A large‐scale forest landscape model incorporating multi‐scale processes and utilizing forest inventory data
A large‐scale forest landscape model incorporating multi‐scale processes and utilizing forest inventory data
复制标题
DOI：
10.1890/es13-00040.1
复制
发表时间：
2013-09
期刊：
影响因子：
2.7
通讯作者：
Wen J. Wang;Hong S. He;M. Spetich;S. Shifley;F. Thompson;D. Larsen;Jacob S. Fraser;Jian Yang
中科院分区：
文献类型：
--
作者：
Wen J. Wang;Hong S. He;M. Spetich;S. Shifley;F. Thompson;D. Larsen;Jacob S. Fraser;Jian Yang
森林景观模型面临的两个挑战是如何在进行大规模(即107ha)模拟的同时模拟精细的林分尺度过程，以及如何利用美国森林调查和分析(FIA)数据等广泛的森林调查数据来初始化和约束模型参数。我们提出了Landis Pro模型，以满足这些需求。Landis PRO增加了资源竞争的密度和大小机制。这是通过在每个栅格单元中按树种年龄队列合并树木数量和胸径来实现的。森林变化是由物种尺度、林分尺度和景观尺度的相互作用决定的。物种尺度的过程包括树木的生长、建立和死亡。林分尺度的过程包括与密度和大小有关的资源竞争，这些竞争调节着自疏和苗木的建立。景观尺度的过程包括种子扩散，以及自然和人为干扰。LANDIS PRO被设计成与森林调查数据直接比较，因此，在预测未来森林变化之前，可以直接利用广泛的FIA数据来初始化和约束模型参数。我们根据FIA的历史数据(1978年)初始化了一大片景观(∼107公顷)，并根据先前的FIA序列数据(1978年至2008年)对经过30年模拟后预测的森林结构和组成进行了统计校准。结果表明，初始条件真实地反映了1978年的历史森林组成和结构，约束模型参数预测了景观和土地类型尺度上的合理结果。随后对模型预测的评估表明，预测的森林组成和结构与老栎林相当；预测的森林演替轨迹与研究区以栎类为主的森林的预期演替格局一致；预测的林分发展格局与已有的林分发展理论相一致。这项研究展示了一个森林景观建模的框架，包括模型初始化、校准和预测评估。
Two challenges confronting forest landscape models (FLMs) are how to simulate fine, stand-scale processes while making large-scale (i.e., >107 ha) simulation possible, and how to take advantage of extensive forest inventory data such as U.S. Forest Inventory and Analysis (FIA) data to initialize and constrain model parameters. We present the LANDIS PRO model that addresses these needs. LANDIS PRO adds density and size mechanisms of resource competition. This is achieved through incorporating number of trees and DBH by species age cohort within each raster cell. Forest change is determined by the interactions of species-, stand-, and landscape-scale processes. Species-scale processes include tree growth, establishment, and mortality. Stand-scale processes include density and size-related resource competition that regulates self-thinning and seedling establishment. Landscape-scale processes include seed dispersal, as well as natural and anthropogenic disturbances. LANDIS PRO is designed to be straightforwardly comparable with forest inventory data, and thus the extensive FIA data can be directly utilized to initialize and constrain model parameters before predicting future forest change. We initialized a large landscape (∼107 ha) from historical FIA data (1978) and the predicted forest structure and composition following 30 years of simulation were statistically calibrated against a prior time-series of sequential FIA data (1978 to 2008). The results showed that the initialized conditions realistically represented the historical forest composition and structure at 1978, and the constrained model parameters predicted reasonable outcomes at both landscape and land type scales. The subsequent evaluation of model predictions showed that the predicted forest composition and structure were comparable with old-growth oak forests; predicted forest successional trajectories were consistent with the expected successional patterns in oak-dominated forests in the study region; and the predicted stand development patterns were in agreement with the established theories of forest stand development. This study demonstrated a framework for forest landscape modeling including model initialization, calibration, and evaluation of predictions.