20 Whys to Map Your (Land) Cover – Part Two

Hello to those of you who have read the first “20 Whys to Map Your (Land) Cover” blog post, and to those of you stopping in just now. I’m still not Paul Simon, and you’re still lucky that you can’t hear me sing. I know you’ve been on the edge of your seat since my last blog, and I figured I would finally let you off the hook by providing a second part to my land cover application examples. Let’s hope the second verse is as good as the first. Hop on the bus, Gus. Land cover can help you make a new plan, Stan. Just listen to me.

11. Assess impervious surface impacts on water quality: Water is an important natural resource, and natural vegetation plays a large role in managing the region’s water supply. When natural vegetation is replaced with impervious surface, the natural hydrology cycle is altered, increasing storm water runoff and reducing groundwater recharge. The result is more frequent flooding, higher flood peak flow, lower base flow in streams, and lower water table levels.

Click HERE to see how the Global Ecosystem Center has used land cover data to assess impacts in Southern California.

12. Assess wetland loss trends: Wetlands are among the most productive environments on Earth. Wetlands provide habitat and food, buffer the impacts of storm surge and flooding, and help control erosion. Wetlands also absorb, store, and filter urban and agricultural runoff to maintain water quality. Understanding wetland changes can help communities identify potential management actions to reverse or mitigate the trend.

Click HERE to visit the U.S. Environmental Protection Agency’s Coastal Wetlands site and access a series of regional reports on wetland trends and causes.

13. Model future development: The Southeast Regional Partnership for Planning and Sustainability was formed in 2005 by the U.S. Department of Defense and state and federal agencies to promote better collaboration in making resource-use decisions. Multiple land cover data sets are being used in predictive urban growth models to simulate where the region’s population growth is likely to occur. These products include 1992, 1996, 2001, and 2006 data from the U.S. Geological Survey’s National Land Cover Database and NOAA’s Coastal Change Analysis Program, along with elevation and transportation data and land suitability maps.

Click HERE to see one example of modeling and evaluation that can help identify easement and development opportunities.

14. Understand forestry management cycles and trends: Healthy forests are a vital part of healthy ecosystems. Although a forest can go through a transitional period after a fire, other natural disaster, or logging operation, typically it can be expected to recover. Such changes in forest growth within the coastal U.S. make up the largest area of change seen in most time periods.

Click HERE to look at recent forest losses and past trends in Pacific County, Washington.

15. Assess fire hazards risk: Southern California is at great risk from wildfires because of its particular combination of weather, topography, and native vegetation, as well as the Santa Ana winds that appear in the spring and late fall. While wildfires are inevitable in this region, over the past few decades the fire risks—including the loss of life and property—have increased with enduring drought conditions and the encroachment of developments into fire-prone lands.

Click HERE to see how the Global Ecosystem Center analyzed risk from wildfire and proximity to urban development using land cover data.

16. Assess flooding risk: A county with more natural areas (such as wetlands and forests) and less development within floodplains typically has a lower exposure to flooding. A county that monitors land cover changes within the floodplain will detect important trends that indicate whether flood exposure is increasing or decreasing. Armed with this information, local leaders can take steps to improve their safety and resilience.

Click HERE to explore the “Flood Exposure” feature of Coastal County Snapshots.

17. Understand impacts of dams to water levels: This example highlights a few lakes and reservoirs that were damaged or destroyed in 2005 due to flooding in Sullivan County, New York. In the months after the flooding, water levels were either naturally lower or were deliberately lowered in order to facilitate repairs and rebuilding. (Note: many of the dams have since been repaired.)

Click HERE to see an example of these impacts for the Swinging Bridge Reservoir in Sullivan County, New York.

18. Assess possible sea level rise impacts to wetlands: The future is uncertain, yet management decisions still need to be made. Models of potential impacts to wetlands due to sea level rise can help guide managers and planners in making decisions that take into account both current and future needs. Data such as land cover that highlight current distributions provide a baseline upon which such modeling can be based.

Click HERE to see how land cover data have been used to inform the modeling of potential future wetlands (see marsh tab).

19. Assess (Forest) Ecosystem Services: Trees are indicators of a community’s ecological health. When trees are large and healthy, the ecological systems that support them are also healthy and provide environmental benefits that can be measured in terms of ecosystem services. With information related to these natural resources, stakeholders can analyze the conditions in specific areas of interest and model scenarios for future growth and development.

Click HERE to see how regional forest services were analyzed in the aftermath of Hurricane Katrina.

20. Evaluate and select restoration sites: The Great Lakes watershed is the largest system of fresh surface water in the world and is a source of abundant natural resources. However, urban and industrial development along the shoreline has degraded water quality, posing threats to wildlife and human health.

Click HERE to see how the Great Lakes Commission identified sound approaches for restoring habitat in a section of the Buffalo River watershed in New York and a sub-watershed of the St. Joseph River drainage basin in Indiana.

I hope these examples (and those I listed in my last blog) highlight uses that will interest you or others in your community. Dig around the Digital Coast Stories from the Field to dive deeper into these applications as well into as all kinds of other data uses.

And get yourself some land cover data, cause there must be more than 20 ways to use that cover.

Comments

Adam Smith's picture

Submitted by Adam Smith (not verified) on Wed, 2013-09-18 16:30

Nate,
The tie between the impervious surface and water quality is an important one. However, I'm wondering how the newer pervious materials for parking lots and such will affect the mapping from imagery. Will we start to overestimate the amount of impervious surface or is there some signature that will help discriminate?

Thanks,
Adam


nate.herold's picture

Submitted by nate.herold on Mon, 2013-09-23 11:33

It might be important to note that we map only the presence/absence of developed impervious features, not their degree of perviousness (and no surfaces that might be impervious, but that are not related to man-made developed features… rock outcrops, or compacted soils, for example). Many of the features I think you are referring to are unlikely to be picked up as being unrelated to developed, or as looking significantly different from more typical surrounding materials (from above, at a 30 meter scale). Though we do try with some things (we tend to capture the crushed gravel along railroad beds as bare, not impervious).

Some of these features may be different enough (grass pavers, for instance) from a standard blacktop or concrete surface to be seen… as long as they make up a significant amount of that 30 meter pixel area (or area of several 30 meter pixels). Though, I’m not sure we've seen enough of this, over significant size areas, to really know for sure (maybe someone else out there can weigh in?). There might be little we can know about how these surfaces (or the specific mitigation techniques used) offset the amount of paved surface, in terms of runoff from remote sensing alone. Both pieces of information are useful, though (do you map a green roof as a building or grass? Both are correct, depending on your purpose). It may be that we need to evaluate this a bit more going into the future (and even potentially change how we are measuring / mapping these types of impervious surfaces).

Until then, users of the data will have to take their own knowledge of an areas building practices into account (which is always the case)... the timing of the changes detected could certainly feed that analysis (if users know when such techniques were introduced to an area)… or it may be that additional classification using more detailed imagery (which could see more of these differences would be required) might be required if this was a need.


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