Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy

Pottier, Julien; Malenovský, Zbyněk; Psomas, Achilleas; Homolová, Lucie; Schaepman, Michael E.; Choler, Philippe; Thuiller, Wilfried; Guisan, Antoine; Zimmermann, Niklaus E.

doi:10.1098/rsbl.2014.0347

Cited by 29 publications

(23 citation statements)

References 17 publications

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“…), hyperspectral aerial images (Pottier et al. ) and spatial modelling (Aalto et al. ) show promise in estimating actual soil moisture at higher resolutions.…”

Section: Discussionmentioning

confidence: 99%

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What we use is not what we know: environmental predictors in plant distribution models

Mod

Scherrer

Luoto

et al. 2016

J Vegetation Science

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Aims The choice of environmental predictor variables in correlative models of plant species distributions (hereafter SDMs) is crucial to ensure predictive accuracy and model realism, as highlighted in multiple earlier studies. Because variable selection is directly related to a model's capacity to capture important species' environmental requirements, one would expect an explicit prior consideration of all ecophysiologically meaningful variables. For plants, these include temperature, water, soil nutrients, light, and in some cases, disturbances and biotic interactions. However, the set of predictors used in published correlative plant SDM studies varies considerably. No comprehensive review exists of what environmental predictors are meaningful, available (or missing) and used in practice to predict plant distributions. Contributing to answer these questions is the aim of this review. Methods We carried out an extensive, systematic review of recently published plant SDM studies (years 2010–2015; n = 200) to determine the predictors used (and not used) in the models. We additionally conducted an in‐depth review of SDM studies in selected journals to identify temporal trends in the use of predictors (years 2000–2015; n = 40). Results A large majority of plant SDM studies neglected several ecophysiologically meaningful environmental variables, and the number of relevant predictors used in models has stagnated or even declined over the last 15 yr. Conclusions Neglecting ecophysiologically meaningful predictors can result in incomplete niche quantification and can thus limit the predictive power of plant SDMs. Some of these missing predictors are already available spatially or may soon become available (e.g. soil moisture). However, others are not yet easily obtainable across whole study extents (e.g. soil pH and nutrients), and their development should receive increased attention. We conclude that more effort should be made to build ecologically more sound plant SDMs. This requires a more thorough rationale for the choice of environmental predictors needed to meet the study goal, and the development of missing ones. The latter calls for increased collaborative effort between ecological and geo‐environmental sciences.

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“…), hyperspectral aerial images (Pottier et al. ) and spatial modelling (Aalto et al. ) show promise in estimating actual soil moisture at higher resolutions.…”

Section: Discussionmentioning

confidence: 99%

“…; Pottier et al. ; He et al. ), and ecologists and ecological modellers should give more attention to collaborative research within the geo‐environmental sciences.…”

Section: Discussionmentioning

confidence: 99%

What we use is not what we know: environmental predictors in plant distribution models

Mod

Scherrer

Luoto

et al. 2016

J Vegetation Science

Self Cite

187

198

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show abstract

“…[21]). Hyperspectral data have also been used, in combination with topographic data, for predicting plant distributions in French and Swiss alpine grasslands [22]. To our knowledge, no studies have modeled the direct relationship between hyperspectral data and plant species diversity in northern European grasslands.…”

Section: Introductionmentioning

confidence: 99%

Airborne Hyperspectral Data Predict Fine-Scale Plant Species Diversity in Grazed Dry Grasslands

et al. 2016

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Semi-natural grasslands with grazing management are characterized by high fine-scale species richness and have a high conservation value. The fact that fine-scale surveys of grassland plant communities are time-consuming may limit the spatial extent of ground-based diversity surveys. Remote sensing tools have the potential to support field-based sampling and, if remote sensing data are able to identify grassland sites that are likely to support relatively higher or lower levels of species diversity, then field sampling efforts could be directed towards sites that are of potential conservation interest. In the present study, we examined whether aerial hyperspectral (414-2501 nm) remote sensing can be used to predict fine-scale plant species diversity (characterized as species richness and Simpson's diversity) in dry grazed grasslands. Vascular plant species were recorded within 104 (4 mˆ4 m) plots on the island of Öland (Sweden) and each plot was characterized by a 245-waveband hyperspectral data set. We used two different modeling approaches to evaluate the ability of the airborne spectral measurements to predict within-plot species diversity: (1) a spectral response approach, based on reflectance information from (i) all wavebands, and (ii) a subset of wavebands, analyzed with a partial least squares regression model, and (2) a spectral heterogeneity approach, based on the mean distance to the spectral centroid in an ordinary least squares regression model. Species diversity was successfully predicted by the spectral response approach (with an error of ca. 20%) but not by the spectral heterogeneity approach. When using the spectral response approach, iterative selection of important wavebands for the prediction of the diversity measures simplified the model but did not improve its predictive quality (prediction error). Wavebands sensitive to plant pigment content (400-700 nm) and to vegetation structural properties, such as above-ground biomass (700-1300 nm), were identified as being the most important predictors of plant species diversity. We conclude that hyperspectral remote sensing technology is able to identify fine-scale variation in grassland diversity and has a potential use as a tool in surveys of grassland plant diversity.

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“…These advances are evidenced by the increasing attempts to combine remote sensing variables and SDM frameworks [6,[22][23][24], mainly to proxy or describe structural or functional attributes of the landscape. In invasion ecology, this incorporation has focused primarily on obtaining predictive variables to improve spatial and temporal representations of species distribution [25].…”

Section: Introductionmentioning

confidence: 99%

Estimating Invasion Success by Non-Native Trees in a National Park Combining WorldView-2 Very High Resolution Satellite Data and Species Distribution Models

et al. 2017

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Invasion by non-native tree species is an environmental and societal challenge requiring predictive tools to assess invasion dynamics. The frequent scale mismatch between such tools and on-ground conservation is currently limiting invasion management. This study aimed to reduce these scale mismatches, assess the success of non-native tree invasion and determine the environmental factors associated to it. A hierarchical scaling approach combining species distribution models (SDMs) and satellite mapping at very high resolution (VHR) was developed to assess invasion by Acacia dealbata in Peneda-Gerês National Park, the only national park in Portugal. SDMs were first used to predict the climatically suitable areas for A. dealdata and satellite mapping with the random-forests classifier was then applied to WorldView-2 very-high resolution imagery to determine whether A. dealdata had actually colonized the predicted areas (invasion success). Environmental attributes (topographic, disturbance and canopy-related) differing between invaded and non-invaded vegetated areas were then analyzed. The SDM results indicated that most (67%) of the study area was climatically suitable for A. dealbata invasion. The onset of invasion was documented to 1905 and satellite mapping highlighted that 12.6% of study area was colonized. However, this species had only colonized 62.5% of the maximum potential range, although was registered within 55.6% of grid cells that were considerable unsuitable. Across these areas, the specific success rate of invasion was mostly below 40%, indicating that A. dealbata invasion was not dominant and effective management may still be possible. Environmental attributes related to topography (slope), canopy (normalized difference vegetation index (ndvi), land surface albedo) and disturbance (historical burnt area) differed between invaded and non-invaded vegetated area, suggesting that landscape attributes may alter at specific locations with Acacia invasion. Fine-scale spatial-explicit estimation of invasion success combining SDM predictions with VHR invasion mapping allowed the scale mismatch between predictions of invasion dynamics and on-ground conservation decision making for invasion management to be reduced. Locations with greater potential to suppress invasions could also be defined. Uncertainty in the invasion mapping needs to be accounted for in the interpretation of the results.

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Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy

Cited by 29 publications

References 17 publications

What we use is not what we know: environmental predictors in plant distribution models

What we use is not what we know: environmental predictors in plant distribution models

Airborne Hyperspectral Data Predict Fine-Scale Plant Species Diversity in Grazed Dry Grasslands

Estimating Invasion Success by Non-Native Trees in a National Park Combining WorldView-2 Very High Resolution Satellite Data and Species Distribution Models

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