Alex Freedman
Intro to GIS
12/16/11
Final Project:
Mapping the Intersection of Agricultural Development and Riparian Buffer Zones
The Manomet Center for Conservation Sciences is working on a program to improve riparian
buffer zones along riverbanks in Maine, especially in agricultural areas. When farmers farm right up to
the water’s edge, there is a greater risk for erosion and agricultural run-off that can damage the
watershed as a whole, and the various ecological services and economic activities associated.
This project examines (Androscoggin River at Rumford Point in Maine. Along the rivers at the
heart of these watersheds, I will look at the area comprised of a 100 ft buffer zone on each side of the
river. In this area, I am tasked with quantifying the types of land cover to determine if riparian buffer
zones exist. The types of groundcover that are NOT considered to be adequate groundcover are
cultivated land, pasture/hay, and grassland/herbaceous categories.
The next part of this project is to identify the parcels that lack an adequate buffer zone and
seek out the address and owner of these parcels. Within the attribute tables of the parcels are Tax ID
numbers. Nevertheless, not all organize their data the same way, with the same identification numbers
available for public view. This process required time and calculations outside of ArcGIS.
Because this is a project for an organization, I wasn’t given a specific research question, but if I
had to sum up one from the projects tasks it would be: What are the types of ground cover that
constitute the riparian zones with the various watersheds, and, using this data, does it provide an
adequate riparian buffer? Where is does not, what are the parcels with “high potential” for
reforestation?
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Data Layers Utilized:
MELCD Land Cover
Description: A detailed raster map of Maine’s land cover at 5m resolution
Source: MEGIS
Website: http://www.maine.gov/megis/catalog/
Metadata: http://geolibportal.usm.maine.edu/geonetwork/srv/en/metadata.show?id=427
Source Scale: 1:24,000
Year: 2004
NLCD Land Cover (Sub-basin 1040002)
Description: A raster map of the national land cover at 30m resolution
Source: USGS, The National Map
Website: http://nationalmap.gov/viewers.html
Year: 2006
HUC 12 Watershed Basin Dataset
Description: A subset of the National Hydrography Dataset (Sub-basin 1040002)
Source: USGS, The National Map
Website: http://nationalmap.gov/viewers.html
Source Scale: “At a minimum, the WBD is being delineated and georeferenced to the USGS 1:24,000
scale topographic base map meeting National Map Accuracy Standards (NMAS)”
Year: 2006
State Organized Town Parcels Dataset
Description: State city-level parcel data
Source: MEGIS
Website: http://www.maine.gov/megis/catalog/
Metadata: http://geolibportal.usm.maine.edu/geonetwork/srv/en/metadata.show?id=886
Source Scale: 1:24,000
Year: 2011
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Analysis Steps:
(Note: I used the following steps for both the MELCD and NLCD layers; however, I only analyzed the
NLCD through Step 4, and the results are only indicated in the spreadsheets I am providing the
Manomet Center.)
Step 1: Prepare Data
After I downloaded the datasets from their respective sources, the first step I took was to
change the coordinate projections to assure that they are all projected under the Universal Transverse
Mercator coordinate system, zone 19 North, using the NAD83 datum and GRS80 spheroid. To do this, I
used the Project feature under the Data Management – Features toolbox. All data taken from the State
of Maine website is in this format, and since much of my data is from that location, it made sense to
convert the rest.
Next, I need to take all the larger, Maine-wide data sets (MELCD, NLDC, streets, and parcel data
sets) and reduce them to the boundaries of the watershed areas (the 10400020207.) To do this, I
Selected by Attribute for the basin indicated above from the National Hydrology Dataset.
From that, I created a separate layer, which I then used as the extent for the groundcover
layers when I used the Raster Clip Tool. Before that action took place, though, I had to change the
Environment settings. I made sure the Output Coordinates were the same as the projection I wanted;
that the processing extent was the basin boundary and the snap raster was the groundcover dataset;
and that the cell size appropriately reflected which ground cover layer I was using (5 or 30 square
meters) with the mask as the buffer polygon I just created (this will be more important in the next step.
The output was a basin-shaped ground cover data layer.
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Step 2: Asses Groundcover in Watershed area
Before analyzing specific buffer zone ground cover types, I need
to establish percentages and acreage of the various land cover types
across the entire basin (a step represented in the materials for
Manomet, but not for the poster project.) This step was not
particularly GIS-heavy; using the attribute tables, I identified the
meanings of each land cover type values. Using the totals in the table, I
manually multiplied them by themselves (5 and 30 for MELCD and
MLCD respectively), and then converted that number into acres to
identify the total acreage of the area for each type.
Step 3: Establish Buffer Zones
Here, I established the buffer zone using the Buffer tool, with the National Hydrography Dataset
layer as the input and 100ft as the width of the buffer. I made sure to set the “Side Type” as ‘Outside
Only’ so that the river itself was not included in the buffer polygon.
Next I adjusted the Environment again, making both the Snap Raster and Mask the buffer layer.
Because of this, I was able to use the Spatial Analyst – Map Algebra – Raster Calculator tool. In the Raster
Calculator tool, I multiplied the raster layer * 1. This creates the new land cover data that is just in the riparian
buffer. (I suspect that I could have used the Raster Clip tool, again, but I decided to try this other
technique as suggested by Barbara.)
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Step 4: Analysis of the Ground Cover in Buffer Zones
In analyzing the groundcover, first I performed the same actions as in Step 2 in order to find the
ground cover breakdowns by acreage and percentage.
Next, I needed to identify the land cover types considered of High, Medium and Low Potential
for reforestation within the buffer zone. In order to facilitate this analysis, and
improve the visual display, I reclassified the raster data set so that High Potential
types (cultivated land, pasture/hay, and
grassland/herbaceous, and blueberry fields) had a value
of 2, Medium Potential types had a value of 1, and Low
Potential types had a value of 0. One step I needed to
assure that I did was change the values to Unique Values, because especially the MELCD layer had over
100 extra null values that would appear and need to be manually reclassified. As a matter of aesthetic
and utility, I made the High Potential values appear in red, Medium Potential in yellow, and Low
Potential (least concern) in blue.
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Step 5: Determine Tax Parcel Data of At-Risk Zones
As the important action step of the process, once I analyzed the ground cover in the buffer
zone, I needed to identify the high potential areas: cultivated land, pasture/hay, and
grassland/herbaceous categories that coincided with the parcels along the river.
In order to do this, I first had to convert the
High Potential Raster values into a vector polygon. I
used the Conversion Tools – From Raster – Raster to
Polygon tool. After downloading the Organized Parcels
layer of Maine. I Selected by Location the Parcels that
intersected with, and then make sure to export that as
a separate data set. This created a layer of just parcels
abutting the High Potential zones.
From here, I will needed to do research beyond the scope of GIS. Using the attribute table
MAP_BLK_LOT field, I was able to search the Bethel, ME Assessor’s Office data for the nam es and
mailing addresses of the land owners in the parcels of concern. This list will provide the Manomet
Center with contacts to pursue in their campaign to regenerate agricultural riparian buffer zones.
Challenges:
The major challenges presented in this project centered on data access and download. The first
struggle was in handling the large ground cover raster sets. When uploading them into ArcGIS, a
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majority of the time it would crash the program, making it impossible to pair it down to a manageable
size. To illustrate my point, the size of the MELCD was so large, and so slow to draw, that I called the
MEGIS to see if they offered a complete dataset, because the area I needed seemed to never load. I
managed to overcome this glitch with patience (for the drawing) and I found that if I imported the
layer when I was zoomed into the layers I was working in, it would load completely enough that I could
Clip out the areas that I needed and discard the larger file.
The second major problem came with access to the Organized Parcel data. For the state of
Maine, not all towns have digitized their maps, making the data incomplete. For this reason, data was
easily accessible for the town of Bethel, ME (majority of my map), but the north shore of the river,
which is another town, has not done the same, meaning that there are missing parcels. I feel lucky,
though, because although a phone call to the MEGIS people (again) told me that no additional town
data would be posted until early 2012, after my phone call, when I downloaded the dataset again, the
parcels I needed for a majority of my map now drew. Was there a correlation? I do not know.
However, I was pleased by the outcome.
Additionally, not all Assessors’ Offices organize their town’s tax parcel information the same
way. Thus, for Bethel, there the MAP_BLK_LOT field aligned with each landowner’s ID information, but
for the 3 parcels in the northeast corner of my map that fell into Rumford, ME, there were no
identification numbers that matched the available data in the attribute table. Because there were so
few, I omitted them from my analysis.
Conclusion:
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Ultimately, I think that this project came out really well, and will provide very useful for the
Manomet Center. While the analysis techniques in their own right were not as complicated as other
projects might have been, the challenges of accessing proper GIS data and then relating it to the
diverse variety of local data has been useful and instructive. It’s a wonder that there is so much
information available, yet it is sometimes so difficult to connect into complimentary analyses. I even
feel that, despite disparities, Maine’s GIS data clearinghouse is remarkably well-organized and up-todate, meaning that in other states, especially in rural areas, this may not be the case. For anyone
working on Maine data, take solace in the fact that a majority of the state parcels are expected to be
digitized by early 2012 and that phone calls to the MEGIS office are quite pleasant, indeed.
Acknoledgements:
This land use analysis is based on the previous work of Bill VanDoren
VanDoren Bill. 2011. Sebago lake watershed land use summaries and analysis of afforestation
potential. Internal report to Manomet Center for Conservation Sciences, February 12, 2011
Literature Review:
“Influence of Landscape Elements in Riparian Buffers on the Conservation of Semiaquatic Amphibians.”
Ficetola, Gentile Francesco, Emilio Padoa-Schioppa, and Fiorenza de Bernardi.
http://digilander.libero.it/lyrgus/Ficetola%20in%20press%20buffers%20amphibians%20online%20first.
pdf
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In this study, the researchers were identifying the type and amount of landscape surrounding
wetlands that are necessary for all the life stages of semi-aquatic species of amphibians in Lombardy,
Italy. The purpose of this analysis was to determine what an “adequate” buffer zone would be around
these wetlands. Looking at this studying is useful for my work, because their methodology for
measuring the land cover present in the buffer zones appears to be similar to what I need to do – “for
the landscapes surrounding each stream, we recorded 3 features on the basis of the Vector Map…We
used ArcView GIS to measure each landscape feature within 16 concentric distances from the midpoint
of the sampled stream…” I would only use one distance, 100ft.
“Watershed Management and Riparian Buffer Analyses using
Remotely Sensed Data.” http://repository.lib.ncsu.edu/ir/bitstream/1840.16/2483/1/etd.pdf
This study also seeks to identify appropriate widths and types of riparian zones, in agricultural
areas utilizing remote sensing and GIS. This is nicely linked to the needs of the study I am conducting,
however because it is focused mostly on determining the width of the buffer zone (which I already
have establish – 100ft), the results don’t mean that much. Additionally, they used only 5 categories of
land cover in their analysis, but I will be using the wide range of NLDC and MELCD categories, which are
much more specific.
“Method to Identify Effective Riparian Buffer Widths for Atlantic Salmon Habitat Protection.”
Haberstock, Alan E. et al. http://onlinelibrary.wiley.com/doi/10.1111/j.1752-1688.2000.tb05726.x/pdf
The purpose of this study is very succinctly described in the title of the article: Method to
Identify Effective Riparian Buffer Widths for Atlantic Salmon Habitat Protection. This paper created a
method for identifying these zones in light of pressures on the areas by logging activity. Most of the
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study is involved with identifying appropriate buffer zones, which is not the purpose of my project.
Rather, I am finding buffer zones based on the definition given to me (only ground cover, with no
regard for geological features like slope or soil composition.) Nevertheless, I think this study pairs
nicely with mine, because it takes place in seven rivers in coastal Maine (although none overlap with
the ones I am studying,) and according it its conclusions, this study may have helped establish the
foundations of what I am doing for this organization.
“Application of remote sensing and geographic information systems to the delineation and analysis of
riparian buffer zones.” Sunil Narumalani, Yingchun Zhou, John R. Jensen
http://www.sciencedirect.com/science/article/pii/S030437709700048X
This study, focusing on the Iowa River basin, served the purpose of explaining how remote
sensing and GIS technology could be both useful and vital for the analysis of riparian buffer zones. In
the process, it analyzed the main rivers and tributaries, finding that 44% of the area along the
tributaries lacked adequate protection from non-point source population from agricultural lands. This
study is exactly what I will be doing for my project, so it’s been really useful to read. Unfortunately, the
article is from the 90s, so the tasks they achieved (like updating the hydrology data layer of the area)
seem more dated than useful for exact replication and above my ability in the process of this project.
(Hopefully I won’t need to do something like that!)
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