Modeling compatible single-tree aboveground biomass equations for masson pine (Pinus massoniana) in southern China

Because of global climate change,it is necessary to add forest biomass estimation to national forest resource monitoring.The biomass equations developed for forest biomass estimation should be compatible with volume equations.Based on the tree volume and aboveground biomass data of Masson pine(Pinus massoniana Lamb.) in southern China,we constructed one-,two-and three-variable aboveground biomass equations and biomass conversion functions compatible with tree volume equations by using error-in-variable simultaneous equations.The prediction precision of aboveground biomass estimates from one variable equation exceeded 95%.The regressions of aboveground biomass equations were improved slightly when tree height and crown width were used together with diameter on breast height,although the contributions to regressions were statistically insignificant.For the biomass conversion function on one variable,the conversion factor decreased with increasing diameter,but for the conversion function on two variables,the conversion factor increased with increasing diameter but decreased with increasing tree height.     

Integrated Stand Growth Model of Mongolian Oak and Its Application

This paper established an integrated stand growth model of Mongolian oak(ISGM_oak) using the data from 61 permanent sample plots measured in 1997 and 2007.ISGM_ oak is a group of nonlinear simultaneous equations.The method of nonlinear error-in-variable simultaneous equations is used to estimate the parameters of ISGMoak with the statistical software Forstat 2.0,so the parameter estimation of the group of correlated equations in ISGMoak is unbiased and the equations are compatible.Model validation using bootstrap method showed that both the average relative error and square error are less than 15 percent.The ISGM_ oak model can be used to simulate the stand growth with different values of site index,stand density and to draw stand density management diagram for decision-making.     

GROWTH AND YIELD MODELS FOR DAHURIAN LARCH PLANTATIONS

Growth and yield models were developed for individual tress and stands based on336 temporary plots with 405 stem analysis trees of dahurian larch(Larix gmelinii(Rupr.)Rupr.)plantations throughout Daxing’anling mountains.Several equations were selected using nonlinearregression analysis.Results showed that the Richards equation was the best model for estimatingtree height,stand mean helght and stand dominant height from age; The Power equation was thebest model for prediction tree volume from DBH and tree height; The logarithmic stand volumeequation was good for predicting stand volume from age,mean height,basal area and other standvariables.These models can be used to construct the volume table, the site index table and other for-estry tables for dahurian larch plantations.     

Generalized Algebraic Difference Site Index Model for Ponderosa Pine in British Columbia,Canada

Based upon 3 widely used base models, a total of 8 ADA/GADA site index models were derived.The data for these models in this study were obtained from 79 pith-split stem analysis plots and the estimation method was "indicator variable approach".We used both fit statistics and visual analysis to select the best-fit model,and attached more importance to the visual analysis.A comprehensive application analysis was also given to the selected model.The results showed:1) GADA outperformed ADA with respect to predictions.2) A GADA model derived from HossfeldⅣpresented the best prediction ability.It was suggested that the model be used to predict dominant height and to estimate site index for ponderosa pine stands ranging 30 -200 years in British Columbia,Canada.3) The best site index age was age of 100 years,based upon relative errors of predictions.     

Estimating soil moisture content using laboratory spectral data

Monitoring soil moisture is important for agriculture and forestry and plays an essential role in land surface processes as well as providing feedback among the earth’s surface ecosystems. Large-scale regional soil moisture spatial data can be obtained with a reliable and operational approach using remote sensing. In this paper, we provide an operational framework for retrieving soil moisture using laboratory spectral data. The inverted Gaussian function was used to fit soil spectral data, and its feature parameters, including absorption depth (AD) and absorption area (AA), were selected as variables for a soil moisture estimate model. There was a significant correlative relationship between soil moisture and AD, as well as AA near 1400 and 1900 nm. A one-variable linear regression model was established to estimate soil moisture. The model was evaluated using the determination coefficients (R2), root mean square error and average precision.Four models were established and evaluated in this study. The determination coefficients of the four models ranged from 0.794 to 0.845. The average accuracy for soil moisture estimates ranged from 90 to 92%. The results prove that it is feasible to estimate soil moisture using remote sensing technology.     

Modeling surface fuels moisture content in Pinus brutia stands

Fuel moisture content is an important variable for forest fires because it affects fuel ignition and fire behavior. In order to accurately predict fuel ignition potential, fuel moisture content must be assessed by evaluating fire spread, fireline intensity and fuel consumption.Our objective here is to model moisture content of surface fuels in normally stocked Calabrian pine(Pinus brutia Ten.) stands in relation to weather conditions, namely temperature, relative humidity, and wind speed in the Mugla province of Turkey. All surface fuels were categorized according to diameter classes and fuel types. Six fuel categories were defined: these were 0–0.3, 0.3–0.6, and0.6–1 cm diameter classes, and cone, surface litter, and duff. Plastic containers 15 9 20 cm in size with 1 9 1 mm mesh size were used. Samples were taken from 09:00 to19:00 h and weighed every 2 h with 0.01 g precision for10 days in August. At the end of the study, samples were taken to the laboratory, oven-dried at 105 °C for 24 h and weighed to obtain fuel-moisture contents. Weather measurements were taken from a fully automated weather station set up at the study site prior to the study. Correlation and regression analyses were carried out and models were developed to predict fuel moisture contents for desorption and adsorption phase for each fuel type categories. Practical fuel moisture prediction models were developed for dry period. Models were developed that performed well with reasonable accuracy, explaining up to 92 and 95.6%of the variability in fuel-moisture contents for desorption and adsorption phases, respectively. Validation of the models were conducted using an independent data set and known fuel moisture prediction models. The predictive power of the models was satisfactory with mean absolute error values being 1.48 and 1.02 for desorption and adsorption as compared to the 2.05 and 1.60 values for the Van Wagner's hourly litter moisture content prediction model. Results obtained in this study will be invaluable for fire management planning and modeling.     

Evaluation of sampling strategies to estimate crown biomass

Background:Depending on tree and site characteristics crown biomass accounts for a significant portion of the total aboveground biomass in the tree.Crown biomass estimation is useful for different purposes including evaluating the economic feasibility of crown utilization for energy production or forest products,fuel load assessments and fire management strategies,and wildfire modeling.However,crown biomass is difficult to predict because of the variability within and among species and sites.Thus the allometric equations used for predicting crown biomass should be based on data collected with precise and unbiased sampling strategies.In this study,we evaluate the performance different sampling strategies to estimate crown biomass and to evaluate the effect of sample size in estimating crown biomass.Methods:Using data collected from 20 destructively sampled trees,we evaluated 11 different sampling strategies using six evaluation statistics:bias,relative bias,root mean square error(RMSE),relative RMSE,amount of biomass sampled,and relative biomass sampled.We also evaluated the performance of the selected sampling strategies when different numbers of branches(3,6,9,and 12)are selected from each tree.Tree specific log linear model with branch diameter and branch length as covariates was used to obtain individual branch biomass.Results:Compared to all other methods stratified sampling with probability proportional to size estimation technique produced better results when three or six branches per tree were sampled.However,the systematic sampling with ratio estimation technique was the best when at least nine branches per tree were sampled.Under the stratified sampling strategy,selecting unequal number of branches per stratum produced approximately similar results to simple random sampling,but it further decreased RMSE when information on branch diameter is used in the design and estimation phases.Conclusions:Use of auxiliary information in design or estimation phase reduces the RMSE produced by a sampling strategy.However,this is attained by having to sample larger amount of biomass.Based on our finding we would recommend sampling nine branches per tree to be reasonably efficient and limit the amount of fieldwork.     

Models and form factors for stand volume estimation in natural forest ecosystems: a case study of Katarniaghat Wildlife Sanctuary (KGWS), Bahraich District, India

In view of the difficulties in stand volume estimation in natural forests, we derived real form factors and models for volume estimation in these types of forest ecosystems, using Katarniaghat Wildlife Sanctuary as a case study. Tree growth data were obtained for all trees (dbh >10 cm) in 4 plots (25 × 25 m) randomly located in each of three strata selected in the forest. The form factor calculated for the stand was 0.42 and a range of 0.42 0.57 was estimated for selected species (density >10). The parameters of model variables were consistent with general growth trends of trees and each was statistically significant. There was no significant difference (p>0.05) between the observed and predicted volumes for all models and there was very high correlation between observed and predicted volumes. The output of the performance statistics and the logical signs of the regression coefficients of the models demonstrated that they are useful for volume estimation with minimal error. Plotting the biases with respect to considerable regressor variables showed no meaningful and evident trend of bias values along with the independent variables. This showed that the models did not violate regression assumptions and there were no heteroscedacity or multiculnarity problems. We recommend use of the form factors and models in this ecosystem and in similar ones for stand and tree volume estimation.     

Development of ecoregion-based merchantable volume systems for Pinus brutia Ten. and Pinus nigra Arnold. in southern Turkey

Estimating individual tree volume is one of the essential building blocks in forest growth and yield models.Ecologically based taper equations provide accurate volume predictions and allow classification by merchantable sizes, assisting in sustainable forest management.In the present study, ecoregion-based compatible volume systems for brutian pine and black pine in the three ecoregions of southern Turkey were developed. Several well-known taper functions were evaluated. A secondorder continuous-time autoregressive error structure was used to correct the inherent autocorrelation in the hierarchical data, allowing the model to be applied to irregularly spaced and unbalanced data. The compatible segmented model of Fang et al.(For Sci 46:1–12, 2000) best described the experimental data. It is therefore recommended for estimating diameter at a specific height, height to a specific diameter, merchantable volume, and total volume for the three ecoregions and two species analyzed. The nonlinearextra sum of squares method indicated differences in ecoregion and tree-specific taper functions. A different taper function should therefore be used for each pine species and ecoregion in southern Turkey. Using ecoregionspecific taper equations allows making more robust estimations and, therefore, will enhance the accuracy of diameter at different heights and volume predictions.     

Prediction of Forest Stock Volume Based on Neural Network Model

BP and RBF neural network to predict forest stock volume were studied,but the study in evaluating both networks’ application effects was not conducted.In order to find a higher forecast precision,more strong applicative method,the comprehensive analysis and evaluation on the two methods were carried out in the practical application. By the correlation analysis,crown density,shady-slope and sunny-slope,TM1,TM2,TM3,TM5, TM7,NDVI,TM,(4-3),TM4/3 were selected as input variables,and the forest volume of Miyun County as output variables,RBF and BP neural network models for forecasting the forest volume were established.And the neural network training step length,training time,prediction accuracy and the applicability model of the two methods were comprehensively analyzed.The results show that the RBF neural network model is superior to the BP neural network model.     

Multi-polarized Radarsat-2 satellite sensor in assessing forest vigor from above ground biomass

Accurate estimates of forest biomass are increasingly important in relation to sequestration of carbon by forest trees.Satellite remote sensing is a useful tool for biomass estimation and monitoring of forest ecological processes.Microwave synthetic aperture radar(SAR) can increase the accuracy of estimations of forest biomass in comparison to optical remote sensing, due to the unique capacities of SAR, including high penetrability, volumetric scattering, interaction with surface roughness, and dielectric property.We studied the potential of multi-polarized C-band Radarsat-2, a SAR technology, with HH, HV and VV polarization for estimating biomass of moist tropical Indian forest.Backscatter values are correlated with fieldbased biomass values and are regressed to generate models for estimating biomass.HH polarization provided maximum information regarding tree biomass.A coefficient of determination of 0.49 was calculated for HH polarized C-band image with in situ measurements.An exponential model was proved to be best suited for estimating forest biomass.Correlation of 0.62 and RMSE of 24.6 t ha~(-1) were calculated for the relationship between estimated and predicted biomass values for the best fit model.The average absolute accuracy of the model was 61%, while Willmott's index of agreement was 0.87.Results suggest that most of the biomass of the area ranged within70 t ha~(-1) a probably due to the saturation of C-band around 60–70 t ha~(-1) for tropical forests.     

Estimating canopy closure density and above-ground tree biomass using partial least square methods in Chinese boreal forests

Boreal forests play an important role in global environment systems. Understanding boreal forest ecosystem structure and function requires accurate monitoring and estimating of forest canopy and biomass. We used partial least square regression (PLSR) models to relate forest parameters, i.e. canopy closure density and above ground tree biomass, to Landsat ETM+ data. The established models were optimized according to the variable importance for projection (VIP) criterion and the bootstrap method, and their performance was compared using several statistical indices. All variables selected by the VIP criterion passed the bootstrap test (p<0.05). The simplified models without insignificant variables (VIP <1) performed as well as the full model but with less computation time. The relative root mean square error (RMSE%) was 29% for canopy closure density, and 58% for above-ground tree biomass. We conclude that PLSR can be an effective method for estimating canopy closure density and above-ground biomass.     

Allometric relationship for estimating above-ground biomass of Aegialitis rotundifolia Roxb. of Sundarbans mangrove forest, in Bangladesh

Tree biomass plays a key role in sustainable management by providing different aspects of ecosystem. Estimation of above ground biomass by non-destructive means requires the development of allometric equations. Most researchers used DBH (diameter at breast height) and TH (total height) to develop allometric equation for a tree. Very few species-specific allometric equations are currently available for shrubs to estimate of biomass from measured plant attributes. Therefore, we used some of readily measurable variables to develop allometric equations such as girth at collar-height (GCH) and height of girth measuring point (GMH) with total height (TH) for A. rotundifolia, a mangrove species of Sundarbans of Bangladesh, as it is too dwarf to take DBH and too irregular in base to take Girth at a fixed height. Linear, non-linear and logarithmic regression techniques were tried to determine the best regression model to estimate the above-ground biomass of stem, branch and leaf. A total of 186 regression equations were generated from the combination of independent variables. Best fit regression equations were determined by examining co-efficient of determination (R2), co-efficient of variation (CV), mean-square of the error (MSerror), residual mean error (Rsme), and F-value. Multiple linear regression models showed more efficient over other types of regression equation. The performance of regression equations was increased by inclusion of GMH as an independent variable along with total height and GCH.     

Modeling height–diameter relationship for Populus euphratica in the Tarim riparian forest ecosystem,Northwest China

Modeling height–diameter relationships is an important component in estimating and predicting forest development under different forest management scenarios. In this paper, ten widely used candidate height–diameter models were fitted to tree height and diameter at breast height(DBH)data for Populus euphratica Oliv. within a 100 ha permanent plots at Arghan Village in the lower reaches of the Tarim River, Xinjiang Uyghur Autonomous Region of China. Data from 4781 trees were used and split randomly into two sets:75 % of the data were used to estimate model parameters(model calibration), and the remaining data(25 %) were reserved for model validation. All model performances were evaluated and compared by means of multiple model performance criteria such as asymptotic t-statistics of model parameters, standardized residuals against predicted height,root mean square error(RMSE), Akaike's informationcriterion(AIC), mean prediction error(ME) and mean absolute error(MAE). The estimated parameter a for model(6) was not statistically significant at a level of a = 0.05. RMSE and AIC test result for all models showed that exponential models(1),(2),(3) and(4) performed significantly better than others. All ten models had very small MEs and MAEs. Nearly all models underestimated tree heights except for model(6). Comparing the MEs and MAEs of models, model(1) produced smaller MEs(0.0059) and MAEs(1.3754) than other models. To assess the predictive performance of models, we also calculated MEs by dividing the model validation data set into 10-cm DBH classes. This suggested that all models were likely to create higher mean prediction errors for tree DBH classes[20 cm. However, no clear trend was found among models.Model(6) generated significantly smaller mean prediction errors across all tree DBH classes. Considering all the aforementioned criteria, model(1): TH ? 1:3 t a= e1 t b?eàc?DBHT and model(6): TH ? 1:3 t DBH2= ea t b?DBH t c ? DBH2T are recommended as suitable models for describing the height–diameter relationship of P. euphratica. The limitations of other models showing poor performance in predicting tree height are discussed. We provide explanations for these shortcomings.     

广宁茶秆竹地上生物量分布特征研究

Based on the diameter, height and biomass of different organs of Pseudosasa amabilis, an individual optimization model is established. The results showed that the relationships among diameter, height, and biomass of different organs followed the power function model, rather than exponential model, linear model, or polynomial model. The biomass proportion of large diameter grade increased apparently after culm harvesting, and diameter was mainly distributed from 4 to 5 cm diameter grade. The standing biomass of cultivated Pseudosasa amabilis was between 48.10 t·hm^-2 and 53.10 t·hm^-2, while standing biomass of non cultivated P. amabilis stand was between 66.90—70.40 t·hm^-2, which is a suitable species for carbon sink. Bamboo individual biomass accounted for 70% of the above-ground biomass and grew multiply as the individual bamboo diameter increasing. The biomass of litter ranged from 8.32% to 18.10% in standing biomass while that of non cultivated bamboo stands was lower than that of cultivated bamboo. There existed a 1∶ 1 constant relationship between leaf biomass and branch biomass.     

闽南麻竹人工林地上部分现存生物量的研究

The construction of aboveground biomass and biomass model of Dendrocalamus latiflorus plantation in South Fujian are studied in this paper. The results are showed as follows (1)The aboveground biomass and the biomass of culm of D.latiflorus may reliably be calculated by the model, m=a*(D²H）^b,but the biomass of its culm and leaf may not. In order to calculate it, it is necessary to introduce the factor, clear-height,and the models,m=a*D^b(H-h）^c and m=a*D^b*［（H－h）/h］^c. (2)The total aboveground biomass of D.latiflorus plantation in South Fujian is 39.518 t*hm^－2.The biomass of 3-year-old is the highest,accounting for 59.17 percent of the total, followed by that of 2-,4- and 5-year-old successively. The biomass of culm is the highest in terms of organ, accounting for 62.81 percent of the fotal, followed by that of branch and leaf successively. The total aboveground biomass and the biomass of culm gradually decrease along the height, and that of branch and leaf gradually decreases from the section of 6～8 m to the top and to the bottom.     

Variable-top stem biomass equations at tree-level generated by a simultaneous density-integral system for second growth forests of roble,raulí,and coigüe in Chile

Variable-top stem biomass models at the tree level for second growth forests of roble(Nothofagus obliqua), raulí(Nothofagus alpina), and coigüe(Nothofagus dombeyi) were fitted by a simultaneous density-integral system, which combines a stem taper model and a wood basic density model. For each model, an autoregressive structure of order 2 and a power equation of residual variance were incorporated to reduce residual autocorrelation and heteroscedasticity, respectively. By using dummy variables in the regression analysis, zonal effects on the parameters in the variable-top stem biomass equations were detected in roble. Consequently, equations for clusters of zones were obtained. These equations presented significant parameters and a high precision in both fitting and validation processes(i.e., CV11.5% and CVp11.9%, respectively), demonstrating that they are unbiased. The advantage of these types of functions is that they provide estimates of volume and biomass of sections of the stem, defined between any two points of the stem in the three species. Thus, depending on the final use of the wood and the dimensions of the tree, a stem fraction can be quantified in units of volume and the remaining fraction in units of weight.     

Modeling height–diameter relationship for <Emphasis Type="Italic">Populus euphratica</Emphasis> in the Tarim riparian forest ecosystem,Northwest China

Modeling height–diameter relationships is an important component in estimating and predicting forest development under different forest management scenarios. In this paper, ten widely used candidate height–diameter models were fitted to tree height and diameter at breast height(DBH)data for Populus euphratica Oliv. within a 100 ha permanent plots at Arghan Village in the lower reaches of the Tarim River, Xinjiang Uyghur Autonomous Region of China. Data from 4781 trees were used and split randomly into two sets:75 % of the data were used to estimate model parameters(model calibration), and the remaining data(25 %) were reserved for model validation. All model performances were evaluated and compared by means of multiple model performance criteria such as asymptotic t-statistics of model parameters, standardized residuals against predicted height,root mean square error(RMSE), Akaike’s informationcriterion(AIC), mean prediction error(ME) and mean absolute error(MAE). The estimated parameter a for model(6) was not statistically significant at a level of a = 0.05. RMSE and AIC test result for all models showed that exponential models(1),(2),(3) and(4) performed significantly better than others. All ten models had very small MEs and MAEs. Nearly all models underestimated tree heights except for model(6). Comparing the MEs and MAEs of models, model(1) produced smaller MEs(0.0059) and MAEs(1.3754) than other models. To assess the predictive performance of models, we also calculated MEs by dividing the model validation data set into 10-cm DBH classes. This suggested that all models were likely to create higher mean prediction errors for tree DBH classes[20 cm. However, no clear trend was found among models.Model(6) generated significantly smaller mean prediction errors across all tree DBH classes. Considering all the aforementioned criteria, model(1): TH ? 1:3 t a= e1 t b?eàc?DBHT and model(6): TH ? 1:3 t DBH2= ea t b?DBH t c ? DBH2T are recommended as suitable models for describing the height–diameter relationship of P. euphratica. The limitations of other models showing poor performance in predicting tree height are discussed. We provide explanations for these shortcomings.     

Improving forest burn severity estimations with partial least squares regression and orthogonal signal correction methods in Daxing'an Mountains,China

Several indices and simple empirical models and ratios of single band from pre-and post-fire Landsat images have been developed to estimate and/or map burn severity. However, these models and indices are usually site-, time-and vegetation-dependent and their applications are limited. The Daxing'an Mountains range has the largest forested area in China and is prone to wildfires. Whether or not the existing models can effectively characterize the burn severity over a large region is unclear. In this study, we used the orthogonal signal correction method based on partial least squares regression(PLSR) to select those variables that better interpret the variance of burn severity. A new index and other commonly used indices were used to construct a new, multivariate PLSR model which was compared with the popular single variable models, according to three assessment indices: relative root mean square error( RMSE %), relative bias(R E %) and Nash–Sutcliffe efficiency( NSE %). The results indicate that the multivariate PLSR model performed better than the other single variable models with higher NSE %(68.2% vs. 67.8%) and less RE %(3.7% vs.-8.7%), while achieving almost the same R MSE%. We also discuss the spectral characteristics of the four selected variables for constructing the multivariate PLSR model and their correlation with the field burn severity data. The new model developed from this study should help to better understand the patterns of forest burn severity and assist in vegetation restoration efforts in the region.     

Disentangling the factors that contribute to variation in forest biomass increments in the mid-subtropical forests of China

Mid-subtropical forests are the main vegetation type of global terrestrial biomes, and are critical for maintaining the global carbon balance. However, estimates of forest biomass increment in mid-subtropical forests remain highly uncertain. It is critically important to determine the relative importance of different biotic and abiotic factors between plants and soil, particularly with respect to their influence on plant regrowth. Consequently,it is necessary to quantitatively characterize the dynamicspatiotemporal distribution of forest carbon sinks at a regional scale. This study used a large, long-term dataset in a boosted regression tree(BRT) model to determine the major components that quantitatively control forest biomass increments in a mid-subtropical forested region(Wuyishan National Nature Reserve, China). Long-term,stand-level data were used to derive the forest biomass increment, with the BRT model being applied to quantify the relative contributions of various biotic and abiotic variables to forest biomass increment. Our data show that total biomass(t) increased from 4.62 9 106 to 5.30 9 106 t between 1988 and 2010, and that the mean biomass increased from 80.19 ± 0.39 t ha-1(mean ± standard error) to 94.33 ± 0.41 t ha-1in the study region. The major factors that controlled biomass(in decreasing order of importance) were the stand, topography, and soil. Stand density was initially the most important stand factor, while elevation was the most important topographic factor. Soil factors were important for forest biomass increment but have a much weaker influence compared to the other two controlling factors. These results provide baseline information about the practical utility of spatial interpolationmethods for mapping forest biomass increments at regional scales.