Predictive Mapping of Landtype Association Maps in Three Oregon National Forests

AN ABSTRACT OF THE THESIS OF

Wendy L. Peterman for the degree of Masters of Science in Soil Science presented on June 10, 2010. Title: Predictive Mapping of Landtype Association Maps in Three Oregon National Forests

Abstract approved:

______Jay S. Noller

Abstract: This study explores the use of predictive mapping techniques in developing

Landtype Association (LTA) maps for use in natural resource management. These maps are produced for the USDA Forest Service on a regional basis at a 1:100,000 scale. The goal of this study is to develop and test a method of producing LTA maps using Decision Tree Analysis (DTA) with existing data sets. The method is intended to be efﬁcient, objective, consistent and accessible to people from many backgrounds and levels of experience. This study maps changes in ecosystem function across the landscape, considers factors affecting DTA accuracy, and compares this method to conventional methods. Three study areas are included in this thesis project: SE Fremont

NF, Deschutes NF and Ochoco NF. Results indicate that subjective interpretation is reduced through machine-learning with a priori data sets.

Predictive Mapping of Landtype Association Maps in Three Oregon National Forests

by Wendy L. Peterman

A THESIS

submitted to

Oregon State University

in partial fulﬁllment of the requirements for the degree of

Master of Science

Presented June 10, 2010 Commencement June 2011

Master of Science thesis of Wendy L. Peterman presented on June 10, 2010

Approved:

______Major Professor, representing Soil Science

______Head of the Department of Crop and Soil Science

______Dean of the Graduate School

I understand that my thesis will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my thesis to any reader upon request.

______Wendy L. Peterman, Author ACKNOWLEDGEMENTS

! I would like to thank Jay Noller for the plethora of wonderful research and educational opportunities he has given me, and for his enthusiasm and support in writing and editing this thesis. Many thanks to Sarah Hash, without whose guidance and support, graduate school would have been much less tolerable. I thank Jay Noller,

Julie Pett-Ridge and Duane Lammers for being on my graduate committee and overseeing my program of study. I also thank Duane Lammers for generously sharing his SRI handbooks and Forest Service connections as well his positive attitude and advice. From the USDA Forest Service, I thank Karen Bennett for her enthusiasm and curiosity about this project, Peter Sussman for his open and efﬁcient sharing of data sets, Jim David for his contribution of detailed Ochoco data sets, and Todd Reinwald,

Terry Craig and the dedicated scientists who attended the August ﬁeld trip to the

Fremont for their feedback. I thank my husband and daughter for their encouragement, support, ﬂexibility and patience during a time of chaotic schedules and intense focus.

Thanks also to Priscilla Woolverton for her help in the ﬁeld. TABLE OF CONTENTS

Page 1.0 Introduction...... 1

! 1.1 Overview...... 2

! 1.2 The Ecoregion Framework...... 4

! 1.3 Study Problem...... 6

! 1.4 Study Areas...... 8

2.0 Methods...... 9

3.0 Results...... 17

! 3.1 Fremont NF/Warner Mountains...... 17

! 3.2 Deschutes NF...... 24

!!3.2.1 Error Analysis...... 30

!!3.2.2 Ground-truthing...... 32

! 3.3 Ochoco National Forest...... 33

!!3.3.1 Spatial Analysis of Ochoco NF maps...... 37

4.0 Discussion...... 40

5.0 Conclusions...... 43

6.0 References...... 44 LIST OF TABLES

Table Page

1 Hierarchy of Ecological Units...... 5

2 Data Sets Used in DTA...... 10

2a Flow Chart of LTA Prediction Process...... 11

3.2.1 Kappa & Overall Error for Deschutes Predictions...... 31

3.2.1a Deschutes Error Analysis...... 32

3.3.1 Comparison between the Ochoco LTA and Ecoregion V Maps...... 39 LIST OF FIGURES

Figure Page

1.1 Physiographic Regions/Geologic Provinces of Oregon...... 2

1.4 Level IV Ecoregions of the Deschutes, Ochoco and Fremont/Warner NF’s...... 9

2 Ground-Truth points in the Deschutes NF...... 13

3.1a Geology of Warner Mountain Portion of Fremont NF...... 18

3.1b Faults of Warner Mountain Portion of Fremont NF...... 20

3.1c Landform Groups of Warner Mountain Portion of Fremont NF...... 18

3.1d Vegetation of Warner Mountain Portion of Fremont NF...... 21

3.2a Elevation Distributions for Ecoregion 4...... 25

3.2b Elevation Distributions for Ecoregion 9...... 25

3.2c Landform Groups of the Deschutes NF...... 26

3.2d Geology of the Deschutes NF...... 27

3.2e Vegetation of the Deschutes NF...... 28

3.3a Landform Groups of the Ochoco NF...... 34

3.3b Geology of the Ochoco NF...... 35

3.2d Vegetation of the Ochoco NF...... 35 LIST OF PLATES

Plate Page

Plate 1 LTA’s of the Warner Mountains portion of the Fremont NF...... 52

Plate 2 LTA’s of the Deschutes NF...... 56

Plate 3 LTA’s of the Ochoco NF...... 59

Plate 4 USFS Ecoregion V map of the Ochoco NF...... 62 LIST OF APPENDICES

APPENDIX...... 51

Appendix A_1 Warner LTA Soils...... 53

Appendix A_2 Warner LTA Landform Groups...... 54

Appendix A_3 Warner LTA Landscape Features...... 55

Appendix A_ 4 Deschutes NF LTA Soils...... 57

Appendix A_5 Deschutes NF LTA Landform Groups...... 57

Appendix A_6 Deschutes NF LTA Landscape Features...... 58

Appendix A_7 Ochoco NF LTA Landform Groups...... 60

Appendix A_8 Ochoco NF LTA Landscape Features...... 61

Appendix A_8.5 Ochoco NF Ecoregions V Descriptions...... 63

Appendix A_9a-d Deschutes NF Prediction Error Matrices...... 65

Appendix A_10a,b Ochoco Prediction Error Matrices...... 75

1.0 Introduction

! Predictive mapping uses a statistical model to quantify landscape relationships from existing maps. With this approach, legacy data and map layers, representing environmental covariates are compared using Decision-Tree Analysis (DTA), resulting in a set of rules applicable to an unmapped area (Hash and Noller, 2009). The output of a modeling exercise based on these rules is a digital map depicting regions of similarity based on the input data sets and training data. Past studies have demonstrated the efﬁciency and usefulness of the machine learning mode of mapping in ecological unit inventories and soil surveys, using the traditional framework of Jenny’s 1941 State

Factor Theory, reformulated for digital soil mapping (DSM) (McBratney et al., 1998,

2008; Hash, 2008; Hash and Noller, 2009). Predictive mapping saves time and money by providing a framework for characterizing environmental covariates with an objective and well-documented approach, thereby reducing error and unnecessary time in the ﬁeld (Hash, 2008; Malone, 2008).

! Landscape modeling is evolving toward improving the understanding of landscape process dynamics and ecosystem functions at various scales (McKenzie,

2000). Predictive models of landscape attributes are useful for environmental modeling and management (Gessler, 1995). For example, a landscape model was developed to investigate and predict the environmental factors affecting wetland habitat change within the Barataria and Terrebonne basins of coastal Louisiana, USA (Reyes, 2000).

Ecological units for the Winema NF were used in a model for predicting forest and 2 riparian units for Landtype Association (LTA) and Landtype phase (LTP) maps in the adjacent Fremont NF (Slevin and Noller, 2008).

! The current study explores the use of predictive mapping techniques in developing LTA maps for use in natural resource management. These maps are produced for the USDA Forest Service on a regional basis. GIS and remote-sensing data have been used extensively to design LTA maps. In Oregon, the Blue Mountains

Ecoregion Map was created through visual inspection and regrouping of several environmental covariate layers (Sasich et al., 2006). The LTA map of the Modoc NF in

California was developed through a combination of visually inspected digital maps and aerial photos, with extensive ﬁeld work (Smith and Davidson, 2003). Machine learning was used in the design of the Fremont NF LTA map (Malone, 2008). Although each region has unique characteristics, this study provides a method for producing LTA maps for any national forest, using a machine learning approach.

1.1 Overview

Figure 1.1 Geomorphic regions of Oregon USFS http://www.fs.fed.us/r6/centraloregon/geology/info/ province.shtml 3 ! Oregon consists of nine physiographic regions (Figure 1.1) formed through combinations of deformation, volcanism, erosion and deposition related to the setting of the convergent plate boundary (Orr and Orr, 1999). During the Tertiary period, voluminous eruptions of ﬂood basalts in eastern Oregon covered extensive territory which later developed into these distinct regions. The Coast Range formed as seamounts on the Farallon Plate as it passed over the Yellowstone hotspot. The resulting volcanic chain was accreted to the western edge of the North American Plate as the two plates converged (Wells et al., 1984). The formation of the Cascade Range is attributed to a series of volcanic arcs that developed in three phases eastward of the

Farallon-North America subduction zone. These phases are coincident with changes in plate velocity, contributing to varying rates of volcanism and deformation on the surface since 35 Ma. (Priest, 1990). Between the two mountain ranges of western

Oregon, the Willamette Valley developed as a tectonic depression and center of deposition for the adjoining uplands. As the Farallon Plate subducted beneath the

North American Plate, extensional stresses in the overlying crust led to the alternating valley and mountain topography of the Basin and Range geologic province. Holocene faults in this region contribute to a northwestward migration of Oregon with relation to the rest of the North American continent at a rate of approximately 5 mm per year, creating chains of grabens and horsts (basins and ranges) (Pezzopane et al., 1993).

! To the northeast, the Blue Mountains region formed as exotic terranes accreted to the Mezozoic shoreline west of Idaho (Orr, 1991). Lahars and mudflows settled between the clusters of mountains, creating the Clarno and John Day formations, and an outpouring of hot lavas, released through crustal extension, covered these, filling 4 valleys and low points. These great basalt flows were carried to central Oregon by the

Deschutes River to form an extensive, level plateau. The construction of the Western

Cascades, succeeded by the High Cascades, pushed the Paciﬁc shore westward, thus altering the previously moist climate by creating a rain shadow to the East and contributing to the current high desert conditions of the Great Basin. In southern

Oregon, this basin experiences the movement of large tectonic blocks as the state rotates northwestward, creating an alternating basin and fault block topography (Orr,

1991).

1.2 The Ecoregion Framework

! Public lands in the US are administered using a framework based on ecosystem concepts. The ecosystem approach to public land management in the U.S. evolved in response to problems arising from too much focus on single-issue topics in environmental research (Omernik, 1995). An interagency task force was established to coordinate ecosystem management in environmental research, leading to a memorandum of agreement promoting the use of a common, hierarchical ecosystem framework (Clark et al, 1997)(Table 1). The choice of which hierarchical scale to use is dependent on the question or management problem at stake (Minshall, 1988).

Ecoregions are developed without a speciﬁc theme, increasing the potential for their general use (Clarke and Bryce, 1997). Environmental factors determining ecosystem characteristics include climate, geology, geomorphology, vegetation communities, soil, land-use, and hydrology (Clark and Bryce, 1997). 5

Table 1. National Hierarchical Framework of Ecological Units adapted from USDA Forest Service, 1993. (unpublished administrative paper). ______

Level Analysis Scale Map Scale Ecological Units Purpose, Objectives and General Use______I Ecoregion Domain Broad applicability for modeling and sampling

II Global/ 1,000,000’s to Continental 10,000 mi2 Division Strategic planning and assessment

III Regional Province International planning

IV Subregion 1,000’s to Section/ Strategic mulit-forest, state-wide 10’s mi2 Subsection and multi-agency analysis

V Landscape 1,000’s to Landtype Forest or area-wide planning, 100’s ac Association and watershed analysis VI Land unit 100’s to Landtype/ Project and management area planning <10 ac Landtype Phase and analysis ______!

! For national forests, a Level V ecoregion (1;63,360 to 1:100,00 scale map) was developed called the Landtype Association (LTA)(Table 1). The LTA map is an ecological inventory identifying related physical and biological processes across a landscape (USDA Forest Service,1993). Landform expression, geology, and potential natural vegetation (PNV) are the three main features used to interpret ecological processes at this scale (USDA Forest Service, 1993). LTA maps developed at OSU have added soils as a fourth interpretive feature for map unit delineation (Malone,

2008; Noller et al., 2009). The intended uses of LTA maps include: 1) monitoring of problem areas, 2) identifying project limitations, 3) identifying commonalities for management areas, and 4) predicting disturbance responses (Sasich, 2006). Some expected planning areas that could be addressed with an LTA map include research, 6 wildlife habitats, soil productivity, timber suitability, range management, and hydrologic properties (Smith & Davidson, 2003; Sasich, 2006).

1.3 Study Problem ! ! Nollerlabs at Oregon State University is contracted with the USDA Forest

Service to produce 1:100,000 maps of national forests in Oregon. This endeavor began with the mapping of the Fremont/Winema NF (Malone, 2008, Slevin, 2008). Some difﬁculties arose with mapping the Warner Mountain portion of the Fremont NF, because this area is separated from the main body of the forest by a wide basin.

Distance from the training area and a high incidence of Holocene faults through the region made it difﬁcult to predict based on the DTA rules established for the main

(NW) forest area. There were also issues with the Fremont NF Soil Resource Inventory

(SRI), which were not resolved during the previous period of study (Malone, 2008).

The prelude to the current study began with an effort to resolve some of these issues and complete the Fremont NF LTA map in time for the September, 2009, report to the

Forest Service. For the US Forest Service (USFS), the Deschutes NF was the next priority as it connects the Fremont/Winema NF and the Ochoco NF. For this reason, the

Deschutes NF is the main focus of this study, building upon skills acquired in mapping the Fremont NF. The mapping techniques developed through this study also are applied to the Ochoco NF to compare the maching learning approach to a traditionally- constructed map, currently in use in that forest.

! A challenge arises in developing a consistent, efficient, objective and accessible method of creating LTA maps representing the ecosystem processes unique to each 7 forest. Traditionally, maps are developed through the documentation of field observations. Although these experiential maps may be very useful to individual forest managers, they can be difficult to for subsequent forest managers to use. New additions to management teams and people from other agencies may not have the frame of reference to interpret the landscape in the same manner as people who have worked there for many years. It is difficult for successors to update, change or recreate maps based on another person’s subjective perception. This difficulty is compounded if the original map is not digitally encoded, as is the condition of the existing Ochoco NF

Ecoregion V map.

! The goal of this study is to develop and test a method of producing LTA maps using Decision Tree Analysis (DTA) with existing data sets. The method is intended to be efﬁcient, objective, consistent and accessible to people from many backgrounds and levels of experience. The purpose of this method is to create a reference map as a foundation for future generations to update and change as new data and landscape models become available.

! In addition to mapping the changes in ecosystem function across the landscape, this study considers factors affecting DTA accuracy and compares this method of LTA development to one developed for the Ochoco NF through a conventional method. For this study, it is assumed that all LTA map makers are working within a certain set of rules, and therefore all LTA maps should provide the same information on a similar spatial scale. ! 8

! This thesis project tests the hypothesis (H1): "At 1:100,000 scale, ecoregion map units are best deﬁned by the environmental co-variates geology, vegetation, and landform groups."Hypothesis H1A: Ecoregion map units are predictable based on geology, vegetation and landform group." The null hypothesis (H0) is stated as: At

1:100,000 scale, ecoregion map units have no predictable relation to these environmental variables.

1.4 Study Areas

! Three study areas are included in this thesis project: SE Fremont NF, Deschutes

NF and Ochoco NF. These three areas correspond to four Level III Ecoregions: Eastern

Cascades Slope and Foothills, Cascades, High Lava Plateaus and Blue Mountains. The

Level III Ecoregions are further divided into Level IV Ecoregions (Figure 1.4). The study begins with the Warner Mountain region of the Fremont NF (circa 150,000 ac/

600 km2). The majority of the Fremont NF was previously mapped by Malone (2008).

The second study area is the Deschutes NF (circa 2,000,000 ac/8,000 km2), which has no previous LTA mapping. The third study area, the Ochoco NF (circa 1,200,000ac/

5,000 km2), has a Level V ecoregion map developed by forest managers. The Warner

Mountain region is used for developing and understanding the LTA mapping process, and the Deschutes and Ochoco NF’s are mapped to test and improve the machine learning methodology. 9

Figure 1.4. EPA Level IV Ecological Units, Thor D. Thorson (NRCS), Sandra A. Bryce (Dynamac Corporation), Duane A. Lammers (USFS), Alan J. Woods (Dynamac Corporation), James M. Omernik (USEPA, retired), Jimmy Kagan (Oregon Natural Heritage Program), David E. Pater (Water Quality Program, Washington Department of Ecology), and Jeffrey A. Comstock (Indus Corporation).

2.0 Methods

! Methods for developing LTA maps in Oregon have evolved from the subjective process or “stained glass" approach to the more objective one using machine learning

(Malone, 2008). A topographic map and hillshade of the 10 m DEM of the study areas were used to delineate major landform groups with on-the-ﬂy digitization. Landform group delineations were based on similarities in slope, elevation, drainage density, relief, and the frequency of smaller, local landforms such as landslides, cinder cones or

ﬂuvial-lacustrine plains.

! All data sets used in prediction (Table 2) were rasterized with a 60 m resolution for use in DTA. Rasterizing and resampling vector data in preparation for DTA ensures 10 that all data sets have identical spatial extents and matching projections (Slevin and

Noller, 2008).

Table 2: Datasets use in DTA

Independent Variable Dataset Scale

Digital Elevation Model USGS 10 m DEM1 Geology USGS OrGEO2 1:500,000 Vegetation Histveg3 GAPveg4 Ochoco PNV5 Soils Fremont/Winema SRI6 1:64,000 Deschutes SRI7 1:64,000 Ochoco SRI Plus5 1:64,000 Ochoco PAG soils5 S. Lake Co SURGO8 1:24,000 Landforms Warner Landform Groups9 1:100,000 Deschutes Landform Groups9 1:100,000 Ochoco Landform Groups9 1:100,000 1 USGS-EROS Data Center: \\DAS-00919\E$\cd_sw\dem10sw 2 3

4 5 Courtesy of Jim David, Ochoco NF ! 6 Courtesy of ______, Fremont NF

7Courtesy of Peter Sussman, Deschutes NF ! 8 http://soildatamart.nrcs.usda.gov/ ! 9 This study

An iterative process of DTA (Table 2a)(documented by Wendy Peterman, 2010), using

ERDAS Imagine’s NLCD sampling tool (Leica Geosystems, 2008) with SEE5

(Quinlan, 2007), was performed twice for the Fremont NF (Warner), ﬁve times for the

Deschutes NF, and twice for the Ochoco NF. DTA successively organizes data sets into 11 increasingly homogenous subsets, producing both a decision tree and a measure of goodness-of -ﬁt based on the reduction in variance (McKenzie and Ryan, 1999). The

NLCD tool sampled 10% of the training data and 5%

Table 2a. Flow chart of LTA prediction process

Independent variables:

Geology Vegetation Soils Digital Elevation Model

GIS DTA Predicted LTA Map

Dependent variable:

Landform data

of the samples for testing the predicted model. Most of the available information in a digital soil map can be represented using relatively few samples, and improvements in accuracy are non-linear with sampling rates greater than 10% (Moran and Bui, 2002).

DTA models were produced with iterative pruning of 25% to increase efficiency and avoid overfitting (Moran and Bui, 2002,Mitterschmidt and Zinck, 2003, Elnaggar and

Noller, 2007). To improve accuracy, the boosting function in See5 was performed ten times for each map. Boosting results in a sequence of decision trees, each removing the misclassification errors from the previous tree, and thereby reducing errors and increasing resistance to random noise in the input data (Quinlan,"1993,

Moran"and"Bui,"2002). After DTA, Arcview majority ﬁlter tool was used to smooth 12 output data by replacing cell values in the raster with the most frequent value of neighboring cells (Hooge, 2008). Others have found that the majority ﬁlter tool decreased error between 2-10% (Elnaggar, 2007, Malone, 2008). Once delineated, the

LTA map units were inspected for their identifying attributes such as slope range, geology, vegetation, representative soils and drainage density. Though some may share a number of characteristics, such as similar geology or vegetation, each one has a combination of environmental variables indicating a unique ecosystem.

! All tables and maps for the Fremont and Deschutes Forests were ground-truthed based on a stratiﬁed random sample of 45 points in the Warner Mountains (1 point per every 13 km2) and 160 points (1 point per every 50 km2) in the Deschutes NF (Figure

2), and no points in the Ochoco NF. For the Ochoco NF, the existing map is, for the purposes of this study, considered to be accurate to the best practice of the USFS (Jay

Noller, personal communication). In the Deschutes, veriﬁcation of vegetation, substrate and landform was performed for 160 randomly generated points within a 150 ft buffer of the major roads through the forest. All selected points were visited, except for"four points that were inaccessible due to snow covering the road."Once each LTA unit is predicted and ground-truthed, the data regarding representative geology, vegetation, soils and drainage density are entered into the LTA attribute tables (Appendices A_1-

A_8). The labels for the LTA’s are created based on the Level III ecoregion and the major Level IV ecoregion represented in the unit. For example, LTA 9.9e.03 is the third map unit delineated in the “Pumice Plateau Forest” (9e) of the “Eastern Cascade Slopes and Foothills” (9). 13

Figure 2. Ground-truth points in the Deschutes NF. %"&"''$'$%(%$)*+'",,(-(+".(/0*"0"'1,(,*2(.3(0*.3$*4567*%&/#&"8*9:$0,$0;*<==>?*2",*@,$)*./* +/8%"&$* .3$* 8/)(-($)* #&/@0)[email protected]* (8"#$* /-* $"+3* B&$8/0.* -/&$,.* C/0$* ./* (.,* 8".+3(0#* %&$)(+.$)* (8"#$D* * !"&"''$'$%(%$)* +'",,(-(+".(/ 0* @,$,* "* ,(8%'$* )$+(,(/0* &@'$* ./* +'",,(-1* 14 8@'.(,%$+.&"'*)"." 9E(+3"&),;*<===?D**F'.3/@#3*.3$*.&"(0(0#*"0)*%&$)(+.$)*8"%,*.3$8,$'G$,*)/* 0/.*+/0."(0*8@'.(,%$+.&"'*)".";*$"+3*'"1$&*(,*.&$".$)*! Error Matrices comparing reference maps ",*(-*(.*2$&$*"*,%$+.&"'*,(#0".@&$D**70*/&)$&*with predicted maps are generated in ./* )$.$&8(0$* .3$* )(8$0,(/0,* /-* $"+3* %"&"''$'$%(%$)* +'",,(-(+".(/0* (0* .3$* (8"#$* )".";* "* ,."0)"&)A)$G(".(/0*Idrisi Taiga (Clark .3&$,3/')* Labs, 2009) -&/8* for .3$* all 8$"0*DTA trials, /-* $"+3* and ,$'$+.$)*data in these +'",,* matrices 2",* +"'+@'".$)* are used H1*to .3$* +'",,(-(+".(/0D*7-*"*#(G$0*%(I$'*G"'@$*9&$%&$,$0.(0#*"*,/('*+'",,?*(,*H$'/2*.3$*3(#3*.3&$,3/')*"0)* "H/G$*.3$*'/2*.3&$,3/')*-/&*"''*/-*.3$*H"0),*H$(calculate the Error of Omission, Error of Commission,0#*+'",,(-($)*9(0*.3(,*+",$*.3$*.2/*,/('*'"1$&,?;* Overall Error and Kappa (.*2",*",,(#0$)*./*.3".*+'",,D**F&$",*&$8"(0(0#*/@.,()$*/-*"01*/-*.3$*%"&"''$'$%(%$)*+'",,$,* 2$&$*)$,(#0".$)*",*@0+'",,(-($)D*Coefficient. This is a new software application, and its use in DTA analysis is thus far F-.$&* .3$* #$0$&"'* %"&"''$'$%(%$)* +'",,(-(+".(/0* 2",* %$&-/&8$);* H/.3* ,(8%'$A)$,+&(%.(G$* "0)*)(,+&$.$A8@'.(G"&(".$*,.".(,.(+,*9:$0,$0;*<==>unprecedented. Error of Omission describes the) 2$&$*@,$)*./*$G"'@".$*.3$*8/)(-($)*#&/@0)Aprobability that the reference sample is [email protected]*(8"#$*"0)*.3$*%&$)(+.$)*(8"#$*/-*$"+3*C/0$D**JG$&"''*"++@&"+1;*%&/)@+$&K,*"++@&"+1;* "0)*@,$&K,*"++@&"+1*"''*-"''*(0./*.3$*+".$#/&1*/-*correctly classified. Error of Comission describes)$,+&(%.(G$*,.".(,.(+,D**L3$*/G$&"''*"++@&"+1*(,* the probability that cells in the +"'+@'".$)*H1*)(G()(0#*.3$*./."'*0@8H$&*/-*+/&&$+.'1*%&$)(+.$)*%(I$',*H1*.3$*./."'*0@8H$&*/-* %(I$',*(0*.3$*+/0-@,(/0*8".&(ID**!&/)@+$&K,*"++@predicted map are allocated to the correct classes.&"+1*(,*,(8('"&*./*/G$&"''*"++@&"+1;*$I+$%.*.3".* The Overall Error, compares the (.*(,*+"'+@'".$)*H1*)(G()(0#*.3$*./."'*0@8H$&*/-*+/&&$+.'1*+'",,(-($)*%(I$',*-/&*/0$*+'",,*H1*"''* /-*.3$*%(I$',*.3".*2$&$*%&$)(+.$)*-/&*.3".*+'total number of correct cells with the total ",,D**!&/)@+$&K,*"++@&"+1*number of pixels analyzed.(,*"*&$-'$+.(/0*/-*3/2* The Kappa "++@&".$*"*+'",,*(,*(0*+/8%"&(,/0*./*.3$*,"8$*+'",,*(0*.3$*#&/@0)[email protected]*(8"#$D**M,$&*"++@&"+1* (,*+"'+@'".$)*H1*)(G()(0#*.3$*./."'*0@8H$&*/-*+/&&$+.'1*%&$)(+.$)*%(I$',*(0*"*+'",,*H1*.3$*./."'*Coefficient (K) is a value from zero to one showing the level of agreement between the 0@8H$&*/-*%(I$',*(0*.3".*+'",,*.3".*2$&$*%&$)(+.$)*(0*"''*+'",,$,D**M,$&*"++@&"+1*&$-'$+.,*.3$* %&/#&"8,*"H('(.1*./*%&$)(+.*-/&*"*+$training data and the predicted data&."(0*+'",,*@,(0#* (Congalton, 1991).(.,*"'#/&(.38,D** It is derived from the formula L3$*)(,+&$.$*8@'.(G"&(".$*8$.3/)*@,$)*./*N@"0.(-1*.3$*"#&$$8$0.*H$.2$$0*.3$*8/)(-($)* #&/@0)[email protected]*(Cohen, 1960): ,/('* @0(.,* "0)* .3$* %&$)(+.$)* ,/('* @0(.,* 2",* .3$* O"%%"* "0"'1,(,* 9P/3$0;* <=>Q?D** L3$*$N@".(/0*-/&*.3$*O"%%"*+/$--(+($0.*(K);*",*@,$)*H1*4'0"##"&*"0)*5/''$&;*RQQS;*(,T** r r N !!xii $ 9xi# V x#i ? KU i""l i l * r R N $ !9xi# V x#i ? i"l , where K = agreement between reference data and predicted data, N = total number of 70* .3(,* $N@".(/0;* N* (,* $N@"'* ./* .3$* ./."'* 0@8H$&* /-*%(I$',* (0* .3$* +/0-@,(/0* 8".&(I;* r* (,* .3$* 0@8H$&*/-*&/2,*(0*.3$*8".&(I;*xii "&$*.3$*)("#/0"'*$0.&($,*/-*.3$*$&&/&*8".&(I;**"0)*xi+*"0)*x+i* pixels in matrix, r = total number of rows in matrix , x+i = sum of rows (l) in matrix , xi+ (0)(+".$*.3$*,@8*/-*&/2*l*"0)*.3$*,@8*/-*+/'@80 l*/-*.3$*+/0-@,(/0*8".&(I;*&$,%$+.(G$'1D**L3$* K G"'@$*(,*&$%&$,$0.".(G$*/-*.3$*"#&$$8$0.*/&*"++@&"+1*H$.2$$0*.3$*#&/@0)[email protected]*(8"#$*@,$)* = sum of columns (l) in matrix. "0)* .3$* %&$)(+.$)* (8"#$D* * L3(,* G"'@$* G"&($,* -&/8* C$&/* ./* /0$;* 23$&$* C$&/* (0)(+".$,* 0/* "#&$$8$0.*"0)*/0$*(0)(+".$,*./."'*"#&$$8$0.*9P/0#"'./0;*<==

! Once the Deschutes NF LTA map was completed, the same machine-learning process used in the Deschutes NF was used to develop an LTA map for the Ochoco NF.

DTA was performed using the Ochoco NF Level V Ecoregion map as the reference map, and the input data sets used in this study’s map were used as the independent variables. Idrisi Taiga was used to produce error matrices for both maps, comparing the original and predicted maps. The ﬁnal product of the Ochoco LTA map was compared with the Level V Ecoregion map currently in use for that forest, using spatial statistics

(Errors of Omission and Commission, Kappa, Average Nearest Neighbor, Overall Area and number of polygons).

! Average Nearest Neighbor and area statistics were generated using Spatial

Statistics Tools in ArcMap. Predictive mapping uses Average Nearest Neighbor (ANN), a Computational Geometry algorithm to compute distances between features and locate clusters of similar environmental variables."Computational Geometry, in computer science, is the study of algorithms that can be stated in terms of geometry, and it is used in GIS to produce mathematical visualizations of data as geometric locations (de Berg et al., 2008). In machine learning, ANN is used to calculate planar subdivisions

(polygons) from the intersections of features on overlayed maps. ArcGIS uses the

Euclidean distance between points to establish clusters of similar attributes, and subsequently calculates the area of the minimum enclosing polygon encompassing 16 these points. ArcGIS calculates the ANN distance based on the centroids of these polygons, making this measurement area-sensitive"(ArcGIS 9.3, 2009). For this reason, it is appropriate to use ANN as a statistical measure of diversity in landscape units!

! Spatial statistics such as ANN measure how the patterns between the two mapping styles differ. ArcGIS calculates the mean distances between neighbors to determine whether the arrangement of the polygons seems to be clustered, dispersed or random. Clusters of polygons would suggest that these map units had similar attributes and might be members of the same LTA. On the contrary, dispersion indicates competition, inferring that the polygons have distinct attributes that don’t co-occur spatially. A random arrangement is without pattern, meaning that the computer can’t distinguish the spatial conﬁguration of map units from a randomly generated one.

! Correspondence analysis for the Ochcoco NF LTA map from this study and the

Ecoregion V map for that forest was performed in ArcGIS. 500 meter buffers were created around all polygon edges in the geology, vegetation and landform maps as well as the two reference maps. The 500 m buffer was chosen to allow for differences in scale between the data sets used in prediction. The reference buffers were intersected with each environmental covariate buffer, and the area of this overlap was calculated and compared to the area of the reference map buffer. The result is a decimal representing the proportion of each data set that corresponds with the total area. 17

3.0 Results

3.1 Fremont National Forest/Warner Mountains

! The Warner Mountains, located at the northwest edge of the Great Basin epitomize the basin and fault block topography of this region. West of the small range lies the Goose Lake Valley, bordered abruptly to the east by the imposing face of Abers

Rim. Throughout the region, numerous escarpments, landslides and ancient volcanoes are interspersed with lava plateaus and ﬂuvial-lacustrine plains. Due to these rapid changes in geomorphology, geology and environmental conditions over small areas, the number of LTA map units in the Warner Range is signiﬁcantly higher than the rest of the Fremont NF. These factors also contribute to a wide range of soil conditions.

! This region is characterized by Ponderosa pine on basalt plateaus and tablelands, with incised fluvial-lacustrine plains to the east and mixed vegetation on tuffaceous mountains and landslides to the west."The tablelands are formed of basalt, and the northernmost plateau is overlain by an additional olivine basalt flow, differentiating a high lava plains landform group (Figure 3.1a) from the lower lava plains."Uplift along the western forest boundary (Figure 3.1b), exposes bedrock via landslide erosion. Along faults, local uplift and down-wasting has resulted in the infilling of low-lying topographic regions by clastic and volcaniclastic materials. In landform groups such as the pyroclastic uplands (Figure 3.1c), locations of tectonic effects are continually changing with respect to volcanic centers, resulting in a complex of bedrock formations (Jay Noller, personal communication). Adjacent to these 18 uplands, a fluvial-lacustrine plain of pyroclastic sediments supports both wet and dry meadow vegetation (Figure 3.1d).

Figure 3.1a. Geology of the Warner Mountain portion of the Fremont NF http://pubs.usgs.gov/of/2005/1305/ 19

Figure 3.1b. Faults of the Warner Mountain portion of the Fremont NF overlaid on landform groups from this study 20 Figure 3.1c. Landform Groups of the Warner Mountain portion of the Fremont NF from this study

Figure 3.1d. Vegetation of the Warner Mountain portion of the Fremont NF

! 22

! Several of the LTA units include landform groups with very steep slopes. A basin rim (15-100% slopes) with basalt ﬂows and tuffaceous and maﬁc vent rocks is included in LTA 9.9h.01. Soils are coarse, moderately deep Mollisols with a xeric moisture regime, and PNV is a variety of sage species. LTA 9.9h.02 is a landslide complex of pyroclastic rocks, with 15-100% slopes. PNV is Ponderosa Pines and sage species.

Soils are Mollisols and rock outcrop complexes. Slopes are 0-80% in LTA 9.9h.06, which contains mountains with fault escarpments. Alpine Mountains (0 - 90% slope) typify LTA 9.9h.08, where sage, pines and Western Juniper are the major vegetation types. The main soils are Andisols and humic Inceptisols. LTA 9.9h.14 includes pyroclastic mountains (0 - 80% slopes) with cover of sages and conifers. Soils are dominantly rock outcrop complexes and Andisols.

! Hilly or mountainous areas with slopes between 0 and 45% are labeled

"Uplands," "Hill slopes" or "Mountains." Uplands and landslides composed of sedimentary rocks, tuff, basalt ﬂows, and maﬁc rocks are included in LTA 9.9h.09.

Vegetation types in this unit are pines, ﬁrs and other conifers, sages and riparian hardwoods. Soils are deep Mollisols with either andic or vertic properties. LTA 9.9h.10 consists of pyroclastic uplands, covered by Mollisols and supporting various conifers and riparian hardwoods. Pyroclastic hill slopes with 0-15% slopes, pines and sages deﬁne LTA 9.9h.15. Soils are moderately deep Mollisols with vertic properties. LTA

9.9h.12. is comprised of tuffaceous and maﬁc hill slope scab lands. Major vegetation types include various patches of conifers, sages and riparian hardwoods. Soils are very deep Mollisols with andic properties. Mountains characterized by ash deposits over 23 pycroclasitc rocks and basalt ﬂows comprise LTA 9.9h.16. Firs and pines are the major vegetation. Soils are very deep Andisols in a Cryic temperature regime. !

! Three LTA’s encompass landforms of plateaus and tablelands. These are distinguished from each other by geology, soils, and vegetation types. LTA 9.9h.03 contains high lava plateaus and fluvial-lacustrine plains of olivine and Columbia River basalts with 0-15% slopes. Soils are Mollisols, some of which contain vertic intergrades. PNV is Ponderosa Pine and sages. Low plateaus and basalt flows with slopes from 0-15%, overlain by tuffaceous and fluvial sedimentary rocks are included in LTA 9.9h.07. Typical vegetation types are conifers, sages, and riparian hardwoods, and soils are Mollisols and Andisols. LTA 80.80g.01 encloses a wide basalt plateau with 0 - 15% slopes. Soils are somewhat shallow to deep Mollisols. PNV includes sages, pines and Western Juniper. !

! Fluvial and lacustrine processes are the main geomorphological drivers in four of the LTA units. LTA 9.9h.04 contains volcaniclastic sediments carried in valley trains with slopes from 0-20%. Soils are deep Mollisols formed in alluvium. PNV is riparian hardwoods, Ponderosa Pine and Wyoming Big Sage. LTA 9.9h.05 contains ﬂuvial- lacustrine plains with 0-5% slopes. Soils are shallow to moderately deep Mollisols formed from lava. LTA 9.9h.11 delineates an expansive prairie (0 - 5% slopes) formed on volcanic, pyroclastic and ﬂuvial rocks with both wet and dry meadow vegetation.

Soils are very deep, poorly drained Mollisols. LTA 9.9h.13 in an aggregate map unit where small prairies were delineated. Slopes in this unit are 0 - 5%, vegetation is 24 typically Wet Meadow, and soils are very deep, poorly drained Mollisols formed in alluvium.

3.2 Deschutes National Forest!

! At the center of the Cascade geologic province is a string of Pliocene and

Pleistocene volcanoes, dramatically altered by glaciers. Some peaks within the

Deschutes NF, such as the Three Sisters, are still glaciated. Down-wasting of constructional landforms within a tectonic graben led to a corresponding array of glacial and paraglacial landform groups with late Quaternary fluvial and post-glacial volcanic activity (Jay Noller, personal communication). Eruptions during the most recent period of volcanism deposited ejecta ranging in size from large lava bombs to pumiceous gravel and cinders to fine ash. Some areas of the Deschutes NF are completely covered with lava, whereas others are influenced to varying degrees by

Sand Mountain ash, Blue Mountain cinders and Mazama pumice. !

! Elevations in the Deschutes NF range from approximately 1900 ft (580m) in the northern ﬂuvial valleys to 10,500 ft (3,200 m) at the peak of the South Sister in the

Cascades, and these elevations are distributed differently within the two Level III ecoregions. In Ecoregion 4, the lowest elevation (2900 ft) is located at the eastern edges of glacial moraines (Table 3.2a), and the highest elevation (10,500 ft) is found on the peak of the South Sister volcano. Except for the Glacial Lakes LTA, all landform groups in this Ecoregion have wide ranges of elevation, characteristic of steep, constructional landforms. In Ecoregion 9, elevations are more evenly distributed. The lowest point (1900 ft) is in the ﬂuvial valleys, and the highest point (9000 ft) is at the 25 top of a cinder cone (Table 3.2b). This is an extreme for that landform group, since

most of the cinder cones within the Deschutes NF are closer to 6,000 ft in elevation.

Ecoregion 4 Table 3.2a Ecoregion 4 elevations, and 3.2b Ecoregion 9 elevationsEcoregion 9 Highest point: 9,000ft (2,750 m) HIghest Point: 10,500 ft Lowest point: 1,900 ft (3,200 m) (580 m) 10000 Lowest Point: 2900 ft 9000 9000 (900 m) 8000 ft 8000 7000 7000 6000 5000 6000 GL MTB 4000 GW 5000 LB TD CC VC 3000 4000 PR MT 2000 3000 FP GM FV

! map unit

! The geology, landform groups and vegetation maps exhibit distinct west-east

trends. At the western boundary, landform groups and geology represent the volcanic

uplift and glacial sculpting typical of the High Cascades (Figures 3.2c and 3.2d).

Mountains of basalt are girded with glacial moraines, lakes, lava ﬂows and cones of

maﬁc ejecta. At the highest elevations, peaks and ridges provide habitat for Paciﬁc Fir

and Mountain Hemlock to the North, and White Fir to the South (Figure 3.2e). At

slightly lower elevations, the western region is covered primarily with Lodgepole Pines,

and secondarily with Ponderosa Pines, interspersed with patches of Subalpine Fir

(Figure 3.2e). A half Lodgepole Pine and half Ponderosa Pine distribution follows the

geomorphology in a southerly direction as the mountains descend into the central

foothills and plains. At the lower elevations north of the foothills, Quaternary

sediments on ﬂuvial-lacustrine plains support only Ponderosa Pines. At the center of

the forest, within a glacioﬂuvial plain, lies a single patch of chaparral. The equal 26 distribution of Lodgepole and Ponderosa Pines re-emerges as the foothills and plains meet the olivine basalt plateau and the basalt/breccia shield volcanoes at the eastern edge of the forest. Crowning the shield volcano is a complex of calderas, obsidian

ﬂows and cinder cones where siliceous vent rocks, ash, and tuffaceous sediments are the major substrates, and various sage species dominate the vegetation.

Figure 3.2c. Landform groups of the Deschutes NF from this study 27 Figure 3.2d. Geology of the Deschutes NF

28 Figure 3.2e.Vegetation of the Deschutes NF

! LTA 4.4c.01 and LTA 9.9d.01 are two landform groups epitomizing early construction and erosion. LTA 4.4c.01 contains Pleistocene and Holocene mountains with 0 - 55% slopes. The geology is primarily basalt and andesite with some glacial deposits on mountain peaks. Soils are Entisols and Spodosols. LTA 9.9d.01 is a

Miocene/Pliocene olivine Basalt plateau, which terminates abruptly at a fault scarp, creating a rim. There is a small delineation of LTA 9.9d.01 within the adjacent LTA

9.9f.02, where a lava plateau rises out of the plain. Slopes range from 0% on the plateau to 55% at the rim. Soils are moderately deep Inceptisols with andic properties. 29

! Three LTA’s are dominated by Pleistocene glacial landforms. LTA 4.4c.03 includes glacial lakes underlain by basalt and glacial deposits. Soils are moderately deep Inceptisols with andic properties and deep Inceptisols with high levels of organic carbon. PNV is Douglas Fir. LTA 4.4c.04 contains lateral and terminal glacial moraines with 0 - 55% slopes underlain with basalt and andesite. Soils are deep, medium ﬁne

Entisols and Spodosols. LTA 9.9d.03 is a glacial meltwater landscape with glacioﬂuvial deposits overlying basalt, rhyolite and tuff. Slopes range from 0 to 15%, and soils are deep, medium ﬁne Entisols and moderately deep Inceptisols with andic intergrades.

! Three LTA’s are dominated by ﬂuvial processes. LTA 9.9d.02 consists of

Miocene/Pliocene ﬂuvial valleys with slopes ranging from 0-30%. Soils are formed in tuffaceous sedimentary rocks, tuff, ash, and alluvium. These soils are somewhat shallow to moderately deep, medium ﬁne Inceptisols and Mollisols with andic properties and a xeric moisture regime. LTA 9.9f.01 consists of Pleistocene/Holocene

Pluvial lakes. The bedrock is basalt, overlain by lacustrine and ﬂuvial deposits. Soils are deep, medium ﬁne Entisols and deep, poorly drained Andisols formed in alluvium.

LTA 9.9f.02 is a long, ﬂuvial-lacustrine plain with wet meadows and bottomlands formed on tuff and a variety of sedimentary rocks. Slopes are from 0 to 15%, and soils are coarse Entisols and Andisols formed in alluvium.

! Four LTA’s contain landforms formed on or covered by volcanic ejecta.

Mazama pumice deposits overlie Holocene rhyolite/dacite, rocky peaks and elevated canyons in LTA 4.4c.02. Slopes range from 0 - 40%, and soils are deep, medium ﬁne

Entisols and Spodosols. LTA 9.9e.02 is a basalt dome with tuff rings and cones 30 overlain with silicic vent rocks. Soils are deep Entisols, derived from basalt colluvium and residuum and volcanic ejecta. In addition to Ponderosa Pine, this landform supports Wyoming Big Sage. LTA 9.9e.04 is a Holocene basalt and andesitic lava ﬂow, most of which is exposed, although some is currently forested. Soils are deep, medium

ﬁne Entisols and Spodosols. Basalt and andesite rocky mountain peaks, cinder cones and steep buttes overlain with maﬁc vent deposits and breccias characterize LTA 9.9e.

05. Soils are deep, coarse Entisols and Andisols with a xeric moisture regime. !

! The expansive Newberry Shield volcano is divided into two LTA units. LTA

9.9e.01 encompasses the majority of the volcano. Slopes are 0-20% on the side of the volcano, and bedrock geology is composed of thin skins of breccias on olivine basalts.

Soils are deep, ashy Entisols and shallow Inceptisols with signiﬁcant amounts of volcanic glass and a xeric moisture regime. Near the center the shield volcano, LTA

4.4c.02 delineates the Newberry Volcanic Complex. Lake Paulina, East Lake and

Paulina Peak are included in this LTA. Landforms consist of craters, lakes, lava flows, obsidian flows and mountains with slopes ranging from 0 to 60%. The underlying bedrock is composed of rhyolite and tuff, whereas surficial geology consists of tuffaceous sedimentary rocks, tuffs, rhyolite and pumice. Soils are deep Entisols and

Spodosols.

3.2.1 Error Analysis

Significant reductions in Overall Error between Trials 2 and 3 and increases between Trials 3 and 4 indicate that Trial 3 represents the optimal prediction for this forest using the rules and data sets from this study. Between the first prediction in 31

September and the second prediction in October, there is a 16.6% decrease in Overall

Error (Table 3.2.1). Overall Error decreases 3.3% on the third prediction, and then it increases 5.4% and 0.5% for the January 7th and 21st predictions respectively.

Table 3.2.1 Kappa and Overall Error for 5 Deschutes Trials

1.00 Kappa Overall Error

0.75

0.50

0.25

0 4-Sep 7-Oct 21-Dec 7-Jan 21-Jan

For the original prediction, the mean Error of Omission is high relative to subsequent trials (25.3%), and the mean Error of Commission is twice or three times those of later predictions (Table 3.2.1a). This indicates a good starting point for the model with plenty of room for DTA improvements. The second trial was based on a reference map that incorporated the machine learning from the first prediction. This resulted in a 15.4% reduction in the mean Error of Commission, and an 8.4% decrease in the mean Error of Omission (Table 3.2.1a). Incorporation of the second prediction into a third model produced a 2.9% reduction in the mean Errors of Omission, and the mean Errors of Commission decreased 2.7%. The number of correctly allocated pixels increased in half of the classes, but decreased in the other half. Although the overall effect was a reduction in Errors of Commission, there was increased confusion between some classes (Appendix A_9c). The net effect of this is a 3.3% reduction in Overall 32

Error and a 0.04 increase in Kappa. Refinement of all delineations based on visual inspection led to a decreasing trend in correctly-allocated pixels between Trials 3, 4 and

5 (Table 3.2.1a), increasing Overall Errors by 5.4% and 0.5% and decreasing Kappa by

0.06 and 0.01, respectively.

Table 3.2.1a. Deschutes Error Analysis Class Trial 1 2 3 4 5 1 4015839 6021290 6326195 3913778 3861070 2 1551343 865922 790678 767397 760407 3 613766 147495 101012 116797 108972 4 108164 6100455 6240103 6087597 6050707 5 817152 145098 141704 141496 137110 6 704244 668429 696478 683266 671877 7 3892329 695748 820525 769345 782325 8 680255 727022 718090 731653 735987 9 144742 1644393 1726285 1668153 1653143 10 3266648 83103 82223 85069 83896 11 710543 236865 218516 220790 215194 12 972542 4071971 4324365 4067829 4075788 13 n/a n/a n/a 1370381 1366348 14 n/a n/a n/a 303205 298783 Pixels correctly allocated 17477567 21407791 22186174 20926756 20801607 Total pixels used 23400572 23457980 23456112 23454722 23457984 Overall Accuracy 74.70% 91.30% 94.60% 89.20% 88.68% Overall Error 25.30% 8.70% 5.40% 10.80% 11.30% Range Errors Omission 6.1% - 59.7%4.7% - 27.4%3.6% - 22.6%3.6% - 39.2%4.9% - 43.4% Mean Errors Omission 20.30% 11.90% 9.00% 14.30% 15.40% Median Errors Omission 14.50% 9.70% 7.60% 12.00% 13.10% Range Errors Commision 15.1% - 45.3% 3.4% - 18.2%2.4% - 16.8%2.5% - 19.9%3.1% - 16.0% Mean Errors Commission 25.40% 10.00% 7.30% 11.10% 11.20% Median Errors Commission 20% 10.10% 6.40% 10.30% 10.00% Kappa 0.71 0.89 0.93 0.87 0.86

3.2.2 Ground-truthing

! Ground-truthing of the Deschutes NF LTA map resulted in low error in predicted vegetation, landforms and substrates, with the lowest error in substrates. "

Vegetation is in agreement with the LTA map for 145 of the 154 points validated (5.8% 33 error)."The vegetation predicted for the public lands is in agreement, however, on the private lands, there are some places where the vegetation has changed due to conversion from forest to pasture."46 out of 154 of the points showed agreement for landform group (5.2% error), and the"substrates are all as predicted (0% error). The major error in landform groups is in LTA unit 9.9d.03, where the prediction indicates glacioﬂuvial plains and canyons. No canyons were conﬁrmed by the data points visited, however, their presence is evident on the 10m hillshade.

3.3 Ochoco National Forest

! Formed through collisions of island blocks and tectonic plates, covered by

Columbia River lava ﬂows and lahars of the John Day and Clarno Formations, the

Ochoco NF is characterized by linear strips of exotic terranes with a southwestern to northwestern orientation. The political boundaries of the forest outline a horizontal cross-section of these vertically-oriented landforms, creating a west-to-east array of lava plateaus, incised lahars (mudﬂows) and varied mountain topography. The north- to-south orientation of the major landform groups across the west to east orientation of forest boundaries results in three distinct differences in landform groups and geology

(Figures 3.3a and 3.3b). The western portion of the forest is the most diverse in landform groups, geology and vegetation. Pine-dominated, olivine basalt plateaus erode into shrub-dominated, sedimentary plains of John Day Formation lahars. River valleys dissect the semi-arid region. Near the center of the forest, landform groups change abruptly to the highly dissected terrain of the andesitic Blue Mountains where

Ponderosa Pines dominate. Still part of the same landform group and andesitic geology, 34 the Maury Mountains to the South support Mountain Big Sage (Figure 3.3c). The eastern edge of this mountainous landform group is marked by a transition zone of landslides and incision, where the underlying John Day Formation is locally exposed. A wide, wet meadow prairie distinguishes a major area of erosion between the pyroclastic mountains to the west and the high-elevation scablands to the east. Less dissected than the western region, these scab lands are characterized by andesites and breccias of the Columbia River Flow group, mixed with other types of volcanic ejecta.

To the North, the scablands weather to plateaus with rims and landslides. To the south, they slope gradually to lower elevations. These southern-aspect hills lopes support

Western Juniper, a distinct difference in vegetation from most of the surrounding pine

Figure 3.3a. Landform groups of the Ochoco NF from this study 35

Figure 3.3b Geology of the Ochoco NF http://pubs.usgs.gov/of/2005/1305/

Figure 3.3c. Vegetation of the Ochoco NF 36

! The western edge of the Ochoco NF adjoins the northeastern portion of the

Deschutes NF, so LTA’s 9.9d.03 and 11.11n.03 reﬂect landforms also found in the

Deschutes NF. LTA 11.11n.03 is a continuation of the olivine basalt plateau from the

Deschutes NF (LTA 9.9d.01). Slopes are 0-5%, and soils are derived from basalt.

Ponderosa Pine, Bitterbrush and Western Juniper are the major vegetation types. LTA

9.9d.03 contains highly eroded cinder cones and buttes with 0-40% slopes, overlain with maﬁc vent rocks. The PNV is Ponderosa Pine. !

! Fluvial-lacustrine processes formed three of the major Ochoco NF landform groups. LTA 11.11n.01 contains ﬂuvial valleys of tuffaceous sedimentary mudstone with 0-25% slopes. Soils are derived from colluvium, residuum and alluvium.

Bitterbrush is the predominant vegetation. Plains with slopes ranging from 0-15% comprise LTA 11.11n.02. Soils are derived from sedimentary rocks. Sages are the main vegetation, and Western Juniper is also signiﬁcant. LTA 11.11o.01 contains an extensive Miocene/Pliocene prairie on sedimentary mudstone, where the main vegetation type is Wet Meadow. !

! Three LTA’s are characterized by similar geology and soils, but exhibit differences in vegetation and landform. The bedrock geology is Columbia River basalts, andesites and breccias, and soils are moderately deep to very deep soils derived from basalt colluvium, residuum, ash and loess. LTA 11.11b.01 contains Oligocene/

Eocene pyroclastic mountains in the Blue and Maury Mountains. The major vegetation is Ponderosa Pine and Mountain Big Sage. High-elevation scablands with

0-50% slopes deﬁne 11.11b.02. The main vegetation is Mountain Big Sage and 37

Western Juniper. LTA 11.11a.01 delineates south-facing hillslopes with 0-50% slopes and Western Juniper vegetation. LTA 11.11l.01 is comprised of Miocene plateaus with escarpments and landslides, and the PNV is Ponderosa and Grand Fir.

3.3.1 Spatial Analysis of Ochoco NF Maps

High overall confusion in the Ecoregion V map leads to a greater range in both

Errors of Omission and Errors of Commission than the LTA map from this study. For example, the Errors of Commission for Class 1 are high (64.3%) (Appendix A_10b) due to the misallocation of 5,947 pixels to Class 1. The 5,892 of these pixels were confused with Class 2, which has an Error of Commission of 90.8%. Class 2 delineations on the Ecoregion V map are very small (Plate 4), and the DTA suggests that these map units are too small for the LTA scale.

The high overall confusion in the Ecoregion V map relates to low correspondence (Table 3.3.1) between this map and the input data sets. In the correspondence analysis, the highest correspondence is between the Ecoregion V map and the vegetation map. Otherwise, correspondence is low with geology and landform groups. This low correspondence raises a question about which landscape attribute is driving these delineations. For this reason, a watersheds data set is included in the

Correspondence Analysis; however the Ecoregion V map also shows low correspondence with the watersheds map.

The lack of clarity between Ecoregion V delineations is further emphasized by the Average Nearest Neighbor analysis. A Z-score of 0.26 (Table 3.3.1) shows a low probability of intentional arrangement. Based on the average distance between the 38 centroids of these map units, the pattern of arrangement is indistinguishable from a randomly generated spatial arrangement. This is due to high overlap in attributes between the map units, leading to high confusion.

DTA of the LTA map from this study results in little confusion throughout.

Errors of Commission range from 1.7% to 15.3% (Appendix A_10a). The only major error in the matrix is in the 38.0% Error of Omission for Class 8, indicating that this delineation should cover more area on the reference map. Average nearest Neighbor analysis suggests a dispersed pattern. The Z-score of 5.67 (Table 3.3.1) implies that there is a high probability of intentional placement of the map units due to high competition or distinct dissimilarities in their attributes.

Correspondence is high between this study’s LTA reference map and all data sets. The highest correspondence is with geology and landform groups. Although it wasn’t used in prediction, the watershed map also shows high correspondence with the

LTA map. This is evidence that the LTA delineations represent changes in hydrology as well as the three main environmental co-variates (geology, landform group and vegetation). 39

Table 3.3.1: Comparison between the Ochoco LTA and Ecoregion V maps. Ochoco Ochoco LTA1 Ecoregion Ochoco LTAV2 Error Assessment3 Mean Error of Commission 6.7% 40.4% Mean Error of Omission 10.1% 24.5% Overall Error 5.6% 46.3% Kappa coefﬁcient 0.92 0.47 Correspondence Analysis5 Watersheds 0.70 high 0.40 low Landform Group 0.90 high 0.30 low Geology 1.0 high 0.40 low Historic Vegetation 0.60 high 0.60 high Spatial Metrics Total # of Map Units 9 9 Total area polygons (acres) 1163914 756231 Max polygon (acres) 453349 221147 Min polygon (acres) 13226 5,451 Median polygon (acres) 59052 29538 Average Nearest Neighbor4 Pattern Dispersed Random Observed/Expected 1.99 0.96 Z-score 5.67 0.26

1 Current Study 2 Courtesy of Jim David, Ochoco NF 3 Refer to correlation matrices of Appendix A_10a & b 4 Euclidean Distance 5 Sources: Watersheds: Oregon Hydro Units 5th, Landform group: current study, Historic Vegetation: Heritage Data, Geology: USGS. 40

4.0 Discussion

! The goals in predicting the LTA maps are efficiency, increased objectivity, and minimal error. Predictive mapping for these three national forests took ten months of approximately twenty hours per week, including field work and writing tables and reports. The majority of this time was spent acquiring data sets and preparing them for the DTA process. Subjective interpretation is not eliminated, but it is significantly reduced through machine-learning with a priori data sets. Error varies between LTA map units, but overall error tends to minimize within two or three passes through the prediction process. After two or three predictions, subsequent maps show minor increases in error. This is due in part to scaling differences between data sets. For example, map units in a 1:500,000 Geology data set will not align perfectly with landforms from a 10 m DEM. Inconsistencies in scale between the environmental data and the predictive map have been linked to misclassification errors in previous predictive mapping studies (Elnaggar, 2007, Malone, 2008). Error may also correspond to imprecise traditional methods used in legacy data which do not accurately depict map unit boundaries (Burrough, 1986, Qi" and" Zhu,"2003). Malone (2008) found that increasing the precision of input data sets via field-checking and visual inspection prior to DTA, reduced error in predictive maps by as much as 10%.

! Visual inspection of hillshades and aerial photos can aid in more detailed delineation of the reference map, making the map appear more accurate to the viewer.

This map may, however, show less agreement with the input data sets and generate more confusion in DTA. It has been shown that increasing map unit complextiy by 41 increasing the edge density (perimeter-to-area ratio) increases DTA errors (Elnaggar" and" Noller," 2008b, Slevin and Noller, 2008). Increased complexity may lead to prediction errors with sampling"strategies that use"fewer data"points"from"small or"irregular"map units" (Wright"and Gallant, 2007,

Elnaggar"and"Noller," 2008b). Map units with larger"perimeter!to!area"ratios"may also contribute more sample points near transitional boundaries, thereby increasing confusion (Qi"and Zhu," 2003, Slevin and Noller, 2008).

! This study indicates that both the largest map units and the smallest map units show less accuracy than medium-sized units at the1:100,000 scale. Previous predictive mapping studies have shown an inverse relationship between map unit areal extent and prediction accuracy (Bryan,"2006;"Elnaggar" and" Noller,"2008b, Slevin and Noller,

2008). At the 1:64,000 scale and below, map units representing smaller areas are not easily discriminated from the input data (Elnaggar, 2007). Slevin and Noller (2008) found that the mean areal extent of individual map units showed stronger correlation to classiﬁcation accuracy than did the total area for each map class."This suggests that adequate coverage is required to cast enough votes for a clear map unit delineation in smaller map units, and that too much coverage by a single map unit can increase confusion by attempting to combine too many disparate attributes across the landscape.

! Although LTA maps can consistently be based on landform, geology, vegetation, and soils, an entirely similar process of LTA map development is a goal yet to be achieved. The difﬁculty lies in the high variability of data available for each national forest. Statewide data sets are accessible for geology, DEM and vegetation, but soils 42 data sets differ greatly in coverage and format. De-convolving SRI maps and decoding the accompanying tables is complicated and time-consuming, because a different approach is required for each format. This step, albeit cumbersome, should not be foregone, because details from the soils data are of vital importance to the accompanying LTA Soils and Landscape Features Tables. In this study, examination of the rules generated in DTA (Slevin, unpublished), showed that the soils data sets ranked third behind geology and elevation in splitting classes for the

Deschutes NF LTA map. DTA can also be used to predict LTA’s beyond the current forest boundaries. However, the conﬁdence level in such extrapolations decreases with distance from ground-truthed delineations (Luoto and"Hjort,"2005, 2007, Slevin and

Noller, in review).

! LTA maps produced with DTA differ from maps developed through conventional methods. It is assumed that all Ecoregion V (LTA) maps are driven by the same environmental factors; therefore, LTA maps of the same forest should show high correspondence to the input data sets. The Ochoco NF LTA map from this study corresponds highly with geology, vegetation, landform and watershed maps (not used in prediction). The Ochoco Ecoregion V map shows moderate correspondence with the vegetation map, but low correspondence with the other maps, suggesting that variables other than geology, landform, and hydrology inﬂuence the delineation of these map units. Support for this is given by the differences in Overall Error (Table 3.3.1). A prediction based on the Ochoco LTA map and ﬁve environmental co-variates yields 43

40.6% less overall error than one based on the Ochoco Ecoregion V map and the same covariates.

5.0 Conclusions

! Predictive mapping is an effective method of developing LTA maps for National

Forests. It requires minimal time, money and ﬁeld work and yields accurate ecological unit delineations at the 1:100,000 scale. The general nature of this level of map makes it easy to view the entire extent of the forest while also immediately seeing the major differences between regions. LTA maps can be quickly created through a streamlined process of predictive mapping and ﬁeld- checking.

! The LTA maps in this study were created consistently and objectively relative to traditional experiential maps, but they aren’t entirely devoid of human interpretation.

De-convolving data sets from diverse sources required expertise and skill in recognizing the common attributes and taxonomy relevant to LTA classification. Initial development of the landform groups map, used as a basis for associating environmental co-variates with landscape features, required human analysis and digitization of these features from GIS data sets. Population of the LTA tables required human evaluation of the data and personal observation of their accuracy. This made the process both efficient and objective, while allowing flexibility for future updates to both input and output data sets. !

! ! 44

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. 51

APPENDIX 52

Plate 1 53

A_1 Warner Mountain Soils Table

Surface Regolith Soil Code Subgroup Great Group Texture Texture Depth, in Parent Materials 102G Xeric Vitricryands Moderately Moderately 60+ Colluvium and Residuum from Rhyolite 12C Vertic Palexerolls CoarseMedium CoarseFine 20 - 40 Colluvium from Tuff 143F Typic Vitricryands Coarse Coarse 60+ Colluvium and Residuum from Rhyolite 161C Lithic Argixerolls Coarse Moderately Fine 10 - 20 Residuum from Basalt and Tuff 170G Moderately complex Pachic/Ultic Argixerolls Coarse Coarse 40 - 60 Residuum from Basalt and Tuff Typic Vitrixerands Medium Medium 40 - 60 Colluvium and Residuum from Pyroclastic Rocks Moderately 171G assoc. Pachic/Ultic Argixerolls Coarse Coarse 40 - 60 Residuum from Basalt and Tuff Moderately Moderately Pachic Argixerolls Coarse Coarse 20 - 40 Residuum from Lava Rocks Lithic Haploxerolls Coarse Coarse 15 Colluvium and Residuum from Volcanic Rocks

209E Typic Vitrixerands Medium Medium 40 - 60 Colluvium and Residuum from Pyroclastic Rocks 210E complex Typic Vitrixerands Medium Medium 40 - 60 Colluvium and Residuum from Pyroclastic Rocks Typic Vitrixerands Coarse Coarse 60+ Colluvium and Residuum from Rhyolite Moderately Pachic/Ultic Argixerolls Coarse Coarse 40 - 60 Residuum from Basalt and Tuff Colluvium and Residuum from Andesite, Basalt & 235E Ultic Haploxerolls Coarse Coarse 20 - 40 Tuff Moderately 244D Pachic/Ultic Argixerolls Coarse Moderately Fine 60+ Colluvium from Basalt & Tuff

24G assoc. Vertic Palexerolls Medium Fine 20 - 40 Colluvium from Tuff Lithic Haploxerolls Coarse Coarse 15 Colluvium and Residuum from Volcanic Rocks Pachic Argixerolls Moderately Moderately 20 - 40 Residuum from Lava Rocks 258E Typic Vitrixerands Coarse Coarse 60+ Colluvium and Residuum from Rhyolite 261A Alluvium from Volcanic and Vitric Pyroclastic complex Cumulic Endoaqolls Fine Moderately Fine 60+ materials Cumulic Endoaquolls Moderately Fine Moderately Fine 60+ Mixed Alluvium Moderately 268C Pachic Argixerolls Coarse Moderately Fine 40 - 60 Colluvium and Residuum from Basalt & Tuff 276C Colluvium and Residuum from Andesite, Basalt & complex Pachic/Ultic Argixerolls Medium Fine 60+ Tuff Colluvium and Residuum from Andesite, Basalt & Ultic Haploxerolls Coarse Coarse 20 - 40 Tuff 279G outcrop/ complex xerolls Coarse Coarse

281G complex Typic Vitrixerands Medium Medium 40 - 60 Colluvium and Residuum from Pyroclastic Rocks outcrop Coarse Coarse Colluvium from Quartzite, Welded Tuff, Chert, 32E Pachic Argixerolls Medium Medium 40 - 80 Argillite, Shale, Conglomerate & Rhyolite Colluvium and Residuum from Andesite, Basalt, 45E Vitrandic Argicryolls Medium Medium 60+ Tuff & Volcanic Ash Moderately Moderately 72F Xeric Humicryepts Coarse Coarse 60+ Colluvium and Residuum from Rhyolite 54

A - 2 Warner Landform Groups

Depth New to Old LTA LTA Slope Bedroc code Code Landform Map Unit ID Elevation, ft/m Bedrock Gradient k (in) Andesite, Basalt, Tuff & 15 - W21.2_13 9.9h.01 Rim BR 5,500 - 7,200 ft, Volcanic Ash 100% 40 +/- Mountains & 0 - W21.2_14 9.9h.02 Landslides LC 6,300 - 7,200 ft, Basalt, Tuff 100% 40 +/- Andesite, Basalt, Tuff & W21.2_15 9.9h.04 Fluvial Valleys RV 5,500 - 7,200 ft, Rhyolite 0 - 20% 60 + Sediments of Andesite, Basalt, Tuff, Volcanic & Fluvial_Lacustrine Pyroclastic W21.2_16 9.9h.05 Plains WMP 5,500 - 8,400 ft, Materials 0 - 5% 40 - 60 Faulted W21.2_17 9.9h.06 Mountains FM 5,500 - 7,200 ft, Basalt, & Tuff 0 - 80% 40 - 60 Andesite, Plateaus & Basalt, Tuff & W21.2_18 9.9h.07 Tablelands LP 5,500 - 7,200 ft, Rhyolite 0 - 15% 40 +/- AL Basalt & W21.2_19 9.9h.08 Mountains (6300 - 7500 ft, Ryolite 0 - 90% 60 + Mountains & UL Andesite, W21.2_20 9.9h.09 Landslides 6,300 - 7,500 ft, Basalt & Tuff 0 - 30% 40 - 60 PU Andesite, 40 - 80 W21.2_21 9.9h.10 Mountains 6,300 - 7,200 ft, Basalt & Tuff 0 - 40% in, Hillslopes & HS Andesite, W21.2_22 9.9h.12 Escarpments 6,300 - 8,400 ft, Basalt & Tuff 0 - 45% 40 +/- Andesite, Basalt, Tuff, Volcanic & Pyroclastic W21.2_23 9.9h.13 Flood Plains FP 5,500 - 8,600 ft, Materials 0 - 5% 40 +/- Mountains & Andesite, W21.2_24 9.9h.14 Landslides ML 5,500 - 7,200 ft, Basalt & Tuff 0 - 80% 40 +/- 80.80g. Lava Plateaus & Andesite, W21.2_25 01 Tablelands WP 4,000 - 6,500 ft, Basalt & Tuff 0 - 15% 40 +/- Andesite, Basalt, Tuff, Volcanic & Hillslopes & Pyroclastic 20 - 40 W21.2_26 9.9h.15 Escarpments PL 5,500 - 7,400 ft, Materials 0 - 15% in, Andesite, 40 - 80 W21.2_27 9.9h.16 Mountains AM 5,500 - 7,200 ft, Basalt & Tuff 0 - 40% in, W21.6_01 9.9h.11 Fluvial_Lacustri PR 5,600 ft, Sediments of 0 - 5% ne Plains Vitric Pyroclastic 40 +/- Materials in, W23.4_01 9.9h.03 Lava Plateaus & HLP 6,400 - 7400 ft, 0 - 15% Fluvial- Lacustrine Andesite, 40 +/- Plains Basalt, Tuff, in, 55

A - 3 Warner Mountain Landscape Features

New Map Map Unit Bedrock/ OLd LTA LTA Unit Long Substrate Surficial Surficial Gelogy Landform Potential Natural Code Code ID Name Bedrock/ Substrate Age Geology Origin Group Vegetation Soils W21.2_13 9.9h.01 BR Basin Rim Basalt flows, Miocene, Colluvium & Andesite, Rim (15 - Wyoming big sage, Ultic Haploxerolls Tuffaceous Sediment, Pliocene, Residuum Basalt, Tuff & 100% slope) low sage, mountain Mafic Rocks Holocene & Volcanic Ash big sage Pleistocene W21.2_14 9.9h.02 LC Landslide Tuffaceous Miocene, Residuum & Basalt & Tuff Mountains & Ponderosa Pine, Pachic/Ultic Complex Sedimentary Rocks, Pliocene, rock outrcops Landslides Wyoming big sage, Argixerolls, & Tuff, Landslide Holocene & (0 - 100% low sage, mountain Rock Outcrop Deposits, Mafic Vent Pleistocene slope) big sage Complexes Rocks W21.2_15 9.9h.04 RV River Basalt flows, Miocene, Alluvium, Andesite, Fluvial Riparian Pachic/Ultic Valleys Tuffaceous Sediment, Pliocene, Colluvium & Basalt, Tuff & Terraces (0 - Hardwoods, Argixerolls, Ultic Mafic Rocks, Holocene & Residuum Rhyolite 20% slope) Ponderosa Pine, Haploxerolls, Lucustrine & Fluvial Pleistocene Wyoming Big Sage Rocks

W21.2_16 9.9h.05 WM Wet Tuffaceous Miocene & Alluvium Andesite, Fluvial- Wet and Dry Pachic Argixerolls P Meadow Sedimentary Rocks, Pliocene Basalt, Tuff & Lacustrine Meadow Prairies Tuff, Fluvial Rocks, Volcanic & Plains (0 - 5% Vegetation Basalt Flows, Pyroclastic slope) Pyroclastic Rocks Materials

W21.2_17 9.9h.06 FM Faulted Tuffaceous Miocene, Residuum & Basalt &Tuff Mountains & Ponderosa Pine, Pachic/Ultic Mountains Sedimentary Rocks, Pliocene, rock outrcops Escarpments Wyoming big sage Argixerolls Tuff, Landslide Holocene & (0 - 80% slope) Deposits, Mafic Vent Pleistocene Rocks W21.2_18 9.9h.07 LP Low Basalt flows, Miocene, Colluvium & Andesite, Plateaus & Ponderosa Pine, Pachic Argixerolls Plateaus Tuffaceaous Pliocene, Residuum Basalt, Tuff & Tablelands (0 Wyoming big & Typic Sedimentary Rocks, Holocene & Rhyolite -15% slope) sage,mixed Vitrixerands Tuff & Fluvial Rocks Pleistocene conifers, riparian hardwoods W21.2_19 9.9h.08 AL Alpine Rhyolite, Dacite, Miocene & Colluvium & Basalt & Mountains (0 Ponderosa Pine, Typic Mountains Basalt Flows, Pliocene Residuum Rhyolite - 90% slope) Wyoming big sage, Vitrixerands, Colombia River Lodgepole Pine, Xeric Basalt Flows, Mafic Western Juniper Humicryepts, Vent Rocks Typic

W21.2_20 9.9h.09 UL Uplands & Tuffaceous Miocene & Colluvium & Andesite, Basalt Mountains & Ponderosa Pine, Vitrandic Landslides Sediementary rocks, Pliocene Residuum &Tuff Landslides (0 - Wyoming big Argicryolls, Vertic Tuff, Mafic Vent 30% slope) sage,White Fir, Palexerolls Rocks, Basalt Flows Riparian Hardwoods

W21.2_21 9.9h.10 PU Pyroclastic Basalt Flows, Miocene, Colluvium & Andesite, Basalt Mountains (0 - Ponderosa Pine, Vertic Palexerolls, Uplands Rhyolite, Dacite & Pliocene, Residuum &Tuff 40% slope) Western Juniper, Pachic Argixerolls Pyroclastic Rocks Holocene & riparian Pleistocene hardwoods, mixed conifers

W21.2_22 9.9h.12 HS Hillslope Tuffaceous Miocene & Colluvium & Andesite, Basalt Hillslopes, & Ponderosa Pine, Vitrandic Scablands Sedimentary Rocks, Pliocene Residuum &Tuff Escarpments Wyoming big sage, Argicryolls, Vertic Tuff, Basalt Flows, (0 -45% slope) White Fir, Riparian Palexerolls Mafic Vent Rocks Hardwoods

W21.2_23 9.9h.13 FP Floodplains Tuffaceous Holocene Alluvium, Andesite, Fluvial- Wet Meadow Aquentic Sedimentary Rocks, Colluvium & Basalt, Tuff, Lacustrine Vegetation Endoaquolls Tuff, Fluvial Rocks, Residuum Volcanic & Plains (0 - Basalt Flows & Pyroclastic 5% slope) Pyroclastic Rocks Materials

W21.2_24 9.9h.14 ML Sage Pyroclastic Rocks, Miocene, Colluvium & Pyroclasitc Mountains (0 - Wyoming big sage, Typic Mountains Tuffaceous Pliocene, Residuum Rocks & Tuff 80% slope) Ponderosa Pine, Vitrixerands, Sedimentary Rocks, Holocene & Mixed Conifers Rock outcrop/ Tuff, Rhyolite & Pleistocene Xerolls Dacite complexes, Vertic Palexerolls

W21.2_25 80.80g. WP Wide Basalt flows, Miocene & Colluvium & Andesite, Basalt Lava Plateaus Low Sage, Vertic Palexerolls, 01 Plateau Tuffaceaous Pliocene Residuum &Tuff & Tablelands (1 Ponderosa Pine, Pachic/Ultic Sedimentary Rocks, - 15% slope) Wyoming Big Sage, Argixerolls & Tuff Western Juniper W21.2_26 9.9h.15 PL Pyroclastic Tuffaceous Miocene & Colluvium & ResiduumAndesite, Hillslopes (0 - Ponderosa Pine, Vertic Palexerolls, Lowlands Sedimentary Rocks, Pliocene Basalt, Tuff, 15% slope) Wyoming big sage, Ultic Tuff, Basalt Flows & Volcanic & low sage Haploxerolls, Pyroclastic Rocks Pyroclastic Materials

W21.2_27 9.9h.16 AM Ashy Basalt flows, Miocene & Colluvium & Andesite, Basalt Mountains(0 - White Fir, Typic Vitricryands Mountains Pyroclastic Rocks, Pliocene Residuum &Tuff 40% slope) Lodgepole Pine Rhyolite & Dacite W21.6_01 9.9h.11 PR Prairie Fluvial Rocks, Miocene, Alluvium Volcanic & Fluvial- Wet and Dry Cumulic Tuffaceous Pliocene, Pyroclastic Lacustrine Meadow Endoaqolls, Sedimentary Rocks & Holocene & Materials Plains (0 - 5% Vegetation Vertic Palexerolls Tuff Pleistocene slope) W23.4_01 9.9h.03 HLP High Lava Olivine Basalt, Basalt Miocene & Colluvium & Andesite, Basalt Lava Plateuas Ponderosa Pine, Pachic/Ultic Plains Flows & Columbia Pliocene Residuum & Tuff & Low Sage, Argixerolls, Vertic River Basalt Flows Fluvial_Lacustr Wyoming Big Sage Palexerolls ine Plains (0 - 15% slope) 56

Plate 2 57

A - 4 Deshcutes NF Soils Table

Soil Code Series Subgroup Great Group Surface Texture Regolith Texture Soil Depth, in/cm Parent Materials 14 N/A Typic Cryorthents Medium Fine Medium Fine 0-60+ in/0-152+cm Basalts & Andesites

16 N/A Entic Cryorthods Medium Fine Medium Fine 60+in / 152+ cm Basalts, Andesites, Tuffs and Breccias 21 N/A Entic Cryandepts Medium Fine Medium Fine 60+ in/ 152+ cm Basalts, Andesites, Tuffs and Breccias 23 N/A Typic Cryandepts Medium Fine Medium Fine 40+ in/ 102+ cm Basalts, Andesites, Tuffs and Breccias 30 N/A Typic Cryofluvents Medium Fine Medium Fine 60+ in/ 152+ cm Basalts,and Glacial Deposits 43 Wickiup Typic Cryaquands Medium Fine Coarse 60+ in/ 152+ cm Pumice, Ash and Fluvial- Lacustrine Deposits

50 N/A Lithic Haploxerolls Medium Fine Medium Fine 20+ in/ 51+ cm Tuffs, Breccias and Volcanic Sediments

51 N/A Andic Xerochrepts Medium Fine Medium Fine 20+ in/ 51+ cm Tuffs and Breccias 52 N/A Andic Cryochrepts Medium Medium Fine 30+ in/ 76+ cm Tuffs and Ash 98 La Pine Xeric Vitricryands Coarse Coarse 60+ in/ 152+ cm Tuffs and Breccias

A - 5 Deschutes NF Landform Groups

LTA Map Unit I.D. Elevation , ft/m Bedrock/Substrate Slope Gradient, % Depth to Bedrock, in/cm 4.4c.01 MT 3,300 - 10,500 ft/ Pumice, Ash, Basalts, Andesites, 0 - 55% 0-60+ in/0-150+ cm 1,000 - 3,200 m Glacial Deposits 4.4c.02 VC 4,600 - 7,700 ft/ 1,400 Pumice, Ash, Tuffaceous 0 - 60% 0-60+ in/0-150+ cm - 2,400 m Sedimentary, Tuffs, Rhyolite and Pumice 4.4c.03 GL 4,800 ft/1,500 m Glacial Deposits, Basalt 0 - 20% 40-60+ in/100-150+ cm 4.4c.04 GM 2,900 - 7,800 ft/ 900 - Pumice, Ash, Glacial Deposits, 0 - 55% 0-60+ in/0-150+ cm 2,400 m Basalt 9.9d.01 PR 3,100 - 5,000 ft/ 950 - Ash and Olivine Basalt 0 - 55% 30+ in/75 cm 1,500 m 9.9d.02 FV 1,900 - 4,100 ft/580 - Tuffaceous Sedimentary 0 - 30% 20-30+ in/50-75+ cm 1,300 m Rocks,Tuff, Ash 9.9d.03 GW 4,500 - 6,700 ft/ 1,400 Glaciofluvial Deposits 0 - 55% 0-60+ in/0-152+ cm - 2,000 m

9.9e.01 SV 4,000 - 7,300 ft/ 1,200 Pumice, Ash, Basalts, Breccias and 0 - 20% 20-60+ in/50-150+ cm - 2,200 m Olivine Basalts 9.9e.02 TD 4,400 - 6,200 ft/ 1,300 Ash and Silicic Vent Rocks 0 - 40% 0-60+ in/0-150+ cm - 1,900 m 9.9e.03 MTB 4,600 - 7,400 ft/1,400 Rhyolite, Dacite, Mazama Pumice 0 - 40% 60+ in/150+ cm - 2,300 m Deposits 9.9e.04 LB 4,300 - 6,000 ft/1,300 Pumice, Ash, Basalts and 0 - 20% 0-60+ in/0-150+ cm - 1,800 m Andesites 9.9e.05 CC 3,600 - 9,000 ft/ 1,100 0 - 40% 0-60+ in/0-150+ cm - 2,700 m Pumice, Ash, Mafic Vent Deposits, Basalts, Andesites, Breccias 9.9f.01 PL 4,400 ft/1,300 m 0 - 20% 0-60+ in/0-150+ cm Pumice, Ash, Basalts, Lacustrine and Fluvial Sedimentary Desposits 9.9f.02 FP 2,700 - 4,800 ft/ 800 - Pumice, Ash, Lacustrine, Fluvial 0 - 15% 0-60+ in/0-150+ cm 1,500 m and Tuffaceous Sedimentary Desposits 58

A - 6 Deschutes NF Landscape Features

Map Bedrock Surficial Potential Drainage LTA Unit Map Unit Geology Bedrock/ Bedrock/ Surficial Gelogy Landform Natural Density Code I.D. Long Name Primary Substrate Substrate Age Geology Origin Group Soils Vegetation (km/km2) 4.4c.01 MT Mountains Igneous, Pumice, Ash, Holocene and Colluvium, Basalt, Mountains Typic Ponderosa Pine, 0.38 with Glacial Sedimentary Basalts, Pleistocene Residuum, Andesite (0 - 55% Cryorthents, Lodgepole Pine Caps Andesites, and Glacial slopes) Entic Glacial Till Cryorthods Deposits 4.4c.02 VC Newberry Igneous Pumice, Ash, Pleistocene Colluvium, Tuff, Craters, Typic Ponderosa Pine, 0.64 Volcanic Tuffaceous and Pliocene Residuum, Rhyolite Lakes, Lava Cryorthents, Lodgepole Pine Complex Sedimentary, Volcanic Flows and Entic Tuffs, Rhyolite Ejecta Mountains Cryorthods and Pumice (0 - 60% slopes)

4.4c.03 GL Glacial Lakes Igneous Glacial Pleistocene Glacial Till Basalt Glacial Lakes Typic Doulas Fir 1.3 Deposits, (0% slopes) Cryandepts, Basalt Typic Cryofluvents 4.4c.04 GM Glacial Sedimentary Pumice, Ash, Pleistocene Glacial Till Basalt, Moraines (0 - Typic Lodgepole Pine, 0.61 Moraines Glacial Andesite 55% slopes) Cryorthents, Ponderosa Pine Deposits, Entic Basalt Cryorthods

9.9d.01 PR Olivine Plateau Igneous Ash and Miocene and Colluvium Basalt Lava Plateau Andic Ponderosa Pine 0.38 and Rim Olivine Basalt Pliocene and and Rim Cryochrepts Residuum (0 - 55% slopes) 9.9d.02 FV Fluvial Valleys Igneous Tuffaceous Pliocene and Colluvium, Tuff Fluvial Andic Ponderosa Pine 1.6 Sedimentary Miocene Residuum, Valleys Cryochrepts Rocks,Tuff, and (0 - 30% , Andic Ash Alluvium slopes) Xerochrepts , Lithic Haploxerolls

9.9d.03 GW Glacial Sedimentary Glaciofluvial Pleistocene Alluvium Basalt, Canyons, Typic Lodgepole Pine, 0.43 Meltwater Deposits Rhyolite, Fluvial- Cryorthents, Ponderosa Pine Landscape Tuff Lacustrine Typic Plains (0 - Cryandepts 55% slopes) 9.9e.01 SV Newberry Igneous Pumice, Ash, Holocene, Colluvium Basalt Hillslopes Typic Ponderosa Pine, 0.18 Shield Volcano Basalts, Pleistocene and (0 - 20% Cryorthents, Lodgepole Pine Breccias and and Pliocene Residuum slopes) Andic Olivine Xerochrepts Basalts 9.9e.02 TD Dome with Igneous Ash and Pliocene, Colluvium, Basalt Mountains Typic Ponderosa Pine, 0.31 Tuff Rings and Silicic Vent Miocene, Residuum, (0 - 40% Cryorthents Wyoming Big Cones Rocks Oligocene and Volcanic slopes) Sage Eocene Ejecta

9.9e.03 MTB Mazama Igneous Mazama Holocene Colluvium, Rhyolite, Mountains Typic Ponderosa Pine 0.43 Tephra Pumice and Residuum, Dacite and Canyons Cryorthents, Blanket Ash Deposits and (0 - 40% Entic Pumice slopes) Cryorthods

9.9e.04 LB Ashy Lava Igneous Pumice, Ash, Holocene Residuum Basalt Lava Flows Typic Ponderosa Pine, 0.12 Beds Basalts and and (0 - 20% Cryorthents, Lodgepole Pine Andesites Andesite slopes) Entic Cryorthods 9.9e.05 CC Cinder Cones Igneous Pumice, Ash, Pleistocene, Colluvium Basalt, Mountains Typic Ponderosa Pine, 0.09 & Buttes Mafic Vent Pliocene and and Andesite (0 - 40% Cryorthents, Lodgepole Pine Deposits, Miocene Residuum slopes) Xeric Basalts, Vitricryands Andesites, Breccias

9.9f.01 PL Pluvial Lakes Igneous, Pumice, Ash, Pleistocene Alluvium Basalt Pluvial Lakes Typic Lodgepole Pine, 0.58 Sedimentary Basalts, and Pliocene (0% slopes) Cryorthents, Ponderosa Pine Lacustrine Typic and Fluvial Cryaquands Sedimentary Desposits

9.9f.02 FP Plains Igneous, Pumice, Ash, Holocene, Alluvium Tuff Fluvial- Typic Lodgepole Pine, 0.5 Sedimentary Lacustrine, Pleistocene Lacustrine Cryorthents, Ponderosa Pine Fluvial and and Pliocene Plains Xeric Tuffaceous (0 -15% Vitricryands Sedimentary slopes) Desposits 59

Plate 3 60

A - 7 Ochoco NF Landform Groups

LTA Map Unit I.D. Elevation , ft/m Bedrock/Substrate Slope Gradient, % 9.9d.04 CC2 3,400 - 4,400 ft/ Mafic vent deposits 0 - 20% 1,000-1,300 m

11.11a.01 SH 3,800-4,800 ft/ Columbia River Basalts, Ash 0 - 50% 1,150-1,450 m 11.11b.01 PM 3,200-6,500 ft/ Columbia River Basalts, Ash, 0 - 60% 1,000-2,000 m Tuffaceous Sedimentary Rocks

11.11b.02 HS 4,300-6,700 ft/ Columbia River Basalts 0 - 50% 1,300-2,000 m 11.11L.01 PRL 2,900-6,000 ft/ Columbia River Basalts 0 - 50% 900-1,900 m 11.11n.01 FV2 1,900-2,900 ft/ Sandstone, Mudstone, Volcanic Ash 0 - 25% 600-900 m 11.11n.02 SP 2,500-4,600 ft/ Sandstone, Mudstone, Volcanic Ash 0 - 5% 750-1,400 m 11.11n.03 OP 3,000-3,400 ft/ Columbia River Basalts 0 - 5% 900-1,000 m 11.11o.01 WMP 4,500-4,600 ft/ Sandstone, Mudstone, Volcanic Ash 0 - 5% 1,300-1,400 m 61

A - 8 Ochoco NF Landscape Features

Bedrock Surficial Potential Drainage Map Unit Map Unit Long Geology Bedrock/ Bedrock/ Surficial Gelogy Landform Natural Density (km/ LTA Code I.D. Name Primary Substrate Substrate Age Geology Origin Group Vegetation km2) 9.9d.04 CC2 Cinder Cones & Igneous Mafic vent Miocene, Colluvium and Basalt, Hillslopes Ponderosa Pine 0.11 Buttes deposits Pliocene and Residuum Andesite (0 - 20% Pleistocene slope)

11.11a.01 SH Southern Aspect Igneous Columbia Miocene Colluvium and Basalt, Mountains Western Juniper 0.88 Hillslopes River Basalts, Residuum Andesite (0 - 50% Ash slopes)

11.11b.01 PM Pyroclastic Igneous Columbia Miocene and Colluvium, Basalts, Mountains Ponderosa Pine, 0.66 Mountains River Basalts, Pliocene Residuum, and Andesites (0 - 60% Big Sage Ash, Loess slopes) Tuffaceous Sedimentary Rocks 11.11b.02 HS High Elevation Igneous Columbia Miocene Colluvium and Basalt, Mountains Mountain Big 0.74 Scablands River Basalts Residuum Andesite, (0-50% slope) Sage, Western Breccia Juniper

11.11L.01 PRL Plateaus with Igneous Columbia Miocene and Colluvium, Basalts, Plateaus, Ponderosa Pine, 0.69 Rims and River Basalts Pliocene Residuum, Andesites Landslides Grand Fir Landslides Volcanic Ejecta (0 - 50% slopes) 11.11n.01 FV2 Fluvial Valleys Sedimentary Sandstone, Miocene and Colluvium, Tuff Fluvial valleys Bitterbrush 1.22 Mudstone, Pliocene Residuum and (0 - 25% Volcanic Ash Alluvium slope)

11.11n.02 SP Sedimentary Sedimentary Sandstone, Miocene and Colluvium and Tuff Fluvial- Wyoming Big 0.74 Plains Mudstone, Pliocene Residuum Lacustrine Sage Volcanic Ash Plains (0 - 5% slopes) 11.11n.03 OP Olivine Plateau Igneous Columbia Miocene and Colluvium and Basalt Plateaus and Ponderosa Pine, 0.36 River Basalts Pliocene Residuum Tablelands Western Juniper, (0-5% slope) Bitterbrush

11.11o.01 WMP Wet Meadow Sedimentary Sandstone, Miocene and Alluvium Tuff Fluvial- Wet Meadow 0.12 Prairie Mudstone, Pliocene Lacustrine Vegetation Volcanic Ash Plains (0 - 5% slopes) 62

Plate 4 63

Level V Ecoregion Map Descriptions March 15, 1999; J.David and C. Gordon

1. This type of classification is an interim step between Level IV Ecoregions (Major Landforms) at the roughly 1/116,000 scale and the Level VII Ecoregion mapping (Order 2, 3 Ecological Unit Mapping) currently in progress on the Ochoco National Forest) at the 1/24,000 scale. The Order 4 SRI ecological units at 1 inch to the mile is at the Level VI ecoregion scale.

2. The Level V Ecoregion Map (currently in draft form in GIS) of Major Landforms is currently being adjusted using 1/41,000 scale (1.5 inches/mile) orthophotos, plant association maps, SRI Order IV maps, more refined geological mapping and slope maps. Currently part of the map refining includes identification of large inclusions of scab stringer terrains on the North Slope Ochoco Major Landform which can be split out at the 1/116,000 scale.

Descriptions of Major Landtypes at the 1/116,000 scale (Level V Ecoregions):

Major Landtype Criteria Description North Slope Ochoco Soils, Slope, Aspect Deep ash soils, high percentage of steep gradients, high productivity due to greater effective moisture Juniper Shrub Steppe Precip, Poten. Natural Comm. Major Land Resource Area (PNC) B10 and B8 on largely basalt and rhyolite bedrock on Crooked River National Grasslands South Slope Ochoco Aspect, PNC, Landform Less effective moisture, less ash depth overall, dissected montane terrain

South Fork John Day Geology, Soils, Aspect Area underlain by Mesozoic Sedimentary sediments, prone to weathering, lots of drainage dissection, more prone to erosion North Slope Snow Mountain Aspect, Geology North aspect area of dry grand fir potential underlain with Mesozoic sediments, basalts and rhyolites , high production due to greater ash depth Juniper Woodland Tuffs Landform, PNC, Geology, Juniper PNC Cover and Precip density, some Juoc-Pipo, Pipo- Juoc, shallow soils, low precip., low understory productivity on dissected 64

rhyolitic plateau Scab Stringer (seperated into Landform, Geology, Aspect Areas of mixed scablands and high elevation and low timbered stringers on largely elevation based on plant Picture Gorge Basalts, resistant association and elevation: the to weathering, residual soils on plant associations groups of scabs, ash soils in drainways Dry Pine, Moist Pine and Dry Definition of = >30% scabs per Doug Fire comprise the Low 80 acres- based on SRI and Elevation Scab Stringer and PNC. Additional acreage of the Dry, Moist and Wet Grand high elevation scablands Fir along with Lodgepole Pine identified through this effort and Subalpine Fir comprise south of Spanish Peak which High Elevation Scab will be added to scab stringer Stringer. major landtype Large Meadow Complex Concave Landform, Soils, Occur on Quaternary alluvium Examples= Big Summit Moisture, Hydrology, PNC or John Day formation, deep Prairie, Little Summit Prairie, alluvial soils, comprise small Williams Prairie, Grey Prairie, inclusions in other subsections. Antler Prairie Large complexes show up on 1/116,000 scale Maury Mountains N Slope Aspect, Ash soils , Slope , North slope aspects with Effective moisture deeper ash soils and higher production than south aspects. Similar to dryer portions of North Slope Ochoco Maury Mountains S Slope Aspect , Soils, Effective Drier south slopes with moisture shallower ash capping than norths, more residual clay soils. Similar to S Slope Ochoco

A_9a Deschutes Trial 1 Error Matrix Analysis Sep 4, 2009

(rows : mapped) 1 2 3 4 6 ------1 | 4015839 61217 98408 1020 9708 0.2028 2 | 67884 1551343 3022 0 20077 0.1785 3 | 22036 3939 613766 2568 96485 0.1717 4 | 13 0 165 108164 25305 0.1912 6 | 33619 25076 55192 76731 817152 0.1927 10 | 119673 20775 0 0 0 0.4530 11 | 55927 63773 0 1010 33108 0.1513 13 | 3385 2455 0 0 0 0.2835 15 | 22802 18708 0 0 0 0.3949 16 | 51545 6212 0 0 1584 0.3931 17 | 26048 42156 0 0 16 0.2300 19 | 3054 1275 0 0 120 0.2027 ------Total | 4421825 1796929 770553 189493 1003555 ErrorO | 0.0918 0.1367 0.2035 0.4292 0.1857

10 11 13 15 16 ------1 | 67832 71584 21553 110534 13889 0.2028 2 | 7403 102168 5070 15854 1895 0.1785 3 | 0 0 0 0 0 0.1717 4 | 0 0 0 0 0 0.1912 6 | 372 438 0 0 0 0.1927 10 | 704244 5008 24127 40916 994 0.4530 11 | 22107 892329 14013 38870 81518 0.1513 13 | 9660 42631 680255 1313 0 0.2835 15 | 8530 14120 4743 144742 0 0.3949 16 | 1718 58153 16 1355 3266648 0.3931 17 | 5235 22707 864 5281 24350 0.2300 19 | 3127 150 0 13 90937 0.2027 ------Total | 830228 4209288 750641 358878 3480231 ErrorO 0.1517 0.0753 0.0938 0.5967 0.0614

17 19 20 Total ErrorC ------1 | 119093 3340 443659 | 5037676 0.2028 2 | 25217 0 88392 | 1888325 0.1785 3 | 0 0 2219 | 741013 0.1717 4 | 0 0 86 | 133733 0.1912 6 | 986 400 2240 | 1012206 0.1927 10 | 16387 0 355270 | 1287394 0.4530 11 | 35033 934 347450 | 4586072 0.1513 13 | 518 0 209211 | 949428 0.2835 15 | 16565 0 8991 | 239201 0.3949 16 | 20569 118003 857122 | 5382925 0.3931 17 | 710543 32248 53288 | 922736 0.2300 19 | 34457 972542 114188 | 1219863 0.2027 ------Total | 979368 1127467 3482116 | 23400572 ErrorO | 0.2745 0.1374 1.0000 | 0.2531 66 ErrorO = Errors of Omission (expressed as proportions) ErrorC = Errors of Commission (expressed as proportions)

90% Confidence Interval = +/- 0.0001 (0.2530 - 0.2533) 95% Confidence Interval = +/- 0.0002 (0.2529 - 0.2533) 99% Confidence Interval = +/- 0.0002 (0.2529 - 0.2533)

KAPPA INDEX OF AGREEMENT (KIA) ------Using LTA3SEPPRED3 as the reference image ... Category KIA ------1 0.7499 2 0.8067 3 0.8224 4 0.8072 6 0.7987 10 0.5304 11 0.8155 13 0.7071 15 0.5990 16 0.5382 17 0.7600 19 0.7870

LTA4SEP3 Category KIA ------1 0.8830 2 0.8513 3 0.7899 4 0.5683 6 0.8059 10 0.8394 11 0.9063 13 0.9023 15 0.3972 16 0.9203 17 0.7142 19 0.8550 20 0.0000

Overall Kappa = 0.7101 67

A_9b Deschutes Trial 2 Error Matrix Analysis 7 Oct, 2009

(rows : mapped) 1 2 3 4 5 ------0 | 14 2 0 3 0 1.0000 1 | 6021290 709 0 33838 0 0.0897 2 | 12214 865922 43032 0 0 0.1226 3 | 0 29555 147495 0 0 0.1687 4 | 83271 0 0 6100455 9183 0.0701 5 | 0 0 0 18353 145098 0.1123 6 | 6315 0 0 0 0 0.0432 7 | 31522 0 0 68785 0 0.1824 8 | 772 0 0 46414 0 0.0615 9 | 89515 24925 0 21752 0 0.1139 10 | 2545 0 0 0 0 0.0336 11 | 5872 0 0 0 0 0.1192 12 | 96591 28887 1867 102342 162 0.0788 ------Total | 6349921 950000 192394 6391942 154443 ErrorO | 0.0518 0.0885 0.2334 0.0456 0.0605

6 7 8 9 10 ------0 | 2 0 0 1 0 1.0000 1 | 30278 140806 2119 140480 5101 0.0897 2 | 1282 0 0 20124 0 0.1226 3 | 0 0 0 0 0 0.1687 4 | 1275 78823 164201 22867 0 0.0701 5 | 0 0 0 0 0 0.1123 6 | 668429 1 0 0 0 0.0432 7 | 0 695748 0 40835 0 0.1824 8 | 0 24 727022 406 0 0.0615 9 | 1146 19543 5 1644393 0 0.1139 10 | 0 9 0 336 83103 0.0336 11 | 0 3472 0 2052 0 0.1192 12 | 44490 20015 0 46894 0 0.0788 ------Total | 746902 958441 893347 1918388 88204 ErrorO | 0.1051 0.2741 0.1862 0.1428 0.0578

11 12 Total ErrorC ------0 | 0 0 | 22 1.0000 1 | 4892 235378 | 6614891 0.0897 2 | 0 44296 | 986870 0.1226 3 | 0 374 | 177424 0.1687 4 | 0 100393 | 6560468 0.0701 5 | 0 0 | 163451 0.1123 6 | 0 23841 | 698586 0.0432 7 | 3917 10172 | 850979 0.1824 8 | 0 0 | 774638 0.0615 9 | 2608 51773 | 1855660 0.1139 10 | 0 0 | 85993 0.0336 11 | 236865 20654 | 268915 0.1192 12 | 6863 4071971 | 4420082 0.0788 68

------Total | 255145 4558852 | 23457980 ErrorO |0.0716 0.1068 | 0.0874

ErrorO = Errors of Omission (expressed as proportions) ErrorC = Errors of Commission (expressed as proportions)

90% Confidence Interval = +/- 0.0001 (0.0873 - 0.0875) 95% Confidence Interval = +/- 0.0001 (0.0873 - 0.0875) 99% Confidence Interval = +/- 0.0002 (0.0872 - 0.0875)

KAPPA INDEX OF AGREEMENT (KIA) ------Using LTA7OCTPRED3 as the reference image ... Category KIA ------0 0.0000 1 0.8770 2 0.8723 3 0.8299 4 0.9036 5 0.8870 6 0.9554 7 0.8098 8 0.9361 9 0.8760 10 0.9663 11 0.8795 12 0.9022

LTA7OCT3

Category KIA ------

1 0.9279 2 0.9076 3 0.7649 4 0.9367 5 0.9391 6 0.8917 7 0.7156 8 0.8075 9 0.8449 10 0.9420 11 0.9275 12 0.8684

Overall Kappa = 0.8906 69 A_9c Deschutes Trial 3 Error Matrix Analysis of Dec 21, 2009

(rows : mapped) 1 2 3 4 5 ------0 | 99 1 51 741 28 1.0000 1 | 4275301 0 0 19 0 0.0005 2 | 0 947874 1174 0 0 0.0019 3 | 0 1077 188046 0 0 0.0057 4 | 184 0 0 6373487 113 0.0003 5 | 0 0 0 88 153102 0.0006 6 | 209 0 0 0 0 0.0010 7 | 774 0 0 0 0 0.0015 8 | 0 0 0 1079 0 0.0012 9 | 660 297 0 0 0 0.0014 10 | 228 0 0 0 0 0.0026 11 | 235 0 0 0 0 0.0031 12 | 1694 148 0 239 0 0.0010 13 | 1602228 0 0 0 0 1.0000 14 | 324322 0 0 0 0 1.0000 ------Total | 6205934 949397 189271 6375653 153243 ErrorO | 0.3111 0.0016 0.0065 0.0003 0.0009

6 7 8 9 10 ------0 | 214 0 0 2 0 1.0000 1 | 12 482 0 628 0 0.0005 2 | 0 0 0 265 0 0.0019 3 | 0 0 0 0 0 0.0057 4 | 0 0 914 0 0 0.0003 5 | 0 0 0 0 0 0.0006 6 | 743810 0 0 0 0 0.0010 7 | 0 1043385 0 522 0 0.0015 8 | 0 0 892439 0 0 0.0012 9 | 0 508 0 1949545 0 0.0014 10 | 0 0 0 0 87975 0.0026 11 | 0 36 0 105 0 0.0031 12 | 319 530 0 1200 0 0.0010 13 | 7 3 0 96 227 1.0000 14 | 0 16 0 682 0 1.0000 ------Total | 744362 1044960 893353 1953045 88202 ErrorO| 0.0007 0.0015 0.0010 0.0018 0.0026

11 12 Total ErrorC ------0 | 0 172 | 1308 1.0000 1 | 142 773 | 4277357 0.0005 2 | 0 378 | 949691 0.0019 3 | 0 0 | 189123 0.0057 4 | 0 753 | 6375451 0.0003 5 | 0 0 | 153190 0.0006 6 | 0 514 | 744533 0.0010 7 | 135 110 | 1044926 0.0015 8 | 0 0 | 893518 0.0012 9 | 224 954 | 1952188 0.0014 70

10 | 0 0 | 88203 0.0026 11 | 254365 407 | 255148 0.0031 12 | 272 4531945 | 536347 0.0010 13 | 0 120 | 1602681 1.0000 14 | 0 11874 | 336894 1.0000 ------Total | 255138 4548000 | 23400558 ErrorO | 0.0030 0.0035 | 0.0837

ErrorO = Errors of Omission (expressed as proportions) ErrorC = Errors of Commission (expressed as proportions)

90% Confidence Interval = +/- 0.0001 (0.0836 - 0.0838) 95% Confidence Interval = +/- 0.0001 (0.0836 - 0.0838) 99% Confidence Interval = +/- 0.0001 (0.0836 - 0.0839)

KAPPA INDEX OF AGREEMENT (KIA) ------Using LTA21DECPRED4 as the reference image ... Category KIA ------0 0.0000 1 0.9993 2 0.9980 3 0.9943 4 0.9996 5 0.9994 6 0.9990 7 0.9985 8 0.9987 9 0.9985 10 0.9974 11 0.9969 12 0.9988 13 0.0000 14 0.0000

LTA21DEC4 Category KIA ------1 0.6193 2 0.9983 3 0.9935 4 0.9995 5 0.9991 6 0.9992 7 0.9984 8 0.9989 9 0.9980 10 0.9974 11 0.9969 12 0.9956

Overall Kappa = 0.8987 71

A_9d Deschutes Trial 4 Error Matrix Analysis Jan 7, 2010

(rows : mapped) 1 2 3 4 5 ------0 | 1 0 0 0 0 1.0000 1 | 3913778 3278 508 5359 0 0.1332 2 | 20384 767397 65111 400 0 0.1632 3 | 12 28257 116797 0 0 0.1987 4 | 37566 1395 0 6087597 12841 0.0699 5 | 0 0 0 12419 141496 0.0821 6 | 5496 0 0 0 0 0.0633 7 | 23959 87 0 79518 0 0.1801 8 | 57 0 0 50762 0 0.0659 9 | 56590 21263 0 20956 0 0.0996 10 | 0 0 0 0 0 0.0253 11 | 4609 0 0 9 0 0.1059 12 | 90286 126406 9782 132828 0 0.1143 13 | 123749 1891 0 251 0 0.1494 14 | 23027 0 0 948 0 0.0967 ------Total | 4299514 949974 192198 6391047 154337 ErrorO | 0.0897 0.1922 0.3923 0.0475 0.0832

6 7 8 9 10 ------0 | 0 0 0 0 0 1.0000 1 | 15778 130538 19 83178 0 0.1332 2 | 0 263 0 22513 0 0.1632 3 | 0 0 0 0 0 0.1987 4 | 4819 102880 160528 18770 0 0.0699 5 | 0 0 0 0 0 0.0821 6 | 683266 177 0 2070 0 0.0633 7 | 0 769345 0 41085 0 0.1801 8 | 0 164 731653 609 0 0.0659 9 | 588 19748 659 1668153 0 0.0996 10 | 0 0 0 0 85069 0.0253 11 | 0 2431 0 1465 0 0.1059 12 | 23767 12550 498 76065 168 0.1143 13 | 18687 1057 139 36676 2966 0.1494 14 | 0 5773 22 1604 0 0.0967 ------Total | 746905 1044926 893518 1952188 88203 ErrorO 0.0852 0.2637 0.1812 0.1455 0.0355

11 12 13 14 Total ErrorC ------0 | 0 0 0 0 | 1 1.0000 1 | 4217 166763 169073 22536 | 4515025 0.1332 2 | 1 26964 14044 0 | 917077 0.1632 3 | 0 689 0 0 | 145755 0.1987 4 | 663 108312 2399 7316 | 6545086 0.0699 5 | 0 231 0 0 | 154146 0.0821 6 | 0 35565 2831 0 | 729405 0.0633 7 | 2710 18257 3097 260 | 938318 0.1801 8 | 0 0 20 0 | 783265 0.0659 72 9 | 2298 48434 12591 1440 | 1852720 0.0996 10 | 0 0 2208 0 | 87277 0.0253 11 |220790 17638 0 0 | 246942 0.1059 12 | 24469 4067829 26340 2041 | 4593029 0.1143 13 | 0 55110 1370381 96 | 1611003 0.1494 14 | 0 1025 69 303205 | 335673 0.0967 ------Total| 255148 4546817 1603053 336894 | 23454722 ErrorO|0.1347 0.1053 0.1451 0.1000 | 0.1078

ErrorO = Errors of Omission (expressed as proportions) ErrorC = Errors of Commission (expressed as proportions)

90% Confidence Interval = +/- 0.0001 (0.1077 - 0.1079) 95% Confidence Interval = +/- 0.0001 (0.1077 - 0.1079) 99% Confidence Interval = +/- 0.0002 (0.1076 - 0.1079)

KAPPA INDEX OF AGREEMENT (KIA) ------Using LTA7JANPRED4 as the reference image ... Category KIA ------0 0.0000 1 0.8369 2 0.8299 3 0.7997 4 0.9039 5 0.9174 6 0.9347 7 0.8115 8 0.9315 9 0.8913 10 0.9746 11 0.8929 12 0.8582 13 0.8397 14 0.9019

LTA7JAN4 Category KIA ------

1 0.8889 2 0.8000 3 0.6052 4 0.9341 5 0.9162 6 0.9121 7 0.7253 8 0.8126 9 0.8420 10 0.9643 11 0.8639 12 0.8690 13 0.8442 14 0.8985 Overall Kappa = 0.8707 73

A_9e Deschutes Trial 5 Error Matrix Analysis of Jan 21, 2010

(rows : mapped) 1 2 3 4 5 ------0 | 22436 601 3127 16294 1199 1.0000 1 | 3861070 3727 614 6903 0 0.1391 2 | 18215 760407 69036 400 0 0.1577 3 | 12 25104 108972 0 0 0.1890 4 | 39753 1375 0 6050707 16133 0.0663 5 | 0 0 0 11567 137110 0.0778 6 | 4555 0 0 0 0 0.0643 7 | 32620 0 0 94119 0 0.2006 8 | 182 0 0 53661 0 0.0696 9 | 61791 18848 0 22910 0 0.1028 10 | 0 0 0 0 0 0.0305 11 | 2868 0 0 0 0 0.0926 12 | 98548 139290 10647 134613 0 0.1213 13 | 136867 646 0 270 0 0.1598 14 | 22173 0 0 501 0 0.0964 ------Total | 4301090 949998 192396 6391945 154442 ErrorO | 0.1023 0.1996 0.4336 0.0534 0.1122

6 7 8 9 10 ------0 | 2541 14 0 25 0 1.0000 1 | 19896 135307 329 87874 463 0.1391 2 | 0 203 0 24174 0 0.1577 3 | 0 0 0 0 0 0.1890 4 | 4827 84743 154809 16867 0 0.0663 5 | 0 0 0 0 0 0.0778 6 | 671877 51 0 87 0 0.0643 7 | 19 782325 451 44895 63 0.2006 8 | 0 0 735987 1083 0 0.0696 9 | 350 18129 584 1653143 0 0.1028 10 | 0 0 0 30 83896 0.0305 11 | 0 2132 0 1091 0 0.0926 12 | 25754 14990 739 79607 61 0.1213 13 | 21638 961 320 42117 3719 0.1598 14 | 0 6119 134 1408 0 0.0964 ------Total | 746902 1044974 893353 1952401 88202 ErrorO | 0.1004 0.2513 0.1762 0.1533 0.0488

11 12 13 14 Total ErrorC ------0 | 0 10845 354 0 | 57436 1.0000 1 | 6837 165849 170146 25778 | 4484793 0.1391 2 | 0 17743 12649 0 | 902827 0.1577 3 | 0 274 0 0 | 134362 0.1890 4 | 0 100990 2835 7522 | 6480561 0.0663 5 | 0 0 0 0 | 148677 0.0778 6 | 0 38611 2832 0 | 718013 0.0643 7 | 3977 16483 3522 176 | 978650 0.2006 8 | 0 0 97 0 | 791010 0.0696 74 9 | 3431 49649 11267 2528 | 1842630 0.1028 10 | 0 0 2609 0 | 86535 0.0305 11 | 215194 15863 0 0 | 237148 0.0926 12 | 25699 4075788 30610 2088 | 4638434 0.1213 13 | 0 53348 1366348 32 | 1626266 0.1598 14 | 0 1526 0 298783 | 330644 0.0964 ------Total | 255138 4546969 1603269 336907 | 23457984 ErrorO | 0.1566 0.1036 0.1478 0.1132 | 0.1132

ErrorO = Errors of Omission (expressed as proportions) ErrorC = Errors of Commission (expressed as proportions)

90% Confidence Interval = +/- 0.0001 (0.1131 - 0.1133) 95% Confidence Interval = +/- 0.0001 (0.1131 - 0.1134) 99% Confidence Interval = +/- 0.0002 (0.1131 - 0.1134)

KAPPA INDEX OF AGREEMENT (KIA) ------Using LTA21JANPRED4 as the reference image ... Category KIA ------0 0.0000 1 0.8297 2 0.8356 3 0.8095 4 0.9088 5 0.9217 6 0.9336 7 0.7900 8 0.9277 9 0.8878 10 0.9694 11 0.9064 12 0.8495 13 0.8285 14 0.9022

LTA21JAN4 Category KIA ------

1 0.8735 2 0.7924 3 0.5639 4 0.9262 5 0.8871 6 0.8964 7 0.7377 8 0.8177 9 0.8337 10 0.9510 11 0.8418 12 0.8708 13 0.8412 14 0.8852 Overall Kappa = 0.8642 75 A_10a Ochoco LTA map (this study) Error Matrix Analysis Mar4, 2010

(rows : mapped) 1 2 3 4 5 ------0 | 0 0 0 0 0 1.0000 1 | 15148 0 0 369 0 0.0239 2 | 0 11842 581 127 0 0.0582 3 | 0 2489 215376 3295 2326 0.0387 4 | 193 513 3062 104588 13 0.0479 5 | 0 0 1698 0 501484 0.0168 6 | 0 0 0 0 4258 0.1139 7 | 0 0 0 0 238 0.1168 8 | 0 0 0 0 437 0.1532 9 | 0 0 0 0 24 0.0328 ------Total | 15341 14844 220717 108379 508780 ErrorO | 0.0126 0.2022 0.0242 0.0350 0.0143

6 7 8 9 10 ------0 | 0 0 0 0 1 1.0000 1 | 0 0 0 0 2 0.0239 2 | 0 0 0 0 24 0.0582 3 | 3 3 0 0 548 0.0387 4 | 0 0 0 0 1484 0.0479 5 | 4161 9 1866 2 854 0.0168 6 | 293549 6036 23182 717 3534 0.1139 7 | 3918 34011 159 0 184 0.1168 8 | 6539 175 41159 0 295 0.1532 9 | 712 0 0 21671 0 0.0328 ------Total | 308882 40234 66366 22390 6926 ErrorO | 0.0496 0.1547 0.3798 0.0321 1.0000

Total ErrorC ------0 | | 1 1.0000 1 | | 15519 0.0239 2 | | 12574 0.0582 3 | | 224040 0.0387 4 | | 109853 0.0479 5 | | 510074 0.0168 6 | | 331276 0.1139 7 | | 38510 0.1168 8 | | 48605 0.1532 9 | | 22407 0.0328 ------Total | | 1312859 ErrorO | | 0.0564

ErrorO = Errors of Omission (expressed as proportions) ErrorC = Errors of Commission (expressed as proportions)

90% Confidence Interval = +/- 0.0003 (0.0561 - 0.0567) 95% Confidence Interval = +/- 0.0004 (0.0560 - 0.0568) 99% Confidence Interval = +/- 0.0005 (0.0559 - 0.0569) 76

KAPPA INDEX OF AGREEMENT (KIA) ------Using WEN_LTA_PREDICT2 as the reference image ... Category KIA ------0 0.0000 1 0.9758 2 0.9411 3 0.9535 4 0.9478 5 0.9725 6 0.8511 7 0.8795 8 0.8386 9 0.9666

WEN_LTA Category KIA ------

1 0.9873 2 0.7958 3 0.9708 4 0.9618 5 0.9765 6 0.9336 7 0.8407 8 0.6056 9 0.9673 10 0.0000

Overall Kappa = 0.9249 77 A_10b Ochco Ecoregion V map Error Matrix Analysis Mar4, 2010

(rows : mapped) 1 2 3 4 5 ------1 | 125045 5892 13 23 19 0.6426 2 | 62 196 0 0 0 0.9084 3 | 0 0 68761 14777 4490 0.5274 4 | 2 0 41521 209216 32685 0.5205 5 | 0 0 7037 17636 209408 0.1672 6 | 0 0 127 1142 1190 0.1182 7 | 0 0 2 1 514 0.0824 8 | 1 0 92 149 80 0.2985 9 | 0 0 3 75 0 0.3711 ------Total | 125110 6088 117556 243019 248386 ErrorO | 0.0005 0.9678 0.4151 0.1391 0.1569

6 7 8 9 10 ------1 | 0 0 45 7 218822 0.6426 2 | 0 0 0 0 1882 0.9084 3 | 375 4 133 0 56943 0.5274 4 | 1696 124 707 51 150353 0.5205 5 | 4525 362 371 0 12110 0.1672 6 | 26543 0 69 39 990 0.1182 7 | 0 6147 0 0 35 0.0824 8 | 30 19 34923 3857 10630 0.2985 9 | 0 0 3301 25776 11831 0.3711 ------Total | 33169 6656 39549 29730 463596 ErrorO | 0.1998 0.0765 0.1170 0.1330 1.0000

Total ErrorC ------1 | | 349866 0.6426 2 | | 2140 0.9084 3 | | 145483 0.5274 4 | | 436355 0.5205 5 | | 251449 0.1672 6 | | 30100 0.1182 7 | | 6699 0.0824 8 | | 49781 0.2985 9 | | 40986 0.3711 ------Total | |1312859 ErrorO | | 0.4622

ErrorO = Errors of Omission (expressed as proportions) ErrorC = Errors of Commission (expressed as proportions)

90% Confidence Interval = +/- 0.0007 (0.4615 - 0.4629) 95% Confidence Interval = +/- 0.0009 (0.4614 - 0.4631) 99% Confidence Interval = +/- 0.0011 (0.4611 - 0.4634) 78 KAPPA INDEX OF AGREEMENT (KIA) ------Using JIM_LTA_PREDICT as the reference image ... Category KIA ------1 0.2897 2 0.0874 3 0.4208 4 0.3612 5 0.7938 6 0.8788 7 0.9172 8 0.6923 9 0.6203

JIM_LTA Category KIA ------1 0.9993 2 0.0306 3 0.5332 4 0.7917 5 0.8059 6 0.7955 7 0.9231 8 0.8784 9 0.8627 10 0.0000

Overall Kappa = 0.4653