Water Resource Monitoring Using Remote Sensing - Coursework Example

Remote sensing applications in water resources management Introduction Remote sensing technology has gained much recognition across the globe today due to its effectiveness in natural resources management. The science of remote sensing relies on the use of laser radiations such as the infrared radiations, radio waves microwaves, etc. Due to the use of satellite imagery in remote sensing techniques and strong radiations to discriminate fine details on various aspects of nature, constant supervision of various natural phenomena has been made essential and effective using remotely sensed data. The platforms used in remote sensing applications on the other hands can be applied even in places where man cannot traverse e.g. above large water bodies, other dangerous places such as flooded regions, diseased areas among others. It is for this reason that remote sensing applications have gained much recognition for use in areas that can only be accessed remotely where fine and accurate details of the occurring phenomena has been gained with utter totality. Due to the availability of remotely sensed data on vast water bodies from satellite images, remote sensing technology has been of great importance in water recourses management. The ability of remote sensing radiations to distinguish different and fine details on various natural phenomena present in different waters gives it the capacity to be used in water resources management very effectively. Remote sensing applications in water resources management have presented many beneficial applications in improving and managing the quality of various waters across the globe. Pollution and diminishing water boundaries due to human and vegetation encroachment are other aspects that have been effectively managed by remote sensing techniques. In this paper, I will address the effectiveness of remote sensing applications in water resources management. I will also address the technological aspect behind the applications of remote sensing techniques in water resources management such as resolution management and fine details detection using remote sensing devices. The radiations used in remote sensing technique such as infrared radiations, UV radiations, x- rays, γ- radiations and other types of radiations can distinguish different tones of colour as well as giving different colour characteristics on various phenomena detected in or above the water surface (Bartholom´e & Belward, 2005). These characteristics give the remote sensing technology its ability to discriminate fine details on the various aspects of the water in various places around the globe. The variations in colour discrimination on various phenomenal characteristics are dependent on the ability of various water features to reflect the light shone on them by the remote sensing devices which are determined by their differing characteristic appearance. Depending on the ray used in data capturing and the platform housing the remote sensing devices, varying degree of results can be distinguished. Based on these observations, the various phenomena such as water chemical content, plants growth in water, varying water PH, and depth of the water as well as other water quality measuring parameters can be detected with much ease and accuracy. Basic concepts in remote sensing of water resources Campo, Caparrini, & Castelli, (2006) note that the substances present in various waters can considerably change the surface waters’ response to varying degree of light shone on them, commonly referred to as the backscattering ability of surface water bodies to respond to light radiations. Remote sensing techniques (Beaureu of Meteorology, 2010) can then measure the changes on spectral characteristics of the water surfaces by distinguishing the spectral signatures backscattered by various phenomena found on the water surfaces. These measures can then be related by empirical as well as analytical models in order to relate the detected image with the reality and help in decision making. Different radiations with varying wavelengths are differently suited for different remote sensing applications and their applicability depend on the different characteristics of the substances being measured, the concentration of these substances in the water and the characteristics of the sensors used in the detection. Among the various aspects that can be effectively measured using remote sensing technology in the management of water quality include the availability of suspended particles on the water surfaces (water turbidity), the presence of algae, chemicals such as nutrients, pesticides, presence of dissolved organic substances, oil spills, presence of aquatic vascular plants in the water, etc. the type of radiations used to detect various factors depend on the type of substances to be detected as prior mentioned in this paper. For instance, the presence of suspended sediments such as algae, oils, or aquatic plants tend to change the energy spectra of the reflected solar or thermal radiations hence can be very easily detected using the remote sensing devices. On the contrary, most chemicals, as well as other pathogens, do not change or affect the spectral qualities or thermal properties of the water surface hence can be detected using the infrared radiations. IKONOS and Landsat as IPCC, (2007) assert are some of the remote sensing software that has gained increasing prominence in water resources management in the later days. IKONOS has been most preferred compared to Landsat technologies in water bodies sensing due to its distinguishing sharp resolution power to be able to discriminate fine details on various aspects of the water bodies. The correct interpretation of remotely sensed data requires an accurate use of a combination of spectral band ratios (Kargel, Abrams, Bishop, Bush, Hamilton, Jiskoot, et al., 2005). Different bands can be distinguished on different waters with different physical characteristics; spectral qualities and radiations used. Both temporal and spatial variations can be detected allowing accurate and most relevant analytical techniques to be initiated on various surfaces. This enables various water management agencies to effectively manage and supervise their waters based on their qualities, and sizes over time and space. Use of remote sensing in determining the optimal properties of clear and turbid groundwater resources The optical characteristics of water vary greatly depending on two primary factors: the variations in the concentration of the constituents present in the water, as well as the variations in the material composition of the various particles in the water i.e. the variation in size as well as the refractive indices of the particles in the water. These variations determine the water reflectance which varies based on the backscattering coefficient; bb (λ) and the absorption coefficient; α (λ). Different water qualities have different spectral capacities giving them the distinguishing characteristics that define their optical properties. According to Immerzeel, (2008), the spectral characteristics of the natural water reflected from the surface vary depending on the ratio bb (λ)/ α (λ). Other factors determining the overall optical reflectance of water bodies are the inherent optical properties (IOPs) determined by the type and concentration of different particles in the water. Turbidity of the ocean water is determined by the amount of foreign particles suspended on its surface. These particles have varying degree of reaction with the various radiation properties defining their nature. Suspended surface particles scatter differently the incident light giving them their distinguishing properties. Clear water surfaces tend to scatter light uniformly giving a continuous beam of reflected radiations. As the turbidity of the surface continues to intensify, the variations in the scatter degree also grow wider designating the surface composition. The inherent colour of the water surfaces can be quantified based on its remote sensing reflectance Rrs (λ). This reflectance provides the measure for the ratio of upwelling and downwelling water characteristics at the surface. Such properties are referred to as the optical properties of water. Different water constituent responds differently to the absorption, (α), and backscattering, (bb), which can be easily detected by the remote sensing devices. Different spectral signatures can, therefore, be derived from these properties and used to distinguish various features present in various water bodies. Snow and ice monitoring with remote sensing Snow and ice melts highly contribute to the quality of water in various water bodies over the earth’s surface. Melt water trickles and find its way into the water bodies in the lowland regions. Analysis of surface water qualities as a result of melt water is determined by the surface temperatures which regulate surface snow melts. Hydrologists, therefore, need to concentrate on the surface temperature analysis in order to be able to deduce the extent of snow melt in a particular region. Kulkarni et al., (2007) asserts that since the shortwave albedo of snow can vary from less than 50 percent to over 90 percent, depending on the snow conditions such as the depth and composition, it has a great significance in determining the rate of snow melts. The extent of melt as well as the quality of the snow water greatly determines the quality and quantity of both surface as well as groundwater resources all over the earths surface. The quality of ice/ snow and melt water can be quantified using the same procedures as those used in remote water assessment. The coefficients of absorption, α (λ), and backscattering, bb (λ), as well as the remote sensing reflectance, Rrs (λ), play a very significant role in determining the various properties of snow water and ice. Higher spatial resolutions and repeat time are, however, suitable for acquiring accurate information on the various characteristic of the ice and snow cover under study. Cloud monitoring using remote sensing and its importance Cloud formation is the true measure of the amount of water resources suspended in the atmosphere and determines the amount of water available on the surface of the earth. Clouds monitoring in water resources management is, therefore, integral and key in determining the quantity and quality of water resources available in a given region. Remote sensors are able to distinguish rain bearing clouds from the non- rain bearing clouds by recording the temperatures on their tops. Rainfall is made from thick convective clouds with rather cold temperatures on their tops. Microwave sensors, near thermally infrared sensors, and sensors using near infrared radiations are suitable for detecting the various properties of clouds including the thickness, and the size of droplets making up a given cloud. Sensor/ systems best for water resources monitoring and analysis Different sensors have been used in water resources analysis. The choice of a sensor is determined by a number of factors including the amount of details to be distinguished, nature of the surface to be studied. IKONOS- 2, Quick Bird imagery systems and LIDAR sensor have been used a great deal in water resources analysis and management due to their high resolution capabilities (Yin, Zhang, Liu, Colella, & Chen, 2008). The earlier two sensors have multispectral digital imagery of resolution power almost equivalent to those of small to medium scale photographic applications. This gives them suitable resolution powers suitable for absolute discrimination of fine details on water quality and other related characteristics. The two sensors have the capability of covering up to 11 x 11 KMs, a surface large enough to enable them monitor large expanses of water bodies (Kargel, Abrams, Bishop, Bush, Hamilton, & Jiskoot et al., 2005). LIDAR, on the other hand, has used a wide range of radiations including ultraviolet, visible, or near infrared radiations to image objects. Different properties of the same object can, therefore, be discriminated in a single illumination. This property makes LIDAR suitable for use in a wide range of detections on different targets found in various water bodies including non- metallic objects, rainfall characteristics, detection of chemical compounds on different surfaces, aerosols, cloud characteristics and even in the detection of single molecules on different surfaces. Based on these reasons, LIDAR has been widely used in atmospheric researches and meteorology to distinguish fine details on various objects under study. Resolution consideration (spectral, spatial and radiometric) in remote sensing applications Spatial resolution is concerned with the ability of the sensor to detect the smallest possible feature. Spatial resolution is highly determined by the distance between the camera and the ground that is dependent on the distance between the platform and the ground. The spatial resolution is determined by the Instantaneous Field of view (IFOV) of the camera. The size of the area viewed by the camera is derived by multiplying the IFOV and the distance between the ground and the platform carrying the sensor which defines the size of a single pixel recorded by the sensor, referred to as the resolution cell (Werner, 2001). The resolution cell determines the optimum spatial resolution of the sensor, i.e. the size of the smallest object detected by the sensor. As Campo, Caparrini, & Castelli, (2006) note, the smaller the distance between the platforms and the ground, the larger the IFOV and the larger the size of the smallest object detected by the sensor hence, the higher the spatial resolution of the camera. Spectral resolution on the other hand is measured by the ability of a sensor to distinguish fine details of the objects under study. Different objects have different emissivity to varying degree of radiations. According to Robock, Mu, Vinnikov, & Robinson, (2003) observations, the finer the spectral resolution, the narrower is the range of wavelength in a given band of radiation. Some remote sensing systems have either multispectral or hyperspectral sensors, able to distinguish a wide range of radiations reflected from the surfaces of various objects. Radiometric resolutions, on the other hand, are the ability of a sensor to define very slight differences in the reflected or emitted energy. Sensors with finer radiometric resolutions have the ability to discriminate very fine details and, therefore, detect very small differences in the energy reflected or emitted from the surfaces of different objects hence their accuracy in defining slight differences in the appearance of different objects. Radiometric resolutions, spectral resolution, as well as the spatial resolution, are very important characteristics in defining the appropriate sensors suitable for various applications in different areas of study. Data processing techniques and procedures Remote data processing goes through a number of processes in order to come up with the correct results required for effective analysis. These procedural stages include the pre- processing stage where any distortion on the imaged data is corrected to suit the demands of the final users. The appearance of the imaged data is then enhanced using techniques such as grey level stretching used to improve the spatial contrasts, spatial filtering mechanisms used to enhance the edges of the images collected. The images are then classified into classes based on the colour and brightness of the features contained in them. This helps the users to distinguish the various features detected during capturing. Case study 1: management of water quality in Minnesota lakes Images derived from IKONOS- two and Landsat were used in detecting and monitoring the quality and property of water in lakes of Minnesota. Landsat technologies were used in determining the clarity of the surface waters and minor modifications i.e. the addition of Lake Polygon layers were applied in order to come up with finer details on the spectral resolutions of the data acquired (Kloiber, Brezonik, Olmanson, & Bauer, 2002). The IKONOS images were then related with the NDVI images in order to derive the relationship between vegetation index and the imperviousness. Case study 2: analysis of snow covers dynamics on the major Asian rivers The moderate resolution imaging spectroradiometer was used in the study to discern the various properties of snow covers such as the overall thickness and the covered area. The limits of the snow cover were detected at intervals, and this compared to the volumes of the rivers and other water bodies present in the region (Dankers & de Jong, 2004). The retreat in the amount of snow cover was found to be significantly related to the level of discharge on the rivers in the region including the Indus, Ganges, Brahmaputra, Irrawaddy, Salween, Mekong, Yangtze and Yellow. Conclusion In conclusion, I would like to mention here that remote sensing technologies have been very instrumental in water resources management and monitoring in order to make concrete and sound decisions on the changing characteristics observed from the images generated. The choice of sensor to be used is determined by the size of the area under study, the amount of details required to be generated and the use of the data generated from such remotely sensed data. The ability of a sensor to distinguish fine details on remote sensing platform determines its appropriateness for use in a particular area of study. Spatial resolution, spectral resolution and the radiometric resolution properties of sensors are the basic determinants for use in studying particular phenomena. References Bartholom´e, E., & Belward, A. S. (2005). GLC2000: a new approach to global land cover mapping from Earth observation data. Int. J. Remote Sens. , 26, 1959–1977. Campo, L., Caparrini, F., & Castelli, F. 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