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

Angela Lausch,Stefan Erasmi,Douglas King,Paul Magdon,Marco Heurich

doi:10.3390/rs9020129

Abstract

Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time- and labour-intensive. Long-term monitoring based on forest inventories provides valuable information about changes and trends of FH. However, abrupt short-term changes cannot sufficiently be assessed through in-situ forest inventories as they usually have repetition periods of multiple years. Furthermore, numerous FH indicators monitored in in-situ surveys are based on expert judgement. Remote sensing (RS) technologies offer means to monitor FH indicators in an effective, repetitive and comparative way. This paper reviews techniques that are currently used for monitoring, including close-range RS, airborne and satellite approaches. The implementation of optical, RADAR and LiDAR RS-techniques to assess spectral traits/spectral trait variations (ST/STV) is described in detail. We found that ST/STV can be used to record indicators of FH based on RS. Therefore, the ST/STV approach provides a framework to develop a standardized monitoring concept for FH indicators using RS techniques that is applicable to future monitoring programs. It is only through linking in-situ and RS approaches that we will be able to improve our understanding of the relationship between stressors, and the associated spectral responses in order to develop robust FH indicators.

Highlights

There is a growing awareness of the multitude of ecosystem services that forests provide and their importance for the wellbeing of humans and conservation of the environment
International Plant Phenotyping Network [72]: (1) Innovative non-invasive techniques such as stereo systems, hyperspectral, RGB, thermal, fluorescence cameras, laser scanners or X-ray tomography; (2) Continuous, very high temporal resolution acquisition of phenotypical traits that provides important reference information for Remote sensing (RS) approaches; (3) spectral traits/spectral trait variations (ST/spectral trait variations (STV)) are saved in databases; (4) Data can be used for calibration and validation of air- and spaceborne RS data
Useful in recording and understanding important processes of soil-plant-atmosphere interactions in tropical montane cloud forests in Brazil, key forest ecosystem processes such as transpiration, carbon uptake and storage, and water stripping from clouds that are affected by climatic variation and the temporal and spatial forest structure

Summary

Introduction

There is a growing awareness of the multitude of ecosystem services that forests provide and their importance for the wellbeing of humans and conservation of the environment. Figure is protein, waterbeing content, leaf area index (LAI) or plant such as photosynthesis They range from spectral analyses of forest species to terrestrial wireless sensor networks as difficult compared to sampling of ground vegetation. They range from spectral analyses of forest species to terrestrial wireless sensor networks as summarized in Figure 2 and Table 2. Phenotypical investigation of woody plants should be augmented in the future, in order to be able to better understand the mechanisms and stress factor interactions involved when interpreting FH indicators observed using RS It is these kinds of comparisons that support research on the effects of the Bidirectional Reflectance Distribution Function (BRDF), scale and different RS platforms as well as different sensor characteristics on model design [69].

Main Findings

Close-Range RS Approaches—Towers

Trends in Air-and Space-Borne RS for Assessing FH

Multi-Sensor Approaches

Findings

Conclusions