Evapotranspiration of a <Emphasis Type="Italic">Populus euphratica</Emphasis> forest during the growing season in an extremely arid region of northwest China using the Shuttleworth–Wallace model

Thus far, measurements and estimations of actual evapotranspiration(ET) in extremely arid areas are still insufficient. Based on successive observations from June–September 2014, we simulated ET of a Populus euphratica Oliv. forest during the growing season in an extremely arid region of northwest China using the Shuttleworth–Wallace(S–W) model. Simulated ET values were compared to those of the eddy-covariance(EC) method on a 1 h interval. With a root mean square error(RMSE),relative error(RE) and mean absolute error(MAE) of0.192, 3.100 and 0.165 mm h-1, respectively, model performance was not satisfactory. In particular, on days with strong winds(Sep. 11–13), deviations between simulated and observed ET values increased to 0.275, 0.878 and0.251 mm h-1, RMSE, RE and MAE respectively. These values were significantly greater than those in other study periods and were most likely owing to sharp increases in wind speed. As a result, there were substantial advective effects, which is not consistent with the assumption of the S–W model that there are no advective effects or mesoscale circulation patterns induced by surface discontinuities.     

Modeling of the height–diameter relationship using an allometric equation model:a case study of stands of Phyllostachys edulis

Understanding the relationship between tree height(H) and diameter at breast height(D) is vital to forest design, monitoring and biomass estimation. We developed an allometric equation model and tested its applicability for unevenly aged stands of moso bamboo forest at a regional scale. Field data were collected for 21 plots. Based on these data, we identified two strong power relationships: a correlation between the mean bamboo height(Hm) and the upper mean H(Hu), and a correlation between the mean D(Dm) and the upper mean D(Du). Simulation results derived from the allometric equation model were in good agreement with observed culms derived from the field data for the 21 stands,with a root-mean-square error and relative root-mean-square error of 1.40 m and 13.41 %, respectively. These results demonstrate that the allometric equation model had a strong predictive power in the unevenly aged stands at a regional scale. In addition, the estimated average height–diameter(H–D) model for South Anhui Province was used to predict H for the same type of bamboo in Hunan Province based on the measured D, and the results were highly similar. The allometric equation model has multiple uses at the regional scale, including the evaluation of the variation in the H–D relationship among regions. The model describes the average H–D relationship without considering the effects caused by variation in site conditions, tree density and other factors.     

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.     

Mixed-effects modeling for tree height prediction models of Oriental beech in the Hyrcanian forests

Height–diameter relationships are essential elements of forest assessment and modeling efforts.In this work,two linear and eighteen nonlinear height–diameter equations were evaluated to find a local model for Oriental beech(Fagus orientalis Lipsky) in the Hyrcanian Forest in Iran.The predictive performance of these models was first assessed by different evaluation criteria: adjusted R~2(R~2_(adj)),root mean square error(RMSE),relative RMSE(%RMSE),bias,and relative bias(%bias) criteria.The best model was selected for use as the base mixed-effects model.Random parameters for test plots were estimated with different tree selection options.Results show that the Chapman–Richards model had better predictive ability in terms of adj R~2(0.81),RMSE(3.7 m),%RMSE(12.9),bias(0.8),%Bias(2.79) than the other models.Furthermore,the calibration response,based on a selection of four trees from the sample plots,resulted in a reduction percentage for bias and RMSE of about 1.6–2.7%.Our results indicate that the calibrated model produced the most accurate results.     

Impact of time lags on diurnal estimates of canopy transpiration and canopy conductance from sap-flow measurements of Populus cathayana in the Qinghai–Tibetan Plateau

Recently, canopy transpiration(Ec) has been often estimated by xylem sap-flow measurements. However, there is a significant time lag between sap flow measured at the base of the stem and canopy transpiration due to the capacitive exchange between the transpiration stream and stem water storage. Significant errors will be introduced in canopy conductance(gc) and canopy transpiration estimation if the time lag is neglected. In this study, a cross-correlation analysis was used to quantify the time lag, and the sap flowbased transpiration was measured to parameterize Jarvistype models of gcand thus to simulate Ecof Populus cathayana using the Penman–Monteith equation. The results indicate that solar radiation(Rs) and vapor pressure deficit(VPD) are not fully coincident with sap flow and have an obvious lag effect; the sap flow lags behind Rsand precedes VPD, and there is a 1-h time shift between Ecand sap flow in the 30-min interval data set. A parameterized Jarvis-type gc model is suitable to predict P. cathayana transpiration and explains more than 80% of the variation observed in gc, and the relative error was less than 25%, which shows a preferable simulation effect. The root mean square error(RMSEs)between the predicted and measured Ecwere 1.91 9 10-3(with the time lag) and 3.12 9 10-3cm h-1(without the time lag). More importantly, Ecsimulation precision that incorporates time lag is improved by 6% compared to the results without the time lag, with the mean relative error(MRE) of only 8.32% and the mean absolute error(MAE) of1.48 9 10-3cm h-1.     

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.     

Analysis of the forest fires in the Antalya region of Turkey using the Keetch–Byram drought index

We investigated the correlation of large fires([300 ha) from 1992 to 2013 within the borders of the Antalya Regional Directorate of Forestry using the Keetch–Byram drought index(KBDI). Daily KBDI values were calculated for each year, and values for the period before the year 2000 differed significantly from those after2000. After 2000(large fires occurred in 2004, 2006, 2007,2008, 2010, and 2013), when KBDI values increased, the KBDI, but not the number of fires, was inversely correlated with the natural log of the burned area(NLBA). While there were both high and low KBDI values when the NLBA was small, only high KBDI values were associated with high NLBA values. Particularly for logarithmic values of 4 and higher, KBDI values increased in parallel with increases in NLBA values. On the basis of a Mann–Whitney U test done in addition to a Pearson correlation test, we found that when the burned areas were grouped according to small and large areas, the KBDI could be used to distinguish the two groups. Using a conditional probability analysis, we found that 4th, 5th and 6th class KBDI values may lead to large fires at the 60 % possibility.Similarly, the possibility of large fires greater than the median burned area in any given 6 years was found to be48 %. In addition, while the mean value of KBDI is 390.51 for the period from May to September for these 6 years, it is 359.93 for the other years. Consequently, the area burned also increased as the KBDI classes(Class 0: 0–99, Class 1:100–199, Class 2: 200–299, Class 3: 300–399, Class 4:400–499, Class 5: 500–599, Class 6: 600–699, and Class 7:700–800) increase.     

Characteristics of canopy interception and its simulation with a revised Gash model for a larch plantation in the Liupan Mountains,China

Canopy interception is a significant proportion of incident rainfall and evapotranspiration of forest ecosystems. Hence, identifying its magnitude is vital for studies of eco-hydrological processes and hydrological impact evaluation. In this study, throughfall, stemflow and interception were measured in a pure Larix principis-rupprechtii Mayr.(larch) plantation in the Liupan Mountains of northwestern China during the growing season(May–October) of 2015, and simulated using a revised Gash model. During the study period, the total precipitation was499.0 mm; corresponding total throughfall, stemflow and canopy interception were 410.3, 2.0 and 86.7 mm,accounting for 82.2, 0.4 and 17.4% of the total precipitation, respectively. With increasing rainfall, the canopy interception ratio of individual rainfall events decreased initially and then tended to stabilize. Within the study period, the simulated total canopy interception, throughfall and stemflow were 2.2 mm lower, 2.5 mm higher and0.3 mm lower than their measured values, with a relative error of 2.5, 0.6 and 15.0%, respectively. As quantified by the model, canopy interception loss(79%) mainly consisted of interception caused by canopy adsorption, while the proportions of additional interception and trunk interception were small. The revised Gash model was highly sensitive to the parameter of canopy storage capacity,followed by the parameters of canopy density and mean rainfall intensity, but less sensitive to the parameters of mean evaporation rate, trunk storage capacity, and stemflow ratio. The revised Gash model satisfactorily simulated the total canopy interception of the larch plantation within the growing season but was less accurate for some individual rainfall events, indicating that some flaws exist in the model structure. Further measures to improve the model's ability in simulating the interception of individual rainfall events were suggested.     

Soil respiration in paludified forests of European Russia

Soil respiration studies in paludified forests of the European part of Russia are quite rare in comparison with those of open peat bogs, which make long-term observations in this region highly relevant. In this study,soil CO_2 emissions were measured by the close chamber method in different microlandscapes of paludified forests.For four summer seasons with different environments, soil respiration ranged from 1078 to 248 mg CO_2 m~(-2) h~(-1) in a paludified spruce forest site with coarse woody debris to659–820 mg CO_2 m~(-2) h~(-1) in a paludified boggy pine forest. The most intensive soil respiration was observed during the hot summer of 2013 and the lowest in the hot and humid summer of 2016. Annual total soil CO_2 emissions in paludified forests in 2015–2016 were approximately 2000–3000 g CO_2 m~(-2). During the year, the lowest CO_2 emission values were observed from November to April(14–84 mg CO_2 m~(-2) h-1) and the maximum were in July and August(522–1205 mg CO_2 m-2 h-1). The contributions of CO_2 emissions in the cold November–Aprilperiod were 6–8.5%. The impacts of temperature on soil respiration were higher(r~2= 0.45–0.57) than those of groundwater levels(r~2= 0.17–0.49). Soil respiration in the paludified spruce forest and in the pine bog generally were higher than emissions from ecosystems with similar hydrothermal conditions in the boreal zone.     

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.     

Optimizing continuous cover management of boreal forest when timber prices and tree growth are stochastic

Background: Decisions on forest management are made under risk and uncertainty because the stand development cannot be predicted exactly and future timber prices are unknown. Deterministic calculations may lead to biased advice on optimal forest management. The study optimized continuous cover management of boreal forest in a situation where tree growth, regeneration, and timber prices include uncertainty.Methods: Both anticipatory and adaptive optimization approaches were used. The adaptive approach optimized the reservation price function instead of fixed cutting years. The future prices of different timber assortments were described by cross-correlated auto-regressive models. The high variation around ingrowth model was simulated using a model that describes the cross- and autocorrelations of the regeneration results of different species and years. Tree growth was predicted with individual tree models, the predictions of which were adjusted on the basis of a climate-induced growth trend, which was stochastic. Residuals of the deterministic diameter growth model were also simulated. They consisted of random tree factors and cross- and autocorrelated temporal terms.Results: Of the analyzed factors, timber price caused most uncertainty in the calculation of the net present value of a certain management schedule. Ingrowth and climate trend were less significant sources of risk and uncertainty than tree growth. Stochastic anticipatory optimization led to more diverse post-cutting stand structures than obtained in deterministic optimization. Cutting interval was shorter when risk and uncertainty were included in the analyses.Conclusions: Adaptive optimization and management led to 6%–14% higher net present values than obtained in management that was based on anticipatory optimization. Increasing risk aversion of the forest landowner led to earlier cuttings in a mature stand. The effect of risk attitude on optimization results was small.     

Modeling height growth for teak plantations in Colombia using the reducible stochastic differential equation approach

Teak( Tectona grandis L.f.) plantations are increasingly being established in tropical regions to meet a rising demand for its highly valued timber. Teak plantations have been established in the Atlantic Coastal Plain region of Colombia, a region climatically suitable for teak growth by having a monsoon climate with a unimodal precipitation pattern. Tree diameter at breast height(DBH, 1.3 m above ground) and mean top height, periodically measured over a 17-year period in 44 permanent sampling plots of size 0.06 and 0.10 ha, were used in this study. A stochastic differential equation(SDE), along with a Bertalanffy–Richards-type height growth model, was used to model and estimate top height growth of teak plantations in Colombia. Environmental noise and height measurement errors were explicitly considered as the main uncertainty sources of mean top height growth. The best model for estimating mean top height, based on statistical performance and biological rationale, had the asymptote defined as a local parameter and the growth rate and shape specified as global parameters. This model outperformed its counterpart that had the growth rate specified as a local parameter and asymptote and shape as global parameters. The selected model also outperformed alternative approaches such as the mixed-effects model, generalized algebraic difference approach, and the dummy variable method. Estimated trajectories for the mean top height of teak in Colombia are biologically sound based on the measured height series and previous studies in Latin America. Results suggest that most of the uncertainty associated with the mean top height growth of teak plantations in Colombia was largely explained by environmental noise. The best estimated model using the SDE approach can be useful for predicting height growth and evaluating site productivity of teak plantations in Colombia and in neighbouring countries with biophysical characteristics similar to those where teak has been planted in Colombia.     

Precision Fire Hazard Divisions for Collective Forest in Southern China

This paper focuses on how to build the model of precision fire hazard divisions in the level of forest resources sub-compartment.Based on 3D GIS technology and characteristics of forest fires in collective forest of southern China,this study utilized Lin’an City,Zhejiang Province as the experimental area.Forest fire factors were divided into 11 indexes from the three categories(social and economic factors,forestry characteristics,and meteorological characteristics) and weighted for analysis.Next,three eigenvectors(one for each category) were created to build a nonlinear mathematical model called precision fire hazard divisions for forests.Then,the model was used to optimize and test forest fire hazard divisions with the least squares.Results showed that experimental and theoretical values of error were less than 0.1. Thus,in the experimental area this model and the fire occurrence history matched.     

Determination of actual evapotranspiration and transpiration in desert sand dunes (Negev Desert) using different approaches

In an arid environment, especially in sandy areas where surface runoff is of no practical importance in the hydrological budget, it is rainfall, dewfall and evapotranspiration that become the most important variables. To assess actual evapotranspiration, several methods (flux-gradient, BREB, eddy correlation) were applied to data from the Nizzana experimental site in the northwestern Negev Desert. Additionally, a model specifically designed for arid environments is introduced in this paper. This zero plane model shows the most reasonable results compared with the other methods, which overestimate evapotranspiration to a large degree. It is shown that plant transpiration is the dominant process in total evapotranspiration while advective processes do not play a major role in the near-ground boundary layer, although the study area is influenced by a sea breeze. Actual transpiration of Artemisia monosperma was measured in a field experiment to validate the calculated evapotranspiration. The vegetation contributed 41% of the calculated total evapotranspiration in a single month.     

Comparison of stand structure and growth between artificial and natural forests of Pinus sylvestiris var, mongolica on sandy land

Mongolian pine (Pinus sylvestiris Linnaeus var. mongolica Litvinov) as a valuable conifer tree species has been broadly introduced to the sandy land areas in “Three North” regions (North, northwest and northeast of China), but many prob-lems occurred in the earliest Mongolian pine plantations in 7hanggutai, 7hangwu County, Liaoning Province (ZZL). In order to clarify the reason, comprehensive investigations were carried out on differences in structure characteristics, growth processes and ecological factors between artificial stands (the first plantation established in ZZL in 1950s) and natural stands (the origin forests of the tree species in Honghuaerji, Inner Mongolia) on sandy land. The results showed that variation of diameter-class distributions in artificial stands and natural stands could be described by Weibull and Normal distribution models, respectively.Chapman-Richards growth model was employed to reconstruct the growth process of Mongolian pine based on the data from field investigation and stem analysis. The ages of maximum of relative growth rate and average growth rate of DBH, height, and volume of planted trees were 11,22 years, 8, 15 years and 35, 59 years earlier than those of natural stand trees, respectively. In respect of the incremental acceleration of volume, the artificial and natural stands reached their maximum values at 14 years and 33 years respectively. The quantitative maturity ages of artificial stands and natural stands were 43 years and 102 years respectively. It was concluded that the life span of the Mongolian pine trees in natural stands was about 60 years longer than those in artificial stands. The differences mentioned above between artificial and natural Mongolian pine forests on sandy land were partially attributed to the drastic variations of ecological conditions such as latitude, temperature, precipitation, evaporation and height above sea level. Human beings‘‘ disturbances and higher density in plantation forest may be ascribed as additional reasons. Those results may be potentially useful for the management and afforestation of Mongolian pine plantations on sandy land in arid and semi-arid areas.     

Nonlinear versus linearised model on stand density model fitting and stand density index calculation: analysis of coefficients estimation via simulation

The stand density index,one of the most important metrics for managing site occupancy,is generally calculated from empirical data by means of a coefficient derived from the“self-thinning rule”or stand density model.I undertook an exploratory analysis of model fitting based on simulated data.I discuss the use of the logarithmic transformation(i.e.,linearisation)of the relationship between the total number of trees per hectare(N)and the quadratic mean diameter of the stand(QMD).I compare the classic method used by Reineke(J Agric Res 46:627–638,1933),i.e.,linear OLS model fitting after logarithmic transformation of data,with the“pure”powerlaw model,which represents the native mathematical structure of this relationship.I evaluated the results according to the correlation between N and QMD.Linear OLS and nonlinear fitting agreed in the estimation of coefficients only for highly correlated(between-1 and-0.85)or poorly correlated([-0.39)datasets.At average correlation values(i.e.,between-0.75 and-0.4),it is probable that for practical applications,the differences were relevant,especially concerning the key coefficient for Reineke’s stand density index calculation.This introduced a non-negligible bias in SDI calculation.The linearised log–log model always estimated a lower slope term than did the exponent of the nonlinear function except at the extremes of the correlation range.While the logarithmic transformation is mathematically correct and always equivalent to a nonlinear model in case of prediction of the dependent variable,the difference detected in my studies between the two methods(i.e.,coefficient estimation)was directly related to the correlation between N and QMD in each simulated/disturbed dataset.In general,given the power law as the“natural”structure of the N versus QMD relationship,the nonlinear model is preferred,with a logarithmic transformation used only in the case of violation of parametric assumptions(e.g.data distributed non-normally).     

The impact of climate change on forest fire danger rating in China’s boreal forest

The Great Xing’an Mountains boreal forests were focused on in the northeastern China.The simulated future climate scenarios of IPCC SRES A2a and B2a for both the baseline period of 1961-1990 and the future scenario periods were downscaled by the Delta Method and the Weather Generator to produce daily weather data.After the verification with local weather and fire data,the Canadian Forest Fire Weather Index System was used to assess the forest fire weather situation under climate change in the study region.An increasing trend of fire weather severity was found over the 21st century in the study region under the both future climate change scenarios,compared to the 1961-1990 baseline period.The annual mean/maximum fire weather index was predicted to rise continuously during 2010-2099,and by the end of the 21st century it is predicted to rise by 22%-52% across much of China’s boreal forest.The significant increases were predicted in the spring from of April to June and in the summer from July to August.In the summer,the fire weather index was predicted to be higher than the current index by as much as 148% by the end of the 21st century.Under the scenarios of SRES A2a and B2a,both the chance of extremely high fire danger occurrence and the number of days of extremely high fire danger occurrence was predicted to increase in the study region.It is anticipated that the number of extremely high fire danger days would increase from 44 days in 1980s to 53-75 days by the end of the 21st century.     

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.     

Theoretical modeling of the effects of temperature and moisture content on the acoustic velocity of Pinus resinosa wood

To investigate the effects of temperature and moisture content(MC) on acoustic wave velocity(AWV)in wood,the relationships between wood temperature,MC,and AWV were theoretically analyzed.According to the theoretical propagation characteristics of the acoustic waves in the wood mixture and the differences in velocity among various media(including ice,water,pure wood or oven-dried wood),theoretical relationships of temperature,MC,and AWV were established,assuming that the samples in question were composed of a simple mixture of wood and water or of wood and ice.Using the theoretical model,the phase transition of AWV in green wood near the freezing point(as derived from previous experimental results) was plausibly described.By comparative analysis between theoretical and experimental models for American red pine(Pinus resinosa) samples,it was established that the theoretically predicted AWV values matched the experiment results when the temperature of the wood was below the freezing point of water,with an averageprediction error of 1.66%.The theoretically predicted AWV increased quickly in green wood as temperature decreased and changed suddenly near 0 °C,consistent with the experimental observations.The prediction error of the model was relatively large when the temperature of the wood was above the freezing point,probably due to an overestimation of the effect of the liquid water content on the acoustic velocity and the limited variables of the model.The high correlation between the predicted and measured acoustic velocity values in frozen wood samples revealed the mechanisms of temperature,MC,and water status and how these affected the wood(particularly its acoustic velocity below freezing point of water).This result also verified the reliability of a previous experimental model used to adjust for the effect of temperature during field testing of trees.     

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.