1. Process and Content of Ukraine's Agricultural Land Use Monitoring
Maksym Martynyuk1, Denys Nizalov2, Denis Bashlyk3
1Ministry of Agrarian Policy and Food, Ukraine; 2University of Kent, United Kingdom; 3Stategeocadastre, Ukraine

2. Ukraine: Change in Land Ownership Structure
Launch of the land reform
(Resolution of the Parliament on № 563-XII)
Registered in the Land Cadaster (by 2017)
Total: 42.2 mln ha (69.9% of total area)
Private – 72%
State – 22.6%
Outdated statistical methods
Based on historical records
Reported by local authorities
Source: Center for Land Reform Policy in Ukraine
No up to date reliable information on land use!

3. State Service of Ukraine for Geodesy, Cartography and Cadastre
Ensure that central and local government authorities, individuals and
legal persons comply with land legislation of Ukraine;
Remote sensing of actual agricultural land use shall be implemented in order to:
Provide for implementation of the State policy for land use
and land protection;
Prevent violations of Ukrainian Law on land use and
land protection, revealing such violations and taking
actions for their elimination;
Ensure that land owners and land users comply with
national standards in the area of land use, protection of
land fertility, preventing land contamination, providing for
environmental protection.

4. State Service of Ukraine for Geodesy, Cartography and Cadastre
Expected Results of the pilot:
А) Analysis of the international and Ukrainian experience of remote sensing of agricultural land
B) Comparative administrative records on land use from the State Land Cadastre with results of remote sensing of actual agricultural land use for pilot territories.
C) Development of technical requirements for data on actual agricultural land use
D) Development of proposals for rolling-out the remote sensing of agricultural land use for the rest of territory of Ukraine

5. Tasks for mapping within pilot project
“Capacity Development for Evidence-based Land and Agricultural Policy-Making in Ukraine”
Recognition of boundaries:
cultivated land;
agricultural crops;
orchards, vineyard, berries;
fallow land;
grassland;
water objects;
forest (evergreen, deciduous, mixed);
developed land;
roads;
irrigated land.
Pilot territories

6. Satellite data Sentinel-2A: Sentinel-1A: Landsat-8:
Swath Width – 100 km
Revisit time: 10 days
Cost - free
Sentinel-1A:
Nominal Scene Size - 250x250 km2
Revisit time - 12 days
Landsat-8:
Scene Size - 185x180 km2
Revisit time - 16 days
Cost – free

7. Machine learning approach for crop classification
4. Geospatial analysis (data fusion from the classification maps and vector information)
3. Map filtration (voting and weighted voting approaches with division parcels into the fields)
2. Universal deep learning approach for time series classification at the regional level
1. No-data pixels restoration (clouds and shadows) using self-organized Kohonen maps

8. Distribution of ground data (training and test samples)
Soy
Winter wheat
Sugar bits
Corn
Sunflower
Bilotserkivskiy district
Snigurivskiy district

9. Crop classification maps
Bila Tserkva district

10. Comparison RS and Cadastral data: Boundary Errors

11. Comparison of RS and administrative data on crops: class misreporting11

12. Comparison of RS and administrative data on crops: area misreporting
10 hа 12

13. Comparison of RS and administrative data on crops: class misreporting
Winter wheat reported as sunflower
Reported as hay land 13

14. Comparison of RS and administrative data on crops: shape misreporting

15. Use of non-registered land

16. Comparison of RS with Statistical Data (Winter wheat and maize)
Bila Tserkva district
Bila Tserkva district
Class
Official statistics (ha)
Classification results (ha)
Error, %
Winter wheat
13730
23721
72.78
Maize
10512
25789
145.34
Snihurivka district
CHECK the definition of errors
Class
Official statistics (ha)
Classification results (ha)
Error, %
Winter wheat
31390
30517
-2.78
Maize
381
2307
505.55

17. Comparison of RS and Cadastral data Class of largest discrepancy
Land cover
Producer Accuracy %
Class of largest discrepancy
Agricultural land
92.10
Artificial (7.3 %)
Artificial
96.56
Agricultural land (1.85%)
Forests
49.23
Agricultural land (14.75%),
Artificial (33.61%),
Hay land (1.99%)
Hay land and natural vegitation
21.35
Artificial (41.10%),
Agricultural land (32.58%),
Forests (2.62%), Water (2.28%)
Swamps*
0.07
Artificial (72.40%),
Hay land (10.63%), Water (9,76%), Agricultural (3.69%), Forests (3.45%)
Water
78.74
Artificial(15.36%),
Hay land (4.68%)
Total accuracy: 81.06%
Gaps: 9 %
Overlaps: 0.46%
Agricultural Land:
RS results hа
Registered in Land Cadaster hа
Cadaster includes: 77.2 % of cultivated land
Оцінювання здійснювалося шляхом побудови матриці помилок (confusion matrix)
* Виділені як перекриття водних об'єктів та лук

18. Next steps
Development of technical and administrative procedures to respond to discrepancies
Scaling up
Facilitate land registration
Development of normative base and software

19. Thank you!