Utilization of Ground-Penetrating Radar and Frequency Domain Electromagnetic for Investigation of Sewage Leaks

Fact 1: Underground sewage pipe systems deteriorate over time, developing cracks and joint defects; therefore, leakage is inevitable. Fact 2: The massive worldwide urbanization process, together with rural development, has meaningfully increased the length of sewage pipelines. Result: The concomitant risk of sewage leaks exposes the surrounding land to potential contamination and environmental harm. It is therefore important to locate such leaks in a timely manner, enabling damage control. Advances in active remote-sensing technologies (GPR and FDEM: ground-penetration radar and frequency domain electromagnetic) were used to identify sewage leaks that might cause pollution and to identify minor spills before they cause widespread damage

Studying Vegetation Salinity: From the Field View to a Satellite-Based Perspective

Salinization of irrigated lands in the semi-arid Jezreel Valley, Northern Israel results in soil-structure deterioration and crop damage. We formulated a generic rule for estimating salinity of different vegetation types by studying the relationship between Cl/Na and different spectral slopes in the visible–near infrared–shortwave infrared (VIS–NIR–SWIR) spectral range using both field measurements and satellite imagery (Sentinel-2). For the field study, the slope-based model was integrated with conventional partial least squares (PLS) analyses. Differences in 14 spectral ranges, indicating changes in salinity levels, were identified across the VIS–NIR–SWIR region (350–2500 nm). Next, two different models were run using PLS regression: (i) using spectral slope data across these ranges; and (ii) using preprocessed spectral reflectance. The best model for predicting Cl content was based on continuum removal reflectance (R2 = 0.84). Satisfactory correlations were obtained using the slope-based PLS model (R2 = 0.77 for Cl and R2 = 0.63 for Na). Thus, salinity contents in fresh plants could be estimated, despite masking of some spectral regions by water absorbance. Finally, we estimated the most sensitive spectral channels for monitoring vegetation salinity from a satellite perspective. We evaluated the recently available Sentinel-2 imagery’s ability to distinguish variability in vegetation salinity levels. The best estimate of a Sentinel-2-based vegetation salinity index was generated based on a ratio between calculated slopes: the 490–665 nm and 705–1610 nm. This index was denoted as the Sentinel-2-based vegetation salinity index (SVSI) (band 4 − band 2)/(band 5 + band 11)

Clay dispersion: An important factor in channel runoff generation in a semi-arid, loess-covered area with very low rain intensities

Overland flow is usually regarded as an important contributor to storm channel flow. This observation is certainly applicable to dryland areas, where base flow is often irrelevant, particularly in small watersheds. This study examines channel runoff generation in the extensive loess-covered areas that characterize the mildly arid area of western Israel, where the average annual rainfall is 280 mm. Hydrological data point to a peculiar hydrological behavior of the ephemeral streams that experience a high frequency of sporadic channel flow events. Even in extreme rain events, peak discharges are exceptionally low, indicative of a limited contributing area. Hydrographs are characterized by very steep rising and falling limbs, usually representative of saturated areas, located in the vicinity of the runoff recording station. Based on this observation, we advanced the hypothesis that storm runoff originated in the limited area of the active channel, with negligible runoff from the adjoining hillslopes. We argue that a quasi-permanent surface seal, at the top of the alluvial deposit, drastically limits the hydraulic conductivity of the alluvial fill, allowing runoff generation at very low rain intensities. The occurrence of the surface seal is ascribed to the combination of two main factors. A high clay content (similar to 40%), where the dominant clays are smectite and illite, characterized by a laminar structure and a high-water absorption capacity. The swelling of the clay particles considerably reduce the porosity of the alluvial material, allowing runoff generation at very low rain intensities while limiting the depth of water penetration in the channel itself. Data presented fit the concept of "Partial Area Contribution" identified in humid areas. However, the application of this concept to dryland areas is based on completely different reasons

Laboratory Measurements of Subsurface Spatial Moisture Content by Ground-Penetrating Radar (GPR) Diffraction and Reflection Imaging of Agricultural Soils

Soil moisture content (SMC) down to the root zone is a major factor for the efficient cultivation of agricultural crops, especially in arid and semi-arid regions. Precise SMC can maximize crop yields (both quality and quantity), prevent crop damage, and decrease irrigation expenses and water waste, among other benefits. This study focuses on the subsurface spatial electromagnetic mapping of physical properties, mainly moisture content, using a ground-penetrating radar (GPR). In the laboratory, GPR measurements were carried out using an 800 MHz central-frequency antenna and conducted in soil boxes with loess soil type (calcic haploxeralf) from the northern Negev, hamra soil type (typic rhodoxeralf) from the Sharon coastal plain, and grumusol soil type (typic chromoxerets) from the Jezreel valley, Israel. These measurements enabled highly accurate, close-to-real-time evaluations of physical soil qualities (i.e., wave velocity and dielectric constant) connected to SMC. A mixture model based mainly on soil texture, porosity, and effective dielectric constant (permittivity) was developed to measure the subsurface spatial volumetric soil moisture content (VSMC) for a wide range of moisture contents. The analysis of the travel times for GPR reflection and diffraction waves enabled calculating electromagnetic velocities, effective dielectric constants, and spatial SMC under laboratory conditions, where the required penetration depth is low (root zone). The average VSMC was determined with an average accuracy of ±1.5% and was correlated to a standard oven-drying method, making this spatial method useful for agricultural practice and for the design of irrigation plans for different interfaces

Frequency Domain Electromagnetic Method (FDEM) as a Tool to Study Contamination at the Sub-Soil Layer

Traditional sheep and cattle grazing in natural semiarid Mediterranean, Asian and African regions is based on night corrals, where animal secretions accumulate. Lack of management and disregard for the long-term effects of using the same sites for corrals on underground soil characters may negatively affect soil values. This locally increases the content of organic matter and nutrients such as nitrogen, potassium, phosphorus and others that are stockpiled in the corrals. As these activities are long-lasting, they affect the soil parameters, leading to nutrient leakage and contamination of the upper and sub-soil surface. This alarming situation demands a technique to reveal and estimate sub-soil contamination in corrals by using the frequency domain electromagnetic method (FDEM) for measuring soil salinity. The aim of this study is to correlate electrical conductivity measurement with the FDEM to study the influence of sheep corrals on the changes within the sub-soils of corrals in the semiarid region of the northern Negev desert. The results show that a correlation was found between the laboratory soil analysis and the electromagnetic analysis in all sites. Plugot forest site results found to be anomalous indicated sub-surface conductivity resulting from the presence of the corral, with a higher conductivity value of about 230 mS/m, while no differences were found between the soil layers outside the active corral and the corral edge. High values were found in the center of the active corral: 960 mS/m by the laboratory analysis and 200 mS/m by the FDEM. The values obtained in the abandoned corral in the laboratory were about 10 times lower than those obtained from the active corral and six times lower that those found with the FDEM. At the Beit Nir site, high values were found in the center of the active corral: 300 mS/m by the laboratory analysis and 130 mS/m by the FDEM. With different sources of manure, cattle and sheep have shown similar patterns of electrical conductivity (EC) obtained in the sub-soil layers between active and abandoned corrals: high in the center and low at the edge and outside the corral and decreased with depth

Spectral Slope as an Indicator of Pasture Quality

In this study, we develop a spectral method for assessment of pasture quality based only on the spectral information obtained with a small number of wavelengths. First, differences in spectral behavior were identified across the near infrared–shortwave infrared spectral range that were indicative of changes in chemical properties. Then, slopes across different spectral ranges were calculated and correlated with the changes in crude protein (CP), neutral detergent fiber (NDF) and metabolic energy concentration (MEC). Finally, partial least squares (PLS) regression analysis was applied to identify the optimal spectral ranges for accurate assessment of CP, NDF and MEC. Six spectral domains and a set of slope criteria for real-time evaluation of pasture quality were suggested. The evaluation of three level categories (low, medium, high) for these three parameters showed a success rate of: 73%–96% for CP, 72%–87% for NDF and 60%–85% for MEC. Moreover, only one spectral range, 1748–1764 nm, was needed to provide a good estimation of CP, NDF and MEC. Importantly, five of the six selected spectral regions were not affected by water absorbance. With some modifications, this rationale can be applied to further analyses of pasture quality from airborne sensors

Spatial and Temporal Monitoring of Pasture Ecological Quality: Sentinel-2-Based Estimation of Crude Protein and Neutral Detergent Fiber Contents

Frequent, region-wide monitoring of changes in pasture quality due to human disturbances or climatic conditions is impossible by field measurements or traditional ecological surveying methods. Remote sensing imagery offers distinctive advantages for monitoring spatial and temporal patterns. The chemical parameters that are widely used as indicators of ecological quality are crude protein (CP) content and neutral detergent fiber (NDF) content. In this study, we investigated the relationship between CP, NDF, and reflectance in the visible–near-infrared–shortwave infrared (VIS–NIR–SWIR) spectral range, using field, laboratory measurements, and satellite imagery (Sentinel-2). Statistical models were developed using different calibration and validation data sample sets: (1) a mix of laboratory and field measurements (e.g., fresh and dry vegetation) and (2) random selection. In addition, we used three vegetation indices (Normalized Difference Vegetative Index (NDVI), Soil-adjusted Vegetation Index (SAVI) and Wide Dynamic Range Vegetation Index (WDRVI)) as proxies to CP and NDF estimation. The best models found for predicting CP and NDF contents were based on reflectance measurements (R2 = 0.71, RMSEP = 2.1% for CP; and R2 = 0.78, RMSEP = 5.5% for NDF). These models contained fresh and dry vegetation samples in calibration and validation data sets. Random sample selection in a model generated similar accuracy estimations. Our results also indicate that vegetation indices provide poor accuracy. Eight Sentinel-2 images (December 2015–April 2017) were examined in order to better understand the variability of vegetation quality over spatial and temporal scales. The spatial and temporal patterns of CP and NDF contents exhibit strong seasonal dependence, influenced by climatological (precipitation) and topographical (northern vs. southern hillslopes) conditions. The total CP/NDF content increases/decrease (respectively) from December to March, when the concentrations reach their maximum/minimum values, followed by a decline/incline that begins in April, reaching minimum values in July

Estimating Pasture Quality of Fresh Vegetation Based on Spectral Slope of Mixed Data of Dry and Fresh Vegetation—Method Development

The main objective of the present study was to apply a slope-based spectral method to both dry and fresh pasture vegetation. Differences in eight spectral ranges were identified across the near infrared-shortwave infrared (NIR-SWIR) that were indicative of changes in chemical properties. Slopes across these ranges were calculated and a partial least squares (PLS) analytical model was constructed for the slopes vs. crude protein (CP) and neutral detergent fiber (NDF) contents. Different datasets with different numbers of fresh/dry samples were constructed to predict CP and NDF contents. When using a mixed-sample dataset with dry-to-fresh ratios of 85%:15% and 75%:25%, the correlations of CP (R2 = 0.95, in both) and NDF (R2 = 0.84 and 0.82, respectively) were almost as high as when using only dry samples (0.97 and 0.85, respectively). Furthermore, satisfactory correlations were obtained with a dry-to-fresh ratio of 50%:50% for CP (R2 = 0.92). The results of our study are especially encouraging because CP and NDF contents could be predicted even though some of the selected spectral regions were directly affected by atmospheric water vapor or water in the plants