Improving precision of biomass estimation in field and remote sensing-based forest monitoring - considering continuous horizontal biomass distributions

森林生物量是现代森林监测的核心变量之一。估计精度是森林监测的一个重要特征。关于森林生物量评估的研究主要侧重于优化抽样设计和加强根据实地测量的树木数据（异速生长模型）和遥感数据（用于区域化和基于模型的推断）建立的统计生物量模型。我们将研究通过改进地上生物量（AGB）的每地块（固定面积地块）预测来提高估计精度的方法：我们引入了一种新的方法，将森林生物量视为具有连续水平分布的变量，以便每地块AGB预测是严格在地块周长限制内的树木生物量。我们称之为连续的方法（CA）的地块生物量预测-而不是传统的离散的方法（DA），其中的总生物量的离散数量的树木被视为地块生物量-无论有多少生物量是真正高于地块面积。在CA中，部分的树内的斜坡超过小区边界被排除，而生物量的树木延伸到小区需要考虑的部分。为了使这种方法的操作，我们介绍了个别树木的水平生物量分布（THBD），并得出其形状的经验数据和理论考虑。我们使用完全映射的立场，比较传统的DA和我们新提出的CA，在那里我们希望更好地了解之间的相互作用的立场特征，地块大小，地块形状和（不可避免的）假设建模的THBD的统计性能。基于试点研究，我们预计在估计精度的增益，我们也评估成本问题，因为CA需要包括额外的外树，有树冠分数内的情节的限制。在这一建议中有两个主要的基础研究的新元素，没有以前的工作：（1）THBD的推导，（2）推导的林分水平分布的树木生物量。使用CA现场小区生物量预测，我们假设，以提高精度的估计（1）森林资源调查现场采样和（2）从监测方法，结合现场观察到的生物量与遥感数据。对两者的分析是本提案研究计划的核心内容。THBD的进一步应用-这里没有研究-被认为是在模拟森林的发展和森林火灾的蔓延。

Forest biomass is among the core variables in modern forest monitoring. Precision of estimation is an important feature in forest monitoring. Research on the assessment of forest biomass focusses largely on optimizing sampling designs and enhancing statistical biomass models from field measured tree data (allometric models) and from remote sensing data (for regionalization and model-based inference). We will look at options to increase precision of estimation by improving the per plot (fixed area plots) prediction of above ground biomass (AGB): we introduce a new approach that looks at the forest biomass as a variable with a continuous horizontal distribution so that the per-plot AGB prediction is the tree biomass which is strictly within the confinements of the plot perimeter. We call this the continuous approach (CA) to plot-biomass prediction - as opposed to the conventional discrete approach (DA), in which the total biomass of the discrete number of in-trees is taken as plot biomass - regardless how much of this biomass is truly above the plot area. In the CA, parts of in-trees that slop over the plot boundary are excluded while biomass fractions of out-trees that extend into the plot need to be considered. To make this approach operational, we introduce the individual tree horizontal biomass distribution (THBD) and derive its shape from empirical data and theoretical considerations. We use fully mapped stands to compare the statistical performance of the conventional DA and our newly proposed CA, where we wish to better understand the interactions between stand characteristics, plot size, plot shape and (the unavoidable) assumptions in modelling the THBD. Based on a pilot study we expect a gain in precision of estimation; we do also evaluate the cost issue as the CA requires including additional out-trees that have crown fractions within the confinements of the plot. There are two major novel elements of basic research in this proposal that had not been worked on before: (1) the derivation of the THBD, (2) the derivation of the stand-wise horizontal distribution of tree biomass. Using the CA to field plot biomass prediction, we hypothesize to increase precision of estimation from both (1) forest inventory field sampling and (2) from monitoring approaches that integrate field observed biomass with remotely sensed data. Analyses on both are a core element of the research plan of this proposal. Further applications of the THBD - not researched here - are seen in the context of modelling the development of forest stands and the spread of forest fires.