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Utilizing GIS and remote sensing to determine sheep grazing patterns for best practices in land management protocols

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Utilizing GIS and remote sensing to determine sheep grazing patterns for best practices in land management protocols

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UTILIZING GIS AND REMOTE SENSING TO
DETERMINE SHEEP GRAZING
PATTERNS FOR BEST PRACTICES IN
LAND MANAGEMENT PROTOCOLS

by

Rachel Rae Miller

A Thesis Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(GEOGRAPHIC INFORMATION SCIENCE AND TECHNOLOGY)

August 2012

Copyright 2012 Rachel Rae Miller
ii

Acknowledgements
I would like to thank my committee chair Dr. Flora Paganelli for her
unconditional time, support, and feedback along with Dr. Travis Longcore and Dr.
Darren Ruddell, my project began to flourish when you all came on board. I would also
like to thank Brian Bean and Tess O’Sullivan for their knowledge and the opportunity to
work with Lava Lake on this project. Many professors within the GIST program also
made the possibility of this project a reality through their teaching, thank you as well.
Last but certainly not least I want to thank my family and friends for their understanding
and loving support throughout.

iii

Table of Contents
Acknowledgements ............................................................................................................. ii
List of Tables ..................................................................................................................... iv
List of Figures ..................................................................................................................... v
Abstract .............................................................................................................................. vi
1. Introduction and Literature Review ................................................................................ 1
1.1 Sustainable Rangeland Management ........................................................................ 1
1.2 Grazing Behavior of Sheep ....................................................................................... 5
1.3 GIS Techniques in Determining Pertinent Land Management Criteria .................... 7
1.4 Remote Sensing Techniques in Determining Pertinent Land Management Criteria 9
2. Land Management Resource Project ............................................................................ 12
2.1 Project Study Area .................................................................................................. 12
2.2 Land Management Protocols ................................................................................... 13
2.3 Project Objectives ................................................................................................... 14
3. Acquired and Processed Data ....................................................................................... 18
3.1 Lava Lake Data ....................................................................................................... 18
3.2 National Elevation Datasets .................................................................................... 19
3.3 National Land Cover Database Imagery ................................................................. 23
3.4 Construction of the Geodatabase............................................................................. 26
4. Methodology ................................................................................................................. 28
4.1 Establishing Land Management Criteria ................................................................. 28
4.2 Capable Sheep Grazing Model ................................................................................ 29
4.3 Sheep Temporal Patterns ......................................................................................... 30
5. Results ........................................................................................................................... 34
5.1 Capability Analysis ................................................................................................. 34
5.2 Time Analysis ......................................................................................................... 39
6. Conclusions and Discussion ......................................................................................... 48
6.1 Capability Analysis and New Criterion .................................................................. 48
6.2 Time Analysis and Seasonal Patterns...................................................................... 50
6.3 Discussion and Future work .................................................................................... 53
References………………………………………………………………………………..58
iv

List of Tables
Table 2.1 Sustainable land management protocols………………………………………14
Table 3.1 Summary of Lava Lake Data…………………………………………….........19
Table 3.2 Summary of NED Data……………………………...……………………...…20
Table 3.6 Summary of NLCD Data…………………………………………………...…25
Table 5.1 Total acreage capable for grazing by allotment……………………………….37
Table 5.2 Total number of days sheep graze……………………...……………………..41

v

List of Figures
Figure 2.1 Lava Lake location and boundaries…………………………………………..17
Figure 3.1 Preprocessing of NED’s for use in the model………………………………..22
Figure 3.2 Unsuitable slope feature class…………………………………………..……24
Figure 3.3 Preprocessing of NLCD data…………………………………………………25
Figure 3.4 LL Model Geodatabase and contents within Esri’s Arc Catalog…………….27
Figure 4.1 Capable Sheep Grazing Model……………………………………………….31
Figure 4.2 Time Slider, Tracking Analyst, and Temporal data wizard…………...……..33
Figure 5.1 Capable terrain for study area…………………………………….………….35
Figure 5.2 Areas capable for grazing in Timmerman Hills North……….………………39
Figure 5.3 Difference in rate of travel during spring, summer, and autumn……....…….45
Figure 5.4 Amount of time spent grazing……………………………….……………….47
Figure 6.1 Dispersion of use as shown through GPS locations………………………….51

vi

Abstract
Sustainable ranching refers to the practice of evaluating livestock quantities that
natural grasses and ecosystems are capable of supporting, with minimal long-term
impacts on the environment. Defining optimal and sustainable stocking rates can be a
complex problem for land managers striving to implement the practice of sustainable
ranching of sheep.
I used a combination of Geographic Information Systems (GIS) with Remote
Sensing (RS) to analyze environmental variables and track movement patterns of sheep
and tested it at the Lava Lake Livestock and Landscape Ranch. A GIS model utilizing
remotely sensed imagery was built to identify areas capable for grazing by sheep across
the study area. Tracking Analyst and Time Slider, which are GIS based time analysis
tools, utilized point data collected from Global Positioning System (GPS) collars to
visualize the rate at which sheep are traveling.
Results show an estimated 85% of the study area is found capable for grazing
with the primary eliminating factors being steeper terrain in the north and lack of water in
the south. Results also outline two contrasting sheep patterns: a slower travel rate in
autumn within the northern regions; a faster travel rate during spring in the more southern
regions of the study area. An improvement in achieving even distribution of grazing,
offering more resources such as water, and planning rest breaks of intensely used areas
can be incorporated in future management plans. A continuation of the project would
benefit from a closer look at vegetation specifically plant species type in the various
terrains and a biomass study as well as factors affecting vegetation such as precipitation.
1

1. Introduction and Literature Review
1.1 Sustainable Rangeland Management
Sustainable agriculture defined is to reserve nonrenewable resources, implement
natural ecosystem cycles, increase environmental quality, maintain fiscal livelihood of
operations, contribute to society, and the lifestyle of farmers (Gold, 1994). Sustainable
rangeland management across grassland landscapes has received increased attention over
the last 20-30 years (Aagesen 2000; Holecheck et al.,1989; Jasmer & Holecheck,1984)
creating an awareness to implement appropriate stocking rates for capable landscapes at
both private and governmental levels (Krausman et al., 2009; Vincent, 2007). In Garret
Hardin’s The Tragedy of the Commons he describes how an unmanaged resource, not
without limits, will be left to depletion because those consuming it act in the best interest
of themselves and not in the interest of all mankind or society, therefore, it is important to
manage resources unsolvable through technology alone by governmental regulations
(1968). Sustainable grazing practices work to develop lower impact protocols in
rangeland management to reduce desertification and maintain healthy riparian habitats.
Such protocols can include: introducing rest breaks (by restricting sheep from overused
areas for a period of time to allow the land to regenerate vegetation), reducing overuse of
riparian areas, and setting grazing capacity and stocking rates at conservative levels.
Desertification and a loss in riparian habitat are two examples of what can result
when unsustainable land management allows for grazing at high levels. It is in the
recognition of these issues that sustainability has taken its place among conversations
within the land management profession. A study conducted in Argentine, Patagonia by
2

Aagesen (2000) determined that the European’s introduction of sheep ranching, about
100 years ago, created a shift from Patagonia’s traditionally lush landscape to an increase
in erosion levels and desertification overtime. Land management initially allowed for
sheep to graze without any quantity or time restrictions leading to the permanent
alteration of Patagonia’s natural ecosystem as the grazing rate was too high compared to
the natural recovery rate of the vegetation.
The grassland and fauna of riparian habitats impact a variety of species living in
and alongside rivers and streams (Armour et al 1994; Glimp & Swanson, 1994; Sarr,
2002). Livestock grazing can reduce streamside vegetation and widen channels, which is
harmful to the ecosystem (Armour et al 1994; Sarr, 2002). Sustainable protocols by the
US Department of the Interior (1978) state the necessity of maintaining riparian areas by
ensuring that an appropriate level of vegetation exits along the stream bank and
sustainable land management practices follow suit to minimize overgrazing in riparian
areas.
Sustainable grazing capacities and conservative stocking rates have a goal of
minimizing negative impacts on grassland landscapes by setting the appropriate number
of sheep to the amount of usable vegetation. Grazing capacity considers the estimated
number of animals a landscape can maintain within a given timeframe (Holecheck, 1989;
Namken & Stuth, 1997), while stocking rate is calculated by the total amount of usable
vegetation divided by how much vegetation a herd of sheep will need to sustain itself
(Bizuwerk et al, 2005; Holecheck, 1989). Both parameters can be costly and time
consuming for land mangers to determine. The traditional way of calculating both
3

grazing capacity and stocking rate is by setting animal unit months (AUM) which is the
amount of vegetation a grazing animal needs to sustain itself for one month. Land
managers establish AUM’s by taking a percentage of an animal’s body weight to
determine how many pounds of dried vegetation it will consume in one month (Alberta
Agriculture and Food, 2007). A primary sustainable grazing protocol denotes the use of
conservative stocking rates, i.e. determining the number of AUM’s a landscape can
sustain and then allowing a portion of that number to be taken as the Bureau of Land
Management (BLM) explained in their Final Environmental Impact Statement written in
relation to a project for proposing revisions to grazing regulations on public lands.
(2004). Rangeland managed to accommodate stocking rates suitable for the grazing
capacity is one way of implementing a sustainable agriculture based practice.
Landscape management is difficult due to the necessary analysis of multiple
variables over vast areas. The containment of these important variables is made easier
through the incorporation of geospatial technology, thus creating a more manageable
environment for analysis. Technological advances within the field of Geographic
Information Sciences (GIS), Remote Sensing (RS), and Global Positioning Systems
(GPS) assist with sustainable rangeland management by looking at grazing patterns and
variables in the landscape (Namken & Stuth, 1997; Sampson & Delgiudice, 2006).
GIS provides an environment where pertinent variables can be analyzed on a
smaller more manageable scale. Bolstad defines GIS as necessary because it can,
“identify the source, location, and extent of adverse environmental impacts, and may help
us devise practical plans for monitoring, managing, and mitigating environmental
4

damage” (2008, 4). The GIS does this by accessing two primary sources of data (location
and attribute information) and stores both in a database which users can ask questions of
and refer to in analysis. For example a polygon representing vegetation can be stored
within the GIS as an object containing both its location (i.e. x and y coordinates as they
appear on the Earth’s surface) and attribute information (i.e. the type of vegetation such
as grassland). This allows for important questions necessary for basic analysis to be
asked such as: Do sheep eat the plant species that are dominant in these areas? And is it
in a location accessible by sheep? If the answer to both is yes then this area is considered
capable of being grazed by sheep and in return tells a land manager area to look more
closely at for suitability of grazing, which addresses locations more susceptible to
adverse impacts such as riparian habitat.
Remote sensing and the use of satellite and aerial imagery allow for feasible
acquisition and analysis of spatial data across large areas due to the wide scan collection
process and temporal repetition in contrast to field data collection (Marsett et al., 2006).
A study in the Ulster Valley (Platcher & Hampicke, 2010) emphasizes the effectiveness
in utilizing RS to gather spatial data across grazing landscapes. The study used aerial
photos, taken from an aircraft, to identify areas across the valley showing signs of impact
by animal grazing. An area was imaged once a month for four consecutive months to
detect changes in vegetation through time. The results were summarized by land
classification in three categories: “affected by vegetation grazing, affected by trampling
[a process where hooves of animals destroy vegetation as they migrate] or not affected or
effects not recognizable on aerial photographs,” (Platcher & Hampicke, 2010, 73) Aerial
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photography allowed for a four month completion of data where if the exact same data
had been collected at the field level it could have taken a year or longer. Remote sensing
expedites the acquisition of information important for landscape analysis, making it an
efficient resource in sustainable land management protocols.
Global positioning system technology allows for the tracking of animal movement
through GPS collars which provides a more precise method to locate and manage live
stock such as sheep. Viewing GPS locations over time show patterns depicting how the
live stock are moving and the potential intensity at which they are grazing, therefore
enabling land managers to identify areas where the animals are staying too long in one
location. One such study utilized this technology to analyze the movement of cattle in
predicting behavior, the amount of pasture used, and performance. Conclusions found a
95% accuracy of the GPS locations collected which provided accurate analysis for where
the cattle were grazing, areas of dispersed movement, and areas of stagnation. Further
analysis of GPS points showed an over use of cattle grazing in certain areas (i.e. shaded,
close to water) during periods with higher temperatures (Turner et al, 2000).
1.2 Grazing Behavior of Sheep
Interactions between sheep and a foraging landscape can be sustainable when
managed appropriately, because the biological relationship between sheep and grasses
can be a symbiotic one where both species benefit from one another. Studies have been
conducted examining livestock grazing as a potential benefit to creating ideal habitat for
certain native bird species and enhancing biodiversity in semi arid landscapes by creating
heterogeneity across the landscape as opposed to homogeneity (Derner et al, 2009;
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Fuhlendorf & Engle, 2001; Toombs et al, 2010) Livestock introduce heterogeneity by
creating different heights in vegetation at various locations creating a variety in habitat
type, which has been shown to increase numbers of certain bird species (i.e. Mountain
Plover, Baird’s Sparrow, Chestnut-collared Longspur) to the area (Fuhlendorf & Engle,
2001) Sheep grazing has also been proposed as a potential tool in habitat management for
wildlife, however this takes considerable awareness in the rangeland ecology to
determine where, if at all, this could be beneficial because it could be at the cost of
another species’ habitat (Mosley, 1994).
Grasses are the primary food source for open free ranged sheep therefore, when
the health of the grassland suffers so does the health of the sheep. Healthy levels of
vegetative land cover in semi-arid landscapes are often determined by measuring
biological soil crusts (BSC) (Muscha & Hild, 2006). Studying soil components identifies
erosion level and water retention, which are factors contributing to the health of
vegetation. Often where there are areas of dense native plant species there are healthy
soil levels (i.e. minimal erosion occurring, nutrient rich) therefore; measuring soil
correlates with the measurement of vegetation health. Livestock grazing has been known
to impact both BSC and soil composition (Muscha & Hild, 2006). There needs to be a
balance in the relationship between vegetation and the grazing of sheep for both species
to coexist in a healthy and sustainable manner (Glimp & Swanson, 1994). The resources
available for land managers to understand both grazing behavior and landscape variables
are what maintain this balance.
7

Grazing refers not only to the consumption of natural foliage, but to any
interaction a sheep has on the landscape such as, trampling or diaspore spread, i.e. release
of fecal matter containing seedlings of various plant species, (Platcher & Hampicke,
2010). Predicting grazing behaviors can aid in answering questions such as: What sheep
are choosing to consume? How intensely are they grazing across the landscape? And
what, if any, necessary actions can land managers take to ensure a sustainable ecology
exists. Research conducted globally on grazing behaviors from sheep suggests they graze
on low land areas (a slope percentage of less than 60), within 2 miles of water, and with
an adequate amount of forage (Holechek et al., 1989). Areas meeting this criterion are
considered capable for grazing, which is the first step in determining conservative
stocking rates by calculating available vegetation of these areas alone. A model
conducted by a group of ecologists examined the behavior of grazing by a variety of large
herbivores and found a combination of abiotic, biotic, and spatial memory to be causative
to the overall grazing patterns developed (Bailey et al., 1996). Certain abiotic factors
(i.e. slope gradient and distance to water) and biotic factors (i.e. forage availability and
type) can create restraints within the overall areas animals choose to graze which can be
amplified through spatial memory of these areas by the animal creating overuse and
fragmentation of use across the landscape.
1.3 GIS Techniques in Determining Pertinent Land Management Criteria
Environmental management has been a long-standing test bed for the
implementation of GIS use as an analytical tool (Goodchild, 2003). A GIS provides an
environment where collected information and data can be used in a spatial framework to
8

predict the behavior of animals over several periods of time. Models designed to mimic
real world applications, in a controlled environment, allow manageable analysis of data
tied to spatial locations resulting in a better understanding of the landscape than field
observations alone (Duvall, 2010; Goodchild, 2003; Gough & Rushton, 2000).
Using GIS to analyze grazing capability at a landscape scale is not a new concept.
A GIS was used to assist in the development of suitable areas for sheep grazing across the
Awash River Basin, located in Ethiopia, as an attempt to identify how efficiently land
managers were utilizing the land (Bizuwerk et al., 2005). The study used Digital
Elevation Models (DEM) to find slope values for the study area as well as water data
layers to show areas within a close proximity to water within a GIS. Areas were given
weighted values based on erosion levels, average rainfall, distance from water, and slope
gradient then analyzed to determine overall suitability. Results concluded that 33% of
the Basin was found suitable for grazing however a large portion of suitable area was
being used for agriculture crop production. Conclusions determined either a reduction in
crop production or moving crops to other locations so as to allow for optimization of
suitable areas for grazing reducing grazing pressure in overused areas (Bizuwerk et al.,
2005).
Another study utilizing GIS to depict rangelands suitable for sheep grazing was
conducted in the semi-arid landscapes of Iran (Amiri, 2009). Utilizing rangeland to graze
sheep is a monetary and culturally entwined process for Iranians, therefore the study
identified areas suitable for such use in order to design grazing management plans in a
way sustainable to the land itself. Slope gradients, highly erosive areas, and areas within
9

close proximity to water were all used to generate an output of suitable grazing areas
classified into three categories moderately suitable, marginally suitable, and unsuitable.
Using the GIS to perform the analysis was said to have offered more accuracy and
suppleness in determining the overall rangeland suitability (Amiri, 2009).
The United States Department of Agriculture’s Natural Resources Conservation
Service also utilized a GIS to design a two part analysis of landscapes used for grazing
(Namken & Stuth, 1997). The first part involved creating a grazing pressure model
which utilized an algorithm to adjust total grazing capacity for unsuitable slope gradients,
distances too far from water, and areas containing high brush densities. The second part
used the model to identify appropriate locations for additional water sites (areas suitable
for grazing but too far away from a water source) and treatment sites (areas of excessive
woody brush in need of reduction treatment to increase desirable forage). The end result
was an effective tool for land managers to determine what limitations were causing areas
to receive intense grazing and how to minimize this where possible by opening up other
limited use areas (Namken & Stuth, 1997).
1.4 Remote Sensing Techniques in Determining Pertinent Land
Management Criteria
Remote sensing has been used extensively over the years as a resource in land
management with the premise being the use of high-resolution satellite imagery to
identify areas where “greenness,” i.e. vegetation, occurs (Homer et al, 2004; Jiang et al,
2006; Knight et al, 2006). One satellite known well for its use in vegetation
classification is the LANDSAT 7 Enhanced Thematic Mapper (ETM) + (Homer et al,
10

2004). As defined on the National Aeronautics and Space Administration’s (NASA)
Introductory LANDSAT Tutorial website
(http://zulu.ssc.nasa.gov/mrsid/tutorial/Landsat%20Tutorial-V1.html) LANDSAT 7 is the
most recent of a series of satellites launched (with one unsuccessful launch for
LANDSAT 6) for the purpose of observing Earth. It is equipped with a high end scanner
called the ETM + which uses 8 bands each reflecting different wavelengths of light. The
Landscape toolbox website explains how the red band (band number 3 on LANDSAT 7
ETM+) and the near infrared band (band number 4 on LANDSAT 7 ETM+) are
important in detecting vegetation, because photosynthesis in healthy vegetation absorbs
red light and reflects near infrared light where the opposite is true of unhealthy vegetation
and non-vegetation reflects light more uniformly (TNC & USDA, 2008). Normalized
Difference Vegetation Index (NDVI) is a well known index used to outline vegetation
(Jiang et al, 2006). NDVI is calculated by subtracting the red bands from the near
infrared and dividing this number by the sum of the red bands to the near infrared (Jiang
et al, 2006):
NDVI= (NIR-R)/ (NIR+R)
The NDVI index is beneficial when used to determine land cover type across landscapes
by classifying the landscape into categories based on the vegetation type within an area.
For example an area within a pasture may have a number of trees, shrubs, and grasses,
however if the grasses make up the majority of the total area the pasture would be given a
grassland land cover type. A study conducted for an estuary system in North Carolina
and Virginia successfully used NDVI to produce a land cover analysis specifying areas of
11

vegetation type and non-vegetation (Knight et al, 2006). A survey of land managers in
the southwest reported a desire in land managers to have timely and reliable vegetation
land cover maps to aid in deciding appropriate protocols (Marsett et al, 2006). Land
cover that is rapidly derived from NDVI is useful for inclusion into land management
protocols because it gives managers the distribution of vegetation in a timely fashion
without having to rely on outdated data.
12

2. Land Management Resource Project
2.1 Project Study Area
The Lava Lake study area is located in south central Idaho neighboring the town
of Hailey and The Craters of the Moon National Monument (Figure 2.1 Lava Lake
Boundaries and Location). The Lava Lake Land and Livestock (Lava Lake) organization
supports conservation of the Pioneers-Craters landscape by managing both private and
public (i.e. U.S. Forest Service, Bureau of Land Management) land. One of Lava Lake’s
conservation efforts is establishing protocols for sustainable sheep grazing across the
landscape. Land managers for Lava Lake have worked to develop protocols consistent
with conservation based practices for sheep ranching (Bradley & O’Sullivan, 2011).
These include, but are not limited to, reducing grazing within riparian habitat (Scheintaub
& O’Sullivan, 2009) introducing rest breaks into grazing, and incorporating outcomes
from scientific analysis into land management decisions.
Lava Lake was founded in 1999 where a collaboration of multiple sheep and
cattle ranches where combined. In 2001 management decisions moved to just the
ranching of sheep where an estimated 7,000 ewes graze across allotments (Bradley &
O’Sullivan, 2011). Vegetative land cover consists of sagebrush, bunchgrasses, forbs, and
shrubland in lower elevations with primarily conifers in higher elevations (Bradley &
O’Sullivan, 2011). The climate consists of higher temperatures with minimal
precipitation in the summer and lower temperatures with a majority of precipitation
occurring in the winter (Bradley & O’Sullivan, 2011).
13

2.2 Land Management Protocols
Lava Lake’s protocols are comparable to land management by other organizations
within the business of grazing sheep at sustainable rates and, therefore, will be used to
define land management protocols at an organizational level. These protocols comply
with federally mandated land management regulations coming from acts managing the
use of grazing livestock on public lands and are: the Taylor Grazing Act of June 28,
1934; the Federal Land Policy and Management Act of 1976; the Public Rangelands
Improvement Act of 1978 (U.S. Department of Interior, 2004). The Code of Federal
Regulations (CFR) established by the United States Department of the Interior
incorporates laws in accordance with these three acts offering a uniform reference for
what federally mandated protocols are (1978). Lava Lake and these protocols share a
common objective to maintain and encourage the sustainability of natural rangeland
ecosystems while allowing livestock to graze in a healthy manner.
This project offers a resource for supporting the implementation of these
protocols by identifying informational criteria both from the landscape and the behavior
of the sheep. Land managers guide the overall grazing process and therefore, to a certain
degree, have the ability to control it in a sustainable way by following protocols to the
best of their ability. Criteria such as the rate at which sheep are grazing, capable grazing
areas, and determinable landscape factors such as slope and water availability assist in
meeting protocols such as limiting overuse, striving for an even allocation of use and thus
providing adequate rest for vegetation to grow by pointing out areas meeting defined
protocols and areas for improvement. In Table 2.1 (Sustainable land management
14

protocols) are summarized Lava Lake protocols, how they relate to federally mandated
protocols, and what land management criteria addresses whether or not these protocols
are being met.
2.3 Project Objectives
In an effort to support appropriate management of livestock grazing a customized
GIS model is used to illustrate grazing patterns for decision making of sustainable
stocking rates. Potential grazing patterns are identified by specifying areas likely to be
grazed, determining where grazing is occurring and at what rate. Land managers can use
this model as a means for spotlighting areas at risk from overgrazing and implement the
derived information in their land resources management practice.
Table 2.1. Sustainable land management protocols: Lava Lake protocols as they apply to federal
regulations and supportive land management criteria helpful in assisting with protocols.
Lava Lake Protocols Code of Federal Regulations
4180.2 Standards and
guidelines for grazing
administration
Land Management
Criterion to assist with
meeting protocols
“Vary timing and intensity of
use for a given area from year
to year.”

“C (1): Maintaining or
promoting adequate amounts
of vegetative ground cover,
including standing plant
material and litter, to support
infiltration, maintain soil
moisture storage, and
stabilize soils”

Identify any areas of
overuse for long periods of
time.*

“Aim for even distribution use
across the largest possible
area of capable terrain.”
“C (10): Maintaining or
promoting the physical and
biological conditions to
sustain native populations
and communities”

Identify areas capable for
grazing and then determine
sheep rate of travel across
these areas to determine
stagnation or even
distribution.*

15

Table 2.1, Continued
Lava Lake Protocols

Code of Federal Regulations
4180.2 Standards and
guidelines for grazing
administration
Land Management Criterion
to assist with meeting
protocols
“Avoidance or altered grazing
of riparian areas known to be
in poor condition, including
many riparian monitoring
sites.”
“C (3): Maintaining, improving
or restoring riparian-wetland
functions including energy
dissipation, sediment capture,
groundwater recharge, and
stream bank stability”

Identify conditions of each
wetland area across the study
area to manage grazing
accordingly
“Institute regular patterns of
rest, with rest one in three
years wherever possible.”
“F 2(xi): Periods of rest from
disturbance or livestock use
during times of critical plant
growth or re-growth are
provided when needed to
achieve healthy, properly
functioning conditions (The
timing and duration of use
periods shall be determined
by the authorized officer.)”

Identify critical times of plant
re-growth for specific plant
species across the study area.
Identify areas of concentrated
use by sheep and work to
manage grazing such that
these areas receive adequate
rest breaks.*
“Stock at conservative levels
that anticipate/assume
continued patterns of drought
and below average
precipitation. “

“F (2) (xv): Grazing on
designated ephemeral (annual
and perennial) rangeland is
allowed to occur only if
reliable estimates of
production have been made,
an identified level of annual
growth or residue to remain
on site at the end of the
grazing season has been
established, and adverse
effects on perennial species
are avoided.”

Identify conservative stocking
rates by 1
st
deciding areas
capable for grazing* and 2
nd

deciphering within these
areas how much grazing
capacity exists.

Source: Data for Lava Lake from Tess O’Sullivan, e-mail message to author, March 12, 2012.
Source: Data for Code of Federal Regulations from U.S. Department of the Interior. (July 5,
1978). Electronic Code of Federal Regulations. Title 43: Public Lands: Interior, 4100-4180
Grazing Administration-Exclusive of Alaska.
*Criterion established through analysis of this thesis
16

GIS based tools used in this project identify heavily grazed and unused areas
offering assistance when establishing stocking rates. A capability analysis specifying
grazing areas based on known grazing behavior of sheep was automated using Esri’s
ArcGIS model builder. Derived capable areas will reflect areas within a 2 mile distance
from water, at a slope percentage lower than 60, and exclude non-vegetative land cover.
Capability outputs are not claiming areas suitable for grazing as suitability is an extension
of capability. Land managers will be able to look at areas probable for grazing across the
allotment based on capability outputs and from these areas determine whether or not they
are suitable for grazing in conjunction with further studies. The “Sheep Grazing
Patterns” model, designed for this project, generates areas probable for grazing allowing
land managers to look at each area individually and apply their professional knowledge
more directly. Sheep movement patterns are identified using two resources within the
Esri ArcGIS software, the “Tracking Analyst extension” and the “Time Slider.” Through
both processes, GIS is able to assist conservation land managers in their goal of
sustainable land use across the landscape.
The land management resource project shows potential areas of overuse across
the Lava Lake study area using a combination of capability analysis (Ogle & Brazee,
2009) that will show the capable areas a sheep is likely to graze (Capable Sheep Grazing
Model) and a time analysis to show probability of how intensely sheep are grazing by
identifying areas of use for extended periods of time. The integrated use of NDVI from
LANDSAT 7 TM+ assisted the Capable Sheep Grazing model by identifying areas where
vegetation exists within the study area. Together they define a more accurate and
17

complete project able to identify prospective grazing patterns then if either were to stand
alone. The result is a useful tool for land managers striving for sustainable ranching.

Figure 2.1. Lava Lake Location and Boundaries in South Central Idaho between the
town of Hailey and Craters of the Moon National Monument.

18

3. Acquired and Processed Data
3.1 Lava Lake Data
Datasets created by Lava Lake (see Table 3.1) were made available for this
project. Lava Lake has incorporated GIS into their practices over the years, acquiring
appropriately formatted data which is privately owned and not made publicly available.
The streams layer was derived from the public Digital Line Graph datasets put out by the
United States Geological Survey.
The Streams and Springs Troughs vector files were both used as water source
layers from which specified criteria in sheep grazing behavior were determined for the
Capable Sheep Grazing model.
The Allotments vector file was used to establish specified areas where sheep graze
across the Lava Lake landscape; the model’s output is generated from the perimeters of
these allotments.
The roads vector file was also used as an additional water source because in areas
with little water, watering trucks will drive water to the sheep.
The temporal analysis uses GPS 2009 and GPS 2010 point data layers which are
two layers consisting of points collected by five Global Positioning System collars worn
by Lava Lake sheep from 2009 to 2010. Both years collected points at an average
Positional Dilution of Precision (PDOP) value of 3.38 degrees, meaning the accuracy of
19

the point being collected was within an average of 3.38 degrees to the exact location in
real time.
Table 3.1. Summary of Lava Lake Data
Name Date Source Format Origin Representation
Springs
Trough
unknown Lava Lake Point Hydrography layers
and coordinates
obtained by (GPS)
Natural occurring
springs and water
troughs placed by
Lava Lake across the
study area
Streams

2002 Lava Lake Line Digital Line Graphs
(DLG) from the
USGS and data
generated from the
Sawtooth National
Forest Service
Intermittent and
perennial streams, lake
and pond boundary
lines, stream braids
and channels, all
found across the study
area
Allotments unknown Lava Lake Polygon Unknown Boundary of all the
allotments managed
by Lava Lake
Roads unknown Lava Lake Line Unknown Access roads where
water trucks can drive
out to sheep
GPS 2009 2009 Lava Lake Point Collected every 4
hours from GPS
collars attached to
sheep
Locations of where the
sheep are grazing for
2009
GPS 2010 2010 Lava Lake Point Collected every 4
hours from GPS
collars attached to
sheep
Locations of where the
sheep are grazing for
2010

3.2 National Elevation Datasets
National Elevation Datasets (NED) (Table 3.2 Summary of NED Data) were
downloaded from the United States Geological Survey’s (USGS) Seamless Data
20

Warehouse (http://seamless.usgs.gov). A total of four NED’s were downloaded in
ArcGIS raster GRID format. They were in a 1/3 arc second resolution (approximately 10
meters), the most precise resolution available for the study area. The USGS offers
NED’s as a resource for acquiring elevation data for the conterminous United States,
((USGS), U.S. Geological Survey, 2009). These datasets were used for the calculation of
slope values in order to specify criteria in sheep grazing behavior (see section1.2).
Table 3.2. Summary of NED Data
Name
Date
Source Format Origin Representation
N43w114 2009 USGS Raster USGS National
elevation dataset
(NED)
Elevation levels for the
south eastern quadrant of
study area
N43w115 2009 USGS Raster USGS’s National
elevation dataset
(NED)
Elevation levels for the
south western quadrant of
study area
N44w114 2009 USGS Raster USGS’s National
elevation dataset
(NED)
Elevation levels for the
north eastern quadrant of
study area
N44w115 2009 USGS Raster USGS’s National
elevation dataset
(NED)
Elevation levels for the
north western quadrant of
study area

Preparation of NEDs
For the NEDs to be used within the model (see Figure 3.1 Preprocessing of
NED’s) additional preparation needed to take place. Generation and grouping (i.e.
reclassification) of accurate slope values were necessary to establish suitable areas of
travel for grazing by sheep. The first step in the process was establishing appropriate
21

units of measurement to calculate accurate slope percentage across the landscape which
determines the percent rise from each cell within the NED to its neighboring cells.
Percent slope is calculated by dividing the rise (the increase in units vertically from one
cell to the next) by the run (the increase in units horizontally from one cell to the next)
and multiplying this value with 100. The NED’s exist in a Geographic Coordinate
System using decimal degrees as their units of measure, and follow the guidelines of the
1983 North American Datum ((USGS), U.S. Geological Survey, 2009). An issue with
incorrect slope percentage will occur when performing slope analysis using decimal
degrees for the horizontal components (x value, y value) and meters for the vertical
component (z values). To derive correct slope percentages each NED was projected into
the Transverse Mercator projection, placing all the x, y, and z values in the same unit
(meters).
The slope percentage dataset was calculated as floating-point and the next step
consisted in the conversion from floating-point to a binary integer output grid when
performing the reclassification using the reclassify tool. The classified slope percentage
was then divided in two groups to which new Boolean values were assigned: from
0.000000-59.999999 % slope were given value “0”, while all values > 60 % slope were
given value “1”. The lowest slope percentage for “n43w114” is 0.000000 and the highest
slope percentage is 348.285553, therefore, while reclassifying this particular NED the
range of values were divided as follows: 0.000000-59.999999% are given a value of “0”
and 60.000000-348.285553% are given a value of “1” the other three NED’s were
reclassified in a duplicate fashion.
22

Figure 3.1. Preprocessing of NED’s for use in the model

The final step created a mosaic database in which all NED’s could be merged
together. Afterward, the raster was converted into a vector polygon format used for
analysis within the model. A query was then performed using select by attribute to
23

query out all of the polygons with a value of “1.” A new feature class was created by
exporting the selected polygons and saving them as their own separate feature class
named unsuitslop. This final feature class was used in the model to eliminate all the
areas with slope percentages of 60 or greater and is shown overlaid to the processed
percent slope of NED “n43w114” in Figure 3.2.
3.3 National Land Cover Database Imagery
The most recent National Land Cover Database (NLCD) was completed in 2001 by
numerous U.S. governing agencies constituting the Multi-Resolution Land Characteristics (MRLC)
Consortium. The NLCD (Table 3.7 Summary of NLCD Data) was derived from imagery,
encompassing all four seasons, taken by the LANDSAT 7 ETM + satellite (Homer et al, 2004).
LANDSAT imagery with a nominal 30 meter ground resolution is better suited for this study and
the detail required in this investigation as opposed to other satellite imagery with smaller
scale ground resolution. NDVI index values were calculated to identify different
vegetation types as well as non-vegetation types.
Preparation of NLCD Imagery
The NLCD was used to extract non-vegetative or vegetative areas incapable for grazing
(i.e. cropland) from the study area. The data was preprocessed before being integrated to
the model as shown in Figure 3.3 (Preprocessing of NLCD data). Land cover types
deemed unqualified were the following: Barren Land, Cultivated Crops,

24

Figure 3.2. Unsuitable slope feature class overlaid to the NED “n43w114” slope values.

25

Table 3.6. Summary of NLCD Data
Name Date Source Format Origin Representation
NLCD 2001 MRLC Raster LANDSAT 7
TM+
Imagery
16 categories of land cover type
 Barren Land
 Cultivated Crops
 Deciduous Forest
 Developed, High
intensity
 Developed, Low
Intensity
 Developed, Medium
Intensity
 Developed, Open Space
 Emergent Herbaceous
Wetland
 Evergreen Forest
 Hay/Pasture
 Herbaceous
 Mixed Forest
 Open Water
 Perennial Snow/Ice
 Shrub/Scrub
 Woody Wetlands

Developed High, Medium, and Low Intensity, Developed Open Space, Open Water, and
Perennial Snow/Ice.
Figure 3.3. Preprocessing of NLCD data

26

3.4 Construction of the Geodatabase
Spatial database systems store geographic locations represented as points, lines, or
polygons while also incorporating pertinent information tied to such locations. They are
unique in comparison to standard database systems because of their ability to manage
geometries providing a framework for the organizing, querying, and analyzing of spatial
data (Yeung & Hall, 2007). Within the Esri ArcMap software spatial databases are called
a geodatabase as explained on Esri’s ArcMap desktop resource center website (2012).
One entitled LL_Model (Lava Lake Model) is created for the project to incorporate
organization, ease of model transferring, and is necessary through preliminary steps in the
project figure 3.4 (LL Model Geodatabase and contents within Esri’s Arc Catalog) shows
the organization of data and the Capable sheep grazing model contained in the
Geodatabase.
All thematic layer feature classes were uploaded into LL_Model providing one
organized location for all data. A requirement for adding data to a geodatabase is all data
layers must be projected in the same geographic coordinate system as the geodatabase.
The LL_Model projection is the North American Datum 1927 Transverse Mercator,
chosen based on appropriateness for the study area and also defining the majority of
existing acquired data.
A benefit to having all the data in one centralized location is the ability to transfer
this data among land managers. Lava Lake works closely together with a variety of land
managers overseeing separate, yet intertwined, departments within the organization as a
whole. Accessing both the Capable Sheep Grazing Model and the data within this project
is readily available for any pertinent individual through one location: the geodatabase.
27

Sharing the project with multiple land managers is also made easier by having all inputs,
processes, and outputs stored in one container allowing the entire project to be transferred
through transferring the geodatabase.
Certain processes within ArcMap require the formation of a geodatabase before
they can be implemented. Two of these processes were necessary for this project they
are: creating the mosaic raster dataset from NED’s (see section 3.2) and creation of the
Capable Sheep Grazing model (see section 4.2). Creating LL_Model provides a storage
platform for the administering and housing of the raster dataset and the model.
Figure 3.4. LL Model Geodatabase and contents within Esri’s Arc Catalog

28

4. Methodology
4.1 Establishing Land Management Criteria
The land management resource project identifies areas capable for sheep grazing
and areas sheep could be grazing more intensely. A capability analysis and a time
analysis are conducted using Esri’s ArcGIS establishing criterion from a suite of datasets
within the LLModel Geodatabase. The criterion identified from this analysis will guide
land management protocols such as identifying any area of overuse, areas capable of
grazing, sheep rate and stagnation, and where to implement rest breaks.
The capability analysis directly addresses protocols of even distribution and
setting appropriate stocking rates as the time analysis addresses protocols of even
distribution and varying timing and intensity of use. Seeing where capable areas are
underutilized offers a way to achieve a more even distribution across the landscape by
expanding into these areas. If initial stocking rates are determined using all areas across
the landscape (both capable and incapable) they will not be appropriate for the level at
which the landscape can sustain itself while being grazed. Eliminating incapable areas
from the overall grazing capacity helps to determine accurate AUM’s which in return is
used to set conservative stocking rates. The Time Slider mimics any stagnation in
movement by sheep pointing out areas of uneven distribution across the landscape as well
as identifies areas being used heavily for a period of a month or longer.
29

4.2 Capable Sheep Grazing Model
A capability analysis specifying grazing patterns based on protocols and known
grazing behavior of sheep is automated using Esri’s ArcGIS Model Builder. Model
builder is structured such that manual processes within ArcGIS are automated through a
flow chart organization. Automation offers ease of use, and organization offers a visual
layout of methods followed in the project. The construction consists of inputs, tools, and
outputs with additional variables and parameters to guide the overall flow (Allen, 2011).
One reason for utilizing model builder was allowance of multiple users, including non
GIS professionals, to map capable grazing areas. Another was to create a manageable
sharing of processes within Lava Lake.
There are two main parts to the capability criteria within the capable sheep
grazing model. The first, producing areas of land within a 2 mile distance to water and
the second showing areas of land with less than 60% slope. In Figure 4.1 (Capable Sheep
Grazing Model) is shown the model and its layout in Model builder, showing all inputs,
tools, and outputs. It is automated to run on a user specified allotment (i.e. boundary of
land ownership across the Lava Lake Landscape) within which it produces areas where
sheep are capable of grazing.
The first step is set up with a user-defined parameter allowing a sequel expression
to be created to define a specified allotment. Then, both the Troughs/Springs and
Streams feature classes are clipped to the selected allotment using the clip tool. A
precondition is applied to both clip tools to allow the selection of the allotment to take
place before the clips are run. Next, both Trough/Springs and Streams feature classes are
buffered at a 2 miles distance using the buffer tool. The buffer outputs are then merged
30

together, in conjunction with a roads buffer layer, using the merge tool and clipped back
to the selected allotment. A precondition is set so the merging of the two buffer classes
will occur before they are clipped to the initially selected allotment. All tools are added
to the capable sheep grazing model by dragging and dropping them from the search menu
in ArcMap. Preconditions are set via the tool properties dialog box, and parameters are
established using the model builder context menu.
The second process uses the new feature class unsuitslop, generated from the
NED’s, and nonvegetation, generated from the NLCD, to erase them from the output of
the first process (i.e. Total Water Distance) using the erase tool. A parameter is attached
to the final output, so the user is prompted to specify the name of the final polygon
feature. This allows the user to choose an appropriate name applicable to the allotment
for which the Capable Sheep Grazing model is being run, as well as allows for running
the model on multiple allotments without overwriting final outputs. It is also indicated to
“Add to display” the final output, so the polygon is added to the map upon completion.
The final output shows areas within a 2 mile distance from water, with slope percentage
lower than 60 and vegetation coverage consisting of grassland, pasture, and herbaceous.
4.3 Sheep Temporal Patterns
There are many patterns of sheep grazing that are unseen when viewing points on a map.
Animating movement as it occurs throughout time lets users see patterns in paths and rate
providing information helpful to grazing management. The ability of Esri’s Tracking
Analyst and Time Slider tools to provide such visual analysis made them appropriate for
use within this thesis.
31

Figure 4.1. Capable Sheep Grazing Model.

Tracking Analyst is a toolbox containing viable time management tools,
permitting recorded time elements in data to be manipulated. Date and time attributes of
a feature class are used to specify time properties producing a time sensitive layer as an
32

output. The time layer is then used in conjunction with the Time Slider tool which plays
each feature in sequential order. The end result is an animation of features as they move
across the landscape through time.
Certain steps are necessary to undertake time analysis. The first is enabling both
tools for use within the GIS. Tracking Analyst is an extension and was accessed by
adding the Tracking Analyst toolbar to ArcMap. Time Slider is an application
programming interface available on the main toolbar, within ArcMap, where it remains
unavailable for use until a time layer is created and added to the map (Figure 4.2 Time
Slider, Tracking Analyst, and the Temporal Data Wizard, shows all of them open within
ArcMap).
The second process is the establishment of time properties for each GPS point
layer. The Concatenate Date and Time tool (available within the Tracking Analyst
toolbox) was used to join the “Date” and “Time”, attributes in the 2009 and 2010 GPS
point feature classes, giving each feature a unique time stamp for the specified day
recorded. The temporal data wizard (made available through the Tracking Analyst
toolbar) was then used to identify time attribute fields, time and date formats, and unique
id fields all properties pertinent to the creation of a time layer. Tracking Analyst was
used to generate six time layers; one for the spring, summer, and autumn seasons within
each year.
After each time layer was created and added to the map, the Time Slider is
available for use. The Time Slider is used to manipulate the extent and rate at which each
feature’s unique time stamp is moved through.
33

Figure 4.2. Time Slider, Tracking Analyst, and the Temporal Data Wizard enabled and running
within ArcMap

The extent was set to one month when visualizing the movement of sheep during
different seasons (Spring: April; Summer: July; Autumn: September), and the extent was
set to one year when looking at how the sheep moved throughout the entire grazing
period (beginning of April to the beginning of October). The rate in all time analysis was
set to move through each features time stamp every second. The extent, (i.e. timeframe)
for each session is set in the Time Slider through its properties, and the speed at which
the Time Slider moves through each timestamp is set through the time layer’s properties.
For example, one hour between timestamps could be played every second, one day
between timestamps could be played every second and it is dependent on the time scale
the user wants to visualize. For the purposes of this analysis the speed is set to move
through each timestamp every second.
34

5. Results
5.1 Capability Analysis
The capability analysis shows areas capable of grazing and provides more
accurate acreage, when considering stocking rate, then accounting for the total acreage a
study area encompasses Figure 5.1 (Capable terrain for the study area). Eliminating
unusable areas when calculating total acres is a first step to setting appropriate stocking
rates. The outcome offers a better perspective of land available to land managers so they
can then look more closely at the suitability of these areas as well as include vegetation
estimates across the landscape from only capable areas. For the year 2009 3,829 GPS
points out of a total 4,084 points located inside an allotment were found within these
capable areas (an accuracy of 94%) and for the year 2010 4,035 points out of 4,146 total
points were found within capable areas (an accuracy of 97%). From a total of 900,000
acres in the study area, 763, 044 acres were found to be capable of being grazed by sheep,
an estimated 85 percent. In Table 5.1 (Total acreage capable for grazing by allotment) it
shows the calculated acreage considered capable of grazing by allotment, and its
subsequent percentage based on total acres for that allotment.
A more accurate calculation of acreage deemed capable for grazing is defined
based on the Capable Sheep Grazing model’s output polygons. The allotments in the
southern and central regions of Lava Lake appeared to be prominent with more flat
terrain (low slope percentages) and greater areas of vegetation. However, this area also
has the least number of water sources available, thus eliminating otherwise viable acreage
due to a distance further then 2 miles from water.
35

Figure 5.1. Capable terrain for the study area

The Timmerman Hills allotments located in the southern region signify these
results as shown in Figure 5.2 (Areas capable for grazing in the Timmerman Hills North
36

allotment). The majority of the Timmerman Hills North allotment was found not capable
for grazing, where the other surrounding allotments were found highly capable of being
grazed (97%-98.5%). Even though there is no steep terrain or non-vegetative areas a
good portion of the allotment is at a distance further then 2 miles from water.
The primary factor contributing to the elimination of areas for capability in the
Northern allotments is the presence of high slope percentages. These allotments are rich
with water supply and have minimal non-vegetative land cover. However, the steep
terrain in this region keeps them at smaller percentages than allotments found in the
central and southern regions.

37

Table 5.1. Total acreage capable for grazing by allotment
Input

Output

Total % Capable
of Grazing
Allotment Name Acres Allotment Name Acres
Balsamroot 3842.501 Balsmrrotgrz 3736.46 97.24
Buckhorn 15022.54 Buckhorngrz 10856.17 72.27
Copper Creek 7735.689 CopCrkgrz 6458.92 83.5
Cottonwood 6312.66 CottWoodgrz 5525.55 87.53
Cove Creek 8921.087 CoveCrkgrz 8225.49 92.2
Crater 4339.437 Cratergrz 2086.15 48.07
Fish Creek S&G 2187.262 FishCrkgrz 1840.66 84.15
Garfield 7303.628 Garfgrz 4680.22 64.08
Hurst Canyon S&G 10853.4 HurstCnygrz 8332.86 76.78
Indian Creek 12632.81 IndCrkgrz 10708.4 84.77
Iron Mine 14164.71 IronMngrz 11843.7 83.61
Kent Canyon 2678.622 KentCyngrz 2665.93 99.53
Kimama 29359.14 Kimamagrz 29124.7 99.2
Laidlaw Park - Middle 31121.25 LaidlawMgrz 30928.9 99.38
Laidlaw Park - North 26734.05 LaidlawNgrz 26617.6 99.56
Laidlaw Park - South 9071.545 LaidlawSgrz 9032.06 99.56
Laidlaw Park - Thumb 19669.98 LaidlawPTgrz 15757.7 80.1
Laidlaw Park-Little Park 7801.078 LaidlawPrklilgrz 6342.7 81.3
Lava Lake 16160.89 LavaLakegrz 13795.7 85.36
Little Wood 6610.808 LilWoodgrz 5794 87.64
Muldoon 18351.95 Muldoongrz 15255.3 83.13
North Fork Big Lost River 21758.86 NrthFrkBLRgrz 16195.1 74.43
North Fork Boulder 34040.6 NrthFrkBldgrz 23150.1 68.01
Park Creek 12236.67 ParkCrkgrz 7221.59 59.02
Pot Creek 10895.28 PotCrkgrz 6631.91 60.87
Quigley 9954.027 Quigleygrz 8373.82 84.12
Richfield - Middle 5123.481 RichFMgrz 4982.95 97.26
Richfield - N. East 7919.617 RichFNEgrz 7725.81 97.55
Richfield - N. West 4684.53 RichFNWgrz 4684.53 100
Richfield - S. East 3343.819 RichFSEgrz 2855.19 85.39
Richfield - S. West 2264.583 RichSWgrz 2233.52 98.63
S. East Fork 1915.04 SEForkgrz 1462.27 76.36
Sheep Creek 8458.178 SheepCrkgrz 8003.35 94.62
Sid Butte - N. East 11317.71 StarLakegrz 10200.7 90.13
Sid Butte - N. West 11715.87 SidButteNEgrz 11074.8 94.53
Sid Butte - S. East 10079.25 SidButteNWgrz 11257.8 111.7
Sid Butte - S. West 11327.17 SidButteSEgrz 9659.92 85.28
Spring Creek 3108.05 SidButteSWgrz 10795.2 347.3

38

Table 5.1, Continued
Input

Output

Total % Capable
of Grazing
Star Lake 10317.32 SpringCrkgrz 2941.11 28.51
Star Lake - Camp 2 18652.88 StarLakeC2grz 18616.6 99.81
Star Lake - Cinder BT 12247.47 StarLakeCBTgrz 11164.9 91.16
Star Lake - Cinder BT E. 7396.429 StarLakeCBTEgrz 7095.45 95.93
Star Lake - E. Bull 1494.44 StarLakeEBgrz 1233.15 82.52
Star Lake - Heifer 477.186 StarLakeHggrz 447.64 93.81
Star Lake - Mallard 5803.559 StarLakeMgrz 5760.86 99.26
Star Lake - Owinza 5713.099 StarLakeOgrz 5579.78 97.67
Star Lake - Sand Blow 1380.42 StarLakeSBgrz 1316.08 95.34
Star Lake - Stage Barn 13871.59 StarLakeStBrngrz 13664.1 98.5
Star Lake - W. Bull 2373.533 StarLakeWBullgrz 2211.75 93.18
Star Lake - Wilson Ridge 18410.84 StarLakeWgrz 18406.2 99.97
Timmerman Hills - Mud Lak 3401.885 TimmHillMLgrz 2695.51 79.24
Timmerman Hills - N 1577.663 TimmHillNgrz 1550.64 98.29
Timmerman Hills - North 13986.89 TimmHillNrthgrz 2813.49 20.12
Timmerman Hills - S 1729.623 TimmHillSgrz 1656.3 95.76
Timmerman Hills - Sonners 3928.299 TimmHillSonngrz 3928.3 100
Timmerman Hills - South 5741.062 TimmHillSthgrz 1678.51 29.24
Timmerman Hills - Wedge 13208.89 TimmHillWedgrz 13162.8 99.65
Trail Creek 24276.61 TrailCrkgrz 14932.7 61.51
Trail Creek S&G 4759.037 TrailCrkSGgrz 4350.73 91.42
Upper Fish Creek 3208.8 UpFishCrkgrz 2393.76 74.6
Upper Rock Creek 5326.183 UpRockCrkgrz 4958.71 93.1
Upper Slaughterhouse 2309.773 UpSlaughtHsgrz 2123.05 91.92
Water Gulch 920.177 WaterGlchgrz 656.98 71.4
Wendell Ct. N. East 2205.211 WendlCtNEgrz 2026.79 91.91
Wendell Ct. N. West 2175.536 WendlCtNWgrz 0 0
Wendell Ct. S. East 2228.649 WendlCtSEgrz 268.67 97.31
Wendell Ct.. S. West 3738.586 WendlCtSWgrz 3683.54 98.53
West Fork 7350.173 WestFrk 7231.18 98.38
Wild Horse 240454 WildHorse 220096.1 91.5
Spring Creek 3108.05 SidButteSWgrz 10795.2 347.3

39

Figure 5.2. Capable grazing in Timmerman Hills North and surrounding allotments (there are no
slope percentage > 60 in the allotment, showing the main eliminating factor is distance to water)

5.2 Time Analysis
The Time Analysis shows that there is a correlation between the time of year and
rate at which the sheep are traveling as well as between the various terrains in the
different regions of the study area and the rate. Based on these findings land managers
can begin to identify patterns in the way their sheep are moving and look at other factors
40

that may be contributing to them. The outcome identifies areas frequented more often by
the sheep, which could translate into areas receiving more grazing pressure. Table 5.2
shows the number of GPS points and the number of days per dates by allotment
representing the amount of sheep activity in each allotment during the different seasons
for the year 2009 and 2010.
The Time Slider animates the overall grazing pattern of sheep monitored by GPS
for the years 2009 (5 collared sheep) and 2010 (5 collared sheep) which is as follows: at
the beginning of April sheep start in the south central allotments and are herded by
horsemen up through the central region, traveling outside study area boundaries with
permission in order to arrive at the most northern allotments by about June, and then
they are herded back down south ending in the north western and north eastern regions by
September staying there until October to early November. Patterns visualized among
each year showed sheep traveling faster during the spring and summer season than during
the autumn, as shown in Table 5.2 a majority of northern allotments have an increased
number of days in conjunction with an increased number of points when compared to the
southern allotments translating into a higher amount of use in this region of the study
area. In the autumn of both years the sheep appeared to stay more stationary in their
movement. Spring appeared to be the season were sheep traveled the fastest as shown in
Figure 5.3 (Difference in rate of travel during spring, summer, and autumn) by the point
locations from the same GPS collar, one day in April, July and September for the 2009
and the 2010 grazing years. An explanation of the time analysis seen while playing
features in the Time Slider is presented in this figure. The points in the two September
41

panels (bottom) are much closer together then those in April and July, showing a
condensed area of travel by sheep in September, and a vast area covered in April and
July.
Table 5.2 Total number of days sheep graze (as represented by GPS generated points) in each
allotment for the 2009 and 2010 grazing year.
Northern Allotments
Name Total points
for 2009
Dates/ # days Total points
for 2010
Dates/ #
days
Total
capable
acreage
Lava Lake 301 6/1-6/14;
10/9-11/29
(34 days)
90 5/7-6/19
(43 days)
13795.70
Cottonwood 101 6/1-6/26
(25 days)
96 6/8-6/28
(20 days)
5525.55
Crater 12 6/15-6/20
(5 days)
23 6/13-6/23
(10 days)
2086.15
Iron Mine 608 6/28-10/17
(111 days)
284 6/27-9/28
(93 days)
11843.7
Balsamroot 75 5/25-6/5
(11 days)
152 5/22-6/20;
9/4-9/16
(41 days)
3736.46
West Fork 138 5/27-6/30
(34 days)
202 5/26-6/28;
9/7-9/26
(52 days)
7231.18
Upper Fish
Creek
109 6/29-9/22
(86 days)
71 7/2-9/26
(87 days)
2393.76
Fish Creek
S&G
41 7/31-8/6
(7 days)
125 7/4-8/1;
9/17-9/24
(35 days)
1840.66
Trail Creek
S&G
64 7/20-8/5
(16 days)
86 7/6-9/17
(72 days)
4350.73
Hurst Canyon 0 0 181 7/19-9/6
(48 days)
8332.86
Muldoon 151 5/30-7/12;
9/14-9/22
(49 days)
228 5/31-7/25;
9/20-9/30
(66 days)
15255.30
Spring Creek 17 10/8-10/12
(5 days)
0 0 10795.20
Garfield 55 7/31-8/12
(13 days)
115 7/30-8/23
(25 days)
4680.22
Copper Creek 148 6/21-9/14
(85 days)
191 6/17-9/20
(95 days)
6458.92

42

Table 5.2, Continued
Name Total points
for 2009
Dates/ # days Total points
for 2010
Dates/ # days Total
capable
acreage
Little Wood 0 0 75 5/31-6/14
(15 days)
5794.00
Buckhorn 109 6/21-6/24;
8/20-9/14
(30 days)
91 6/19-6/20;
7/4-7/10;
9/1-9/16
(26 days)
10856.17
Pot Creek 0 0 0 0 6631.91
Sheep Creek 142 5/31-6/23
(24 days)
160 6/14-7/11
(38 days)
8003.35
Upper
Slaughter
House
27 6/24-6/29
(6 days)
24 7/12-7/16
(5 days)
2123.05
Water Gulch 0 0 0 0 656.98
Quigley 73 6/29-7/4;
9/19-10/14
(32 days)
38 7/16-7/22;
9/29-10/2
(11 days)
8373.82
Indian Creek 93 6/17-6/27;
7/9-7/14;
9/16-9/19
(21 days)
56 9/15-9/27
(13 days)
10708.4
Upper Rock
Creek
58 5/28-6/7
(10 days)
57 5/31-6/10
(11 days)
4958.71
Kent Canyon 41 6/8-6/16
(9 days)
0 0 2665.93
Trail Creek 43 8/2-8/6;
9/12-9/15
(9 days)
96 7/28-8/5;
9/17-9/25
(18 days)
14932.7
Park Creek 48 8/7-8/11;
9/6-9/12
(12 days)
72 8/6-8/9;
9/7-9/16
(14 days)
7221.59
North Fork
Big Lost
River
133 8/12-9/5
(24 days)
148 8/11-9/6
(26 days)
16195.10
Name Total points
for 2009
Dates/ # days Total points
for 2010
Dates/ # days Total
capable
acreage
North Fork
Boulder
133 6/29-7/31
(33 days)
189 6/13-7/26
(44 days)
23150.10
S. East Fork 0 0 4 9/15-9/17
(3 days)
1462.27
Cove Creek 343 7/1-9/17
(18 days)
342 7/17-9/28
(73 days)
8225.49

43

Table 5.2, Continued
Southern Allotments
Name Total points
for 2009
Dates/ # days Total points
for 2010
Dates/ # days Total capable
acreage
Laidlaw Park-
North
0 0 22 5/15-5/17
(3 days)
26617.60
Laidlaw Park
Middle
64 5/9-5/20
(11 days)
48 5/6-5/18
(12 days)
30928.9
Laidlaw Park-
South
25 5/5-5/9
(5 days)
13 5/4-5/6
(3 days)
9032.06
Laidlaw Park-
Thumb
0 0 0 0 15757.70
Laidlaw Park-
Little park
9 5/20-5/21
(2 days)
11 5/18-5/19
(2 days)
6342.70
Kimama 36 4/4-4/17
(13 days)
0 0 29124.7
Wild Horse 631 4/18-5/23
(6 days)
563 4/22-5/17
(25 days)
220096.10
Timmerman
Hills-North
41 5/12-5/24
(13 days)
13 5/22-5/24
(3 days)
2813.49
Timmerman
Hills-South
28 5/11-5/17
(7 days)
12 5/14-5/22
(9 days)
1678.51
Timmerman
Hills-Sonners
19 5/14-5/17
(4 days)
18 5/16-5/18
(3 days)
3928.30
Timmerman
Hills-Wedge
30 5/17-5/22
(6 days)
37 5/19-5/25
(7 days)
13162.80
Timmerman
Hills-N
16 5/22-5/24
(3 days)
9 5/25-5/26
(2 days)
1550.64
Timmerman
Hills-S
0 0 2 5/25
(1 day)
1656.30
Timmerman
Hills-Mud Lak
6 5/13
(1 day)
6 5/15
(1 day)
2695.51
Richfield-
North West
0 0 0 0 4684.53
Richfield-North
East
2 5/12
(1 day)
8 5/20-5/21
(2 days)
7725.81
Richfield-
Middle
0 0 0 0 4982.95
Richfield-South
West
0 0 0 0 2233.52
Richfield-South
East
0 0 17 5/17-5/19
(3 days)
2855.19
Star Lake-
Wilson Ridge
1 4/10
(1 day)
34 4/18-4/23
(6 days)
18406.2
Star Lake-
Stage Barn
63 4/6-4/17
(12 days)
8 4/22-4/25
(4 days)
13664.1
44

Table 5.2, Continued
Name Total points
for 2009
Dates/ # days Total points
for 2010
Dates/ # days Total
capable
acreage
Star Lake-
Mallard
30 4/4-4/10
(7 days)
0 0 5760.86
Star Lake-
Owinza
19 4/10-4/19
(10 days)
28 4/18-4/21;
4/25-4/26
(6 days)
5579.78
Star Lake-
East Bull
1 4/18
(1 day)
1 4/22
(1 day)
1233.15
All remaining
Star Lake
0 0 0 0 43793.53
All Sid Butte 0 0 0 0 52988.42
All Wendell
Ct.
0 0 0 0 5979

There is also a correlation between the rate of travel by the sheep and the terrain
they are traveling on during different times of the year. While the sheep are grazing in
the autumn months (September to the beginning of October) they are located primarily in
the north western and north eastern allotments. These allotments are steeper and at
higher elevation then those in the south central region, therefore these factors could be
contributing to the overall reduction in travel rate.
Through these results it appears that seasonal differences, steep terrain, or a
combination of the two factors are contributing to a higher concentration of use in the
northern allotments as opposed to the protocol of achieving a more even distribution of
use across capable terrain. Comparing the amount of time (number of days) the sheep are
grazing to the amount of capable grazing available in each allotment there is a trend of
excessive time spent in the northern allotments where there is less capable acreage. For
example Figure 5.4 (Amount of time spent grazing) shows an increase number of days
spent grazing in allotments that have less capable grazing acreage while sheep are in the
45

north (i.e. Iron and Mine had sheep graze 111 days in 2009 and 93 days in 2010 with only
about 12,000 capable acres) and the reverse while grazing in the south (i.e. the Sid Butte
allotments combined had no sheep grazing in them for 2009 & 2010 however has an
estimated 53,000 capable acres). One could assume these areas are receiving higher
levels of grazing pressure. However, further analysis of other primary contributing
factors (i.e. vegetation) would need to be conducted in order to test this hypothesis.
Figure 5.3 Difference in rate of travel during spring, summer, and autumn (all shown at a scale of
1:20,000). Small point clusters between the hours of midnight and around 8 in the morning are
possible bedding sites. The distance between the daytime points show a faster rate of travel in the
Spring and Summer allotments during April and July then in the Autumn allotments during
September. This correlates between both 2009 (right panel) and 2010 (left panel).

April 2009 April 2010

46

Figure 5.3, Continued
July 2010 July 2009
Sept 2009 Sept 2010
47

Figure 5.4. Amount of time spent grazing: The number of days spent grazing in each allotment
for the 2009 and 2010 grazing year (left axis) compared to the amount of capable grazing acreage
available for the allotments (right axis). Maximum values are set to the highets number of days
and highest number of capable acreage for the entire study area.

0
50000
100000
150000
200000
250000
0
20
40
60
80
100
120
Lava Lake
Cottonwood
Crater
Iron Mine
Balsamroot
West Fork
Upper Fish Creek
Fish Creek S&G
Trail Creek S&G
Hurst Canyon
Muldoon
Spring Creek
Garfield
Copper Creek
Little Wood
Buckhorn
Pot Creek
Sheep Creek
Upper Slaughter …
Water Gulch
Quigley
Indian Creek
Upper Rock Creek
Kent Canyon
Trail Creek
Park Creek
North Fork Big Lost …
North Fork Boulder
S. East Fork
Cove Creek
Acres
# Days Grazed in Northern allotments
2009 2010 Total capable acreage
0
50000
100000
150000
200000
250000
0
20
40
60
80
100
120
Laidlaw Park-North
Laidlaw Park Middle
Laidlaw Park-South
Laidlaw Park-…
Laidlaw Park-Little …
Kimama
Wild Horse
Timmerman Hills-…
Timmerman Hills-…
Timmerman Hills-…
Timmerman Hills-…
Timmerman Hills-N
Timmerman Hills-S
Timmerman Hills-…
Richfield- North …
Richfield-North East
Richfield-Middle
Richfield-South …
Richfield-South East
Star Lake-Wilson …
Star Lake- Stage …
Star Lake-Mallard
Star Lake-Owinza
Star Lake-East Bull
All the rest of Star …
All Sid Butte …
All Wendell Ct. …
Acres
Days
Allotments
# Days Grazed in Southern allotments
2009 2010 Total capable acreage
48

6. Conclusions and Discussion
Sustainable land management is driven by protocols where identification of
pertinent land management criterion addresses how well these protocols are being
implemented. Resulting criterion determines strengths as well as areas for improvement
in addressing whether or not sustainable protocols are being met. The analysis of GIS
model outputs in conjunction with the time tools show a more accurate representation of
areas where sheep could graze and how they are grazing across the Lava Lake rangeland
which in return offers valuable information for areas which may be at risk of overgrazing.
6.1 Capability Analysis and New Criterion
The capability analysis addresses criteria important to the implementation of
sustainable land management protocols. In particular it identifies areas capable of
grazing that are not being accessed efficiently and therefore prompts to provide respite of
overused areas by completing the first step to assign appropriate stocking rates. Given
this information, additional land management decisions such as changing grazing routes,
opening up incapable areas through introduction of necessary criterion, and identifying
the overall grazing area by capable boundaries as opposed to allotments can be made.
The northern allotments show the highest concentration of use, as shown in
Figure 6.1 (Dispersion of use as shown through GPS locations for 2009 and 2010),
therefore the potential stress induced on the vegetation in these areas identify them as
prime candidates for looking at implementing rest breaks, where appropriate, while still
viable for the health of the sheep. Within the northern allotments there are areas capable
of being grazed that are not heavily used. The outlined distributions could guide the
49

grazing of sheep in a sustainable way by increasing the use of less concentrated areas and
reducing the use of stressed and overused areas, therefore allowing for vegetation re-
growth and assisting with sustainable management protocols in future years. Areas
eliminated due to unsatisfying requirements of distance from water sources could also be
utilized if decisions were made to fulfill these requirements where applicable. For
example, in the southern allotments the capability analysis showed many areas eliminated
because of a distance greater than 2 miles away from water, however if water were to be
made available in these areas, through troughs or other such resources, this would make
them available for use providing additional means for alleviating stressed areas.
The analysis of the grazing capability of the entire study area showed a vast
percentage of the land as useful for grazing confirming that the study area appropriate for
the management of sheep. The definition of the capable boundaries maps however offer a
more precise method to identify areas that are and will be grazed compared to a
generalized map view of the land sheep are grazing that was previously used by land
managers. This is the first step in setting conservative stocking rates for the landscape
because it eliminates all the area most unlikely to be grazed, therefore when Lava Lake
takes the next steps toward establishing how much capacity (i.e. total estimated dry
weight vegetation biomass) exists only the capable areas will be taken into consideration
defining a more accurate quantification of the overall vegetation available. This estimate
will also provide land managers an ideal number of sheep they should allow to graze
while still being within sustainable ranching practices by deciphering how much
50

vegetation they have to sustain the optimal number of sheep and then managing numbers
below this.
6.2 Time Analysis and Seasonal Patterns
The time analysis shows how patterns begin forming in the way in which the
sheep and the landscape are relating to one another. Once these patterns are identified
land managers can determine whether or not protocols introducing variety in timing and
use of the landscape, from year to year, satisfy an even distribution and sustainable
practices. This analysis shows a need for improvement in reaching these protocols by
reducing the grazing intensity while sheep are in the northern allotments.
Results from the time analysis show that sheep traveling at slower rates are within areas
of greater concentration. This combination could negatively impact the vegetation within
these areas causing them to be at greater risk of desertification or loss in habitat. The rate
could be slowed due to the steeper terrain in the northern allotments or it could correlate
to higher temperatures during the time of year in which sheep graze in these areas. Also,
the northern allotments are used by Lava Lake during the majority of the grazing year (4
to 5 out of 7 to 8 months total) identifying areas of overuse for long periods of time.
These factors are shown in the analysis as stagnation in movement which is
unconstructive to achieving both protocols of variety in timing and use from year to year
of an even distribution across the land. Land managers could act based on the results by
adding additional shade resources in areas where there is evidence of high temperatures
stress during the year, or decide to use different allotments during different seasons in
variation from year to year in a hope of encouraging more even use across the landscape.
51

Figure 6.1 Distribution of use as shown through GPS locations for 2009= 4,544 points total (top
panel) and 2010= 4,459 points total (bottom panel).

52

Figure 6.1, Continued

53

6.3 Discussion and Future work
The land management project is consistent with other studies conducted in
determining terrain sheep are grazing, where vegetation and non-vegetation exists, as
well as what additional sources can be added to open up unutilized areas, however it is
not as accurate in comparison when defining areas suitable for sheep to be grazing.
Compared with the study in the Awash River Basin (Bizuwerk et al, 2005) this project
was compatible in identifying areas sheep are grazing as evidence by 94% in 2009 and
97% in 2010 of the GPS locations being within model outputs of capable areas. The
project did not however, identify areas suitable for grazing by incorporating soil erosion
levels, precipitation values, or an extended assessment of healthy vegetation levels where
the Awash River Basin study did. As in the studies conducted in Virginia and North
Carolina (Knight et al, 2006) as well as for land managers in the southwest (Marsett,
2006) this project’s use of NDVI derived data provided maps of vegetative land cover
type assisting land managers with representing where vegetation and non-vegetation
exists. The land management project was comparable with the Natural Resources
Conservation Service’s study (Namkun & Stuth, 1997) in identifying non-grazing areas at
high slope gradients and further then 2 miles distance from water; both offering an
explanation for where additional water resources could be implemented opening up
underutilized areas. However, the land management project did not identify areas of
increased brush densities to determine treatment sites and in return provided a tool for
locating areas of extended use for long periods of time.
54

Continuous assessment of how protocols are managed and implemented across
the landscape is valuable to the sustainment of both the sheep and the ecosystem. The
project was consistent with supporting sustainable ranching practices in identifying areas
of extended use and generating more accurate acreage for use in capacity assessments.
However, results show that influential factors such as the scale of data and scale in
analysis, overall behavior of sheep, and additional grazing not documented in this project
are introducing elements of uncertainty that should be addressed in future work as each
plays a specific role in the final outcomes.
Scale is often an important factor to take into account when performing analysis
with GIS and RS. This is due primarily to the way ground level objects are represented
and related to one another within the computer. Therefore, when performing analysis
across varying areas of a landscape it is crucial to do so in the same scale. In this study
an appropriate use of scale was taken into account for both the time analysis (1:20,000
map to land unit ratio) performed within the GIS and when selecting and analyzing the
NED (10 meter resolution) and NLCD (30 meter resolution) imagery.
When performing the time analysis and comparing different areas it is important
to ensure the scale ratio is set to be identical. The scale ratio can influence the proximity
of the GPS point data to one another, consequently impacting the overall representation
of rate of travel, therefore all the small scale comparisons were set to the exact same ratio
and all the large scale analysis is viewed and compared at the same ratio.
The scale in the RS imagery (referred to as resolution) impacts the overall
analysis when inappropriately selected since it could introduce increased uncertainty in
55

the delineation/extraction of raster to polygon elements used in the analysis. The ground
resolution for all RS imagery used for this project offered a relatively accurate scale
across the Lava Lake study area. Other RS imagery were available for the study area,
however their courser resolution (250 meter ground resolution) was found to be
inadequate compared to the 10 meter resolution that offered a more localized
representation. To map the vegetation present in the study area better results could be
achieved to this extent if higher resolution RS could be used. Higher RS imagery is
expensive and unavailable therefore our selection was the best compromise between costs
and reasonable achievable results.
The grazing behavior of sheep could affect the results of this analysis. The
overall habits of sheep behavior are different in given temperature, certain times of the
year and certain biological necessities. Temperature, relating to the seasons, is an
influential factor contributing to a sheep’s rate of travel because heat can increase
stagnation where the cold can increase mobility. Seasons and temperature are closely
related to one another in the study area located in south central Idaho. Summer and late
autumn are traditionally higher in temperature, therefore sheep tend to graze in cooler
areas (i.e. under shaded trees), and closer to water. Depending on the layout of the terrain
this could lessen the overall area the sheep are grazing, thus slowing down their rate of
travel.
Another pertinent behavior is mating, which plays a role by contributing to areas
where sheep are located during different times of the year, and could also contribute to
their rate of travel. Sheep mate in flat terrain so that the rams (male sheep) can perform
56

without excessive exertion. Sheep could be navigating to flatter terrain at the time of
mating. Therefore, the increase in travel rate could be due to mating behavior,
temperature of the season, the ease of navigation across flatter terrain, or all three.
Where this project was specific to the study area it focused only on land
management and grazing behavior of sheep by Lava Lake. However, it is worth noting
that while Lava Lake utilizes the northern allotments throughout the majority of the year,
other operators use the southern allotments more frequently. Most of the southern region
allotments are shared with other operators who graze both sheep and cattle, therefore
while Lava Lake does not graze their sheep excessively on the southern allotments these
areas may still be at risk from over grazing by other operators, whose activity should also
be included in future analysis for a more complete model.
Factors for Future work
There are certainly other pertinent factors when looking at other potential
contributions to the analysis and the most relevant is vegetation. Vegetation is a direct
link between the sheep and the landscape, therefore future analysis of factors contributing
to vegetation such as plant species, biomass consumption, and precipitation will
contribute to the overall determination of how sheep are interacting with the landscape.
In return this would assist in specifying areas where sustainable ranching protocols are
thriving and areas where they need further implementation.
Plant species may vary depending on season and location as well herbs, forbs,
shrubs, and grasses grow differently during the spring, summer, and autumn months
potentially due to how sheep choose to consume them throughout the grazing season.
Elevation can also impact the types of plant species growing in lower terrain versus areas
57

higher in elevation. Future work on this project could consider identifying types of plant
species that characterize the vegetative land cover across the study area, and compare this
to how the average sheep consumes them.
A main determining factor for grazing protocols is biomass consumption (i.e. the
total amount of dry weight vegetation a sheep needs to sustain itself based on pounds).
Varying types of plant species produce different types of biomass weight, so as to say
areas where certain plants make up the majority of land cover will have a different
biomass compared to areas with a different plant cover.
Precipitation has an impact on how vast and large a plant grows, thus contributing
to a plant’s total biomass. In addition to having less water sources, the southern
allotments also receive less rainfall and snow than the northern ones. The more southern
regions receive on average ten inches of precipitation a year, as you move further north it
increases to around thirty inches a year (Brian Bean personal interview 3/5/2012)
therefore it may be the case that the northern allotments have a higher overall biomass
then the southern allotments.
Since both vegetation type and consequently biomass varies so greatly, between
the southern and northern regions, further studies may show the northern allotments more
adept at sustaining grazing for longer periods of time. A means to show what areas are
truly at risk would also require incorporating vegetation type and biomass weight.

58

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Abstract (if available)

Abstract Sustainable ranching refers to the practice of evaluating livestock quantities that natural grasses and ecosystems are capable of supporting, with minimal long-term impacts on the environment. Defining optimal and sustainable stocking rates can be a complex problem for land managers striving to implement the practice of sustainable ranching of sheep. ❧ I used a combination of Geographic Information Systems (GIS) with Remote Sensing (RS) to analyze environmental variables and track movement patterns of sheep and tested it at the Lava Lake Livestock and Landscape Ranch. A GIS model utilizing remotely sensed imagery was built to identify areas capable for grazing by sheep across the study area. Tracking Analyst and Time Slider, which are GIS based time analysis tools, utilized point data collected from Global Positioning System (GPS) collars to visualize the rate at which sheep are traveling. ❧ Results show an estimated 85% of the study area is found capable for grazing with the primary eliminating factors being steeper terrain in the north and lack of water in the south. Results also outline two contrasting sheep patterns: a slower travel rate in autumn within the northern regions

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Asset Metadata

Creator Miller, Rachel Rae (author)

Core Title Utilizing GIS and remote sensing to determine sheep grazing patterns for best practices in land management protocols

School College of Letters, Arts and Sciences

Degree Master of Science

Degree Program Geographic Information Science and Technology

Publication Date 07/27/2012

Defense Date 05/29/2012

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag GIS,land management protocols,OAI-PMH Harvest,remote sensing

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Paganelli, Flora (committee chair), Longcore, Travis R. (committee member), Ruddell, Darren M. (committee member)

Creator Email rachelrae.miller@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-74079

Unique identifier UC11289102

Identifier usctheses-c3-74079 (legacy record id)

Legacy Identifier etd-MillerRach-1048.pdf

Dmrecord 74079

Document Type Thesis

Rights Miller, Rachel Rae

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA