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 Stratification-Based Forest Aboveground Biomass Estimation in a Subtropical Region Using Airborne Lidar Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1101 ◽  
Author(s):  
Xiandie Jiang ◽  
Guiying Li ◽  
Dengsheng Lu ◽  
Erxue Chen ◽  
Xinliang Wei
Keyword(s):  
Aboveground Biomass ◽  
Forest Type ◽  
Forest Stand ◽  
Airborne Lidar ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Forest Types ◽  
Estimation Model ◽  
Forest Aboveground Biomass

Species-rich subtropical forests have high carbon sequestration capacity and play important roles in regional and global carbon regulation and climate changes. A timely investigation of the spatial distribution characteristics of subtropical forest aboveground biomass (AGB) is essential to assess forest carbon stocks. Lidar (light detection and ranging) is regarded as the most reliable data source for accurate estimation of forest AGB. However, previous studies that have used lidar data have often beenbased on a single model developed from the relationships between lidar-derived variables and AGB, ignoring the variability of this relationship in different forest types. Although stratification of forest types has been proven to be effective for improving AGB estimation, how to stratify forest types and how many strata to use are still unclear. This research aims to improve forest AGB estimation through exploring suitable stratification approaches based on lidar and field survey data. Different stratification schemes including non-stratification and stratifications based on forest types and forest stand structures were examined. The AGB estimation models were developed using linear regression (LR) and random forest (RF) approaches. The results indicate the following: (1) Proper stratifications improved AGB estimation and reduced the effect of under- and overestimation problems; (2) the finer forest type strata generated higher accuracy of AGB estimation but required many more sample plots, which were often unavailable; (3) AGB estimation based on stratification of forest stand structures was similar to that based on five forest types, implying that proper stratification reduces the number of sample plots needed; (4) the optimal AGB estimation model and stratification scheme varied, depending on forest types; and (5) the RF algorithm provided better AGB estimation for non-stratification than the LR algorithm, but the LR approach provided better estimation with stratification. Results from this research provide new insights on how to properly conduct forest stratification for AGB estimation modeling, which is especially valuable in tropical and subtropical regions with complex forest types.

scholarly journals Leaf area index estimation of even-aged oak (Quercus petraea) forests using in situ stand dendrometric parameters

2021 ◽  
Author(s):  
Briere Maxime ◽  
Christophe Francois ◽  
Francois Lebourgeois ◽  
Ingrid Seynave ◽  
Francois Ningre ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Basal Area ◽  
Quercus Petraea ◽  
Forest Stand ◽  
Stand Age ◽  
Estimation Errors ◽  
Data Set ◽  
Area Index ◽  
Net Productivity

The leaf area index (LAI) is a key characteristic of forest stand aboveground net productivity (ANP), and many methods have been developed to estimate the LAI. However, every method has flaws, e.g., methods may be destructive, require means or time and/or show intrinsic bias and estimation errors. A relationship using basal area (G) and stand age to estimate LAI was proposed by Sonohat et al. (2004). We used literature data in addition to data form measurements campaign made in the northern half of France to build a data set with large ranges of pedoclimatic conditions, stand age and measured LAI. We validated the Sonohat et al. (2004) relationship and attempted to improve or modify it using other stand/dendrometric characteristics that could be predictors of the LAI. The result is a series of three models using the G, age and/or quadratic mean diameter (Dg), and the models were able to estimate the LAI of an oak only even-aged forest stand with good confidence (root mean square error, RMSE < 0.75) While G is the main predictor here, age and Dg could be used conjointly or exclusively given the available data, with variable precision in the estimations. Although these models could not, by construction, relate to the interannual variability of the LAI, they may provide the theoretical LAI of an untouched forest (no meteorological, biotic or anthropogenic perturbation) in recent years. additionally, the use of this model may be more interesting than an LAI measurement campaign, depending on the means to be invested in such a campaign.

scholarly journals A Comparison of Prism Plots and Modified Point-Distance Sampling to Calculate Tree Stem Density and Basal Area

2006 ◽  
Vol 23 (3) ◽  
pp. 218-221 ◽  
Author(s):  
Ian D. Thompson ◽  
Darcy A. Ortiz ◽  
Christopher Jastrebski ◽  
Daniel Corbett
Keyword(s):  
Boreal Forest ◽  
Distance Sampling ◽  
Basal Area ◽  
Forest Stand ◽  
The Other ◽  
Stem Density ◽  
Normal Density ◽  
Forest Stands ◽  
Point Distance ◽  
Tree Stem

Abstract Two common methods of measuring forest stand woody stem attributes include prism plots for basal area and modified point-distance for stem density. The data from each method can be used for the other calculation; that is, prism data can provide stem density, and point-distance datacan provide estimated basal area. We examined data from the same 10 stands using the two techniques to determine whether the results for each calculation were comparable and/or consistent. There was a significant correlation between the estimated tree (defined as stems >10 cm) basal areas,and between tree stem densities, derived from the two methods (P < 0.01). Prism plots provided significantly higher estimated tree stem densities (+23.5%; P < 0.05) compared to estimates from the point-distance technique, but there was no difference between estimated treebasal area. For all stems, that is also including stems <10 cm dbh, there was no difference between the two methods for estimated basal area or stem density. There was no correlation between total stem densities derived from the two methods. This is likely because the prism plot method(two-factor metric prism) sampled relatively few trees with small diameters, whereas the point distance technique, as used, sampled small trees independently from trees using a diameter distinction. When we removed two young stands with <50 trees/ha, there was no difference in estimatesof stem density. We concluded that, for boreal forest stands with a normal density of trees (i.e., >10 cm dbh and 900 to 3,000 stems/ha), either method would provide comparable estimates of stem density and basal area. We found no time difference in conducting surveys using either method.

scholarly journals Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data

2020 ◽  
Vol 12 (18) ◽  
pp. 3019 ◽  
Author(s):  
Kourosh Ahmadi ◽  
Bahareh Kalantar ◽  
Vahideh Saeidi ◽  
Elaheh K. G. Harandi ◽  
Saeid Janizadeh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Temperate Forests ◽  
Elevation Gradient ◽  
Basal Area ◽  
Forest Stand ◽  
Stem Volume ◽  
Support Vector ◽  
Stem Density ◽  
Stand Characteristics ◽  
Sentinel 2

The estimation and mapping of forest stand characteristics are vital because this information is necessary for sustainable forest management. The present study considers the use of a Bayesian additive regression trees (BART) algorithm as a non-parametric classifier using Sentinel-2A data and topographic variables to estimate the forest stand characteristics, namely the basal area (m2/ha), stem volume (m3/ha), and stem density (number/ha). These results were compared with those of three other popular machine learning (ML) algorithms, such as generalised linear model (GLM), K-nearest neighbours (KNN), and support vector machine (SVM). A feature selection was done on 28 variables including the multi-spectral bands on Sentinel-2 satellite, related vegetation indices, and ancillary data (elevation, slope, and topographic solar-radiation index derived from digital elevation model (DEM)) and then the most insignificant variables were removed from the datasets by recursive feature elimination (RFE). The study area was a mountainous forest with high biodiversity and an elevation gradient from 26 to 1636 m. An inventory dataset of 1200 sample plots was provided for training and testing the algorithms, and the predictors were fed into the ML models to compute and predict the forest stand characteristics. The accuracies and certainties of the ML models were assessed by their root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2) values. The results demonstrated that BART generated the best basal area and stem volume predictions, followed by GLM, SVM, and KNN. The best RMSE values for both basal area (8.12 m2/ha) and stem volume (29.28 m3/ha) estimation were obtained by BART. Thus, the ability of the BART model for forestry application was established. On the other hand, KNN exhibited the highest RMSE values for all stand variable predictions, thereby exhibiting the least accuracy for this specific application. Moreover, the effectiveness of the narrow Sentinel-2 bands around the red edge and elevation was highlighted for predicting the forest stand characteristics. Therefore, we concluded that the combination of the Sentinel-2 products and topographic variables derived from the PALSAR data used in this study improved the estimation of the forest attributes in temperate forests.

scholarly journals A Comparison of Imputation Approaches for Estimating Forest Biomass Using Landsat Time-Series and Inventory Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1825 ◽  
Author(s):  
Trung Nguyen ◽  
Simon Jones ◽  
Mariela Soto-Berelov ◽  
Andrew Haywood ◽  
Samuel Hislop
Keyword(s):  
Time Series ◽  
Forest Biomass ◽  
Basal Area ◽  
Imputation Method ◽  
Stem Volume ◽  
Biomass Estimation ◽  
Stem Density ◽  
Nearest Neighbour ◽  
Dead Trees ◽  
Model Scenarios

The prediction of forest biomass at the landscape scale can be achieved by integrating data from field plots with satellite imagery, in particular data from the Landsat archive, using k-nearest neighbour (kNN) imputation models. While studies have demonstrated different kNN imputation approaches for estimating forest biomass from remote sensing data and forest inventory plots, there is no general agreement on which approach is most appropriate for biomass estimation across large areas. In this study, we compared several imputation approaches for estimating forest biomass using Landsat time-series and inventory plot data. We evaluated 18 kNN models to impute three aboveground biomass (AGB) variables (total AGB, AGB of live trees and AGB of dead trees). These models were developed using different distance techniques (Random Forest or RF, Gradient Nearest Neighbour or GNN, and Most Similar Neighbour or MSN) and different combinations of response variables (model scenarios). Direct biomass imputation models were trained according to the biomass variables while indirect biomass imputation models were trained according to combinations of forest structure variables (e.g., basal area, stem density and stem volume of live and dead-standing trees). We also assessed the ability of our imputation method to spatially predict biomass variables across large areas in relation to a forest disturbance history over a 30-year period (1987–2016). Our results show that RF consistently outperformed MSN and GNN distance techniques across different model scenarios and biomass variables. The lowest error rates were achieved by RF-based models with generalized root mean squared difference (gRMSD, RMSE divided by the standard deviation of the observed values) ranging from 0.74 to 1.24. Whereas gRMSD associated with MSN-based and GNN-based models ranged from 0.92 to 1.36 and from 1.04 to 1.42, respectively. The indirect imputation method generally achieved better biomass predictions than the direct imputation method. In particular, the kNN model trained with the combination of basal area and stem density variables was the most robust for estimating forest biomass. This model reported a gRMSD of 0.89, 0.95 and 1.08 for total AGB, AGB of live trees and AGB of dead trees, respectively. In addition, spatial predictions of biomass showed relatively consistent trends with disturbance severity and time since disturbance across the time-series. As the kNN imputation method is increasingly being used by land managers and researchers to map forest biomass, this work helps those using these methods ensure their modelling and mapping practices are optimized.

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.

The Effect of Stand Age and Climate on Aboveground Biomass Accumulation of Pinus tabulaeformis Forests

2013 ◽  
Vol 726-731 ◽  
pp. 4266-4269
Author(s):  
Fei Li ◽  
Hua Yong Zhang ◽  
Zhong Yu Wang ◽  
Yang Su ◽  
Lu Han
Keyword(s):  
Aboveground Biomass ◽  
Predictive Power ◽  
Explanatory Power ◽  
Biomass Accumulation ◽  
Pinus Tabulaeformis ◽  
Stepwise Multiple Regression ◽  
Stand Age ◽  
Mean Annual Precipitation ◽  
Mean Annual Temperature ◽  
Better Than

In order to investigate the effect of stand age and climate hydrothermic factors on aboveground biomass accumulation (ABA), data from 65 typical Pinus tabulaeformis forest stands were compiled from published literatures. By means of stepwise multiple regression, the variations in ABA were examined across the range of stand age and gradients of mean annual precipitation (MAP) and mean annual temperature (MAT). For comparison, stand age was also used as explaining variable alone. The results show that, stand age and MAP could explain 85.1% of variation in ABA, the predictive power is much better than stand age alone. The explanatory power of stand age and MAP were 70.7% and 15.3% respectively. In comparison with stand age, MAP has a relatively poor but significant effect. ABA is not significantly related to MAT, which implies that water availability is more important than thermal condition for ABA of Pinus tabulaeformis forests.

Biomass of understory species of Pinus halepensis Mill. forests in the Kassandra Peninsula - Chalkidiki

1998 ◽  
Vol 63 ◽  
Author(s):  
P. Smiris ◽  
F. Maris ◽  
K. Vitoris ◽  
N. Stamou ◽  
P. Ganatsas
Keyword(s):  
Moisture Content ◽  
Aboveground Biomass ◽  
Quercus Ilex ◽  
Pinus Halepensis ◽  
Biomass Estimation ◽  
Regression Equations ◽  
Pistacia Lentiscus ◽  
Quercus Coccifera ◽  
Understory Species ◽  
Cistus Incanus

This  study deals with the biomass estimation of the understory species of Pinus halepensis    forests in the Kassandra peninsula, Chalkidiki (North Greece). These  species are: Quercus    coccifera, Quercus ilex, Phillyrea media, Pistacia lentiscus, Arbutus  unedo, Erica arborea, Erica    manipuliflora, Smilax aspera, Cistus incanus, Cistus monspeliensis,  Fraxinus ornus. A sample of    30 shrubs per species was taken and the dry and fresh weights and the  moisture content of    every component of each species were measured, all of which were processed  for aboveground    biomass data. Then several regression equations were examined to determine  the key words.

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