A multi-sensor, multi-scale approach to mapping tree mortality in woodland ecosystems

Abstract

Woodland ecosystems, dominant on nearly 4% of all terrestrial land globally, are faced with a variety of threats, including increasingly prolonged and severe droughts, invasive insect outbreaks, and the rapid spread of pathogens. While many remote sensing methods have been developed for the detection and quantification of mortality in forested environments, woodland ecosystems present unique challenges to accurately mapping tree die-off due to relatively lower canopy covers, smaller and irregularly-shaped tree crowns, and greater influence of understory vegetation and soil cover on reflectance. To address these challenges, we developed a multi-sensor, multi-scale approach combining the analytical strengths of centimeter-resolution unmanned aerial system imagery for interpreting individual tree-level mortality, airborne lidar for crown mapping and quantifying percent canopy mortality, and Landsat imagery for upscaling mortality estimates to a regional scale. This approach utilizes a new algorithm for delineating the shapes of small, irregular woodland tree crowns using lidar. To demonstrate the application of this method, we map the extent and severity of a recent tree mortality event in piñon-juniper (PJ) woodlands of southeastern Utah. Our results suggest that 39% of PJ in this region has experienced some level of mortality, with patches exceeding 50% mortality. An analysis of potential mortality drivers revealed that canopy cover, terrain, and recent winter precipitation conditions are most directly linked with mortality, although the explanatory power of the mortality driver model was low. Our approach demonstrates a methodology that could be used for tree mortality mapping and scaling in a variety woodland ecosystems, and can provide a strong basis for further ecophysiological, ecological, and carbon cycle studies involving woodland tree mortality.

Plant phenotyping relevance

複数センサーと新規アルゴリズムにより、個体・樹冠レベルの樹木枯死状態とその重症度を抽出・スケール化する方法が研究の中心である。

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