WO2012096684A1

WO2012096684A1 - Systems and methods for automated mapping and analysis of a region

Info

Publication number: WO2012096684A1
Application number: PCT/US2011/034546
Authority: WO
Inventors: Kurt A. Reichold; Scott VASSEUR; Keith W. KENNEDY; Maynard J. RILEY
Original assignee: Diversey, Inc.
Priority date: 2011-01-14
Filing date: 2011-04-29
Publication date: 2012-07-19

Abstract

Systems and methods for analyzing and mapping a region, such as a body of water. The system is configured to obtain and analyze characteristic data associated with the body of water in an automated or at least partially automated manner. For example, a data gathering device (e.g., a non-submersible, dirigible vessel) is controlled to gather data throughout the body of water. After the data has been gathered, the data is compressed, processed, and analyzed. The body of water is divided into a number of areas or coordinates for which compressed, processed, and analyzed data is associated. The analyzed data includes, for example, statistical analysis of the body of water and topographical maps that graphically illustrate the characteristics within the body of water.

Description

SYSTEMS AND METHODS FOR AUTOMATED MAPPING

AND ANALYSIS OF A REGION

RELATED APPLICATIONS

[0001] This application claims the benefit of previously-filed, co-pending U.S.

Provisional Patent Application No. 61/432,900, filed January 14, 2011, the entire content of which is hereby incorporated by reference.

BACKGROUND

[0002] This invention relates to automated mapping and analysis of a region, such as a lagoon or another body of water.

[0003] If the lagoon has, for example, too much sludge (e.g., a sludge blanket level that is too thick), the amount of water the lagoon is able to hold is reduced. This results in reduced hydraulic retention time and diminished lagoon performance (e.g., higher than optimal levels of effluent water pollution). Various chemical treatments are available to improve the conditions (e.g., sludge blanket level, odor level, etc.) of the lagoon; however, the effects of the treatments are difficult to discern without extensive monitoring and analysis of the contents and composition of the lagoon. Monitoring the conditions of the lagoon is important because, for example, if a lagoon has too much sludge, the lagoon may have to be dredged at a significant cost to a company, municipality, or the like. Monitoring the conditions of the lagoon is often accomplished by mapping and analyzing the contents and composition of the lagoon and determining, among other things, whether a chemical treatment is necessary, whether a chemical treatment is working, or whether the lagoon requires alternative treatment methods (e.g., dredging). Monitoring the conditions of the lagoon can involve the use of a model remote control boat that is directed to different locations of the lagoon for the purposes of gathering test data. Such boats are often gasoline-powered, direct-drive devices designed for forward, straight-line travel.

SUMMARY

[0004] Although mapping provides beneficial information related to the condition of a lagoon, mapping is often a time and labor intensive process that requires the correlation of significant amounts of measured characteristic data (e.g., sludge depth data) with a corresponding amount of location data. For example, sludge blanket level is currently determined by taking manual core samples at regular grid intersection points and calculating the percent of the lagoon that is occupied by sludge. Such a technique for lagoon mapping is approximate and labor intensive. The characteristic data then must be analyzed by experienced professionals familiar with the content and chemical composition of lagoons. The amount of time and labor required for mapping a single lagoon, and the level of training required to analyze the corresponding data, limits the wide-scale, time-effective mapping and analysis of lagoons and other bodies of water.

[0005] As such, the invention provides automated systems and methods for mapping and analyzing lagoons and other bodies of water. In one construction, the system includes a data gathering device and a data processing and analysis device. The data gathering device is configured to navigate a lagoon and gather data corresponding to one or more characteristics of the lagoon (e.g., sludge blanket depth, conductivity, dissolved oxygen, pH, oxidation reduction potential ("ORP"), suspended solids, temperature, chlorophyll content, ammonia concentration, nitrate concentration, temperature, exposure to sunlight, turbidity, sulfide concentration, and chemical oxygen demand ("COD"), microbial content, etc.). In some constructions, the data gathering device is configured to gather water samples a one or more predetermined water depths using water sampling equipment. The data gathering device operates autonomously based on a predetermined path retrieved from a memory and based on GPS coordinates, or is controlled directly or remotely by a user. The gathered characteristic data and corresponding location data (e.g., GPS coordinates, latitudes and longitudes, etc.) are stored on a memory such as an SD card, a flash drive, a server, or the like. The data processing and analysis device is configured to access the stored characteristic data and location data from the memory and compress, process, and analyze the characteristic data and location data. The data processing and analysis device is also configured to generate graphical and numerical representations of the characteristics of the lagoon efficiently and without requiring assistance from experienced lagoon mapping professionals.

[0006] In one embodiment, the invention provides a method of analyzing a region (e.g., a lagoon). The method includes obtaining a set of characteristic data that corresponds to a characteristic of the region, obtaining a set of location data corresponding to locations within the region, and correlating the set of characteristic data with the set of location data. The set of characteristic data includes a plurality of characteristic data samples, and the location data includes a plurality of location coordinates. For example, the location data includes location coordinates for each of the plurality of characteristic data samples in the set of characteristic data. The method also includes storing the set of characteristic data and the set of location data in a memory, and accessing the stored characteristic data and the location data. After the data has been accessed, the data is compressed, for example, by combining the samples within the plurality of characteristic data samples that have corresponding location coordinates. More specifically, the samples that have corresponding location coordinates can be combined (e.g., averaged), and each of the location coordinates can be associated with a coordinate within a grid (e.g., a rectangular grid, a square grid, etc.). For each grid coordinate, the averaged characteristic data samples corresponding to each of the associated location coordinates are combined to determine an output characteristic value for the grid coordinate. The output characteristic value for each grid coordinate is then used to generate a representation (e.g., graphical, numerical, etc.) of the region.

[0007] In another embodiment, the invention provides a method of analyzing a lagoon. The method includes obtaining a set of characteristic data corresponding to a characteristic of the lagoon. The set of characteristic data includes a plurality of characteristic data samples. A set of location data corresponding to a plurality of locations within the lagoon is obtained. The set of location data includes a plurality of location coordinates. The method also includes correlating the set of characteristic data with the set of location data, storing the set of characteristic data and the set of location data to a memory, accessing the stored characteristic data and the location data from the memory, and compressing the characteristic data by combining each of the characteristic data samples having corresponding location coordinates. Each of the plurality of location coordinates are then associated with one of a plurality of grid coordinates, and the compressed characteristic data samples that correspond to each of the plurality of grid coordinates are combined. The method then includes determining an output characteristic value for each of the plurality of grid coordinates, and generating a representation of the lagoon based on the output characteristic value for each of the plurality of grid coordinates.

[0008] In another embodiment, the invention provides a method of analyzing a body of water. The method includes obtaining a set of characteristic data corresponding to a characteristic of the body of water. The set of characteristic data includes a plurality of characteristic data samples. A set of location data corresponding to a plurality of locations within the body of water is obtained. The set of location data includes a plurality of location coordinates. The method also includes correlating the set of characteristic data with the set of location data, compressing the characteristic data by combining each of the characteristic data samples having corresponding location coordinates, and associating each of the plurality of location coordinates with one of a plurality of grid coordinates. The method then includes combining the compressed characteristic data samples that correspond to each of the plurality of grid coordinates, determining an output characteristic value for each of the plurality of grid coordinates, and generating a representation of the body of water based on the output characteristic value for each of the plurality of grid coordinates.

[0009] In another embodiment, the invention provides a system for mapping a characteristic of a lagoon. The system includes a non-submersible, dirigible vessel and a memory. The vessel includes a platform, a propulsion device, a testing device, and a location device. The platform includes an upper surface, a lower surface, and an aperture located at an interior portion of each of the upper surface and the lower surface. The aperture is configured to allow access to the lagoon through the interior portion of the platform. The upper surface is located above the surface of the lagoon. The propulsion device is operable to propel the vessel in a first direction and a second direction. The propulsion device is positioned above the upper surface and is coupled to the platform or stationary with respect to the platform. The testing device is configured to obtain a plurality of characteristic data samples, and the location device is configured to determine a plurality of location coordinates corresponding to the location of the vessel. The memory is configured to store a set of characteristic data corresponding to a characteristic of the lagoon. The set of characteristic data includes the plurality of characteristic data samples. The memory is also configured to store a set of location data corresponding to a plurality of locations within the lagoon. The set of location data includes the plurality of location coordinates.

[0010] Other aspects of the invention will become apparent by consideration of the detailed description and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Fig. 1 is a block diagram of a data gathering device.

[0012] Fig. 2 is a front view of a data gathering device according to an embodiment of the invention.

[0013] Fig. 3 is a section view of the data gathering device of Fig. 2.

[0014] Fig. 4 is a top view of a data gathering device of Fig. 2. [0015] Fig. 5 is a block diagram of a data processing and analysis device.

[0016] Figs. 6-7 represent a process for analyzing lagoon data.

[0017] Fig. 8 illustrates a grid according to an implementation of the invention.

[0018] Fig. 9 illustrates a data gathering path for the grid of Fig. 5.

[0019] Fig. 10 represents a process for compressing data according to an implementation of the invention.

[0020] Fig. 11 illustrates a relationship between a data point and a grid coordinate.

[0021] Fig. 12 illustrates a plurality of data points associated with a plurality of grid coordinates.

[0022] Figs. 13-14 represent a process for processing lagoon data. [0023] Figs. 15-16 represent a process for analyzing lagoon data.

[0024] Figs. 17-19 illustrate graphical and numerical representations of analyzed lagoon data.

[0025] Fig. 20 illustrates a sludge height topographical map for a first lagoon. [0026] Fig. 21 illustrates a sludge height topographical map for a second lagoon. DETAILED DESCRIPTION

[0027] Before any embodiments of the invention are explained in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The invention is capable of other embodiments and of being practiced or of being carried out in various ways.

[0028] The invention described herein relates to systems and methods for analyzing and mapping a region or area, such as a body of water. The body of water is, for example, freshwater, a wastewater stabilization lagoon such as an industrial lagoon (e.g., dairies, food processing plants, rendering plants, etc.), agricultural concentrated animal feeding operations ("CAFOs"), commercial retention or fire protection ponds (e.g., for shopping centers, department stores, etc.), residential run-off ponds, municipal treatment lagoons (e.g., residential sewage lagoons), and the like. For descriptive purposes, bodies of water such as those listed above are referred to herein as lagoons. Characteristics of lagoons, such as sludge blanket depth, conductivity, dissolved oxygen, pH, oxidation reduction potential ("ORP"), suspended solids, temperature, chlorophyll content, ammonia concentration, nitrate concentration, temperature, exposure to sunlight, turbidity, sulfide concentration, microbial content, and chemical oxygen demand ("COD") are monitored to determine treatment strategies and actions for the lagoon. However, determining these characteristics is often time consuming and labor intensive. As such, the invention provides an automated, or at least partially automated, system for gathering and analyzing characteristic data for the lagoon, and corresponding processes. For example, a data gathering device (e.g., a boat, a raft, a remote controlled vessel, a platform, etc.) is remotely or manually controlled such that it gathers data from most of or an entire lagoon. The lagoon can generally be divided into a number of areas or coordinates for which data is to be gathered. After the data has been gathered, the data is compressed, processed, and analyzed. The analyzed data includes, for example, a statistical analysis of the lagoon characteristics and topographical maps that illustrate the lagoon characteristics. The topographical maps can be generated and stored for each of the characteristics of the lagoon. The topographical maps can then be compared to maps from, for example, different times of the year, different times of the day, etc. as a series of before-and-after maps configured for the evaluation of one or more remediation services, optimization products, or management practices.

[0029] Fig. 1 illustrates a device 10 for gathering information related to a lagoon. The device 10 is, for example, a non- submersible, dirigible vessel. The device 10 includes a controller 15, a user interface module 20, one or more indicators 25, a power supply module 30, a communications module 35, a GPS module 40, a sound navigation and ranging

("SONAR") module 45, and one or more motors 50. The controller 15 includes, or is connected to an external device (e.g., a computer), which includes combinations of software and hardware that are operable to, among other things, control the operation of the device 10, control the movement of the device 10, and activate the one or more indicators 25 (e.g., LEDs or a liquid crystal display ("LCD")), raise and lower testing equipment, etc. The controller 15 includes, for example, a processing unit 55 (e.g., a microprocessor, a microcontroller, or another suitable programmable device), a memory 60, and a bus. The bus connects various components of the controller 15, including the memory 60, to the processing unit 55. [0030] The memory 60 includes, for example, a read-only memory ("ROM"), a random access memory ("RAM"), an electrically erasable programmable read-only memory

("EEPROM"), a flash memory, a hard disk, an SD card, or another suitable magnetic, optical, physical, or electronic memory device. The processing unit 55 is connected to the memory 60 and executes software that is capable of being stored in the RAM (e.g., during execution), the ROM (e.g., on a generally permanent basis), or another non-transitory computer readable medium such as another memory or a disc. Additionally or alternatively, the memory 60 is included in the processing unit 55. The controller 15 also includes an input/output ("I/O") system 65 that includes routines for transferring information between components within the controller 15 and other components of the device 10. Software included in the

implementation of the device 10 is stored in the memory 60 of the controller 15. The software includes, for example, firmware, one or more applications, program data, one or more program modules, and other executable instructions. The controller 15 is configured to retrieve from memory and execute, among other things, instructions related to the control processes and methods described below. In other constructions, the controller 15 or external device includes additional, fewer, or different components.

[0031] The power supply module 30 supplies a nominal AC or DC voltage to the device 10. For example, the power supply module 30 is powered by one or more batteries or battery packs (e.g., 12V batteries), a generator, or the like. In other constructions, the device 10 is powered by mains power having nominal line voltages between, for example, 100V and 240V AC and frequencies of approximately 50-60Hz, and supplies higher or lower voltages to operate circuits and components within the device 10.

[0032] The motor 50 or another device (e.g., a fan-motor combination) configured to provide propulsion or motion to the device 10 around the lagoon is controlled by control signals received from the controller 15. In some implementations, the controller 15 is configured to control the motor 50 and the motion of the device 10 autonomously using a plurality of sensors and a stored program for traversing the lagoon (e.g., latitude and longitude coordinates along a data gathering path are stored in a memory and accessed by the device 10). In other implementations, the device 10 is controlled based on signals received from an antenna (e.g., a remote control antenna) within the communications module 35. For example, the device 10 can be controlled remotely by a user standing on shore or at another location from which the user is able to monitor the position of the device 10. The motor 50 receives power either through the controller 15 or directly from the power supply module 30. In other constructions, the device 10 is navigated around the lagoon using one or more ropes (e.g., being pulled by people, vehicles, etc.).

[0033] The user interface module 20 is used to control or monitor the device 10. For example, the user interface module 20 is operably coupled to the controller 15 to control the output of the motor 50. The user interface module 20 can include a combination of digital and analog input or output devices required to achieve a desired level of control and monitoring for the device 10. For example, the user interface module 20 can include a display and input devices such as a touch-screen display, a plurality of knobs, a plurality of dials, a plurality of switches, a plurality of buttons, or the like. The user interface module 20 can also be configured to display the lagoon data in real-time or a period of time after it has been gathered (e.g., as a two-dimensional animation). In some constructions, such as those where the device 10 is remotely controlled, the user interface module 20 can be separate from and operably connected to the device 10.

[0034] The GPS module 40 is also connected to the controller 15. In some

implementations, the GPS module 40 is used to control the movement and operation of the device 10. For example, the GPS module 40 can be integrated with a trolling motor such that a lagoon is mapped only once and a mapping path is stored in a memory. The stored mapping path can then be retrieved. The GPS module 40 is configured to work in conjunction with the controller 15 to control the movement of the device 10 and ensure that the lagoon is mapped according to a retrieved mapping path. In other constructions, the GPS module 40 provides coordinates to the controller 15 such that the controller 15 can determine where the device 10 is located, or in order to store data related to the characteristics of the lagoon in the memory 60 with corresponding GPS coordinates.

[0035] The SONAR module 45 is configured to determine, for example, depths associated with the lagoon. The SONAR module 45 can be a conventional fish-finder or similar ranging device. The depth readings are sent to the controller 15 to be stored in the memory 60 along with corresponding GPS coordinates. In some constructions, the GPS module 40 and the SONAR module 45 are combined into a single module or are combined into a single component within the device 10. In other constructions, the SONAR module 45 is one of a variety of modules and chemical sampling instruments used to assess or determine characteristics of the lagoon (e.g., with respect location and depth). The other modules and instruments include, for example, modules and instruments necessary to gather water samples and/or determine characteristics of the lagoon such as conductivity, dissolved oxygen, pH, ORP, suspended solids, temperature, chlorophyll content, ammonia levels, nitrate levels, temperature, exposure to sunlight, COD, microbial content, etc. Additionally or alternatively, the device 10 includes a digital camera for capturing images of the body of water (e.g., above or below the water), a video camera for capturing video of the body of water (e.g., above or below the water), etc.

[0036] The SONAR module 45 is also configured to gather depth data for use in mapping the integrity of structural components of the lagoon (e.g., the design footprint of the lagoon's floor, the lagoon' s side embankments, the lagoon's suspended dividing walls, etc.). For example, the data gathered by the SONAR module 45 can be used to identify cavities or recessed areas on the lagoon floor. These cavities or recessed areas can be caused by, among other things, mechanical aerators (e.g., from erosion due to high velocity mixing forces), solar-powered water level circulators (e.g., from forces exerted that alter the lagoon floor), or dredging equipment used to remove sediment, vegetative build-up, etc. The lagoon' s side embankments can be compromised naturally by weed and plant growth, erosion, and by the presence of invasive wildlife. The lagoon' s side embankments can also be compromised by excavation equipment. Such conditions can be detrimental to the performance of the lagoon and may contribute to permit and/or operational violations.

[0037] Lagoons also often include suspended curtains that are distributed throughout the lagoon to, for example, reduce short-circuiting, increase retention time, enhance operational performance, etc. The SONAR module 45 is configured to gather side-scan or wide-scan data that can be used to identify defects or gaps associated with the suspended curtains. The data can also be used to evaluate the performance of the lagoon based on the build up of solids that results from, for example, improper directional water flow. Mapping the structural components of the lagoon as described above to determine the curvature and flow

characteristics within the lagoon can be beneficial to lagoon treatment. Knowing the location of deep spots in the lagoon or the location of recessed areas in the lagoon can be used to more effectively administer, for example, herbicides, treatments to break up mounds of organic waste, etc. In some implementations, such mapping is also used to provide information for shoreline treatment applications, zone- targeted lagoon treatments, performance enhancements (e.g., increasing mixing based on aerator location), etc. [0038] The characteristic data associated with a lagoon is also sensitive to testing parameters such as the speed at which the measurements are able to be taken. The longer it takes to gather the characteristic measurements, the more likely the lagoon is to undergo changes which skew or corrupt the gathered data. For example, changes in the amount of sunlight shining on the lagoon, changes in the temperature of the lagoon, as well as other testing parameters vary with time. In turn, changes in these testing parameters affect the measured characteristics of the lagoon. As such, the ability to measure characteristics of the lagoon in an amount of time that limits the variance in the testing parameters of the lagoon maximizes the reliability of the characteristic data.

[0039] Figs. 2-4 illustrate a lagoon research drone ("LRD") 10 for quickly and efficiently gathering characteristic data. The LRD 10 is, for example, a non- submersible, dirigible vessel. The LRD 10 includes a flotation device 70, power sources 75, propulsion devices 80, a winch 85, a pulley 90, an antenna 95, a circular spool platform 100, a winch drop line 105, and a component shelf 110. In some constructions, the LRD 10 is also configured for the application of chemicals and compounds to a body of water. For example, the LRD 10 can be equipped with a spray boom, pumps, and nozzles to uniformly apply chemicals and compounds throughout the body of water.

[0040] The platform 100 in the illustrated construction of the LRD is substantially circular in shape. However, in other constructions, the platform 100 is square, rectangular, elliptical, etc. The platform includes an aperture 115, an upper surface 120, and a lower surface 125. The platform 100 is constructed from, for example, a plastic, wood, metal, or another suitable material. In some constructions, the platform 100 is centrally hinged for storage or transport. For example, the various components connected to the platform 100 (e.g., the flotation device 70, the power sources 75, the propulsion devices 80, the winch 85, etc.) can be removed to allow the LRD 10 to be stored or transported in a modular manner.

[0041] With reference to Fig. 4, the aperture 115 is centrally located at an interior portion of the platform (i.e., away from the edge of the platform). The aperture 115 is configured to allow access to a body of water through the platform 100 (e.g., for lowing testing equipment into the body of water). The testing equipment is lowered into the body of water using the winch 85, pulley 90, and the winch drop line 105. The aperture 115 reduces the likelihood of or prevents the testing equipment from becoming tangled or snagged. For example, if the testing equipment were to hang off an outer edge of the LRD, the testing equipment would be susceptible to becoming tangled or snagged on the shore or other obstacles (e.g., debris) within the body of water. The testing equipment can be lowered into the body of water to a variety of depths. For example, the testing equipment can be lowered anywhere from a few inches in depth to several feet in depth. However, for a given survey of a body of water, the testing equipment can be held at a constant depth.

[0042] In some constructions, the flotation device 70 is an inner tube configured to surround an outer portion of the platform 100. In other constructions, the flotation device is positioned at an interior portion of the platform 100. In constructions of the invention in which the flotation device 70 is an inner tube, the inner tube can be covered with or encased in a lightweight woven nylon cover. The cover protects the flotation device 70 from being punctured by sharp objects or bottom dragging, and the cover may be removable for washing or sanitization. Additionally or alternatively, the flotation device 70 is made from a heavy duty rubber material that provides added protection from being punctured, and can be inflated using a standard 12V air pump. In some constructions, the flotation device 70 and the platform 100 are like-shaped (e.g., substantially circularly shaped).

[0043] The propulsion devices 80 are, for example, high-rpm 24V DC fans that can be remotely controlled by way of the antenna 95 (e.g., an RC antenna) and RC controls associated with the component shelf 110. The propulsion devices 80 each include one or more motors and are operable to propel the LRD 10 in either a forward direction or a reverse direction (e.g., by rotating its fan blades in a first or a second direction). The propulsion devices 80 are coupled to the platform 100 and are stationary with respect to the platform 100 or are configured to swivel or rotate with respect to the platform 100 to enable controlled turning and rotation of the LRD 10. In some constructions, the LRD 10 has a zero-turn radius. The propulsion devices 80 are placed above the surface of a body of water to increase the controllability of the LRD 10 and decrease disruption of the body of water. For example, a variety of characteristics of the body of water that may be tested using the LRD 10 are sensitive to turbulence and other disruptions. Underwater propellers, boat hulls, etc., contribute to the disruption of the bodies of water and, potentially, corrupt the characteristic measurements associated with the body of water. An output of the propulsion devices 80 and an angle of rotation of the propulsion devices 80 can be individually controlled via, for example, the antenna 95 and a remote control. The propulsion devices allow for precise control of the position of the LRD 10 (e.g., the LRD 10 can be accurately positioned at a location corresponding to specified GPS coordinates). [0044] The component shelf 110 includes, among other things, the controller 15, the communications module 35, the GPS module 40, the SONAR module 45, RC controls, winch controls, etc., described above. In some constructions, the component shelf 110 is partially or completely enclosed in a water tight housing to prevent electronics from being damaged by, for example, water.

[0045] The information and data that is gathered by the device or LRD 10 is sent or transferred to a second device 200 (see Fig. 5). The second device 200 is, for example, a personal computer, a laptop computer, a mobile phone, tablet computer, personal digital assistance ("PDA"), e-reader, or the like. In some implementations, the data is transferred via a wireless local area network ("LAN"), a neighborhood area network ("NAN"), a home area network ("HAN"), or personal area network ("PAN") using any of a variety of

communications protocols, such as Wi-Fi, Bluetooth, ZigBee, or the like. Additionally or alternatively, the data is transferred to the device 200 over a wide area network ("WAN") (e.g., a TCP/IP based network, Global System for Mobile Communications ("GSM"), General Packet Radio Service ("GPRS"), Code Division Multiple Access ("CDMA"), Evolution-Data Optimized ("EV-DO"), Enhanced Data Rates for GSM Evolution ("EDGE"), 3GSM, Digital Enhanced Cordless Telecommunications ("DECT"), Digital AMPS ("IS- 136/TDMA"), or Integrated Digital Enhanced Network ("iDEN"), a Digital Advanced Mobile Phone System ("D-AMPS"), or the like).

[0046] The device 200 includes a controller 205, a user interface module 210, a display 215, a power supply module 220, and a communications module 225. The controller 205 includes, or is connected to an external device (e.g., a computer), which includes

combinations of software and hardware that are operable to, among other things, control the operation of the device 200 and the information that is presented on the display 215. The display 215 is, for example, a liquid crystal display ("LCD"), a light-emitting diode ("LED") display, an organic LED ("OLED") display, an electroluminescent display ("ELD"), a surface-conduction electron-emitter display ("SED"), a field emission display ("FED"), a thin-film transistor ("TFT") LCD, or the like. In other constructions, the display 215 is a Super active-matrix OLED ("AMOLED") display. The controller 205 includes, for example, a processing unit 230 (e.g., a microprocessor, a microcontroller, or another suitable programmable device), a memory 235, and a bus. The bus connects various components of the controller 205, including the memory 235, to the processing unit 230. [0047] The memory 235 includes, for example, a ROM, a RAM, an EEPROM, a flash memory, a hard disk, an SD card, or another suitable magnetic, optical, physical, or electronic memory device. The processing unit 230 is connected to the memory 235 and executes software that is capable of being stored in the RAM (e.g., during execution), the ROM (e.g., on a generally permanent basis), or another non-transitory computer readable medium such as another memory or a disc. Additionally or alternatively, the memory 235 is included in the processing unit 230. The controller 205 also includes an input/output ("I/O") system 240 that includes routines for transferring information between components within the controller 205 and other components of the device 200. Software included in the implementation of the device 200 is stored in the memory 235 of the controller 205. The software includes, for example, firmware, one or more applications, program data, one or more program modules, and other executable instructions. The controller 205 is configured to retrieve from memory and execute, among other things, instructions related to the control processes and methods described below. The information received from the device 200 is received through, for example, the communications module 225. For example, the communications module 225 includes one or more antennas, one or more network interface cards ("NICs"), or the like for communicating over one or more of the networks described above. In other constructions, the controller 205 or external device includes additional, fewer, or different components. In some constructions, the device 10 and the device 200 are combined into a single device that is configured to gather and analyze the data associated with the lagoon.

[0048] The power supply module 220 supplies a nominal AC or DC voltage to the device 200. For example, the power supply module 220 is powered by mains power having nominal line voltages between, for example, 100V and 240V AC and frequencies of approximately 50-60Hz, and supplies higher or lower voltages to operate circuits and components within the device. In other constructions, the device 200 is powered by one or more batteries or battery packs, a generator, or the like.

[0049] The user interface module 210 is used to control or monitor the device 200. For example, the user interface module 210 is operably coupled to the controller 205 to control the information presented on the display 215. The user interface module 210 can include a combination of digital and analog input or output devices required to achieve a desired level of control and monitoring for the device 200. For example, the user interface module 210 can include a display and input devices such as a touch-screen display, a plurality of knobs, a plurality of dials, a plurality of switches, a plurality of buttons, or the like. [0050] In some implementations, the device 200 is also configured to connect to a network (e.g., a WAN, a LAN, or the like) to access a service or utility for rendering a graphical representation of the data associated with the lagoon. For example, the lagoon data is input to a web-based service, such as Google Earth, that is configured to interpret files having extensions associated with GPS coordinates. Using the service, the characteristics of the lagoon are graphically displayed on, for example, the display 215.

[0051] Figs. 6 and 7 illustrate a process 300 for analyzing characteristic data associated with a lagoon. The steps of the process 300 are described in a serial manner for descriptive purposes. Various steps described herein with respect to the process 300 are capable of being executed simultaneously, in parallel, or in an order that differs from the illustrated serial manner of execution. At step 305, data associated with one or more characteristics of the lagoon is obtained. For example, the data includes sludge depths, conductivities, dissolved oxygen levels, pH levels, ORP levels, suspended solid levels, temperatures, microbial content, COD levels for the lagoon, etc. The data is then stored to a memory (step 310) for retrieval at a later time. The data can be gathered at a variety of user selectable or predefined sampling rates, such as 0.5Hz, lHz, 10Hz, etc. The memory is, for example, an SD card or another removable storage device that allows the data to be easily moved from one device to another. Additionally or alternatively, the data is stored on a server that is accessible by a variety of devices (e.g., Internet-enabled devices).

[0052] After the data has been stored to memory, the data is transferred (step 315). For example, the data is transferred to the device 200 for processing and analysis, or to a monitor or display such that the data can be viewed graphically. In some implementations, the data is transferred by removing a removable memory (e.g., an SD card, a flash drive, etc.) from one device and inserting it into another. In other implementations, the data is transferred from one device to another using a short range communications protocol such as Bluetooth, ZigBee, or the like, or is accessed and downloaded from a server or a similar networked device.

[0053] Following step 315, a user is able to view the data (step 320). If the data is to be viewed, the user selects whether the data is to be uploaded to a hosted service (step 325), such as Google Earth. Such a service allows the gathered characteristic data and GPS data to be viewed graphically in Google Earth (step 330). Additionally or alternatively, the data can be viewed as an animation (e.g., a two-dimensional animation) on a monitor (step 335). Following each of steps 330 and 335, the user is able to view the data again or using a different display option. If the user does not view the data or does not want to view the data, the process 300 proceeds to section B shown in and described with respect to Fig. 7.

[0054] At step 340 in Fig. 7, the data is accessed by a program or application that is configured to process and analyze the data. For example, the data is accessed or imported into a spreadsheet that is configured to execute one or more functions (e.g., VBA functions) or programs. Following step 340, the data is converted to facilitate processing and analysis (step 345). The accessed data is in, for example, a tabular form including a date/time stamp, latitude data, longitude data, and lagoon characteristic data (e.g., sludge depth). The latitude and longitude data (i.e., in degrees, minutes, seconds, etc.) is converted to a unit of distance or length such as miles, feet, meters, etc. A reference point is selected as the origin (i.e., (0, 0)) for a grid representing the lagoon. For example, a grid having a width, W, and a length, L, is used to represent the area of the lagoon. In some implementations, the size of the grid can be selected or modified based on the size of the lagoon. The implementations of the invention described herein are described with respect to a grid having seven rows (i.e., W = 7) and ten columns (i.e., L = 10), although other sized grids can be used. The reference point corresponds to an edge or corner of the lagoon, such as the south-east corner. The conversion of the latitude and longitude coordinates to lengths and ultimately to coordinates within the grid is also complicated by the orientation of the lagoon with respect to lines of latitude and longitude. For example, the latitude and longitude coordinates for a lagoon that is rectangular and running North-to-South along its length correspond well to lengths and widths in a rectangular grid. However, when the lagoon is skewed at an angle to the cardinal directions, one or more geometric transformations or rotations are executed to align the latitude and longitude coordinates of the lagoon with the cardinal directions and a rectangular grid.

[0055] Following the conversion of the data, the data is compressed to reduce the total number of samples used to represent the lagoon (step 350). For example, depending on the sampling rate at which the lagoon was mapped, there are likely to be several points of data that correspond to the same latitude and longitude coordinates. As such, the compression of the data includes identifying each of the data points having the same latitude and longitude coordinates, and combining (e.g., averaging) the lagoon characteristic data values for each of those data points. In one implementation, the data is compressed from approximately 15,000 data points to approximately 1,000 data points. Following step 350, the data is processed to associate each pair of latitude and longitude coordinates and its corresponding averaged characteristic data value with a single grid coordinate (step 355). The characteristic data values associated with a particular grid coordinate are then averaged to determine a single, equally- spaced characteristic value for each grid coordinate. After step 355, the data is analyzed (step 360) to generate numerical and graphical representations of the lagoon characteristic data. The compression, processing, and analysis of the data are described in greater detail below with respect to various implementations of the invention.

[0056] Fig. 8 illustrates a grid 400 that is seven units wide and ten units long. The grid 400 represents a lagoon that is to be mapped. Fig. 9 illustrates the grid 400 as well as a path 405 along which data is gathered. For example the device 10 is used to traverse the path 405 and gather data at a plurality of points along the path 405. The amount of data that is gathered depends on the rate at which the device 10 is traveling and a sampling rate. In some implementations, the sample rate is set sufficiently high to ensure that a plurality of data points are gathered for each pair of latitude and longitude coordinates. Due to the redundancy in data gathering, the data is compressed as described above.

[0057] Fig. 10 represents a process 500 for compressing the lagoon characteristic data. The steps of the process 500 are described in an iterative manner for descriptive purposes. Various steps described herein with respect to the process 500 are capable of being executed simultaneously, in parallel, or in an order that differs from the illustrated serial and iterative manner of execution. At step 505, a variable, C, is set equal to one, and a row variable, ROW, is set equal to the variable, C (step 510). The row variable, ROW, points to a row within a table of lagoon data. As described above, the data can be in a tabular form when it is accessed. Each row of the table has a time/date stamp, location coordinates (e.g., a latitude coordinate and a longitude coordinate), and one or more lagoon characteristic values. By setting the row variable, ROW, equal to one, the process 400 is pointing at the first row in the table of lagoon data. The processing and analysis of the data described herein uses similar pointers to select rows of data.

[0058] At step 515, the latitude coordinate for the selected row is compared to the latitude coordinate for the next row (i.e., ROW+1). If the latitude coordinates are not corresponding, the lagoon characteristic values for rows having the corresponding latitude and longitude coordinates are averaged (step 520). Location coordinates (e.g., latitude coordinates, longitude coordinates, etc.) are described as corresponding when, for example, the coordinates are the same, substantially the same, within a predetermined error (e.g., the resolution error associated with GPS coordinates), approximately the same, etc. For descriptive purposes, coordinates are described herein as being either the same or different, but these terms are used to describe whether the coordinates are corresponding or are not corresponding, respectively. As such, when comparing coordinates, the coordinates do not have to be identical.

[0059] If the first row of data has a latitude coordinate that differs from the latitude coordinate of the second row, the lagoon characteristic values corresponding to the latitude and longitude coordinates in the first row are unchanged. If the first five rows all have the same latitude and longitude coordinates, the lagoon characteristic values for the first five rows of data are averaged to produce a single set of lagoon characteristic values for the corresponding pair of latitude and longitude coordinates. If, at step 515, the latitude coordinates have the same value, the longitude coordinate of the selected row is compared to the longitude coordinate of the next row (step 525). If the longitude coordinate for the selected row has the same value as the longitude coordinate for the next row, the variable, C, is incremented (step 530) and the row variable, ROW, is set equal to the new value of the variable, C (step 510). Steps 515 and 525 are then repeated. If, at step 525, the longitude coordinate for the selected row does not have the same value as the longitude coordinate for the next row, the lagoon characteristic values for rows having the same latitude and longitude characteristics are averaged (step 520), and the averaged values for the lagoon characteristics are saved with respect to the corresponding latitude and longitude coordinates. After the lagoon characteristic values have been averaged, the process 500 proceeds to section D where the variable, C, is incremented and steps 510-530 are repeated until all rows of data have been compressed based on the latitude and longitude coordinates. The averaged

characteristic values are stored to memory.

[0060] Following the compression of the data using process 500, each of the latitude and longitude coordinates is associated with a coordinate within the grid 400 of Fig. 8. For example, as described above, the latitude and longitude coordinates (e.g., in degrees, minutes, and seconds) are converted to widths and lengths, respectively, that can be placed within the grid 400. Fig. 11 illustrates an intersection 600 of two rows, A and B, with two columns, 1 and 2. The intersections of the rows and columns correspond to coordinates within the grid 400. The location of a data point 605 within the grid 400 is used to calculate a separation between the data point 605 and each of the coordinates in the grid 400. The separations between the data point 605 and the grid coordinates are described generally with respect to distances. However, the separations can also be based on, for example, ratios, products, sums, differences, or the like between the data point 605 and the grid coordinates. The separations correspond generally to an intervening space or gap between points, values, quantities, objects, locations, and the like. For example, the component length, a, and the component width, b, are used to calculate the distance, c, between the data point 605 and the grid coordinate 610. The calculated distances are used to associate each data point with one of the plurality of coordinates in the grid 400. Fig. 12 illustrates the distribution of data points with respect to a portion 700 of the grid 400. The data points are associated with the nearest grid coordinate (e.g., the coordinate having the smallest separation from the data point).

[0061] A process 800 for calculating the distance between each data point and each grid coordinate is shown in Figs. 13 and 14. Like the process 500 described above, the steps of the process 800 are described in an iterative manner for descriptive purposes. Various steps described herein with respect to the process 800 are capable of being executed

simultaneously, in parallel, or in an order that differs from the illustrated serial and iterative manner of execution. At step 805, a variable, E, is set equal to one, and a row variable, ROW, is then set equal to the variable, E (step 810). As previously described, the lagoon data is in tabular form and each row corresponds to a time/date stamp, a latitude coordinate, a longitude coordinate, and lagoon characteristic data. Following the data conversion described above, the latitude coordinate and the longitude coordinate can be referred to as lengths and widths that fall within the grid 400. At step 815, a row width variable, RW, is set equal to the width of the selected row. Similarly, a row length variable, RL, is set equal to the length of the selected row (step 820). Following step 820, a second variable, F, is set equal to one (step 825), and the process 800 proceeds to section G shown in and described with respect to Fig. 14. At step 830, a location variable, LOC, (e.g., corresponding to a grid coordinate) is set equal to F. Following step 830, a coordinate width variable, CW, is set equal to the width of LOC (step 835), and a coordinate length variable, CL, is set equal to the length of LOC (step 840).

[0062] The separation or distance between the row coordinate designated by (RW, RL) and a grid coordinate designated by (CW, CL) is calculated at step 845. The distance is stored in memory (step 850), and the value of LOC is compared to the last grid coordinate in the grid 400 (step 855). If LOC is not the last grid coordinate, the second variable, F, is incremented (step 860), and steps 830-860 are repeated. The steps 830-860 are repeated until a distance between the row coordinate and each of the grid coordinates within the grid 400 has been calculated. In some implementations, the distance between the row coordinate and each of the grid coordinates within the grid 400 is not calculated, and distances are only calculated for a portion of the grid coordinates. For example, the four or six closest grid coordinates for each row coordinate are inferred or estimated, and the corresponding distances between the row coordinate and each of the four or six grid coordinates is calculated. In other implementations, different numbers of distances are calculated (e.g., eight distances, twelve distances, etc.). If, at step 855, the value of LOC corresponds to the last grid coordinate in the grid 400, the process 800 proceeds to section H of Fig. 13 where the variable, E, is incremented (step 865). The row variable, ROW, is then set equal to the new value of the variable, E (step 810). The process 800 is repeated until the distances have been calculated for each row (i.e., each data point). After the distances for each of the data points have been calculated, each data point is associated with the grid coordinate having the smallest distance. In terms of the tabular data, a column can be added to each row that identifies the grid coordinate nearest the data point.

[0063] Following the identification of the grid coordinate nearest each data point, the lagoon data is further processed to determine a single value for each of the lagoon characteristics for each grid coordinate. Figs. 15 and 16 illustrate a process 900 for processing and analyzing the compressed lagoon data. For descriptive purposes, the process 900 is described with respect to an implementation of the invention in which the lagoon characteristic is a sludge depth. However, the process 900 can be applied to implementations of the invention in which other lagoon characteristics are being processed and analyzed. For example, dissolved oxygen concentration can be determined and analyzed with respect to the depth of the lagoon at a particular location, and a distance from that location to a water inlet of the lagoon. Like the processes 500 and 800 described above, the steps of the process 900 are described in an iterative manner for descriptive purposes. Various steps described herein with respect to the process 900 are capable of being executed simultaneously, in parallel, or in an order that differs from the illustrated serial and iterative manner of execution.

[0064] At step 905, a total depth variable, TOT, is initialized to a value of zero. The total depth variable, TOT, corresponds to a cumulative depth for the entire lagoon (i.e., a summation of average depths for each grid coordinate), as described in greater detail below. A variable, J, is set equal to one (step 910), and a location variable, LOC, is set equal to the variable, J (step 915). Following step 915, a depth variable, DEPTH, is initialized to zero (step 920), and a counter variable, COUNTER, is initialized to zero (step 925). The depth variable, DEPTH, corresponds to a cumulative depth for a particular grid coordinate, and the counter variable, COUNTER, corresponds to the number of data points associated with the particular grid coordinate. After step 925, a second variable, K, is set equal to one (step 930), and a row variable, ROW, is set equal to the second variable, K (step 935).

[0065] After each of the variables has been initialized or set in steps 905-935, the location of the selected row is compared to the location variable, LOC (step 940). If the location of the selected row is not equal to the location variable, LOC, the process 900 proceeds to section L and the second variable, K, is incremented (step 945). The row variable, ROW, is then reset to the new value of the second variable, K, (step 935) and step 940 is repeated. If, at step 940, the location of the selected row is equal to the location variable, LOC, the process 900 proceeds to section M shown in and described with respect to Fig. 16.

[0066] At step 950 in Fig. 16, the depth variable, DEPTH, is updated as shown below in EQN. 1.

DEPTH = DEPTH + DEPTH (ROW ) EQN. 1

For the first iteration of the process 900, the depth variable, DEPTH, is initialized to zero. As such, EQN. 1 resets the value of DEPTH to the depth of the first row when the location of the first row is the same as LOC. The depth variable, DEPTH, is updated each time the location of the selected row is equal to the location variable, LOC. As such, the depth variable, DEPTH, is a cumulative depth value associated with a particular grid coordinate. After step 950, the counter variable, COUNTER, is also incremented (step 955). The counter variable, COUNTER, corresponds to the number of data points associated with the selected grid coordinate (i.e., the number of data points that match the location variable, LOC). Following step 955, the row variable, ROW, is compared to the last row in the lagoon data (step 960). If the selected row is not the last row in the lagoon data, the process 900 proceeds to section L in Fig. 15 where the second variable, K, is incremented (step 945). The row variable, ROW, is then reset to the new value of the second variable, K (step 935). If, at step 960, the selected row is the last row in the lagoon data, an average depth for the grid coordinate is calculated (step 965). The average depth for the selected grid coordinate is calculated by taking the value for the depth variable, DEPTH, and dividing it by the counter variable, COUNTER, as shown below in EQN. 2. AVERAGE DEPTH (LOC) = EQN. 2

COUNTER

[0067] The total depth variable, TOT, is then updated (step 970) as shown below in EQN. 3.

TOT = TOT + AVERAGE DEPTH {LOC) EQN. 3

[0068] Following step 970, the location variable, LOC, is compared to the last grid coordinate in the grid 400 (step 975). If the location variable, LOC, does not correspond to the last grid coordinate, the process 900 proceeds to section N in Fig. 15 where the variable, J, is incremented (step 980). The location variable, LOC, is then reset to the new value of the variable, J (step 915). Steps 915-980 repeat until the location variable, LOC, corresponds to the last grid coordinate in the grid 400. If, at step 975, the location variable, LOC, corresponds to the last grid coordinate in the grid 400, an overall average depth for the lagoon is calculated (step 985). The overall average depth for the lagoon is calculated as shown below in EQN. 4.

TOT

OVERALL AVERAGE DEPTH = EQN. 4

LOC

[0069] After the overall average depth for the lagoon is calculated, the results of the analysis are displayed (step 990). For example, the results of the analysis can be displayed as topographical maps, a histogram, numerical results, etc. Although the process 900 described above is described with respect to the determination of an average depth, additional statistical analysis can also be performed. For example, one or more cumulative distribution functions can be used to describe the distribution of sludge depths in the lagoon (e.g., a percent distribution based on depth). Additionally statistical analyses can also be performed, such as standard deviation, standard error mean, upper 95% mean, lower 95% mean, etc.

[0070] Figs. 17-19 illustrate various combinations of distributions and statistical analyses 1000, 1100, and 1200 for lagoons. Fig. 17 illustrates a graphical plot 1005 (e.g., a histogram) of the sludge depths of the grid points, a percentage distribution 1010 (i.e., quantiles) with respect to depth, and additional statistical analyses 1015 (e.g., moments) associated with the lagoon sludge depth data. The graphical plot 1005 includes a bar graph showing the distribution of grid points corresponding to each depth between zero and twenty-five feet. The percentage distribution 1010 provides a breakdown of depths for the grid coordinates. For example, the maximum sludge depth is 23.3ft, and the median sludge depth is 5.6 feet. The additional statistical analyses 1015 include the mean or average depth of the sludge, a standard deviation, the standard error mean, the upper 95% mean, the lower 95% mean, and the number of acquired data points or samples, N. Fig. 18 illustrates a graphical plot 1105, percentage distribution 1110, and statistical analyses 1115 similar to those described with respect to Fig. 17, but uses fewer data points, N. Fig. 19 illustrates a graphical plot 1205, a percentage distribution 1210, and statistical analyses 1215 that differ slightly from those shown in Figs. 17 and 18. In Fig. 19, the analysis is with respect to the height of the sludge (e.g., the height of the sludge as it is measured from the bottom of the lagoon).

[0071] Figs. 20 and 21 illustrate first and second topographical maps 1300 and 1400 of sludge height or first and second lagoons, respectively. The topographical maps 1300 and 1400 combine the compressed and processed lagoon data corresponding to a set of grid coordinates with the analysis of the lagoon data to provide a graphical representation of sludge height throughout a lagoon. In some implementations, the topographical maps 1300 and 1400 also include a plane positioned at the average or mean value for the sludge height to readily identify which portions of the lagoon are above or below average in sludge height. The analyzed lagoon data can also be stored in memory and retrieved at a later time to compare past and present lagoon data and determine, for example, whether a particular treatment technique is working.

[0072] Thus, the invention provides, among other things, systems and methods for the mapping and analysis of a region, such as a wastewater stabilization lagoon or another body of water. Various features and advantages of the invention are set forth in the following claims.

Claims

CLAIMS What is claimed is:

1. A method of analyzing a lagoon, the method comprising:

obtaining a set of characteristic data corresponding to a characteristic of the lagoon, the set of characteristic data including a plurality of characteristic data samples;

obtaining a set of location data corresponding to a plurality of locations within the lagoon, the set of location data including a plurality of location coordinates;

correlating the set of characteristic data with the set of location data;

storing the set of characteristic data and the set of location data to a memory;

accessing the stored characteristic data and the location data from the memory;

compressing the characteristic data by combining each of the characteristic data samples having corresponding location coordinates;

associating each of the plurality of location coordinates with one of a plurality of grid coordinates;

combining the compressed characteristic data samples that correspond to each of the plurality of grid coordinates;

determining an output characteristic value for each of the plurality of grid coordinates; and

generating a representation of the lagoon based on the output characteristic value for each of the plurality of grid coordinates.

2. The method of claim 1, wherein the lagoon is one of a wastewater stabilization lagoon, a concentrated animal feeding operation ("CAFO") lagoon, and a treatment lagoon.

3. The method of claim 1, wherein the characteristic of the lagoon is at least one of sludge blanket depth, conductivity, dissolved oxygen, pH, oxidation reduction potential ("ORP"), suspended solids, temperature, chlorophyll content, ammonia concentration, nitrate concentration, temperature, exposure to sunlight, turbidity, sulfide concentration, microbial content, and chemical oxygen demand ("COD").

4. The method of claim 1, wherein the representation of the lagoon includes a graphical representation of a topographical map.

5. The method of claim 1, wherein the location coordinates include a latitude coordinate and a longitude coordinate.

6. The method of claim 5, wherein compressing the characteristic data includes averaging each of the characteristic data samples having substantially the same latitude coordinate and substantially the same longitude coordinate, and

wherein combining the compressed characteristic data includes averaging the compressed characteristic data samples associated with each of the plurality of grid coordinates.

7. A method of analyzing a body of water, the method comprising:

obtaining a set of characteristic data corresponding to a characteristic of the body of water, the set of characteristic data including a plurality of characteristic data samples;

obtaining a set of location data corresponding to a plurality of locations within the body of water, the set of location data including a plurality of location coordinates;

correlating the set of characteristic data with the set of location data;

generating a representation of the body of water based on the output characteristic value for each of the plurality of grid coordinates.

8. The method of claim 7, wherein the lagoon is one of a freshwater body of water, a wastewater stabilization lagoon, a concentrated animal feeding operation ("CAFO") lagoon, commercial retention pond, a fire protection pond, a run-off pond, and a treatment lagoon.

9. The method of claim 7, wherein the characteristic of the lagoon is at least one of sludge blanket depth, conductivity, dissolved oxygen, pH, oxidation reduction potential ("ORP"), suspended solids, temperature, chlorophyll content, ammonia concentration, nitrate concentration, temperature, exposure to sunlight, turbidity, sulfide concentration, microbial content, and chemical oxygen demand ("COD").

10. The method of claim 7, wherein the representation of the lagoon includes a graphical representation of a topographical map.

11. The method of claim 7, wherein the location coordinates include a latitude coordinate and a longitude coordinate.

12. The method of claim 11, wherein compressing the characteristic data includes averaging each of the characteristic data samples having substantially the same latitude coordinate and substantially the same longitude coordinate, and

13. A system for mapping a characteristic of a lagoon, the system comprising:

a non-submersible, dirigible vessel including

a platform having an upper surface and an aperture located at an interior portion of the upper surface, the aperture configured to allow access to the lagoon through the interior portion of the platform, the upper surface located above the lagoon,

a propulsion device operable to propel the vessel in a first direction and a second direction, the propulsion device positioned above the upper surface and coupled to the platform,

a testing device configured to obtain a plurality of characteristic data samples, and

a location device configured to determine a plurality of location coordinates corresponding to the location of the vessel; and

a memory configured to

store a set of characteristic data corresponding to a characteristic of the lagoon, the set of characteristic data including the plurality of characteristic data samples, and store a set of location data corresponding to a plurality of locations within the lagoon, the set of location data including the plurality of location coordinates.

14. The system of claim 13, wherein the lagoon is one of a freshwater body of water, a wastewater stabilization lagoon, a concentrated animal feeding operation ("CAFO") lagoon, commercial retention pond, a fire protection pond, a run-off pond, and a treatment lagoon.

15. The system of claim 13, wherein the characteristic of the lagoon is at least one of sludge blanket depth, conductivity, dissolved oxygen, pH, oxidation reduction potential ("ORP"), suspended solids, temperature, chlorophyll content, ammonia concentration, nitrate concentration, temperature, exposure to sunlight, turbidity, sulfide concentration, microbial content, and chemical oxygen demand ("COD").

16. The system of claim 13, wherein the vessel further includes a second propulsion device operable to propel the vessel, the second propulsion device positioned above the upper surface and coupled to the platform.

17. The system of claim 16, wherein the propulsion device and the second propulsion device are fans.

18. The system of claim 13, wherein the platform is substantially circularly shaped.

19. The system of claim 18, further comprising a flotation device, wherein the flotation device and the platform are like-shaped.

20. The system of claim 13, further comprising a processor configured to

correlate the set of characteristic data with the set of location data,

compress the characteristic data by combining each of the characteristic data samples within the plurality of characteristic data samples having corresponding location coordinates, associate each of the plurality of location coordinates with one of a plurality of grid coordinates,

combine the compressed characteristic data samples corresponding to each of the plurality of grid coordinates,

determine an output characteristic value for each of the plurality of grid coordinates, and

generate a representation of the lagoon based on the output characteristic value for each of the plurality of grid coordinates.

21. The system of claim 20, wherein the representation of the lagoon includes a graphical representation of a topographical map.

22. The system of claim 20, wherein the location coordinates include a latitude coordinate and a longitude coordinate.

23. The system of claim 22, wherein compressing the characteristic data includes averaging each of the characteristic data samples having substantially the same latitude coordinate and substantially the same longitude coordinate, and

24. The system of claim 20, wherein the vessel further includes an antenna for transmitting the set of characteristic data and the set of location data to the processor.