C o p e r n i c u s L a n d M o n i t o r i n g S e r v i c e

Transcription

1 C o p e r n i c u s L a n d M o n i t o r i n g S e r v i c e Grassland Harvest Estimation (Bavaria, Germany) Copernicus EU Copernicus EU Copernicus EU

2 G R A S S L A N D H A R V E S T E S T I M A T I O N Introduction Grassland is one of the main (indirect) protein sources in Bavaria (Southern Germany), important for producing milk and meat. One third of the agricultural land of Bavaria is grassland, mostly in the Alpine Foreland. Cattle and dairy farming is one of the most important sources of agricultural income. Grassland is also a growing energy source for bio-gas production. Even slight changes in grassland harvest have strong impacts on the milk/meat and energy market and related prices. 2

3 G R A S S L A N D H A R V E S T E S T I M A T I O N The Challenge The grassland harvest in Bavaria can currently only be estimated roughly, on county level. No weighing system or reporting obligation exists. Variation of 3-6 cuts per year for intensively used grassland areas Current estimations based on few surveys on harvest intensity and amount. Harvest estimation of individual parcels can deviate by 100% from real conditions Aim: Overcoming the existing information gap, in a spatially explicit manner, contributing to federal reporting obligations Source: LfL 3

4 G R A S S L A N D H A R V E S T E S T I M A T I O N Introduction of use case The Bavarian agricultural administration wants to: a) know the number of grassland harvests per season b) use / improve a model for grassland productivity The case study has been implemented by: a) esamt für wirtschaft LFL (state authority for agricultural research) providing test areas for verification / validation b) CAU (University of Kiel) for modelling grassland productivity c) Service providers (GAF AG / e-geos) for radar remote sensing data processing 4

5 G R A S S L A N D H A R V E S T E S T I M A T I O N Available Input Data COPERNICUS can provide information on Grassland through: the High Resolution Layer (HRL) Grassland ( currently HRL 2012; large improvements expected from HRL 2015 The Copernicus Local component products: Natura2000, Riparian Zones, Urban Atlas comprising VHR grassland polygons, amongst others Satellite Radar Data from Sentinel-1 and Copernicus Contributing Missions (here: COSMO-SkyMed) to analayse dates and quantity of harvests IACS Integrated Administrative and Control System : administrative information system on farm and field level from the agricultural administrations for EU subsidies and environmental measures 5

6 G R A S S L A N D H A R V E S T E S T I M A T I O N Change detection approaches based on SAR data: 1) Change detection by comparing pairs of images (of the same sensor) in a time series: Fixed geometries of the sensors allow four data acquisitons every few days image-to-image comparison, depending on sensor e.g. at Days 1, 3, 4, 8, 9, 12, etc. (click Video) 2) Improvement/adjusting of timelines by combining and comparing the detected changes of different times series Variable geometries of the sensors allow for up to four data acquisitions per day improving the data acquisition cycles, as needed by the respective applications (click Video ) 6

7 G R A S S L A N D H A R V E S T E S T I M A T I O N Grassland harvest as seen by SAR: 7

8 G R A S S L A N D H A R V E S T E S T I M A T I O N Grassland harvest as seen by SAR: 8

9 GRASSLAND HARVEST ESTIMATION Detecting Grassland cut dates

10 GRASSLAND HARVEST ESTIMATION Demonstration site Bruckmühl in southern Bavaria parcel-based changes in May: Ratio: change of signal from image at date 1 to image at date 2 Ratio: Ratio: Change: Change: Ratio: Change: IACS parcels/field boundaries 1 0 GAF AG 2016

11 GRASSLAND HARVEST ESTIMATION Demo Bruckmühl Retrieved harevest dates of observed parcels: 1st - 17th May 2015 Data: COSMO-SkyMed, 3 pairs of data, 2 observation geometries Time of harvest May 2015: unknown IACS Parcels (reference parcels) GAF AG 11

12 G R A S S L A N D H A R V E S T E S T I M A T I O N Harvest Intensity Source: LfL

13 G R A S S L A N D H A R V E S T E S T I M A T I O N Accuracy of Grassland harvest detection, May 2015 S S CSK CSK "Cuts" correctly captured "No Cuts" correctly captured "Cuts" not captured "Cuts" erroneously captured Detection Rate 88,2% 92,3% 91,8% 85,9% 340 Test areas 13

14 G R A S S L A N D H A R V E S T E S T I M A T I O N Modelling of grass/biomass growth in-between cuts Determining quantity and quality of grassland growth phases between the cuts through harvest models Using a combination of weather data, agrometeorological data and crop type information Alternative Approach (still a research topic): direct measurement of biomass between cuts by radar remote sensing 14

15 G R A S S L A N D H A R V E S T E S T I M A T I O N 15

16 G R A S S L A N D H A R V E S T E S T I M A T I O N Dry Matter production (g/m²) Measured vs. modelled dry matter development of grassland 16

17 G R A S S L A N D H A R V E S T E S T I M A T I O N Thank you for your interest! Markus Probeck 17