Change Agent Classification Based on All Available Landsat Data

Zhe Zhu Texas Tech University

Zhiqiang Yang Oregon State University

Landsat Science Team Meeting, 01/11/2017, Boston

Why classifying change agent?

• To better understand change, it is important to know the cause of change.

• Different type of change agent has quite different impacts to the environment.

Mathematical prediction models fit to clear observations

Reference: Zhu, Z. and C.E. Woodcock. 2014. Continuous change detection and classification of

land cover using all available Landsat data. Remote Sensing of Environment 144:152–171.

Continuous Change Detection and

Classification (CCDC) “Breaks”

CCDC Breaks vs Change agents

CCDC breaks indicate occurrence of spectral changes, but not all spectral changes are real change or meaningful change!

o Ephemeral break (i.e., moisture change, aerosols, clouds, shadows)

o Recovery break (i.e., break between re-growing stage to mature stage)

Wet

Dry

Regrowth

Mature

Training “Breaks”: Ephemeral and Recovery Breaks from USFS

• Cohen et al., Forest disturbance across the conterminous United States from 1985–2012: The emerging dominance of forest decline (2016).

• Simple random of 7,200 pixels from 180 individual frames that provide time segments of stable, recovery, and other disturbances.

• Breaks in stable segments for training ephemeral breaks.

• Breaks in recovery segments for training recovery breaks.

Training “Breaks”: Change agents from USGS LANDFIRE project

Change agents from USGS LANDFIRE project

Confidence Prescribed Fire Wildland Fire Wildland Fire Use Planting Reforestation Seeding Biological Chemical Herbicide Insecticide Low 10032 1 2 384 0 301 216223 51 18 0

Low/Moderate 10 0 0 0 0 0 0 0 0 0 Moderate 35574 3 124 135 0 136 5 208 33 0

Moderate/High 0 2 0 0 0 0 0 0 0 0 High 3688 4 13 19882 180 496 0 1978 52 0

Unchanged 0 0 0 0 0 0 0 0 0 0

Confidence Thinning Harvest Clearcut Development Mastication Other Mechanical Weather Insects Insects/Disease Disease Wildfire Low 26474 514 0 0 306 184 5046 333 111 0 1925

Low/Moderate 0 0 0 0 0 0 651 0 0 0 1 Moderate 38936 13615 306 0 219 20687 1567 1824 0 431 2177

Moderate/High 0 0 0 0 0 0 123 0 0 0 0 High 21 4890 8374 706 2593 13747 423 18019 92 0 1690

Unchanged 0 0 0 0 0 0 0 0 0 0 785 Agent Harvest Mechanical Weather Insets/disease fire

Extract breaks randomly for each category and subcategory

• 1,000 breaks per category

• Ephemeral (500) + Recovery (500) -> Others

• Harvest (500) + Mechanical (500) -> Mechanical

• Weather (500) + disease/insect (500) -> Nonmechanical

• Fire (1000) -> Fire

How to use CCDC outputs to classify different breaks?

Pre-change curves

Post-change curves

During-change vector

10 repeated cross validation 80% training & 20% validation

Change Agents Others Mechanical Nonmechanical

(insects/disease + weather) Fire Total Users Others 1796 143 14 46 1999 90%

Mechanical 119 1801 109 92 2121 85% Nonmechanical 29 11 1804 8 1852 97%

Fire 72 32 0 1904 2008 95% Total 2016 1987 1927 2050 7980

Producers 89% 91% 94% 93% Overall 91.54%

Change Agents Others Mechanical Insect/disease Weather Fire Total Users Others 1757 134 10 3 39 1943 90%

Mechanical 111 1912 32 122 74 2251 85% Insect/disease 1 6 947 2 0 956 99%

Weather 20 0 0 850 0 870 98% Fire 85 39 0 0 1836 1960 94%

Total 1974 2091 989 977 1949 7980 Producers 89% 91% 96% 87% 94% Overall 91.50%

Variables No DEM No Thermal No Thermal No DEM DEM & Thermal

Overall 89.40% 90.63% 90.88% 91.50%

Conclusion

• The CCDC algorithm can classify change agent with high accuracies.

• The insect/disease and weather related change can be well separated by the CCDC algorithm.

• Both DEM and thermal band are helpful for change agent classification.

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