Sistema para el Seguimiento del funcionamiento de ecosistemas en la Red de Parques Nacionales de España mediante Teledetección

Cabello, Javier; Alcaraz‐Segura, Domingo; Reyes, Andrés; Lourenço, Patrícia; Requena, J. M.; Bonache, Jorge; Castillo, Pablo R.; Valencia, Santiago; Naya, Jorge; Ramı́rez, Lucı́a; Serrada, Jesús

doi:10.4995/raet.2016.5731

Cited by 11 publications

(11 citation statements)

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“…Ecological research based on spectral vegetation indices plays an important role in biodiversity conservation 315 (Cabello et al, 2012;Pettorelli, 2016Pettorelli, , 2018 and management (Pelkey et al, 2003;Cabello et al, 2016) and for the study of biodiversity and ecosystems responses to environmental changes (Alcaraz-Segura et al, 2017; Pérez-Luque et al 2015). In fact, numerous studies have demonstrated the usefulness of satellite image time series to evaluate the functional changes in ecosystems at regional scale (Alcaraz-Segura et al, 2010) and at the protected area level (Alcaraz-Segura et al, 2009;Lourenço et al, 2018).…”

Section: Data Applications For Research and Conservation / Example Ofmentioning

confidence: 99%

A remote sensing-based dataset to characterize the ecosystem functioning and functional diversity of a Biosphere Reserve: Sierra Nevada (SE Spain)

Cazorla

Cabello

Reyes

et al. 2020

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Abstract. Conservation Biology faces the challenge of safeguarding the ecological processes that sustain biodiversity. Characterization and evaluation of these processes can be carried out through attributes or functional traits related to the exchanges of matter and energy between vegetation and the atmosphere. Nowadays, the use of satellite imagery provides useful methods to produce a spatially continuous characterization of ecosystem functioning and processes at regional scales. Our dataset characterizes the patterns of ecosystem functioning in Sierra Nevada (Spain) from the vegetation greenness dynamics captured through the spectral vegetation index EVI (Enhanced Vegetation Index) since 2001 to 2018 (product MOD13Q1.006 from MODIS sensor). First, we provided three Ecosystem Functional Attributes (EFAs) (i.e., descriptors of annual primary production, seasonality, and phenology of carbon gains), as well as their integration into a synthetic mapping of Ecosystem Functional Types (EFTs). Second, we provided two measures of functional diversity: EFT richness and EFT rarity. Finally, in addition to the yearly maps, we calculated interannual summaries, i.e., means and inter-annual variabilities. Examples of research and management applications of these data sets are also included to highlight the value of EFAs and EFTs to improve the understanding and monitoring ecosystem processes across environmental gradients. The datasets are available in two open-source sites (PANGAEA: https://doi.pangaea.de/10.1594/PANGAEA.904575 (Cazorla et al. 2019) and http://obsnev.es/apps/efts_SN.html), and bring to scientists, managers and the general public valuable information on the first characterization of the functional diversity at ecosystem level developed in a Mediterranean hotspot. Sierra Nevada represents an exceptional ecology laboratory of field conditions, where a long-term monitoring (LTER) program was established 10 years ago. The data availability on biodiversity, climate, ecosystem services, hydrology, land-use changes and management practices from Sierra Nevada, will allow to explore the relationships between these other environmental data and ecosystem functional data that we provide in this work.

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Section: Data Applications For Research and Conservation / Example Ofmentioning

confidence: 99%

A remote sensing-based dataset to characterize the ecosystem functioning and functional diversity of a Biosphere Reserve: Sierra Nevada (SE Spain)

Cazorla

Cabello

Reyes

et al. 2020

Preprint

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“…We used one-year of cloud-free time series of FAPAR (see Section 2.2.1) to describe, classify, and map the intra-annual functional activity of the vegetation cover considering existing methodologies for functional characterisations of ecosystems [1,[4][5][6][7][8][9]11].…”

Section: Analysis and Characterisation Of Functional-trait Dynamicsmentioning

confidence: 99%

“…The characterisation of ecosystems based on functional-trait dynamics derived from remotely sensed data complements structural studies for describing the heterogeneity of ecosystems [1,3]. Several studies have been developed for characterising functional activity of vegetation cover at a regional scale [1,[4][5][6][7][8][9]11]. Most of these studies covered large regional scales, with remote sensors of medium and low spatial resolution (e.g., MODIS and NOAA/AVHRR), where seasonal dynamics were characterised using the NDVI or the Enhanced Vegetation Index (EVI) as proxies of the FAPAR.…”

Section: Analysis and Characterisation Of Functional-trait Dynamicsmentioning

confidence: 99%

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Characterisation of Functional-Trait Dynamics at High Spatial Resolution in a Mediterranean Forest from Sentinel-2 and Ground-Truth Data

et al. 2018

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The characterisation of functional-trait dynamics of vegetation from remotely sensed data complements the structural characterisation of ecosystems. In this study we characterised for the first time the spatial heterogeneity of the intra-annual dynamics of the fraction of absorbed photosynthetically active radiation (FAPAR) as a functional trait of the vegetation in Prades Mediterranean forest in Catalonia, Spain. FAPAR was derived from the Multispectral Instrument (MSI) on the Sentinel-2 satellite and validated by comparison with the ground measurements acquired in June 2017 at the annual peak of vegetation activity. The validation results showed that most of points were distributed along the 1:1 line, with no bias nor scattering: R2 = 0.93, p < 0.05; with a root mean square error of 0.03 FAPAR (4.3%). We classified the study area into nine vegetation groups with different dynamics of FAPAR using a methodology that is objective and repeatable over time. This functional classification based on the annual magnitude (FAPAR-M) and the seasonality (FAPAR-CV) from the data on one year (2016–2017) complements structural classifications. The internal heterogeneity of the FAPAR dynamics in each land-cover type is attributed to the environmental and to the specific species composition variability. A spatial autoregressive (SAR) model for the main type of land cover, evergreen holm oak forest (Quercus ilex), indicated that topographic aspect, slope, height, and the topographic aspect x slope interaction accounted for most of the spatial heterogeneity of the functional trait FAPAR-M, thus improving our understanding of the explanatory factors of the annual absorption of photosynthetically active radiation by the vegetation canopy for this ecosystem.

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“…En este contexto se ha utilizado la teledetección, que ha demostrado ser efectiva en análisis de diferentes coberturas: vegetación (Nie et al, 2018), agua (Di Vittorio y Georgakakos, 2018) y suelo (Bansal et al, 2018), ya que ofrece herramientas para caracterizar y seguir el funcionamiento de los ecosistemas, debido a que proporciona índices que informan reiteradamente de los intercambios de materia y energía que tienen lugar entre la biota y la superficie terrestre (Cabello et al, 2016); es decir, que las imágenes satelitales tienen el potencial de proveer valiosa información para la gestión y conservación de los bofedales (García y Lleellish, 2012). En este sentido las imágenes Landsat se han utilizado en distintos estudios de humedales, debido a que es un sistema de satélites diseñados y operados para observar repetidas veces la cubierta de la Tierra con una resolución moderada (Halabisky et al, 2016).…”

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Estudio de bofedales en los Andes ecuatorianos a través de la comparación de imágenes Landsat-8 y Sentinel-2

et al. 2019

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<p>The objective of the present study was to compare the Landsat-8 and Sentinel-2 images to calculate the wetland´s extension, distribution and degree of conservation, in Reserva de Producción de Fauna Chinborazo (RPFCH) protected area located in the Andean region of Ecuador. This process was developed with in situ work in 16 wetlands, distributed in different conservation levels. The Landsat-8 and Sentinel-2 images were processed through a radiometric calibration (restoration of lost lines or píxels and correction of the stripe of the image) and an atmospheric correction (conversion of the digital levels to radiance values), to later calculate the Vegetation spectral indexes: NDVI, SAVI (L = 0.5) where L is a constant of the soil brightness component, EVI2 (improved vegetation index 2), NDWI (standard difference water index), WDRI (wide dynamic range vegetation index) and the Red Edge model that only this one has in Sentinel-2 in this study. Making a classification of the Bofedal ecosystem in satellite images by applying Random Forest, the most important variables with Landsat-8 were EVI2 (37.72%) and SAVI with L = 0.5 (30.97%), while with Sentinel-2 the most important variables correspond to the Red Edge (38.54%) and WDRI (27.06%). With the indices calculated, two categories of analysis were determined: a) wetland integrated by the levels: intervened [1], moderately conserved [2] and conserved [3] and b) other than wetland [4] integrated by areas that do not correspond to this ecosystem. Landsat-8 shows that the percentage of correct classifications of píxels belonging to the wetland category corresponds to: [1] 72.76%, [2] 58.38%, [3] 68.42%, while for the category other [4] were correct 95.15%. With Sentinel-2, the percentage of correct classifications corresponds to [1] 95.00%, [2] 82.60%, [3] 96.25%, while for the category other [4] the correct answers were 98.13%. In this way with Landsat-8 the wetland corresponds to 21.708,54 ha (41.21%), while with Sentinel-2 the wetland represents a total of 20,518 ha (38.95%), of the 52,560 ha that belong to the RPFCH, concluding that Sentinel-2, due to its better spatial resolution, and the incorporation of its new bands in Red Edge, obtains better results in image classification.</p>

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Sistema para el Seguimiento del funcionamiento de ecosistemas en la Red de Parques Nacionales de España mediante Teledetección

Cited by 11 publications

References 25 publications

A remote sensing-based dataset to characterize the ecosystem functioning and functional diversity of a Biosphere Reserve: Sierra Nevada (SE Spain)

A remote sensing-based dataset to characterize the ecosystem functioning and functional diversity of a Biosphere Reserve: Sierra Nevada (SE Spain)

Characterisation of Functional-Trait Dynamics at High Spatial Resolution in a Mediterranean Forest from Sentinel-2 and Ground-Truth Data

Estudio de bofedales en los Andes ecuatorianos a través de la comparación de imágenes Landsat-8 y Sentinel-2

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