Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data
Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data
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DOI：
10.1016/j.rse.2020.112122
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发表时间：
2021-01-01
影响因子：
13.5
通讯作者：
Malhi, Yadvinder
中科院分区：
文献类型：
--
作者：
Aguirre-Gutierrez, Jesus;Rifal, Sami;Malhi, Yadvinder
由于不断变化的环境条件和人类的直接影响，热带森林生态系统正在迅速转变。然而，我们不能充分理解，监测或模拟热带生态系统对环境变化的反应，而不捕捉物种丰富的热带地区的植物功能特征的高度多样性。如果不这样做，我们对生态系统对环境干扰的反应的理解就会过于简单化。需要创新的方法和数据产品来跟踪热带森林生态系统功能特征组成在时间和空间上的变化。本研究旨在通过将Sentinel-2衍生变量与使用标准化方法从热带地区2434棵树中收集的冠层功能性状的精确定位原位测量的独特数据集相结合，来跟踪关键功能性状。功能性状和植被普查收集了来自澳大利亚，巴西，秘鲁，加蓬，加纳和马来西亚，跨越四个热带大陆的国家的47个田间小区。绘制了胸径大于10 cm的树木个体的空间位置图，并记录了它们的冠层大小和形状。使用地理定位的树冠大小和形状数据，社区一级的性状值估计在相同的空间分辨率的哨兵-2图像（即10米像素）。然后，我们使用地理随机森林（GRF）在我们的地块上建模和预测功能性状。我们证明，关键的植物功能性状可以准确地预测整个热带地区使用高空间和光谱分辨率的哨兵2图像结合气候和土壤信息。图像纹理参数被认为是遥感信息的关键组成部分，用于预测热带森林和木本稀树草原的功能性状。叶片厚度（R ~ 2 = 0.52）对形态和结构性状的预测精度最高，叶片含碳量（R ~ 2 = 0.70）和最大光合速率（R ~ 2 = 0.67）对叶片化学和光合相关性状的预测精度最高。总体而言，最高的预测精度获得叶化学和光合特性相比，形态和结构特征。我们的方法为测绘、监测和了解地球上物种最丰富的生态系统中的生物多样性和生态系统变化提供了新的机会。
Tropical forest ecosystems are undergoing rapid transformation as a result of changing environmental conditions and direct human impacts. However, we cannot adequately understand, monitor or simulate tropical ecosystem responses to environmental changes without capturing the high diversity of plant functional characteristics in the species-rich tropics. Failure to do so can oversimplify our understanding of ecosystems responses to environmental disturbances. Innovative methods and data products are needed to track changes in functional trait composition in tropical forest ecosystems through time and space. This study aimed to track key functional traits by coupling Sentinel-2 derived variables with a unique data set of precisely located in-situ measurements of canopy functional traits collected from 2434 individual trees across the tropics using a standardised methodology. The functional traits and vegetation censuses were collected from 47 field plots in the countries of Australia, Brazil, Peru, Gabon, Ghana, and Malaysia, which span the four tropical continents. The spatial positions of individual trees above 10 cm diameter at breast height (DBH) were mapped and their canopy size and shape recorded. Using geo-located tree canopy size and shape data, community-level trait values were estimated at the same spatial resolution as Sentinel-2 imagery (i.e. 10 m pixels). We then used the Geographic Random Forest (GRF) to model and predict functional traits across our plots. We demonstrate that key plant functional traits can be accurately predicted across the tropicsusing the high spatial and spectral resolution of Sentinel-2 imagery in conjunction with climatic and soil information. Image textural parameters were found to be key components of remote sensing information for predicting functional traits across tropical forests and woody savannas. Leaf thickness ((R)2 = 0.52) obtained the highest prediction accuracy among the morphological and structural traits and leaf carbon content (R-2 = 0.70) and maximum rates of photosynthesis (R-2 = 0.67) obtained the highest prediction accuracy for leaf chemistry and photosynthesis related traits, respectively. Overall, the highest prediction accuracy was obtained for leaf chemistry and photosynthetic traits in comparison to morphological and structural traits. Our approach offers new opportunities for mapping, monitoring and understanding biodiversity and ecosystem change in the most species-rich ecosystems on Earth.