Vegetation Biodiversity Vegetation Biodiversity using Remote Sensingusing Remote Sensing

Morgan DeanMorgan Dean

EES 5053EES 5053

12/1/0612/1/06

Reviewing Articles:Reviewing Articles:

Landscape Ecology and Diversity Patterns in the Landscape Ecology and Diversity Patterns in the

Seasonal Tropics from Landsat TM Imagery Seasonal Tropics from Landsat TM Imagery (1)(1) by: Jose M. Rey-Benayas; Kevin O. Popeby: Jose M. Rey-Benayas; Kevin O. Pope

Identifying Conservation-Priority Areas in the Identifying Conservation-Priority Areas in the Tropics: A Land-Use Change Modeling ApproachTropics: A Land-Use Change Modeling Approach (2)(2) by: Shaily Menon; R. Gill Pontius Jr; Joseph Rose; M. L. by: Shaily Menon; R. Gill Pontius Jr; Joseph Rose; M. L.

Khan; Khan; Hamaljit S. BawaHamaljit S. Bawa Remote Sensing of Vegetation, Plant Species Remote Sensing of Vegetation, Plant Species

Richness, and Regional Biodiversity HotspotsRichness, and Regional Biodiversity Hotspots (3)(3) by: William Gouldby: William Gould

Introduction for Article 1Introduction for Article 1

Landsat Thematic Mapper ( TM ) imagery was used to analyze patterns Landsat Thematic Mapper ( TM ) imagery was used to analyze patterns of landscape diversity in the seasonal tropical forests of northeastern of landscape diversity in the seasonal tropical forests of northeastern GuatemalaGuatemala

TM radiance and the radiance coefficient of variation (CV) are significant TM radiance and the radiance coefficient of variation (CV) are significant in discriminating land-cover typesin discriminating land-cover types

Cluster analysis of TM4, TM5, and TM7 radiance produced six distinct Cluster analysis of TM4, TM5, and TM7 radiance produced six distinct land-cover typesland-cover types

Green leaf biomass from TM4 and canopy closure and degree of Green leaf biomass from TM4 and canopy closure and degree of senescence from TM5 and TM7 represent the most important variables in senescence from TM5 and TM7 represent the most important variables in discriminating between land-cover types in the uplands and the lowland discriminating between land-cover types in the uplands and the lowland swamps respectivelyswamps respectively

Patterns of landscape diversity reflected in three landscape indices: the Patterns of landscape diversity reflected in three landscape indices: the number of land-cover types (LCT), the Shannon-Weaver index of number of land-cover types (LCT), the Shannon-Weaver index of lanscape evenness (S-W), and a topographic index (TI)lanscape evenness (S-W), and a topographic index (TI)

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Primary ObjectivePrimary Objective

To demonstrate that TM analyses, without To demonstrate that TM analyses, without extensive field data, provide valuable extensive field data, provide valuable information on landscape diversity information on landscape diversity patterns to aid conservation and patterns to aid conservation and development plans when time and development plans when time and resources do not permit intensive field resources do not permit intensive field studies.studies.

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BackgroundBackground

TM4 (0.76-0.90 µm) or TM3 (0.63-0.69 µm) reflectance TM4 (0.76-0.90 µm) or TM3 (0.63-0.69 µm) reflectance can be used as a measure of green leaf biomasscan be used as a measure of green leaf biomass TM4 is shown to measure vegetation density relating to leaf TM4 is shown to measure vegetation density relating to leaf

area, green-leaf biomass, and photosynthetic activityarea, green-leaf biomass, and photosynthetic activity TM3 is also related to green-leaf biomass due to its inverse TM3 is also related to green-leaf biomass due to its inverse

relationship with chlorophyll contentrelationship with chlorophyll content TM5 (1.55-1.75 µm) and TM7 (2.08-2.35 µm) provide a TM5 (1.55-1.75 µm) and TM7 (2.08-2.35 µm) provide a

measure of canopy closure and relative amounts of measure of canopy closure and relative amounts of green vs. senescent biomassgreen vs. senescent biomass TM5 and TM7 have a longer wavelength infrared reflectance TM5 and TM7 have a longer wavelength infrared reflectance

the is inversely related to moisture, and can provide a the is inversely related to moisture, and can provide a measure of standing dead biomass, or senescent woody measure of standing dead biomass, or senescent woody biomass because the biomass is drier than live, biomass because the biomass is drier than live, photosynthetically active tissuephotosynthetically active tissue

These bands provide a measure of canopy closure, whereby These bands provide a measure of canopy closure, whereby reflectance increases as more forest floor is detected from reflectance increases as more forest floor is detected from spacespace

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Study AreaStudy Area

Located in the Located in the northeastern corner of northeastern corner of the Department of El the Department of El Peten, in the Republic Peten, in the Republic of Guatemala of Guatemala

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Study AreaStudy Area

Typical of tropical karst regions, with conical hills and closed Typical of tropical karst regions, with conical hills and closed depressionsdepressions

Rainfall is highly variable, both spatially and temporally, 1200 to Rainfall is highly variable, both spatially and temporally, 1200 to 2000 mm annually, over half of which falls between June and 2000 mm annually, over half of which falls between June and September.September.

The four most common forest associations typical for the study The four most common forest associations typical for the study area, beginning in the bajo (karst depression) center and area, beginning in the bajo (karst depression) center and extending to the top of a conical karst hill, are: extending to the top of a conical karst hill, are: Tintal – a low (5-11 m), open swamp forest with palms in the Tintal – a low (5-11 m), open swamp forest with palms in the

understoryunderstory Escobal – a slightly higher swamp forest with palms in the understoryEscobal – a slightly higher swamp forest with palms in the understory Botanal – a medium-high (15-25 m) swamp forest on better drained Botanal – a medium-high (15-25 m) swamp forest on better drained

soils with many isolated tall treessoils with many isolated tall trees Zapotal – a high (25 m with isolated trees to 40 m) multi-tiered, closed Zapotal – a high (25 m with isolated trees to 40 m) multi-tiered, closed

canopy forestcanopy forest

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MethodsMethods

Three sites were selected (Dos Lagunas, Bajo Azucar, Three sites were selected (Dos Lagunas, Bajo Azucar, and Holmul) and analyzed with different and Holmul) and analyzed with different geomorphology and vegetation distributions to sample geomorphology and vegetation distributions to sample a variety of natural landscape types with as little human a variety of natural landscape types with as little human disturbance, clouds, or hazedisturbance, clouds, or haze

A dry-season Landsat TM image was selected A dry-season Landsat TM image was selected because of suspected differences between forest types because of suspected differences between forest types in the dry/wet season due to seasonal, drought-in the dry/wet season due to seasonal, drought-induced senescence.induced senescence.

Microimages’ Map and Image Processing System Microimages’ Map and Image Processing System (MIPS), for Landsat image processing, principal (MIPS), for Landsat image processing, principal components analysis (PCA) and the first step in the components analysis (PCA) and the first step in the cluster analysis (cluster analysis (kk-means classification), SAS for the -means classification), SAS for the centroid clustering, discriminate analysis (DA), analysis centroid clustering, discriminate analysis (DA), analysis of variance (ANOVA), and the correlation and of variance (ANOVA), and the correlation and regression analysesregression analyses

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MethodsMethods

The three indices of landscape diversity were examined: LCT, S-The three indices of landscape diversity were examined: LCT, S-W, and TIW, and TI

Land-Cover Type was considered absent in a cell when it Land-Cover Type was considered absent in a cell when it accounted for <2% of the total number of pixelsaccounted for <2% of the total number of pixels

The Shannon-Weaver index was used as a measure of evenness. The Shannon-Weaver index was used as a measure of evenness. Expressed by Expressed by

HH = -∑ = -∑ppii xx ln ln ppi i

Where Where ppii is the probability of finding a land-cover type in a cell is the probability of finding a land-cover type in a cell

The topographic index is equal to the sum of the area of each The topographic index is equal to the sum of the area of each land-cover type in a cell multiplied by its topographic rank, and land-cover type in a cell multiplied by its topographic rank, and divided by the total area of the cell.divided by the total area of the cell. 1 ranking the lowest to 6 ranking the highest, or most rich, along the 1 ranking the lowest to 6 ranking the highest, or most rich, along the

topographic gradienttopographic gradient

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ResultsResults

Results of PCA indicate that near-infrared reflectance, Results of PCA indicate that near-infrared reflectance, as measured by TM4, is the main source of variability as measured by TM4, is the main source of variability in the imagery at the pixel levelin the imagery at the pixel level

TM4 is highly correlated (TM4 is highly correlated (rr = 0.99, = 0.99, PP < 0.0001) with < 0.0001) with PC1, accounting for 59.8% of the total variancePC1, accounting for 59.8% of the total variance

TM5 is the second most highly correlated (TM5 is the second most highly correlated (rr = 0.94, = 0.94, PP < < 0.0001) with PC2, accounting for 26.4% of total 0.0001) with PC2, accounting for 26.4% of total variancevariance

TM7 is the third most highly correlated (TM7 is the third most highly correlated (rr = 0.35, = 0.35, PP < < 0.0001) with PC1 and (0.0001) with PC1 and (rr = 0.62, = 0.62, PP < 0.0001) with PC2, < 0.0001) with PC2, accounting for 8.1% of total varianceaccounting for 8.1% of total variance

Dos Lagunas – The most uneven distribution of Dos Lagunas – The most uneven distribution of vegetation types (S-W = 0.53, TI = 4.41)vegetation types (S-W = 0.53, TI = 4.41)

Bajo Azucar – highest TI (S-W = 0.62, TI = 4.62)Bajo Azucar – highest TI (S-W = 0.62, TI = 4.62) Holmul – most even distribution (S-W = 0.89, TI = 3.81)Holmul – most even distribution (S-W = 0.89, TI = 3.81)

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Introduction for Article 2Introduction for Article 2

Methods that allow identification of conservation-priority area have Methods that allow identification of conservation-priority area have been proposed. been proposed.

Two major types of information are necessary for setting Two major types of information are necessary for setting conservation priorities: the conservation value of an area and its conservation priorities: the conservation value of an area and its vulnerabilityvulnerability

An analysis of the overall pattern of land use in a given area could An analysis of the overall pattern of land use in a given area could be a guide to identifying vulnerable areasbe a guide to identifying vulnerable areas

In the Old World tropics, 80% of the countries have lost over half In the Old World tropics, 80% of the countries have lost over half their wildlife habitat, and 65% of primary forest habitat has been their wildlife habitat, and 65% of primary forest habitat has been lost in tropical Asialost in tropical Asia

Propose a method for identifying conservation-priority areas Propose a method for identifying conservation-priority areas based on a predictive, land-use change modeling approachbased on a predictive, land-use change modeling approach Unprotected natural areas most susceptible to land-use change by Unprotected natural areas most susceptible to land-use change by

virtue of their geophysical and socioeconomic characteristics can be virtue of their geophysical and socioeconomic characteristics can be ranked as the highest-priority areas for in-depth field inventories of ranked as the highest-priority areas for in-depth field inventories of biodiversity distributionbiodiversity distribution

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ObjectivesObjectives

To use a geographic information system To use a geographic information system and spatially explicit modeling to and spatially explicit modeling to

Examine patterns of land-use change in Examine patterns of land-use change in Arunachal PradeshArunachal Pradesh

Examine the correlation of land-use patterns with Examine the correlation of land-use patterns with biogeophysical characteristicsbiogeophysical characteristics

Predict areas most suscepitible to future Predict areas most suscepitible to future deforestation and biodiversity loss based on deforestation and biodiversity loss based on geophysical and developmental variablesgeophysical and developmental variables

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Study AreaStudy Area

The state of ArunachalThe state of Arunachal

Pradesh (lat 26˚-29˚ N, Pradesh (lat 26˚-29˚ N,

long 91˚-97˚E), whichlong 91˚-97˚E), which

covers 83,743 kmcovers 83,743 km22, and, and

has one of the richest has one of the richest

floras in the worldfloras in the world

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Study AreaStudy Area

Tropical wet-evergreen Tropical wet-evergreen forests, occurring up to forests, occurring up to elevations of 900 melevations of 900 m

Subtropical forests, located Subtropical forests, located between 800 and 1900 mbetween 800 and 1900 m

Pine forests, extending into Pine forests, extending into both the subtropical and both the subtropical and temperate belts between temperate belts between 1000 and 1800 m 1000 and 1800 m

Temperate forests, occurring in all districts as a continuous belt Temperate forests, occurring in all districts as a continuous belt between 1800 and 3500 m elevationbetween 1800 and 3500 m elevation

Alpine forests, which occur on peaks above 4000 mAlpine forests, which occur on peaks above 4000 m Tropical semievergreen forest, which occurs along the foothills Tropical semievergreen forest, which occurs along the foothills

and river banks up to 600 m thoroughout the stateand river banks up to 600 m thoroughout the state

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MethodsMethods

Source for 1988 land-cover and land-use information was a series Source for 1988 land-cover and land-use information was a series of 1:250,000-scale thematic paper maps prepared by visual of 1:250,000-scale thematic paper maps prepared by visual interpretation of false-color composites of satellite imageryinterpretation of false-color composites of satellite imagery Landsat TM imagery from 1987 and IIRS LISS-II imagery from 1988Landsat TM imagery from 1987 and IIRS LISS-II imagery from 1988

Digitized:Digitized: land cover - evergreen forests, deciduous forests, degraded forests, land cover - evergreen forests, deciduous forests, degraded forests,

forest blanks, wastelands, water, and snowforest blanks, wastelands, water, and snow land use – forest plantations, shifting agriculture, grazing land, other land use – forest plantations, shifting agriculture, grazing land, other

agriculture, and townsagriculture, and towns District boundaries, towns, roads, rivers, and reservoirsDistrict boundaries, towns, roads, rivers, and reservoirs

Maps were digitized in a vector format with the GIS package Maps were digitized in a vector format with the GIS package CAMRISCAMRIS The coverage's were estimated to have a positional accuracy of 60 m The coverage's were estimated to have a positional accuracy of 60 m

based on the errors introduced during digitizing and importingbased on the errors introduced during digitizing and importing A U.S. Geological Survey GTOPO30 elevation map was used to A U.S. Geological Survey GTOPO30 elevation map was used to

generate a slope map and an aspect map, each with resolution of generate a slope map and an aspect map, each with resolution of 1 km1 km22 per cell per cell

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MethodsMethods

Convert the vector coverage's into raster grids, in preparation for Convert the vector coverage's into raster grids, in preparation for the GEOMOD2the GEOMOD2

Reclassified the 1988 land use map into three categories:Reclassified the 1988 land use map into three categories: Forest, disturbed, and otherForest, disturbed, and other

GEOMOD2 simulation selected forested grid cells to convert to GEOMOD2 simulation selected forested grid cells to convert to the disturbed category according to two rules:the disturbed category according to two rules: Specify the quantity of forest disturbanceSpecify the quantity of forest disturbance Prioritize locations with the greatest risk of disturbancePrioritize locations with the greatest risk of disturbance

GEOMOD2 computed the risk of disturbance by comparing the GEOMOD2 computed the risk of disturbance by comparing the 1988 land-use map to each of six geophysical attributes:1988 land-use map to each of six geophysical attributes: Elevation, slope, aspect, buffer around towns, buffer around roads, Elevation, slope, aspect, buffer around towns, buffer around roads,

and buffer around rivers and reservoirsand buffer around rivers and reservoirs

A risk-of-disturbance map was createdA risk-of-disturbance map was created

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ResultsResults

Areas closer to roads and towns have Areas closer to roads and towns have fewer evergreen forests, whereas areas fewer evergreen forests, whereas areas more than 6 km from roads or towns are more than 6 km from roads or towns are about 70% forestedabout 70% forested

It is projected that 50% of the state’s It is projected that 50% of the state’s 1988 forests will be lost by 2021, based 1988 forests will be lost by 2021, based on exponential growth of the human on exponential growth of the human population and resulting resource use.population and resulting resource use.

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Introduction for Article 3Introduction for Article 3

Diversity estimation and mapping techniques Diversity estimation and mapping techniques take advantage of the relationship between take advantage of the relationship between species richness and habitat diversity, where species richness and habitat diversity, where species richness increases as environmental species richness increases as environmental heterogeneity increases at a variety of scalesheterogeneity increases at a variety of scales

Mapping of diversity is accomplished by Mapping of diversity is accomplished by analyzing variation of some spectral signal, analyzing variation of some spectral signal, and correlation this variation with measures of and correlation this variation with measures of landscape or taxa diversitylandscape or taxa diversity

Results obtained are compared by analyzing:Results obtained are compared by analyzing: Normalized difference of vegetation index (NDVI) Normalized difference of vegetation index (NDVI)

variabilityvariability A satellite-derived vegetation map with ground-A satellite-derived vegetation map with ground-

based measures of species richnessbased measures of species richness

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GoalsGoals

To analyze species diversity and landscape To analyze species diversity and landscape heterogeneity in an artic landscape by:heterogeneity in an artic landscape by: Mapping the vegetation of the Hood River Region of the Mapping the vegetation of the Hood River Region of the

Central Canadian ArticCentral Canadian Artic Developing techniques to predict and map variation in plant Developing techniques to predict and map variation in plant

species richness using remote sensingspecies richness using remote sensing Assessing and comparing the techniques used to estimate Assessing and comparing the techniques used to estimate

species richnessspecies richness Regional variation in plant species richness was Regional variation in plant species richness was

estimated by:estimated by: Analyzing variation in NDVI measures obtained from Landsat Analyzing variation in NDVI measures obtained from Landsat

Thematic Mapper ( TM ) dataThematic Mapper ( TM ) data Analyzing the regional vegetation map created for this study in Analyzing the regional vegetation map created for this study in

relation to intensive ground-based measures of plant species relation to intensive ground-based measures of plant species richness and plant community compositionrichness and plant community composition

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Study AreaStudy Area

Bear-slave Uplands, Bear-slave Uplands, low topographic low topographic relief, rolling granitic relief, rolling granitic hills, and shallow, hills, and shallow, discontinuous cover discontinuous cover of glacial tills of glacial tills dissected by dissected by numerous lakes and numerous lakes and drainage basinsdrainage basins

Bathurst Lowlands, greater relief, extensive marine deposits, and Bathurst Lowlands, greater relief, extensive marine deposits, and non-acidic bedrock outcropsnon-acidic bedrock outcrops

Low-shrub tundra subzoneLow-shrub tundra subzone Vegetation is a mosaic of dwarf and low shrubs, shrub-graminoid Vegetation is a mosaic of dwarf and low shrubs, shrub-graminoid

and graminoid tundra, riparian shrubs, and rock-lichenand graminoid tundra, riparian shrubs, and rock-lichen

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MethodsMethods

Image and field sampling areas were chosen in this Image and field sampling areas were chosen in this study with the goal of characterizing species richness study with the goal of characterizing species richness and landscape heterogeneity within a roughly 0.5 kmand landscape heterogeneity within a roughly 0.5 km22 area to better understand and map richness patterns at area to better understand and map richness patterns at the mesoscalethe mesoscale

Among-pixel variation was sampled at the same scale Among-pixel variation was sampled at the same scale as the field-measured species richness and community as the field-measured species richness and community datedate

The relationships between richness estimated by The relationships between richness estimated by variation among pixels, vegetation type diversity, and variation among pixels, vegetation type diversity, and ground-measured species richness were all determined ground-measured species richness were all determined from the same pixel areas and species richness from the same pixel areas and species richness mapping was based on these relationshipsmapping was based on these relationships

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Methods – Methods – Vegetation Vegetation studiesstudies

17 0.5 km17 0.5 km22 study sites at Hood River valley study sites at Hood River valley Species richness was measured within each Species richness was measured within each

site by determining the vascular plant species site by determining the vascular plant species present within a set of eight randomly placed present within a set of eight randomly placed 100 x 3 m plots100 x 3 m plots

Sampling focused along the riparian corridor Sampling focused along the riparian corridor because of easier river access, and all major because of easier river access, and all major regional vegetation types can be found along regional vegetation types can be found along the corridorthe corridor

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Methods – Methods – Vegetation MapVegetation Map

A land cover map was derived from a supervised A land cover map was derived from a supervised classification of Landsat TM scene covering the areaclassification of Landsat TM scene covering the area

A single Landsat TM scene (path 46 row 13) was usedA single Landsat TM scene (path 46 row 13) was used Atmospheric correction using dark object subtraction, Atmospheric correction using dark object subtraction,

converted to reflectance and calibrated based on converted to reflectance and calibrated based on scene acquisition date and sun angel, and scene acquisition date and sun angel, and georeferencedgeoreferenced

TM bands 1-5 and 7 were used in a maximum TM bands 1-5 and 7 were used in a maximum likelihood algorithm for supervised classification with likelihood algorithm for supervised classification with ground-truthed target areas used for interpretationground-truthed target areas used for interpretation

Training sites for the supervised classification were Training sites for the supervised classification were chosen from homogeneous areas for which detailed chosen from homogeneous areas for which detailed vegetation descriptions were availablevegetation descriptions were available

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Methods – Methods – Richness Richness estimatesestimates

A weighting factor was determined from field samples of vascular A weighting factor was determined from field samples of vascular plant richness and used in conjunction with the classified plant richness and used in conjunction with the classified vegetation map to remotely estimate plant richnessvegetation map to remotely estimate plant richness

Detailed floristic data for each study site enabled weighting the Detailed floristic data for each study site enabled weighting the land cover types based on relative vascular richness within a typeland cover types based on relative vascular richness within a type

The weighting factors were determined by dividing the sum of The weighting factors were determined by dividing the sum of potentially occurring species of a cover type by the sum from the potentially occurring species of a cover type by the sum from the least species-rich vegetation typeleast species-rich vegetation type

Richness values for each 500-pixel area were determined by Richness values for each 500-pixel area were determined by multiplying the number of pixels of each class by the weighting multiplying the number of pixels of each class by the weighting factor and determining the mean value of the 500-pixel areafactor and determining the mean value of the 500-pixel area

This data was then used in regression analysis to determine the This data was then used in regression analysis to determine the relationship between measured and estimated species richnessrelationship between measured and estimated species richness

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Methods – Methods – NDVI variabilityNDVI variability

An NDVI image was created using TM data An NDVI image was created using TM data from the peak of the growing season and used from the peak of the growing season and used in the analysis of variation in NDVIin the analysis of variation in NDVI

Non-positive values in the NDVI image were Non-positive values in the NDVI image were set to zero, this removed some of the variance set to zero, this removed some of the variance associated with non-vegetated surfacesassociated with non-vegetated surfaces

Regression analysis was used to determine the Regression analysis was used to determine the relationship between measured species relationship between measured species richness, NDVI variability, and weighted richness, NDVI variability, and weighted vegetation type abundance of the 17 intensive vegetation type abundance of the 17 intensive study sitesstudy sites

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Methods – Methods – Richness MappingRichness Mapping

Species richness estimates were determined for a Species richness estimates were determined for a central pixel of each 500-pixel area on the NDVI image central pixel of each 500-pixel area on the NDVI image and vegetation map using a 25 x 20 pixel filter and and vegetation map using a 25 x 20 pixel filter and regression equationsregression equations

A multiple regression analysis of vNDVI and weighted A multiple regression analysis of vNDVI and weighted abundance (WA) with measured species richness (Sv) abundance (WA) with measured species richness (Sv) was performed to determine how well the combined was performed to determine how well the combined methods explain variability in species richness at the methods explain variability in species richness at the 17 study sites17 study sites

A final map was made to display the areas where the A final map was made to display the areas where the three methods of estimating richness, are in most and three methods of estimating richness, are in most and least agreementleast agreement

Cover types, richness levels, and degree of difference Cover types, richness levels, and degree of difference in richness estimates were tabulatedin richness estimates were tabulated

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Results - Results - VegetationVegetation

Ten land cover classes were determined:Ten land cover classes were determined:WaterWater

snow and icesnow and ice

rock-lichen barrens - most species poor rock-lichen barrens - most species poor (total 61.4%)(total 61.4%)

sand and gravel barrens - most species rich sand and gravel barrens - most species rich (total 14.5%)(total 14.5%)

dry acidic dwarf-shrub tundra - most species poor dry acidic dwarf-shrub tundra - most species poor

dry non-acidic dwarf-shrub tundra - most species poor dry non-acidic dwarf-shrub tundra - most species poor

low-shrub tundra, tall riparian shrubslow-shrub tundra, tall riparian shrubs

moist shrub-graminoid tundra - most species rich moist shrub-graminoid tundra - most species rich

moist and wet graminoid tundramoist and wet graminoid tundra

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Results – Results – Richness CorrelationsRichness Correlations

Simple regressions between measured and Simple regressions between measured and estimated species richness indicate variation in estimated species richness indicate variation in NDVI explains 65% of the variance in species NDVI explains 65% of the variance in species richness (richness (rr22 = 0.653, = 0.653, PP < 0.0001) < 0.0001)

The weighted abundance of vegetation types The weighted abundance of vegetation types explains 34% (explains 34% (rr2 2 = 0.340, = 0.340, PP < 0.014) < 0.014)

A multiple regression analysis indicates that A multiple regression analysis indicates that together, these two variables significantly together, these two variables significantly explain 79% of the variance in species explain 79% of the variance in species richness at the 17 study sites along the Hood richness at the 17 study sites along the Hood River (adjusted River (adjusted rr22 = 0.788, = 0.788, PP < 0.0001) < 0.0001)

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ReferencesReferences

(1)(1) J.M. Rey-Benayas, and K.O. Pope. 1995. J.M. Rey-Benayas, and K.O. Pope. 1995. Landscape Landscape Ecology and Diversity Patterns Ecology and Diversity Patterns in the Seasonal Tropics from in the Seasonal Tropics from Landsat TM Landsat TM Imagery. Ecological Applications, Vol.5, Imagery. Ecological Applications, Vol.5, No.2 No.2 May:386-394May:386-394

(2)(2) S. Menon, R. G. Pontius Jr., J. Rose, M. L. S. Menon, R. G. Pontius Jr., J. Rose, M. L. Khan, K. S. Khan, K. S. Bawa. 2001. Identifying Bawa. 2001. Identifying Conservation-Priority Areas in the Conservation-Priority Areas in the Tropics: Tropics: A Land-Use Change Modeling Approach. A Land-Use Change Modeling Approach.

Conservation Biology, Vol. 15, No. 2 Conservation Biology, Vol. 15, No. 2 April:501-512April:501-512(3)(3) W. Gould. 2000. Remote Sensing of Vegetation, Plant Species W. Gould. 2000. Remote Sensing of Vegetation, Plant Species

Richness, and Regional Biodiversity Hotspots. Richness, and Regional Biodiversity Hotspots. Ecological Ecological Applications, Vol. 10, No. 6 Dec:1861-1870Applications, Vol. 10, No. 6 Dec:1861-1870