Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models
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 spe...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-02-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/9/2/129 |
| _version_ | 1818870348105383936 |
|---|---|
| author | Angela Lausch Stefan Erasmi Douglas J. King Paul Magdon Marco Heurich |
| author_facet | Angela Lausch Stefan Erasmi Douglas J. King Paul Magdon Marco Heurich |
| author_sort | Angela Lausch |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T12:05:35Z |
| format | Article |
| id | doaj.art-caa8e9220d3d4c28b708d9ee0f3a826e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-19T12:05:35Z |
| publishDate | 2017-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-caa8e9220d3d4c28b708d9ee0f3a826e2022-12-21T20:22:21ZengMDPI AGRemote Sensing2072-42922017-02-019212910.3390/rs9020129rs9020129Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data ModelsAngela Lausch0Stefan Erasmi1Douglas J. King2Paul Magdon3Marco Heurich4Department Computational Landscape Ecology, Helmholtz Centre for Environmental Research—UFZ, Permoserstr. 15, Leipzig D-04318, GermanyCartography GIS & Remote Sensing Section, Institute of Geography, Georg–August–University Göttingen, Goldschmidtstr. 5, Göttingen D-37077, GermanyGeomatics and Landscape Ecology Lab, Department of Geography and Environmental Studies, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, CanadaChair of Forest Inventory and Remote Sensing, Georg-August-University Göttingen, Büsgenweg 5, Göttingen D-37077, GermanyBavarian Forest National Park, Department of Conservation and Research, Freyunger Straße 2, Grafenau D-94481, GermanyStress 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.http://www.mdpi.com/2072-4292/9/2/129spectral traits (ST)spectral trait variations (STV)in-situremote sensing (RS) approachesplant phenomics facilitieswireless sensor networks (WSN)RADARopticalLiDARRS models |
| spellingShingle | Angela Lausch Stefan Erasmi Douglas J. King Paul Magdon Marco Heurich Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models Remote Sensing 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 |
| title | Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models |
| title_full | Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models |
| title_fullStr | Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models |
| title_full_unstemmed | Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models |
| title_short | Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models |
| title_sort | understanding forest health with remote sensing part ii a review of approaches and data models |
| topic | 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 |
| url | http://www.mdpi.com/2072-4292/9/2/129 |
| work_keys_str_mv | AT angelalausch understandingforesthealthwithremotesensingpartiiareviewofapproachesanddatamodels AT stefanerasmi understandingforesthealthwithremotesensingpartiiareviewofapproachesanddatamodels AT douglasjking understandingforesthealthwithremotesensingpartiiareviewofapproachesanddatamodels AT paulmagdon understandingforesthealthwithremotesensingpartiiareviewofapproachesanddatamodels AT marcoheurich understandingforesthealthwithremotesensingpartiiareviewofapproachesanddatamodels |