The Special Natural Reserve of Malpaís de Güímar is a recent volcanic enclave, colonized by typical vegetation formations of the basal floor of the south of Tenerife (Canary Islands, Spain). The beekeeping that is currently carried out in this area requires a reliable mapping of the flora that conforms it. This work analyses the potential of hyperspectral images of 10 cm, captured from a UAV in June 2018, to make a preliminary thematic map of the species of interest. The study focuses on a reduced area of 6 ha, in order to assess the feasibility of the methodology and then apply it to the whole of the protected area. The results of applying a traditional algorithm to two sets of spectral bands obtained from the original 150, between 400 and 1000 nm, were compared. The first, result of a study of spectral separability and the second from the Principal Components Analysis. The best-classified species was the Cardon with an omission error of 17.70% and a commission error of 12.26%.
Chestnuts have been part of the landscape and popular culture of the Canary Islands (Spain) since the sixteenth century.
Many crops of this species are in state of abandonment and an updated mapping for its study and evaluation is needed.
This work proposes the elaboration of this cartography using two satellite images of very high spatial resolution captured
on two different dates and representing well-differentiated phenological states of the chestnut: a WorldView-2 image of
March 10th, 2015 and a WorldView-3 image of May 12th, 2015 (without and with leaves respectively). Two study areas
were selected within the municipality of La Orotava (Tenerife Island). One of the areas contains chestnut trees dispersed
in an agricultural and semi-urban environment and in the other one, the specimens are grouped forming a forest merged
with Canarian pines and other species of Monteverde. The Maximum Likelihood (ML), the Artificial Neural Networks
(ANN) and the Spectral Angle Mapper (SAM) classification algorithms were applied to the multi-temporal image
resulting from the combination of both dates. The results show the benefits of using the multi-temporal image for
Pinolere with the ANN algorithm and for Chasna area with ML algorithm, in both cases providing an overall accuracy
close to 95%.
Regular updating of fuels maps is important for forest fire management. Nevertheless complex and time consuming field work is usually necessary for this purpose, which prevents a more frequent update. That is why the assessment of the usefulness of satellite data and the development of remote sensing techniques that enable the automatic updating of these maps, is of vital interest. In this work, we have tested the use of the spectral bands of OLI (Operational Land Imager) sensor on board Landsat 8 satellite, for updating the fuels map of El Hierro Island (Spain). From previously digitized map, a set of 200 reference plots for different fuel types was created. A 50% of the plots were randomly used as a training set and the rest were considered for validation. Six supervised and 2 unsupervised classification methods were applied, considering two levels of detail. A first level with only 5 classes (Meadow, Brushwood, Undergrowth canopy cover >50%, Undergrowth canopy cover <15%, and Xeric formations), and the second one containing 19 fuel types. The level 1 classification methods yielded an overall accuracy ranging from 44% for Parellelepided to an 84% for Maximun Likelihood. Meanwhile, level 2 results showed at best, an unacceptable overall accuracy of 34%, which prevents the use of this data for such a detailed characterization. Anyway it has been demonstrated that in some conditions, images of medium spatial resolution, like Landsat 8-OLI, could be a valid tool for an automatic upgrade of fuels maps, minimizing costs and complementing traditional methodologies.
Reliable and updated maps of vegetation in protected natural areas are essential for a proper management and conservation. Remote sensing is a valid tool for this purpose. In this study, a methodology based on a WorldView-2 (WV-2) satellite image and in situ spectral signatures measurements was applied to map the Canarian Monteverde ecosystem located in the north of the Tenerife Island (Canary Islands, Spain). Due to the high spectral similarity of vegetation species in the study zone, a Multiple Endmember Spectral Mixture Analysis (MESMA) was performed. MESMA determines the fractional cover of different components within one pixel and it allows for a pixel-by-pixel variation of endmembers. Two libraries of endmembers were collected for the most abundant species in the test area. The first library was collected from in situ spectral signatures measured with an ASD spectroradiometer during a field campaign in June 2015. The second library was obtained from pure pixels identified in the satellite image for the same species. The accuracy of the mapping process was assessed from a set of independent validation plots. The overall accuracy for the ASD-based method was 60.51 % compared to the 86.67 % reached for the WV-2 based mapping. The results suggest the possibility of using WV-2 images for monitoring and regularly updating the maps of the Monteverde forest on the island of Tenerife.
Reliable and updated maps of vegetation in protected natural areas are essential for a proper management and conservation. Remote sensing is a valid tool for this purpose. In this study, a methodology based on a WorldView-2 (WV-2) satellite image and in situ spectral signatures measurements was applied to map the Canarian Monteverde ecosystem located in the north of the Tenerife Island (Canary Islands, Spain). Due to the high spectral similarity of vegetation species in the study zone, a Multiple Endmember Spectral Mixture Analysis (MESMA) was performed. MESMA determines the fractional cover of different components within one pixel and it allows for a pixel-by-pixel variation of endmembers. Two libraries of endmembers were collected for the most abundant species in the test area. The first library was collected from in situ spectral signatures measured with an ASD spectroradiometer during a field campaign in June 2015. The second library was obtained from pure pixels identified in the satellite image for the same species. The accuracy of the mapping process was assessed from a set of independent validation plots. The overall accuracy for the ASD-based method was 60.51 % compared to the 86.67 % reached for the WV-2 based mapping. The results suggest the possibility of using WV-2 images for monitoring and regularly updating the maps of the Monteverde forest on the island of Tenerife.
The emergence of high-resolution satellites with new spectral channels and the ability to change its viewing angle has highlighted the importance of modeling the atmospheric effects. So, atmospheric correction serves a critical role in the processing of remotely sensed image data, particularly with respect to identification of pixel content. Efficient and accurate realization of images in units of reflectance, rather than radiance, has proven to be a crucial point in the pre-processing of images in remote sensing applications, acquired under a variety of measurement conditions. However, reflectance of the objects recorded by satellite sensors is generally affected by atmospheric absorption and scattering, sensor-targetillumination geometry, and sensor calibration. These normally result in distortion of the actual reflectance of the objects that subsequently affects the extraction of information from images. The use of atmospheric models has significantly improved the results of the corrections. In this study we have proceeded to make the atmospheric correction of the eight multispectral bands of high resolution WorldView-2 satellite by three different atmospherics models (COST, DOS, 6S) defining the geometry of the satellite observation, viewing angle and setting the weather conditions more suited for the acquired images of the study area (Granadilla, Canary Islands). For this purpose, the reflectance obtained by COST, DOS and 6S atmospheric correction techniques are compared with the Top of Atmosphere (TOA) reflectance. Specifically, the 6S atmospheric correction model, based on radiative transfer theory, provides patterns which describe properly atmospheric conditions in this specific study area for monitoring turbid coastal environments. To check the proper functioning of the atmospheric correction comparison was performed between ground-based measurements and corresponding obtained by the eight multispectral satellite channels through the 6S atmospheric model, with similar date, weather and lighting conditions.
Sea surface temperature can be estimated from infrared satellite radiances. The NOAA-AVHRR radiometer has been used to produce SST fields during the last decades. The aim of this study is to examine, with Pathfinder Oceans Matchup Database, the SST accuracy deviations that occur when we use in situ data from different latitudinal and longitudinal bands to compute, by linear regression, the NLSST algorithm coefficients. We have restricted the in situ SST to 8
latitudinal bands and 18 longitudinal bands. Applying the resulting coefficients of the equatorial region (20N to 20S) and from the polar zone to the global case, we have found that the mean of residuals (SST in situ minus estimated SST) is greater than -0.6°C and the standard deviation near to 1°C in both cases. When we use the coefficients from longitudinal bands in global data set, the residuals show a bias lower than -0.1°C in about the 80% of cases. We conclude that the mean errors for the longitudinal algorithms are small when compared to the latitudinal, but in both cases have a substantial dependence on the SST in situ.
The water vapor is an atmospheric component which is not evenly distributed. If we consider the total water vapor content in a vertical column, i.e. the precipitable water, W it may change from 0.5 g/cm2 for high latitudes to about 6 g/cm2 for equatorial zones. All that makes it impossible to know its distribution with only radiosondes data, because their validity is restricted to a few km2. In order to correctly characterize the water vapor content over the sea around the Canary Islands, we have used brightness temperatures of TOVS sensors onboard NOAA satellites. We have compared the total water vapor content obtained with radiosondes data, launched by Instituto Nacional de Meteorologia at Santa Cruz de Tenerife, with the nearest free-clouds data from satellite for a set of fifty days of 1994. The statistics generated with the comparison are shown and the validity of the humidity fields determined with only TOVS data are discussed.
In this work we study the complete evolution of an episode of Saharan dust invasion over Canary Island. To describe adequately this phenomena two magnitudes are calculated from NOAA-AVHRR satellite data: aerosols optical depth (AOD) in the channel 1 and the ratio channel 1-channel 2 (R12) which gives us rough information about the aerosols size distribution. The aerosols optical depth, calculated to 500 nm with a ground based sunphotometer, situated above the inversion layer is used too.
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