Pacific Southwest Research Station
Attn: Tiff van Huysen
Ecologist & Tahoe Science Program Coordinator
TCES Suite, 320
291 Country Club Drive
Incline Village, NV, 89451
Jarlath   O’Neil ‐ Dunne   ,   Dr.
David   Saah,   Tadashi   Moody,   Travis   Freed,   Dr.
Qi
Chen,   and   Jason   Moghaddas   on   behalf   of:
Spatial   Informatics   Group,   LLC
3248   Northampton   Ct.
Pleasanton,   CA   94588   USA
Email:   dsaah@sig ‐ gis.com
Phone:   (510)   427 ‐ 3571
Report   Date:   March   18th,   2014
Page   1   of   29

Contents
Abstract   .........................................................................................................................................................
3
Introduction   ..................................................................................................................................................
3
Research   Objectives   ..............................................................................................................................
4
Methods   ........................................................................................................................................................
5
Study   Area   .................................................................................................................................................
5
Data   ...........................................................................................................................................................
5
Classification   Scheme   ................................................................................................................................
6
Feature   Extraction   .....................................................................................................................................
7
Results   .....................................................................................................................................................
10
Accuracy   Assessment   ..............................................................................................................................
10
Land   Cover   Products   Derived   from   LiDAR   Data   ..........................................................................................
12
Land   Cover   Products   ...............................................................................................................................
12
Hydrography   Polygons   ........................................................................................................................
12
Transportation   Linear   Network   ..........................................................................................................
12
Building   Polygons   ................................................................................................................................
13
Impervious   Surfaces   ............................................................................................................................
14
Accuracy   ..................................................................................................................................................
15
Distribution   .............................................................................................................................................
16
Broad   Scale   Summaries   of   Impervious   Surfaces   .........................................................................................
18
Statistical   Summaries   ..............................................................................................................................
18
Tahoe   Basin   (TRPA   Boundary)   .............................................................................................................
18
Parcels   .................................................................................................................................................
19
Watersheds   .........................................................................................................................................
23
Current   LiDAR   Derived   Impervious   Surface   Coverage   by   Land   Capability   Class   .................................
24
Discussion   ...................................................................................................................................................
26
Acknowledgements   .....................................................................................................................................
26
Literature   Cited   ...........................................................................................................................................
27
Non ‐ Discrimination   Statement   ...................................................................................................................
29
Page   2   of   29

Abstract
Conversion   of   land   to   impervious   cover   threatens   the   environmental   quality   of   the   Lake   Tahoe   Basin
(LTB)   (Bailey   1974)   by   reducing   water   percolation   into   the   soil   and   increasing   runoff,   and   thus   sediment,   nutrient,   and   pollutant   transfer   into   Lake   Tahoe   (Arnold   and   Gibbons   1996).
If   impervious   cover,   both   hard   (e.g.
paved   or   roofed   areas)   and   soft   (e.g.
disturbed   or   compacted   soil)   expands   to   unsustainable   or   irreversible   levels,   many   if   not   all   of   the   environmental   goals   or   “thresholds”   central   to   management   efforts   within   the   Basin   could   be   adversely   affected.
Therefore,   effective   management   and   monitoring   of   impervious   surfaces   within   the   LTB   requires   an   impervious   cover   data   set   that   is   detailed,   accurate,   and   meaningful.
Spatial   Informatics   Group   (SIG)   mapped   both   hard   and   soft   impervious   surfaces   in   the
LTB   by   leverage   existing   investments   in   high ‐ resolution   multispectral   imagery,   Light   Detection   and
Ranging   (LiDAR),   and   vector   GIS   data.
This   was   accomplished   through   a   repeatable   and   cost ‐ effective   analytical   methodology,   centered   on   Object ‐ Based   Image   Analysis   (OBIA)   techniques   (e.g.
O’Neil ‐ Dunne   et   al.
2012).
The   end   product   consists   of   a   spatially   detailed,   accurate,   attribute ‐ rich,   and   realistic   map   of   impervious   cover   within   the   LTB   based   on   August   2010   ground   conditions.
Impervious   surface   information   was   summarized   by   varying   geographic   units   of   analysis,   including   ownership   (e.g.
parcels),   environmental   gradients/boundaries   (e.g.
watershed,   soil   type,   soil   capability   class)   and   political   boundaries   (e.g.
counties).
Finally,   a   pilot   implementation   of   the   Lake   Tahoe   Land   Cover   and
Disturbance   Monitoring   Plan   (SIG   2009)   was   carried   out   using   the   derived   impervious   surface   cover.
This   analysis   indicated   that   Land   Capability   Types   1b   and   2   exceeded   allowable   coverage   prescribed   by   Bailey
(1974)   to   maintain   the   “environmental   balance”   for   combined,   LiDAR   derived   hard   and   soft   impervious   cover   types,   which   was   consistent   with   the   2011   Threshold   Report.
Utilizing   this   approach   to   monitoring   impervious   surfaces   may   provide   a   repeatable,   cost   effective   approach   to   detecting   watershed   and   LTB   wide   changes   in   impervious   coverages   in   the   future.
Introduction
Land   conversion   to   impervious   cover   can   accelerate   sediment   and   nutrient   deposition   by   “short ‐ circuiting”   the   hydrological   cycle   and   increasing   soil   erosion   (TRPA   2001   as   cited   in   TRPA   2007).
Other   indicators   of   ecosystem   health   including   pollutant   transfer,   flooding   intensity,   stream   flow,   stream   temperature   and   habitat   loss   are   influenced   by   the   amount   of   impervious   cover   (Arnold   and   Gibbins
1996,   Tilley   and   Slonecker   2007).
If   impervious   cover   exists   or   expands   to   excessive   or   irreversible   levels   it   can   affect   all   of   TRPA’s   nine   threshold   categories   or   areas   of   concern   for   the   Tahoe   Basin   (e.g.
wildlife,   fisheries,   water   quality,   vegetation,   etc.)   (Minor   and   Cablk   2004).
To   control   for   this,   the   Tahoe   Regional
Planning   Agency   (TRPA)   has   placed   limitations   on   land   conversion   (e.g.,   development)   and   disturbance
(e.g.
grading)   within   the   Lake   Tahoe   Basin   (LTB)   based   on   amounts   that   are   considered   allowable   to   meet   the   soil   conservation   threshold   standards   (TRPA   2012).
TRPA’s   impervious   coverage   standard,   adopted   in   1982   is   based   on   the   Bailey   Land   Capability
Classification   System   (Bailey   1974).
The   Bailey   classification   consists   of   seven   “land   capability
Page   3   of   29

class/district”   ranks,   which   are   principally   based   on   geomorphic   conditions   and   soil   type   (Bailey,   1974).
Each   one   of   the   seven   ranks   has   an   associated   allowable   base   coverage   of   impervious   surfaces.
Increased   deposition   of   fine   sediment   and   nutrients   are   key   factors   reducing   water   clarity   in   Lake   Tahoe
(Swift   et   al.
2006).
Up ‐ to ‐ date   assessments   of   impervious   cover   in   LTB   are   required   to   determine   if   threshold   standard   –   acres   and   percent   of   impervious   coverage   (hard   and   soft),   by   land   capability   class   are   in   attainment,   to   inform   management   decisions   at   the   parcel   and   watershed   scale   and   to   serve   as   a   baseline   for   future   comparative   analyses   (SNPLMA   2010).
These   needs   are   further   outlined   in   the   proposed   “Lake   Tahoe   Land   Cover   and   Disturbance   Monitoring   Plan”   (SIG   2009).
Impervious   cover   is   composed   of   hard,   soft,   and   natural   features.
Hard   cover   refers   to   land   that   is   covered   by   materials   such   as   buildings,   pavement,   exposed   rock   and   concrete,   and   soft   cover   refers   to   disturbed   and/or   degraded   soil   (Cablk   and   Perlow,   2004)   and   as   defined   in   the   TRPA    Regional   Plan ‐
Code   of   Ordinances   (2012).
Detecting,   mapping,   and   identifying   each   subtype   of   impervious   cover   and   its   connectivity   is   important   for   evaluating   impacts   of   impervious   cover   to   environmental   goals   and   developing   appropriate   management   strategies.
There   is   thus   a   need   to   develop   a   cost ‐ effective   and   consistent   approach   for   long ‐ term   impervious   surface   monitoring   that   leverages   passive   and   active   remotely ‐ sensed   data   to   differentiate   between   impervious   surface   types.
This   project   had   four   primary   research   goals   and   three   research   objectives,   all   of   which   were   achieved.
The   goals   and   objectives   were   to:
1.
Develop   a   cost ‐ effective   and   repeatable   approach   suitable   for   the   long ‐ term   monitoring   of   impervious   surfaces   within   the   LTB   that   leverages   existing   investments   in   remotely   sensed   imagery,
LiDAR,   and   vector   GIS   datasets.
2.
Generate   products   that   provide   decision   makers   and   land   managers   with   meaningful   information   about   impervious   surfaces   at   a   variety   of   scales.
3.
Produce   a   GIS   dataset   that   is   an   accurate   and   realistic   representation   of   impervious   surfaces   within   the   LTB   such   that   it   is   suitable   for   Effective   Impervious   Area   (EIA)   hydrologic   modeling   (e.g.
Subtheme   2b)   and   believable   when   viewed   by   the   general   public.
4.
Use   this   dataset   to   pilot   implement   the   Lake   Tahoe   Land   Cover   and   Disturbance   Monitoring   Plan
(SIG   2009).
Research Objectives
1.
Map   hard   and   soft   impervious   surfaces   within   the   LTB   such   that   the   user’s   and   producer’s   accuracies   for   both   types   exceed   95%   for   the   entire   study   area.
2.
Quantify   the   amount   and   extent   of   the   various   impervious   cover   types   in   the   LTB   by   ownership,   political,   and   environmental   boundaries.
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3.
Use   this   dataset   to   pilot   implement   the   proposed   Lake   Tahoe   Land   Cover   and   Disturbance
Monitoring   Plan   (SIG   2009).
Methods
Study Area
The   study   area   consisted   of   the   Lake   Tahoe   drainage   basin   plus   Tahoe   Regional   Planning   Agency   (TRPA)   jurisdictional   boundary   that   includes   some   of   the   drainage   area   associated   with   the   Truckee   River   outfall.
Impervious   surface   mapping   was   carried   out   for   the   entirety   of   the   study   area   ( Figure   1 ).
Figure   1.
Impervious   mapping   study   area.
Data
Source   data   for   the   impervious   mapping   consisted   of   a   combination   of   remotely   sensed   and   GIS   vector   data   ( Table   1 ).
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Table   1.
Source   datasets   for   impervious   surface   mapping.
Dataset
WorldView ‐ 2   pan ‐ sharpened   8 ‐ band   multispectral   imagery
LiDAR   Normalized   Digital   Surface   Model   (nDSM)
LiDAR   Digital   Elevation   Model   (DEM)
LiDAR   Normalized   Digital   Terrain   Model   (nDTM)
LiDAR   Intensity
Property   Parcels
Road   centerlines
Trails
Hydrology   polygons
Format
Raster
Raster
Raster
Raster
Raster
Vector
Vector
Vector
Vector
2010
Year
2010
2010
2010
2010
2009
Unknown
2009
2008
Horizontal
Resolution
0.46m
0.5m
0.5m
0.5m
0.5m
N/A
N/A
N/A
N/A
Classification Scheme
The   impervious   surface   classification   is   depicted   in   Figure   2 .
The   classification   scheme   consisted   of   hard   and   soft   surface   types   and   building,   road,   trail,   and   other   (driveways,   parking   lots)   feature   types.
Figure   2.
Impervious   surface   classification   scheme.
Photo ‐ interpretation   keys   were   constructed   by   visiting   representative   areas   for   all   the   feature   and
surface   types   listed   in   Figure   2   on   the   ground   where   one   or   more   photographs   were   collected   along   with
Page   6   of   29

GPS   coordinates.
Keys   were   also   developed   for   features   that   could   be   easily   misclassified   as   impervious
(e.g.
exposed   rock).
The   point   locations   and   associated   ground   photographs   were   stored   in   KML   file
( Figure   3 ).
Figure   3.
Example   photo   interpretation   key   for   exposed   rock,   a   feature   type   that   could   be   confused   with   hard   impervious   surfaces.
Feature Extraction
The   overall   approach   to   feature   extraction   consisted   of   both   automated   and   manual   processes.
The   automated   mapping   centered   on   an   Object ‐ Based   Image   Analysis   (OBIA)   system   developed   using   eCognition®   version   8.7.
The   design,   development,   and   deployment   of   the   OBIA   system   followed   O’Neil ‐
Dunne   et   al.
(2012).
Manual   editing   of   data   was   carried   out   using   ArcGIS®   10.0.
The   feature   extraction   cycle   went   through   four   stages   ( Figure   4 ):   1)   hydrology   polygons,   2)   linear   transportation   network,   3)   building   polygons,   and   finally   4)   the   comprehensive   impervious   surface   dataset.
This   incremental   approach   was   designed   to   eliminate   false   positives   by   extracting   non ‐ impervious   features   (e.g.
water)   while   at   the   same   time   iteratively   increasing   the   amount   of   contextual   information   available   for   the   classification   algorithms   (e.g.
distance   to   roads).
Each   one   of   the   stages   resulted   in   a   distinct   product   that   was   fed   into   the   next   stage,   cumulating   with   the   impervious   surface   classification.
Page   7   of   29

Figure   4.
Feature   extraction   stages.
Figure   5   depicts   the   cyclical   nature   of   the   workflow   for   each   one   of   the   four   stages.
During   each   stage   1)   the   data   were   processed   into   data   stacks   and   loaded   into   the   OBIA   system,   2)   features   were   extracted
OBIA   techniques,   and   3)   the   quality   of   the   data   were   improved   using   manual   editing   techniques.
Figure   5.
Impervious   mapping   workflow.
Page   8   of   29

Creating   data   stacks   involved   first   dicing   the   data   into   tiles   3000x3000   pixels   in   size   with   300   pixels   of   overlap.
The   diced   data   were   then   loaded   into   the   OBIA   system   via   a   customized   import   routine   implemented   in   XML.
Each   data   stacked   contained   the   imagery,   LiDAR,   and   vector   datasets   ( Figure   6 ).
Following   each   stage   the   data   stack   was   updated   with   the   new/improved   data.
Figure   6.
OBIA   data   stacks.
Page   9   of   29

Automated   feature   extraction   was   performed   using   a   rule ‐ based   expert   system   developed   using   the   Cognition   Network   Language   (CNL).
Figure   7   shows   an   example   of   the   CNL   rule   set   used   in   the   final   impervious   classification.
The   CNL   rule   set   integrated   image   processing,   surface   calculations,   segmentation,   classification,   fusion,   and   morphology   algorithms   into   a   single   process.
Separate   rule   sets   were   developed   for   each   of   the   four   mapping   iterations   (Figure   4)   and   an   example   rule   set   is   shown   in   Figure   7.
For   a   given   iteration,   a   single   rule   set   was   applied   to   all   data   stack   tiles.
Rule   sets   were   developed   based   on   the   reference   areas   in   the   photo   interpretation   keys   then   applied   to   other   areas   and   refined   as   necessary.
Post ‐ processing   consisted   of   assembling   the   output   tiles   into   a   single   basin ‐ wide   mosaic.
Manual   corrections   were   carried   out   via   heads ‐ up   digitizing   techniques   using   the   LiDAR   and   WorldView ‐ 2   imagery   as   the   source   data.
Review   of   the   data   occurred   at   a   scale   of   1:2,000.
Figure   7.
An   example   CNL   rule   set   for   impervious   surface   feature   extraction.
Results
Accuracy Assessment
The   accuracy   assessment   was   carried   out   following   Congalton   and   Green   (2009).
A   stratified   sampling   approach   was   employed   in   which   500   points   were   randomly   placed   on   areas   assigned   to   one   of   the   impervious   surface   classes   and   500   points   were   placed   on   areas   not   classified   as   impervious   surfaces.
This   stratified   approach   was   designed   to   quantify   errors   of   omission   (the   lack   of   inclusion   of   an   impervious   surface   when   it   in   fact   existed   on   the   landscape)   and   commission   (the   inclusion   of   impervious   surface   when   it   did   not   exist   on   the   landscape).
Each   point   was   independently   assigned   to   one   of   the   impervious   surface   classes   ( Figure   2 )   by   two   technicians   using   the   2010   WorldView ‐ 2   imagery   and   LiDAR   as   the   reference   data   ( Figure   8 ).
In   the   cases   where   the   two   technicians   differentially   defined
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the   point,   the   point   was   revaluated   and   a   consensus   reached.
Producer’s,   user’s,   and   overall   accuracy   were   computed   for   both   the   impervious   feature   types   and   the   impervious   surface   types.
Figure   8.
Accuracy   assessment   reference   points.
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Land Cover Products Derived from LiDAR Data
Land Cover Products
Hydrography Polygons
The   hydrography   polygons   generated   as   part   of   this   project   represent   a   marked   improvement   over   the   existing   National   Hydrography   Dataset   (NHD)   polygons   ( Figure   9 ).
The   precision   of   the   water ‐ land   interface   was   refined,   false   water   features   were   removed,   and   previously   unmapped   features   were   added.
Figure   9.
Hydrography   polygons   generated   to   support   the   impervious   mapping   (blue)   compared   to   those   from   the   National   Hydrography   Dataset   (red)   for   Susie   Lake.
Transportation Linear Network
The   transportation   linear   network   generated   as   part   of   the   project   represent   a   substantial   upgrade   of   road   and   trail   network   both   in   terms   of   feature   types,   accuracy   and   precision   (Figure   10).
This   updated
Page   12   of   29

dataset,   with   some   modifications   to   its   topology,   could   be   used   for   transportation   modeling   and   analysis.
Figure   10.
Transportation   linear   network.
Building Polygons
The   building   polygon   dataset   represents   the   first   comprehensive   inventory   of   buildings   for   the   Tahoe
Basin.
Use   of   the   LiDAR   enabled   buildings   to   be   extracted   even   if   portions   of   the   buildings   were   under   tree   canopy   ( Figure   11 ).
The   building   data   will   support   many   uses   ranging   from   attributing   property   parcels   with   building   area   information   to   identifying   structures   that   have   an   elevation   fire   risk.
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Figure   11.
Building   polygons   (black   lines)   overlaid   on   the   LiDAR   Normalized   Digital   Surface   Model   (nDSM).
Impervious Surfaces
The   objective   of   mapping   both   hard   and   soft   impervious   surfaces   was   achieved.
The   ability   to   distinguish   between   various   surface   types   and   feature   types   will   enable   TRPA   to   use   this   dataset   for   a   broad   range   of   management   and   regulatory   purposes,   to   include   implementing   the   monitoring   plan   and   applying   the   Bailey   Land   Capability   Classification   System   (Figure   12).
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Figure   12.
Final   impervious   surface   dataset   depicting   both   the   feature   type   and   surface   type.
Accuracy
The   overall   accuracy   of   mapped   hard   and   soft   impervious   types   was   94%   ( Table   2 ).
Not   surprisingly,   the   user’s   accuracy   of   hard   impervious   surfaces   was   better   than   that   of   soft   impervious   surfaces   as   soft   impervious   surfaces   were   confused   with   non ‐ impervious   features   on   the   landscape   that   were   also   void   of   vegetation   (e.g.
bare   soil   and   exposed   rock).
The   overall   accuracy   of   mapped   impervious   feature   types   was   90%   ( Table   3 ).
The   user’s   accuracy   of   buildings,   roads,   and   other   (driveways/parking   lots)   features   all   exceeded   90%,   but   the   user’s   accuracy   of   trails   was   only   59%.
This   can   be   attributed   to   the   narrow   configuration   of   trails   in   combination   with   the   fact   that   they   are   often   obscured   by   overhead   canopy.
It   is   important   to   note   that   the   estimates   of   accuracy   presented   in   this   report   are   for   the   entirety   of   the
Tahoe   Basin.
It   was   not   feasible   to   conduct   assess   the   accuracy   at   multiple   units   of   analysis   (e.g.
parcels
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and   watershed)   for   this   project.
As   such,   these   tables   should   not   be   used   to   draw   conclusions   on   the   accuracy   for   individual   watersheds   or   parcels.
Table   2.
Impervious   surface   type   error   matrix.
Not
Not
472
Hard
6
Reference   Data
Soft Total Producer's   Accuracy
6 484 98%
Hard 7 362 5 374 97%
Soft
Total
User's   Accuracy
25
504
94%
15
383
95%
102 142
113 1000
90%
72%
94%
Table   3.
Impervious   feature   type   error   matrix.
Not
Road
Trail
Building
Other
Total
User's   Accuracy
Not
473
7
29
1
23
533
89%
Road
Reference   Data
Trail Building Other Total
2
168
1
1
8
180
93%
5
2
20
0
7
34
59%
1
2
0
100
2
105
95%
2
6
2
4
134
148
91%
483
185
52
106
174
1000
Producer's   Accuracy
98%
91%
56%
94%
77%
90%
Distribution
A   hot   spot   analysis   was   carried   out   to   highlight   concentrations   of   impervious   surfaces   within   the   Tahoe
Basin.
Impervious   surface   percentages   were   summarized   to   100m   grid   cells   then   the   Getis ‐ Ord   Gi*   statistic   (ESRI   2013)   was   computed   using   ArcGIS.
The   Gi*   statistic   is   a   Z   score.
“For   statistically   significant   positive   Z   Scores,   the   larger   the   Z   score   is,   the   more   intense   the   clustering   of   high   values.
For   statistically   significant   negative   Z   scores,   the   smaller   the   Z   Score   is,   the   more   intense   the   clustering   of   low   values”   (ESRI   2013).
Not   surprisingly,   hard   impervious   surfaces   are   concentrated   in   the   more   urbanized   portions   of   the   Lake
Tahoe   Basin   close   to   the   lake   ( Figure   13 ).
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Figure   13.
Hard   impervious   surface   hot   spot   analysis   based   on   average   percent   impervious   in   100m   grid   cells.
Higher   GiZ   scores   are   indicative   of   clusters   (“Hot   Spots”)   of   hard   impervious   surfaces.
Cluster   analysis   is   similar   to,   but   not   the   same   as   a   density   analysis.
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Broad Scale Summaries of Impervious Surfaces
Statistical Summaries
Impervious   surface   metrics   were   computed   for   the   following   geographic   boundaries:
1.
The   Lake   Tahoe   Basin   (watershed   plus   other   administrative   boundaries)
2.
Property   parcels   a.
Property   boundaries   b.
Ownership   c.
Parcel   land   use   classes
3.
Census   block   groups
4.
Watersheds   a.
Primary   b.
Sub   basins   184
For   each   geography   unit   of   analysis   the   area   of   each   impervious   feature   type   and   surface   type   was   summarized   along   with   the   percent   of   land   covered   by   each   impervious   feature   type   and   surface   type.
Tahoe Basin (TRPA Boundary)
Overall   (including   area   associated   with   water   bodies),   1.9%   of   the   Tahoe   Basin   is   covered   by   hard   impervious   surfaces,   totaling   6,175   acres   and   0.5%   of   the   Tahoe   Basin   is   covered   by   soft   impervious   surfaces,   totaling   1,810   acres,   totaling   approximately   7,942   aces.
This   estimated   total   impervious   coverage   acreage   represents   about   3.95%   of   the   land   area   in   the   LTB   (study   area   boundaries).
In   terms   of   impervious   surface   types   the   largest   single   type   of   impervious   surface   are   roads,   followed   by   other
(driveway/parking   lots),   buildings,   and   trails.
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Figure   14.
Impervious   surface   type   and   feature   type   summaries   for   the   Tahoe   Basin   based   on   the   TRPA   boundary.
Parcels
Impervious   surface   metrics   were   computed   for   all   62,962   parcels   within   the   TRPA   boundary.
An   example   of   per ‐ parcel   hard   impervious   is   shown   in   ( Figure   15 ).
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Figure   15.
Per ‐ parcel   hard   impervious   metrics.
The   majority   of   parcels   (65%)   have   20%   or   less   of   their   land   area   covered   by   impervious   surfaces   ( Figure
16 ).
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Figure   16.
Distribution   of   parcels   by   %   impervious.
An   analysis   of   parcel   ownership   categories   shows   that   the   majority   of   impervious   surfaces   are   on   private   property   ( Figure   17 ).
When   the   ownership   patterns   are   broken   down   by   surface   types   most   of   the   hard   impervious   surfaces   are   on   private   land,   but   most   of   the   soft   impervious   is   on   land   owned   by   the   Forest
Service.
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Figure   17.
Impervious   surfaces   by   ownership   type.
The   size   of   the   circles   in   the   bubble   chart   corresponds   to   the   area   of   hard   impervious   and   the   color   gradient   (bottom   of   graphic)   the   area   of   soft   impervious.
Note   than   roads   do   not   have   an   ownership   type   and   thus   are   exclude   from   this   analysis.
In   terms   of   land   use   the   vast   majority   of   hard   impervious   surfaces   are   in   the   rights ‐ of ‐ way   (ROW)   and   on   single ‐ family   dwellings   ( Figure   18 ).
Impervious   surfaces   in   the   ROW   are   primarily   roads,   whereas   impervious   surfaces   in   single ‐ family   dwellings   are   a   combination   of   buildings   and   driveways.
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Figure   18.
Impervious   surface   metrics   broken   out   by   land   use.
The   size   of   the   box   represents   hard   impervious   acres   by   land   use,   the   color   represents   the   area   of   soft   impervious   per   land   use   class.
Watersheds
Impervious   metrics   were   generated   for   both   the   primary   sub ‐ watersheds   and   the   184   sub ‐ basins.
Sub ‐ watersheds   in   the   urbanized   areas   near   the   lake   have   the   highest   percentage   of   their   land   area   covered   by   hard   impervious   surfaces   ( Figure   19 ).
Page   23   of   29

Figure   19.
Percent   of   land   in   each   primary   sub ‐ watersheds   covered   by   hard   impervious   surfaces.
The   white   polygon   in   the   northwest   of   the   Basin   has   precisely   0%   impervious   surfaces.
Current LiDAR Derived Impervious Surface Coverage by Land Capability Class
The   LiDAR   derived   soft   and   hard   impervious   cover   map   derived   by   Spatial   Informatics   Group   (SIG)   was   utilized   to   estimate   impervious   land   coverage   by   Land   Capability   Class   (Bailey   1974)   (Table   4).
Only   two
Land   Use   Capability   types,   1b   and   2,   exceeded   the   Bailey   (1974)   coverage   for   combined   totals   of   both   soft   and   impervious   coverage.
Within   Land   Capability   Type   1b,   the   coverage   was   exceeded   by   668   acres   or   5.9%   above   the   total   acreage   recommended   in   Bailey   1974.
Within   Land   Capability   Type   2,   coverage   was   exceeded   by   42.9
acres   or   0.2%.
These   findings   are   consistent   with   key   findings   reported   in   the
2011   Threshold   Report,   who   also   noted   Land   Capability   Classes   1b   and   2   “…to   be   exceeding   allowable   coverage   targets   by   670   and   43   acres..
“   (Choy   et   al.
2011).
Page   24   of   29

Table   4.
Hard,   soft,   and   total   impervious   coverage   for   the   Lake   Tahoe   Basin   in   2010   by   Bailey   (1974)   Land   Capability   Class.
Current   impervious   coverages   are   compared   with   Bailey   (1974)   allowable   coverages,   with   acres   and   percent   over   or   below   these   thresholds   reported   on   the   far   right   columns   of   Figure   20.
Land
Capability
Class
Total
Area
1a
1b
1c
2
3
4
5
6
7
Grand
Total
Percent   of   LTB
Area
1
Acres   (%)
23,477.0
12%
11,288.3
6%
53,578.0
27%
23,626.1
12%
16,786.2
8%
32,250.7
16%
10,294.9
5%
24,260.2
12%
5,513.5
3%
201,074.9
100%
Pervious   Coverage
Acres   (%)
23,302.7
99.3%
10,506.6
93.1%
53,077.3
99.1%
23,346.9
98.8%
16,434.0
97.9%
30,988.8
96.1%
9,199.0
89.4%
22,046.8
90.9%
4,230.4
76.7%
193,132.6
96.1%
Hard   Impervious
Coverage
Acres   (%)
75.7
0.3%
673.6
6.0%
261.5
0.5%
134.8
0.6%
184.3
1.1%
918.1
2.8%
912.2
8.9%
1,847.8
7.6%
1,160.5
21.0%
6,168.5
3.1%
Soft   Impervious
Coverage
Total
Impervious
Coverage
Acres   (%)   Acres   (%)
98.6
0.4%
108.1
1.0%
239.2
0.4%
174.3
781.7
500.7
0.7%
6.9%
0.9%
144.4
0.6%
167.9
1.0%
343.8
1.1%
183.7
1.8%
365.5
1.5%
122.6
2.2%
1,773.8
0.9%
279.2
352.2
1,261.9
1.2%
2.1%
3.9%
1,095.9
10.6%
2,213.3
9.1%
1,283.1
7,942.3
23.3%
3.9%
Bailey
Allowed
Coverage
Acres
234.8
112.9
535.8
(%)
1.0%
1.0%
19,915.0
9.9%
1.0%
236.3
839.3
1.0%
5.0%
6,450.1
20.0%
2,573.7
25.0%
7,278.1
30.0%
1,654.1
30.0%
Exceeding   or   (Below)
Bailey   Allowable
Thresholds
Acres
(60.5)
668.8
(35.1)
(
(%)
‐
0.3%)
5.9%
( ‐ 0.1%)
42.9
(487.1)
0.2%
( ‐ 2.9%)
(5,188.2)   ( ‐ 16.1%)
(1,477.8)   ( ‐ 14.4%)
(5,064.7)   ( ‐ 20.9%)
(370.9)   ( ‐ 6.7%)
(11,972.7)   ( ‐ 6.0%)
Page   25   of   29

Discussion
This   project   succeeded   in   mapping   both   hard   and   soft   impervious   surfaces   in   the   Lake   Tahoe   Basin   with   a   high   degree   of   accuracy.
Crucial   to   the   project’s   success   was   the   availability   of   high ‐ resolution,   high ‐ quality   remotely   sensed   data.
The   combination   of   LiDAR   and   spectral   imagery   allowed   for   the   differentiation   of   impervious   surfaces   based   on   both   their   structure   and   material.
By   having   impervious   surface   data   summarized   at   multiple   geographical   units,   managers   have   the   information   they   need   to   identify   and   target   specific   areas   from   the   watershed   to   the   parcel,   for   the   purposes   of   either   removing   impervious   surfaces   or   disconnecting   them   from   the   hydrologic   network.
Furthermore,   this   dataset   served   to   identify   land   capability   classes   that   are   not   incompliance   with   development   standards.
The   dataset   produced   as   part   of   this   study   should   serve   as   the   foundation   for   future   impervious   surface   mapping,   lowering   the   cost   of   future   mapping   efforts   and   assisting   mangers   in   understanding   how   impervious   surfaces   are   changing   in   the   Basin.
Acknowledgements
We   would   like   to   thank   the   Tahoe   Regional   Planning   Agency   and   local   land   management   experts   for   their   assistance   over   the   course   of   this   project.
Funding   for   this   project   was   provided   by   the   Southern
Nevada   Public   Land   Management   Act   (SNPLMA).
Page   26   of   29

Literature Cited
Alley,   W.
M.,   and   J.
E.
Veenhuis.
1983.
Effective   impervious   area   in   urban   runoff   modeling.
Journal   of
Hydraulic   Engineering   109(2):   313 ‐ 319.
Arnold,   C.
L.,   and   C.
J.
Gibbons.
1996.
Impervious   surface   coverage:   the   emergence   of   a   key   environmental   indicator.
Journal   of   the   American   Planning   Association   62(2):   243 ‐ 258.
Bailey,   R.
G.
1974.
Land ‐ Capability   Classification   of   the   Lake   Tahoe   Basin,   California ‐ Nevada:   A   Guide   for
Planning .
South   Lake   Tahoe,   CA:   US   Forest   Service,   US   Department   of   Agriculture   in   cooperation   with   the   Tahoe   Regional   Planning   Agency.
Brabec,   E.,   S.
Schulte,   and   P.
L.
Richards.
2002.
Impervious   surfaces   and   water   quality:   a   review   of   current   literature   and   its   implications   for   watershed   planning.
Journal   of   Planning   Literature
16(4):   499 ‐ 514.
Cablk,   M.E.,   and   L.E.
Perlow.
2006.
A   classification   system   for   impervious   cover   in   the   Lake   Tahoe   Basin .
Final   Report.
Reno,   NV:   Nevada   Water   Resources   Research   Institute.
Choy,   J.,   Romsos,   S.,   Loftis,   W.,   O’Neil ‐ Dunne,   J.,   Nielson,   P.,   Saah,   D.,   and   Oehrli.
2011.
Chapter   5,   Soil
Conservation,   In :   2011   Threshold   Evaluation.
Tahoe   Regional   Planning   Agency.
175p.
http://www.trpa.org/regional ‐ plan/threshold ‐ evaluation/
ESRI   (Environmental   Systems   Research   Institute)   2013.
How   Hot   Spot   Analysis:   Getis ‐ Ord   Gi*   (Spatial
Statistics)works.
Accessed   from   http://edndoc.esri.com/arcobjects/9.2/NET/shared/geoprocessing/spatial_statistics_tools/how
_hot_spot_analysis_colon_getis_ord_gi_star_spatial_statistics_works.htm
on   12/30/2013
Long,   J.
2010.
Tahoe   Science   Update   Report .
Incline   Village,   NV:   US   Department   of   Agriculture,   US   Forest
Service,   Pacific   Southwest   Research   Station.
Minor,   T.B.,   and   M.E.
Cablk.
2004.
Estimation   of   impervious   cover   in   the   Lake   Tahoe   Basin   using   remote   sensing   and   geographic   information   systems   data   integration.
Journal   of   the   Nevada   Water
Resources   Association   1(1):   58 ‐ 75.
O’Neil ‐ Dunne,   Jarlath   P.M.,   Sean   W.
MacFaden,   Anna   R.
Royar,   and   Keith   C.
Pelletier.
2012.
An   Object ‐ based   System   for   LiDAR   Data   Fusion   and   Feature   Extraction.
Geocarto   International   (May   29):   1–
16.
doi:10.1080/10106049.2012.689015.
Spatial   Informatics   Group   (SIG).
2009.
Lake   Tahoe   Land   Cover   and   Disturbance   Monitoring   Plan .
Stateline,   NV:   Tahoe   Regional   Planning   Agency.
Swift,   T.
J.,   J.
Perez ‐ Losada,   S.
G.
Schladow,   J.
E.
Reuter,   A.
D.
Jassby,   and   C.
R.
Goldman.
2006.
Water   clarity   modeling   in   Lake   Tahoe:   Linking   suspended   matter   characteristics   to   Secchi   depth.
Aquatic   Sciences ‐ Research   Across   Boundaries   68(1):   1 ‐ 15.
Page   27   of   29

Tahoe   Regional   Planning   Agency.
2001.
Regional   plan   for   the   Lake   Tahoe   Basin:   2001   Threshold   evaluation   draft.
Stateline,   NV.
Tahoe   Regional   Planning   Agency.
———.
2007.
Threshold   Evaluation   Report.
Stateline,   NV.
Tahoe   Regional   Planning   Agency.
Tetra   Tech.
2007.
Watershed   hydrologic   modeling   and   sediment   and   nutrient   loading   estimation   for   the
Lake   Tahoe   total   maximum   daily   load.
Lahontan   Regional   Water   Quality   Control   Board.
http://www.waterboards.ca.gov/lahontan/water_issues/programs/tmdl/lake_tahoe/docs/peer
_review/tetra2007.pdf
Tilley,   J.
S.,   E.
T.
Slonecker,   2006.
Quantifying   the   components   of   impervious   surfaces .
U.S.
Geological
Survey.
Open ‐ File   Report   2006 ‐ 1008.
USGS   publications   website.
http://pubs.usgs.gov/of/2007/1008/ofr2007 ‐ 1008.pdf
.
Page   28   of   29

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“In   accordance   with   Federal   law   and   U.S.
Department   of   Agriculture   policy,   this   institution   is   prohibited   from   discriminating   on   the   basis   of   race,   color,   national   origin,   sex,   age   or   disability.
(Not   all   prohibited
bases   apply   to   all   programs.)
To   file   a   complaint   of   discrimination:   write   USDA,   Director,   Office   of   Civil   Rights,   Room   326 ‐ W,   Whitten
Building,   1400   Independence   Avenue,   SW,   Washington,   D.C.
20250 ‐ 9410   or   call   (202)   720 ‐ 5964   (voice   and   TDD).
USDA   is   an   equal   opportunity   provider   and   employer.”
Page   29   of   29