Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives

Lausch, Angela; Bannehr, Lutz; Beckmann, Michael; Boehm, Christoph; Feilhauer, Hannes; Hacker, Jörg; Heurich, Marco; Jung, András; Klenke, Reinhard; Neumann, Christoph; Pause, Marion; Rocchini, Duccio; Schaepman, Michael E.; Schmidtlein, Sebastian; Schulz, Karsten; Selsam, Peter; Settele, Josef; Skidmore, Andrew K.; Cord, Anna F.

doi:10.1016/j.ecolind.2016.06.022

Cited by 150 publications

(171 citation statements)

References 273 publications

(245 reference statements)

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“…• RS is a physically-based system that can only directly or indirectly record the Spectral Traits (ST) and spectral trait variations (STV)" [16] in FES [136,262].…”

Section: Discussionmentioning

confidence: 99%

“…Although a footprint size of 25 m will not enable the high resolution mapping that we are familiar with from small footprint LiDAR, the scientific objectives of the mission include modelling finer scale structure [133]. A combination of Landsat, EnMAP, FLEX, HySPIRI and GEDI LiDAR will combine structural and spectral information and improve the modelling, prediction and understanding of FH [134][135][136][137][138][139]. Examples of the main findings from LiDAR studies in forest analysis are given in Table 6.…”

Section: Light Detection and Ranging (Lidar)mentioning

confidence: 99%

“…Various studies have shown that the implementation of multi-sensor RS approaches improves the discrimination of ST/STV over time and thus the accuracy of estimation of FH indicators [14,[134][135][136][137]190]. 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].…”

Section: Multi-sensor Approachesmentioning

confidence: 99%

“…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.…”

Section: Multi-sensor Approachesmentioning

confidence: 99%

“…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].…”

Section: Multi-sensor Approachesmentioning

confidence: 99%

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

et al. 2017

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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.

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“…• RS is a physically-based system that can only directly or indirectly record the Spectral Traits (ST) and spectral trait variations (STV)" [16] in FES [136,262].…”

Section: Discussionmentioning

confidence: 99%

Section: Light Detection and Ranging (Lidar)mentioning

confidence: 99%

Section: Multi-sensor Approachesmentioning

confidence: 99%

Section: Multi-sensor Approachesmentioning

confidence: 99%

Section: Multi-sensor Approachesmentioning

confidence: 99%

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

et al. 2017

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Multiscale mapping of species diversity under changed land use using imaging spectroscopy

Paz‐Kagan

Caras

Herrmann

et al. 2017

Ecological Applications

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Land use changes are one of the most important factors causing environmental transformations and species diversity alterations. The aim of the current study was to develop a geoinformatics-based framework to quantify alpha and beta diversity indices in two sites in Israel with different land uses, i.e., an agricultural system of fruit orchards, an afforestation system of planted groves, and an unmanaged system of groves. The framework comprises four scaling steps: (1) classification of a tree species distribution (SD) map using imaging spectroscopy (IS) at a pixel size of 1 m; (2) estimation of local species richness by calculating the alpha diversity index for 30-m grid cells; (3) calculation of beta diversity for different land use categories and sub-categories at different sizes; and (4) calculation of the beta diversity difference between the two sites. The SD was classified based on a hyperspectral image with 448 bands within the 380-2500 nm spectral range and a spatial resolution of 1 m. Twenty-three tree species were classified with high overall accuracy values of 82.57% and 86.93% for the two sites. Significantly high values of the alpha index characterize the unmanaged land use, and the lowest values were calculated for the agricultural land use. In addition, high values of alpha indices were found at the borders between the polygons related to the "edge-effect" phenomenon, whereas low alpha indices were found in areas with high invasion species rates. The beta index value, calculated for 58 polygons, was significantly lower in the agricultural land use. The suggested framework of this study succeeded in quantifying land use effects on tree species distribution, evenness, and richness. IS and spatial statistics techniques offer an opportunity to study woody plant species variation with a multiscale approach that is useful for managing land use, especially under increasing environmental changes.

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Light detection and ranging explains diversity of plants, fungi, lichens, and bryophytes across multiple habitats and large geographic extent

Moeslund

Zlinszky

Ejrnæs

et al. 2019

Ecological Applications

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Effective planning and nature management require spatially accurate and comprehensive measures of the factors important for biodiversity. Light detection and ranging (LIDAR) can provide exactly this, and is therefore a promising technology to support future nature management and related applications. However, until now studies evaluating the potential of LIDAR for this field have been highly limited in scope. Here, we assess the potential of LIDAR to estimate the local diversity of four species groups in multiple habitat types, from open grasslands and meadows over shrubland to forests and across a large area (~43,000 km2), providing a crucial step toward enabling the application of LIDAR in practice, planning, and policy‐making. We assessed the relationships between the species richness of macrofungi, lichens, bryophytes, and plants, respectively, and 25 LIDAR‐based measures related to potential abiotic and biotic diversity drivers. We used negative binomial generalized linear modeling to construct 19 different candidate models for each species group, and leave‐one‐region‐out cross validation to select the best models. These best models explained 49%, 31%, 32%, and 28% of the variation in species richness (R 2) for macrofungi, lichens, bryophytes, and plants, respectively. Three LIDAR measures, terrain slope, shrub layer height and variation in local heat load, were important and positively related to the richness in three of the four species groups. For at least one of the species groups, four other LIDAR measures, shrub layer density, medium‐tree layer density, and variations in point amplitude and in relative biomass, were among the three most important. Generally, LIDAR measures exhibited strong associations to the biotic environment, and to some abiotic factors, but were poor measures of spatial landscape and temporal habitat continuity. In conclusion, we showed how well LIDAR alone can predict the local biodiversity across habitats. We also showed that several LIDAR measures are highly correlated to important biodiversity drivers, which are notoriously hard to measure in the field. This opens up hitherto unseen possibilities for using LIDAR for cost‐effective monitoring and management of local biodiversity across species groups and habitat types even over large areas.

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Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives

Cited by 150 publications

References 273 publications

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

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

Multiscale mapping of species diversity under changed land use using imaging spectroscopy

Light detection and ranging explains diversity of plants, fungi, lichens, and bryophytes across multiple habitats and large geographic extent

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