Forest resilience research using remote sensing and GIS - A systematic literature review

Abstract

Forest resilience assessment is increasingly important given the current global environmental change. However, attributes and indicators to quantify forest resilience still need to be explored. Remote sensing (RS) and Geographical Information System (GIS) techniques are widely applied for forest resilience modeling. A bibliometric analysis was conducted to obtain insights concerning methods for quantifying forest resilience using RS/GIS. VosViewer and Bibliometrix R software were applied to analyze 117 articles from the Web of Science global database covering a period of 2011-2021. Using inclusion-exclusion criteria, 31 studies were examined, covering local, regional, and transnational ecosystem types. Satellite devices were used in 28 studies, whilst GIS dataset frameworks were used in the remaining studies. Multiple satellites and sensors were preferable to maximize results for modeling forest resilience. To estimate resilience, ecological attributes (above-ground biomass, tree-ring, and basal area increments) and remote-sensing derived indicators (vegetation indices, forest cover changes, deforestation rates, and forest productivity) were analyzed using conventional statistical tests or machine learning techniques. Studies combined experiments, observations, and process-based models demonstrated better results. Scale and resolution, indicator uncertainty, and data availability were among the constraints reported using RS/GIS. Therefore, a standardized framework for forest resilience assessment incorporating field observation with RS/GIS is needed.