Zan Image Printer v5.0.7 serial key or number

Free Download PDFDesk Easy PDF Creator 3.0 Full Version Serial Number/ Key

Free Download PDFDesk Easy PDF Creator 3.0 Full Version Serial Number/ Key

Introduction: PDFDesk Easy PDF Creator 3.0
Free Download PDFDesk Easy PDF Creator 3.0 Full Version Serial Number/ Key offers an easy and less expensive way of creating high-quality PDF documents out of almost any windows application. Free Download PDFDesk Easy PDF Creator 3.0 Full VersionSerial Number/ Key/ installs as if it were a real printer, making the creation of PDF files as easy as printing. Common documents can be converted to PDF files at an incredibly fast speed while retaining the look and feel of the original documents as well as many options that give users flexibility in PDF creation. More than 500 applications and file formats are natively supported.

 The software supports such features of the PDF format as paper size, resolution, orientation, compatibility, fonts embedding, live hyperlink, embedded images, formulas, tables, figures, 40-bit and 128-bit encryption, password protection, document usage control, document metadata, automatic output and batch conversion.
With Easy PDF Creator Plug-ins, existing PDF documents can be easily converted into HTML, RTF, TXT format with a minimum loss of information while preserving the layout and graphics of the original documents. All useful plug-ins are included in the software package, and new ones will be published on the website for download free of charge.
As professional PDF creation software, Easy PDF Creator gives businesses the means to effectively deliver existing business documents to partners, customers and employees.

Features
- Create PDF documents from any Windows application that supports printing
- Microsoft .NET platform and Microsoft Office 2012 compatibility
- Merge PDF documents
- Automatic output PDF file to specific path
- PDF content encryption with passwords (48-bit and 128-bit support)
- Usage control prevents PDF documents from being modified, copied, converted, printed, signed, converted, etc.
- Live hyperlink support
- Up to 2540×2540 dpi printing resolution
- Custom paper size support
- PDF document information, including title, subject, keyword, and author
- PDF compatibility option for Adobe PDF format
- PDF font embedding options
- PDF printing scaling
- Convert PDF files into HTML, RTF and TXT format.
- Automatic installation and configuration on Windows x86 and Windows x64
- Printer sharing
- Native support for 500+ Windows applications
- Batch conversion (DOC to PDF, XLS to PDF, PPT to PDF, TXT to PDF, RTF to PDF)
- Plug-ins support for conversion of AutoCAD drawings (DWG to PDF, DXF to PDF)
- Live update for new versions and plug-ins
- Free upgrades in the future for registered users.

Download Links:
Password: |daoud|

• 3232 Accesses

• 4 Citations

• 4 Altmetric

• Metrics details

Salinity intrusion is a pressing issue in the coastal areas worldwide. It affects the natural environment and causes massive economic loss due to its impacts on the agricultural productivity and food safety. Here, we assessed the salinity intrusion in the Tra Vinh Province, in the Mekong Delta of Vietnam. Landsat 8 OLI image was utilized to derive indices for soil salinity estimate including the single bands, Vegetation Soil Salinity Index (VSSI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Salinity Index (NDSI). Statistical analysis between the electrical conductivity (EC_1:5, dS/m) and the environmental indices derived from Landsat 8 OLI image was performed. Results indicated that spectral values of near-infrared (NIR) band and VSSI were better correlated with EC_1:5 (r² = 0.8 and r² = 0.7, respectively) than the other indices. Comparative results show that soil salinity derived from Landsat 8 was consistent with in situ data with coefficient of determination, R² = 0.89 and RMSE = 0.96 dS/m for NIR band and R² = 0.77 and RMSE = 1.27 dS/m for VSSI index. Findings of this study demonstrate that Landsat 8 OLI images reveal a high potential for spatiotemporally monitoring the magnitude of soil salinity at the top soil layer. Outcomes of this study are useful for agricultural activities, planners, and farmers by mapping the soil salinity contamination for better selection of accomodating crop types to reduce economical loss in the context of climate change. Our proposed method that estimates soil salinity using satellite-derived variables can be potentially useful as a fast-approach to detect the soil salinity in the other regions with low cost and considerable accuracy.

Salinity intrusion affects agricultural activities in many parts of the world (Suarez 1989; Shammi et al. 2019). It has become a pressing issue in the Mekong Delta of Vietnam, which has been ranked among top five countries most likely to be affected by climate change coupled with sea level rise. Depending on the emissions pathway, it is estimated that about 6–12 million people will be potentially affected by coastal flooding by 2070–2100 and climate change will reduce the national income by up to 3.5% by 2050 (World Bank Group 2018). In particular, Tra Vinh Province characterized by low-lying coastal and river delta region is of very high vulnerability to saline intrusion, fluvial flooding, and cash crops. This area is experiencing effects of annual monsoon of tropical climate zone with long rainy season that source of salt brought from the sea by the tides and sea level rise. This region’s climate is also impacted by the EI Niño Southern Oscillation (ENSO), which influences monsoonal circulation, showing enhanced influence of sea level rise and drought (World Bank Group 2018). That is to say, soil salinization is a dynamic phenomena, which can occur through a natural process (primary salinization due to sea level rise) or as a result of human activities (secondary salinization due to urbanization or irrigation) of enriching the soil with soluble salts to deteterious levels at or near the soil surface (Metternicht et al. 2008; Gorji et al. 2017), resulting in modification of biochemical features of soil. Salinity intrusion occurs when salts are dissolved in water and accumulated in the soil at a level that affects agricultural production, environment, and economics. During the initial phase, salinity affects the metabolism of soil organisms and reduces the productivity of land. In the next phase, it destroys all the plants and other organisms living in the soil. Salinity intrusion is thus one of the influencial factors leading to land degradation and triggering a significant threat on the sustainable growth and economic benefits (Wiegand et al. 1994; Sertel and Tanik 2017). Globally speaking, it is estimated that farmland affected by salinity is approximately 45 million hectares with an increased rate from 200,000 to 500,000 hectares a year (CGIAR 2016). In Vietnam, according to recent report of Directorate of Water Resources (DWR)-Ministry of Agriculture and Rural Development (MARD), the discharge of water in the upstream of the Mekong River was reduced to 900 m³/s. Meanwhile, the water level in the middle and downstream of Mekong River was increased by 0.1–1.5 m. The intrusion of seawater increases salinity of water surface by about 4 g/l spreading through Hau and Tien Rivers, deep into to 45–65 km and 55–60 km from the coast, respectively (Nguyen and Tanaka 2007; Nguyen et al. 2008; Nowacki et al. 2015). In contrast, the drought led to a decline in underground water level and salinity intrusion in the sweeping 90 years, focusing primarily in Tra Vinh, Ben Tre, and Kien Giang Provinces. In addition, according to the report of DWR-MARD in Tra Vinh Province, in the dry season in 2016, 12,346 ha of rice were damaged due to the impact of drought and salinity intrusion, mainly in the Tra Cu, Cau Ngang, Tieu Can, Chau Thanh, and Duyen Hai districts and Tra Vinh City (CGIAR 2016) Provincial Report of Tra Vinh, 2011-2015.

Because of the devastating impacts of salinization on soil fertility and agricultural production, finding ways to mitigate the negative impacts on the soil is essential. A prime step is to map its spatial extent and classify the severity of salinity in the affected areas for sustainable agricultural planning. Ultilizing the satellite data and remote sensing techniques for monitoring soil salinity has become more efficient and economical (Dehni & Lounis 2012; Taghadosi et al., 2018, Seghal et al, 1988. Different approaches have been developed and utilized such as spectral reflectivity, combined spectrum channels, statistical analysis, mathematical modeling, and geophysical measurements. Fernandez-Buces et al. (2006) Among the various satellite sensors and techniques used to map salinity and produce EC maps, multispectral satellite imagery has been widely studied in the literature and found to be a very promising tool for mitigating this type of task (Richards 1954; Sehgal et al. 1988; Prentice 1972; Richardson et al. 1976; Dwivedi and Rao 1992; Dwivedi and Sreenivas 1998). Verma et al. (1994) studied soil contamination and found that the degree of soil contaminated by salt affects the land cover characteristics, resulting in different appearances (color, texture, shape, etc.) on the Landsat TM images. Ivits et al. (2013) addressed the limitation of productivity of salt-affected soils across the European continent by using soil maps and SPOT time series satellite images. Scudiero et al. (2015) used Landsat ETM+ canopy reflectance for assessing soil salinity at region scale. In most cases, multiple bands were transformed into a single index that is more sensitive to soil salinity than a single band (Madani 2005; Fernandez et al. 2006; Weng et al. 2010; Aldakheel 2011; Allbed et al. 2014; Zhang et al. 2015; Fan et al. 2015). Results of these studies indicated the high possibility of mapping salinity using Landsat data. Recently, Landsat TM/ETM+ data have been used to monitor a variety of environmental issues for overall vulnerability assessment (Nguyen et al. 2016; Liou et al. 2017). This served as a base to develop a global map of eco-environmental vulnerability (Nguyen and Liou 2019a) with methodology detailed in Nguyen and Liou (2019b) and a conceptual framework to further examine vulnerability and adaptive capacity (VAC) using geospatial techniques at regional scales, applied over Vietnam (Nguyen et al. 2019.) Furthermore, Sommerfeldt et al. (1984) and Douaoui et al. (2006) investigated the correlation between indices derived from Landsat 8 OLI images and amount of salt obtained from in situ data. Sommerfeldt et al. (1984) used different techniques to analyze the salinity of the soil. Their research confirmed that salty soil can be reasonably well detected by using Landsat data. Douaoui et al. (2006) compared 11 indices derived from satellite imagery with 20 m resolution collected during the 1997 summer sampling campaign and found that regression–kriging systematically provided the best validation statistics (bias, accuracy, rank of method), while vegetation indices (NDVI) was proven to be poor predictors of soil salinity within this context. In contrast, Sumfleth and Duttmann, 2008 modified a method for predicting the arrangement of land bases in paddy rice fields by utilizing in situ data and satellite information as an index number. Their correlation analysis showed that the distribution of salt content has a close relationship with NDVI and that it substantially corresponded to the terrain features such as relative altitude, e.g., the altitude of drainage channel, and topographical wetness. Several studies explored the potential of soil salinity mapping by using very high spatial resolution (VHSR), i.e., QuickBird and IKONOS imageries through vegetation indices (Setia et al. 2013; Allbed et al. 2014; Sidike et al. 2014). However, the major limitation of VHRS is not freely available and, thus, not economic to apply for large areas. Additionally, Tao et al. (2012) retrieved soil salt content (SSC) from the Advanced Land Imager (ALI) multi-spectral image based on generalized regression neural network (GRNN). Partial least squares regression (PLSR) model has been widely used in estimation of soil parameters (Farifteh et al. 2007; Zhang et al. 2011; Nawar et al. 2014 ). Goldshleger et al. (2012) integrated passive and active remote sensing methods for assessing soil salinity. Yang et al. (2015) used a dataset of 92 points with salt data at depth of 30–40 cm that was divided into two subsets for prediction and validation.

In general, the techniques and methods used in the previous studies to extract soil salinity information from satellite images have been successful to some extent. However, the study of soil salinity in the low-lying coastal plain where tidal effects associated with topographical conditions and sea level rise, for instance, Mekong Delta, is relatively poorly investigated. Hence, we used simultaneous sampling soil data with Landsat 8 image acquisition. Soil samples were analyzed to obtain EC_1:5, and the coefficient of determination between EC_1:5 of soil content and spectral reflectance bands at the location of the sampling points on Landsat 8 were investigated. Based on the two sources of data, regression analysis was performed to relate satellite remote sensing data and ground measured salinity to derive the soil salinity map for Tra Vinh Province. This method can rapidly assess the soil salinity for the whole Mekong Delta less commonly found in the previous studies, especially under continuous drought and seawater conditions rising in this coastal plain.

Study area

The Tra Vinh Province is selected for this study (Fig. 1). Its geographical location ranges from 9° 31′ 46″ N to 10° 04′ 05″ N and from 105° 57′ 16″ E to 106° 36′ 04″ E, covering an area of 2341.2 km². Tra Vinh is characterized by the lower delta terrain, belonging to the last point of downstream of the Mekong River with an average elevation ranging from 0.6 m to 1.0 m (occupying 66% of the area of the province). Its soil is attributed by high sediments mainly deposited due to annual flooding. Its sedimentations were from the river and sea to form alluvial plains, lowland, swamp, and sand dunes. The sand dunes have an average height of 1.0 m to 3.0 m, particularly with heights of up to 5.0 m in Long Son and 10 m in Long Toan. For example, high altitude terrains in the Tra Vinh Province can be seen in Fig. 1. Located in the tropical monsoon climate, Tra Vinh’s climate is divided into two distinct seasons, namely rainy and dry seasons. The rainy season starts from May to October with prevailing wind of southwest monsoon and causes heavy rain. The dry season starts from November to April. In a year, rainfall is unevenly distributed, forming two distinct seasons corresponding to the monsoon regime in the area. The rainy season associated with the southwest monsoon starts in May and ends in October. At Cang Long weather station, the average rainfall in the rainy season is 1617 mm with less annual fluctuation. The dry season is associated with less damping northeast winds, starting in November and ending in April of the next year. The average dry season rainfall is 198 mm and fluctuates significantly through the season. The temperatures in the area are relatively high (26–27.6 °C), while their difference is not much during the season. The lowest temperature in a year falls in January and the highest temperature is in April. The average humidity in a year is 83–85 %. The driest months are in February and March. Average evapotranspiration varies from 48 mm in July to 111 mm in March. The highest evapotranspiration occurs in the dry season from December to April, during which timeframe rainfall is negligible. The Tra Vinh Province is directly affected by the semi-tide regime of the East Sea through the Co Chien River and Hau River (Provincial Report in 2011–2015). The tidal effect gradually decreases from the sea to the inland, mainly in coastal areas. The tide also causes negative consequences of salinity intrusion into the interior, changing the quality of soil in the direction of increased salinity.

Saline water intrudes into Tra Vinh simultaneously with the tide regime, depending on the seasons of the year, generally highest in March, April, and May. According to observation data, the salinity is about 25–32 g/l at the gates of Cung Hau and Dinh An. On the pedology side (Fig. 2), land of Tra Vinh Province is dominated by alluvial soils and acid sulphate soils, with 56% of saline-affected soils and 24.3% of acid soils, including three major soil groups: (i) sandy soil has an area of 15,169.3 hectares, mainly distributed in Tra Cu, Cau Ngang, Duyen Hai, and Chau Thanh districts and scattered in the other districts ; (ii) alluvial land covers an area of 132,983.4 hectares, primarily distributed along sand dunes and concentrated in Cau Ngang, Tra Cu, Duyen Hai, and Chau Thanh; and (iii) acidic soil area is 55,719.4 hectares, mainly distributed in Cang Long and Cau Ke with some scattered in Tieu Can, Chau Thanh, and Tra Vinh town.

Groundwater in coastal areas of Tra Vinh Province exists in two forms, namely shallow groundwater and deep groundwater. The shallow groundwater is under the sand dunes. The water source is mainly from local rainwater stored as groundwater with depth of less than 100 m and small reserves. The deep groundwater also known as Pleistocene groundwater is at depth of more than 100 m. The water source is enough, around 97,000 m³/day, for daily life and living with ability to exploit.

Materials

(1) Landsat 8 OLI satellite image Level 1T product with radiometrically corrected and co-registered to a cartographic projection and terrain corrections acquired on February 14, 2017, with a resolution of 30 m.

(2) Field salinity survey was conducted by taking soil samples over Tra Vinh Province. In total, 44 soil samples, between 0 and 20 cm depths, were collected with 41 of them used in this study since 3 samples were eliminated due to their locations being underneath of cloudy sky. The locations of samples were selected based on the soil classification map of Tra Vinh Province (Fig. 2) to meet the requirement that samples are located at different soil types, as shown in Fig. 3. The soil samples were taken in the field at different soil types on 13–14 February 2017 (i.e., during the dry season) and in weather conditions with no rainfall, but sunny (Fig. 4). Each soil sample weighs 2 kg. Sample preparation for laboratory analysis is addressed as follows: soil samples from the field must be crushed down in time, picking up the plant, gravel stone, etc., and dried in the air. The sample room must be ventilated and free of chemicals. Then, the soil samples were analyzed for quantifying EC_1:5 (Richards 1954). The electrical conductivity is measured at 25 °C from an unfiltered 1:5 soil to deionized water suspension. Soil suspensions were prepared using 35 mL of distilled water and 7 g of soil into 50 mL plastic centrifuge tubes (Cat. No. 06-443-20, Fisherbrand). Soil suspensions were continuously shaken by using a mechanical shaker (132 rpm) at 25 °C for 60 min to dissolve soluble salts. EC_1:5 is defined as the first of three steps to estimate soil salinity (ECe). It was determined by mixing 1 part soil with 5 parts distilled or deionized water. After mixing the sample and allowing the sediment to settle, the electrical conductivity of the solution was tested (Slinger and Tension 2005). Therefore, after 15 min settling time, EC was determined using a conductivity probe (Conductivity Meter SCM 902A, Apel Instrument). The EC meter was calibrated by KCl standard solution (1.413 dS/m) (Cat. No. 2974326, Hach Company, Loveland, CO, USA) prior to soil suspensions measurement. Among 41 samples, 29 samples were used for training and 12 samples for validation. The samples used for training and validation have to meet the condition of being representative for typical land use types in the regions of concern with evenly spatial distributed over the Province (Table 4 in Appendix).