No peace for the forest:
Land changes in the Andes-Amazon region following the Colombian internal conflict
Paulo J. Murillo-Sandoval
Conflict is common.
Source: PRIO Institute, Conflict Catalog,
HYDE population dataset and UN
Circle size represents number of deaths
Rate of deaths per 100.000 people
Conflict Catalog (1400-2000)
PRIO Institute
(1946-2013)
Conflict impacts urban areas
Germany, Nuremberg, 1945
Source: National Archives and Records Administration
Syria, Aleppo, 2017
Conflict impacts forested and agricultural areas
Source: National Geographic
Illegal wood / coca farming
Agricultural land abandonment
Source: https://globalforestatlas.yale.edu/amazon
Colombia - Amazon
Myanmar - Asia
D.R Congo - Africa
Source: UCDP/Google Earth Engine
Conflict occurs in the tropics
Forest cover
Conflict events
Colombia is an excellent place for studying armed conflict influences on land system dynamics.
~60% of Colombia remains as forests.
Rich in biodiversity
Total deaths:
Internally displaced persons (IDPs):
Civilians:
Combatants:
By guerrillas:
By Paramilitaries:
FARC
Germany
Peace agreement:
Colombian Government and FARC
November 2016
Former president: Santos
Nobel Peace Prize 2016
FARC leader:
Timochenko
Political participation in Colombian
Congress
Source: ACLED https://blogs.lse.ac.uk/latamcaribbean/2020/09/29/como-entender-la-ola-de-asesinatos-de-lideres-sociales-en-colombia-durante-la-pandemia/
“Peace” is fragile
Number of social leaders killed 2019-2020
Source: Murillo-Sandoval, August 2019
La Julia (Meta-Colombia).
Juan de Jesus Arroyo: Leader ~102 ex-FARC combatants
Overarching question:
How does armed conflict and the current post-conflict situation impact land system in the Colombian Andes-Amazon region?
The relationship between conflict processes and land system is difficult to study:
3. The impact of illicit land activities (i.e., coca farming) on landscape during conflict and post-conflict periods are unknown.
2. Mostly speculative, causal effects or mechanisms are not well understood.
Andes-Amazon transition belt
Strongholds of FARC
Specific massacres by Paramilitaries
IDPs
Historical land grabbing & land abandonment resulting in complex mosaic of forest, forest recovery and agricultural expansion
Land system →
Land cover and land use types
Conflict → A fluid process, not a discrete series of isolated events
Analytical model
1.
Did the 2016 declaration of peace increase forest disturbance?
1. Methodology
bfastMonitor: Intrannual forest disturbances (2010-2018)
Spatial representation
Source: Murillo-Sandoval et al., 2020
Disturbancess
Forest stable
1. Methodology
Ethnographic data: Collected by Kristina Van Dexter (2016-2018) and Paulo Murillo (2019). n = 80.
Source: Murillo-Sandoval (La Julia, August, 2019)
Spatial representation
Workshop with FARC ex-combatants, campesinos, WWF and national park managers.
Disturbancess
Forest stable
Ch1. Methodology
Juan de Jesus Monroy (aka “Albeiro”) explains the agricultural projects, location and future goals.
Source: Murillo-Sandoval (La Julia, August 2019 )
Chapter 1. Results
1. Results: Choropleth map
1. Forest disturbances
Murillo et al., Env Research Letters, 2020.
1. Results:
Protected Areas
1. What is happening?
Wartime
2010-2012
Post-peace Agreement
2017-present
Negotiations
2013-2016
Everyone respects the environmental laws created by FARC
<2ha per year
Keep 30% of farm as forest.
Uncertainty laws
Campesinos afraid from new government rules.
People opt for coca
> 7ha
More than 60% of forest farm could be cleared
Land tenure is not the goal/
Capitalize land and get rent or interest on the money.
New outsider investors, large landholders, campesinos and FARC dissidents
1. Implications
Small sample (2010-2018) and no info about other land cover / land use types….
Forest was not central element in the peace accord.
Forest is a victim of “peace.”
Massive deforestation in sensitive biodiversity hotspots.
2.
How did conditions during the conflict and post-conflict period affect agricultural uses and forest land cover dynamics (1988-2019)?
2. Methodology
Source: https://emapr.github.io/LT-GEE/landtrendr.html
Land cover mapping using LandTrendr + RandomForest
LandTrendr video over Tinigua!
LandTrendr video over Tinigua!
2. Connecting maps with conflict events
Armed confrontation between two groups led to at least one person killed
2. Connecting maps with conflict events
Armed confrontation between two groups led to at least one person killed
2. Connecting maps with conflict events
Armed confrontation between two groups led to at least one person killed
We use a diff-in-diff framework to estimate the causal effect of a conflict event on land cover change and landscape metrics for agriculture.
2. Lots of change from Forest to Agriculture
Map difference: 1988-2019
Fast
Slow
Loss of stable forest
Murillo et al., Global Env Change, 2021.
Significant conversion to agriculture
Murillo et al., Global Env Change, 2021.
Loss of secondary forest
Murillo et al., Global Env Change, 2021.
2. Heterogeneous effects in conflict events
Municipalities are separated by low-population (population below the 50th percentile) and high-population (population >= 50th).
Significance levels of the estimates are represented by: * p<.10, ** p<.05, *** p<.01.
2. Implications
How, where and what type of illicit land activities are happening….
Secondary forests are deforested faster after the peace accord
Violent actors dictated land changes during conflict
but
Wealthy actors aggressively acted during “peace”.
Low populated areas experience quick ag expansion
3.
How are policy regimes such as the war on drugs (i.e coca substitution and aerial fumigation campaigns), and the recent peace accord related to the expansion of illicit land activities?
>40% of deforestation is linked to illicit land activities (Lawson 2014), but finding them is very hard:
1. People intentionally work to obscure traces of illicit land activities.
2. Available remote sensing data is not linked with illicit land activities
3. They might have the same pattern as legal activities.
4. We do not know about the magnitude of illicit land activities (i.e., coca and illegal cattle ranching) during and after conflict.
Socializing the pixel
Pixelizing the social
Pattern:
e.g., Fishbone
Process:
Urban settlement
Known “pattern” with suspected processes that drive it
Tellman et al., 2020
Known “process” or “driver”
with suspected land outcome.
Process:
e.g., Forest management
Pattern:
Regular solid plots
Kennedy et al., 2015
Two pixel-based approaches
How do these two pixel-based approaches help disentangle coca farming and cattle ranching farms?
Note: If you have enough on the ground data you do not need socializing the pixel
Linking observable patterns with known historical and institutional processes
3. Semantic segmentation
Ch3. Semantic segmentation
Landsat image 2018
Field map
Deep learning model
Residual UNet (He et al., 2020, Ronneberger et al., 2015; 2016)
Six Landsat bands+slope+
elevation
Three classes: Forest, coca and cattle farms
By 2018. Coca→ 50kha ; Cattle ranching → 3000kha
Within
legal frontier
Outside legal frontier
frontier
3. Implications
Still, we do not know about other illicit land activities (oil palm, mining, illegal timber….)
Coca farming is NOT the initial spearhead of deforestation
Illegal cattle ranching is pushing deeper into the Amazon watershed
War on drugs is ineffective, “peace” encourages more illicit land activities
The relationship between conflict processes and land system is difficult to study:
Dissertation’s contributions to the land system science
Methodological contribution:
bfastMonitor/LandTrendr/deep learning + ethnography/stats/social approaches
Dissertation’s contributions to the land system science
2. Mostly speculative, causal effect or mechanisms are less understood.
Empirical contribution:
Conflict was beneficial (slow changes) for Amazon land system but during post-conflict (fast change) massive forest loss and ag expansion
Conflict events cause deforestation and ag expansion
Dissertation’s contributions to the land system science
3. The impact of illicit land activities (i.e., coca farming) on landscape during conflict and post-conflict periods are unknown.
Conceptual contribution:
First worldwide example connecting illicit land activities (i.e., coca and illegal cattle ranching) with specific policies. A framework that can be extended in other countries.
Source: Semana, January 8, 2017
Backup
Coca
“Balloon effect”
Pasture
“Cattle ranching”
Land grabbing
“No cows yet”
Ch2. Empirical Strategy
We use a diff-in-diff framework to estimate the causal effect of a conflict event on land cover and landscape metrics for agriculture.
After these shocking numbers, Colombia looks an interesting study case, but globally, Is war a common process, where/when armed conflict occurs?
Bubble’ size represents the size of each event with respect to the total casualties (raw: C, rescaled: D)
Source: Cirilo and Taleb 2016
Colombia - Amazon
Myanmar - Asia
D.R Congo - Africa
Urban areas, but also agricultural and forested regions are affected by armed conflict
Source: Baumann et al., 2016
Fighting and Foliage:
Land system dynamics in the Colombian Andes-Amazon region
Ch2. Results: Land cover pathways
Landsat image 2018
Field data 2018
Deep learning 2018
Ch2. Results - Heterogeneous effects
Municipalities are separated by low-population (population below the 50th percentile) and high-population (population >= 50th).
Significance levels of the estimates are represented by: * p<.10, ** p<.05, *** p<.01.
After these shocking numbers, Colombia looks an interesting study case, but globally, Is war a common process, where/when armed conflict occurs?
Source: UCDP
Conflict: Internal armed confrontation between two groups, that leads at least 25 battle-related deaths in one calendar year.
Land system: Mosaic that combine land cover and land uses
Post-conflict: difficult to actually define but when a decrease of deaths, peace accords, cease of fire are declared we can identify when this period starts.