scholarly journals Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy

2014 ◽  
Vol 10 (7) ◽  
pp. 20140347 ◽  
Author(s):  
Julien Pottier ◽  
Zbyněk Malenovský ◽  
Achilleas Psomas ◽  
Lucie Homolová ◽  
Michael E. Schaepman ◽  
...  
Keyword(s):  
Plant Species ◽  
Species Distribution ◽  
Imaging Spectroscopy ◽  
Added Value ◽  
Leaf Water Content ◽  
Alpine Grasslands ◽  
Distribution Models ◽  
Area Index ◽  
Leaf Chlorophyll ◽  
Airborne Imaging

Remote sensing using airborne imaging spectroscopy (AIS) is known to retrieve fundamental optical properties of ecosystems. However, the value of these properties for predicting plant species distribution remains unclear. Here, we assess whether such data can add value to topographic variables for predicting plant distributions in French and Swiss alpine grasslands. We fitted statistical models with high spectral and spatial resolution reflectance data and tested four optical indices sensitive to leaf chlorophyll content, leaf water content and leaf area index. We found moderate added-value of AIS data for predicting alpine plant species distribution. Contrary to expectations, differences between species distribution models (SDMs) were not linked to their local abundance or phylogenetic/functional similarity. Moreover, spectral signatures of species were found to be partly site-specific. We discuss current limits of AIS-based SDMs, highlighting issues of scale and informational content of AIS data.

scholarly journals The distribution of woody species in relation to climate and fire in Yosemite National Park, California, USA

Fire Ecology ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Jan W. van Wagtendonk ◽  
Peggy E. Moore ◽  
Julie L. Yee ◽  
James A. Lutz
Keyword(s):  
Plant Species ◽  
Sierra Nevada ◽  
Species Distribution ◽  
National Park ◽  
Woody Plant ◽  
Fire Regime ◽  
Yosemite National Park ◽  
Distribution Models ◽  
Ecological Zones ◽  
Woody Plant Species

Abstract Background The effects of climate on plant species ranges are well appreciated, but the effects of other processes, such as fire, on plant species distribution are less well understood. We used a dataset of 561 plots 0.1 ha in size located throughout Yosemite National Park, in the Sierra Nevada of California, USA, to determine the joint effects of fire and climate on woody plant species. We analyzed the effect of climate (annual actual evapotranspiration [AET], climatic water deficit [Deficit]) and fire characteristics (occurrence [BURN] for all plots, fire return interval departure [FRID] for unburned plots, and severity of the most severe fire [dNBR]) on the distribution of woody plant species. Results Of 43 species that were present on at least two plots, 38 species occurred on five or more plots. Of those 38 species, models for the distribution of 13 species (34%) were significantly improved by including the variable for fire occurrence (BURN). Models for the distribution of 10 species (26%) were significantly improved by including FRID, and two species (5%) were improved by including dNBR. Species for which distribution models were improved by inclusion of fire variables included some of the most areally extensive woody plants. Species and ecological zones were aligned along an AET-Deficit gradient from cool and moist to hot and dry conditions. Conclusions In fire-frequent ecosystems, such as those in most of western North America, species distribution models were improved by including variables related to fire. Models for changing species distributions would also be improved by considering potential changes to the fire regime.

scholarly journals Correction: Oleas, N.H., et al. Muddy Boots Beget Wisdom: Implications for Rare or Endangered Plant Species Distribution Models. Diversity, 2019, 11, 10

Diversity ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 401
Author(s):  
Nora H. Oleas ◽  
Kenneth J. Feeley ◽  
Javier Fajardo ◽  
Alan W. Meerow ◽  
Jennifer Gebelein ◽  
...  
Keyword(s):  
Plant Species ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Endangered Plant ◽  
Distribution Models ◽  
Endangered Plant Species

An error on our paper [...]

scholarly journals Comparative analysis of different retrieval methods for mapping grassland leaf area index using airborne imaging spectroscopy

2015 ◽  
Vol 43 ◽  
pp. 19-31 ◽  
Author(s):  
Clement Atzberger ◽  
Roshanak Darvishzadeh ◽  
Markus Immitzer ◽  
Martin Schlerf ◽  
Andrew Skidmore ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Imaging Spectroscopy ◽  
Area Index ◽  
Airborne Imaging

scholarly journals EVALUATION OF VARIOUS SPECTRAL INPUTS FOR ESTIMATION OF FOREST BIOCHEMICAL AND STRUCTURAL PROPERTIES FROM AIRBORNE IMAGING SPECTROSCOPY DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 961-966
Author(s):  
L. Homolová ◽  
R. Janoutová ◽  
Z. Malenovský
Keyword(s):  
Imaging Spectroscopy ◽  
Beech Forest ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Spectroscopy Data ◽  
Area Index ◽  
The Czech Republic ◽  
Sensor Field ◽  
Airborne Imaging ◽  
Study Sites

In this study we evaluated various spectral inputs for retrieval of forest chlorophyll content (Cab) and leaf area index (LAI) from high spectral and spatial resolution airborne imaging spectroscopy data collected for two forest study sites in the Czech Republic (beech forest at Štítná nad Vláří and spruce forest at Bílý Kříž). The retrieval algorithm was based on a machine learning method – support vector regression (SVR). Performance of the four spectral inputs used to train SVR was evaluated: a) all available hyperspectral bands, b) continuum removal (CR) 645 – 710 nm, c) CR 705 – 780 nm, and d) CR 680 – 800 nm. Spectral inputs and corresponding SVR models were first assessed at the level of spectral databases simulated by combined leaf-canopy radiative transfer models PROSPECT and DART. At this stage, SVR models using all spectral inputs provided good performance (RMSE for Cab < 10 μg cm<sup>&minus;2</sup> and for LAI < 1.5), with consistently better performance for beech over spruce site. Since application of trained SVRs on airborne hyperspectral images of the spruce site produced unacceptably overestimated values, only the beech site results were analysed. The best performance for the Cab estimation was found for CR bands in range of 645 – 710 nm, whereas CR bands in range of 680 – 800 nm were the most suitable for LAI retrieval. The CR transformation reduced the across-track bidirectional reflectance effect present in airborne images due to large sensor field of view.

scholarly journals Application of consensus theory to formalize expert evaluations of plant species distribution models

2013 ◽  
Vol 17 (3) ◽  
pp. 528-542 ◽  
Author(s):  
Maarten van Zonneveld ◽  
Nora Castañeda ◽  
Xavier Scheldeman ◽  
Jacob van Etten ◽  
Patrick Van Damme
Keyword(s):  
Plant Species ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Consensus Theory

Species distribution models applied to plant species selection in forest restoration: are model predictions comparable to expert opinion?

New Forests ◽  
2014 ◽  
Vol 45 (5) ◽  
pp. 641-653 ◽  
Author(s):  
Aitor Gastón ◽  
Juan I. García-Viñas ◽  
Alfredo J. Bravo-Fernández ◽  
César López-Leiva ◽  
Juan A. Oliet ◽  
...  
Keyword(s):  
Plant Species ◽  
Expert Opinion ◽  
Species Distribution ◽  
Forest Restoration ◽  
Species Distribution Models ◽  
Species Selection ◽  
Distribution Models ◽  
Model Predictions

scholarly journals EVALUATION OF VARIOUS SPECTRAL INPUTS FOR ESTIMATION OF FOREST BIOCHEMICAL AND STRUCTURAL PROPERTIES FROM AIRBORNE IMAGING SPECTROSCOPY DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 961-966 ◽  
Author(s):  
L. Homolová ◽  
R. Janoutová ◽  
Z. Malenovský
Keyword(s):  
Imaging Spectroscopy ◽  
Beech Forest ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Spectroscopy Data ◽  
Area Index ◽  
The Czech Republic ◽  
Sensor Field ◽  
Airborne Imaging ◽  
Study Sites

In this study we evaluated various spectral inputs for retrieval of forest chlorophyll content (Cab) and leaf area index (LAI) from high spectral and spatial resolution airborne imaging spectroscopy data collected for two forest study sites in the Czech Republic (beech forest at Štítná nad Vláří and spruce forest at Bílý Kříž). The retrieval algorithm was based on a machine learning method – support vector regression (SVR). Performance of the four spectral inputs used to train SVR was evaluated: a) all available hyperspectral bands, b) continuum removal (CR) 645 – 710 nm, c) CR 705 – 780 nm, and d) CR 680 – 800 nm. Spectral inputs and corresponding SVR models were first assessed at the level of spectral databases simulated by combined leaf-canopy radiative transfer models PROSPECT and DART. At this stage, SVR models using all spectral inputs provided good performance (RMSE for Cab &lt; 10 μg cm&lt;sup&gt;&minus;2&lt;/sup&gt; and for LAI &lt; 1.5), with consistently better performance for beech over spruce site. Since application of trained SVRs on airborne hyperspectral images of the spruce site produced unacceptably overestimated values, only the beech site results were analysed. The best performance for the Cab estimation was found for CR bands in range of 645 – 710 nm, whereas CR bands in range of 680 – 800 nm were the most suitable for LAI retrieval. The CR transformation reduced the across-track bidirectional reflectance effect present in airborne images due to large sensor field of view.

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