National Park Service

U.S. Department of the Interior

Natural Resource Stewardship and Science

NPScape Landscape Analysis for Mount Rainier 

National Park

Housing, Population, and Roads Metrics

Natural Resource Report NPS/NCCN/NRR—2015/1079
ON THE COVER

Map showing 2100 projected housing density for the NPScape ‘area of analysis’ for Mount Rainier National Park. Data from 

Spatially Explicit Regional Growth Model (SERGoM) developed by Theobald (2005).
NPScape Landscape Analysis for Mount Rainier 

National Park

Housing, Population, and Roads Metrics

Natural Resource Report NPS/NCCN/NRR—2015/1079

Lise Grace1

, Mark H. Huff2, Catharine Copass3

1

National Park Service

North Coast and Cascades Inventory and Monitoring Program

North Cascades National Park

810 State Route 20

Sedro-Woolley, WA 98284

2

National Park Service

North Coast and Cascades Inventory and Monitoring Program

Mount Rainier National Park

55210 238th Ave E.

Ashford, WA 98304-9751

3

National Park Service

North Coast and Cascades Inventory and Monitoring Program

Olympic National Park

600 E. Park St.

Port Angeles, WA 98362

November 2015

U.S. Department of the Interior

National Park Service

Natural Resource Stewardship and Science 

Fort Collins, Colorado
ii

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Please cite this publication as:

Grace, L. P., M. H. Huff, and C. Copass. 2015. NPScape landscape analysis for Mount Rainier 

National Park: Housing, population, and roads metrics. Natural Resource Report NPS/NCCN/NRR—

2015/1079. National Park Service, Fort Collins, Colorado.

NPS 105/130511, November 2015
iii

Contents 

Page

Figures...................................................................................................................................................iv

Tables....................................................................................................................................................iv

Acknowledgments..................................................................................................................................v

Introduction............................................................................................................................................1

Methods..................................................................................................................................................3

Area of Analysis.............................................................................................................................3

Housing metrics..............................................................................................................................4

Data ...........................................................................................................................................4

Processing..................................................................................................................................5

Population metrics..........................................................................................................................5

Data ...........................................................................................................................................5

Processing..................................................................................................................................6

Roads metrics.................................................................................................................................6

Data ...........................................................................................................................................6

Processing..................................................................................................................................7

Results....................................................................................................................................................8

Housing ..........................................................................................................................................8

Population.....................................................................................................................................12

Roads............................................................................................................................................18

Additional Considerations............................................................................................................22

Literature Cited ....................................................................................................................................24
iv

Figures 

Page

Figure 1. Location of parks within the North Coast and Cascades Network........................................2

Figure 2. Location of Mount Rainier National Park in the regional context of other public 

lands and wilderness areas. ....................................................................................................................4

Figure 3. Change in percent area of the private/undeveloped, rural, and exurban housing 

density categories in the Mount Rainier National Park Area of Analysis, 1970-2100........................10

Figure 4. Change in percent area of the suburban, urban, commercial/industrial, and 

urban-regional park housing density categories in the Mount Rainier National Park Area 

of Analysis, 1970-2100........................................................................................................................10

Figure 5. Size and spatial distribution of housing density categories in Mount Rainier 

National Park Area of Analysis between 1970 and 2100. ...................................................................11

Figure 6. Total population by Census block in 1990, 2000, and 2010 for Mount Rainier 

National Park Area of Analysis. ..........................................................................................................13

Figure 7. Trends in population density between 1990, 2000, and 2010 for the Mount 

Rainier National Park Area of Analysis...............................................................................................16

Figure 8. Density of all roads in the Mount Rainier National Park Area of Analysis. .......................19

Figure 9. Comparison of road density within the Mount Rainier National Park boundary 

and entire Area or Analysis..................................................................................................................19

Figure 10. Distance from all roads in the Mount Rainier National Park Area of Analysis. ...............20

Figure 11. Roadless areas (patches >500 m from any road) in the Mount Rainier National 

Park Area of Analysis. .........................................................................................................................21

Figure 12. Comparison of roadless area (as percentage of total area) within Mount 

Rainier National Park (MORA) boundary and entire Area of Analysis. .............................................21

Tables 

Page

Table 1. Percentage of Mount Rainier National Park Area of Analysis in each housing 

density class, 1970-2010, 2040, 2070, and 2100. ..................................................................................9

Table 2. Change in total population and population density between 1990, 2000, and 

2010 for the Mount Rainier National Park Area of Analysis. .............................................................12
v

Acknowledgments

We are grateful to Bill Monahan, Lisa Nelson, and Mara Kali from the Inventory and Monitoring 

Program Division of the National Park Service’s Natural Resource Stewardship and Science for all 

their guidance, assistance in selecting data sets, and in refining and customizing the tools to run the 

analyses for the North Coast and Cascades Network parks. This project was made possible by a grant 

from the North Coast and Cascades Science Learning Network.

1

Introduction 

Responding to the need for landscape-scale information within and surrounding national parks, in 

2009 the Inventory and Monitoring (I&M) Program of the National Park Service (NPS) funded

NPScape, a service-wide inventory of landscape-scale data sets pertaining to all national parks

(general information about NPScape can be found at 

http://science.nature.nps.gov/im/monitor/npscape/). Working with data from existing sources, the 

I&M Program provided the landscape information from NPScape to nearly 300 parks and I&M 

networks--covering six major categories of environmental drivers and attributes (metrics) that are 

key to conservation planning in NPS units. The categories are 1) population, 2) housing density, 3) 

roads, 4) land cover, 5) patterns of forest/grassland patches, and 6) conservation status. The metrics 

for these categories include historic (where data are available) and current condition, and for certain 

categories, future projections. The I&M Program envisions that I&M networks will refine the base 

information provided, select ecologically relevant study areas for analyses, and use higher resolution, 

local data sets if available to develop products that inform park resource management and planning.

Landscape Dynamics is one of the North Coast and Cascades Network’s (NCCN) primary 

monitoring vital signs. Between 2007 and 2011, NCCN I&M developed and updated a vital sign 

monitoring protocol to assess landscape change using satellite imagery (Kennedy et al 2007, 

Antonova et al 2012). To date, protocol implementation has focused on detecting landscape 

disturbances in the Network’s three largest parks: Mount Rainier (MORA), North Cascades, and 

Olympic National Parks. It has not yet been implemented in the Network’s smaller, historical parks. 

Landscape-scale questions most pertinent to the smaller parks may not be best addressed with the 

techniques that the Network has targeted for the larger parks, and the NPScape data could prove a 

useful compliment to NCCN Landscape Dynamics Monitoring.

The availability of NPScape data is a relatively new opportunity for networks and parks and 

represents an untapped resource to assess landscape change. For this study, our overarching goal was 

to explore the utility of the NPScape data for supplementing the Network’s Landscape Dynamics 

protocol, especially for the smaller parks. Our aim for this report was two-fold: 1) to assess the 

NPScape data for accessibility and ease of use in analyzing and producing landscape change 

products, and 2) to provide landscape-scale products to park managers and planners for use in natural 

resource conservation assessments and plans. 

We selected four parks for this study: Ebey’s Landing National Historical Reserve (EBLA), Lewis 

and Clark National Historical Park (LEWI), Mount Rainier National Park (MORA), and San Juan 

Island National Historical Park (SAJH) (Figure 1). In this report, we present analyses conducted for 

MORA on three key NPScape attributes: roads, population, and housing.
2

Figure 1. Location of parks within the North Coast and Cascades Network.
3

Methods

This NCCN project was conducted in the summer 2011, when NPScape was relatively new, and the 

products were released in two ‘phases.’ Calculating the metrics for NCCN parks required 

identification of appropriate input data sets, specifying an Area of Analysis (AOA) for each park, and 

running Python scripts developed by the Inventory and Monitoring Division (IMD SO) to calculate 

the landscape metrics. NPScape landscape data products provided by IMD SO were developed using 

data sets with national coverage and thus required evaluation of the input datasets for spatial 

accuracy and relevancy at the park scale. Where local data sets were available and deemed more 

accurate, those were substituted as inputs when the selected metrics for NCCN parks were “recalculated.” Selection of AOAs and input data sets are described here in general terms for the NCCN 

parks and for MORA specifically.

Area of Analysis

The first step in conducting the landscape-scale analyses was identifying a study area, or area of 

analysis (AOA), which would incorporate the range of influences of processes that affect park 

resources but that was also relevant given the resolution of the various data sets to be used. Typically, 

this would include the area of the park itself and some region surrounding the park. 

Hansen et al. (2011) developed a methodology for delineating boundaries around protected areas, 

like national parks, that encompass the spatial extent of ecosystem processes and direct human 

influence as well as critical habitat for native organisms. This framework, the Protected Area 

Centered Ecosystem (PACE), offers parks and protected areas a means of customizing AOAs as 

needed. Hansen et al. (2011) created a PACE model for MORA, soliciting input from park staff on 

resource management concerns. We explored the PACE boundary developed for MORA but decided 

that for the purposes of this NPScape product summary, the PACE included areas for which the 

source data sets were not at appropriate spatial scale. For example, the PACE boundary included

narrow riparian corridors along several rivers, some of which extended 100 km from the Park 

boundary, and were narrower than the minimum 30 km buffer area recommended by IMD SO for the 

100 x 100 m cell size housing data. Moreover, the PACE boundary included areas far enough away 

from the Park that for the metrics summarized here, there would likely be minimal impact on Park 

resources.

The default AOA for NPScape products was a 30 km buffer around each park. For MORA, this 

included the gateway communities of Enumclaw, Eatonville, and Packwood, as well as important 

habitat connectivity with adjacent United States Forest Service (USFS) wilderness areas to the north, 

east, and south of the Park (Figure 2). This area encompassed by the 30 km buffer was considered a 

reasonable area of analysis to describe landscape-scale drivers that may impact park natural 

resources. We used this default NPScape AOA for the MORA analyses.
4

Figure 2. Location of Mount Rainier National Park in the regional context of other public lands and 

wilderness areas.

Housing metrics

The housing metrics selected for the NCCN project were historic, current, and projected housing 

density by decade from 1970-2100. The full suite of NPScape housing metrics produced by the I&M 

Program are summarized on the NPScape website at 

http://science.nature.nps.gov/im/monitor/npscape/measures.cfm; note that different metrics are 

available currently than were available at the time of this project.

Data

The housing density data were generated by the Spatially Explicit Regional Growth Model 

(SERGoM) developed by Theobald (2005), which modeled past, current, and future housing density 

from 1970 to 2100. These data included an update in 2010 (by the developer) to the original 

SERGoM data (version 3) provided in the Phase 1 release of NPScape. The SERGoM data were 
5

developed by combining U.S. Census housing data with information on land ownership, protected 

areas, water bodies, and the density of major roads (interstates, highways, and county roads) in order 

to provide a more accurate allocation of housing units, across the landscape (housing units were 

assumed not to occur in these protected areas, including national parks, nor in waterbodies). Historic 

and current housing density patterns were used to develop a simulation model. Data were projected 

for future time steps by assuming that future growth patterns will be similar to those of the past. The 

data were resampled to 100 m x 100 m cell size representing 14 housing density classes for each 

decade from 1970 to 2100. 

Processing

The SERGoM data were not modified, therefore processing entailed extracting the housing density 

values for the AOA. Housing density was calculated for the 14 density classes identified in the 

SERGoM data set. These classes were then lumped into seven density categories following Theobald 

(2005): Private undeveloped, Rural, Exurban (sprawl beyond the urban/suburban fringe), Suburban, 

Urban, Commercial/industrial, and Urban-regional park. For detailed steps in processing these 

metrics (e.g., running the Python scripts, generating the statistics), see NPScape Housing Measure –

Phase 1 Metrics Processing SOP (NPS 2010a).

Population metrics

The Phase 1 release population metrics selected by the NCCN project were total population and 

population density by Census block group for 1990, 2000, and 2010. A Census block is the smallest 

geographical unit for which the United States Census Bureau tabulates 100% decennial census data, 

(information collected from every inhabitant and housing unit in the United States). A Census block 

group is a geographical unit used by the Census. Block group unit size on average is 30 blocks, but 

can vary considerably depending on population density and tabulates sample data (information 

collected from ~17% of the population) from the decennial census. The full suite of NPScape 

population metrics produced by the I&M Program are summarized on the NPScape website at 

http://science.nature.nps.gov/im/monitor/npscape/measures.cfm. 

Data

A review of the IMD SO-developed products for NCCN parks indicated that the historic and 

projected county-level population metrics were not that informative at the small NCCN park scale, 

but the Census block group-level data for 1990 and 2000 were. The initial release of the Phase 1 

NPScape products did not yet include the 2010 Census data, but during the course of this project, 

those data became available. IMD SO performed QA/QC on them for the NCCN parks so that 

population metrics could be calculated for this most recent decade.

Waterbodies (lakes and ponds), protected areas, and the park itself, do not have people living in them 

and needed to be excluded from the analyses. The National Hydrography Database waterbody data 

were selected for use in the NCCN project because these data included more known features from the 

parks than the IMD SO-provided data from the National Atlas Water Feature Areas. The protected 

areas data we used were those provided by IMD SO and were from the Gap Analysis Program (GAP) 

Protected Areas Database (PAD-US) (USGS 2011). The IMD SO-provided scripts were written with 

the assumption that people did not live within park boundaries. Recognizing that this assumption 
6

may not be valid for all parks, the scripts excluded park boundaries from the PAD-US data set to 

allow population analysis to be conducted within park boundaries. For parks where the assumption is 

valid, parks could exclude park areas from the analysis using their own park boundary data. MORA 

does have a very small year-round population of park and concessions staff (<10 people) living

within the Park boundary (Roger Andrascik, Chief of Natural and Cultural Resources, MORA, pers. 

comm., November 2015), but because it is so small and varies little from year to year, we treated 

MORA as having no population for this analysis. We used the Park boundary Geographic 

Information System (GIS) layer to exclude the Park area from the analysis, thereby preventing the 

model from calculating population increases within the Park boundary in the future projections.

Processing

Total population and population density were calculated for Census block group data for 1990, 2000, 

and 2010. The IMD SO Python script for calculating population metrics for Census block groups 

‘pushed’ population numbers for block groups within park boundaries to surrounding areas outside 

park boundaries under the assumption that people do not live within park boundaries. In other words, 

a greater number of people (formerly counted in block groups spanning a park boundary) were now 

‘squeezed’ into a smaller area - block groups adjacent to the park (or clipped by park boundary) -

resulting in density metrics for those block groups adjacent to a park being slightly higher after 

processing. Similarly, areas identified as water (lakes and ponds) as well as protected areas from the 

GAP PAD-US were excluded from the analysis. Thus, population density was area-weighted,

excluding areas where people were not expected to live. See NPScape Population Measure – Phase 1 

Metrics Processing SOP (NPS 2010b) for details on these protected areas exclusions and analysis 

processing steps. These assumptions were generally valid for MORA, so no additional special 

processing was required.

Roads metrics

The roads metrics selected for the NCCN project were density of all roads and distance from all 

roads (Phase 2 release), and area without roads (>500 m from all roads; Phase 1 release). The full 

suite of NPScape road metrics produced by the I&M Program are summarized on the NPScape 

website at http://science.nature.nps.gov/im/monitor/npscape/measures.cfm; note that different 

metrics are available currently than were available at the time of this project.

Data

The NPScape roads metrics produced by IMD SO were calculated using ESRI’s national roads data 

set. This data set was of highly variable accuracy in and around NCCN parks, so for the NCCN 

project, the road metrics were developed primarily from the Bureau of Land Management (BLM) 

Ground Transportation Roads Publication and Highways geodatabases, downloaded from the BLM 

Oregon State Office website in 2008 (BLM 2008). These data covered both Washington and Oregon 

and, after reviewing multiple data sources, were deemed to be the most accurate and representative of 

the roads within the AOAs surrounding all the NCCN parks. For MORA, all the roads from the BLM 

data set within the Park boundary were replaced with the roads in the Park’s GIS roads layer as these 

were much more accurate.
7

Processing

The metrics selected for the NCCN project were calculated for the entire MORA AOA. Density of all 

roads and area without roads were also analyzed separately for the area within the Park boundary for 

comparison to the larger AOA. For detailed processing steps in calculating areas without roads, see 

NPScape Road Measure – Phase 1 Metrics Processing SOP (NPS 2010c) and for calculating density 

of all roads and distance from roads, see NPScape Roads Measure – Phase 2 Road Metrics 

Processing SOP (NPS 2010d).
8

Results

Summaries of the calculated landscape metrics for MORA are presented here. Results from the other 

NCCN parks will be summarized in future reports.

Housing

Of the 14 housing density classes calculated for the MORA AOA, the classes representing residential 

housing densities (i.e., not including the private/undeveloped, commercial/industrial, and urbanregional park categories) were lumped into the ‘rural,’ ‘exurban,’ ‘suburban,’ and ‘urban’ categories 

following Theobald (2005) for the time period of 1970-2010 and the additional future projections of 

2040, 2070, and 2100 (Table 1). There was a general trend, over the entire time period analyzed, 

towards increasing density in the exurban (the three densest of the four classes), suburban, and urban 

categories and decreasing density in the private undeveloped and rural (except the middle density 

class) categories. There was no change in the commercial/industrial or urban-regional park classes

(Table 1).

The greatest change among housing density categories between 1970 and 2010 occurred in the 

private/undeveloped, rural, and exurban categories, with a marked decrease in private/undeveloped 

density offset by a similar increase in exurban density (Figure 3). The rural housing density category 

showed a very slight increase over the 1970-2000 time period, followed by a slight decrease starting 

in 2010 and continuing in the future projections through 2100. Of the remaining housing density 

categories, the suburban category showed a dramatic increase, particularly in the future projections 

(after 2010). The urban category showed only a very slight increase, and there was no change 

indicated for the commercial/industrial and urban-regional park categories over the entire 1970-2100 

time period (Figure 4).

The pronounced shift from low-density categories to high-density categories (excluding the lack of 

change in commercial and urban-regional park categories) in terms of area and spatial distribution is 

presented in Figure 5.
9

Table 1. Percentage of Mount Rainier National Park Area of Analysis in each housing density class, 1970-2010, 2040, 2070, and 21001.

Housing density class Density categories2 1970 1980 1990 2000 2010 2040 2070 2100

Private undeveloped Private undeveloped 14.355 11.838 10.467 8.482 8.188 8.145 8.125 8.113

< 1.5 units / sq. km.

Rural

8.915 8.887 8.587 9.330 8.964 8.545 8.510 8.464

1.5 - 3 units / sq. km. 2.011 2.690 3.324 3.400 3.447 3.629 3.604 3.564

4 - 6 units / sq. km. 2.060 2.176 2.173 2.112 1.564 1.329 1.258 1.234

7 - 12 units / sq. km.

Exurban

1.646 2.153 2.083 2.133 1.454 0.979 0.964 0.942

13 - 24 units / sq. km. 0.831 1.399 1.827 2.197 2.488 1.490 1.362 1.146

25 - 49 units / sq. km. 0.485 0.777 1.120 1.382 2.393 2.385 2.045 1.629

50 - 145 units / sq. km. 0.292 0.546 0.754 1.059 1.483 3.217 3.436 3.482

146 - 494 units / sq. km. Suburban 0.090 0.196 0.297 0.466 0.535 0.765 1.164 1.840

495 - 1,234 units / sq. km. 0.016 0.037 0.064 0.130 0.146 0.177 0.190 0.235

1,235 - 2,470 units / sq. km. Urban 0.001 0.003 0.007 0.021 0.021 0.022 0.025 0.032

> 2,470 units / sq. km. 0 0 0 0.003 0.002 0.003 0.003 0.003

Commercial/industrial Commercial/industrial 0.191 0.191 0.191 0.191 0.191 0.191 0.191 0.191

Urban-regional Park Urban-regional park 0.119 0.119 0.119 0.119 0.119 0.119 0.119 0.119

1 Housing density projections were provided by SERGoM for each decade from 2010-2100 but not all decades presented here.

2 For residential housing densities, following Theobald (2005).
10

Figure 3. Change in percent area of the private/undeveloped, rural, and exurban housing density 

categories in the Mount Rainier National Park Area of Analysis, 1970-2100.

Figure 4. Change in percent area of the suburban, urban, commercial/industrial, and urban-regional park

housing density categories in the Mount Rainier National Park Area of Analysis, 1970-2100.
11

Figure 5. Size and spatial distribution of housing density categories in Mount Rainier National Park Area 

of Analysis between a) 1970 and b) 2100.

a)

b)
12

Projected housing density values are based on socioeconomic forecasts; where these forecasts prove 

incorrect, the model will not perform well. Further, the model has not performed well projecting 

future housing density in coastal areas or on Puget Sound islands where no change was projected in 

housing density categories after 2010 or 2020 (L. Nelson, GIS Specialist, NPS, pers. comm., August 

2011). Within the MORA AOA, the model projected future housing density changes for each decade 

up to 2100. There were, however, some illogical changes in certain housing density classes that 

suggest some room for improvement in the model assumptions or specifications. Most notably, this

included a slight decrease in the densest housing category (>2,470 units/sq km) within the AOA 

between 2000 and 2010 (Table 1).

Population

Both total population and population density increased dramatically (61 and 64%, respectively) over 

the 1990-2010 time period for the MORA AOA (Table 2, Figures 6 and 7). 

Table 2. Change in total population and population density between 1990, 2000, and 2010 for the Mount 

Rainier National Park Area of Analysis.

Metric 1990 2000 2010

Increase between 

1990-2010 (%)

Total population 97,183 133,502 156,794 61

Area-weighted density (people/km2) 11 15 18 64
13

Figure 6. Total population by Census block in a) 1990, b) 2000, and c) 2010 for Mount Rainier National 

Park Area of Analysis. 

a)
14

Figure 6. Total population by Census block in a) 1990, b) 2000, and c) 2010 for Mount Rainier National 

Park Area of Analysis (continued). 

b)
15

Figure 6. Total population by Census block in a) 1990, b) 2000, and c) 2010 for the Mount Rainier 

National Park Area of Analysis (continued). 

c)
16

Figure 7. Trends in population density between a) 1990, b) 2000, and c) 2010 for the Mount Rainier 

National Park Area of Analysis.

a)
17

Figure 7. Trends in population density between a) 1990, b) 2000, and c) 2010 for the Mount Rainier 

National Park Area of Analysis (continued).

b)
18

Figure 7. Trends in population density between a) 1990, b) 2000, and c) 2010 for the Mount Rainier 

National Park Area of Analysis (continued).

Roads

Road density was dramatically lower within the Park boundary and other protected areas than in the 

rest of the AOA (Figures 8 and 9). 

c)
19

Figure 8. Density of all roads in the Mount Rainier National Park Area of Analysis.

Figure 9. Comparison of road density within the Mount Rainier National Park (MORA) boundary and 

entire Area or Analysis (AOA).
20

The distance from roads is much greater within the Park boundary as well as in the areas directly east 

and southeast of the Park as compared to other areas in the AOA (Figure 10). The regions of the 

AOA to the west and northwest of the Park include the suburban communities of the greater Seattle 

and Tacoma areas whereas the regions to the east and southeast of the Park are largely USFS 

wilderness areas (Norse Peak, William O. Douglas, Goat Rocks, and Tatoosh), (Figure 2). More 

importantly, the areas to the west, southwest, and northwest also encompass privately owned timber 

lands in active forest management which includes building and maintaining logging roads, and these 

logging roads were included in the BLM roads dataset.

Figure 10. Distance from all roads in the Mount Rainier National Park Area of Analysis.

Roadless area patches greater than 500 m from roads are shown in Figure 11, and the largest of these 

patches (>100 km2

) closely correspond to the aforementioned wilderness areas. A comparison of 

area without roads as a percentage of total area within the Park boundary and the entire AOA

showed that the Park had a significantly larger proportion of roadless area (84.7%) than the AOA

(36.2%) (Figure 12).
21

Figure 11. Roadless areas (patches >500 m from any road) in the Mount Rainier National Park Area of 

Analysis.

Figure 12. Comparison of roadless area (as percentage of total area) within Mount Rainier National Park 

(MORA) boundary and entire Area of Analysis (AOA).
22

Additional Considerations

In downloading and utilizing the NPScape products, which included datasets, processing tools, and 

SOPs, we determined the accessibility and ease of use was at an intermediate level. The NCCN was 

one of the first I&M networks to evaluate the NPScape products, adapt the products to customized, 

park-specific AOAs (except for the MORA AOA), and recalculate the metrics using at least one local 

data set. There was a significant amount of troubleshooting involved in downloading some of the 

very large data sets from the IMD SO server as well as in running the Python scripts. IMD SO staff 

were exceptionally helpful in providing assistance with these obstacles, most of which are presumed 

to be resolved in the current versions of NPScape products. Substituting a local data set (roads) for 

one of the IMD SO-developed products was straightforward in this case, and the SOPs were clear on 

format and processing requirements for using local data sets.

We have shown here how the NPScape metrics can be calculated using both IMD SO-provided and

local data sets, using the NPScape tools, and presented in a format potentially useful to assess 

landscape changes occurring in, and especially adjacent to, parks. This information may also inform 

conservation planning. Future NPScape assessments should consider including other metrics not 

selected for this project, such as Landcover and Pattern, as these could provide additional 

information about changes occurring within and adjacent to park borders (see NPScape website, 

http://science.nature.nps.gov/im/monitor/npscape/, for more detail on these metrics). Furthermore, 

we recommend exploring the use of other local data sets in conjunction with a customized PACE 

boundary to focus a future NPScape analysis to more specific park resource management priorities.

24

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Landsat-based monitoring of landscape dynamics in North Coast and Cascades Network Parks: 

Version 2. Natural Resource Report NPS/NCCN/NRR—2012/601. National Park Service, Fort 

Collins, Colorado.

Hansen, A. J., C. R. Davis, N. Piekielek, J. Gross, D .M. Theobald, S. Goetz, F. Melton, and R. 

DeFries. 2011. Delineating the ecosystems containing protected areas for monitoring and 

management. BioScience 61(5):363-373.

Kennedy, R. E., W. B. Cohen, A. A. Kirschbaum, and E. Haunreiter. 2007. Protocol for Landsatbased monitoring of landscape dynamics and North Coast and Cascades Network Parks. 2-G1. 

U.S. Geological Survey Techniques and Methods, Reston, Virginia. Available from: 

http://pubs.usgs.gov/tm/2007/tm2g1/pdf/tm2g1.pdf (accessed 19 June 2012).

National Park Service (NPS). 2010a. NPScape housing measure – phase 1 metrics processing SOP: 

Current housing density, historic housing density, and projected housing density metrics. Natural 

Resource Report NPS/NRPC/IMD/NRR—2010/251. National Park Service, Fort Collins, 

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Theobald, D. M. 2005. Landscape patterns of exurban growth in the USA from 1980 to 2020. 

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http://www.ecologyandsociety.org/vol10/iss1/art32/ES-2005-1390.pdf.

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Geospatial dataset. Available from: http://www.blm.gov/or/gis/data.php.

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the United States (PADUS) version 1.2. Available at 

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The Department of the Interior protects and manages the nation’s natural resources and cultural heritage; provides scientific 

and other information about those resources; and honors its special responsibilities to American Indians, Alaska Natives, and 

affiliated Island Communities.

NPS 105/130511, November 2015
National Park Service

U.S. Department of the Interior

Natural Resource Stewardship and Science

1201 Oakridge Drive, Suite 150

Fort Collins, CO 80525

www.nature.nps.gov

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