monitoring cropland areas using remote sensing, murali krishna gumma

Monitoring cropland areas using Remote sensing

Murali K Gumma1 Prasad S Thenkabail2 and Jun Xiong2

1International Crops Research Institute for the Semi-Arid Tropics, Patancheru-502324, India2 U.S. Geological Survey (USGS), Western Geographic Science Center, Flagstaff, AZ 8600, USA

Operationalizing the Regional Collaborative Platform to address water consumption, Water productivity and Drought management’ in

Agriculture27-29 October, 2015

Fairmont Nile City Hotel, Cairo

Global Croplands using AVHRR, SPOT Vegetation, and Secondary Data

Thenkabail and Gumma, 2013

Global datasets

http://www.croplands.org/app/#/?lat=0&lng=0&zoom=2

GFSAD30 Cropland Products of South Asia @ MODIS 250m

Outline of Today’s Presentation

Outline1. Goals and Objectives2. Data: MODIS time-series3. Cropland Knowledge Creation through Ground

survey Data4. MethodsDecision Trees algorithms SMTs for baseline cropland product generation

5. Results Cropland products from SMTs using MODIS

250 m time series;6. Challenges/Way forward


Goals and Objectives

The overarching goal of this research is to develop and implement spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCA’s) for production of multi-year cropland products that in turn will help address food security issues using MODIS 250m time-series data and Landsat 30m.

Four distinct cropland products will be produced. These are: A. Cropland Extent\Areas, B. Irrigated and Rainfed CroplandsC. Cropping Intensities (single, double, triple, or continuous cropping), and D. Crop Type and\or Dominance. F. Change over space and timeG. Length of growing periodF. Other products (e.g., study drought in rainfed and irrigated)

GFSAD30 Cropland Products of Africa / South Asia @ Nominal 250 mGoals and Objectives:

ACCA- Automated

Decision Tree

DT/SMT- Interpretation

based on knowledge

MODIS NDVI, EVI & LSWI/ Landsat

Crop Extent/Mask Crop Signatures

Baseline Map(2014 for now)

Reference Bank

FAO statistics

Annual Dynamic Map(2003-2014)

VHRI

Approach: Flowchart

Feb 2011_MVC Mar 2011_MVC Apr 2011_MVC May 2011_MVC

Temporal satellite images(MODIS 250m) Rescaled NDVI0 1

Jan 2014 Feb 2014 Mar 2014 Apr 2014

May 2014 Jun 2014 Jul 2014 Aug 2014

Sep 2014 Oct 2014 Nov 2014 Dec 2014

Jun 2010_MVC Jul 2010_MVC Aug 2010_MVC Sept 2010_MVC

Oct 2010_MVC Nov 2010_MVC Dec 2010_MVC Jan 2011_MVC

Feb 2011_MVC Mar 2011_MVC Apr 2011_MVC May 2011_MVC

Generating mega datasets:Time-series multi-date, multi-sensor data

MODIS 8- day 7b data for the year 2010: 322 bands

GFSAD30 Cropland Products of Africa / South Asia @ MODIS 250m

Knowledge Base on Croplands:Ground survey data

• 1. Known, accurate sources (e.g., USDA cropland data layer or CDL);

• 2. Ground Data

Knowledge Base on Croplands Type and Approaches to Collecting Knowledge Base

LegendRainfed croplandsIrrigated croplandsOther LULC

Ground data collection

• Coordinates: latitude, longitude• Land cover percentages• Land use categories• Irrigated area class types (eg. small scale, large scale)• Crop types• Cropping pattern• Cropping calendar• Watering Method (eg; surface water, ground water, tank)• Others: eg; digital photos, detailed descriptions

Watering method Irrigation type Crop type* Scale Intensity

LULC

SW GW

e.g. Rice, Wheat, Maize, or others

Large scale Small scale

Singlecrop

Doublecrop

Continuouscrop

+ + + +

FragmentConjunctive use

Knowledge Base on Croplands Details on Ground Data


METHODSIdeal Spectra Generation for Irrigated

cropland

Knowledge Base on Croplands Irrigated Classes: INITIAL IDEAL SPECTRA

Knowledge Base on Croplands Irrigated classes: INITIAL IDEAL SPECTRA

0.00

0.20

0.40

0.60

0.80

1.00

1.20

May-10 Jun-10 Aug-10 Sep-10 Nov-10 Jan-11 Feb-11 Apr-11 Jun-11

NDV

I

Month

IS_1 IS_2 IS_3 IS_4 IS_5 IS_6 IS_7 IS_8 IS_9 IS_10 IS_11 IS_12 IS_13









IS_130 IS_131 IS_132 IS_134 IS_135 IS_136 IS_137 IS_138 IS_140 IS_141

Knowledge Base on Croplands Irrigated class: FINAL UNIQUE IDEAL SPECTRA

Knowledge Base on Croplands Irrigated Classes: FINAL UNIQUE IDEAL SPECTRA

Knowledge Base on Croplands Rainfed Classes: FINAL UNIQUE IDEAL SPECTRA


METHODSDecision tree algorthms

Knowledge Base on Croplands Irrigated Information Classes

Knowledge Base on Croplands Irrigated Information Classes: MODIS NDVI Spectral Profiles

Initial 70 Classes or Initial Class Spectra

Grouping Calsses to Unique Categories Irrigated Information Classes: MODIS NDVI Spectral Profiles

Initial Class Spectra to 20 groups


METHODSSpectral Matching Techniques


Spectral matching techniques: Irrigated-double crop


Initial 100 Classes or Initial Class Spectra to 20 groups

Grouping Calsses to Unique Categories Rainfed Information Classes: MODIS NDVI Spectral Profiles

Spectral matching techniques: Rainfed-single crop


Initial 100 Classes or Initial Class Spectra to 20 groups

RESULTSFinal Cropland Classes of Africa &South

AsiaCropland Extent\Areas

Crop intensityIrrigated/rainfed

LGPAbiotic stresses

Total Gross Cropland areas (TGCA) = 282 Mha

Total Net Cropland Area (TNCA) = 257 Mha

SMT Derived Product 4

Mapping crop land areas in Africa(2014)SMTs

Mapping crop land areas in Malawi (2014)

Mapping crop land areas in Africa(2014)

Major crops (2014)01. Rainfed-sc-sorghum02. Rainfed-sc-millets/sorghum03. Rainfed-sc-groundnut04. Rainfed-sc-pigeonpea05. Rainfed-SC-maize/sorghum/millet06. Other crops

Croplands of South Asia 2014-2015

LULC 2000-14 (Area '000) (%)01. Irrigated-SW-DC-rice-wheat (20047 ) (11%)02. Irrigated-GW-DC-rice-rice (4741 ) (3%)03. Irrgated-SW/GW-DC-sugarcane/rice-rice (5880 ) (3%)04. Irrigated-GW-DC-millet/sorghum-wheat/mustartd (13724 ) (8%)05. Irrgated-GW-DC-potato-wheat/chickpea (4452 ) (2%)06. Rainfed-SC-groundnut/millets/sorghum (2034 ) (1%)07. Rainfed-SC-pigeonpea/mixedcrops (5155 ) (3%)08. Rainfed-SC-cotton/pigeonpea/mixedcrops (3615 ) (2%)09. Irrigated-GW-DC-rice-fallow-rice (6883 ) (4%)10. Irrigated-SW-mixedcrops-wheat-LS (5754 ) (3%)11. Irrgated-SW-DC-beans-wheat (6180 ) (3%)12. Irrigated-SW-DC-millet-wheat (3183 ) (2%)13. Rainfed-DC-rice-fallows-jute/rice/mixed crops (8714 ) (5%)14. Rainfed-SC-cotton/pigeonpea/mixedcrops (9145 ) (5%)15. Rainfed-DC-millet-fallows/mixedcrops- (12616 ) (7%)16. Rainfed-SC-pigeonpea/cotton (23192 ) (13%)

17. Rainfed-SC-groundnut/millets/sorghum (9602 ) (5%)18. Irrigated-SW-DC-rice-rice/pulses (3663 ) (2%)19. Irrigated-SW-DC-Sugarcane/rice-rice (2265 ) (1%)20. Rainfed-SC-mixedcrops/Plantations (8843 ) (5%)21. Rainfed-SC-fallow-chickpea- (1796 ) (1%)22. Rainfed-DC-millets-chickpea/Fallows (2294 ) (1%)23. Irrigated-TC-fallow-jute-rice (386 ) (0%)24. Irrigated-DC-fallows/pulses-rice-fallow (3085 ) (2%)25. Irrigated-SC-rice-fallow/pulses (2412 ) (1%)26. Irrigated-SW-DC-rice-maize (1583 ) (1%)27. Irrigated-GW-DC-rice-maize/chickpea (1625 ) (1%)28. Rainfed-SC-fallow-rice-fallow (338 ) (0%)29. Irrigated-SW-DC-rice-fallow-rice (3133 ) (2%)30. Irrgated-TC-rice-mixedcrops-mixedcrops (2991 ) (2%)31. Irrgated-Mixed crops/ Shrublands 60%32. Other LULC

Total land area = 446 MhaTotal cropland area = 210.4 Mha

GFSAD30 Cropland Products of South Asia @ Nominal 250 m 2. Irrigated versus Rainfed Croplands

Agriculture area (2000-14)Irrigated croplandsRainfed croplands

CroplandsNet areas (000'ha) %

Irrigated croplands 96037 46Rainfed croplands 114363 54Total cropland 210400

Crop intensity

Crop intensity (2000-14)Single crop (67,035) (35%)Double crop (114,957) (59%)Triple crop (10533) (6%)Other LULC

Kharif: Length of growing periods (2000-14)

Kharif sown area = 189 Mha

Rabi: Length of growing periods (2000-14)

Rabi sown area = 115 Mha

Summer: Length of growing periods (LGP)

Summer sown area = 24 Mha

Planting dates

Planting dates01. Jun, 1st to 30th02. Jul, 1st to 15th03. Jul, 16th to 30th04. Aug, 1st to 15th05. Aug, 16th to 30th06. Sept, 1st to 15th07. Sept, 16th to 30th

Rice – Fallows (Rabi fallows) in South Asia (2010-11)

Rice – fallows (Rabi fallows) areas(2010-11)

Country

MODIS–based Estimates ('000ha)

Total net rice (area)

Rice-fallows (rainfed)

Rice-fallows (irrigated)

Rice-fallows (rainfed) (%)

Rice-fallows (irrigated) (%)

Bangladesh 6,834 2,270 128 33 6

Bhutan 16 2 0 12 0

India 45,117 11,519 2,205 26 19

Nepal 1,231 109 0 9 0

Pakistan 1,490 23 12 2 52

Sri Lanka 1,139 357 1 31 0

Crop Intensity (rice): Bangladesh

Boro rice: 5.01 Mha Aus rice: 1.1 Mha Aman rice: 5.8 Mha

Drought & Submergence areas: South Asia(Agriculture areas)

/ submergence

Identifying drought villages

a) Abiotic stresses

Abiotic stressesFlood damageMild drought

Moderate droughtSevere drought

RESULTSAutomated Cropland Classification

Algorithm (ACCA) for

Producing Cropland Products for Baseline Year 2010

Mapping crop land areas in Africa(2014)SMT Vs ACCA

Total Gross Cropland areas (TGCA) = 282 MhaTotal Net Cropland Area (TNCA) = 257 Mha

Total Gross Cropland areas (TGCA) = 282 MhaTotal Net Cropland Area (TNCA) = 259 Mha

SMT Derived Product 4 ACCA Derived Product 4

Semi-automated, input-dependency Automated, standalone

Data available on https://croplands.org/app/map

2009 2010 2011 2012 2013 2014

2003 2004 2005 2006 2007 2008

Temporal cropland extent (2003 to 2013)

Drought & Submergence areas: South Asia(cropland areas)

http://irri.org/our-work/research/policy-and-markets/mapping/remote-sensing-derived

Temporal irrigated area changes: South Asia(Irrigated area)

http://irri.org/our-work/research/policy-and-markets/mapping/remote-sensing-derived


RESULTSFinal Cropland Classes of South Asia

Accuracy: Remote Sensing Vs. ground Data

Accuracy assessment(based on ground survey data)

Reference data (Ground survey data)

Classified data

01. R

ainf

ed-S

C-ric

e-fa

llow

s

02. R

ainf

ed-

supp

lem

enta

l-ric

e-fa

llow

s

03. I

rrig

ated

-SC-

rice-

fallo

ws

04. I

rrig

ated

-GW

-SC-

rice-

fallo

ws/

mix

edcr

ops

05. I

rrig

ated

-GW

/SW

-ric

e-pu

pses

06. I

rrig

ated

-SW

-DC-

rice-

rice

07. O

ther

LU

LC

Row

Tot

al

Num

ber C

orre

ct

Prod

ucer

s Ac

cura

cy

Use

rs A

ccur

acy

Kapp

a

01. Rainfed-SC-rice-fallows 41 1 2 0 0 11 4 55 41 75% 69% 67%

02. Rainfed-supplemental-rice-fallows 0 15 0 2 0 6 0 28 15 54% 65% 64%03. Irrigated-SC-rice-fallows 2 1 19 0 1 0 1 34 19 56% 79% 78%

04. Irrigated-GW-SC-rice-fallows/mixedcrops 0 0 0 6 1 0 0 19 6 32% 86% 85%

05. Irrigated-GW/SW-rice-pupses 1 3 1 0 15 10 5 22 15 68% 43% 41%06. Irrigated-SW-DC-rice-rice 0 5 6 8 2 150 15 217 150 69% 81% 70%07. Other LULC 11 3 6 3 3 40 220 245 220 90% 77% 62%Column Total 55 28 34 19 22 217 245 620 466

Overall Classification Accuracy = 75.16% Overall Kappa Statistics = 0.6442

Comparisons with National Statistics

N= 735 districts


RESULTSFinal Cropland Classes of South Asia

Utilization at field level

ICRISAT Mandate Crops in South Asia

Millet (5348103 hectares)

Kharif + Rabi sorghum ( 4322140 hectares)

ICRISAT Crops (2013-14)SorghumMilletsGroundnutPigeonpeaChickpea

Groundnut (774300 hectares)

Groundnut (1.67 Mha)

Crop Types at 30m: Anantapur Dist.,

Crop type map (2013-14): Rayadurg taluk

Anantapur Dist.,RayadurgTaluk

Geographical area: 175000haNo. of villages: 80

Planting dates and LGP (2013-14): Rayadurg taluk

Drought frequency (2000-10): Rayadurg taluk

2003-05 2011-12

Tracking of NRM technologiesTemporal Land use / land cover

GW-Irri. 537 ha GW-Irri. 1005 ha

GW-Irri. 1719 ha

1

Conclusions

• Map cropland areas/types/systems for Asia and

Africa at 250m

• An automated cropland classification algorithm

(ACCA) is developed integrating segmentation and

knowledge-based decision trees;

• Remote sensing derived statistics are compared to

conventional statistics;

Next steps• Map cropland areas/types/systems for Asia and

Africa at 30m using Landsat data

• Develop automated techniques for data processing

• Ground data collection for ESA/WCA

Thank you!!Questions, Comments, Discussions?

Mapping crop land areas in Malawi (2014)

Land use / land cover Area (ha)01.Rainfed-DC Maize/mixed crops 637229202.Rainfed-SC Maize/sorghum 181514103.Rainfed-SC tef, sorghum, Maize 200165904.Rainfee-SC-tef/wheat,barly 247943705.Rainfed mixed Crops (vegetablesetc) 324508506.Irrigated-SC-sugarcane-VLS 74292907.Irrigated_mixedcrops 388535808.Rainfed_Rice 42510709.Rangeland/fallow 870712610.Range lands/Shrublands 3138813611.Shrublands/Wasteland tress 3315750412.Barenlands/Sanddunes 1374750613.Forest 467117014.Waterbodies 69764515.Builtup 40946

11,33,77,041 Level5_mod250_2014_lulc_15cls.img

01.Rainfed-DC Maize/mixed crops02.Rainfed-SC Maize/sorghum03.Rainfed-SC tef, sorghum, Maize04.Rainfee-SC-tef/wheat,barly05.Rainfed mixed Crops06.Irrigated-SC-sugarcane-VLS07.Irrigated_mixedcrops08.Rainfed_Rice09.Rangeland/fallow10.Range lands/Shrublands11.Shrublands/Wasteland tress12.Barenlands/Sanddunes13.Forest14.Waterbodies15.Builtup

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monitoring cropland areas using remote sensing, murali krishna gumma

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