Advanced Earth observation techniques in forest biodiversity and carbon sequestration mapping

Abstract

Integrating multisource Earth observation (EO) data and methods allows studying forest biodiversity and carbon sequestration related questions at various spatial and temporal scales. We aim to develop and produce novel remotely sensed variables describing biodiversity and ecosystem properties using a multi-sensor approach. We utilize 1) optical satellite images (e.g. Sentinel, Landsat), 2) airborne laser scanning data, and 3) unmanned aircraft systems (UAS). Optical remote sensing covers large geographical areas at 10-30 m spatial resolution and temporal span of several decades. Laser scanning is a superb method to capture the 3D structure of forested ecosystems with sub-meter accuracy, and has been used in growing numbers to study wildlife habitats and biodiversity. Novel UAS methods contribute for bridging the gap between field and airborne measurements and providing ultra-high spatial and temporal resolution imagery for detailed assessment of different ecosystems properties. Because of the potential for rapid deployment, spatially explicit data from UASs can be acquired irrespective of many of the costs, scheduling, logistic and weather limitations to satellite or piloted aircraft missions. We will develop further on the concept of spectral traits (ST) in boreal environments1. Biotic traits, especially functional traits, are becoming increasingly important concept in ecology, conservation biology and sustainable resource management. They can be biochemical, physiological, morphological, structural, phenological or functional characteristics of plants, populations or communities. Spectral traits are traits that can be directly or indirectly recorded using remote sensing. Deriving spectral traits from various remote sensing data can provide detailed valuable information for biodiversity research. Calculating the spatial composition and configuration of spectral traits plays a crucial role in distinguishing different forest biotopes, communities and species, and linking biodiversity variables with ecosystem functioning and biogeochemical processes. In addition to optical remote sensing, the planned use of lidar data allows studying traits related to forest and vegetation structure in 3D. As a case study, multi-source remote sensing data will be collected from old-growth forest sites located in Central and Eastern Finland, where various field data (e.g. the occurrence of deadwood and polyporous fungi) are available. By combining these information, we aim at finding efficient remote sensing methods for mapping the indicators of forest biodiversity.

References 1. Lausch et al. 2016. Ecol. Ind. 70: 317–339.