Anderson, E. R.

Griffin, Robert

Macharia, Denis

Markert, K.

Odour, Phoebe

English

NASA Technical Reports Server

Considering 10 growing “crowd 
collections” of landslide events: 
1) improves the accuracy of 
national landslide hazard maps 
from unknown to 78.1% 
and
2) signiﬁcantly changes how we 
would depict populations’ 
exposure to landslides in Rwanda
Results and Discussion
Hazard: Collect 1 / Sim 2 (n=62)          Hazard: Collect 1 / Sim 20 (n=44)    Hazard: Collect 10 (n=507)
MODEL PERFORMANCE
Model performance under growing 
landslide inventory data. Accuracies 
hovered between 70% and 80% 
across growing collections, but 
attention should be paid to 
signiﬁcance values. Fewer than half 
of the simulations yielded signiﬁcant 
results under low-data conditions 
(Collections #1 and #2). From 
Collection #4 (~200 samples) and 
beyond, more than 90% of the 
simulations yielded statistically 
signiﬁcant results.
LIMITATIONS
● Simulated “crowd” data required experience in interpreting 
satellite imagery (i.e., not any citizen scientist)
● Time horizons of land use planning vs. hazard analysis diﬀer
IMPLICATIONS
● Depictions of populations’ exposure to landslide hazard 
should vary widely given diﬀerent inventories. 
● This adds to previous analyses based on expert opinion
 
Conclusions and future work
● We can glean valuable information from freely-available 
data to enhance the characterization of landslide hazard 
and exposure in Rwanda
● Incorporation of new landslide reports into NASA GLC and 
MINEMA (formerly MIDIMAR) records
● Re-assessment of planning / zoning decisions that can be 
informed with new, freely-available Earth observations data
Acknowledgements
This work builds on previous research by Piller et al. (2015) under the supervision of 
the primary author. Support for this work was provided through the joint US Agency 
for International Development (USAID) and National Aeronautics and Space 
Administration (NASA) initiative SERVIR, particularly through the NASA Applied 
Sciences Capacity Building Program, NASA Cooperative Agreement NNM11AA01A.
References
1. Fick and Hijmans, 2017. Worldclim 2...  International Journal of Climatology.
2. HOTOSM, 2019. https://data.humdata.org/dataset/hotosm_rwa_roads 
3. MIDIMAR, 2015, The National Risk Atlas of Rwanda
4. NASA Shuttle Radar Topography Mission Global 1 arc second V003
5. Piller, A., et al., 2015. Landslide Hazard Mapping in Rwanda..., NH41B-1813, AGU
6. Shedlock, et al., 2000, The GSHAP Global Seismic Hazard Map
Anderson, E.R.1,2, Markert, K.1, Griﬃn, R.1,2, Odour, P.3, and D. Macharia3
(1) Earth System Science Center, The University of Alabama in Huntsville, (2) NASA/SERVIR Science Coordination Oﬃce, (3) Regional Centre for Mapping of Resources for Development (RCMRD)
Hypotheses
If we can augment existing landslide inventories with “expert 
collections” of landslide events as observed in high resolution 
satellite imagery, then we can demonstrate how self-updating 
landslide hazard maps change the depiction and 
understanding of populations’ exposure to landslides. We will 
also be able to quantitatively assess the accuracy of landslide 
hazard maps under sparse, growing, and rich data conditions.
Methods
* crowd collections adapted from Piller et al. (2015)
** down-selected from a list of ~20 potential explanatory variables (ibid)
● “Collection 1” includes ~50 randomly-selected “presence” and 
“absence” landslide points, from a total of 507
○ 100 simulations of randomly-selected points for each 
collection = 1000 total simulations
● Each collection adds ~50 new landslide records
Context
Rwanda is very landslide prone, yet data access and availability 
issues inhibit comprehensive landslide risk reduction eﬀorts.
NASA Earth Science Division | Applied Sciences Program | Capacity Building Program
“The application of statistical or probabilistic methods [was] not possible because of the lack of a suﬃciently complete national 
landslide inventory.
— Ministry of Disaster Management and Refugee Aﬀairs , 2015
Demonstration of self-updating landslide hazard maps 
with dynamic crowd-sourced data in Rwanda  
dynamic hazard 
assessment
3.  New collection
“Grow” landslide 
inventory with new 
“crowd collection”*
4.  Re-run 
statistical tests
Logistic regression 
5.  Re-generate hazard map
Including descriptive statistics (McFadden R2, 
AIC, Signiﬁcance tests, etc.) and Accuracy 
1. Initial landslide inventory data (e.g., 
NASA Global Landslide Catalog)
2. Potential explanatory variables:** 
30m slope, soil moisture factor, distance 
to roads, 4) earthquake hazard, and 30m 
Height above nearest drainage
Questions and suggestions: eric.anderson@nasa.gov
● Landslide
○ NOT landslide
https://ntrs.nasa.gov/search.jsp?R=20190034029 2020-03-11T16:15:44+00:00Z


Demonstration of Self-Updating Landslide Hazard Maps with Dynamic Crowd-Sourced Data in Rwanda

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