Grain and Extent Considerations Are Integral for

Article

Grain and Extent Considerations Are Integral forMonitoring Landscape-Scale Desired Conditions inFire-Adapted Forests dagger

Tzeidle N Wasserman 1 Andrew J Saacutenchez Meador 12 and Amy E M Waltz 1

1 Ecological Restoration Institute Northern Arizona University Flagstaff AZ 86011-5017 USA

AndrewSanchezMeadornauedu (AJSM) AmyWaltznauedu (AEMW)2 School of Forestry Northern Arizona University Flagstaff AZ 86011-5018 USA

Correspondence TzeidleWassermannauedu Tel +928-523-7488

dagger This manuscript is adapted from an annual report submitted to the USDA Forest Service which provided

funding for this research

Received 1 May 2019 Accepted 25 May 2019 Published 29 May 2019

Abstract Remotely-sensed data are commonly used to evaluate forest metrics such as canopy cover

to assess change detection and to inform land management planning Often canopy cover is measured

only at the scale of the spatial data product used in the analysis and there is a mismatch between the

management question and the scale of the data We compared four readily available remotely sensed

landscape data productsmdash Light detection and ranging (LiDAR) Landsat-8 Sentinel-2 and National

Agriculture Imagery Program (NAIP) imagery mdashat different spatial grains and multiple extents to

assess their consistency and efficacy for quantifying key landscape characteristics of forest canopy

patches and sensitivity to change We examined landscape-scale patterns of forest canopy cover

across three landscapes in northern Arizona and assessed their performance using six landscape

metrics Changes in grain and extent affect canopy cover patch metrics and the inferences that can be

made from each data product Overall data products performed differently across landscape metrics

When performing analyses and choosing data layers it is essential to match the scale of the data

product to the management question and understand the limitations inherent in using canopy cover

as a stand-alone metric

Keywords canopy cover landsat LiDAR sentinel naip landscape metrics spatial resolution patch

1 Introduction

United States federal land management agencies are directed to manage national forests to promote

resilient landscapes defined as within their natural range of variability [1] Public lands in the western

US require restoration to address the uncharacteristic current conditions such as high tree densities

high fuel loads bark beetle infestations and subsequent high fire hazard [1ndash3] Currently forests

are subject to a variety of disturbances including large wildfires insects pathogens and post-fire

flooding While these disturbances were always components of the historical ecosystem they are now

strongly influenced by human activities and climate change and uncharacteristic disturbances have

become more frequent higher in intensity and cover larger areas than historically [45] USDA Forest

Service restoration goals need to be accomplished at an accelerated pace to maintain resiliency [6ndash8]

In addition public lands have seen both leadership direction and political pressure to include increased

participation in planning and decision making from multiple agencies and stakeholders [9] As a

result both land managers and stakeholders are interested in understanding landscape-level processes

at scales appropriate to planning and implementation and quantifying metrics that are indicative of

resilient and restored ecosystems that capture the natural range of variability

Forests 2019 10 465 doi103390f10060465 wwwmdpicomjournalforests

Forests 2019 10 465 2 of 21

Understanding spatial and temporal patterns in forested landscapes is an important component of

ecosystem complexity and forest ecology However these are difficult concepts to quantify and capture

for land management practitioners and non-technical stakeholders [10ndash12] Forests are composed of a

mosaic of patches differing in their structure shape and spatial arrangement and these patterns affect

overall ecological processes [101314] Adjacent patches interact with one another to compose the

larger landscape and influence landscape patterns and processes at larger scales yet forest management

historically has been done on traditional stand-level scales Recent work examining the distribution and

proportion of forest attributes across the landscape informs questions concerning forest restoration and

resilience wildfire hydrology and wildlife habitat management [1013] but is not fully incorporated

into land management planning and active management

Quantifying spatial heterogeneity of forest structure can be used as a tool to measure the ecological

integrity of a system and forest structural and compositional complexity Scale is an important

component of our ecological view of the landscape Metrics used to describe forest structure and spatial

heterogeneity of a landscape will vary dependent on the scale at which they are examined [1015]

Scale within a landscape context can be defined in terms of both grain and extent [15ndash17] The spatial

grain of the landscape is the smallest resolution of the data Extent is the overall size of the landscape

or area encompassed by the observation Extent and grain determine the upper and lower limits of the

observation and changes in both grain and extent affect the patterns detected and the inferences we

make Canopy cover is a metric that is widely used in modeling species habitat relationships spatial

patterns fragmentation forest resilience stand density and many other areas of interest [131819]

It has also been used in land management direction as a surrogate for wildlife habitat quality However

canopy cover is measured or extracted in multiple ways at multiple spatial scales and extents that can

vary by the user-defined parameters Often this metric is used without explicitly defining how canopy

cover was measured or classified

Remote sensing is an effective tool for understanding current landscape conditions monitoring

forest change and understanding how forest ecosystems respond to disturbances The advancement

in technology and available data sources provide a means to assess a range of spatial information and

real-time data conduct surveys and provide continuous data sources across large landscapes [20]

There is often a lack of spatially explicit data that characterize forest heterogeneity at multiple scales

In many cases field or ground data are not readily available they are time-consuming and costly to

collect and are often not spatially replicated across large areas or available at the scale of the analysis

Therefore using readily available and spatially explicit remotely sensed data products can aid in

elucidating landscape patterns of canopy cover and other forest structure metrics when field data

are not available or not feasible Remotely sensed data products can provide spatially continuous

information with higher efficiency and lower cost

Light detection and ranging (LiDAR) has become increasingly useful in the three-dimensional

characterization of forest canopy and other forest attributes such as stand structure biomass and tree

density [2122] Airborne LiDAR data can be used to characterize tree-level forest structure and

over large areas [2324] and individual-tree detection methods can be used to characterize forest

structure tree spatial patterns and canopy metrics [2526] Additionally LiDAR can provide accurate

high-resolution descriptions of overstory structure and patterns with high accuracy at multiple

scales [27]

Remotely sensed data coupled with field observations can be an important data source to support

the monitoring of forest restoration via mechanical thinning and burning treatments and provide

baseline forest inventory data where there is none This study examines readily available remotely

sensed landscape data products to assess their consistency and efficacy for quantifying key landscape

characteristics of forest canopy patches and sensitivity to change due to forest management actions

or diversity In this analysis we compared four remotely sensed data products at different spatial

grains and multiple spatial extents to examine landscape-scale patterns of forest canopy cover across

three landscapes in northern Arizona USA Our objectives were to observe how changes in grain and

Forests 2019 10 465 3 of 21

extent affect canopy cover patch characteristics We specifically asked 1) How do readily available

and repeatable remotely sensed productsmdashLandsat-8 Sentinel-2 the National Agriculture Imagery

Program and LiDARmdashthat are at different spatial grains compare in their ability to accurately predict

canopy cover and 2) how do forest canopy patch metrics vary across remotely sensed data products

and spatial extents

2 Materials and Methods

21 Study Area

We examined landscape patterns of canopy cover across three ponderosa pinegambel oak

(Pinus ponderosa var scopulorum EngelmQuercus gambelii Nutt) forests spanning a gradient of

intact un-managed ponderosa pine forest to a highly managed forest in northern Arizona USA

Our three landscapes were Powell Plateau and Tepeats Creek near the North Rim of the Grand Canyon

and Windmill DrawJacks Canyon located on the Mogollon Rim (Figure 1) Powell Plateau (~1800 ha

in size) represents a relict landscape of ponderosa pine and oak on limestone soils that has remained a

relatively intact functional landscape with a natural fire disturbance history into the 20th century [28]

Tepeats Creek (~2100 ha) represents a ponderosa pine and oak forest on limestone soils with fire

exclusion and timber management since the late 19th century and Windmill DrawJacks Canyon

(~2400 ha hereafter referred to as ldquoWindmill Drawrdquo) represents a ponderosa pine and oak forest on

basalt soils with fire exclusion and timber management since the late 19th century both typical of

current southwestern forest conditions [29]

Figure 1 Map of study landscapes in northern Arizona USA Powell Plateau and Tepeats Creek on the

North Rim of the Grand Canyon and Windmill DrawJacks Canyon on the Mogollon Rim

22 Remotely Sensed Data

We used remotely sensed data from four sources to examine patterns of canopy cover across

our three study landscapes We used Landsat-8 data (30 m cell resolution) Sentinel-2 data (10 m

cell resolution) orthographic aerial imagery from the National Agriculture Imagery Program (NAIP)

(1 m cell resolution) and aerial LiDAR data (1 m cell resolution) across our three landscapes In this

analysis we compared landscape metrics calculated from these four spatial products (ie different

Forests 2019 10 465 4 of 21

grainsmdashLandsat-8 Sentinel-2 NAIP and LiDAR) and then make comparisons across landscapes

The Landsat and Sentinel data are easily available to land management agencies and are released

relatively often while NAIP imagery is released every two to four years depending on location LiDAR

although more accurate is currently at a higher cost and flown by individual agencies or coalitions

of natural resource management stakeholders Currently it is not consistently collected across the

nation but the USGS 3D Elevation Program (3DEP) has set a goal to complete nationwide LiDAR data

coverage by 2023 [30] Our objective was to address how remotely sensed canopy cover patch metrics

vary over multiple spatial extents and how the grain (cell resolution) of normally used products affects

landscape metrics commonly used to assess restoration success and thus patterns detected

Airborne LiDAR data were used to create a digital elevation model (DEM) digital surface model

(DSM) and a canopy height model (CHM) for each site at 1 m spatial resolution The Airborne LiDAR

data were collected over two acquisition periods AugustndashSept 2012 for Powell and Tepeats Creek and

AugustndashSept 2013 for Windmill Draw The data were requisitioned by the USDA Forest Service and

acquired by Quantum Spatial using Leica ALS50 Phase II and ALS60 systems either dual mounted

or separately in a Cessna Caravan and a Partenavia fixed-wing aircraft respectively The sensor

was flown at an altitude of 900 m aboveground with ge50 side-lap (ge100 overlap) resulting in an

average volumetric point-density of ge8 pulses per m2 These data were then classified as ground or

non-ground by the vendor using TerraScan v12004 and v13008 which uses the Axelsson algorithm

to classify the DTM points [31] The horizontal and vertical positioning errors in the point cloud data

were le15 cm Resulting CHM pixels above 2 m were classified as either canopy or not canopy with no

effort undertaken to differentiate between deciduous and coniferous vegetation

Additionally we utilized National Agriculture Imagery Program (NAIP) imagery collected for

Arizona in 2015 This imagery is available free via Google Earth Engine (GEE) [32] and includes four

bands (red green blue and infrared) with a 1 m ground sample distance At a minimum the horizontal

accuracy of the imagery must be within 6 m and cloud cover must be less than 10 per quarter USGS

topographic quadrangle tile NAIP Imagery was loaded into R (httpswwwr-projectorg) using

the raster package (httpscranr-projectorgwebpackagesraster) and a ratio band was calculated

by dividing the near-infrared and red (bands 4 and 1 respectively) [33] The ratio band was merged

with the red green blue and near-infrared bands Test classifications indicated that 25 classes limited

confusion between tree canopy and forest openings and a 25-class unsupervised classification was

run using the RStoolbox package (httpscranr-projectorgwebpackagesRStoolbox) and then each

class was evaluated by overlaying with LiDAR CHM to verify canopy and not canopy classes We

then classified the resulting layer into a binary map of canopy and not canopy cover at 1 m spatial

resolution and georeferenced this layer to the LiDAR CHM We then generated 1000 random points

within each landscape and performed an accuracy assessment using the LiDAR classified canopy and

not canopy image as truth Resulting overall classification accuracies were 633 618 and 763 for

Powell Tepeats Creek and Windmill Draw areas respectively

Sentinel-2 is part of the Copernicus Earth Observation Program of the European Union We utilized

cloud-free (lt10) fall imagery (October 2015 for Windmill Draw and August 2016 for Powell plateau

and Tepeats Creek) from Sentinel-2 were used to characterize canopy cover across the study area

The satellite is equipped with a wide swath multi spectral instrument (MSI) which collects near-nadir

imagery in 13 spectral bands (coastal aerosol red green blue three red edge two near-infrared

water vapor and three shortwave infrared) ranging from 10 to 60 m resolution [34] For this

study we used the 10 m resolution visible and near-infrared bands and each Sentinel-2 image was

atmospherically corrected to bottom of atmosphere reflectance using the Sen2Cor algorithm provided

by the European Space Agency Sentinel Application Platform [35] Using the same approach as we

used with the NAIP imagery we performed a 25-class unsupervised classification with the resulting

image being reclassified into a binary map of canopy and not canopy cover at 10 m spatial resolution

To perform an accuracy assessment with this product we aggregated the LiDAR canopy and not canopy

image to 10 m using the raster package and reclassified pixels to canopy where ge50 (an approximate

Forests 2019 10 465 5 of 21

non-linear interpolation based on the FAO classification using 10 cover at 05 ha [36] Hansen et alrsquos

classification of cover using 25 at 30 m [37] and an assumed 90 cover at 1 m for LiDAR) of the

LiDAR canopy image pixels were classified as canopy This layer was then compared to the classified

Sentinel-2 canopy image using 1000 randomly generated points and resulting overall classification

accuracies were 724 568 and 631 for Powell Tepeats Creek and Windmill Draw respectively

Lastly Landsat-8 imagery was used to assess canopy cover at the coarsest grain 30 m spatial

resolution We obtained Landsat-8 operational land imagery from the fall (OctoberndashNovember) 2015

These images contain seven bands (five visible and near-infrared bands and two short-wave infrared)

processed to orthorectified surface reflectance by GEE using the Landsat surface reflectance code

(LaSRC) which makes use of the coastal aerosol band to perform aerosol inversion tests uses auxiliary

climate data from MODIS and uses a unique radiative transfer model We used a cloud cloud-shadow

and cirrus cloud mask included in the SR data using CFMASK [38] As with the other products

we performed a 25-class unsupervised classification with the resulting image being reclassified into

a binary map of canopy and not canopy cover at 30 m spatial resolution To perform an accuracy

assessment with this product we aggregated the LiDAR canopy and not canopy image to 30 m using

the raster package and reclassified pixels to canopy where ge25 [37] of the LiDAR canopy image

pixels were classified as canopy This layer was then compared to the classified Landsat-8 canopy

image using 1000 randomly generate points and resulting overall classification accuracies were 746

693 and 853 for Powell Tepeats Creek and Windmill Draw respectively

23 Use of LiDAR as Reference Data

Airborne LiDAR is a widely accepted method of collecting and generating precise georeferenced

spatial information about features of the earthrsquos surface shape and physical characteristics [39]

Recent advances have produced mapping technologies that allow scientists to examine environmental

features such as forests watersheds and ecosystem attributes with accuracy and precision [39]

LiDAR technology provides horizontal and vertical information at high spatial resolution and vertical

accuracies It has been used to accurately assess forest canopy metrics and other forest attributes

such as stand structure biomass and tree density [2122] LiDAR provides very detailed wall-to-wall

maps of forest canopy cover (via reclassification of the CHM to covernot cover binary maps) that

are problematic and laborious to produce with traditional field and photointerpretation methods

Furthermore validating the accuracy of LiDAR-derived canopy cover using highly uncertain field

observations should be avoided as the results of ocular and vertical sighting methods are prone to

sampling problems and are difficult to confirm [40] As a result of these uncertainties LiDAR-derived

canopy cover maps are increasingly being used as reference data for assessing accuracies of alternative

methods which typically show agreement with LiDAR-derived canopy cover estimates but are subject

to high levels of bias (underestimate low and to overestimate high canopy cover) and imprecision [41ndash43]

Given that LiDAR-derived estimates are continuous and high resolution making them useful in forest

monitoring and management we chose to use LiDAR-derived metrics as reference data and compare

the performance of the three other spatially derived products to LiDAR

For each landscape the four data products that vary in grain (Landsat-8 30 m Sentinel-2 10 m

NAIP 1 m and LiDAR 1 m) were compared over multiple spatial extents (Figure 2) For all four

data products each patch was delineated by contiguous pixels of canopy cover and we used the

eight neighbor rule for cell-based indices- meaning contiguous cells were defined as those that were

either directly or adjacently contiguous After classification of each data layer into canopynot canopy

we used landscape metrics to quantify key characteristics of forest canopy patches and to quantify forest

cover patterns and make comparisons among different landscapes A total of six patch metrics were

examined to explain the variability of patches across landscapes of different extent (Table 1) With our

simple binary classification the proportion of landscape (PLand) patch density (PD) and mean patch

area (MPA) are basic descriptors of the canopy distribution These characteristics inform the descriptors

Forests 2019 10 465 6 of 21

of spatial connectedness and distribution including the aggregation index (AI) patch cohesion index

(PCI) and landscape division index (LDI)

Figure 2 Remotely sensed data products and associated grain of each data source Landsat-8 (30 m)

Sentinel-2 (10 m) NAIP (1 m) and LiDAR (1 m) The top panel (a) shows the canopy cover of Windmill

Draw landscape with the blue areas representing the relative size of each extent (10ndash1000 ha) analyzed

The lower panel (b) shows size of the spatial grain (resolution) and level of information available for

each data product within an example extent

Table 1 Description of landscape metrics evaluated across grain and extent on three landscapes

in northern Arizona USA Metrics derived from FRAGSTATS (wwwumassedulandecoresearch

fragstatsfragstatshtml)

Metric

bull PLand (proportion of landscape) is the total proportion of the patchmdashcanopy covermdashacross each landscape

bull PD (patch density) is the number of patches of canopy cover per unit area hectares in this analysis

bull MPA (mean patch area) is the average area of patches (ha) composed of canopy cover across each landscape

bull AI (aggregation index) is the area of canopy cover on the landscape that has like adjacencies of canopyclumped into a compact patch and is weighted proportionally by its area in the landscape It is a landscapemetric of how aggregated canopy cover is at each extent

bull PCI (patch cohesion index) is a connectivity metric that measures the physical connectedness of thecorresponding patch type Patch cohesion increases as canopy cover becomes more clumped or aggregated inits distribution and more physically connected

bull LDI (landscape division index) is the probability that two randomly chosen pixels in the landscape are notin the same patch and is based on the cumulative patch area distribution The sum is across the proportionalarea of each patch of canopy cover

For each landscape and each remote sensing product we performed a landscape pattern analysis

of canopy cover using the SDM Tool package (httpsCRANR-projectorgpackage=SDMTools)

in R (R Studio httpswwwr-projectorg) SDM Tools calculates landscape metrics (similar to

FRAGSTATS [44]) and outputs values for each metric We sampled each landscape metric across five

to seven different spatial extents 10 25 50 75 100 300 600 1000 ha We performed 100 Markov

Chain Monte Carlo (MCMC) simulations to sample each landscape at these multiple extents Our three

Forests 2019 10 465 7 of 21

landscapes varied in size therefore all three landscapes did not get sampled at the largest extents

Powell Plateau was sampled 10 handash300 ha Tepeats Creek was sampled 10 handash600 ha and Windmill

Draw was sampled 10 handash1000 ha (Figure 2)

3 Results

General patterns of patch characteristics were observed across spatial extents and between the four

data products (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) that match our understanding of

expected changes of patches with scale and grain This study more specifically tested readily available

remotely sensed data products to assess their consistency and efficacy for quantifying key characteristics

of forest canopy and sensitivity to change due to forest management or diversity Here we report the

differences in data products (grain) and how the patterns change across spatial extents

31 Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics ofCanopy Cover

The proportion of landscape (PLand) comprised of canopy cover was underpredicted by NAIP

and overpredicted by Landsat-8 and Sentinel-2 across our three landscapes PLand was best predicted

by the Sentinel-2 data (10 m) which produced means most similar to the LiDAR data (1 m) on Powell

Plateau our remnant functional landscape with an intact natural disturbance regime (Table 2 Figure 3)

NAIP (1 m) while still underestimated effective1y best characterized PLand on Windmill Draw

a highly managed forest landscape experiencing both fire exclusion and extensive timber management

Landsat-8 (30 m) overestimated PLand on all three landscapes

Table 2 Proportion of landscape (PLand) comprised of canopy cover across each landscape and data

product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents

Mean values and standard error (SE) are shown

Proportion of Landscape () Extent (ha)

10 25 50 75 100 300 600 1000

Powell Plateau

Landsat-80633

(0019)0619

(0017)0635

(0013)0648

(0011)0660

(0009)0584

(0000)

Sentinel-20568

(0011)0559

(0010)0569

(0006)0578

(0005)0577

(0004)0550

(0000)

NAIP0314

(0006)0319

(0005)0330

(0003)0332

(0003)0334

(0003)0329

(0000)

LiDAR0537

(0010)0535

(0007)0552

(0006)0560

(0005)0560

(0004)0543

(0000)

Tepeats Creek

Landsat-80743

(0013)0734

(0011)0742

(0009)0732

(0009)0737

(0008)0744

(0003)0748

(0001)

Sentinel-20824

(0011)0797

(0009)0807

(0008)0795

(0008)0796

(0007)0790

(0003)0797

(0000)

NAIP0439

(0007)0422

(0006)0427

(0005)0418

(0004)0417

(0004)0420

(0001)0418

(0000)

LiDAR0596

(0010)0571

(0008)0565

(0008)0560

(0007)0550

(0007)0546

(0002)0568

(0001)

Forests 2019 10 465 8 of 21

Table 2 Cont

10 25 50 75 100 300 600 1000

Windmill Draw

Landsat-80835

(0010)0826

(0009)0832

(0007)0824

(0006)0838

(0005)0826

(0002)0828

(0001)0831

(0000)

Sentinel-20832

(0009)0819

(0009)0828

(0007)0816

(0007)0824

(0006)0813

(0002)0814

(0001)0809

(0001)

NAIP0356

(0008)0336

(0007)0347

(0006)0339

(0006)0341

(0005)0321

(0002)0321

(0001)0318

(0000)

LiDAR0492

(0010)0473

(0009)0484

(0008)0476

(0008)0480

(0007)0461

(0002)0467

(0001)0470

(0000)

Figure 3 Violin plots (colored by extent and depicting probability density of the data at different values

smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)

for the proportion of landscape (ha) under forested canopy cover for each landscape and data product

(Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets

with rescaled y-axis provided for clarity

The number of patches of canopy cover or patch density (PD) was not accurately predicted

by any of our spatial products across landscapes or spatial extents NAIP at 1 m resolution highly

over-predicted the patch density by 25 to 3 times across all three landscapes (Table 3 Figure 4)

The small pixel size can fragment patches that the LiDAR point cloud keeps aggregated thus resulting

in many more patches across the landscape at all extents Landsat (30 m) and Sentinel-2 (10 m) highly

underestimated PD at their native cell resolutions Mean Patch Area (MPA) the average area composed

of patches of canopy cover was highly overestimated (one to two orders of magnitude) by Landsat

and Sentinel-2 as compared to LiDAR (Table 4 Figure 5) NAIP underestimated mean patch area by

three to four times on all landscapes across all spatial extents

Forests 2019 10 465 9 of 21

Table 3 Patch density (PD) the average number of patches of canopy cover on each landscape

delineated by each data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across

multiple spatial extents Mean values and standard error (SE) are shown

Patch Density (ha) Extent (ha)

10 25 50 75 100 300 600 1000

Powell Plateau

Landsat-80214

(0015)0138

(0011)0090

(0006)0063

(0004)0061

(0004)0075

(0000)

Sentinel-21047

(0074)0883

(0053)0706

(0035)0597

(0028)0590

(0025)0673

(0002)

NAIP57969(1079)

56659(0695)

53808(0516)

53300(0499)

52735(0448)

50537(0025)

LiDAR15428(0661)

15170(0536)

13349(0377)

12601(0352)

12375(0302)

12950(0011)

Tepeats Creek

Landsat-80153

(0010)0085

(0007)0051

(0004)0041

(0003)0038

(0003)0019

(0001)0011

(0000)

Sentinel-20310

(0032)0259

(0023)0189

(0017)0207

(0015)0185

(0014)0182

(0005)0157

(0000)

NAIP41339(1283)

40980(1027)

39345(0835)

40110(0723)

39728(0626)

37624(0212)

34257(0041)

LiDAR15310(0750)

16167(0617)

16325(0616)

16551(0526)

16973(0487)

16740(0135)

15306(0022)

Windmill Draw

Landsat-80138

(0007)0056

(0004)0032

(0002)0030

(0003)0021

(0002)0013

(0001)0011

(0000)0008

(0000)

Sentinel-20205

(0018)0153

(0018)0099

(0009)0104

(0008)0081

(0006)0073

(0002)0072

(0001)0067

(0000)

NAIP52483(1502)

54286(1227)

52324(1362)

52001(1231)

52385(1146)

55683(0579)

54084(0352)

51462(0104)

LiDAR23018(0853)

23109(0691)

22289(0706)

21886(0663)

21978(0618)

22724(0238)

21838(0135)

21666(0040)

for patch density (patches per ha) Patch density is the number of patches per landscape area under

forested canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1

m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity

Forests 2019 10 465 10 of 21

Table 4 Mean Patch Area (MPA) (ha) the average area composed of patches of canopy cover across

each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across

Mean Patch Area (ha) Extent (ha)

10 25 50 75 100 300 600 1000

Powell Plateau

Landsat-8 4647(0299)

9474(0776)

13988(1296)

19518(1840)

20520(2052)

7819(0035)

Sentinel-2 1094(0132)

1158(0129)

1096(0069)

1245(0067)

1206(0061)

0817(0002)

NAIP 0006(0000)

0006(0000)

0007(0000)

LiDAR 0045(0003)

0042(0002)

0046(0002)

0048(0001)

0042(0000)

Tepeats Creek

Landsat-8 6351(0264)

13949(0736)

25349(1542)

31540(2152)

34500(2566)

47600(2326)

71374(1401)

Sentinel-2 5420(0344)

7333(0700)

12646(1459)

8224(1062)

7709(0663)

4834(0187)

5082(0014)

NAIP 0012(0001)

0011(0000)

0012(0000)

0011(0000)

0012(0000)

LiDAR 0059(0006)

0045(0003)

0046(0004)

0041(0003)

0038(0002)

0033(0000)

0037(0000)

Windmill Draw

Landsat-8 7248(0255)

18257(0611)

33437(1340)

42230(2231)

58305(2789)

95256(6883)

85781(3900)

104836(0889)

Sentinel-2 6083(0302)

11371(0782)

17674(1437)

19018(2053)

20103(2224)

12738(0508)

11511(0152)

12085(0100)

NAIP 0008(0001)

0007(0000)

0008(0000)

0007(0000)

0006(0000)

LiDAR 0035(0005)

0028(0004)

0030(0004)

0029(0003)

0028(0002)

0021(0000)

0022(0000)

for mean patch area (ha) (average size of patches) for each landscape and data product (Landsat-8 30 m

Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis

provided for clarity

Forests 2019 10 465 11 of 21

Canopy cover was highly aggregated an attribute that was detected by all four data products

across all spatial extents sampled (Figure 6) Sentinel-2 was able to detect aggregation more accurately

on Tepeats Creek and Windmill Draw but under-predicted aggregation on Powell Plateau Landsat-8

best predicted aggregation on Windmill Draw and under-predicted aggregation on Tepeats Creek and

Powell Plateau (Table 5 Figure 6) NAIP under-estimated aggregation on all three landscapes and

across all extents especially on Powell Plateau

for aggregation index (0ndash100) Aggregation index indicates how adjacent patches of canopy cover are

to each other for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR

1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity

Table 5 Aggregation index (AI) () of canopy cover across each landscape and data product (Landsat-8

30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Mean values and

standard error (SE) are shown

Aggregation Index Extent (ha)

10 25 50 75 100 300 600 1000

Powell Plateau

Landsat-876761(1175)

76553(0986)

77397(0802)

78012(0712)

78950(0581)

74823(0021)

Sentinel-269008(0748)

68585(0636)

68873(0473)

69394(0406)

69600(0323)

66906(0009)

NAIP77402(0246)

77603(0191)

78206(0113)

78239(0122)

78429(0105)

78773(0003)

LiDAR91447(0146)

91467(0106)

91810(0074)

91921(0070)

91941(0063)

91810(0001)

Tepeats Creek

Landsat-882820(0625)

82027(0543)

81855(0490)

81684(0425)

81706(0426)

82238(0139)

82418(0047)

Sentinel-290755(0471)

89497(0390)

90100(0311)

89688(0269)

89613(0221)

89824(0094)

90086(0012)

NAIP81195(0239)

81281(0183)

81362(0150)

81272(0137)

81256(0121)

81784(0047)

81914(0013)

LiDAR91847(0181)

91468(0144)

91330(0146)

91286(0127)

91147(0118)

91083(0041)

91506(0013)

Forests 2019 10 465 12 of 21

Table 5 Cont

10 25 50 75 100 300 600 1000

Windmill Draw

Landsat-887235(0775)

86813(0595)

86940(0466)

86730(0364)

87511(0305)

86643(0168)

86639(0073)

86754(0010)

Sentinel-289880(0514)

89913(0344)

90184(0312)

89781(0323)

89827(0298)

89461(0091)

89408(0061)

88922(0032)

NAIP79129(0287)

78427(0252)

78715(0271)

78745(0256)

78549(0231)

77921(0159)

78242(0102)

78734(0020)

LiDAR88984(0192)

88656(0170)

88852(0175)

88824(0163)

88834(0151)

88463(0057)

88651(0035)

88751(0008)

All three landscapes had a high patch cohesion index and high physical patch connectivity of

canopy cover across all four data grains and across all spatial extents (Figure 7 Table 6) Patch cohesion

is sensitive to aggregation of canopy cover and increases as canopy cover becomes more clumped or

aggregated in its distribution NAIP and Sentinel-2 most accurately predicted patch cohesion on all

three landscapes across all extents while Landsat-8 consistently and increasingly under-predicted

patch cohesion at extents 50 ha and smaller (Table 6)

for patch cohesion index () Patch cohesion index describes the physical connectedness of patches of

canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR

Forests 2019 10 465 13 of 21

Table 6 Patch cohesion (PC) index measuring the physical connectedness of patches of canopy cover for

spatial extents Mean values and standard error (SE) are shown

Patch Cohesion Index () Extent (ha)

10 25 50 75 100 300 600 1000

Powell Plateau

Landsat-87733

(0066)8496

(0036)8998

(0014)9205

(0008)9318

(0005)9560

(0000)

Sentinel-29183

(0020)9476

(0012)9654

(0004)9723

(0002)9760

(0001)9855

(0000)

NAIP9539

(0015)9624

(0013)9686

(0008)9686

(0006)9705

(0004)9733

(0000)

LiDAR9859

(0007)9914

(0003)9947

(0001)9957

(0001)9963

(0001)9979

(0000)

Tepeats Creek

Landsat-87970

(0024)8700

(0012)9063

(0010)9229

(0009)9331

(0007)9627

(0001)9731

(0000)

Sentinel-29327

(0008)9569

(0004)9697

(0002)9749

(0002)9785

(0001)9877

(0000)9911

(0000)

NAIP9758

(0013)9793

(0011)9835

(0009)9844

(0008)9850

(0008)9913

(0003)9947

(0000)

LiDAR9887

(0006)9919

(0004)9936

(0003)9947

(0002)9949

(0002)9971

(0000)9981

(0000)

Windmill Draw

Landsat-88070

(0015)8767

(0006)9126

(0003)9283

(0002)9378

(0001)9638

(0000)9743

(0000)9798

(0000)

Sentinel-29333

(0005)9571

(0005)9698

(0002)9752

(0002)9784

(0002)9876

(0000)9912

(0000)9932

(0000)

NAIP9476

(0021)9471

(0019)9551

(0020)9538

(0020)9561

(0019)9518

(0012)9554

(0009)9627

(0002)

LiDAR9759

(0012)9793

(0010)9839

(0008)9843

(0009)9858

(0008)9876

(0004)9906

(0002)9921

(0000)

Landscape division index was best predicted by NAIP especially on Powell Plateau and Windmill

Draw but poorly captured within-extent variability (Figure 8 Table 7) Landsat-8 and Sentinel-2 highly

under-predicted LDI by two to four times across all landscapes and extents NAIP over-estimated LDI

on Powell Plateau but performed well at larger spatial extents 300 ha and 600 and 1000 ha on Tepeats

Creek and Windmill Draw (Table 7)

Forests 2019 10 465 14 of 21

for landscape division index () Landscape division index is presented for each landscape and data

and is defined as the probability that two animals placed in different areas somewhere in the extent

might find one another Insets with rescaled y-axis provided for clarity

Table 7 Landscape division index (LDI) of canopy cover across all three landscapes across for each

data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial

extents Mean values and standard error (SE) are shown

Landscape Division Index Extent

10 25 50 75 100 300 600 1000

Powell Plateau

Landsat-80585

(0024)0628

(0025)0615

(0020)0594

(0016)0581

(0014)0741

(0001)

Sentinel-20708

(0015)0726

(0014)0708

(0010)0692

(0008)0695

(0006)0745

(0000)

NAIP0996

(0000)0997

(0000)0998

(0000)0999

(0000)1000

(0000)

LiDAR0869

(0012)0879

(0010)0867

(0007)0864

(0006)0859

(0006)0881

(0000)

Tepeats Creek

Landsat-80446

(0021)0468

(0018)0474

(0018)0489

(0017)0486

(0016)0453

(0006)0444

(0001)

Sentinel-20326

(0020)0382

(0017)0367

(0016)0394

(0016)0382

(0013)0385

(0005)0374

(0000)

NAIP0959

(0005)0977

(0003)0980

(0003)0986

(0002)0988

(0002)0994

(0000)0994

(0000)

LiDAR0775

(0017)0839

(0013)0852

(0015)0869

(0013)0893

(0012)0939

(0004)0936

(0001)

Forests 2019 10 465 15 of 21

Table 7 Cont

10 25 50 75 100 300 600 1000

Windmill Draw

Landsat-80305

(0018)0319

(0015)0310

(0012)0325

(0010)0301

(0008)0322

(0004)0318

(0001)0312

(0000)

Sentinel-20319

(0018)0344

(0017)0337

(0015)0358

(0016)0353

(0015)0366

(0006)0366

(0005)0354

(0001)

NAIP0983

(0005)0992

(0003)0990

(0003)0994

(0002)0995

(0002)1000

(0000)1000

(0000)

LiDAR0925

(0014)0957

(0011)0952

(0011)0964

(0009)0965

(0009)0997

(0000)0998

(0000)

32 Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent

We examined how patch metrics varied across the landscapes and extents for each data product

PLand varied across landscapes and extent for all four spatial products In addition to the median and

mean values the ability to capture the variability on the landscape is important to both land managers

and stakeholders For this metric there was little observed variability at extents larger than 300 ha

and was best observed using extents smaller than 75 ha or 100 ha (Table 2 Figure 3) The values of

the PLand varied across landscapes However not consistently across remotely sensed products and

spatial grain The LiDAR data product showed similar canopy cover across Tepeats Creek and Powell

Plateau with lower values at Windmill Draw Conversely the Landsat-8 and Sentinel-2 data products

both had the highest canopy cover at Windmill Draw NAIP at 1 m resolution underestimated PLand

at Powell Plateau across all spatial extents

We standardized patch density (PD) by area in hectares (Figure 4) There were consistent patterns

of high overestimation of PD by NAIP and high underestimation of PD by Sentinel-2 and Landsat-8

compared to LiDAR On Powell Plateau at 100 ha extent LiDAR predicted 124 patches while NAIP

predicted 527 patches Landsat-8 predicted 006 patches and Sentinel-2 predicted 059 patches (Table 3)

Patch density of both single trees or groups of trees are categorized as a patch and in these forests

canopy patches on the landscape were dominated by many small patches This trend was steady

across all spatial extents The metric was amplified by a higher number of small trees and density

decreased by the area sampled For both LiDAR and NAIP patch density was not sensitive to the

extent measured However using Landsat-8 data (30 m grain) and Sentinel-2 data (10 m grain) patch

density decreased with spatial extent by nearly an order of magnitude from smallest (10 ha) to largest

(300 ha) This correlates with the inability of these spatial products at those spatial grains to detect

small patches Landsat-8-derived patch density at a 10-ha extent was limited by this extent and showed

higher patch density

Patch density (PD) and mean patch area (MPA) are closely related and remotely sensed products

with high PD will have lower MPA For example On Tepeats Creek at 50 ha extent LiDAR predicted

the mean patch area to be 0046 ha in size NAIP predicted it to be 0006 ha in size Sentinel-2 predicted

1096 and Landsat-8 predicted 139 ha in size (Table 4 Figure 5) The effect of extent was similar to the

patterns in PD Landsat-8 and Sentinel-2 data had an increasing MPA with an increased spatial extent

on all three landscapes which was expected due to the larger cell resolution that samples the data

NAIP and LiDAR showed little sensitivity to spatial extents (Table 4)

Aggregation (AI) was high on all three landscapes (Figure 6 Table 5) and varied little across

all landscapes as estimated using LiDAR (ie reported standard errors ranged from 0001 to 0192)

The NAIP had consistently lower AI values than the other products on Tepeats and Windmill Draw

areas This was directly related to the native cell resolution of NAIP at 1 m the high PD and low

MPA Landscape Aggregation was well estimated using the Landsat-8 data This was due to the larger

cell resolution (30 m) which tended to form more contiguous patches NAIP at 1 m cell resolution

Forests 2019 10 465 16 of 21

detected more discrete single patches that are not aggregated At the landscape-scale NAIP at 1 m cell

resolution under-predicted aggregation All products were consistent across extents showing little

sensitivity to extent

Patch Cohesion (PC) increased as canopy cover became more clumped or aggregated in its

distribution and therefore more physically connected All three landscapes had a high patch cohesion

consistent with the aggregation index and supporting the observation that canopy cover was highly

connected This pattern was found across all products and landscapes (Figure 7 Table 6) Although

NAIP had much different values than LiDAR on patch description metrics (PLand PD MPA) for patch

cohesion it was more similar to LiDAR on all three landscapes and across all extents Sentinel-2

performed well at estimating patch cohesion across all spatial extents (Table 6) Landsat-8 consistently

under-predicted patch cohesion at smaller extents (10ndash25 ha) and was more accurate at predicting

patch cohesion at ge100 ha (Table 6) Patch cohesion was sensitive to the aggregation of canopy cover

and increased as canopy cover becomes more clustered or aggregated in its distribution

Landscape division index (LDI) is the probability that two randomly chosen pixels in the landscape

are not in the same patch and is based on the cumulative patch area distribution NAIP had a high

probability of landscape division across all spatial extents as it tended to break up large patches

into many smaller patches yet it was accurate on Windmill Draw (Table 7) Landsat-8 had a lower

probability of LDI because it measured canopy cover across the landscape as a smaller number of

larger sized patches The four data products displayed variability of patterns across spatial extents

due to their native cell resolutions NAIP showed consistent unimodal distributions of LDI at smaller

extents LiDAR only showed this at the Windmill Draw landscape

4 Discussion

This analysis provided insights into remotely sensed data products their native cell resolutions

(grain) and how metrics can describe patch and landscape characteristics of canopy cover We examined

six forest canopy patch metrics and described their differences across data products (Landsat-8 30 m

Sentinel-2 10 m NAIP 1 m and LiDAR 1 m) spatial extent and among study landscapes LiDAR

was used as a reference for evaluating landscapes as well as for assessing the accuracy of the other

products In the United States little resources are available for ground-based assessment and remote

sensing techniques are increasingly explored for usability with minimal ground-truthing Our accuracy

assessment on each remotely sensed data product put us within the acceptable range of accuracy for

spatial products [45]

Spatial heterogeneity directly affects ecosystem patterns and processes but can be difficult

to measure in a consistent way to inform federal land management priorities Heterogeneity is

characterized by a focal patch type interacting with the mosaic of surrounding patches or patch

types across an area of interest [46] and can influence a variety of ecological responses including

animal movement species interactions fire spread and behavior and overall ecosystem function [47]

For land managers landscape heterogeneity is important to support multiple seral stages as habitat

for wildlife species biodiversity ecosystem function and can aid in characterizing fuels and wildfire

spread [48ndash51] We chose a variety of metrics to quantify landscape pattern attributes and assess

sensitivity to management practices found in western frequent-fire forests

Patch attributes such as mean patch density (PD) that depend on discriminating smaller patches

are highly underestimated by coarser grained products (Landsat-8 and Sentinel-2) The grain of both

Landsat-8 and Sentinel-2 is too large to detect individual small patches Small patches are lumped

together resulting in fewer but larger patches The proportion of landscape of canopy cover (PLand)

can be a highly variable metric especially at smaller extents (10ndash25 ha) but is more stable at larger

extents (ge300 ha) On all three landscapes fine-scale heterogeneity becomes harder to detect by each

data product at scales 300 ha and larger On our highly managed landscape Windmill Draw there is

less fine-scale landscape variability of canopy cover Landsat-8 (30 m grain) and Sentinel-2 (10 m grain)

over-predicted proportion of canopy cover because each pixel size is larger so when the pixel moves

Forests 2019 10 465 17 of 21

across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it

as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and

spatial extents because the 1 m data product cell resolution was too small to accurately estimate the

true pattern of PLand across the landscapes

Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration

and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern

US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife

habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the

processes identified at small scales are not always the drivers at larger spatial scales When this happens

incorrect inferences can be made for the phenomena or species of interest and incorrect management

actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis

or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy

cover-informed spatial metrics combined with additional forest structural information including

tree size (height and diameter) age species composition and tree densities are all important factors

in determining if management actions have restored forest structure to within the natural range

of variability [55ndash57] Used together these variables are more informative for quantifying wildlife

habitat potential fire behavior landscape connectivity and forest restoration success and resilience to

disturbances [5157ndash59]

Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]

To capture this remotely LiDAR is a very robust data source that can accurately produce models of

canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)

cell resolution and the user has to classify shadows bare ground shrubs and more therefore there

are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of

25 m could help reduce some classification error and increase the accuracy of a binary canopynot

canopy classification From a management perspective and in a landscape ecology context forest

heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where

patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with

frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape

As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future

disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across

large scales will be useful to assess treatment success Using other information in tandem with canopy

cover such as tree size and age classes structural composition and species composition will be more

informative and helpful in elucidating landscape patterns and processes than using canopy cover

alone Although analyses should include structural and compositional variables remotely sensed data

acquisitions may be faster and better assessed to quickly inform adaptive management

Limitations of This Study

In this study we calculated landscape metrics on a single class (canopy cover) and therefore can

only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe

spatial patterns of patches and openings nor make comparisons among classes Additionally our three

landscapes varied in size and therefore landscape metrics were calculated on different maximum

extents Differences in data product and grain (cell resolution) caused differences in how landscape

patterns of canopy cover were calculated and were more evident than differences due to spatial extent

Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while

acceptable by most classification standards this is still an important source of uncertainty The LiDAR

data used in this analysis is the best estimate of canopy cover in our study area and interpretations of

results should be made accordingly

Forests 2019 10 465 18 of 21

5 Conclusions

Spatial products at different resolutions performed differently across patch metrics and across our

three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully

characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to

accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m

resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness

and distribution of patches such as aggregation index and patch cohesion index were well described

by all three spatial data products LiDAR data provide the most accurate and high-resolution data

that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three

landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data

product at different grains and at multiple spatial extents to describe forest heterogeneity and structural

characteristics within a landscape

Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM

Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute

Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data

Conflicts of Interest The authors declare no conflict of interest

References

1 USDA Forest Service National Forest System Land Management Planning Available online https

wwwfsusdagovplanningrule (accessed on 4 February 2019)

2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015

3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire

management For Ecosyst 2015 2 1418 [CrossRef]

4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western

US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]

5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring

Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]

6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects

GAO-15-398 United States Government Accountability Office Washington DC USA 2015

7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report

United States Department of Agriculture Forest Service Washington DC USA 2012

8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating

and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069

9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration

documentscflrptitleIVpdf (accessed on 4 February 2019)

10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process

Springer New York NY USA 2001

11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a

Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]

12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity

for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]

13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics

In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397

Forests 2019 10 465 19 of 21

14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht

The Netherlands 2006

15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]

16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620

17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc

Ecol 1989 3 245ndash252 [CrossRef]

18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest

management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156

[CrossRef]

19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape

in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]

20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors

2009 9 1768ndash1793 [CrossRef] [PubMed]

21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics

with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371

22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand

structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]

23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale

effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag

2013 287 17ndash31 [CrossRef]

24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale

ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63

[CrossRef]

25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree

Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]

26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest

resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013

291 442ndash457 [CrossRef]

27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference

models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]

28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes

with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]

29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since

Euro-American settlement J For 1994 92 39ndash47

30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D

Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35

31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the

Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117

32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine

Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]

33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial

Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43

34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)

35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-

release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)

36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online

httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)

37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad

Sci USA 2010 107 8650ndash8655 [CrossRef]

38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ

Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote

Sens Environ 2017 194 379ndash390 [CrossRef]

Forests 2019 10 465 20 of 21

39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101

An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston

SC USA 2012

40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies

In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014

41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring

across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern

Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of

Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235

42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and

airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]

43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery

and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]

44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for

Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on

4 February 2019)

45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K

Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a

decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354

46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142

[CrossRef]

47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015

[CrossRef]

48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological

Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21

49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes

50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J

Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14

101ndash112 [CrossRef]

51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG

Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based

Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture

Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013

52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes

as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection

p17192coll1id861rec25 (accessed on 4 February 2019)

53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire

behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]

54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96

[CrossRef]

55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management

pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]

56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing

spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499

[CrossRef]

57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest

structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA

Ecol Manag 2018 422 147ndash160 [CrossRef]

58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the

Grand Canyon USA J Biogeogr 2002 29 31ndash47

Forests 2019 10 465 21 of 21

59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM

Integrating single-species management and landscape conservation using regional habitat occurrence models

The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]

60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern

following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90

copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access

article distributed under the terms and conditions of the Creative Commons Attribution

(CC BY) license (httpcreativecommonsorglicensesby40)

Introduction

Materials and Methods

Study Area

Remotely Sensed Data

Use of LiDAR as Reference Data

Results

Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover

Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent

Discussion

Conclusions

References