ABSTRACT

Climate and land-use change profoundly affect plant species distribution (SD) and composition, and the impact of these processes is expected to increase in the coming years. As a proxy of global changes, knowledge of SD and diversity along climatic gradients is essential to determine the efforts needed for species conservation. Plant spectral diversity is an emerging approach used as a proxy for species diversity based on remote sensing. Thus, the research aim was to develop a comprehensive methodology based on spectral diversity for SD and richness mapping and to study their relations with environmental and human-derived factors, demonstrated along Mediterranean to semi-arid climatic gradient. The study addresses two main knowledge gaps regarding spectral diversity: (1) improving the accuracy of woody species classification by features extraction and selection, and by using texture analysis in an ecosystem characterized by high spatial variability and relatively small-sized and sparse woody vegetation; and (2) developing a better estimate of the local species ‎richness and their response to environmental and human-derived factors (i.e. climate, topography, substrate, and land cover factors) across a transition zone between Mediterranean woodlands and semi-arid dwarf shrublands. A hyperspectral image was acquired for a 43-km strip along the study area using an airborne flight of AISA-FENIX (380–2500 nm, 420 bands) at the end of the 2017 rainy season. The dominant species were surveyed, with a total number of 247 trees and shrubs, to train a machine learning support vector machine (SVM) classification for species distribution mapping, which yielded an overall accuracy of 86.1%. A feature extraction and selection methodology was developed, combining principal component analysis and neighborhood component analysis techniques, facilitating the identification of 33 spectral diagnostic bands out of 330 spectral bands. The classification accuracy was decreased by about 2% to 84.2% using only 33 spectral bands. The classification accuracy improved by about 7.1% for the seven large crown species (93.3%) by adding texture information. Later, the local species richness was calculated by utilizing the alpha diversity index (i.e. the Shannon Index) for 30-m grid cells and was tested in response to environmental (i.e. climate, substrate, and topography) and human-derived factors (i.e. land cover). The highest sensitivity to alpha diversity factors was mean annual precipitation, slope, and land surface temperature. The alpha diversity showed higher richness in the natural Mediterranean shrubland and the guarrigue located in the northern part of the climate gradient. We suggest that the approach presented here significantly improves the estimation of woody species distribution and diversity in areas characterized by high spatial heterogeneity along steep climatic gradients.

摘要

气候和土地利用变化深刻影响植物物种分布 (SD) 和组成，预计这些过程的影响在未来几年会增加。作为全球变化的代表，了解气候梯度上的 SD 和多样性对于确定物种保护所需的努力至关重要。植物光谱多样性是一种新兴的方法，用作基于遥感的物种多样性代理。因此，研究目的是开发一种基于光谱多样性的综合方法，用于 SD 和丰富度绘图，并研究它们与环境和人为因素的关系，在地中海到半干旱气候梯度上得到证明。该研究解决了关于光谱多样性的两个主要知识差距：(1) 在空间变异性大、木本植被相对较小稀疏的生态系统中，通过特征提取和选择、纹理分析提高木本物种分类的准确性；(2) 更好地估计当地物种丰富度及其对地中海林地和半干旱矮灌木地之间过渡地带的环境和人为因素（即气候、地形、基质和土地覆盖因素）的响应. 在 2017 年雨季结束时，使用 AISA-FENIX（380-2500 nm，420 波段）的空中飞行获取了沿研究区 43 公里长的带状高光谱图像。调查了优势种，共有乔木和灌木247株，训练用于物种分布映射的机器学习支持向量机 (SVM) 分类，其总体准确率为 86.1%。开发了一种特征提取和选择方法，结合主成分分析和邻域成分分析技术，有助于识别 330 个光谱带中的 33 个光谱诊断带。仅使用 33 个光谱带，分类准确度降低了约 2% 至 84.2%。通过添加纹理信息，七种大型树冠物种（93.3%）的分类精度提高了约 7.1%。随后，利用 30 米网格单元的 alpha 多样性指数（即香农指数）计算当地物种丰富度，并针对环境（即气候、基质和地形）和人为因素（即 土地覆盖）。对阿尔法多样性因子的最高敏感性是年平均降水量、坡度和地表温度。阿尔法多样性在位于气候梯度北部的天然地中海灌木地和瓜里格中表现出更高的丰富度。我们建议这里提出的方法显着改善了对沿陡峭气候梯度具有高度空间异质性的地区的木本物种分布和多样性的估计。