Evaluating the riparian forest quality index (QBR) in the Luchena River by integrating remote sensing, machine learning and GIS techniques

Abstract

The Water Framework Directive (WFD 20 0 0/60/EU) is a mandatory standard that aims to improve and protect water quality in Europe. It covers, among other issues, the need to establish particular reference conditions for assessing river ecosystems and defines the ecological status of water bodies and conserve the hydromorphological characteristics of rivers. The quality of riparian vegetation is an important component of stream status and contributes directly to a river’s ecological stability. QBR index (“Qualitat del Bosc de Rib- era”) is one of the most widely used methods of evaluating riparian quality. This paper presents a new methodological version of the QBR index (QBR-GIS) to assess the ecological status of riparian forests. For this purpose, we have considered the four major conceptual blocks of the QBR index (total vegetation cover, cover structure, cover quality and chan- nel alteration) using geographically referenced information, remote sensing and machine learning techniques. To obtain the cover quality indicator, several vegetation indices were calculated and a sensitivity analysis was performed. The QBR-GIS was validated from the results obtained from the QBR index. QBR-GIS provides greater reliability and objectivity in the results. Furthermore, it reduces the time spent on field visits and increases accuracy in obtaining the status of riparian quality. Furthermore, it is a useful tool for landscape planning and management, improved ability to apply the QBR Index to larger areas of the river catchment, resulting in more information on riparian quality.