A Review of Sampling and Modeling Techniques for Forest Biomass Inventory

To reveal the changes on the stock of the litter layer and its nutrient storage capacity during Moso bamboo expansion in subtropical coniferous and broad-leaved forests, permanent plots were set up in the transitional zone in Wuxie National Park, Zhuji, Zhejiang, China. The plots contained conifer and broad-leaved forests (CFs), transition forests (TFs), and Moso bamboo forests (MFs), which represented three stages of the expansion of Moso bamboo to the surrounding forests. Litter samples were collected and analyzed by un-decomposed, semi-decomposed, and decomposed layers. The stock of the litter layer, the content and storage of the main nutrient elements, and their release rate were measured. It was revealed that the stock of the litter layer and each decomposition layer decreased as the bamboo expands. However, the litter decomposition rate exhibited a positive correlation with the expansion of Moso bamboo, which might be due to the change in the physical properties of the litter. Meanwhile, there were no significant differences in the un-decomposed and semi-decomposed layers of the litter contents of C, N, and P between the three forests, but the contents of C, N, and P in the decomposed layer gradually decreased with the expansion of Moso bamboo. There were no remarkable differences in the N content, C/N, C/P, and lignin/N values of the un-decomposed layer of the three forests, indicating that the litter quality was not the principal reason affecting the decomposition rate. The total nutrient storage in the litter layer decreased significantly with the bamboo expansion, and the release rate of nutrient elements increased, which was adverse to the accumulation and storage of the nutrients. The material cycle of the original forest ecosystem is likely to deteriorate gradually with the bamboo expansion.

“…In March 2021, we randomly arranged six 1 m × 1 m sampling points in every 20 m × 20 m sampling plot [24]. Litter was collected from every small quadrat and then taken back to the lab.…”

Section: Stock Of the Litter Layermentioning

confidence: 99%

Effects of Moso Bamboo (Phyllostachys pubescens) Expansion on the Stock and Nutrient Characteristics of the Litter Layer in Subtropical Coniferous and Broad-Leaved Mixed Forests, China

Chen,

Liu

2024

“…The efficient and accurate monitoring of the forest volume and its dynamic change is a popular topic of research in the field of natural resource investigation and monitoring [1][2][3],…”

Section: Introductionmentioning

confidence: 99%

“…A third approach is to update the initial estimates obtained from an estimator, which can be design-or model-based, for the current year [1]. The optimization of the sampling system, which aims to improve the efficiency in order to reduce the workload that is currently in effect in forest resource investigation and monitoring, is necessary [2,3]. Sampling methods and techniques are key to obtaining forest resource information.…”

Section: Introductionmentioning

confidence: 99%

Multistage Sampling and Optimization for Forest Volume Inventory Based on Spatial Autocorrelation Analysis

Tian

et al. 2023

It is important to achieve estimates at the minimum cost, with no greater uncertainty than that which is appropriate for the objectives of the inventory. The aim of this study was to estimate the forest volume efficiently and accurately by sampling and analyzing the existing forest survey data, which is also a technical challenge. In this work, we used the spatial statistics tools in the ArcGIS software to analyze spatial autocorrelations with the data from the sixth to ninth continuous forest inventories (CFI) of Sichuan Province from 2002, 2007, 2012, and 2017. Based on the sampling framework of the CFI, we divided the sampling units into five groups using different methods to create the second-stage samples. Combined with the spatial autocorrelation analysis results, we selected certain samples from the collection of second-stage samples through stratified sampling to form the third-stage sampling units. We applied the sampling ratio, sampling accuracy, workload, and costs as the evaluation indexes for the sampling efficiency analysis. The main results are as follows: Before conversion, the forest volume density had a positively skewed distribution. There was substantial positive spatial autocorrelation, and its intensity was affected by the distance scale, especially at 187.3 km, where the spatial processes of clustering were most pronounced. At the significance level of α = 0.01, the high-volume stands were mainly concentrated in the Aba Prefecture, Garze Prefecture, and Liangshan Prefecture, while the low-volume stands were mainly concentrated in the Sichuan Basin region. The heterogeneous gatherings were staggered between the high-volume areas and low-volume areas, while the transition zone between the three prefecture regions and basin region was randomly distributed. With 95% reliability, the average estimation accuracy of the systematic sampling, random sampling, and cluster sampling in the second stage was 94.09%, which is less accurate than the CFI estimation accuracy. The mean correlation coefficients (R) between the estimated value of the forest volume and the observations of the systematic sampling, random sampling, and cluster sampling in the second stage were 0.95, 0.98, and 0.96, respectively. The relative differences (RD%) were −0.52, −0.39, and −0.36, respectively. The spatial stratified sampling in the third stage, which is based on spatial distribution pattern information, significantly reduced the sampling ratio to 1.68 per 10,000, compared with the average ratios of the CFI sampling and second-stage sampling, which were 13.73 per 10,000 and 2.75 per 10,000, respectively. With 95% reliability, the mean accuracy of the spatial stratified sampling in the third stage was 93.05%, the R was 0.94, and the RD% was −0.09. Spatial stratified sampling is more in line with the actual work conducted in annual surveys because it effectively reduces the sample size using prior spatial information, which can better meet the requirements of the annual output.

“…Estimating forest biomass accurately is critical for predicting carbon sinks and addressing climate change activity [1,2]. Generally, stand biomass estimation methods can be categorized into two groups: one obtains biomass directly from inventory data and the other uses prediction, combining remote sensing images with survey data [3,4]. However, both methodologies require a carbon conversion coefficient to convert the volume of plant material (e.g., wood, branches, foliage, bark, and roots) into forest biomass.…”

Section: Introductionmentioning

confidence: 99%

A Compatible Estimation Method for Biomass Factors Based on Allometric Relationship: A Case Study on Pinus densata Natural Forest in Yunnan Province of Southwest China

Li,

Xu,

et al. 2023

Using various biomass factors, such as biomass expansion factor (BEF) and biomass conversion and expansion factor (BCEF), yields different results for estimating forest biomass. Therefore, ensuring compatibility between total biomass and its components when employing different biomass factors is crucial for developing a set of rapid and efficient models for large-scale biomass calculation. In this study, allometric equations were utilized to construct independent models and the proportional values (root-to-shoot ratio (Rra), crown-to-stem ratio (Rcs), bark-to-wood ratio (Rbw), foliage-to-bark ratio (Rfb), and wood biomass-to-wood volume (ρ)) by using the mean height (Hm) and the mean diameter at breast height (Dg) of 98 Pinus densata plots in Shangri-La, Yunnan province, China. The compatible methods were applied to reveal the compatibility between the total biomass and each component’s biomass. The results showed the following: (1) Both the independent model and compatible model had a higher accuracy. The values were greater than 0.7 overall, but the foliage biomass accuracy was only 0.2. The total biomass and the component biomass showed compatibility. (2) The accuracy of BEF and BCEF exceeded 0.87 and the total error was less than 0.1 for most components. (3) The mean BEF (1.6) was greater than that of the Intergovernmental Panel on Climate Change (IPCC) (M = 1.3), and the mean BCEF was smaller than that of the IPCC; the values were 0.6 and 0.7, respectively. The range of BEF (1.4–2.1) and BCEF (0.44–0.89) were all within the range of the IPCC (1.15–3.2, 0.4–1.0). This study provides a more convenient and accurate method for calculating conversion coefficients (BEF and BCEF), especially when only Rcs data is available.