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Remote Sens. 2017, 9(2), 129; doi:10.3390/rs9020129

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

1
Department Computational Landscape Ecology, Helmholtz Centre for Environmental Research—UFZ, Permoserstr. 15, Leipzig D-04318, Germany
2
Department of Geography, Lab for Landscape Ecology, Humboldt Universität zu Berlin, Rudower Chaussee 16, 12489 Berlin, Germany
3
Cartography GIS & Remote Sensing Section, Institute of Geography, Georg–August–University Göttingen, Goldschmidtstr. 5, Göttingen D-37077, Germany
4
Geomatics and Landscape Ecology Lab, Department of Geography and Environmental Studies, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
5
Chair of Forest Inventory and Remote Sensing, Georg-August-University Göttingen, Büsgenweg 5, Göttingen D-37077, Germany
6
Bavarian Forest National Park, Department of Conservation and Research, Freyunger Straße 2, Grafenau D-94481, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Lars T. Waser, Clement Atzberger, Richard Gloaguen and Prasad S. Thenkabail
Received: 11 September 2016 / Revised: 9 January 2017 / Accepted: 23 January 2017 / Published: 5 February 2017
(This article belongs to the Special Issue Remote Sensing of Forest Health)
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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. View Full-Text
Keywords: spectral traits (ST); spectral trait variations (STV); in-situ; remote sensing (RS) approaches; plant phenomics facilities; wireless sensor networks (WSN); RADAR; optical; LiDAR; RS models spectral traits (ST); spectral trait variations (STV); in-situ; remote sensing (RS) approaches; plant phenomics facilities; wireless sensor networks (WSN); RADAR; optical; LiDAR; RS models
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lausch, A.; Erasmi, S.; King, D.J.; Magdon, P.; Heurich, M. Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models. Remote Sens. 2017, 9, 129.

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