1 / 29

Policy oriented study on remote sensing agricultural drought monitoring methods

Policy oriented study on remote sensing agricultural drought monitoring methods. Prof. Dr. János Tamás. Integrated Drought Management Programme in Central and Eastern Europe , National Consultation Budapest, HUNGARY Date: 3. December, 2013. Overview. Project activity Case study

cookmary
Télécharger la présentation

Policy oriented study on remote sensing agricultural drought monitoring methods

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Policy oriented study on remote sensing agricultural drought monitoring methods Prof. Dr. János Tamás Integrated Drought Management Programme in Central and Eastern Europe, National Consultation Budapest, HUNGARY Date: 3. December, 2013.

  2. Overview Project activity • Casestudy • Drought Monitoring/Observations • USA • Europe • How can measure? • How can we built Farms’ Drought Monitoring

  3. Policy oriented study on remote sensing agricultural drought monitoring methods Partners of Activity GWP HUNGARY University of Debrecen University of Oradea Institute of Hydrology of the Slovak Academy of Sciences

  4. Hungary (University of Debrecen and GWP HU): Applied hydrological remote sensing and GIS; Spatial Decision Supporting Systems Romania (University of Oradea): - Geography and Integrated watershed management Slovakia (Institute of Hydrology of the Slovak Academy of Sciences): - Agricultural water management, Soil hydrology Key qualifications of partners

  5. Relation of the different drought form

  6. •  PA: Percentage of Precipitation Anomalies •  CDD: Consecutive Dry Days •  SPI: Standard Precipitation Index •  MI: Relative Moisture Index •  SPEI: Standardized Precipitation – Evapotranspiration Index •  MCDI: Meteorological Comprehensive Drought Index Meteorological Drought Indices Agricultural andEcological Soil Moisture Drought Indices

  7. How can compare one index to other?

  8. Model based meteorolological drought sensitivity Source: Bihari Zita, Kovács Tamás Lakatos Mónika, Móring Andrea, Nagy Andrea, Németh Ákos, Szentimrey Tamás: Délkelet-Európai Aszálykezelő Központ: az aszály monitoringja és hatásai. 2011. Éghajlati Osztály Országos Meteorológiai Szolgálat

  9. Agricultural Drought Indices- From Science To Practice • Sponsors • World Meteorological Organization • UN Strategy for International DisasterReduction (UNISDR) • HydrographicConfederation of Segura, Spain • United StatesDepartment of Agriculture • National DroughtMitigation Center • University of Nebraska, Lincoln, Nebraska, USA Sivakumar, Mannava V.K., Raymond P. Motha, Donald A. Wilhite and Deborah A. Wood (Eds.). 2011. Agricultural Drought Indices. Proceedings of the WMO/UNISDR Expert Group Meeting on Agricultural Drought Indices, 2-4 June 2010, Murcia, Spain: Geneva, Switzerland: World Meteorological Organization. AGM-11, WMO/TD No. 1572; WAOB-2011. 219 pp.

  10. The drought types: meteorological, hydrological and agricultural The drought indexes of meteorological and hydrological drought parameters well-measurable and widely tested (temperature, precipitation, humidity, water level etc.) The agricultural drought least quantified in soil-water-plant environment, the most uncertain drought type. The main objective of this case study is to formulate concrete practical agricultural drought monitoring method and intervention levels with calibrating for the important crops and fruits (wheat, corn and apple) Task definition

  11. 2. RS tools for vegetation indices 3. Agricultural drought decision support parameters 1. Analysis of green and brown water status 3. Agricultural drought decision support parameters 2. RS tools for vegetation indices Finalize OUTPUT 1: An analysis report on the role of soil and crop water content status in waterbalance within different agricultural, landuse and water management practices at rain fed and irrigated systems for the most important crops and fruit (wheat, corn and apple) Finalize OUTPUT 3: Report on integration of RS and GIS tools and intervention levels into drought monitoring system Finalize OUTPUT 2: Toolbox with the concrete identification of remote sensing and GIS data tools for agricultural drought monitoring and forecast May 2014 – Jan 2015 Sept 2013 – Jun 2014 June 2013-Dec 2013

  12. Process flow of RS agricultural drought monitoring methods Meteorological Data Calibrationwithavailablewatercontent Soil Physical Data CalibrationwithDrought Index SDSS Classification NDVI Time Series CalibrationwithYieldstatistical data PlantSpecificDroughtRiskEvaluation Landusemask Alert Varning Watch

  13. Why will be RS strategically tool of DM? To measure crop water stress symptoms Advantage: Freely available Regional Standard meteorological Drought Index Biomass Combined Drought Indicator, based on SPI, soil moisture and fAPAR.

  14. http://edo.jrc.ec.europa.eu/edov2/php/index.php?id=1000

  15. STUDY AREA-SITE SELECTION The Tisa River Basin is the largest sub-basin in the Danube River Basin, covering 157,186km² (19.5%) of the Danube Basin. Sk HU Ro Drought Risk on Hungarian Great Plain

  16. Drought of CEE (2013) 08/ 3 dec 08/ 1 dec 06/1st dec 07/1st dec 10/1 st dec 09/2 dec

  17. SPI3

  18. SPI - SPI has been selected by the WMO as a key indicator for monitoring drought. Spatial scale: o SYNOP stations: Station SPI interpolated to 0.25 degr. GPCC (all products): 1degr. o E-OBS: 0.25 - 0.5 degr.

  19. Product: SOIL MOISTURE INDEX The LISFLOOD model is a hydrological rainfall-runoff model that is capable of simulating the hydrological processes that occur in a catchment. LISFLOOD has been developed by the floods group of the Natural Hazards Project of the Joint Research Centre (JRC) of the European Commission.

  20. NDWI: Normalized Difference Water Index The Normalized Difference Water Index (NDWI) is a remote sensing derived index estimating the leaf water content at canopy level. o Geographic coverage: available for Europe o Spatial scale: 1.2km o Temporal scale: every 10 days aligned on the first day of each month, which corresponds to 3 images per month (day 1-10, day 11-20, day 21-last day of month). Data source: MODIS spectral bands 2 and 6 are provided by the German Aerospace Centre (DLR) and pre-processed by the FOREST Action (IES, JRC). o Frequency of data collection: every day

  21. FAPAR anomaly: Anomaly of Fraction of AbsorbedPhotosynthetically Active Radiation o Geographic coverage: available for Europe o Spatial scale: 1.2km ( from 1/1/2002 to 2011), 1 Km (from 2012) o Temporal scale: every 10 days aligned on the first day of each month, which corresponds to 3images per month (day 1-10, day 11-20, day 21-last day of month). o Data source: fAPAR data isaccessed through the JRC’s Community Image Data (CID) portal (http://cidportal.jrc.ec.europa.eu ). The JRC provider of the fAPAR data is the Flemish institute for technological research (VITO). o Frequency of data collection: every 10 days

  22. Winter wheat yield T/County- Tisza Hungarian region Winter Wheat - Yield data sources Winter wheat –yield/ area- Hungary Hectically yield (Drought effect) Same Growing Area Source: AKI, Hungary

  23. Modis Terra/Aqua Ground res. From 250 m 36 band, Cycle: 1 d 6 Y LONG TIME SERIES of WHEAT NDVI DROUGHT IMPACT

  24. NDVI DROUGHT IMPACT ON NDVI BIOMASS

  25. Relation of NDVI Biomass and Drought YIELD LOSS Relative Yield Loss Potential yield loss is changing in time End result is depend on climatic, soil condition If we calibrate the NDVI TS with real yield loss data and combined with soil data, and meteorological Drought Index, we can estimate the expected different crops yield loss by region by region. Biomass -NDVI

  26. The case study will utilize the available database prepared for the Tisza River Basin. Database Building Crop data – Remote sensing time series Selection of training sites Spectral data noise filtering Rectification (UTM system) Cropping and mosaicing of reference area Indexing Statistical time series data of yields Soil data- digital soil map Common soil physical database of reference area Common topology and coordinate system of reference area Calculation of available water capacity Calculation of water balance on watersheds Meteorological data – Drought Index SPI, fAPAR Sources: USGS, ESA, Literature, Scientific reports, Publications, Media, Statistical reports, Owner data,

  27. Physical Implementation of different stake holder intervention points - Watch: When a plant water stress is observed in sensitive phenological phases - Early Warning: When relevant a plant water stress is observed, available soil moisture is close to critical, Predicted potential yield loss <10%- Preparation to intervention Warning: When this plant stress translates into significant biomass damage Potential yield loss <20%Alert: when these two conditions are accompanied by an anomaly in the irreversible vegetation damage Potential yield loss <30% Catastrophe: When have to mitigate serious damages. Potential yield loss <40%

  28. SUMMARY • Drought phenomena very complex process • Drought indexing important to measure magnitude and duration of drought • Important to know the content of key indexes • RS based drought indexes move to proxy standard application, but important the accurate local time-space dependent calibration and validation to increase monitoring accuracy

  29. THANK YOU FOR ATTENTION Integrated Drought Management Programme in Central and Eastern Europe, National Consultation Budapest, HUNGARY Date: 21-23. November, 2013.

More Related