Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics

Abstract

Anthropogenic stress and disturbance of forest ecosystems (FES) has been increasing at all scales from local to global. In rapidly changing environments, in-situ terrestrial FES monitoring approaches have made tremendous progress but they are intensive and often integrate subjective indicators for forest health (FH). Remote sensing (RS) bridges the gaps of these limitations, by monitoring indicators of FH on different spatio-temporal scales, and in a cost-effective, rapid, repetitive and objective manner. In this paper, we provide an overview of the definitions of FH, discussing the drivers, processes, stress and adaptation mechanisms of forest plants, and how we can observe FH with RS. We introduce the concept of spectral traits (ST) and spectral trait variations (STV) in the context of FH monitoring and discuss the prospects, limitations and constraints. Stress, disturbances and resource limitations can cause changes in FES taxonomic, structural and functional diversity; we provide examples how the ST/STV approach can be used for monitoring these FES characteristics. We show that RS based assessments of FH indicators using the ST/STV approach is a competent, affordable, repetitive and objective technique for monitoring. Even though the possibilities for observing the taxonomic diversity of animal species is limited with RS, the taxonomy of forest tree species can be recorded with RS, even though its accuracy is subject to certain constraints. RS has proved successful for monitoring the impacts from stress on structural and functional diversity. In particular, it has proven to be very suitable for recording the short-term dynamics of stress on FH, which cannot be cost-effectively recorded using in-situ methods. This paper gives an overview of the ST/STV approach, whereas the second paper of this series concentrates on discussing in-situ terrestrial monitoring, in-situ RS approaches and RS sensors and techniques for measuring ST/STV for FH.

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

“…Traits and trait variations in FES display specific spectral responses in RS data that are caused by the taxonomy, phenology and phylogenetics of forest species and communities as well as by natural and anthropogenic stressors [16,17]. The spectrum of responses ranges from additive reinforcing effects to contrary effects, reductive or even exclusive signals.…”

“…In the first part of this paper series, Lausch et al [16] provided a working definition of FH and reviewed FH indicators, as well as their importance and relevance for the development of FHM programs. They introduced the concept of Spectral Traits (ST) and spectral trait variations (STV) as a framework for FHM.…”

“…Multi-sensor approaches on airborne platforms can include hyperspectral, multispectral, TIR, RGB, LiDAR, active RADAR and passive microwave sensors to quantify spectral trait indicators of FH [198]. For example, the Goddard LiDAR hyperspectral and thermal (G-LiHT) airborne imaging system integrates a combination of lightweight and portable multi-sensors for measuring vegetation structure, foliar spectra and surface temperatures at a high spatial resolution (1 m) to map plant species composition, plant functional types, biodiversity, biomass as well as plant growth [16,136].…”

“…Depending on their sensor characteristics (spatial, radiometric, spectral, temporal or angular resolution), RS sensors can record specific ST/STV and thus discriminate between certain species, populations, communities, habitats and biomes of FES [16,136]. Reviews, advantages and limitations using multi-and hyperspectral RS data for monitoring FH and FES diversity have been conducted [16,136]. Therefore, in this section, we focus on innovative present and future optical and multi-sensor approaches to monitor FH.…”

“…Estimation of ST/STV of FH [14,16,138,139] Implementation of multi-sensor RS in modelling approaches improves the discrimination, quantification and accuracy when estimating ST/STV. [197] Multiple platforms for a given sensor type, e.g., the forthcoming Radarsat Constellation of three platforms provides potential for more frequent data acquisition.…”