climate landscape response (clare) phenometrics for ... · class 1 class 2 class 3 class 4 class 5...

Climate Landscape Response (CLaRe) phenometrics for southern AZ using Prism

& MODIS data to identify nascent populations of Buffelgrass

Devesh Khosla Graduate Student - UA MSGIST

January 12th, 2018

2

1) Background

2) Objectives

3) Study area

4) Data Collection/Methods

5) Results

6) Conclusions

7) Future Directions

PRESENTATION OUTLINE

3

Background

4

WHAT IS THE ISSUE?

Buffelgrass (Cenchrus cillare) is an invasive perennial

Grasses transform desert into flammable grassland

Effect of climate

We need to predict, monitor, and apply proper treatment to stop

the growth of Buffelgrass in Southern USA.

5

With Treatment

Without Treatment

6

Objectives

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PROJECT GOALS

Prediction and treatment of Buffelgrass

1) To promote Buffelgrass management in the southern region of the United States, specifically the state of Arizona.

2) Map and locate new infestations.

3) To identify when and where Buffelgrass is “green” (i.e., photosynthetically active) and suitable for herbicide treatment.

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Study Area

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Data Collection/Methods

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Data/Tools Required

Remote sensing satellite data

Field data

Tools - ERDAS, ARC GIS, SPSS, R, and database

Hardware - Computer to run and process heavy images

Projection used UTM NAD 1986 North 12

12

METHODS

CLaRe-ppt123 using PICO, 2011-2016 Predict Buffel grass &

validate

Case study Ajo with field

data

Identify temporal patterns of

new infestations

Explore options for web-based app to optimize

treatment

Prism Data Set

8 days stack and Re- projection

PPT for prior 24 days & subset NDVI & subset AZ

Batch/Re-projection

MODIS Data Set

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MODIS DATA


validate


data


new infestations


treatment

Prism Data Set



Batch/Re-projection

MODIS Data Set

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Data 1 MODIS NDVI 8-day composite data for 2011 through 2016

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PRISM DATA


validate


data


new infestations


treatment

Prism Data Set



Batch/Re-projection

MODIS Data Set

16

Data 2 PRISM PPT123 for the

composite, i.e., the cumulative ppt for 3 prior time periods

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Processing


validate


data


new infestations


treatment

Prism Data Set



Batch/Re-projection

MODIS Data Set

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Data 3 Two types from the

SWreGAP vegetation map (red=57, orange=60)

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Data 4 Field Data provided by

Jim Malusa

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CLaRe


validate


data


new infestations


treatment

Prism Data Set



Batch/Re-projection

MODIS Data Set

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CLaRe metrics were calculated from MODIS and PRISM ppt123 data

This is the correlation coefficient between current NDVI greenness for a MODIS 8-day composite and the cumulative precipitation for the prior 3 time periods.

PiCo

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Results

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CLaRe Results


validate

Case study AJO Identify temporal patterns of

new infestations


treatment

Prism Data Set



Batch/Re-projection

MODIS Data Set

CLaRe Metrics at SNP-E

Average correlation values across a suite of CLaRe values for native vegetation in Saguaro

National Park

SWreGAP Vegetation Map

CLaRe = 0

Gre

en

ne

ss

(ND

VI)

PPT Value (inches)


Average correlation values across a suite of CLaRe values for native vegetation in Saguaro

National Park


CLaRe = 1

CLaRe = 0

Gre

en

ne

ss

(ND

VI)

PPT Value (inches)


Average correlation values between MODIS NDVI and cumulative lagged precipitation for native vegetation compared to averages for various densities of Buffelgrass. Note that small

amounts of Buffelgrass can dramatically increase the correlation values.

Sonoran-Paloverde Mixed

Cacti Desert Scrub


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Threshold


validate


data


new infestations


treatment

Prism Data Set



Batch/Re-projection

MODIS Data Set

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Model Differencing

Threshold model

Vegetation type

Full year & Season1

Analysis- 1, Analysis- 2, Analysis-3

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Chi-Square

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Type 57 Expected Observed Chi-Square

BG 521.2 1359 1346.5

Native 2110.7 1273 332.5

Total 2632 2632 1679.0

Alpha: 0.05, Significant

Type 60 Expected Observed Chi-Square

BG 41.80 60 7.90

Native 405.19 387 0.81

Total 447 447 8.73

Alpha: 0.05, Significant

Field data was used to calculate chi-square results of model ≈2012 for both vegetation types because most of these data were collected before 2013.

Observed versus expected partitioning of the Buffelgrass presence data are significant, confirming the model results.


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Threshold


validate


data


new infestations


treatment

Prism Data Set



Batch/Re-projection

MODIS Data Set

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T-test

Pico Full Year

Year Mean(BG) Mean(Ran) BG:R Significant

2011 0.264 0.127 > **

2012 0.194 0.081 > **

2013 0.157 0.108 > **

2014 0.165 0.120 > **

2015 0.032 0.021 > **

2016 0.026 0.020 > **

**Significant at the p = 0.001 level

Pico Season1

Season Mean(BG) Mean(Ran) BG:R Significant

2011 0.245 0.178 > **

2012 0.104 0.092 > **

2013 0.278 0.193 > **

2014 0.051 0.046 > **

2015 0.072 0.039 > **

2016 0.048 0.036 > **

**Significant at the p = 0.001 level

1. Field points 2. Random points


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Nascent Population


validate


data


new infestations


treatment

Prism Data Set



Batch/Re-projection

MODIS Data Set

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𝐶𝐿𝑎𝑅𝑒 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 − 𝑀𝑒𝑎𝑛 𝐶𝐿𝑎𝑅𝑒(𝑣𝑒𝑔𝑒𝑡𝑎𝑡𝑖𝑜𝑛 𝑡𝑦𝑝𝑒

𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝐶𝐿𝑎𝑅𝑒(𝑣𝑒𝑔𝑒𝑡𝑎𝑡𝑖𝑜𝑛 𝑡𝑦𝑝𝑒

Z-Score Z-transformation is the number of standard deviations from the mean a CLaRe pixel is.

An unsupervised classification of the stacked 6 years (2011-2016) of z-score transformed CLaRe metrics.


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-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

1 2 3 4 5 6

Z-Sc

ore

2011 2012 2013 2014 2015 2016

Class 1 Class 2 Class 3 Class 4 Class 5

Class 6 Class 7 Class 8 Class 9 Class 10

Spectrum Signature

Class 10 (with dashed signature) exhibits the highest z-score across the years; chi-squared results with BG field data (L) show BG presence points are strongly concentrated in this class. Class 4 (red) presents a profile expected of a new BG

invasion, with “average” (near 0) CLaRe dynamics 2011 through 2015 and a spike to above average CLaRe in 2016.


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Conclusions

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SUMMARY

This study shows promising results toward detecting nascent Buffelgrass populations using multi-temporal CLaRe metrics.

Model differencing reveal native to Buffelgrass transitions with similar patterns and geographies to the multi-temporal z-score class with expected signature of “average” to “above average” CLaRe.

Appropriate field data are required to validate these results and refine the methods.

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Future Directions

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NEXT STEPS….

Future Mobile App

Design options for data served on web-based app and mobile app to optimize timing of herbicide treatment.

Evaluate CLaRe metrics 2010-2016 to identify temporal pattern of new infestations, areas prone to invasive spread.

App can be utilized by agencies and/or organizations (e.g., Department of Transportation) to determine timely treatment.

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Dr. Cynthia Wallace

Dr. Christopher Lukinbeal

U.S. Geological Survey

University of Arizona

GIS Co-op Meeting

climate landscape response (clare) phenometrics for ... · class 1 class 2 class 3 class 4 class 5...

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