Lluís Pesquer

Parallel ordinary kriging interpolation incorporating automatic variogram fitting

This work introduces a methodology for reducing the execution time of the kriging interpolation method without losing the quality of the model results, as occurs in simplified moving neighborhood solutions. The proposed solution distributes the computation applying parallel programming using MPI (Message Passing Interface) libraries in a HPC (High Performance Computing) environment. For the solution to be automatic and adaptable to different spatial patterns the variogram was automatically fitted; this preliminary modeling step is usually interactive in this interpolation method. The experimental results show the validity of the implemented solution, as it significantly reduces (in one of the examples the execution time decreases from 2h 38min to only 3min) the final execution time of the entire process. The proposed solution is not exclusive to a particular architecture or operating system and can be applied in various environments and spatial resolutions of the generated raster model as well as at different magnitudes of the data to be interpolated.

Spatial pattern alterations from JPEG2000 lossy compression of remote sensing images: massive variogram analysis in high performance computing

Abstract We evaluate the implications of JPEG2000 lossy compression of remote sensing images for spatial analytical purposes. The main issue is to identify which cases and conditions in geostatistical studies are suitable for using lossy compressed images. For these purposes, an extensive test using Landsat, compact airborne spectrographic imager (CASI), and moderate resolution imaging spectroradiometer (MODIS) image series has been analyzed, through applying and comparing two-dimensional and three-dimensional (spectral and time domains) compression methods with a wide range of compression ratios for several dates, different landscape regions, and spectral bands. Due to the massive test bed and consequently to the high time consuming executions, a parallel solution was specifically developed. Variogram analyses showed that all the compression ratios maintain the variogram shapes, but high compression ratios ( > 20 ∶ 1 ) degrade the spatial patterns of the remote sensing images. These alterations are lower for the three-dimensional compression method, which was a considerable improvement (25%) on the two-dimensional method for large three-dimensional series (CASI, MODIS). However, the two methods behave similarly in the Landsat case. Finally, the parallel solution in a distributed environment demonstrates that high performance computing offers a suitable scientific platform for highly demanding time execution applications, such as geostatistical analyses of remote sensing images.

Radiometric Correction of Simultaneously Acquired Landsat-7/Landsat-8 and Sentinel-2A Imagery Using Pseudoinvariant Areas (PIA): Contributing to the Landsat Time Series Legacy

The use of Pseudoinvariant Areas (PIA) makes it possible to carry out a reasonably robust and automatic radiometric correction for long time series of remote sensing imagery, as shown in previous studies for large data sets of Landsat MSS, TM, and ETM+ imagery. In addition, they can be employed to obtain more coherence among remote sensing data from different sensors. The present work validates the use of PIA for the radiometric correction of pairs of images acquired almost simultaneously (Landsat-7 (ETM+) or Landsat-8 (OLI) and Sentinel-2A (MSI)). Four pairs of images from a region in SW Spain, corresponding to four different dates, together with field spectroradiometry measurements collected at the time of satellite overpass were used to evaluate a PIA-based radiometric correction. The results show a high coherence between sensors (r2 = 0.964) and excellent correlations to in-situ data for the MiraMon implementation (r2 > 0.9). Other methodological alternatives, ATCOR3 (ETM+, OLI, MSI), SAC-QGIS (ETM+, OLI, MSI), 6S-LEDAPS (ETM+), 6S-LaSRC (OLI), and Sen2Cor-SNAP (MSI), were also evaluated. Almost all of them, except for SAC-QGIS, provided similar results to the proposed PIA-based approach. Moreover, as the PIA-based approach can be applied to almost any image (even to images lacking of extra atmospheric information), it can also be used to solve the robust integration of data from new platforms, such as Landsat-8 or Sentinel-2, to enrich global data acquired since 1972 in the Landsat program. It thus contributes to the program’s continuity, a goal of great interest for the environmental, scientific, and technical community.

A Geostatistical Approach for Selecting the Highest Quality MODIS Daily Images

The aim of this work was to develop a new methodology for automatic selection of the highest quality MODIS daily images, MOD09GA Surface Reflectance product. The methodology developed here complements the quality assessment of MODIS products with a geostatistical analysis of spatial pattern images based on variogram tools. The resulting selection is formed by 26 high-quality images (from an initial dataset of 365) from throughout 2007. Most images with geometric distortion problems, such as the bow-tie effect, were rejected. The automatic selection was validated by comparing it to manual selection, which showed that it achieved an overall accuracy of 71.4%.

Improving Mean Minimum and Maximum Month-to-Month Air Temperature Surfaces Using Satellite-Derived Land Surface Temperature

Month-to-month air temperature (Tair) surfaces are increasingly demanded to feed quantitative models related to a wide range of fields, such as hydrology, ecology or climate change studies. Geostatistical interpolation techniques provide such continuous and objective surfaces of climate variables, while the use of remote sensing data may improve the estimates, especially when temporal resolution is detailed enough. The main goal of this study is to propose an empirical methodology for improving the month-to-month Tair mapping (minimum and maximum) using satellite land surface temperatures (LST) besides of meteorological data and geographic information. The methodology consists on multiple regression analysis combined with the spatial interpolation of residual errors using the inverse distance weighting. A leave-one-out cross-validation procedure has been included in order to compare predicted with observed values. Different operational daytime and nighttime LST products corresponding to the four months more characteristic of the seasonal dynamics of a Mediterranean climate have been considered for a thirteen-year period. The results can be considered operational given the feasibility of the models employed (linear dependence on predictors that are nowadays easily available), the robustness of the leave-one-out cross-validation procedure and the improvement in accuracy achieved when compared to classical Tair modeling results. Unlike what is considered by most studies, it is shown that nighttime LST provides a good proxy not only for minimum Tair, but also for maximum Tair. The improvement achieved by the inclusion of remote sensing LST products was higher for minimum Tair (up to 0.35 K on December), especially over forests and rugged lands. Results are really encouraging, as there are generally few meteorological stations in zones with these characteristics, clearly showing the usefulness of remote sensing to improve information about areas that are difficult to access or simply with a poor availability of conventional meteorological data.

Radiometric Correction of Landsat-8 and Sentinel-2A Scenes Using Drone Imagery in Synergy with Field Spectroradiometry

The main objective of this research is to apply unmanned aerial system (UAS) data in synergy with field spectroradiometry for the accurate radiometric correction of Landsat-8 (L8) and Sentinel-2 (S2) imagery. The central hypothesis is that imagery acquired with multispectral UAS sensors that are well calibrated with highly accurate field measurements can fill in the scale gap between satellite imagery and conventional in situ measurements; this can be possible by sampling a larger area, including difficult-to-access land covers, in less time while simultaneously providing good radiometric quality. With this aim and by using near-coincident L8 and S2 imagery, we applied an upscaling workflow, whereby: (a) UAS-acquired multispectral data was empirically fitted to the reflectance of field measurements, with an extensive set of radiometric references distributed across the spectral domain; (b) drone data was resampled to satellite grids for comparison with the radiometrically corrected L8 and S2 official products (6S-LaSRC and Sen2Cor-SNAP, respectively) and the CorRad-MiraMon algorithm using pseudo-invariant areas, such as reflectance references (PIA-MiraMon), to examine their overall accuracy; (c) then, a subset of UAS data was used as reflectance references, in combination with the CorRad-MiraMon algorithm (UAS-MiraMon), to radiometrically correct the matching bands of UAS, L8, and S2; and (d) radiometrically corrected L8 and S2 scenes obtained with UAS-MiraMon were intercompared (intersensor coherence). In the first upscaling step, the results showed a good correlation between the field spectroradiometric measurements and the drone data in all evaluated bands (R2 > 0.946). In the second upscaling step, drone data indicated good agreement (estimated from root mean square error, RMSE) with the satellite official products in visible (VIS) bands (RMSEVIS < 2.484%), but yielded poor results in the near-infrared (NIR) band (RMSENIR > 6.688% was not very good due to spectral sensor response differences). In the third step, UAS-MiraMon indicated better agreement (RMSEVIS < 2.018%) than the other satellite radiometric correction methods in visible bands (6S-LaSRC (RMSE < 2.680%), Sen2Cor-SNAP (RMSE < 2.192%), and PIA-MiraMon (RMSE < 3.130%), but did not achieve sufficient results in the NIR band (RMSENIR < 7.530%); this also occurred with all other methods. In the intercomparison step, the UAS-MiraMon method achieved an excellent intersensor (L8-S2) coherence (RMSEVIS < 1%). The UAS-sampled area involved 51 L8 (30 m) pixels, 143 S2 (20 m) pixels, and 517 S2 (10 m) pixels. The drone time needed to cover this area was only 10 min, including areas that were difficult to access. The systematic sampling of the study area was achieved with a pixel size of 6 cm, and the raster nature of the sampling allowed for an easy but rigorous resampling of UAS data to the different satellite grids. These advances improve human capacities for conventional Remote Sens. 2018, 10, 1687; doi:10.3390/rs10111687 www.mdpi.com/journal/remotesensing Remote Sens. 2018, 10, 1687 2 of 26 field spectroradiometry samplings. However, our study also shows that field spectroradiometry is the backbone that supports the full upscaling workflow. In conclusion, the synergy between field spectroradiometry, UAS sensors, and Landsat-like satellite data can be a useful tool for accurate radiometric corrections used in local environmental studies or the monitoring of protected areas around the world.

On the interest of the spectral bands in the automatic selection of high quality MODIS data through spatial pattern identification

MODIS (MODerate resolution Imaging Spectroradiometer) daily surface reflectance data is distributed with one of the most complete quality ancillary data sets. Such amount of quality information is essential for automatically selecting the highest quality MODIS daily images, for example using geostatistical analysis of the image spatial pattern. However, the success of this automatic selection certainly could depend on the spectral information of each MODIS band. This work studies the influence of MODIS spectral bands on the automatic identification of high quality daily images by analyzing their variogram and aiming at the identification of the most suitable spectral band (or band combination) for the spatial characterization of a given geographical region. The analysis tests the influence of each of the reflectance bands of the 2009 MOD09GA Daily Surface Reflectance product and the first component of its Principal Component Analysis over an area of 32 000 km2 , Catalonia (northeast of the Iberian Peninsula). Specifically, the combination of quality data and the variogram analysis allows the detection of different anomalies by the correspondence between the variability among the pixels and the fitted variogram parameters: nugget, sill and range. The variogram analysis is reaffirmed as an extremely useful approach for the automatic selection of high quality images while highlighting the need of high computational techniques for such huge processing. Finally, it reveals that is crucial to select the appropriate spectral band in order to, not only optimize, but substantially improve the automatic selection of remote sensing images using geostatistical analysis based on variogram tools.

Geostatistical analysis of Landsat-TM lossy compression images in a high-performance computing environment

The main goal of this study is to characterize the effects of lossy image compression procedures on the spatial patterns of remotely sensed images, as well as to test the performance of job distribution tools specifically designed for obtaining geostatistical parameters (variogram) in a High Performance Computing (HPC) environment. To this purpose, radiometrically and geometrically corrected Landsat-5 TM images from April, July, August and September 2006 were compressed using two different methods: Band-Independent Fixed-Rate (BIFR) and three-dimensional Discrete Wavelet Transform (3d-DWT) applied to the JPEG 2000 standard. For both methods, a wide range of compression ratios (2.5:1, 5:1, 10:1, 50:1, 100:1, 200:1 and 400:1, from soft to hard compression) were compared. Variogram analyses conclude that all compression ratios maintain the variogram shapes and that the higher ratios (more than 100:1) reduce variance in the sill parameter of about 5%. Moreover, the parallel solution in a distributed environment demonstrates that HPC offers a suitable scientific test bed for time demanding execution processes, as in geostatistical analyses of remote sensing images.

Quality analysis in N-dimensional lossy compression of multispectral remote sensing time series images

This work aims to determine an efficient procedure (balanced between quality and compression ratio) for compressing multispectral remote sensing time series images in a 4-dimensional domain (2 spatial, 1 spectral and 1 temporal dimension). The main factors studied were: spectral and temporal aggregation, landscape type, compression ratio, cloud cover, thermal segregation and nodata regions. In this study, the authors used three-dimensional Discrete Wavelet Transform (3d-DWT) as the compression methodology, implemented in the Kakadu software with the JPEG2000 standard. This methodology was applied to a series of 2008 Landsat-5 TM images that covered three different landscapes, and to one scene (19-06-2007) from a hyperspectral CASI sensor. The results show that 3d-DWT significantly improves the quality of the results for the hyperspectral images; for example, it obtains the same quality as independently compressed images at a double compression ratio. The differences between the two compression methodologies are smaller in the Landsat spectral analysis than in the CASI analysis, and the results are more irregular depending on the factor analyzed. The time dimensional analysis for the Landsat series images shows that 3d-DWT does not improve on band-independent compression.