Timber production assessment of a plantation forest: An integrated framework with field-based inventory, multi-source remote sensing data and forest management history
Timber production assessment of a plantation forest: An integrated framework with field-based inventory, multi-source remote sensing data and forest management history
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人工林木材生产评估：包含实地清查、多源遥感数据和森林管理历史的综合框架
DOI：
10.1016/j.jag.2016.06.004
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发表时间：
2016-10
影响因子：
7.5
通讯作者：
Huang Liyan
中科院分区：
文献类型：
--
作者：
Gao Tian;Zhu Jiaojun;Deng Songqiu;Zheng Xiao;Zhang Jinxin;Shang Guiduo;Huang Liyan
木材生产是管理种植园林的目的，其空间和数量信息对于指导管理战略至关重要。以往的研究主要集中在蓄积量增长（GSV），这代表了目前的木材生产潜力，但很少有研究调查历史过程采伐木材。这导致在木材生产的综合生态系统服务评估中出现空白。在本文中，我们建立了一个管理过程为基础的木材生产（MPT）框架，整合当前的GSV和采伐木材来自历史的采伐制度，试图综合评估木材生产的历史时期。在MPT框架中，年龄级和当前GSV通过使用“空间换时间”替代来确定历史间伐的时间和相应的采伐量。根据各次间伐的历史采伐量和当前的总蓄积量可以估算出总木材产量。为了验证该MPT框架，对华北地区43，946 ha落叶松种植园（LP）进行了1962 - 2010年的实证研究。基于实地的清单数据与ALOS PALSAR（高级陆地观测卫星相控阵L波段合成孔径雷达）和Landsat-8 OLI（业务陆地成像仪）数据相结合，用于估计LP的年龄级和当前GSV。PALSAR后向散射强度通道和OLI波段作为输入预测变量的随机森林模型，年龄组分类的Kappa系数为0.59，准确率为67.9%。使用PALSAR数据的回归模型产生的均方根误差（RMSE）为36.5 m3 ha −1。林分总木材产量为7.27 × 106 m3，其中现存蓄积量为4.87 × 106 m3，历史间伐量为2.40 × 106 m3。历史加工采伐占木材总产量的33.0%，这一部分在种植园林现状评价中被忽略。综合考虑预测GSV的RMSE和年龄级的错误分类，木材生产的误差范围为-55.2 ~56.3 m3 ha −1。MPT框架可用于在更大的空间尺度上评估其他树种的木材生产，为更好地了解森林生态系统服务提供关键信息。
Timber production is the purpose for managing plantation forests, and its spatial and quantitative information is critical for advising management strategies. Previous studies have focused on growing stock volume (GSV), which represents the current potential of timber production, yet few studies have investigated historical process-harvested timber. This resulted in a gap in a synthetical ecosystem service assessment of timber production. In this paper, we established a Management Process–based Timber production (MPT) framework to integrate the current GSV and the harvested timber derived from historical logging regimes, trying to synthetically assess timber production for a historical period. In the MPT framework, age-class and current GSV determine the times of historical thinning and the corresponding harvested timber, by using a “space-for-time” substitution. The total timber production can be estimated by the historical harvested timber in each thinning and the current GSV. To test this MPT framework, an empirical study on a larch plantation (LP) with area of 43,946 ha was conducted in North China for a period from 1962 to 2010. Field-based inventory data was integrated with ALOS PALSAR (Advanced Land-Observing Satellite Phased Array L-band Synthetic Aperture Radar) and Landsat-8 OLI (Operational Land Imager) data for estimating the age-class and current GSV of LP. The random forest model with PALSAR backscatter intensity channels and OLI bands as input predictive variables yielded an accuracy of 67.9% with a Kappa coefficient of 0.59 for age-class classification. The regression model using PALSAR data produced a root mean square error (RMSE) of 36.5 m3ha−1. The total timber production of LP was estimated to be 7.27 × 106m3, with 4.87 × 106m3in current GSV and 2.40 × 106m3in harvested timber through historical thinning. The historical process-harvested timber accounts to 33.0% of the total timber production, which component has been neglected in the assessments for current status of plantation forests. Synthetically considering the RMSE for predictive GSV and misclassification of age-class, the error in timber production were supposed to range from −55.2 to 56.3 m3ha−1. The MPT framework can be used to assess timber production of other tree species at a larger spatial scale, providing crucial information for a better understanding of forest ecosystem service.
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影响因子：
13.5
作者：
M. Shimada;T. Itoh;T. Motooka;Manabu Watanabe;T. Shiraishi;R. Thapa;R. Lucas
通讯作者：
M. Shimada;T. Itoh;T. Motooka;Manabu Watanabe;T. Shiraishi;R. Thapa;R. Lucas
影响因子：
1.7
作者：
Dengqiu Li;W. Ju;W. Fan;Z. Gu
通讯作者：
Dengqiu Li;W. Ju;W. Fan;Z. Gu
影响因子：
3.7
作者：
Xiangping Wang;S. Ouyang;O. Sun;Jingyun Fang
通讯作者：
Xiangping Wang;S. Ouyang;O. Sun;Jingyun Fang
影响因子：
3.7
作者：
Gao T;Yang X;Jin Y;Ma H;Li J;Yu H;Yu Q;Zheng X;Xu B
通讯作者：
Xu B
影响因子：
13.5
作者：
T. Chowdhury;C. Thiel;C. Schmullius
通讯作者：
T. Chowdhury;C. Thiel;C. Schmullius