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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
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1) Background
2) Objectives
3) Study area
4) Data Collection/Methods
5) Results
6) Conclusions
7) Future Directions
PRESENTATION OUTLINE
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Background
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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.
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With Treatment
Without Treatment
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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
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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
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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Data 1 MODIS NDVI 8-day composite data for 2011 through 2016
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PRISM DATA
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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Data 2 PRISM PPT123 for the
composite, i.e., the cumulative ppt for 3 prior time periods
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Processing
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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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
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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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
CLaRe-ppt123 using PICO, 2011-2016 Predict Buffel grass &
validate
Case study AJO 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
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)
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 = 1
CLaRe = 0
Gre
en
ne
ss
(ND
VI)
PPT Value (inches)
CLaRe Metrics at SNP-E
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
SWreGAP Vegetation Map
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Threshold
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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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.
Analysis- 1, Analysis- 2, Analysis-3
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Threshold
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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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
Analysis- 1, Analysis- 2, Analysis-3
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Nascent Population
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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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.
Analysis- 1, Analysis- 2, Analysis-3
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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.
Analysis- 1, Analysis- 2, Analysis-3
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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
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