Coupling SAR C-band and optical data for soil moisture and leaf area index retrieval over irrigated grasslands

International audienceThe objective of this study was to develop an approach for estimating soil moisture and vegetation parameters in irrigated grasslands by coupling C-band polarimetric Synthetic Aperture Radar (SAR) and optical data. A huge dataset of satellite images acquired from RADARSAT-2 and LANDSAT-7/8, and in situ measurements were used to assess the relevance of several inversion configurations. A neural network (NN) inversion technique was used. The approach for this study was to use RADARSAT-2 and LANDSAT-7/8 images to investigate the potential for the combined use of new data from the new SAR sensor SENTINEL-1 and the new optical sensors LANDSAT-8 and SENTINEL-2. First, the impact of SAR polarization (mono-, dual- and full-polarizations configurations) and the Normalized Difference Vegetation Index (NDVI) calculated from optical data for the estimation error of soil moisture and vegetation parameters was studied. Next, the effect of some polarimetric parameters (Shannon entropy and Pauli components) on the inversion technique was also analyzed. Finally, configurations using in situ measurements of the fraction of absorbed photosynthetically active radiation (FAPAR) and the fraction of green vegetation cover (FCover) were also tested.The results showed that HH polarization is the SAR polarization most relevant to soil moisture estimates. An RMSE for soil moisture estimates of approximately 6 vol.% was obtained even for dense grassland cover. The use of in situ FAPAR and FCover only improved the estimate of the leaf area index (LAI) with an RMSE of approximately 0.37 m²/m². The use of polarimetric parameters did not improve the estimate of soil moisture and vegetation parameters. Good results were obtained for the biomass (BIO) and vegetation water content (VWC) estimates for BIO and VWC values lower than 2 and 1.5 kg/m², respectively (RMSE is of 0.38 kg/m² for BIO and 0.32 kg/m² for VWC). In addition, a high under-estimate was observed for BIO and VWC higher than 2 and 1.5 kg/m², respectively (a bias of -0.65 kg/m² on BIO estimates and -0.49 kg/m² on VWC estimates). Finally, the estimation of vegetation height (VEH) was carried out with an RMSE of 13.45 cm

A study of Lumpy skin disease outbreak in Thi Qar Province

In this study the clinical diagnosis of lumpy skin disease was depended. A total 1606 serum samples and skin biopsies collected from infected cows clinically with skin nodule of different size which change to a necrotic nodule or form a deep scab, from wide different areas and cities of Thi Qar , extended from June to November at 2016.      ELISA were performed for identification of LSDV antibodies, LSDV isolation was carried out on CAM of ECE  and Viral DNA was extracted from skin biopsy for PCR.     All serum samples was show seropositivity against LSD. Isolation of LSD virus from skin biopsy was conducted on CAM and the isolated virus was identified by PCR. collection and processing of clinical samples, viral isolation and PCR assay, for LSDV are much sensitive and rapid diagnostic of LSD their importance in controlling the rapid spread of disease in Iraq

A study of Lumpy skin disease outbreak in Thi Qar Province

In this study the clinical diagnosis of lumpy skin disease was depended. A total 1606 serum samples and skin biopsies collected from infected cows clinically with skin nodule of different size which change to a necrotic nodule or form a deep scab, from wide different areas and cities of Thi Qar , extended from June to November at 2016.      ELISA were performed for identification of LSDV antibodies, LSDV isolation was carried out on CAM of ECE  and Viral DNA was extracted from skin biopsy for PCR.     All serum samples was show seropositivity against LSD. Isolation of LSD virus from skin biopsy was conducted on CAM and the isolated virus was identified by PCR. collection and processing of clinical samples, viral isolation and PCR assay, for LSDV are much sensitive and rapid diagnostic of LSD their importance in controlling the rapid spread of disease in Iraq

Coupling potential of ICESat/GLAS and SRTM for the discrimination of forest landscape types in French Guiana

The Shuttle Radar Topography Mission (SRTM) has produced the most accurate nearly global elevation dataset to date. Over vegetated areas, the measured SRTM elevations are the result of a complex interaction between radar waves and tree crowns. In this study, waveforms acquired by the Geoscience Laser Altimeter System (GLAS) were combined with SRTM elevations to discriminate the five forest landscape types (LTs) in French Guiana. Two differences were calculated: (1) penetration depth, defined as the GLAS highest elevations minus the SRTM elevations, and (2) the GLAS centroid elevations minus the SRTM elevations. The results show that these differences were similar for the five LTs, and they increased as a function of the GLAS canopy height and of the SRTM roughness index. Next, a Random Forest (RF) classifier was used to analyze the coupling potential of GLAS and SRTM in the discrimination of forest landscape types in French Guiana. The parameters used in the RF classification were the GLAS canopy height, the SRTM roughness index, the difference between the GLAS highest elevations and the SRTM elevations and the difference between the GLAS centroid elevations and the SRTM elevations. Discrimination of the five forest landscape types in French Guiana was possible, with an overall classification accuracy of 81.3% and a kappa coefficient of 0.75. All forest LTs were well classified with an accuracy varying from 78.4% to 97.5%. Finally, differences of near coincident GLAS waveforms, one from the wet season and one from the dry season, were analyzed. The results showed that the open forest LT (LT12), in some locations, contains trees that lose leaves during the dry season. These trees allow LT12 to be easily discriminated from the other LTs that retain their leaves using the following three criteria: (1) difference between the GLAS centroid elevations and the SRTM elevations, (2) ratio of top energy in the wet season to top energy in the dry season, or (3) ratio of ground energy in the wet season to ground energy in the dry season

Borel-Cantelli sequences

A sequence $\{x_{n}\}_1^\infty$ in $[0,1)$ is called Borel-Cantelli (BC) if for all non-increasing sequences of positive real numbers $\{a_n\}$ with $\underset{i=1}{\overset{\infty}{\sum}}a_i=\infty$ the set $$\underset{k=1}{\overset{\infty}{\cap}} \underset{n=k}{\overset{\infty}{\cup}} B(x_n, a_n))=\{x\in[0,1)\mid |x_n-x|<a_n \text{for} \infty \text{many}n\geq1\}$$ has full Lebesgue measure. (To put it informally, BC sequences are sequences for which a natural converse to the Borel-Cantelli Theorem holds). The notion of BC sequences is motivated by the Monotone Shrinking Target Property for dynamical systems, but our approach is from a geometric rather than dynamical perspective. A sufficient condition, a necessary condition and a necessary and sufficient condition for a sequence to be BC are established. A number of examples of BC and not BC sequences are presented. The property of a sequence to be BC is a delicate diophantine property. For example, the orbits of a pseudo-Anosoff IET (interval exchange transformation) are BC while the orbits of a "generic" IET are not. The notion of BC sequences is extended to more general spaces.Comment: 20 pages. Some proofs clarifie

Regional scale rain-forest height mapping using regression-kriging of spaceborneand airborne lidar data: application on French Guiana

IGARSS 2015, Milan, ITA, 26-/07/2015 - 31/07/2015International audienceLiDAR remote sensing has been shown to be a good technique for the estimation of forest parameters such as canopy heights and aboveground biomass. Whilst airborne LiDAR data are in general very dense but only available over small areas due to the cost of their acquisition, spaceborne LiDAR data acquired from the Geoscience Laser Altimeter System (GLAS) have a coarser acquisition density associated with a global cover. It is therefore valuable to analyze the integration relevance of canopy heights estimated from LiDAR sensors with ancillary data such as geological, meteorological, and phenological variables in order to propose a forest canopy height map with good precision and high spatial resolution.In this study, canopy heights extracted from both airborne and spaceborne LiDAR, were first extrapolated from available environmental data. The estimated canopy height maps using random forest (RF) regression from the airborne or GLAS calibration datasets showed similar precisions (RMSE better than 6.5 m). In order to improve the precision of the canopy height estimates regression-kriging (kriging of RF regression residuals) was used. Results indicated an improvement in the RMSE (decrease from 6.5 to 4.2 m) for the regression-kriging maps from the GLAS dataset, and from 5.8 to 1.8 m for the regression-kriging map from the airborne LiDAR dataset

Capability of GLAS/ICESat data to estimate forest canopy height and volume in mountainous forests of Iran

International audienceThe importance of measuring biophysical properties of forest for ecosystem health monitoring and forest management encourages researchers to find precise, yet low cost methods especially in mountainous and large area. In the present study Geoscience Laser Altimeter System (GLAS) on board ICESat was used to estimate three biophysical characteristics of forests located in north of Iran: 1) maximum canopy height (Hmax), 2) Lorey's height (HLorey), and 3) Forest volume (V). A large number of Multiple Linear Regressions (MLR) and also Random Forest (RF) regressions were developed using different set of variables: waveform metrics, Principal Components (PCs) produced from Principal Component Analysis (PCA) and Wavelet Coefficients (WCs) generated from wavelet transformation. To validate and compare different models, statistical criteria were calculated based on a five-fold cross validation. The best model concerning the maximum canopy height was an MLR with an RMSE of 5.0 m which combined two metrics extracted from waveforms (waveform extent "Wext" and height at 50% of waveform energy "H50"), and one from the Digital Elevation Model (Terrain Index: TI). The mean absolute error (MAPE) of maximum canopy height estimates is about 16.4%. For Lorey's height, a simple MLR model including two metrics (Wext and TI) represents the highest performance (RMSE=5.1 m, MAPE=24.0%). Totally, MLR models showed better performance rather than RF models, and accuracy of height estimations using waveform metrics was greater than those based on PCs or WCs. Concerning forest volume, employing regression models to estimate volume directly from GLAS data led to a better result (RMSE=128.8 m3/ha) rather than volume-HLorey relationship (RMSE=167.8 m3/ha)

Spread of imipenem-resistant Acinetobacter baumannii co-expressing OXA-23 and GES-11 carbapenemases in Lebanon

© 2015 The Authors. Objectives: The acquisition of carbapenemases by Acinetobacter baumannii is reported increasingly worldwide, but data from Lebanon are limited. The aims of this study were to evaluate the prevalence of imipenem-resistant A. baumannii in Lebanon, identify resistance determinants, and detect clonal relatedness. Methods: Imipenem-resistant A. baumannii were collected from nine Lebanese hospitals during 2012. Antimicrobial susceptibility, the cloxacillin effect, and ethylenediaminetetraacetic acid (EDTA) synergy were determined. Genes encoding carbapenemases and insertion sequence IS. Aba1 were screened via PCR sequencing. IS. Aba1 position relative to genes encoding Acinetobacter-derived cephalosporinases (ADCs) and OXA-23 was studied by PCR mapping. Clonal linkage was examined by enterobacterial repetitive intergenic consensus PCR (ERIC-PCR). Results: Out of 724 A. baumannii isolated in 2012, 638 (88%) were imipenem-resistant. Of these, 142 were analyzed. Clavulanic acid-imipenem synergy suggested carbapenem-hydrolyzing extended-spectrum β-lactamase. A positive cloxacillin test indicated ADCs, while EDTA detection strips were negative. Genotyping indicated that 90% of isolates co-harbored blaOXA-23 and blaGES-11. The remaining strains had blaOXA-23, blaOXA-24, blaGES-11, or blaOXA-24 with blaGES-11. ISAba1 was located upstream of blaADC and blaOXA-23 in 97% and 100% of isolates, respectively. ERIC-PCR fingerprinting revealed 18 pulsotypes spread via horizontal gene transfer and clonal dissemination. Conclusion: This survey established baseline evidence of OXA-23 and GES-11-producing A. baumannii in Lebanon, indicating the need for further surveillance

Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne lidar data : application on French Guiana

LiDAR data has been successfully used to estimate forest parameters such as canopy heights and biomass. Major limitation of LiDAR systems (airborne and spaceborne) arises from their limited spatial coverage. In this study, we present a technique for canopy height mapping using airborne and spaceborne LiDAR data (from the Geoscience Laser Altimeter System (GLAS)). First, canopy heights extracted from both airborne and spaceborne LiDAR were extrapolated from available environmental data. The estimated canopy height maps using Random Forest (RF) regression from airborne or GLAS calibration datasets showed similar precisions (~6 m). To improve the precision of canopy height estimates, regression-kriging was used. Results indicated an improvement in terms of root mean square error (RMSE, from 6.5 to 4.2 m) using the GLAS dataset, and from 5.8 to 1.8 m using the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on canopy height estimates precision, six subsets were derived from the initial airborne LiDAR dataset. Results indicated that using the regression-kriging approach a precision of 1.8 m on the canopy height map was achievable with a flight line spacing of 5 km. This precision decreased to 4.8 m for flight line spacing of 50 km