Application of remote sensing and GIS for modeling and assessment of land use/cover change in Amman/Jordan

Modeling and assessment of land use/cover and its impacts play a crucial role in land use planning and formulation of sustainable land use policies. In this study, remote sensing data were used within geographic information system (GIS) to map and predict land use/cover changes near Amman, where half of Jordan's population is living. Images of Landsat TM, ETM+ and OLI were processed and visually interpreted to derive land use/cover for the years 1983, 1989, 1994, 1998, 2003 and 2013. The output maps were analyzed using GIS and cross-tabulated to quantify land use/cover changes for the different periods. The main changes that altered the character of land use/cover in the area were the expansion of urban areas and the recession of forests, agricultural areas (after 1998) and rangelands. The Markov Chain was used to predict future land use/cover, based on the historical changes during 1983-2013. Results showed that prediction of land use/cover would depend on the time interval of the multi-temporal satellite imagery from which the probability of change was derived. The error of prediction was in the range of 2% - 5%, with more accurate prediction for urbanization and less accurate prediction for agricultural areas. The trends of land use/cover change showed that urban areas would expand at the expense of agricultural land and would form 33% of the study area (50km x 60km) by year 2043. The impact of these land use/cover changes would be the increased water demand and wastewater generation in the future

Geospatial Techniques for Improved Water Management in Jordan

This research shows a case from Jordan where geospatial techniques were utilized for irrigation water auditing. The work was based on assessing records of groundwater abstraction in relation to irrigated areas and estimated crop water consumption in three water basins: Yarmouk, Amman-Zarqa and Azraq. Mapping of irrigated areas and crop water requirements was carried out using remote sensing data of Landsat 8 and daily weather records. The methodology was based on visual interpretation and the unsupervised classification for remote sensing data, supported by ground surveys. Net (NCWR) and gross (GCWR) crop water requirements were calculated by merging crop evapotranspiration (ETc), calculated from daily weather records, with maps of irrigated crops. Gross water requirements were compared with groundwater abstractions recorded at a farm level to assess the levels of abstraction in relation to groundwater safe yield. Results showed that irrigated area and GCWR were higher than officially recorded cropped area and abstracted groundwater. The over abstraction of groundwater was estimated to range from 144% to 360% of the safe yield in the three basins. Overlaying the maps of irrigation and groundwater wells enabled the Ministry of Water and Irrigation (MWI) to detect and uncover violations and illegal practices of irrigation, in the form of unlicensed wells, incorrect metering of pumped water and water conveyance for long distances. Results from the work were utilized at s high level of decision-making and changes to the water law were made, with remote sensing data being accredited for monitoring water resources in Jordan

Evaluation of CHIRPS and CFSR precipitation products over the Mujib Basin, Jordan

Open-source climate products provide the possibility of complementing observed data, which sometimes suffer from the scarcity and inconsistency issues. This study aims to evaluate the accuracy of two open-source climate products, Climate Hazards Group Infrared Precipitation with Station (CHIRPS 0.05) and Climate Forecast System Reanalysis (CFSR), in capturing precipitation over the Mujib Basin, Jordan, from 2002 to 2012. Both products were compared with observed data collected from ten climate stations using the point-to-pixel comparison approach at the daily, monthly, seasonal, and annual scales. The coefficient of determination (R2 ), the root mean square error (RMSE), the mean absolute error (MAE), and the relative bias (RB) were used to evaluate the efficiency of CHIRPS and CFSR. While, categorical statistics such as the probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), Heidke skill score (HSS), and frequency bias index (FBI), were used to analyze the precipitation detection capability. Results indicated good correlations between open-source climate products and observed data in the monthly time period, where the R 2 values ranged from 0.65 (CFSR) to 0.76 (CHIRPS). Besides that, CHIRPS performed better than CFSR for the daily, monthly, and seasonal time steps, with a better ability in detecting precipitation. Therefore, CHIRPS is recommended to fill the missing gaps of observed data and to detect the drought conditions over the Mujid Basin

Remote Sensing for Agricultural Water Management in Jordan

This study shows how remote sensing methods are used to support and provide means for improving agricultural water management (AWM) in Jordan through detailed mapping of irrigated areas and irrigation water consumption (IWC). Digital processing and classification methods were applied on multi-temporal data of Landsat 8 and Sentinel-2 to derive maps of irrigated areas for the period 2017–2019. Different relationships were developed between the normalized difference vegetation index (NDVI) and the crop coefficient (Kc) to map evapotranspiration (ET). Using ground data, ET maps were transferred to IWC for the whole country. Spatial analysis was then used to delineate hotspots where shifts between ET and groundwater abstraction were observed. Results showed that the applied remote sensing methods provided accurate maps of irrigated areas. The NDVI-Kc relationships were significant, with coefficients of determination (R2) ranging from 0.89 to 0.93. Subsequently, the ET estimates from the NDVI-Kc relationships were in agreement with remotely sensed ET modeled by SEBAL (NSE = 0.89). In the context of Jordan, results showed that irrigated areas in the country reached 98 thousand ha in 2019, with 64% of this area located in the highlands. The main irrigated crops were vegetables (55%) and fruit trees and olives (40%). The total IWC reached 702 MCM in 2019, constituting 56% of the total water consumption in Jordan, with 375 MCM of this amount being pumped from groundwater, while reported abstraction was only 235 MCM. The study identified the hotspots where illegal abstraction or incorrect metering of groundwater existed. Furthermore, it emphasized the roles of remote sensing in AWM, as it provided updated figures on groundwater abstraction and forecasts for future IWC, which would reach 986 MCM in 2050. Therefore, the approach of ET and IWC mapping would be highly recommended to map ET and to provide estimates of present and future IWC

Remote Sensing-Based Agricultural Water Accounting for the North Jordan Valley

Remote sensing can provide important and updated information for agricultural water accounting (AWA). In this study, data from the open-access portal (WaPOR) of the Food and Agricultural Organization was used in AWA to assess levels of agricultural water consumption and to provide possible solutions for water deficiency in the North Jordan Valley (NJV). Consolidated procedures have been applied to complement and validate the WaPOR products. These included the use of climatic and ground data, the multispectral remote-sensing data of Sentinel-2 and Landsat 8 to derive land use/cover maps, GIS layers, and calibrated evapotranspiration (ET) estimates using the surface energy balance algorithm for land (SEBAL). The data of water inflows and outflows were analyzed using the water accounting plus (WA+) system. Results showed that the WaPOR data of actual ET and interception (AETI) were highly correlated with SEBAL-ET, with WaPOR data overestimating ET for irrigated areas. Precipitation data from WaPOR, on the other hand, were underestimating inflow from rainfall, although significant correlations were observed between these data and rainfall records. As a result, the quality of WaPOR data affected the outputs from agricultural water accounting. The main impact on water accounting outputs was the underestimation of percolated water that could be utilized as a possible solution to water deficiency in the NJV. In addition, the water accounting performance indicators were relatively affected, although they reflected the nature of the study area where water deficiency predominated as a result of inter-basin transfer. The study compared outputs from water accounting in terms of the possible solutions to water deficiency in the NJV and concluded that considerable amounts of recoverable water could be developed when compared with the option of developing surface water from the side wadis. Also, it emphasized the important role of remote-sensing sources for providing information for AWA needed for improved water management and governance

Geomatics, Natural Hazards and Risk Modelling desertification risk in the north-west of Jordan using geospatial and remote sensing techniques Modelling desertification risk in the north-west of Jordan using geospatial and remote sensing techniques

Remote sensing, climate, and ground data were used within a geographic information system (GIS) to map desertification risk in the north-west of Jordan. The approach was based on modelling wind and water erosion and incorporating the results with a map representing the severity of drought. Water erosion was modelled by the universal soil loss equation, while wind erosion was modelled by a dust emission model. The extent of drought was mapped using the evapotranspiration water stress index (EWSI) which incorporated actual and potential evapotranspiration. Output maps were assessed within GIS in terms of spatial patterns and the degree of correlation with soil surficial properties. Results showed that both topography and soil explained 75% of the variation in water erosion, while soil explained 25% of the variation in wind erosion, which was mainly controlled by natural factors of topography and wind. Analysis of the EWSI map showed that drought risk was dominating most of the rainfed areas. The combined effects of soil erosion and drought were reflected on the desertification risk map. The adoption of these geospatial and remote sensing techniques is, therefore, recommended to map desertification risk in Jordan and in similar arid environments

SCADA and related technologies

Presented at SCADA and related technologies for irrigation district modernization, II: a USCID water management conference held on June 6-9, 2007 in Denver, Colorado.Includes bibliographical references.A study was conducted in Mafraq, Jordan, between 32°15' and 32°50' north latitude and 36°15' and 36°50' east longitude, to investigate the potential use of remotely sensed data to estimate evapotranspiration (ET). Evapotranspiration values were estimating by integrating high resolution (ASTER) and coarse resolution (MODIS) data in the ALARM model. The first part of the study focused on identifying crop types and developing a relationship between plant canopy height (PH) and Normalized Difference Vegetation Index (NDVI) from ASTER. The second part of the study concentrated on modeling actual ET through the integration of data from the previous stage and from the MODIS satellite with the ALARM model. Field surveys and data collection, from March to October 2005, included 37 farms with a total of 247 plots representing irrigated vegetable crops in the area. The ET was calculated using the ALARM model with input parameters of land surface temperature, leaf area index, surface albedo, view angle, view time from 1-km MODIS data and plant canopy height derived from its empirical relationship with ASTER NDVI. Results showed that ASTER satellite imagery could provide an adequate identification of different irrigated vegetable crops in the study area. The use of estimated PH derived from its relationships with ASTER-NDVI instead of ground measurements was not a significant source of error for estimating ET. The average performance of the ALARM model showed a strong spatial variability from one site to another depending on the individual components of each site (total irrigated area and type of irrigated crops). The calculation approach of ET using the ALARM model with MODIS satellite data and crop parameters from ASTER data can be used to provide spatial distribution of actual ET. Therefore, the calibrated approach from this study could be used as a new tool for estimating ET for the irrigated area of Mafraq and similar irrigated regions in Jordan. The study also demonstrated the importance of radiometric correction for satellite images before using them in similar studies

Modelling desertification risk in the north-west of Jordan using geospatial and remote sensing techniques

Remote sensing, climate, and ground data were used within a geographic information system (GIS) to map desertification risk in the north-west of Jordan. The approach was based on modelling wind and water erosion and incorporating the results with a map representing the severity of drought. Water erosion was modelled by the universal soil loss equation, while wind erosion was modelled by a dust emission model. The extent of drought was mapped using the evapotranspiration water stress index (EWSI) which incorporated actual and potential evapotranspiration. Output maps were assessed within GIS in terms of spatial patterns and the degree of correlation with soil surficial properties. Results showed that both topography and soil explained 75% of the variation in water erosion, while soil explained 25% of the variation in wind erosion, which was mainly controlled by natural factors of topography and wind. Analysis of the EWSI map showed that drought risk was dominating most of the rainfed areas. The combined effects of soil erosion and drought were reflected on the desertification risk map. The adoption of these geospatial and remote sensing techniques is, therefore, recommended to map desertification risk in Jordan and in similar arid environments

Characteristics and outcomes of COVID-19 patients admitted to hospital with and without respiratory symptoms

Background: COVID-19 is primarily known as a respiratory illness; however, many patients present to hospital without respiratory symptoms. The association between non-respiratory presentations of COVID-19 and outcomes remains unclear. We investigated risk factors and clinical outcomes in patients with no respiratory symptoms (NRS) and respiratory symptoms (RS) at hospital admission. Methods: This study describes clinical features, physiological parameters, and outcomes of hospitalised COVID-19 patients, stratified by the presence or absence of respiratory symptoms at hospital admission. RS patients had one or more of: cough, shortness of breath, sore throat, runny nose or wheezing; while NRS patients did not. Results: Of 178,640 patients in the study, 86.4 % presented with RS, while 13.6 % had NRS. NRS patients were older (median age: NRS: 74 vs RS: 65) and less likely to be admitted to the ICU (NRS: 36.7 % vs RS: 37.5 %). NRS patients had a higher crude in-hospital case-fatality ratio (NRS 41.1 % vs. RS 32.0 %), but a lower risk of death after adjusting for confounders (HR 0.88 [0.83-0.93]). Conclusion: Approximately one in seven COVID-19 patients presented at hospital admission without respiratory symptoms. These patients were older, had lower ICU admission rates, and had a lower risk of in-hospital mortality after adjusting for confounders