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Quantifying Live Aboveground Biomass and Forest Disturbance of Mountainous Natural and Plantation Forests in Northern Guangdong, China, Based on Multi-Temporal Landsat, PALSAR and Field Plot Data

机译:基于多时相Landsat,PALSAR和田地图数据量化粤北山区天然和人工林地上生物量和森林扰动

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Spatially explicit knowledge of aboveground biomass (AGB) in large areas is important for accurate carbon accounting and quantifying the effect of forest disturbance on the terrestrial carbon cycle. We estimated AGB from 1990 to 2011 in northern Guangdong, China, based on a spatially explicit dataset derived from six years of national forest inventory (NFI) plots, Landsat time series imagery (1986–2011) and Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radars (PALSAR) 25 m mosaic data (2007–2010). Four types of variables were derived for modeling and assessment. The random forest approach was used to seek the optimal variables for mapping and validation. The root mean square error (RMSE) of plot-level validation was between 6.44 and 39.49 (t/ha), the normalized root-mean-square error (NRMSE) was between 7.49% and 19.01% and mean absolute error (MAE) was between 5.06 and 23.84 t/ha. The highest coefficient of determination R 2 of 0.8 and the lowest NRMSE of 7.49% were reported in 2006. A clear increasing trend of mean AGB from the lowest value of 13.58 t/ha to the highest value of 66.25 t/ha was witnessed between 1988 and 2000, while after 2000 there was a fluctuating ascending change, with a peak mean AGB of 67.13 t/ha in 2004. By integrating AGB change with forest disturbance, the trend in disturbance area closely corresponded with the trend in AGB decrease. To determine the driving forces of these changes, the correlation analysis was adopted and exploratory factor analysis (EFA) method was used to find a factor rotation that maximizes this variance and represents the dominant factors of nine climate elements and nine human activities elements affecting the AGB dynamics. Overall, human activities contributed more to short-term AGB dynamics than climate data. Harvesting and human-induced fire in combination with rock desertification and global warming made a strong contribution to AGB changes. This study provides valuable information for the relationships between forest AGB and climate as well as forest disturbance in subtropical zones.
机译:在大范围内对地面生物量(AGB)进行空间明确的了解对于准确进行碳核算和量化森林干扰对陆地碳循环的影响非常重要。我们根据来自六年国家森林清单(NFI)地块,Landsat时间序列影像(1986-2011)和高级陆地观测卫星(ALOS)的空间显性数据集,估计了1990年至2011年中国广东北部的AGB阵列L波段合成孔径雷达(PALSAR)25 m镶嵌数据(2007–2010)。导出了四种类型的变量以进行建模和评估。随机森林方法用于寻找映射和验证的最佳变量。地块级验证的均方根误差(RMSE)在6.44至39.49(t / ha)之间,归一化均方根误差(NRMSE)在7.49%至19.01%之间,平均绝对误差(MAE)为在5.06和23.84吨/公顷之间。 2006年报告的最高测定系数R 2为0.8,最低NRMSE为7.49%。1988年之间,平均AGB从最低值13.58 t / ha到最高值66.25 t / ha都有明显的增长趋势。与2000年相比,2000年之后出现了上升的波动,2004年的平均AGB峰值为67.13 t / ha。通过将AGB的变化与森林扰动相结合,扰动面积的趋势与AGB的下降趋势紧密对应。为了确定这些变化的驱动力,采用了相关性分析,并使用探索性因子分析(EFA)方法来找到使这种方差最大化的因子旋转,并代表影响AGB的9个气候要素和9个人类活动要素的主导因素动力学。总体而言,人类活动对短期AGB动态的贡献大于气候数据。采伐和人为引发的火灾,加上岩石荒漠化和全球变暖,为AGB的变化做出了重要贡献。这项研究为亚热带森林AGB与气候以及森林干扰之间的关系提供了有价值的信息。

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