Time‐lapse monitoring of soil water content using electromagnetic conductivity imaging

The volumetric soil water content (θ) is fundamental to agriculture because its spatiotemporal variation in soil affects the growth of plants. Unfortunately, the universally accepted thermogravimetric method for estimating volumetric soil water content is very labour intensive and time‐consuming for use in field‐scale monitoring. Electromagnetic (EM) induction instruments have proven to be useful in mapping the spatiotemporal variation of θ. However, depth‐specific variation in θ, which is important for irrigation management, has been little explored. The objective of this study was to develop a relationship between θ and estimates of true electrical conductivity (σ) and to use this relationship to develop time‐lapse images of soil θ beneath a centre‐pivot irrigated alfalfa (Medicago sativa L.) crop in San Jacinto, California, USA. We first measured the bulk apparent electrical conductivity (EC_a – mS/m) using a DUALEM‐421 over a period of 12 days after an irrigation event (i.e. days 1, 2, 3, 4, 6, 8 and 12). We used EM4Soil to generate EM conductivity images (EMCIs). We used a physical model to estimate θ from σ, accounting for soil tortuosity and pore water salinity, with a cross‐validation RMSE of 0.04 cm³/cm³. Testing the scenario where no soil information is available, we used a three‐parameter exponential model to relate θ to σ and then to map θ along the transect on different days. The results allowed us to monitor the spatiotemporal variations of θ across the surveyed area, over the 12‐day period. In this regard, we were able to map the soil close to field capacity (0.27 cm³/cm³) and approaching permanent wilting point (0.03 cm³/cm³). The time‐lapse θ monitoring approach, developed using EMCI, has implications for soil and water use and management and will potentially allow farmers and consultants to identify inefficiencies in water application rates and use. It can also be used as a research tool to potentially assist precision irrigation practices and to test the efficacy of different methods of irrigation in terms of water delivery and efficiency in water use in near real time.     

Functional porous carbons from waste cotton fabrics for dyeing wastewater purification

A new functional porous carbons (PC-WF) is prepared by activation-pyrolysis method use waste cotton fabrics (WF) as an abundant, cheap and available precursor for removal of Brilliant Crocein (BC-GR) and Cationic Red 2GL (CR-GL) from aqueous phase. The PC-WF was characterized by BET, FTIR, SEM, and XRD techniques, the surface area, total pore volume, average pore diameter was found as 1463.5 m² g^-1, 0.783 cm³ g^-1 and 2.14 nm, respectively. The influences on BC-GR and CR-GL adsorption of various experimental factors such as initial concentration and temperature were investigated. Adsorption kinetics was found to be best represented by the pseudo-second order model. The adsorption capacity was 319.8 mg g^-1 for BC-GR and 842.5 mg g^-1 for CR-GL at 30 °C, respectively. The results indicate that for waste cotton fabrics in particular, the practical application of this process to the production of porous carbon would be possible.     

Characterizing long-term forest disturbance history and its drivers in the Ning-Zhen Mountains,Jiangsu Province of eastern China using yearly Landsat observations (1987–2011)

Forest losses or gains have long been recognized as critical processes modulating the carbon flux between the biosphere and the atmosphere. Timely, accurate and spatially explicit information on forest disturbance and recovery history is required for assessing the effectiveness of existing forest management. The major objectives of our research focused on testing the mapping efficacy of the vegetation change tracker (VCT) model over a forested area in China. We used a new version of VCT algorithm built upon the Landsat time series stacks (LTSS). The LTSS consisted of yearly image acquisitions to map forest disturbance history from 1987 to 2011 over the Ning-Zhen Mountains, Jiangsu Province of east China. The LTSS consisted of TM and ETM+ scenes with different projections due to distinct data sources (Beijing remote sensing ground station and the USGS EROS Center). The validation results of the disturbance year maps showed that most spatial agreement measures ranged from 70 to 86 %, comparable with the VCT accuracies reported for many places in USA. Very low accuracies were identified in 1995 (38.3 %) and 1992 (56.2 %) in the current analysis. These resulted from the insensitivity of the VCT algorithm to detect low intensity disturbances and also from the mis-registration errors of the image pairs. Major forest disturbance types existing in our study area were identified as agricultural expansion (39.8 %), urbanization (24.9 %), forest management practice (19.3 %), and mining (12.8 %). In general, there was a gradual decreasing trend in forest cover throughout this region, caused principally by China’s economic, demographic, environmental and political policies and decisions, as well as some weather events. While VCT has largely been used to assess long term changes and trends in the USA, it has great potential for assessing landscape level change elsewhere throughout the world.