The Water Conservation and Transfer Project (Water Transfer Project) currently being implemented in Southern California is one of the largest agricultural-to-urban water transfer projects in the entire world. Conserved water is made available for transfer by infrastructure and technology improvements to water delivery systems and by on-farm water conservation practices.
The long-term transfer of up to 303,000 acre-feet of water annually from Imperial Irrigation District (IID) to the San Diego County Water Authority and the Coachella Valley Water District means more water supply reliability for drought stricken California. It also requires mitigation of potential impacts on the Salton Sea, a large, saline, terminal lake that used to receive the inflows that are now being conserved (see figure 1).
Fig. 1: The Salton Sea is a unique saline, terminal lake in need of conservation monitoring.
Formation Environmental (FE) is an environmental consulting firm, based in Boulder, Colorado, specializing in the application of advanced remote sensing to support its clients. FE's clients use information derived from remote sensing to make better management decisions related to water resources, agricultural water usage, long–term environmental stewardship, and air quality (see figure 2). FE is currently working with the Imperial Irrigation District and other multi-disciplinary team members in modeling, monitoring, and mitigating the effects of the Water Transfer Project.
Fig. 2: Formation Environmental, a Boulder-based environmental consulting firm, looks at the effect of dust emissions on the Salton Sea floor.
The Water Transfer Project is just one of the factors, along with drought conditions, reduced inflows from Mexico, and climate change, expected to affect Salton Sea inflows and water balance. The net result of these changes will be the accelerated exposure of the Salton Sea floor.
As the Sea continues to recede, there is potential for windblown dust emissions from the exposed dry lakebed, a.k.a., the playa. A significant portion of this windblown dust is called PM10, particulate matter with an aerodynamic diameter of 10 µm or less. The PM10 are approximately 1/7th the thickness of a human hair, are small enough to be inhaled. This represents a potential human health risk to the Imperial and Coachella valleys (see figure 3).
Fig. 3: Particulate matter from the extreme drought conditions represent a health risk to the surrounding areas.
To combat this problem, the Salton Sea Air Quality Program is being developed to detect, locate, assess and mitigate potential PM10 dust emissions. Remote sensing techniques are a key component of this program and are used to map playa surfaces, vegetation cover, and active dust source areas.
Satellite, aerial, and Unmanned Aerial Vehicle (UAV) imaging technology, together with LiDAR and image analysis software, are used to collect and derive the geospatial intelligence needed to understand and address air quality (see figures 4 and 5). Remote sensing techniques are used, among other things, to inform design and evaluate the long-term effectiveness of dust control measures, such as surface roughening and vegetation enhancement.
Fig. 4: Satellite imagery plays a key role in mapping and conservation efforts.
Fig. 5: UAS equipped with high accuracy cameras are a cost-effective way to gather geospatial data.
Mapping and Sensor Data Help Pinpoint Dust Source Areas
The Salton Sea Air Quality Program requires remote sensing at various scales from mapping the entire surrounding desert basin about 6,000 square miles to monitoring local dust control projects at the scale of centimeters; all in an effort to manage PM10 dust emissions. Basin-wide mapping leverages publically available aerial photography and satellite imagery to gather intelligence on dust source areas. For example, FE is currently using remote sensing and image analysis software to map the surficial geology and vegetation of the entire basin to determine which natural areas surrounding the Salton Sea have the most emission potential (see figure 6).
Fig. 6: Basin-wide mapping leverages aerial photography and satellite imager to gather intelligence on dust source areas.
The ability to create detailed geo-spatial inputs for air quality models is the key to understanding how factors such wind speed, vegetation distribution, and surficial geology interact. Understanding and modeling these interactions is crucial to accurately assessing the emission potential of specific areas during meteorological events.
"Our mapping efforts feed into air quality models, which are built to predict when and where we should expect dust emissions" explained Aaron J. Smith, senior remote analyst for Formation Environmental, LLC.
The air quality models are sensitive to several inputs such as surface type and surface roughness. Surface roughness is a key factor in estimating emission potential from source areas, so understanding the spatial distribution of vegetation is very important. This is a challenging task because the vegetation, e.g., small plants, in the natural desert environment surrounding the Salton Sea tends to be quite sparsely distributed and small in stature. This means that the team can't rely upon coarse resolution satellite imagery, e.g. Landsat images. Instead they use near-infrared (NIR) aerial photography at a 1-meter resolution or greater. Through the use of remote sensing data and sophisticated image analysis software, the team is developing a ruleset that will spatially identify different types of vegetation.
Field work is at the heart of this project, according to Smith. "When we create maps, we need to make sure that the remote sensing derived information accurately reflects the conditions on the ground."
To collect ground truth data on potential emissivity of different ground surfaces, the team uses a sensor known as a PI-SWERL. Essentially, the PI-SWERL creates a micro-tornado on the earth's surface, which to measure how bound PM10 particles are to the surface. Several other in-situ and remote sensing technologies, such as earth imaging, LiDAR, video cameras, and meteorological stations, help the team develop a comprehensive understanding of how a range of wind speeds interact with the various surface types found at different locations. This allows them to both quantify potential PM10 emissions (before they become airborne), and to monitor actual PM10 emission events in real time (see figure 7).
Fig. 7: Image analysis software creates models to accurately reflect conditions on the ground.
Informing Design of Dust Control Measures
After determining the surface characteristics of the newly exposed playa, team engineers develop a mitigation strategy for the potentially emissive surfaces. One of the most straightforward ways of mitigating dust emissions is to simply apply water. However, using water for dust control is extremely expensive and runs counter to the overall water conservation objectives of the project.
Fortunately, several other cost- and resource-effective measures are available. For example, increasing surface roughness with equipment or vegetation is highly effective. As Smith explains, "When you increase the roughness of the surface, you can control PM10 dust emissions. It works by disrupting the air flow across the smooth playa surface; roughening the playa limits the abrasion that rapidly moving sand-sized particles can cause in a silt dominated area. Breaking up the surface air-flow with vegetation or mechanical roughening treatments can control dust without the need for large quantities of water."
Two treatments considered to control dust include:
- Vegetation – if there is a water source and an area that will support vegetation, simply increasing the amount of vegetation in particular areas can limit the amount of dust emissions.
- Surface roughening – essentially done by plowing the land in such a way that creates micro-topographies (or trenches) to impede the flow of sand-sized particles across the surface of the exposed area (see figure 8).
Fig. 8: Surface roughening creates micro-topographies, impeding flow of particulate matter across exposed areas.
For one of the dust control pilot projects, the goal was to increase the surface roughness around newly exposed playa by plowing deep furrows into the smooth surface. The lifespan and effectiveness of the treatments are dictated by the surface material type and by deposition and erosion over time.
The team uses highly detailed and spatially accurate imaging to keep track of where the roughening needs to be maintained or replaced. In order to map the micro-topography, the team uses a UAV to capture extremely detailed imagery and then applies photogrammetric techniques to help derive point clouds from the images. Newly available UAV technology makes this project possible because the UAV is able to inexpensively capture 1.7-cm resolution imagery.
This approach wouldn't be feasible for long term monitoring if another technology, such as airborne LiDAR was used. In addition to the high cost of deploying a manned aircraft for routine monitoring, typical airborne LiDAR scanning wouldn't produce the point density required to measure the subtle changes in the geometry of the roughening trenches, which are only about 1 to 2 meters wide.
Imaging analysis software allows the team to extract information and gather intelligence for each individual trench created and then track it through time. In order to achieve this level of understanding, all data is collected geo-spatially and then software is used to fuse and catalog data from different dates and sources. The team evaluates the micro-topographic changes to the trench, and measures which areas are effective in capturing dust by looking at changes in the depth and width of the trench over time. Being able to combine all of the gathered data (from UAV imagery, point clouds, remote sensing, etc.) into a single software platform gives the team the ability to make use of all the extremely high-density data sets to understand what is happening on the ground.
Enabling a Data Value Chain
"Ultimately what we need is a consistent workflow to analyze and track the effectiveness of dust control over time," said Smith. For example, Smith and his team wanted to profile a single trench: "As the slope of this trench changes over time, we can figure out how long its dust control effectiveness will last. Once we know how long it will last, we can understand maintenance requirements for surface roughening on various playa surfaces."
Robust processing and analysis tools are key to efficiently extracting information from the proliferation of satellite and imaging data. Efficient workflows are critical for effective decision making, within the team and for the project overall. The remote sensing data feeds into a processing technology suite that addresses information needs across many different stages of the process: collection, processing, modelling, and analysis. It also allows the team to streamline the processing and integration of multiple types of data in order to extract valuable information faster – creating a chain of data that provides value to the entire project.
The project managers are now able to extrapolate future project costs, estimated man-hours, and additional development considerations that will be business-critical moving forward. Value of the geospatial data collected on the ground extends deep into the office. The ultimate goal is providing decision-makers with actionable intelligence.
About the Author
Christian Hoffmann is the Market Manager for Photogrammetry and Remote Sensing Applications at Trimble. He has more than 10 years' experience in Remote Sensing, Image Processing, eCognition, GIS, and in various horizontal and vertical positions.