Creating agricultural drought Creating agricultural drought statistics for developing countries statistics for developing countries using historic data from satellite using historic data from satellite

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Dr. G.V.KumariDr. G.V.KumariBITS Pilani Goa Campus, India.BITS Pilani Goa Campus, India.

ICAS IV Conference, October 2007ICAS IV Conference, October 2007

Where are the drought statistics?Where are the drought statistics?

In the disaster relief records of international In the disaster relief records of international and national relief agenciesand national relief agencies

In the memories of the local residentsIn the memories of the local residents As communal knowledgeAs communal knowledge As indigenous knowledge passed on by the farmer As indigenous knowledge passed on by the farmer

to his sonto his son As cropping systems that have mixed crops that As cropping systems that have mixed crops that

can tolerate drought, for risk management.can tolerate drought, for risk management.

Who needs them?Who needs them?

The (national/international) development The (national/international) development planner/activist who needs to target areas with planner/activist who needs to target areas with drought with drought management policies drought with drought management policies like “weather insurance” or “drought tolerant like “weather insurance” or “drought tolerant crop variety” crop variety”

The insurance agency to measure risk and set The insurance agency to measure risk and set premiums.premiums.

……. .

Traditional Drought AnalysisTraditional Drought Analysis Drought is deviation from normal rainfall.Drought is deviation from normal rainfall. Rainfall data is standardized, and drought is known to Rainfall data is standardized, and drought is known to

occur when the rainfall is one or two standard occur when the rainfall is one or two standard deviation away from normal!deviation away from normal!

But what happens if you want to spatially But what happens if you want to spatially differentiate and find out which regions are more differentiate and find out which regions are more drought prone?drought prone?

STANDARDIZATION erases spatial difference.STANDARDIZATION erases spatial difference. The probability of drought proneness will be equal!The probability of drought proneness will be equal!

Drought Prone AreasDrought Prone AreasDon’t have normally distributed rainfall pattern!Don’t have normally distributed rainfall pattern!

Hence there is a movement towards REMOTE SENSING METHODS for drought analysis

Literature Literature on drought on drought delineated delineated

using NDVI using NDVI based based

drought drought indicesindices

NDVI is acronym for Normalized Difference Vegetation Index.

NDVI = (NIR — Red)/(NIR + Red)

NIR = Near Infra Red RadiationRed = Visible Red Radiation

(-1) < NDVI < (+1)

Definition and Illustration of NDVI

Data Data

NDVI from SPOT Vegetation (French Satellite)NDVI from SPOT Vegetation (French Satellite)

Region: South East Asia Region: South East Asia (Includes India and China)(Includes India and China)

Period: 1998-2007 (8 years)Period: 1998-2007 (8 years)Spatial Resolution: 1.1km * 1.1km areaSpatial Resolution: 1.1km * 1.1km areaTemporal Resolution: Every 10 days (decadal) Temporal Resolution: Every 10 days (decadal)

DECAD – 10 days – 36 decads per yearDECAD – 10 days – 36 decads per yearDECADE – 10 yearsDECADE – 10 yearsIf you take a 2000 km by 2000km data, you have got 4 million data points for If you take a 2000 km by 2000km data, you have got 4 million data points for

every 10 days for 8 years – imagine the amount of data!every 10 days for 8 years – imagine the amount of data!The abundant resource that we can mine with intelligent questions!The abundant resource that we can mine with intelligent questions!

Flow Diagram of Process StepsFlow Diagram of Process Steps

The useful part of the variability in data valuesThe useful part of the variability in data values

Spatial Clarity:Spatial Clarity: A pixel that is usually dry is not A pixel that is usually dry is not a drought area. a drought area. For example, the Thar desert is not a drought zone.For example, the Thar desert is not a drought zone.

Temporal Clarity:Temporal Clarity: A pixel that is drier A pixel that is drier seasonally will not be identified as a drought seasonally will not be identified as a drought pixel in that dry season. pixel in that dry season. Dry season is not drought season.Dry season is not drought season.

ANNUALAVERAGERAINFALL

Drought Analysis Example: October Drought Analysis Example: October Last YearLast Year

a. Original Data Oct 21 2006a. Original Data Oct 21 2006 b. Maximum for Oct 21 2006b. Maximum for Oct 21 2006

Decline in Percent Carrying Capacity = (Maximum Greenness – Current Greenness) * 100/ Maximum Greenness

Drought in October 2006Drought in October 2006

PCC Map for Oct 2006PCC Map for Oct 2006 PCC LegendPCC Legend(>60 % decline snow & rain)(>60 % decline snow & rain)

N.E.Monsoon!

Drought Evidence - 1Drought Evidence - 1Records vs. PCC AnalysisRecords vs. PCC Analysis

Discussion - 1Discussion - 1

Drought has been recorded in April. Drought has been recorded in April. It could have been recorded well after the actual It could have been recorded well after the actual

occurrence, during the time of relief sanction or fund occurrence, during the time of relief sanction or fund transfer. transfer.

Real Drought has been observed only in February Real Drought has been observed only in February (not in April) in Orissa, Andhra Pradesh, Gujarat, (not in April) in Orissa, Andhra Pradesh, Gujarat, Rajasthan and some eastern parts of Madhya Pradesh. Rajasthan and some eastern parts of Madhya Pradesh. Not much drought evident in Maharashtra even in the Not much drought evident in Maharashtra even in the month of February.month of February.

Drought Evidence - 2Drought Evidence - 2Records vs. PCC AnalysisRecords vs. PCC Analysis

Discussion - 2Discussion - 2

Drought has been recorded immediately in May Drought has been recorded immediately in May during the occurrence of drought, possibly because it during the occurrence of drought, possibly because it covers the capital city (New Delhi) area. covers the capital city (New Delhi) area.

Drought has been observed in the PCC Map of May Drought has been observed in the PCC Map of May 2001 in Gujarat, Rajasthan, New Delhi and Orissa. 2001 in Gujarat, Rajasthan, New Delhi and Orissa.

The map also points to drought in some areas of The map also points to drought in some areas of Andhra Pradesh and Uttar Pradesh which have not Andhra Pradesh and Uttar Pradesh which have not been recorded.been recorded.

Drought Data SourceDrought Data Source

http://http://www.glidenumber.net/glide/public/search/searwww.glidenumber.net/glide/public/search/search.jspch.jsp

Limitations of the studyLimitations of the study NDVI data related limitationsNDVI data related limitations(Eg: not accurate for tropical rain forests – high biomass (Eg: not accurate for tropical rain forests – high biomass

areas, better for low bio mass areas)areas, better for low bio mass areas) Does reduction in greenness always mean drought?Does reduction in greenness always mean drought?(Drought has been captured by the PCC, but there are (Drought has been captured by the PCC, but there are

several other things that came along, which need to several other things that came along, which need to be filtered out)be filtered out)

How much has been traded off because of the spatial How much has been traded off because of the spatial resolution of 1.1Km * 1.1Kmresolution of 1.1Km * 1.1Km

(But its better than 8km data, better than relying on just (But its better than 8km data, better than relying on just the previous year data …) the previous year data …)

Future Directions – Future Directions – Online Drought MapsOnline Drought Maps

Web GIS technologies enable presentation of these Web GIS technologies enable presentation of these maps online with interactive capacities. maps online with interactive capacities. User (NGO or local scientists) zoom into the data of their User (NGO or local scientists) zoom into the data of their

area, they would be able to get information relevant to their area, they would be able to get information relevant to their area area

cross verify against their local knowledge of the drought cross verify against their local knowledge of the drought phenomenon and send feedbacks on the utility and user phenomenon and send feedbacks on the utility and user friendliness, accuracy and precisionfriendliness, accuracy and precision

The feedback can then be used for detection of systematic The feedback can then be used for detection of systematic errors in the processes. errors in the processes.

The drought mapping project hence will evolve over The drought mapping project hence will evolve over time with continuous feedback between local reality time with continuous feedback between local reality and remotely sensed methods.and remotely sensed methods.

Project RelevanceProject Relevance

International Potato Center (CGIAR)International Potato Center (CGIAR)

Indian Council of Agricultural ResearchIndian Council of Agricultural Research

Where is the targetWhere is the target

From CIP (International Agricultural From CIP (International Agricultural Research Center) Vision point of viewResearch Center) Vision point of view

In the target location how much are we really In the target location how much are we really contributing to Millenium development goals?contributing to Millenium development goals?

In simple terms what is the impact on poverty, In simple terms what is the impact on poverty, and malnutrition because of these heat and and malnutrition because of these heat and drought tolerant varieties based on the drought tolerant varieties based on the demography of the target location demography of the target location

Specific objectives of the projectSpecific objectives of the project

To create drought prone area maps corresponding to each NDVI To create drought prone area maps corresponding to each NDVI image using PCC methodimage using PCC method To delineate drought prone areas (1.1 km * 1.1 km) for every 10 days. To delineate drought prone areas (1.1 km * 1.1 km) for every 10 days.

(so that they know which region will benefit from the drought tolerant (so that they know which region will benefit from the drought tolerant variety)variety)

Alternatively, using the same resource, to predict drought prone decads Alternatively, using the same resource, to predict drought prone decads (three parts of each month) for every square kilometer area. (so that (three parts of each month) for every square kilometer area. (so that they know if drought happens during the potato cropping season of that they know if drought happens during the potato cropping season of that drought prone region … and when and how long)drought prone region … and when and how long)

To create drought probability maps (probability in space and To create drought probability maps (probability in space and time) by accumulating the drought prone area maps of 9 yearstime) by accumulating the drought prone area maps of 9 years

ReferencesReferencesGurusamy, Kumari (2005). “Using NDVI-based Measures to Derive Geographic Gurusamy, Kumari (2005). “Using NDVI-based Measures to Derive Geographic

Information on Drought-Prone Areas for Developing Countries,” Dissertation, Information on Drought-Prone Areas for Developing Countries,” Dissertation, University at Albany, State University of New York, USAUniversity at Albany, State University of New York, USA

Jensen, J. R. (2005). Introductory Digital Image Processing: A Remote Sensing Jensen, J. R. (2005). Introductory Digital Image Processing: A Remote Sensing Perspective. Upper Saddle River, NJ 07458, Prentice Hall.Perspective. Upper Saddle River, NJ 07458, Prentice Hall.

Kassa, A. (1999). Drought Risk Monitoring for the Sudan using NDVI 1982- 1993. Kassa, A. (1999). Drought Risk Monitoring for the Sudan using NDVI 1982- 1993. Geomatic Engineering. London, University College London: 47.Geomatic Engineering. London, University College London: 47.

Kogan, F. N. (1998). "Global drought and flood watch from NOAA polar orbitting Kogan, F. N. (1998). "Global drought and flood watch from NOAA polar orbitting satellites." satellites." Advances in Space ResearchAdvances in Space Research 21(3): 477-480. 21(3): 477-480.

Liu, W. T. and R. I. N. Juarez (2001). "ENSO drought onset prediction in northeast Liu, W. T. and R. I. N. Juarez (2001). "ENSO drought onset prediction in northeast Brazil using NDVI." International Journal of Remote Sensing 22 Part 17: 3483-3502.Brazil using NDVI." International Journal of Remote Sensing 22 Part 17: 3483-3502.

McVicar, T. R. and P. N. Bierwirth (2001). "Rapidly assessing the 1997 drought in McVicar, T. R. and P. N. Bierwirth (2001). "Rapidly assessing the 1997 drought in Papua New Guinea using composite AVHRR imagery." International Journal of Papua New Guinea using composite AVHRR imagery." International Journal of Remote Sensing 22 Part 11: 2109-2128.Remote Sensing 22 Part 11: 2109-2128.

Singh, R., S. Roy and F.Kogan. (2003). "Vegetation and temperature condition indices Singh, R., S. Roy and F.Kogan. (2003). "Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India." International Journal from NOAA AVHRR data for drought monitoring over India." International Journal of Remote Sensing 24, no 22: 4393-4402 (10 pages).of Remote Sensing 24, no 22: 4393-4402 (10 pages).

Washington-Allen, R. A., R. D. Ramsey, B.E.Norton and N.E.West. (1998). "Change Washington-Allen, R. A., R. D. Ramsey, B.E.Norton and N.E.West. (1998). "Change detection of the effect of severe drought on subsistence agropastoral communities on detection of the effect of severe drought on subsistence agropastoral communities on the Bolivian Altiplano." International Journal of Remote Sensing 19(7): 1319-1333.the Bolivian Altiplano." International Journal of Remote Sensing 19(7): 1319-1333.

Song, X., G. Saito, M.Kodama and H.Sawada. (2004). "Early detection system of Song, X., G. Saito, M.Kodama and H.Sawada. (2004). "Early detection system of drought in East Asia using NDVI from NOAA/AVHRR data." International Journal drought in East Asia using NDVI from NOAA/AVHRR data." International Journal of Remote Sensing 25, no 16: 3105-3111 (7 pages).of Remote Sensing 25, no 16: 3105-3111 (7 pages).

Questions and CommentsQuestions and Comments

THANK YOU!THANK YOU!

PCC method is precise but accuracy PCC method is precise but accuracy needs to be improvedneeds to be improved

2.1 Steps in the method: 2.1 Steps in the method: Download zip files of NDVI for the region of interest (in this project Download zip files of NDVI for the region of interest (in this project

it was South East Asia since it included India the study focus area) it was South East Asia since it included India the study focus area) for all the available decads. For this project the source data NDVI for all the available decads. For this project the source data NDVI files were downloaded from SPOT free vegetation data website. One files were downloaded from SPOT free vegetation data website. One could create a batch process to automatically download the files could create a batch process to automatically download the files using wget commands, since we are downloading 100s of files.using wget commands, since we are downloading 100s of files.

Unzip them and store only the NDVI file in HDF format for the Unzip them and store only the NDVI file in HDF format for the analysis. The unzipping process also can be automated using shell analysis. The unzipping process also can be automated using shell scripts for batch processing.scripts for batch processing.

Change the NDVI file names to names that identify the space and Change the NDVI file names to names that identify the space and time it represents. (For example, SEAsia_1998Mar21_NDVI.HDF time it represents. (For example, SEAsia_1998Mar21_NDVI.HDF represents the NDVI file of the third decad of March 1998 for the represents the NDVI file of the third decad of March 1998 for the geographic extent of South East Asia)geographic extent of South East Asia)

Convert the HDF format files into a format that is helpful for GIS Convert the HDF format files into a format that is helpful for GIS analysis and raster algebra. In this project FAO’s free WINDISP analysis and raster algebra. In this project FAO’s free WINDISP program was used to do the analysis. Hence all the HDF format files program was used to do the analysis. Hence all the HDF format files were converted into WINDISP format files.were converted into WINDISP format files.

STEPS - INSIDE WINDISP STEPS - INSIDE WINDISP Add, edit and verify that the header of all the files Add, edit and verify that the header of all the files

contains the geographic co-ordinates and the projection contains the geographic co-ordinates and the projection systems recorded properly.systems recorded properly.

Make lists of files corresponding to each decad, to Make lists of files corresponding to each decad, to conduct the analysis, maintaining the temporal identity. In conduct the analysis, maintaining the temporal identity. In WinDISP list files can be used to do raster algebra on WinDISP list files can be used to do raster algebra on specific sets of files. For example, “SEAsia_Mar21.ls” specific sets of files. For example, “SEAsia_Mar21.ls” will contain the list of names of the third decad of March will contain the list of names of the third decad of March files for all 9 years starting from 1998 to 2007.files for all 9 years starting from 1998 to 2007.

Raster algebra functions can be used to find the pixel Raster algebra functions can be used to find the pixel among the 9 years, with the maximum NDVI value, for among the 9 years, with the maximum NDVI value, for each spatial unit. each spatial unit.

CALCULATING DROUGHT INDICES:CALCULATING DROUGHT INDICES: Quality check for the data files have to be done at least at this point Quality check for the data files have to be done at least at this point

of time, because if the original NDVI data files had stripes in them, of time, because if the original NDVI data files had stripes in them, the maximum NDVI files would have picked them up. the maximum NDVI files would have picked them up.

Once the maximum NDVI pixels have been stored in the max NDVI Once the maximum NDVI pixels have been stored in the max NDVI files of the corresponding decads, they can be used to calculate files of the corresponding decads, they can be used to calculate percent carrying capacity of each pixel for each decad in the 9 years. percent carrying capacity of each pixel for each decad in the 9 years.

The only cautionary note during this step is that the original data The only cautionary note during this step is that the original data NDVI should be compared with the maximum NDVI for that NDVI should be compared with the maximum NDVI for that specific decad. Using raster algebra the PCC formula can be input specific decad. Using raster algebra the PCC formula can be input into the GIS program and the results can be stores as a PCCMap file into the GIS program and the results can be stores as a PCCMap file for each decad corresponding to each original NDVI data file. for each decad corresponding to each original NDVI data file.

Macro commands can be written within the GIS software for Macro commands can be written within the GIS software for repeating the steps for all the decads and thus the whole process can repeating the steps for all the decads and thus the whole process can be automated.be automated.

Collect ground truths for verification of calculated dataCollect ground truths for verification of calculated data. From . From a drought disaster relief database collect a few known drought a drought disaster relief database collect a few known drought observations with clear indications of time and space. The observations with clear indications of time and space. The higher the resolution of the spatial unit in the observed data, higher the resolution of the spatial unit in the observed data, more rigorous is the verification process. Hence select drought more rigorous is the verification process. Hence select drought disaster information at least at admin 2 (state/province) level, disaster information at least at admin 2 (state/province) level, if not at admin 3(district/county) level. Information at country if not at admin 3(district/county) level. Information at country level is not very useful. The time stamp on the drought level is not very useful. The time stamp on the drought information may indicate the time at which the relief money information may indicate the time at which the relief money was sent. It may not correspond to the time period of drought was sent. It may not correspond to the time period of drought occurrence. There is no specific or uniform time gap between occurrence. There is no specific or uniform time gap between actual occurrence of drought and relief record time stamp to actual occurrence of drought and relief record time stamp to use in the verification process. One could assume that drought use in the verification process. One could assume that drought occurred during a crop calendar if a drought relief was sent in occurred during a crop calendar if a drought relief was sent in the end of a cropping season.the end of a cropping season.

The PCC calculated maps should display PCC thematically in The PCC calculated maps should display PCC thematically in different colors for varying values of PCC. During manual different colors for varying values of PCC. During manual verification process for a random number of records from the verification process for a random number of records from the drought relief database, one could figure out the delineating drought relief database, one could figure out the delineating PCC value that causes a drought relief process to begin. In PCC value that causes a drought relief process to begin. In other words, the verification process will point to a range of other words, the verification process will point to a range of PCC value in which it is possible to relate to drought relief PCC value in which it is possible to relate to drought relief records. For example, if the PCC value fell above the range of records. For example, if the PCC value fell above the range of 30% for all the verified drought records, it means when the 30% for all the verified drought records, it means when the deviation from maximum NDVI (optimal rainfall) caused deviation from maximum NDVI (optimal rainfall) caused more than 30 percent reduction in carrying capacity, drought is more than 30 percent reduction in carrying capacity, drought is recorded. It should also be noted that values about 60 could be recorded. It should also be noted that values about 60 could be due to “clouds.” The most meaningful range in which drought due to “clouds.” The most meaningful range in which drought can be observed is possibly between 30 and 60. This range can be observed is possibly between 30 and 60. This range would get more precise and more accurate with rigorous would get more precise and more accurate with rigorous verification methods. In this project 30%-60% loss in carrying verification methods. In this project 30%-60% loss in carrying capacity is assumed to be drought.capacity is assumed to be drought.