Cloud Filtering Method Research Articles

Airborne LiDAR Point Cloud Filtering Method Based on Multiconstrained Connected Graph Segmentation

Airborne LiDAR Point Cloud Filtering Method Based on Multiconstrained Connected Graph Segmentation

An Elliptical Distance Based Photon Point Cloud Filtering Method in Forest Area

An Elliptical Distance Based Photon Point Cloud Filtering Method in Forest Area

Evapotranspiration Estimation in the Sahel Using a New Ensemble-Contextual Method

In many tropical areas, evapotranspiration is the most important but least known component of the water cycle. An innovative method, named E3S (for EVASPA S-SEBI Sahel), was developed to provide spatially-distributed estimates of daily actual evapotranspiration (ETd) from remote sensing data in the Sahel. This new method combines the strengths of a contextual approach that is used to estimate the evaporative fraction (EF) from surface temperature vs. albedo scatterograms and of an ensemble approach that derives ETd estimates from a weighted average of evapotranspiration estimated from several EF methods. In this work, the two combined approaches were derived from the simplified surface energy balance index (S-SEBI) model and the EVapotranspiration Assessment from SPAce (EVASPA) tool. Main innovative aspects concern (i) ensemble predictions of ETd through the implementation of a dynamic weighting scheme of several evapotranspiration estimations, (ii) epistemic uncertainty of the estimation of ETd from the analysis of the variability of evapotranspiration estimates, and (iii) a new cloud filtering method that significantly improves the detection of cloud edges that negatively affect EF determination. E3S was applied to MODIS/TERRA and AQUA datasets acquired during the 2005–2008 period over the mesoscale AMMA-CATCH (Analyse Multidisciplinaire de la Mousson Africaine—Couplage de l’Atmosphère Tropicale et du Cycle Hydrologique) observatory in South-West Niger. E3S estimates of instantaneous and daily available energy, evaporative fraction, and evapotranspiration were evaluated at a local scale based on two field-monitored plots representing the two main ecosystem types in the area—a millet crop and a fallow savannah bush. In addition to these ground-based observations, the local scale evaluation was performed against continuous simulations by a locally-calibrated soil-vegetation-atmosphere transfer model for the two plots. The RMSE (root mean square error) from this comparison for E3S’s ETd estimates from combined AQUA/TERRA sources was 0.5 mm·day−1, and the determination coefficient was 0.90. E3S significantly improved representation of the evapotranspiration seasonality, compared with a classical implementation of S-SEBI or with the original EVASPA’s non-weighted ensemble scheme. At the mesoscale, ETd estimates were obtained with an average epistemic uncertainty of 0.4 mm·day−1. Comparisons with the reference 0.25°-resolution GLEAM (global land evaporation Amsterdam model) product showed good agreement. These results suggested that E3S could be used to produce reliable continuous regional estimations at a kilometric resolution, consistent with land and water management requirements in the Sahel. Moreover, all these innovations could be easily transposed to other contextual approaches.

3D point cloud filtering method for pose measurement application of space non-cooperative targets

3D point cloud filtering method for pose measurement application of space non-cooperative targets

An Improved High-Resolution SST Climatology to Assess Cold Water Events off Florida

Cloud filters developed for high-resolution (1-km) Advanced Very High Resolution Radiometer (AVHRR) satellite-derived sea surface temperature (SST) observations are generally inadequate to capture extreme cold events. Such events impacted shallow waters in Florida Bay and other coastal regions in January 2010 with fatal consequences for large numbers of corals and associated organisms. Raw AVHRR images were reprocessed to understand whether historical knowledge of daily and interannual SST variations could be used to derive a practical cloud-filtering technique. This approach, however, misidentified valid water temperature pixels in nearly 20% of 2703 images collected during the month of January for each year between 1995 and 2010. To create an improved SST climatology, this cloud-filtering method was combined with manually delineated overrides of falsely masked regions. During the January 2010 cold event, this climatology indicated negative SST anomalies of up to 11.6°C in the Big Bend region and 14°C in Florida Bay, with high spatial heterogeneity throughout. Our findings highlight the need for improved autonomous cloud-masking techniques to detect cold events in near real time.