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Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data

机译:利用高光谱和模拟多光谱Sentinel-2数据区分热带森林类型,优势物种和功能行会的制图

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摘要

To answer new scientific and ecological questions and monitor multiple forest changes, a fine scale characterizationof these ecosystems is needed, and could imply the mapping of specific species, of detailed forest types, andof functional composition. This characterization can be now provided by the novel Earth Observation tools. Thisstudy aims to contribute to understanding the innovation in forest and ecological research that can be brought inby advanced remote sensing instruments, and proposes the guild mapping approach as a tool to efficiently monitor the varied tropical forest resources. We evaluated, in tropical Ghanaian forests, the ability of airbornehyperspectral and simulated multispectral Sentinel-2 data, and derived vegetation indices and textures, to: distinguishbetween two different forest types; to discriminate among selected dominant species; and to separatetrees species grouped according to their functional guilds: Pioneer, Non Pioneer Light Demanding, and Shade Bearer. We then produced guild classification maps for each area using hyperspectral data. Our results showedthat with both hyperspectral and simulated Sentinel-2 data these discrimination tasks can be successfully accomplished.Results also stressed the importance of texture features, especially if using the lower spectral and spatial Sentinel-2 resolution data, and highlighted the important role of the new Sentinel-2 data for ecological monitoring.Classification results showed a statistically significant improvement in overall accuracy using Support Vector Machine, over Maximum Likelihood approach. We proposed the functional guilds mapping as an innovativeapproach to: (i) monitor compositional changes, especially with respect to the effects of global climate changeon forests, and particularly in the tropical biome where the occurrence of hundreds of species prevents mapping activities at species level; (ii) support large-scale forest inventories. The imminent Sentinel-2 data could serve to open the road for the development of new concepts and methods in forestry and ecological research.
机译:为了回答新的科学和生态问题并监测多种森林的变化,需要对这些生态系统进行精细的表征,这可能意味着需要对特定物种,详细的森林类型和功能组成进行制图。现在可以通过新颖的地球观测工具提供此特征。这项研究旨在帮助理解先进的遥感仪器可以带来的森林和生态研究创新,并提出行会制图方法,作为有效监测各种热带森林资源的工具。我们评估了加纳热带森林中空中高光谱和模拟多光谱Sentinel-2数据的能力,并推导了植被指数和质地,以区分两种不同的森林类型。区分选定的优势种;并根据其功能行会将其分为以下几个树种:“先驱者”,“非先驱者”和“遮荫者”。然后,我们使用高光谱数据为每个区域生成行会分类图。我们的结果表明,利用高光谱和模拟的Sentinel-2数据均可成功完成这些识别任务。结果还强调了纹理特征的重要性,尤其是在使用较低光谱和空间的Sentinel-2分辨率数据的情况下,并强调了图像识别的重要作用。新的Sentinel-2数据用于生态监测。分类结果表明,与最大似然法相比,使用支持向量机在总体准确性上具有统计学上的显着提高。我们提出功能性行会制图作为一种创新方法,它是:(i)监测成分变化,尤其是在全球气候变化对森林的影响方面,尤其是在热带生物群落中,因为数百种物种的出现阻止了物种水平的制图活动; (ii)支持大型森林清单。即将到来的Sentinel-2数据可以为林业和生态学研究的新概念和方法的发展开辟道路。

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