Integrating SAR and Optical Remote Sensing for Conservation-Targeted Wetlands Mapping

The Prairie Pothole Region (PPR) contains numerous depressional wetlands known as potholes that provide habitats for waterfowl and other wetland-dependent species. Mapping these wetlands is essential for identifying viable waterfowl habitat and conservation planning scenarios, yet it is a challenging task due to the small size of the potholes, and the presence of emergent vegetation. This study develops an open-source process within the Google Earth Engine platform for mapping the spatial distribution of wetlands through the integration of Sentinel-1 C-band SAR (synthetic aperture radar) data with high-resolution (10-m) Sentinel-2 bands. We used two machine-learning algorithms (random forest (RF) and support vector machine (SVM)) to identify wetlands across the study area through supervised classification of the multisensor composite. We trained the algorithms with ground truth data provided through field studies and aerial photography. The accuracy was assessed by comparing the predicted and actual wetland and non-wetland classes using statistical coefficients (overall accuracy, Kappa, sensitivity, and specificity). For this purpose, we used four different out-of-sample test subsets, including the same year, next year, small vegetated, and small non-vegetated test sets to evaluate the methods on different spatial and temporal scales. The results were also compared to Landsat-derived JRC surface water products, and the Sentinel-2-derived normalized difference water index (NDWI). The wetlands derived from the RF model (overall accuracy 0.76 to 0.95) yielded favorable results, and outperformed the SVM, NDWI, and JRC products in all four testing subsets. To provide a further characterization of the potholes, the water bodies were stratified based on the presence of emergent vegetation using Sentinel-2-derived NDVI, and, after excluding permanent water bodies, using the JRC surface water product. The algorithm presented in the study is scalable and can be adopted for identifying wetlands in other regions of the world

Integrating SAR and Optical Remote Sensing for Conservation-Targeted Wetlands Mapping

The Prairie Pothole Region (PPR) contains numerous depressional wetlands known as potholes that provide habitats for waterfowl and other wetland-dependent species. Mapping these wetlands is essential for identifying viable waterfowl habitat and conservation planning scenarios, yet it is a challenging task due to the small size of the potholes, and the presence of emergent vegetation. This study develops an open-source process within the Google Earth Engine platform for mapping the spatial distribution of wetlands through the integration of Sentinel-1 C-band SAR (synthetic aperture radar) data with high-resolution (10-m) Sentinel-2 bands. We used two machine-learning algorithms (random forest (RF) and support vector machine (SVM)) to identify wetlands across the study area through supervised classification of the multisensor composite. We trained the algorithms with ground truth data provided through field studies and aerial photography. The accuracy was assessed by comparing the predicted and actual wetland and non-wetland classes using statistical coefficients (overall accuracy, Kappa, sensitivity, and specificity). For this purpose, we used four different out-of-sample test subsets, including the same year, next year, small vegetated, and small non-vegetated test sets to evaluate the methods on different spatial and temporal scales. The results were also compared to Landsat-derived JRC surface water products, and the Sentinel-2-derived normalized difference water index (NDWI). The wetlands derived from the RF model (overall accuracy 0.76 to 0.95) yielded favorable results, and outperformed the SVM, NDWI, and JRC products in all four testing subsets. To provide a further characterization of the potholes, the water bodies were stratified based on the presence of emergent vegetation using Sentinel-2-derived NDVI, and, after excluding permanent water bodies, using the JRC surface water product. The algorithm presented in the study is scalable and can be adopted for identifying wetlands in other regions of the world

Trend analysis of hydro-climatic variables in the north of Iran

Trend analysis of climate variables such as streamflow, precipitation, and temperature provides useful information for understanding the hydrological changes associated with climate change. In this study, a nonparametric Mann-Kendall test was employed to evaluate annual, seasonal, and monthly trends of precipitation and streamflow for the Neka basin in the north of Iran over a 44-year period (1972 to 2015). In addition, the Inverse Distance Weight (IDW) method was used for annual seasonal, monthly, and daily precipitation trends in order to investigate the spatial correlation between precipitation and streamflow trends in the study area. Results showed a downward trend in annual and winter precipitation (Z < −1.96) and an upward trend in annual maximum daily precipitation. Annual and monthly mean flows for most of the months in the Neka basin decreased by 14% significantly, but the annual maximum daily flow increased by 118%. Results for the trend analysis of streamflow and climatic variables showed that there are statistically significant relationships between precipitation and streamflow (p value < 0.05). Correlation coefficients for Kendall, Spearman’s rank and linear regression are 0.43, 0.61, and 0.67, respectively. The spatial presentation of the detected precipitation and streamflow trends showed a downward trend for the mean annual precipitation observed in the upstream part of the study area which is consistent with the streamflow trend. Also, there is a good correlation between monthly and seasonal precipitation and streamflow for all sub-basins (Sefidchah, Gelvard, Abelu). In general, from a hydro-climatic point of view, the results showed that the study area is moving towards a situation with more severe drought events

Dielectric properties of chloroadamantane and adamantanone

International audienc

Molecular motions in glassy crystal cyanoadamantane : a proton spin-lattice relaxation study

Cyanoadamantane C$_{10}$H$_{15}$CN exhibits four different solid phases : two cubic plastic (I and I$'$), one cubic glassy (I$_{\rm g}$) and one monoclinic ordered (II). In cubic plastic phases (I, I$'$) three types of motion coexist : a uniaxial rotation of the molecule around its C—C$\equiv$N axis, a tumbling reorientation of this dipolar axis between the $\langle 001\rangle$ directions and a vacancy self-diffusion. In the cubic glassy state (I$_{\rm g}$) the tumbling motion is frozen and therefore only the uniaxial rotation survives. In the ordered phase (II), the molecules only perform a 3-fold uniaxial rotation among identical positions. These different molecular motions in the four solid phases have been studied by the analysis of the $T_{1 z}$ and $T_{\rm 1 \rho}$ spin-lattice relaxation times in $^1$H-NMR. The derived residence time are compared, when possible, to values previously deduced from quasi-elastic neutron scattering, dielectric relaxation and second moment of the $^1$H-NMR lineshape.Le cyanoadamantane C$_{10}$H$_{15}$CN possède quatre phases solides différentes : deux plastiques cubiques (I et I$'$), une vitreuse cubique (I$_{\rm g}$) et une ordonnée monoclinique (II). Dans les phases plastiques cubiques (I, I$'$) trois types de mouvements coexistent : une rotation uniaxiale de la molécule autour de son axe C—C$\equiv$N, un basculement de cet axe dipolaire entre les directions $\langle 001\rangle$ et une diffusion moléculaire. Dans l'état vitreux cubique (I$_{\rm g}$), le mouvement de basculement est gelé et seule la rotation uniaxiale subsiste. Enfin dans la phase ordonnée (II), les molécules effectuent une rotation uniaxiale d'ordre 3 entre positions indiscernables. Ces différents mouvements dans les quatre phases solides ont été évalués par l'analyse des temps de relaxation spin-réseau $T_{1 z}$ et $T_{1 \rho}$ en $^1$H-RMN. Les temps de résidence qui en sont déduits sont comparés (lorsque cela est possible) aux valeurs correspondantes déduites précédemment par diffusion quasi-élastique des neutrons, par relaxation diélectrique et par mesure du second moment de la raie RMN