2019 SWCS International Annual Conference July 28-31, 2019 Pittsburgh, Pennsylvania

1. Philip Heilman and Guillermo Ponce
With help from the BLM, Forest
Service, and U. Arizona and Montana

4. Strengths
 Cover whole allotment and area around it
 Can go back in time (Landsat 5 to 1984)
 Can compare within and across allotments
Weaknesses
 Broader, but shallow (no composition)
 Not directly comparable to field measurements
 Error – but in many cases improvable
 Need interpretation
 A “Big Data” problem – takes skill and time

5. Strengths
 In fashion buzzword that makes it sound like you are on
the cutting edge (!)
 Veg Production = f(inputs) relative to others
 Reduce error even with very messy, non-linear systems
(now easier to try a range of modeling solutions
LM < RF < GBM < Deep Learning?)
Weaknesses
 Less interpretability than one would want
 Requires more hardware, programming, database and
GIS skills, truly a “Big Data” problem

6. 2018 Overview and examination of 1 variable
(MaxNDVI as proxy for production)
2019 Expand to 2 proxys for production and 2
for cover
2020 Evaluation with Public Land Managers
(GIS issues, workflow?)
Data -> Information -> Knowledge -> Wisdom

8. Sensor Production Cover
Landsat (1984-) Maximum NDVI Value in the
Year
30 m Spatial Resolution
Annual 2000-2013
SATVI Derived Total
(Green + Senescent)
Foliar Cover
30 m Spatial Resolution
Pre and Post Monsoon
2008-2011
MODIS (2000-) Integrated Enhanced
Vegetation Index (iEVI)
250 m Spatial Resolution
Every 16 Days. 2000-2013
SATVI Derived Total
(Green + Senescent)
Foliar Cover
500 m Spatial Resolution
Every 8 Days, 2000-2013

9. AZ NM
Johnny Creek
Bonita Creek

10. MODIS (8 Day) % Cover

12. 500m
100m

14. Algorithm
Field Measurements Used to Develop SATVI to Cover Relationship
Cover:
• 5% measured
•10% Landsat
(ID: 123-0.022)
Cover:
• 17% measured
•22% Landsat
(ID: 54-0.094)

16. Sensor Production Cover
Landsat (1984-) Maximum NDVI Value in the
Year
30 m Spatial Resolution
Annual 2000-2013
SATVI Derived Total
(Green + Senescent)
Foliar Cover
30 m Spatial Resolution
Pre and Post Monsoon
2008-2011
Landsat (1984-)
Products of Brady
Allred and team at U
Montana
Landsat and MODIS derived
terrestrial primary production
by Robinson et al., 2018, in
Remote Sensing in Ecology
and Conservation
Plant functional type
cover maps by Jones
M.O. et al., 2018 in
Ecosphere
https.rangelands.app

43. The road to wisdom? Well its plain
and simple to express:
Err
and err,
and err again,
but less
and less
and less. by Piet Hein

44.  Field data becoming more expensive to collect
 With limited monitoring, how to defend public land
management?
 Catalog of free (raw) imagery (Landsat 1984-;
MODIS 2000-; Google Earth Engine)
 Supporting technology is improving (GPS; GIS
datasets; aerial photography; big data technology,
but still difficult!)
Remote sensing + machine learning is on the verge of
being able to complement, but not replace, field data