Landscape history confounds the ability of the <scp>NDVI</scp> to detect fine‐scale variation in grassland communities

Löfgren, Oskar; Prentice, Honor C.; Moeckel, Thomas; Schmid, Barbara; Hall, Karin

doi:10.1111/2041-210x.13036

Cited by 9 publications

(12 citation statements)

References 41 publications

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“…Separating the roles played by different abiotic variables and history during grassland community assembly may be challenging (Fagan et al, ; Karlík & Poschlod, ; Löfgren, Prentice, Moeckel, Schmid, & Hall, ). For example, we can expect that species that have preferences for older grasslands will also show negative relationships with phosphorus — simply because older grasslands have lower levels of soil phosphorus than younger grasslands (Table ).…”

Section: Discussionmentioning

confidence: 99%

Grasslands ancient and modern: Soil nutrients, habitat age and their relation to Ellenberg N

Löfgren

Hall

Schmid

et al. 2020

J Vegetation Science

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Questions:To what extent does the long-term process of grassland succession reflect changes in nutrient availability or other effects of grassland history? Plant communities in ancient, semi-natural pastures include many species associated with nutrient-poor soils. However, semi-natural pasture communities can also develop on previously arable sites -as nutrient levels decline over time. In Europe, Ellenberg N-values represent species' overall nutrient preferences and are often used as a proxy for soil nutrient availability. But how well do N-values actually reflect species' relationships with measured nutrient concentrations during grassland succession?Location: A successional series of grazed, previously arable to ancient, grasslands on the Baltic island of Öland, Sweden. Methods:We collected data on community composition and soil nutrient (phosphorus, ammonium, nitrate) concentrations. We used Bayesian joint-community modelling to parameterize species' relationships with nutrients and grassland age, and quantified the relative contributions of the variables. Species responses were then compared with Ellenberg N-values. Results:Phosphorus was the best explanatory variable for most species. However, species occurrences were not simply explained by gradients in particular nutrients, but by combinations of different nutrients and grassland age. There was overall agreement between N-values and species' nutrient responses -although the occurrences of species with identical N-values may be explained by different nutrients.Species with high and low N-values represent more reliable nutrient indicators than intermediate-N species, but their occurrences also reflect other factors that, as with nutrients, depend on the grassland age. Conclusions:Our results confirm that Ellenberg N provides a robust indication of the overall nutrient preferences of individual grassland species. However, in grassland sites developing on previously arable land -where nutrient availability is strongly associated with habitat age -N-values may represent an integrated response not only to nutrients but also to other historical processes that drive grassland community assembly. |

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Section: Discussionmentioning

confidence: 99%

Grasslands ancient and modern: Soil nutrients, habitat age and their relation to Ellenberg N

Löfgren

Hall

Schmid

et al. 2020

J Vegetation Science

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“…The nine intensity values from Sentinel-2 were treated as a vector with 9 numeric elements. Using the raw intensity values instead of the traditional indices like NDVI (see e.g., reference [31]) has previously been shown to work well for this kind of modelling [22]. To investigate if the links between the imagery and the EIVs were influenced by habitat type, this information was also included as a predictor (categorical variable).…”

Section: Discussionmentioning

confidence: 99%

Predicting Abiotic Soil Characteristics Using Sentinel-2 at Nature-Management-Relevant Spatial Scales and Extent

Moeslund,

Damgaard

2024

Preprint

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Knowledge of local plant community characteristics is imperative for practical nature planning and management, and for understanding plant diversity and distribution drivers. Today, retrieving such data is only possible by fieldwork and is hence costly both in time and money. Here we used 9 bands from multispectral high-to-medium resolution (10–60 m) satellite data (Sentinel-2) and machine learning to predict the local vegetation plot characteristics at broad extent (approx. 30.000 km2) in terms of plants’ preferences for soil moisture, soil fertility, and pH, mirroring the levels of the corresponding actual soil factors. These factors are believed to be among the most important for local plant community composition. Our results showed that there are clear links between the Sentinel-2 data and plants abiotic soil preferences and using solely satellite data we achieved predictive powers between 26–59% improving to about 70% when habitat information was included as a predictor. This show that plants abiotic soil preferences can be detected quite well from space, but also that retrieving soil characteristics using satellites is complicated and that perfect detection of soil conditions using remote sensing – if at all possible – needs further methodological and data development.

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“…To determine key dates that facilitate the discrimination by phenology, we analyzed the separability of the vegetation classes across an NDVI time series from 2020 at a 10 m resolution (Figure 3). NDVI was selected due to its importance as a standard index for vegetation classifications [8,41]. The time series of NDVI averaged for the vegetation classes revealed three time periods defined as the early dry season (D1), from January to April.…”

Section: Selection Of Datesmentioning

confidence: 99%

Efficacy of Multiseason Sentinel-2 Imagery for Classifying and Mapping Grassland Condition

Guevara-Torres,

Facelli,

Ostendorf

2024

Journal of Sensors

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Assessing the condition of ecosystems is imperative for understanding their degree of degradation and managing their conservation. The increasing availability of remote sensing products offers unprecedented opportunities for mapping vegetation with high detail and accuracy. However, mapping complex ecosystems, like grasslands, remains challenging due to their heterogeneity in vegetation composition and structure. Furthermore, degraded ecosystems affected by invasive vegetation present different condition levels within vegetation classes, limiting the accuracy of classifications and condition assessment. Here, we evaluated the capacity of Sentinel-2 multispectral time series imagery as an input for classifying different levels of cover within a vegetation class to detect the subtle differences needed to assess the condition of degraded ecosystems. Our study was conducted in the iron-grasslands of South Australia, a perennial tussock grassland dominated by iron-grasses (Lomandra spp.) and severely affected by invasive annual grasses. We developed random forest models to discriminate classes defined by the cover of iron-grasses, wild oats (Avena barbata), and woodland (training points = 250). We tested the importance of data seasonality, spatial resolution, spectral bands, and vegetation indices. The combination of spatial, temporal, and spectral detail produced the best classification results. Random forest classifications performed best at 10 m resolution, suggesting that detailed resolution outweighs spectral detail for discriminating vegetation patterns in systems with high spatial heterogeneity. The model at 10 m resolution combining all periods and all variables (spectral bands and vegetation indices) produced a mean kappa coefficient of 56% and a mean overall accuracy of 67%. The dry season imagery and vegetation indices emerged as the most informative, suggesting that vegetation classes presented different phenological properties critical for their discrimination. Our study contributes to mapping complex ecosystems, facilitating the discrimination of different levels of condition in grasslands degraded by invasive species, and thus benefits the conservation of native grasslands and other ecosystems.

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Landscape history confounds the ability of the NDVI to detect fine‐scale variation in grassland communities

Cited by 9 publications

References 41 publications

Grasslands ancient and modern: Soil nutrients, habitat age and their relation to Ellenberg N

Grasslands ancient and modern: Soil nutrients, habitat age and their relation to Ellenberg N

Predicting Abiotic Soil Characteristics Using Sentinel-2 at Nature-Management-Relevant Spatial Scales and Extent

Efficacy of Multiseason Sentinel-2 Imagery for Classifying and Mapping Grassland Condition

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