Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data

Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass and its changes in Southwest China.     

Using ALS raster data in forest planning

Raster type of forest inventory data with site and growing stock variables interpreted for small squareshaped grid cells are increasingly available for forest planning.In Finland,there are two sources of this type of lattice data:the multisource national forest inventory and the inventory that is based on airborne laser scanning(ALS).In both cases,stand variables are interpreted for 16 m×16 m cells.Both data sources cover all private forests of Finland and are freely available for forest planning.This study analyzed different ways to use the ALS raster data in forest planning.The analyses were conducted for a grid of 375×375 cells(140,625 cells,of which 97,893 were productive forest).The basic alternatives were to use the cells as calculation units throughout the planning process,or aggregate the cells into segments before planning calculations.The use of cells made it necessary to use spatial optimization to aggregate cuttings and other treatments into blocks that were large enough for the practical implementation of the plan.In addition,allowing premature cuttings in a part of the cells was a prerequisite for compact treatment areas.The use of segments led to 5–9%higher growth predictions than calculations based on cells.In addition,the areas of the most common fertility classes were overestimated and the areas of rare site classes were underestimated when segments were used.The shape of the treatment blocks was more irregular in cell-based planning.Using cells as calculation units instead of segments led to 20 times longer computing time of the whole planning process than the use of segments when the number of grid cells was approximately 100,000.     

Development of mobile GIS system for forest resources second-class inventory

A special mobile GIS(Geographic Information System) system used for forest resources second-class inventory was developed on the basis of traditional forest resources inventory,remote sensing,GPS(Globe Positioning System) and embedded technology.Portable instrument,embedded development and the integration technology of RS(Remote Sensing),GIS and GPS are all used in this special mobile GIS system.Further,the system composition,key techniques,and current situation of the practical application in China were analyzed in the study.The results are important for applying modern high-tech for the planning and design of digital forest resources to improve the precision and efficiency of inventory and reduce the labor cost and financial investment.     

A management planning system for even-aged and uneven-aged forests in northeast China

The most common scientific approach to numerical landscape-level forest management planning is combinatorial optimization aimed at finding the optimal combination of the treatment alternatives of stands. The selected combination of treatments depends on the conditions of the forest, and the objectives of the forest landowners. A two-step procedure is commonly used to derive the plan. First, treatment alternatives are generated for the stands using an automated simulation tool. Second,the optimal combination of the simulated treatment schedules is found by using mathematical programming or various heuristics. Simulation of treatment schedules requires models for stand dynamics and volume for all important tree species and stand types present in the forest.A forest planning system was described for Northeast China. The necessary models for stand dynamics and tree volume were presented for the main tree species of the region. The developed models were integrated into the simulation tool of the planning system. The simulation and the optimization tools of the planning system were described. The optimization tool was used with heuristic methods, making it possible to easily solve also spatial forest planning problems, for instance aggregate cuttings.Finally, the use of the system is illustrated with a case study, in which nonspatial and spatial management plans are developed for the Mengjiagang Forest District.     

Analysis of Forest Biodiversity Changes in China

By reference of the evaluative data of forest biodiversity changes in China from 1973 to 1998, the variation analysis models of the pressure index of forest biodiversity, forest ecosystem diversity and forest species diversity, as well as the general index of forest biodiversity are developed using Statistical Package for the Social Sciences (SPSS). Furthermore established is the relevant model of mutation of forest diversity potential functions. This paper points out that changes of forest biodiversity are generally unsteady, especially for the pressure index . The ecosystem diversity index and the species diversity index always fluctuate, resulting in the erratic changes or mutations of general index of forest biodiversity in China.     

Goal programming approach for sustainable forest management （case study in Iranian Caspian forests）

We used a goal programming technique to determine the optimal harvest volume for the Iranian Caspian forest. We collected data including volume, growth, wood price at forest roadside, and variable harvesting costs. The allometric method was used to quantify seques- trated carbon. Regression analysis was used to derive growth models. Expected mean price was estimated using wood price and variable harvesting costs. Questionnaire was used to determine the constraints and the equation coefficients of the goal programming model. The optimal volume was determined using the goal programming method according to multipurpose forest management. LINGO software was used for analysis. Results indicated that the optimum volumes of species were 250.25 m3.ha-1 for beech, 59 m3.ha-1 for hornbeam, 73 m3.ha-1 for oak, 41 m3.ha-1 for alder, and 32 m3.ha-1 for other species. The total optimum volume is 455.25 m3.ha-1.     

Valuation of forest carbon sinks in China within the framework of the system of national accounts

To better promote forest resource management and strengthen the development of forest carbon sink marketization, this paper studied the accounting of forest carbon sinks from 2003 to 2008 based on a system of national accounts(SNA) and data from the latest forest resources inventory in China. The study calculated the value of forest carbon stocks at a total of RMB817.13 × 10~9 yuan in 2003 and RMB 839.93× 109 yuan in 2008, with an average annual increase of 0.55 % from an increase in physical carbon sinks. The total value of forest carbon sinks in 2003 and 2008 was RMB 26.73 × 10~9 yuan and RMB 29.77 × 10~9 yuan, respectively, with an average annual growth of 2.18 %. From 2003 to 2008, both stock and flow value of forest carbon sinks increased, but the total net flow value of carbon sinks decreased. The growth rate for the environmentally adjusted Gross Domestic Product(ea GDP) for China's forest carbon sinks was 17.23 %, outstripping the average growth rate of 9.5 % for the GDP during the same period. The study also indicates that China's forest carbon sinks affects the GDP in the range of 0.25–0.26 %, and its economic potential is not relatively huge.     

Estimation of forest parameters based on TM imagery and statistical analysis

One of the primary forestry research interests lies in estimating forest stand parameters by applying empirical or semi-empirical model to establish the relationship between the forest stand parameters and remote sensing data. Using remote sensing image and the inventory data from 2 compartments in northeast Florida, U.S.A., this paper explored the correlation between forest stand parameters and Landsat TM spectral digital number （DN） value. Results showed that less than 50% of the total variance could be explained by linear regression models with only either a single band or such vegetation indices as vegetation index （VI） or normalized difference vegetation index （NDVI） as predicators. In consequence, multi-linear regression models which synthesized more predicators were introduced to estimate forest parameters. Regression results were tested in terms of the other group of data, and verification showed a better capability of explaining over 75% variance except for forest density. The weakness and further improvement of prediction models were also discussed in the article. This paper is expected to provide a better understanding of the relationship between TM spectral and forest characteristics     

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.     

A spatially-explicit count data regression for modeling the density of forest cockchafer （Melolontha hippocastani） larvae in the Hessian Ried （Germany）

Background： In this paper, a regression model for predicting the spatial distribution of forest cockchafer larvae in the Hessian Ried region （Germany） is presented. The forest cockchafer, a native biotic pest, is a major cause of damage in forests in this region particularly during the regeneration phase. The model developed in this study is based on a systematic sample inventory of forest cockchafer larvae by excavation across the Hessian Ried. These forest cockchafer larvae data were characterized by excess zeros and overdispersion. Methods： Using specific generalized additive regression models, different discrete distributions, including the Poisson, negative binomial and zero-inflated Poisson distributions, were compared. The methodology employed allowed the simultaneous estimation of non-linear model effects of causal covariates and, to account for spatial autocorrelation, of a 2-dimensional spatial trend function. In the validation of the models, both the Akaike information criterion （AIC） and more detailed graphical procedures based on randomized quantile residuals were used. Results： The negative binomial distribution was superior to the Poisson and the zero-inflated Poisson distributions, providing a near perfect fit to the data, which was proven in an extensive validation process. The causal predictors found to affect the density of larvae significantly were distance to water table and percentage of pure clay layer in the soil to a depth of I m. Model predictions showed that larva density increased with an increase in distance to the water table up to almost 4 m, after which it remained constant, and with a reduction in the percentage of pure clay layer. However this latter correlation was weak and requires further investigation. The 2-dimensional trend function indicated a strong spatial effect, and thus explained by far the highest proportion of variation in larva density. Conclusions： As such the model can be used to support forest practitioners in their decision making for regenerati     

Propagating the errors of initial forest variables through stand- and tree-level growth simulators

Developments in the field of remote sensing have led to various cost-efficient forest inventory methods at different levels of detail. Remote-sensing techniques such as airborne laser scanning (ALS) and digital photogrammetry are becoming feasible alternatives for providing data for forest planning. Forest-planning systems are used to determine the future harvests and silvicultural operations. Input data errors affect the forest growth projections and these effects are dependent on the magnitude of the error. Our objective in this study was to determine how the errors typical to different inventory methods affect forest growth projections at individual stand level during a planning period of 30 years. Another objective was to examine how the errors in input data behave when different types of growth simulators are used. The inventory methods we compared in this study were stand-wise field inventory and single-tree ALS. To study the differences between growth models, we compared two forest simulators consisting of either distance-independent tree-level models or stand-level models. The data in this study covered a 2,000-ha forest area in southern Finland, including 240 sample plots with individually measured trees. The analysis was conducted with Monte Carlo simulations. The results show that the tree-level simulator is less sensitive to errors in the input data and that by using single-tree ALS data, more precise growth projections can be obtained than using stand-wise field inventory data.     

Modeling individual tree height growth of Norway spruce and Scots pine from national forest inventory data in Norway

We developed individual tree height growth models for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) in Norway based on national forest inventory data. Potential height growth is based on existing dominant height growth models and reduced due to competition by functions developed in this study. Three spatially explicit and two spatially non-explicit competition indices were tested. Distance effects and diameter ratio effects were estimated from the data simultaneously with parameters of the potential modifier functions. Large height measurement errors in the national forest inventory data caused large residual variation of the models. However, the effects of competition on height growth were significant and plausible. The potential modifier functions show that height growth of dominant trees is largely unaffected by competition. Only at higher levels of competition, height growth is reduced as a consequence of competition. However, Scots pine also reduced height growth at very low levels of competition. Distance effects in the spatially explicit competition indices indicated that the closest neighbors are most important for height growth. However, for Scots pine also competitors at larger distance affected height growth. The five competition indices tested in this study explained similar proportions of the variation in relative height growth. Given that unbiased predictions can only be expected for the same plot size, we recommend a spatially explicit index, which describes the distance function with a negative exponential, for use in growth simulators.     

Predicting stand damage and tree survival in burned forests in Catalonia (North-East Spain)

The study developed models for predicting the post-fire tree survival in Catalonia. The models are appropriate for forest planning purposes. Two types of models were developed: a stand-level model to predict the degree of damage caused by a forest fire, and tree-level models to predict the probability of a tree to survive a forest fire. The models were based on forest inventory and fire data. The inventory data on forest stands were obtained from the second (1989–1990) and third (2000–2001) Spanish national forest inventories, and the fire data consisted of the perimeters of forest fires larger than 20 ha that occurred in Catalonia between the 2nd and 3rd measurement of the inventory plots. The models were based on easily measurable forest characteristics, and they permit the forest manager to predict the effect of stand structure and species composition on the expected damage. According to the stand level fire damage model, the relative damage decreases when the stand basal area or mean tree diameter increases. Conversely, the relative stand damage increases when there is a large variation in tree size, when the stand is located on a steep slope, and when it is dominated by pine. According to the tree level survival models, trees in stands with a high basal area, a large mean tree size and a small variability in tree diameters have a high survival probability. Large trees in dominant positions have the highest probability of surviving a fire. Another result of the study is the exceptionally good post-fire survival ability of Pinus pinea and Quercus suber.     

云南省森林资源二类调查中的生长量计算分析

以云南省某县的森林资源规划设计调查数据为基础，利用森林资源连续清查的生长率数据及云南省已有各树种的生长率数据，对森林资源规划设计调查中的生长量计算问题进行探讨，分析存在差异的原因，提出相应的建议措施。     

Large-scale scenario analysis in forest ecology and forest management

Large-scale scenario models have been developed to combine forest inventory data and forest growth models to explore impacts of changes in environmental conditions and consequences of changes in forest management, and to support decision-making and policy development. This article reviews some of the scenario studies done by EFI researchers in the past, and attempts to identify current limitations as well as future challenges in this field of research. Main emphasis so far has been on science-driven, technical scenario studies focussing on timber supply and carbon budget studies. While the need remains to improve the science-driven part of large-scale scenario analysis, there is an urgent need to extend the analysis to include value-driven aspects of forest management strategies, to arrive at consistent and comprehensive scenarios for possible future developments in European forestry that can be used in policy-making processes.     

天然林区小班森林资源数据的更新模型

以吉林省汪清林业局为例,根据１９９７年森林经理调查的８４８块固定样地数据,与全林整体模型方法相结合,建立了适合于天然林区林业局（场）无人为干预小班森林资源数据更新的林分级生长模型组。该组模型包括林分密度指数,平均高,断面积,形高,郁闭度等林分测算因子的生长或变化模型。     

Modelling dominant height growth from national forest inventory individual tree data with short time series and large age errors

We developed dominant height growth models for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) in Norway using national forest inventory (NFI) data. The data were collected for a different purpose which potentially causes problems for dominant height growth modelling due to short time series and large age errors. We used the generalized algebraic difference approach and fitted 15 different models using nested regression techniques. Despite the potential problems of NFI data the models fitted to these data were unbiased for most of the age and site index range covered by the NFI data when tested against independent data from long-term experiments (LTE). Biased predictions for young stands and better site indices that are better represented in the LTE data, led us to fit models to a combined data set for unbiased predictions across the total data range. The models fitted to the combined data that were unbiased with little residual variation when tested against an independent data set based on stem analysis of 73 sample trees from southeastern Norway. No indications of regional differences in dominant height growth across Norway were detected. We tested whether the better growing conditions during the short time series (22 years) of the NFI data had affected our dominant height growth models relative to long-term growing conditions, but found only minor bias. The combination with LTE data that have been collected during a longer period (91 years) reduced this potential bias. The dominant height growth models presented here can be used as potential height growth models in individual tree-based forest growth models or as site index models.     

森林资源监测中未测生长量估计方法分析

对未测生长量的概念、计算方法及其在森林资源监测中的应用作了回顾与分析 ,并用部分省近两期连续清查数据作为实例 ,比较了三种方法的特点 ,证明新系统中采用的基于单株模拟的未测生长量计算方法最优