1. Monitoring Performance and Impact of Sustainable Land Management projects in Ethiopia
World Bank Land and Poverty Conference
March 2018
Daniel Ali, Klaus Deininger, Daniel Monchuk
The World Bank

2. This study…
Combines data from different sources to assess the effectiveness of soil and water conservation interventions in Ethiopia (under the Tana-Beles Integrated Water Resources Development (TBIWRD) Project )
Project M&E data on activities and outcomes
Remote sensing data extracted from medium resolution satellite imagery

3. 5 critical watersheds - 85,700 ha, 163 micro-watersheds – 526ha average, natural resource management investments

5. Main Activities/Interventions of the NRM Component of the Project
Reported in M&E
Output Soil and water conservation measures undertaken on cultivated lands
Output Degraded land (hillside, grazing and forestry land) treated
Output New area planted by community forestry and agro-forestry systems to stabilize landscape and produce fuel wood and timber

6. Micro-watershed average treated area (ha) by output activity
5 critical watersheds
85,700 ha
163 micro-watersheds with average area of 526 ha

7. Can NDVI (using LS7) pick-up meaningful change?
areas in the 'y' as having been part of a tree planting activity

8. Processing imagery?? The old way… The new way…
Download 11 years worth of LS7 imagery -> ~2 MONTHS from World Bank country office
Batch routines
Filter out clouds, shadows, etc.
Merging raster files
The new way…
Google Earth Engine developer platform -> some javascript -> sit back, relax…
-> 22 images/ year (16 day pass), 4 images span area of interest, 11 years, 250 MB, 4GB/day from country office

9. Three main seasons
Daily rainfall pattern in the 5 hydro-stations located in the TBIWRD study areas
Dry, pre-rainy and rainy seasons
Seasonal vegetation indices are thus computed for analysis
October 1-Jan 31: dry season; Feb1 – May 31: pre-rainy season; June-Sep: rainy season

10. Data for analysis ~30m pixel resolution Control – 5km buffer
~3 million pixels -> ~99 million obs in panel (11 years, 3 seasons/year)
Remote sensed data: seasonal average of NDVI, SAVI, and LSWI are computed using L7 imagery
Rainfall - Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) which incorporates 0.05° (~5km) resolution satellite imagery with ground-based weather station data to create gridded rainfall time series

11. Imagery-based outcome variables (No dense forest)
NDVI
SAVI
LSWI
Control
Treatment
Dry season (Bega)
0.384
0.388
0.226
0.110
0.120
0.387
0.396
0.231
0.234
0.106
0.118
0.363
0.376
0.211
0.216
0.078
0.094
Pre-rainy season (Belg)
0.206
0.214
0.123
0.126
-0.015
-0.004
0.202
0.121
0.124
-0.019
-0.007
0.235
0.249
0.141
0.147
-0.009
0.006
Rainy season (Kiremt)
0.476
0.489
0.297
0.305
0.232
0.253
0.529
0.532
0.330
0.331
0.263
0.273
0.464
0.477
0.291
0.299
0.223
0.239
NDVI (normalized difference vegetation index) ranges between -1 and 1: close to -1, water; around zero (-.1 to .1), barren land; values .2 to .5 represent crops, shrubs, and grass land, above 0.5 dense forest
SAVI (soil adjusted vegetation index): adjusts for soil brightness when the vegetation cover is low…The lower the value, the lower the amount/cover of green vegetation
LSWI (Land surface water index): ranges between -1 and 1: helps to monitor evapotranspiration and plant and soil water content – positive values for green vegetation and negative values for dried (brown) leaves

12. M&E: Daily water flow and sediment load at hydro station level
Enkulal
G/Mechaw
Tikur wu
Toma
Flow in m3/s
2010
0.101
0.530
0.305
0.353
2011
0.089
0.380
0.071
0.425
2012
0.098
0.141
0.052
0.341
2013
0.082
0.234
0.064
0.460
2014
0.135
0.311
0.147
0.765
2015
0.104
0.201
0.240
0.749
Mean
0.102
0.300
0.516
Daily sediment load in t/d
1.731
3.460
2.942
3.104
0.408
1.858
0.757
2.210
0.677
0.728
0.476
1.646
0.619
0.930
0.294
2.049
0.632
1.079
0.390
3.490
0.563
0.852
1.368
3.416
0.772
1.485
1.038
2.653
- Stream flow or discharge: the volume of water that moves through a specific point in a stream during a given period of time
Steep terrain: less time to soak into the ground and runoff will be greater; if level train or dense vegetation: plenty of time to soak into the ground
Sediment load: the amount of solid load that a stream an carry – measured in metric ton per day passing a given location

13. Treatment Indicator
Used M&E data to construct three treatment variables separately for cultivated land, degraded land, and new area planted as:
the ratio of (cumulative) area of land treated as a share of total micro-watershed.

15. Impact of micro-watershed treatment on vegetation cover (95% confidence interval)

16. Impact of micro-watershed treatment on stream flows (95% confidence interval)

17. Impact of micro-watershed treatments on sediment load (95% confidence interval)

18. Summary Ex-post evaluation possible
“Easy” to process imagery and scalable (if good intervention data available)
Caveats:
Lack of data on the cost of specific interventions to value the impact in economic terms  M&E Data
Lack of information on the location or timing of interventions makes it difficult to assess impact say by slope or pre-project levels of land degradation  CAPI helps