Land degradation risk assesment with remote sensing presentation for Evidenz workshop, December 6, 2016

1. Land degradation risk
monitoring with remote
sensing
Institute of agroecology and natural management
National University of Kyiv-Mohyla Academy
Tetyana Kuchma
tanyakuchma@gmail.com

2. fragmentation of landscapes Lack of water protection zones and shelterbelts Plow to the boundary of forest and water bodies
Climate change mitigation requires the improvement of technologies to reduce the risks of
land degradation, especially in regions with deficient landscape structure and erodibility risk
Land degradation risk 2

3. Soil erosion monitoring
- 15 mln ha of erodive lands in Ukraine
- 15-20 ton of soil loss per year
- 100,000 ha/year the erosion rate
(Tarariko, 2016)
3

4. Soil erosion monitoring
Legend
soil samples
soil class 1 (chornozem clayey)
soil class 2 (chornozem normal)
soil class 3 (chornozem on granites)
soil class 4 (chornozem on sand)
soil class 5 (meadow soils)
Humus
-1,5 - 2,03
2,04 - 2,87
2,88 - 3,5
3,51 - 4,09
4,1 - 5,43
Multiple regression analysis of humus samples and reflection
combination (Landsat-8, 11 bands) for different soil types
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5. Soil erosion monitoring
Humus
-1,5 - 2,03
2,04 - 2,87
2,88 - 3,5
3,51 - 4,09
4,1 - 5,43
Sample
humus
Model humus Model and sample humus
difference
Soil
class
Error
(%)
5
Total accuracy > 85%

6. Soil erosion monitoring
vegetation
soil erodibility
very high
high
moderate
low
Soil erodibility risk assessment and recommendations
development for crop structure and land use
management
• development of contour reclamation project (forestbelts
and terraces development)
• exclusion the lands with very high soil erodibility from
intensive agriculture
6

7. Soil erosion monitoring
Soil erodibility risk assessment and recommendations
development
Сontour reclamation project (Tarariko, 1990), decreasing a soil erodibility risk and crop condition according to the field
observation
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8. Soil moisture monitoring
Soil moisture data is essential for planning agricultural
activities, crop condition monitoring and drought prediction.
InSAR remote sensing technology offers a means of measuring
surface soil moisture, however temporal and spatial data
resolution is crucial for effective remote sensing soil moisture
data integration in decision making.
NASA SMAP mission provides volumetric measurement of
water content in surface soil with three day revisit time
producing to the wide number of time series with global
coverage.
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9. Soil moisture monitoring
0
0,1
0,2
0,3
0,4
0,5
0,6
0 10 20 30 40 50 60
R = 0,66
Precipitation, mm
Soilmoisture,cm3/cm3
Comparison of soil moisture map, obtained from SMAP data
and ground measurements of precipitation from gauging
stations (Ukrainian Hydrometcenter)
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10. Soil moisture dynamic in Kyiv region
Data product time series were developed for the vegetation period (May– September 2016)
Soil moisture monitoring
0
0,05
0,1
0,15
0,2
0,25
0,3
0,35
0,4
12.05.2016
19.05.2016
26.05.2016
02.06.2016
09.06.2016
16.06.2016
23.06.2016
30.06.2016
07.07.2016
14.07.2016
21.07.2016
28.07.2016
04.08.2016
11.08.2016
18.08.2016
25.08.2016
01.09.2016
soil moisture dynamic
Since the loss of radar of SMAP satellite there
are only low spatial resolution data products
of 40 km is available from SMAP radiometer.
Dry soils are observed since 20th of June, 2016
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11. Soil moisture monitoring
L3_SM_40km Gridded Radiometer Brightness Temperature / Soil Moisture and Sentinel-1A images were used to merge
with Sentinel-1 Radar data
> 70 % accuracy based on SMAP 9-km soil moisture L3 GRID
Combined use of Sentinel-1 and SMAP data for soil moisture mapping
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12. Conclusions
• Need for continuous monitoring using common and clear for
agrarians land degradation indicators at national level
• Need for recommendations development and scenario modelling
algorithms to mitigate land degradation at local level
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13. Thank you for your attention
Tetyana Kuchma, PhD
tanyakuchma@gmail.com
Oleshky Sands, one of the largest European desert (>160 km2)