Crop Damage Assessment using Remote Sensing & Agrometeorological data Mahalanobis National Crop Forecast Centre, DACFW, New Delhi Space Applications Centre,

Slides:



Advertisements
Similar presentations
Operational information FAO RFE for Sudan FAO & Sudan Meteorology Authority Mauro Evangelisti &Stefano Alessandrini (FAO Consultant)
Advertisements

Dr. Adriana-Cornelia Marica & Alexandru Daniel
Project Overview Isabelle Piccard (VITO) Presented by, Lieven Bydekerke (VITO) CODIST-ii, UNECA, 5 May 2011.
Selected results of FoodSat research … Food: what’s where and how much is there? 2 Topics: Exploring a New Approach to Prepare Small-Scale Land Use Maps.
Walloon Agricultural Research Centre Extending Crop Growth Monitoring System (CGMS) for mapping drought stress at regional scale D. Buffet, R. Oger Walloon.
Predicting and mapping biomass using remote sensing and GIS techniques; a case of sugarcane in Mumias Kenya Odhiambo J.O, Wayumba G, Inima A, Omuto C.T,
January 6. January 7 January 8 January 9 January 10.
Active Remote Sensing Systems March 2, 2005 Spectral Characteristics of Vegetation Temporal Characteristics of Agricultural Crops Vegetation Indices Biodiversity.
Monitoring forage production with MODIS data for farmers' decision making Gonzalo Grigera, Martín Oesterheld and Fernando Pacín IFEVA, Facultad de Agronomía,
Early warning Systems in Sudan Meteorological Authority Ahmed M Abdel Karim Sudan Meteorological Authority Crop and RAngeland Monitoring.
Drought Management – Integration of
Dr. Sujay Dutta Crop Inventory & Modelling Division ABHG/EPSA Space Applications Centre ISRO Ahmedabad – Monitoring.
Crop Yield Modeling through Spatial Simulation Model.
CROPPING SYSTEM ANALYSIS
Presented By Abhishek Kumar Maurya
Development of a combined crop and climate forecasting system Tim Wheeler and Andrew Challinor Crops and Climate Group.
Use of remote sensing on turfgrass Soil 4213 course presentation Xi Xiong April 18, 2003.
CSIRO LAND and WATER Estimation of Spatial Actual Evapotranspiration to Close Water Balance in Irrigation Systems 1- Key Research Issues 2- Evapotranspiration.
Relationships Between NDVI and Plant Physical Measurements Beltwide Cotton Conference January 6-10, 2003 Tim Sharp.
EXPERT MEETING ON WEATHER, CLIMATE AND FARMERS November 2004, Geneva, Switzerland ZOLTÁN DUNKEL OMSZ -Hungarian Meteorological Service H-1525 Budapest.
WMO / COST 718 Expert Meeting on Weather, Climate and Farmers November 2004 Geneva, Switzerland.
Remote Sensing & Satellite Imagery Messana Science 8.
Agricultural, Water, and Health-Related Satellite Products from NESDIS-STAR Felix Kogan NOAA/NESDIS Center for Satellite Applications and Research October.
Temperature. Seasonal changes in temperature
Proposed architecture for IMD. Integrating GIS in an operational Agromet advisory system RDBMS MODELS GIS Remote sensing Weather Crop Info Web WAN Point.
Application of GI-based Procedures for Soil Moisture Mapping and Crop Vegetation Status Monitoring in Romania Dr. Adriana MARICA, Dr. Gheorghe STANCALIE,
Differences b etween Red and Green NDVI, What do they predict and what they don’t predict Shambel Maru.
AVHRR-NDVI satellite data is supplied by the Climate and Water Institute from the Argentinean Agriculture Research Institute (INTA). The NDVI is a normalized.
Child POPULATION AND Child SEX RATIO. Child population in India 2  Total population 1,21,05,69,573  Males 62,31,21,843  Females 58,74,47,730  Child.
Summer Colloquium on the Physics of Weather and Climate ADAPTATION OF A HYDROLOGICAL MODEL TO ROMANIAN PLAIN MARS (Monitoring Agriculture with Remote Sensing)
Remote Sensing for agricultural statistics Main uses and cost-effectiveness in developing countries Insert own member logo here Pietro Gennari, Food and.
Detection of Fog Using Derived Dual Channel Difference of MODIS Data Dr. Devendra Singh,Director Satellite Meteorology Division,India Meteorological Department,
Results of Long-Term Experiments With Conservation Tillage in Austria Introduction On-site and off-site damages of soil erosion cause serious problems.
Weather Forecasting Chapter 9 Dr. Craig Clements SJSU Met 10.
Remote Sensing of Vegetation. Vegetation and Photosynthesis About 70% of the Earth’s land surface is covered by vegetation with perennial or seasonal.
Chapter 9: Weather Forecasting Surface weather maps 500mb weather maps Satellite Images Radar Images.
EG2234: Earth Observation Interactions - Land Dr Mark Cresswell.
Remote sensing for surface water hydrology RS applications for assessment of hydrometeorological states and fluxes –Soil moisture, snow cover, snow water.
X. Cai, B.R Sharma, M.Matin, D Sharma and G. Sarath International workshop on “Tackling Water and Food Crisis in South Asia: Insights from the Indus-Gangetic.
Improving Quality of Area & Production Estimates of Crops By Directorate of Economics & Statistics Department of Agriculture & Cooperation Ministry of.
14 ARM Science Team Meeting, Albuquerque, NM, March 21-26, 2004 Canada Centre for Remote Sensing - Centre canadien de télédétection Geomatics Canada Natural.
12/3/2015 3:16 AM 1 Influence of Land Use Land Cover (LULC) on Cyclone Track Prediction – A Study during AILA Cyclone COSPAR Training and Capacity Building.
PRICING A FINANCIAL INSTRUMENT TO GUARANTEE THE ACCURACY OF A WEATHER FORECAST Harvey Stern and Shoni S. Dawkins (Bureau of Meteorology, Australia)
Rhythm of Seasons PRESENTED BY: GROUP- C PRESENTED BY: GROUP- C.
Satellite Imagery for Agronomic Management Decisions.
A Remote Sensing Approach for Estimating Regional Scale Surface Moisture Luke J. Marzen Associate Professor of Geography Auburn University Co-Director.
Current approaches to assess land use and surface atmosphere interactions: Irrigation, salinity and drought Joop Kroes.
Interactions of EMR with the Earth’s Surface
India has multiple hazards that it must combat namely: 1.Drought 2. Floods 3.Cyclone 4.Earthquake While previous disasters form the basis of developing.
NAME : JATIN V KAPIL Social News. Heat wave sweeps north India.
Estimating intra-annual changes in the surface area of Sand Mesa Reservoir #1 using multi-temporal Landsat images Cody A. Booth 1 with Ramesh Sivanpillai.
Date of download: 6/28/2016 Copyright © 2016 SPIE. All rights reserved. Seasonal frequency of Chortoicetes terminifera infestation in eastern Australia,
بسم الله الرحمن الرحيم In the Name of God In the Name of God
Mapping Variations in Crop Growth Using Satellite Data
Using vegetation indices (NDVI) to study vegetation
Askar Choudhury, James Jones, John Kostelnick, Frank Danquah,
VEGA-GEOGLAM Web-based GIS for crop monitoring and decision support in agriculture Evgeniya Elkina, Russian Space Research Institute The GEO-XIII Plenary.
Mapping wheat growth in dryland fields in SE Wyoming using Landsat images Matthew Thoman.
Wheat Scenario: Global & India
Some Applications of Remote Sensing and GIS
E.V. Lukina, K.W. Freeman,K.J. Wynn, W.E. Thomason, G.V. Johnson,
Konstantin Ivushkin1, Harm Bartholomeus1, Arnold K
Satellite Sensors – Historical Perspectives
PRADHAN MANTRI FASAL BIMA YOJANA (PMFBY)
By Blake Balzan1, with Ramesh Sivanpillai PhD2
Figure11.2 Air mass source regions and their paths.
Image Information Extraction
The Pagami Creek Wildfire
Rice monitoring in Taiwan
Indian Support to GEOGLAM Activities
Presentation transcript:

Crop Damage Assessment using Remote Sensing & Agrometeorological data Mahalanobis National Crop Forecast Centre, DACFW, New Delhi Space Applications Centre, ISRO, Ahmedabad

3 Case Studies 1.Wheat Rust 2.Hailstorm Damage of Wheat 3.White Fly in Cotton

Scientific NamePictureOptimum Conditions Stem Rust,Black rust (Puccinia graminis) humid and warmer temperatures (15 -35°C) Stripe rust,Yellow Rust (Puccinia striiformis ) Humid and low temperature(2-15°C) Leaf rust,Brown rust (Puccinia triticina ) Humid and medium temperature(10-30°C)

Study Area

Remote Sensing Data: 1.Resourcesat-2 AWiFS Data(56m) 2.Landsat 8 OLI data (30m) Agro-meteorological data: 1.Minimum and Maximum Air Temperature 2.Minimum and Maximum Relative Humidity 3.Rainfall 4.Surface heat Flux Data Used for Assessment

Flow chart of the methodology

Seasonal Variation of Air Temperature and Relative Humidity in Yamuna Nagar District (Haryana) Air temperature : C Relative Humidity:70-100%

Seasonal Variation of Air Temperature and Relative Humidity in Karnal District (Haryana) Air temperature : C Relative Humidity:70-100%

Seasonal Variation of Air Temperature and Relative Humidity in Ambala District (Haryana) Air temperature : C Relative Humidity:70-100%

Seasonal Variation of Air Temperature and Relative Humidity in Rupnagar District (Punjab) Air temperature : C Relative Humidity: %

Seasonal Variation of Air Temperature and Relative Humidity in Hoshiarpur District (Punjab) Air temperature : C Relative Humidity: %

Seasonal Variation of Air Temperature and Relative Humidity in Gurdaspur District (Punjab) Air temperature : C Relative Humidity: %

Fig.9: Probable conductive zone for stripe rust based on air temperature ( C) and relative humidity ( %) December 2014

Fig.10: Probable conductive zone for stripe rust based on air temperature ( C) and relative humidity ( %) January 2015

Fig.11: Probable conductive zone for stripe rust based on air temperature ( C) and relative humidity ( %) February 2015

Hailstorm Damage

(Source: IMD Gridded Rainfall)

21 March March 2014 AWiFS Data of Sheopur District in MP showing changes Shows lower Poor Crop Condition in 2015, compared to similar period in 2014.

21 March March 2014 AWiFS Data of Bundi District in Rajasthan showing changes Bundi Kota Shows lower Poor Crop Condition in 2015, compared to similar period in 2014.

Field Photos (Mewat District, Haryana) (Barabanki District, UP) (Udaipur District, Rajasthan) (Betul District, MP) (Hoshangabad District, MP)

NDVI Images of 2014, 2015 for Haryana showing changes NDVI is an indicator of crop condition. Shows lower NDVI values in 2015 during similar period

Final Assessment based on NDVI Deviation and Rainfall

District-wise percentage Affected Area

06-13 Mar Mar 2015 Normal Mild Moderate Moderate-Severe Detailed Map of Affected Area (Derived by National Remote Sensing Centre, ISRO) Map at Grid (5 km*5km) Level Map at Pixel level

Crop Cutting Experiment Results AreaAvg. Yield (kg/ha) Range (kg/ha) No. of sites Comments Affected % reduction in yield Unaffected MP (Betul, Hoshangabad)

Wheat Production Estimates Acreage was estimated using Remote Sensing data Yield was estimated from a combination of Remote Sensing based models, agro-meteorological models and crop simulation model Then, a loss factor was determined by developing an empirical model between NDVI and crop yield (from CCE data) and yield loss per unit NDVI decrease (from 2014 to 2015) was computed. Accordingly estimated yield, at district level, was reduced. State F3 F2 Estimated Reduction in Production (%) (compared to F2) Haryana Madhya Pradesh Punjab Rajasthan Uttar Pradesh All India F2: February End estimate, F3: Early April Estimate

White Fly Damage in Cotton

21 Aug,20156 Sept, Oct,2015 Village: Khatwan, Fazilka Fig.12: Seasonal variation of spectral reflectance Profile shows that most of damage by white fly occurred after 6 th September 2015 Which is visible in satellite data of 8 th October.There is sharp change in Red and NIR reflectance

18 Aug, Sept, Oct,2014

18 Aug, Sept, Oct, Aug,20156 Sept,20158 Oct,

Based on the analysis of satellite data (Landsat 8 OLI (21 Aug, 6 Sept, 8Oct (2015))

Based on the analysis of satellite data (Landsat 8 OLI (21 Aug, 6 Sept, 8 Oct, 2015) Accuracy of Assessment : 73.92%

Kirdhan,Fatehabad Bighad,Fatehabad Uchana,Jind Bayankhera,Hisar Budain,Jind Jandikalan,Jind Normal White Fly effect Mealybug & White Fly effect GT photo were taken after

Plans for Meteorological Modeling for Probable Zone 2.Field Data from DWR 3.Remote Sensing (ground and field data) Analysis 4.Possible Drone based imaging Thank You.