Mr. Jose Antonio Caparros-Santiago Profile

Jose Antonio Caparros-Santiago

at Univ of Seville

SPIE Involvement:

Author

Publications (4)

Proceedings Article | 12 September 2021 Presentation + Paper

Characterising the spring and autumn land surface phenology of Macaronesian species using Sentinel-2 data: the case of Canary Island

Lorenzo Quesada-Ruiz, Jose Caparros-Santiago, Miguel Garcia-Perez, Victor Rodriguez-Galiano

Proceedings Volume 11856, 1185608 (2021) https://doi.org/10.1117/12.2600125

KEYWORDS: Vegetation, Satellites, Ecosystems, Satellite imaging, Earth observing sensors, Multispectral imaging, High resolution satellite images

Read Abstract +

Land surface phenology (LSP), the study of seasonal dynamics of vegetation analysing phenological metrics -phenometrics- derived from vegetation indices time series (VI), has emerged as an important research focus in recent decades as LSP patterns have been considered as an important ecological indicator for understanding the functioning of terrestrial ecosystems. LSP from high-spatial-resolution satellite imagery in ecosystems with significant heterogeneity of plant species, such as Macaronesian ecosystems, are needed for a better understanding on how these ecosystems function. The objective of this study was to monitor LSP dynamics of representative species of the Canary Islands: Olea Cerasiformis, Pistacia atlantica, Juniperus turbinata, Pinus canariensis, Myrica Faya and Erica arborea. NDVI (Normalised Difference Vegetation Index) Sentinel-2 time series at a spatial and temporal resolution of 10 meters and 5 days were generated for the 2018-2020 period. Atmospheric disturbances and noise were reduced using a double-logistic function. SOS (start of the growing season), EOS (end of the growing season) and LOS (length of the growing season) were extracted using a threshold-based method. Thermophilus species, such as Olea Cerasiformis and Pistacia atlantica had the SOS in October-November and the EOS between June and July. Juniperus turbinata showed double seasonality in La Palma, being the first growing season between November-December and April-May and the second growing season between May-June and September-October. Growing season of Pinus canariensis started in September-October and ended in April-June, nevertheless a double seasonality was observed in some locations of Pinus canariensis, probably associated to the understory. Subtropical laurel forest composed by different plant species, such as Myrica Faya and Erica arborea, did not show a clear seasonality. The species-specific LSP patterns for the Canary Islands can contribute to stablishing a baseline to monitor future impacts of climate change in Macaronesian biogeographical region.

Proceedings Article | 12 September 2021 Presentation + Paper

A case of study of land surface phenology for CAP management: using Sentinel-2 data to obtain phenometrics for winter cereals in Andalusia, Spain

Miguel Garcia-Perez, Lorenzo Quesada-Ruiz, Jose Caparros-Santiago, Esperanza Sanchez-Rodriguez, Victor Rodriguez-Galiano

Proceedings Volume 11856, 1185607 (2021) https://doi.org/10.1117/12.2600114

KEYWORDS: Vegetation, Satellites, Climate change, Agriculture, Sensors, Remote sensing, Geographic information systems

Read Abstract +

Proceedings Article | 20 September 2020 Paper

Vegetation phenology dynamics across ecoregions of Iberian Peninsula from MODIS NDVI time series: 2001-2017

Jose Caparros-Santiago, Lorenzo Quesada-Ruiz, Miguel Perez-Garcia, Victor Rodriguez-Galiano

Proceedings Volume 11528, 115280K (2020) https://doi.org/10.1117/12.2573390

KEYWORDS: Vegetation, MODIS, Climate change, Carbon, Agriculture, Forestry, Ecosystems, Composites, Reflectivity, Spatial resolution

Read Abstract +

Vegetation phenology, the study of the timing of biological cycles of plants and their relation with environmental factors, is considered an important ecological indicator of climate change. Different ecological processes, such as water and carbon cycles, energy fluxes or species interactions, can be altered by changes in the phenological patterns of plants. Furthermore, these changes could also have important consequences on economy (e. g. agriculture, forestry). Iberian Peninsula is one of the regions with the greatest diversity of ecosystems in European continent. It is therefore an excellent natural laboratory for monitoring vegetation dynamics. The goal of this study was to monitor the vegetation phenology dynamics across ecoregions of the Iberian Peninsula for the period between 2001 and 2017. 782 8-day composites of NDVI (Normalised Difference Vegetation Index) images were produced from the surface reflectance product MOD09Q1 at a spatial resolution of 250 meters. A Savitzky-Golay filter was applied to smooth the NDVI time series and a threshold-based method was used to extract three phenometrics: the start of the growing season (SOS), the end of growing season (EOS) and the length of the growing season (LOS). Results of this research showed how both SOS and EOS are significantly different between the northern and southern ecoregions. Cantabrian mixed forests and Pyrenees conifer and mixed forests presented the latest SOS and EOS. Iberian conifer forests, Northwest Iberian montane forests and Northeastern Spain and Southern France Mediterranean forests showed the highest internal variability in the phenological dates, which may be related with the behaviour of different land covers (e. g., phenology of natural vegetation vs. crop phenology) and the altitude effects on climatic conditions (e. g. increases in precipitation, decrease of the temperature). Phenological patterns of the Iberian ecoregions could contribute to improve the understanding of the potential environmental effects of climate change.

Proceedings Article | 5 October 2017 Presentation + Paper

Discriminating the Mediterranean Pinus spp. using the land surface phenology extracted from the whole MODIS NDVI time series and machine learning algorithms

Victor Rodriguez-Galiano, David Aragones, Jose Caparros-Santiago, Rafael Navarro-Cerrillo

Proceedings Volume 10428, 104280U (2017) https://doi.org/10.1117/12.2278557

KEYWORDS: Coastal modeling, MODIS, Composites, Machine learning, Vegetation, Remote sensing, Animal model studies, Climatology, Satellite imaging, Satellites

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years