Assignment of rainfall confidence values using multispectral satellite data at mid-latitudes: first results

The authors propose a new method for the assignment of rainfall confidences on a pixel basis using cloud properties derived from optical satellite data during daytime. This approach is based on the concept model that the probability for precipitation is a function of the liquid water path, which in turn can be computed using the satellite-retrieved cloud optical thickness and the cloud effective droplet radius. In order to evaluate the principal potential of this idea, scenes from the Terra-MODIS sensor during the severe European summer floods in 2002 have been analysed in order to derive a corresponding regression function that interlinks the liquid water path with the rainfall probability or better with the confidence that a pixel which is classified as raining does actually rain. A first evaluation against ground-based radar data during March 2004 shows good skill of this new method

Discriminating raining from non-raining clouds at mid-latitudes using multispectral satellite data

International audienceWe propose a new method for the delineation of precipitation using cloud properties derived from optical satellite data. This approach is not only sufficient for the detection of mainly convective driven precipitation by means of the commonly used connection between infrared cloud-top temperature and rainfall probability but enables the detection of stratiform precipitation (e.g., in connection with mid-latitude frontal systems). The scheme presented is based on the concept model, that precipitating clouds must have both a large enough vertical extent and large enough droplets. Therefore, we have analyzed Terra-MODIS scenes during the severe European summer floods in 2002 and retrieved functions for the computation of an auto-adaptive threshold value of the effective cloud droplet radius with respect to the corresponding optical thickness which links these cloud properties with rainfall areas on a pixel basis

Discriminating raining from non-raining clouds at mid-latitudes using Meteosat Second Generation daytime data

International audienceA new method for the delineation of precipitation during daytime using multispectral satellite data is proposed. The approach is not only applicable to the detection of mainly convective precipitation by means of the commonly used relation between infrared cloud top temperature and rainfall probability but enables also the detection of stratiform precipitation (e.g. in connection with mid-latitude frontal systems). The presented scheme is based on the conceptual model that precipitating clouds are characterized by a combination of particles large enough to fall, an adequate vertical extension (both represented by the cloud water path (cwp)), and the existence of ice particles in the upper part of the cloud. The technique considers the VIS0.6 and the NIR1.6 channel to gain information about the cloud water path. Additionally, the channel differences ?T8.7-10.8 and ?T10.8-12.1 are considered to supply information about the cloud phase. Rain area delineation is realized by using a minimum threshold of the rainfall confidence. To obtain a statistical transfer function between the rainfall confidence and the channel differences, the value combination of the four variables is compared to ground based radar data. The retrieval is validated against independent radar data not used for deriving the transfer function and shows an encouraging performance as well as clear improvements compared to existing optical retrieval techniques using only IR thresholds for cloud top temperature

First results on a process-oriented rain area classification technique using Meteosat Second Generation SEVIRI nighttime data

A new technique for process-oriented rain area classification using Meteosat Second Generation SEVIRI nighttime data is introduced. It is based on a combination of the Advective Convective Technique (ACT) which focuses on precipitation areas connected to convective processes and the Rain Area Delineation Scheme during Nighttime (RADS-N) a new technique for the improved detection of stratiform precipitation areas (e.g. in connection with mid-latitude frontal systems). The ACT which uses positive brightness temperature differences between the water vapour (WV) and the infrared (IR) channels (ΔT<sub>WV-IR</sub>) for the detection of convective clouds and connected precipitating clouds has been transferred from Meteosat First Generation (MFG) Metesoat Visible and Infra-Red Imager radiometer (MVIRI) to Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI). RADS-N is based on the new conceptual model that precipitating cloud areas are characterised by a large cloud water path (<i>cwp</i>) and the presence of ice particles in the upper part of the cloud. The technique considers information about both parameters inherent in the channel differences ΔT<sub>3.9-10.8</sub>, ΔT<sub>3.9-7.3</sub>, ΔT<sub>8.7-10.8</sub>, and ΔT<sub>10.8-12.1</sub>, to detect potentially precipitating cloud areas. All four channel differences are used to gain implicit knowledge about the <i>cwp</i>. ΔT<sub>8.7-10.8</sub> and ΔT<sub>10.8-12.1</sub> are additionally considered to gain information about the cloud phase. First results of a comparison study between the classified rain areas and corresponding ground based radar data for precipitation events in connection with a cold front occlusion show encouraging performance of the new proposed process-oriented rain area classification scheme

Discriminating raining from non-raining clouds at mid-latitudes using multispectral satellite data

International audience(CE, juge des référés, 11 févr. 2005, n° 276376, B. c/ AMF, Bull. Joly Bourse 2005.143, note M. Dimitrijevic et G. Dolidon ; Banque et droit, mai-juin 2005, p. 48, 1re esp., obs. H. de Vauplane et J.-J. Daigre ; CE, juge des référés, 12 mai 2005, n° 279011, Z. c/ AMF, Banque et droit, mai-juin 2005, p. 48, 2e esp., obs. H. de Vauplane et J.-J. Daigre ; CA Paris, 1re ch., sect. H, ord. du 19 avr. 2005, n° 05/07263, M. c/ Autorité des marchés financiers

Disturbance can slow down litter decomposition, depending on severity of disturbance and season: an example from Mount Kilimanjaro

Deforestation and land-use change affect ecosystem processes such as carbon cycling. Here, we present results from a litter decomposition experiment in six natural and six disturbed vegetation types along an elevation gradient of 3600 m on the southern slopes of Mount Kilimanjaro, Tanzania. We exposed litter bags with a standard material for up to 12 weeks each in two seasons. In the cold wet season we sampled the full elevation gradient and in the warm wet season we repeated the sampling in the lower part of the elevation gradient. Though we found significantly negative effects of disturbance in forest ecosystems, this was only due to differences between natural and burned Podocarpus forests. Disturbance characterized by a more open vegetation structure in many of the studied vegetation types had no general effect when we studied the full elevation gradient; this also included non-forest vegetation types. Land-use intensity had a significant negative effect on decomposition rates but only in the warm wet season, not in the cold wet season. Temperature and humidity were the most important drivers of decomposition overall and for all subsets of vegetation types and seasons. Our study shows that negative effects of disturbance or land-use intensity on decomposition depended on the severity of disturbance and on the season. Nevertheless, climate was generally the most relevant driver of decomposition. Therefore, vegetation types with moderate levels of disturbance can retain high functionality in regards to carbon cycling over short periods of time. More and longer decomposition studies are necessary to better predict consequences of land-use change for carbon cycling in the Afrotropics.</p

The semianalytical cloud retrieval algorithm for SCIAMACHY I. The validation

A recently developed cloud retrieval algorithm for the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) is briefly presented and validated using independent and well tested cloud retrieval techniques based on the look-up-table approach for MODeration resolutIon Spectrometer (MODIS) data. The results of the cloud top height retrievals using measurements in the oxygen A-band by an airborne crossed Czerny-Turner spectrograph and the Global Ozone Monitoring Experiment (GOME) instrument are compared with those obtained from airborne dual photography and retrievals using data from Along Track Scanning Radiometer (ATSR-2), respectively

Quality Assessment of Photogrammetric Methods—A Workflow for Reproducible UAS Orthomosaics

Unmanned aerial systems (UAS) are cost-effective, flexible and offer a wide range of applications. If equipped with optical sensors, orthophotos with very high spatial resolution can be retrieved using photogrammetric processing. The use of these images in multi-temporal analysis and the combination with spatial data imposes high demands on their spatial accuracy. This georeferencing accuracy of UAS orthomosaics is generally expressed as the checkpoint error. However, the checkpoint error alone gives no information about the reproducibility of the photogrammetrical compilation of orthomosaics. This study optimizes the geolocation of UAS orthomosaics time series and evaluates their reproducibility. A correlation analysis of repeatedly computed orthomosaics with identical parameters revealed a reproducibility of 99% in a grassland and 75% in a forest area. Between time steps, the corresponding positional errors of digitized objects lie between 0.07 m in the grassland and 0.3 m in the forest canopy. The novel methods were integrated into a processing workflow to enhance the traceability and increase the quality of UAS remote sensing.This research was funded by the Hessian State Ministry for Higher Education, Research and the Arts, Germany, as part of the LOEWE priority project Nature 4.0—Sensing Biodiversity. The grassland study was funded by the Spanish Science Foundation FECYT-MINECO through the BIOGEI (GL2013- 49142-C2-1-R) and IMAGINE (CGL2017-85490-R) projects, and by the University of Lleida; and supported by a FI Fellowship to C.M.R. (2019 FI_B 01167) by the Catalan Government

Radar vision in the mapping of forest biodiversity from space

Recent progress in remote sensing provides much-needed, large-scale spatio-temporal information on habitat structures important for biodiversity conservation. Here we examine the potential of a newly launched satellite-borne radar system (Sentinel-1) to map the biodiversity of twelve taxa across five temperate forest regions in central Europe. We show that the sensitivity of radar to habitat structure is similar to that of airborne laser scanning (ALS), the current gold standard in the measurement of forest structure. Our models of different facets of biodiversity reveal that radar performs as well as ALS; median R² over twelve taxa by ALS and radar are 0.51 and 0.57 respectively for the first non-metric multidimensional scaling axes representing assemblage composition. We further demonstrate the promising predictive ability of radar-derived data with external validation based on the species composition of birds and saproxylic beetles. Establishing new area-wide biodiversity monitoring by remote sensing will require the coupling of radar data to stratified and standardized collected local species data