PFG 2014 / 5, 0383–0392 Article Stuttgart, October 2014 Analysing Phenological Characteristics Extracted from Landsat NDVI Time Series to Identify Suitable Image AcquisitionDatesforCannabisMappinginAfghanistan MATTEO MATTIUZZI,COEN BUSSINK &THOMAS BAUER, Vienna, Austria Keywords: illicitcropmonitoring,cannabis,NDVI,phenology,seasonality,senescence Summary: Since2009,theUnitedNationsOffice Zusammenfassung: Analyse phänologischer Ei- on Drugs and Crime (UNODC) has carried out genschaften basierend auf Landsat NDVI Zeitrei- various annual surveys of cannabis cultivation in hen für die Bestimmung geeigneter Zeitpunkte für Afghanistan. The mapping of cannabis fields is die Aufnahme von Satellitenbildern für die Kartie- based on very high resolution satellite imagery. The rung von Cannabis in Afghanistan. Das Büro für image acquisition dates have a strong influence on Drogen- und Verbrechensbekämpfung der Verein- whether cannabis can be visually differentiated ten Nationen (UNODC) führt seit 2009 jährlich from other crops. Expert knowledge shows that the eine Auswertung der Anbaufläche von Cannabis in idealacquisitiondatesareattheendofthegrowing Afghanistan durch. Die Kartierung von Cannabis- period (senescence) of cannabis. For future surveys feldernerfolgtmitHilfevonsehrhochauflösenden amethodwasdevelopedtopredicttheoptimumob- Fernerkundungsdaten. Der Aufnahmezeitpunkt servationdatesindependentoftemporaldifferenc- der Daten ist sehr bedeutend dafür, ob Cannabis es of the phenology. The study includes an analysis von anderen Feldfrüchten visuell eindeutig unter- ofLandsat-basedNDVItimeseriesandthecalcula- schieden werden kann. Nach Expertenansicht liegt tionofinformationaboutthesenescencestagesof dieser Zeitpunkt am Ende der Wachstumsperiode cannabis and other vegetation. The approach was (Seneszenz) von Cannabis. Fürzukünftige Aus- applied to four test sites in Afghanistan. wertungenwurdeeineMethodeentwickelt,den idealen Aufnahmezeitpunkt unabhängig von zeitli- chen Unterschieden der Phänologie im Voraus zu bestimmen. Der Ansatz umfasst eine Analyse von NDVI Zeitreihen basierend auf Landsat-Daten und die Ableitung von Informationen über die Senes- zenz-Phasen von Cannabis und anderen Pflanzen. Die Methode wurde in vier Testgebieten in Afgha- nistan angewandt. 1Introduction for alternative development and law enforce- ment.Inordertoascertaintheimpactofthese Illicit crops like cannabis, opium poppy and measures, the United Nations Office on Drugs coca bush form the basis for highly lucrative andCrimemonitorsillicitcropproduction, narcotic drug production. Illicit crop cultiva- providing technical assistance to its member tion(cannabisandopiumpoppy)inAfghan- statesonmethodology.Oneofthekeycompo- istantakesplaceinareaswhereinsecurity nents to estimate plant-based drug production andinsurgenciesareprevalentandisstrong- istheareaundercultivation,whichismostly ly linked with organized criminal networks done with the help of satellite images and ap- (UNODC 2013a).Policymeasurestotacklethe propriate statistical techniques. These estima- problem require an integrated approach of sta- tionsfacevariouschallengesandinparticular bilization measures, creating opportunities the optimum timing of the satellite imagery. © 2014 E. Schweizerbart'sche Verlagsbuchhandlung, Stuttgart, Germany www.schweizerbart.de DOI: 10.1127/1432-8364/2014/0231 1432-8364/14/0231 $ 2.50 384 Photogrammetrie • Fernerkundung • Geoinformation 5/2014 This is especially difficult for monitoring can- tection through remote sensing using spectral nabisinAfghanistanduetothepossiblecon- differences detected from aerial platforms or fusion with other crops. hyperspectral images. DAUGHTRY & WALTHALL UpuntilnowUNODChasconductedfour (1998) and AZARIA etal.(2012)identifiedspe- annual cannabis surveys in Afghanistan to cific spectral ranges that are optimum for dif- determine the area under cultivation (2009 ferentiating cannabis from other crops that – 2012). In addition UNODC has been con- areattheirbestwhenthecannabisfieldshave ductingannualopiumpoppysurveysinAf- densecanopies.Althoughthereisalotofin- ghanistansince1994andsince1999with formalknowledgeonthebehaviourofcanna- satellite imagery. For estimating the area, bisplantsandhowtheyareaffectedbytheen- UNODC used satellite imagery that were ac- vironment and cultivation practices, as far as quired for locations based on a spatial sam- theauthorscouldverify,thereisnoscientific plingapproach.Theveryhighresolutionsat- literature on monitoring the phenology of can- ellite images (VHR) obtained on a single ac- nabiswithremotesensingtimeseries. quisition date were used to identify cannabis Phenology is defined as the study of the fields(Fig.1)andtocalculatetheareaunder timing of recurring plant life cycle events that cultivation, which was extrapolated to a larg- are driven by environmental factors (MORI- er cannabis risk area (UNODC 2013b). Reliable SETTE etal.2009).Thedocumentationofthe information on the growing cycle of cannabis timing of phenological events often relies on plants (phenology) is essential for the selec- visual observations in the field. In order to col- tion of the ideal acquisition time of the VHR lect data on phenology over large geographic images. Within the growing cycle that is sub- areas, measurements of reflected electromag- jecttoseasonalandregionalvariations,anat- neticradiationfromthelandsurfacecanbe temptismadetotryandfindthemostappro- used. The timing of recurring changes in re- priatetimefortheacquisitionofthesatellite flectance is defined as land surface phenology imageswheretheillicitcropcanbestbeiden- (HANES etal.2014).Inthispaper“phenology” tifiedanddistinguishedfromothervegetation refers to this term. Information on phenology bymeansofavisualinterpretation.According asderivedfromremotelysensedimagesdiffer to local UNODC experts in Afghanistan can- from conventional phenology data in that the nabis is harvested later than most other crops. information relates to the aggregated charac- Studiesonthemappingofcannabisare teristics of the vegetation within the areal unit often directed towards the early detection measured by satellite sensors. Phenological of plants in order to guide eradication cam- changescanbemonitoredbymeansofremote paigns, e.g. by mapping of potential high-risk sensingasplantschangeinappearanceand areas to reduce the search area (LISITA et al. structure during their growth cycle. 2013).Therearevariousunpublishedstudies Theanalysisasdescribedinthispaperis butonlyafewscientificpublicationsonde- based on the normalized difference vegetation Fig. 1: Left: cannabis fields at flowering stage, as seen on a colour infra-red (CIR) satellite image, right: as seen from helicopter in Nangarhar province in 2012 (UNODC 2013). Matteo Mattiuzzi et al., Analysing Phenological Characteristics 385 index(NDVI).TheNDVIisamathematical termine the number of seasons or seasonal pa- combinationoftheRedandtheNIR(near-in- rameters,e.g.,thebeginningortheendofthe frared) band. Annual time series of satellite- season. ZHANG et al. (2003) used MODIS and derived vegetation indices and biophysical AVHRR data for modelling the key pheno- metricsthatincorporatethereflectanceofthe logical phases: green up, maturity, senescence NIR and the red wavelengths generally cap- anddormancy.Asoftwaretooldevelopedby ture the spectral changes associated with leaf LANGE & DOKTOR (2013)canalsobeusedto growth and senescence in the large areal units determine such parameters. monitored by the sensor (HANES et al. 2014). Theobjectiveofthispaperistotestwheth- In order to better distinguish cannabis from er the cannabis phenology in different regions otherplantsandtodeterminetheidealacqui- in Afghanistan is different from the general sition dates for VHR images, focus is laid on a vegetation at the end of the growing season, more detailed analysis of the senescence. Dur- when cannabis can best be identified on VHR ing the onset of senescence, deterioration of satellite imagery. In addition, the research cell walls in the mesophyll tissue produces a looks at whether the development of cannabis distinctive decline in infrared reflectance; an in comparison to the general vegetation pro- accompanying increase in visible brightness vides a basis for the development of a forecast- maybetheresultofdeclineintheabundance ingtooltodeterminethebestdatesforVHR andeffectivenessofchlorophyllasanabsorb- imageacquisition.Thisisdonebyanalysing erofvisibleradiation(CAMPBELL &WYNNE twoparameters:i)thetimespanbetweenthe 2006).Thisleadstoasignificantdecreaseof dates when the differences of the NDVI val- NDVI values. uesofcannabisandnon-cannabisvegetation While many studies have analysed pheno- reach a maximum; and ii) the maximum num- logicaltrendsingeneral(e.g.MORISETTE et al. ber of occurrences of senescence within each 2009),onlyafewstudieshavefocussedon testsite.ThemaximumdifferenceofNDVI informationonsenescencederivedfromre- between cannabis and non-cannabis is expect- motely sensed images. JÖNSSON &EKLUNDH ed to be the date where cannabis is most eas- (2004) for example developed a method to de- ilydistinguishable.Themaximumnumberof Fig. 2: Test sites in Afghanistan and Landsat scenes necessary to cover the areas. 386 Photogrammetrie • Fernerkundung • Geoinformation 5/2014 occurrencesofsenescenceisusedtodefine 3 Methodology a comparable phenological stage sensitive to seasonal changes between the years. 3.1 Data Theanalysiswascarriedoutusingatime 2StudyArea series of archived medium resolution satel- liteimages.Inthiscaseallavailabledataof Cannabis