Detection of Spatial and Temporal Patterns of Liana Infestation Using Satellite-Derived Imagery
Detection of Spatial and Temporal Patterns of Liana Infestation Using Satellite-Derived Imagery
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DOI：
10.3390/rs13142774
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发表时间：
2021-07
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影响因子：
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通讯作者：
Chris J. Chandler;G. V. D. Heijden;D. Boyd;G. Foody
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作者：
Chris J. Chandler;G. V. D. Heijden;D. Boyd;G. Foody
藤本植物对树木的生长、死亡和更新具有重要影响，在热带森林动态中起着关键作用。评估大片地区的藤本植物侵扰对于了解驱动其空间分布的因素和监测随时间的变化至关重要。然而，目前尚不清楚卫星图像是否可用于检测郁闭森林中的藤本植物侵扰，因此，卫星观测到的藤本植物侵扰变化是否可随着时间的推移和气候条件的变化而检测出来。在这里，我们的目标是确定基于卫星的遥感探测的空间和时间模式的藤本植物侵染在婆罗洲的沙巴，主要和选择性记录的季节性森林。我们使用来自机载高光谱数据的预测藤本植物侵扰来训练神经网络分类，以预测2016年至2019年四个基于Sentinel-2卫星的图像。我们的研究结果表明，藤本植物侵染是呈正相关的绿色指数（GI），一个简单的度量有关的光合作用活跃的绿色叶的数量增加。此外，这种关系在不同的森林类型中以及在厄尔尼诺引起的干旱期间（2016年）和之后（2017-2019年）观察到。使用神经网络分类，我们评估了随着时间的推移藤本植物的侵扰，并显示严重（>75%）藤本植物侵扰像素的百分比从2016年的12.9% ± 0.63（95%CI）增加到2019年的17.3% ± 2。这意味着藤本植物丰富度增加的报告可能比目前假设的更广泛。这是第一项研究表明，利用卫星图像可以准确地检测到整个封闭树冠热带森林中的藤本植物侵扰。此外，在干旱和潮湿的年份和不同森林类型的藤本植物感染的检测表明，这种方法应广泛适用于整个热带森林。因此，这项工作提高了我们探索在多个空间和时间尺度上藤本植物侵染模式的驱动因素的能力，并量化了藤本植物对全球热带森林碳动态的影响。
Lianas (woody vines) play a key role in tropical forest dynamics because of their strong influence on tree growth, mortality and regeneration. Assessing liana infestation over large areas is critical to understand the factors that drive their spatial distribution and to monitor change over time. However, it currently remains unclear whether satellite-based imagery can be used to detect liana infestation across closed-canopy forests and therefore if satellite-observed changes in liana infestation can be detected over time and in response to climatic conditions. Here, we aim to determine the efficacy of satellite-based remote sensing for the detection of spatial and temporal patterns of liana infestation across a primary and selectively logged aseasonal forest in Sabah, Borneo. We used predicted liana infestation derived from airborne hyperspectral data to train a neural network classification for prediction across four Sentinel-2 satellite-based images from 2016 to 2019. Our results showed that liana infestation was positively related to an increase in Greenness Index (GI), a simple metric relating to the amount of photosynthetically active green leaves. Furthermore, this relationship was observed in different forest types and during (2016), as well as after (2017–2019), an El Niño-induced drought. Using a neural network classification, we assessed liana infestation over time and showed an increase in the percentage of severely (>75%) liana infested pixels from 12.9% ± 0.63 (95% CI) in 2016 to 17.3% ± 2 in 2019. This implies that reports of increasing liana abundance may be more wide-spread than currently assumed. This is the first study to show that liana infestation can be accurately detected across closed-canopy tropical forests using satellite-based imagery. Furthermore, the detection of liana infestation during both dry and wet years and across forest types suggests this method should be broadly applicable across tropical forests. This work therefore advances our ability to explore the drivers responsible for patterns of liana infestation at multiple spatial and temporal scales and to quantify liana-induced impacts on carbon dynamics in tropical forests globally.