveground Biomass at the Regional Scale

Shu

Wang

et al. 2022

Remote Sensing

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Accurately estimating forest aboveground biomass (AGB) based on remote sensing (RS) images at the regional level is challenging due to the uncertainty of the modeling sample size. In this study, a new optimizing method for the samples was suggested by integrating variance function in Geostatistics and value coefficient (VC) in Value Engineering. In order to evaluate the influence of the sample size for RS models, the random forest regression (RFR), nearest neighbor (K-NN) method, and partial least squares regression (PLSR) were conducted by combining Landsat8/OLI imagery in 2016 and 91 Pinus densata sample plots in Shangri-La City of China. The mean of the root mean square error (RMSE) of 200 random sampling tests was adopted as the accuracy evaluation index of the RS models and VC as a relative cost index of the modeling samples. The research results showed that: (1) the statistical values (mean, standard deviation, and coefficient of variation) for each group of samples based on 200 experiments were not significantly different from the sampling population (91 samples) by t-test (p = 0.01), and the sampling results were reliable for establishing RS models; (2) The reliable analysis on the RFR, K-NN, and PLSR models with sample groups showed that the VC decreases with increasing samples, and the decreasing trend of VC is consistent. The number of optimal samples for RFR, K-NN, and PLSR was 55, 54, and 56 based on the spherical model of variance function, respectively, and the optimal results were consistent. (3) Among the established models based on the optimal samples, the RFR model with the determination coefficient R2 = 0.8485, RMSE = 12.25 Mg/hm2, and the estimation accuracy P = 81.125% was better than K-NN and PLSR. Therefore, they could be used as models for estimating the aboveground biomass of Pinus densata in the study area. For the optimal sample size and sampling population, the RFR model of Pinus densata AGB was established, combining 26 variable factors in the study area. The total AGB with the optimal samples was 1.22 × 107 Mg, and the estimation result with the sampling population was 1.24 × 107 Mg based on Landsat8/OLI images. Respectively, the average AGB was 66.42 Mg/hm2 and 67.51 Mg/hm2, with a relative precision of 98.39%. The estimation results of the two sample groups were consistent.

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Model uncertainty in forest biomass estimation

Cited by 3 publications

References 0 publications

A Review of Sampling and Modeling Techniques for Forest Biomass Inventory

A Review of Sampling and Modeling Techniques for Forest Biomass Inventory

Estimation of Quercus Biomass in Shangri-La Based on GEDI Spaceborne Lidar Data

Optimization of Samples for Remote Sensing Estimation of Forest Aboveground Biomass at the Regional Scale

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