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Mapping seasonal trends in vegetationusing AVHRR-NDVI time series in theYucatán Peninsula, MexicoNeeti Neeti a , John Rogan a , Zachary Christman b , J. RonaldEastman a , Marco Millones a , Laura Schneider c , Elsa Nickl d ,Birgit Schmook e , Billie L. Turner II a f g & Bardan Ghimire aa Graduate School of Geography, Clark University, Worcester, MA,01610, USAb Department of Geography, Middlebury College, Middlebury, VT,USAc Department of Geography, Rutgers, The State University of NewJersey, Piscataway, NJ, USAd Department of Geography, University of Delaware, Newark, DE,USAe El Colegio de la Frontera Sur (ECOSUR), Chetumal, QuintanaRoo, Mexicof School of Geographical Sciences and Urban Planning, ArizonaState University, Tempe, AZ, USAg School of Sustainability, Arizona State University, Tempe, AZ,USA

Version of record first published: 27 Sep 2011.

To cite this article: Neeti Neeti, John Rogan, Zachary Christman, J. Ronald Eastman, MarcoMillones, Laura Schneider, Elsa Nickl, Birgit Schmook, Billie L. Turner II & Bardan Ghimire (2012):Mapping seasonal trends in vegetation using AVHRR-NDVI time series in the Yucatán Peninsula,Mexico, Remote Sensing Letters, 3:5, 433-442

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Remote Sensing LettersVol. 3, No. 5, September 2012, 433–442

Mapping seasonal trends in vegetation using AVHRR-NDVI time seriesin the Yucatán Peninsula, Mexico

NEETI NEETI†, JOHN ROGAN*†, ZACHARY CHRISTMAN‡, J. RONALDEASTMAN†, MARCO MILLONES†, LAURA SCHNEIDER§, ELSA NICKL¶,BIRGIT SCHMOOK|, BILLIE L. TURNER II†††‡‡ and BARDAN GHIMIRE†

†Graduate School of Geography, Clark University, Worcester, MA 01610, USA‡Department of Geography, Middlebury College, Middlebury, VT, USA

§Department of Geography, Rutgers, The State University of New Jersey, Piscataway,NJ, USA

¶Department of Geography, University of Delaware, Newark, DE, USA|El Colegio de la Frontera Sur (ECOSUR), Chetumal, Quintana Roo, Mexico

††School of Geographical Sciences and Urban Planning, Arizona State University,Tempe, AZ, USA

‡‡School of Sustainability, Arizona State University, Tempe, AZ, USA

(Received 12 May 2011; in final from 17 August 2011)

This research examines the spatio-temporal trends in Advanced Very HighResolution Radiometer (AVHRR) Global Inventory Modelling and MappingStudies (GIMMS) normalized difference vegetation index (NDVI) time series toascribe land use change and precipitation to observed changes in land cover from1982 to 2007 in the Mexican Yucatán Peninsula, using seasonal trend analysis(STA). In addition to discrete land cover transitions across the study region, pat-terns of agricultural intensification, urban expansion and afforestation in protectedareas have enacted changes to the seasonal patterns of apparent greenness observedthrough STA greenness parameters. The results indicate that the seasonal variationin NDVI can be used to distinguish among different land cover transitions, andthe primary differences among these transitions were in changes in overall green-ness, peak annual greenness and the timing of the growing season. Associationsbetween greenness trends and precipitation were weak, indicating a human-dom-inated system for the 26 years examined. Changes in the states of Campeche,Quintana Roo and Yucatán appear to be associated with pasture cultivation,urban expansion-extensive cultivation and urban expansion-intensive cultivation,respectively.

1. Introduction

The Yucatán Peninsula of south-eastern Mexico possesses extraordinary biodiver-sity within its expansive seasonal tropical forests (Toledo 1994). Adapted to climatevariability (Magaña et al. 1999, Pavia et al. 2006), these forests have registered ashotspots of deforestation (Velázquez et al. 2010), threatened by chronic anthropogenic

*Corresponding author. Email: jrogan@clarku.edu

Remote Sensing LettersISSN 2150-704X print/ISSN 2150-7058 online © 2011 Taylor & Francis

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disturbances (Schmook et al. 2011). In the last 30 years, almost 4.2 million hectaresof forest (1977–2000) in the peninsula (Diaz-Gallegos et al. 2010) have been lost toagricultural expansion (Roy Chowdhury and Turner 2006), pasture expansion and thedevelopment of the Maya Riviera along the north-eastern coast (Torres and Momsen2005). These land use changes appear to have been accompanied by changes in themagnitude and timing of vegetation green-up. This article maps and characterizesstatistically significant changes in vegetation abundance and condition in the penin-sula using coarse spatial resolution normalized difference vegetation index (NDVI)imagery (1982–2007). It ascribes land use changes and possible climate variability tothe observed changes in NDVI.

Remotely sensed data provide comprehensive spatio-temporal portraits of mid-to long-term (years to decades) vegetation dynamics, especially with fine temporalresolution data from the Advanced Very High Resolution Radiometer (AVHRR)(Tucker et al. 1985) and Moderate Resolution Imaging Spectroradiometer (MODIS)(Huete et al. 2002) sensors. Time series of AVHRR and MODIS vegetation indexdata effectively represent such earth surface phenomena as (a) subtle temporal vari-ation in vegetation condition; (b) the influence of precipitation and temperature onvegetation reflectance; and (c) phenological patterns, including seasonal variation inmagnitude and timing of green-up and green-down of different land covers (de Beursand Henebry 2010).

Annual variability in vegetation condition can be influenced by climate fluctua-tions (e.g. rainfall) and disturbances (e.g. hurricanes), and anthropogenic activities(e.g. agriculture, urbanization). In the Yucatán Peninsula, attention has been focusedon anthropogenic land cover conversion (e.g. Schmook et al. 2011), and more recently,on the forest damage resulting from Hurricane Dean in 2007 (e.g. Rogan et al. 2011).Much less attention has been given to annual and inter-annual productivity cycles andtiming or to the effects of regional climate change – important issues that cannot beaddressed using conventional land cover map comparison.

Time series of remotely sensed data can be used to analyse seasonal trends using avariety of methods, including Fourier transform, harmonic analysis and logistic func-tion, which involve fitting a predefined theoretical function based on empirical datavalues. The challenge to the successful application of time series analysis is determin-ing what constitutes true change in a remotely sensed variable (e.g. vegetation index)versus the presence of scene noise (e.g. smoke, cloud contamination). The detectionof true change depends on the ability of a method to account for scene variability atone scale (e.g. annual), while accounting for variability at another scale (e.g. multi-year trends) because interactions between changes at multiple scales lead to complexlandscape dynamics.

Seasonal trend analysis (STA) is an improved method to analyse seasonal trends intime series data, which can potentially identify vegetation change while characterizinginteraction at different temporal scales (Eastman et al. 2009). To date, the STA methodhas not been used to detect different anthropogenic and natural land cover transitions.This article utilizes the STA to document the inter-annual, annual and semi-annualpatterns of changes in vegetation in the Yucatán Peninsula, utilizing monthly AVHRRGlobal Inventory Modelling and Mapping Studies (GIMMS) NDVI data for 1982–2007. These changes are related to modifications in land use practices and climatevariability across the region.

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Seasonal trends analysis of NDVI in Yucatán Peninsula 435

2. Study area: the Yucatán Peninsula

The Yucatán Peninsula in Mexico is constituted of three states: Yucatán, Campecheand Quintana Roo (figure 1). The climate across the peninsula is warm, with seasonalpatterns of rainfall that range from 750 to 1400 mm/year, with annual amountsincreasing with decreasing latitude (Veleva et al. 1997). A distinctive wet season occursfrom May to October (Magaña et al. 1999); in the dry period, November–April,evapotranspiration exceeds precipitation, causing a loss of soil moisture and spurringleaf loss across much forest. Precipitation falls on a karstic terrain are characterizedby calcium-rich, shallow rendzina soils, in which phosphorus is the limiting nutrient(Lawrence et al. 2007), and thick clays in solution sinks. Large-scale sugar cane pro-duction and cattle grazing are undertaken in the south-east of Quintana Roo andsouth-western Campeche, respectively. Most extant forest is concentrated in the inte-rior upland of the peninsula where recent land use changes include the expansion ofrotational cultivation and pasture. Four major urban areas, all located on the coastalshelves, continue to expand, with implications for land use in their hinterlands (Turneret al. 2001).

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Figure 1. The Yucatán Peninsula study area with six sample locations of land cover changechosen on the basis of contextual Mann–Kendall significance test.

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3. Data

The 8 km spatial resolution biweekly NDVI (1982–2007) product from GIMMS wascomposited to monthly values using maximum value compositing, and then used toexamine annual and inter-annual vegetation condition trends across the study area.The National Aeronautics Space Administration/National Oceanic and AtmosphericAdministration (NASA/NOAA) Pathfinder AVHRR Land (PAL) NDVI data set(James and Kalluri 1994) and the data from the NASA GIMMS group at theLaboratory for Terrestrial Physics (Tucker et al. 2001) are the longest time series ofremotely sensed data available for studying long-term multi-decadal vegetation trends(Fensholt et al. 2009). General land cover transitions were examined with the use ofcategorical maps of land cover for 1976 and 2000 of 30 m spatial resolution producedby the Mexican National Institute of Statistics and Geography (INEGI) in conjunc-tion with the Center of Environmental Geographical Investigations (CIGA) of theNational Autonomous University of Mexico (UNAM) (Velázquez et al. 2010). Dataspanning 1980–2008 from four climate stations were used to determine potential linksbetween NDVI trends and precipitation. Monthly average precipitation data werecompiled for Ria Lagartos (north), Chetumal (south-east), Escarcega (south-west)and Zoh Laguna (south central).

4. Methods

The four-step methodology involves (a) noise removal from the NDVI time series, (b)STA applied to the de-noised NDVI series, (c) significance testing of STA trends usingthe contextual Mann–Kendall (CMK) measure and (d) visualizing seasonal curves atlocations with substantial NDVI change.

Step 1: noise removal

A major concern using AVHRR-NDVI image time series in tropical regions is thepresence of cloud contamination in the data. Such noise in the NDVI time serieswas removed using inverse T-mode principal component analysis (PCA). T-modePCA considers time as the statistical variable and pixels as statistical observations(Machado-Machado et al. 2011) and performs spectral decomposition with maximumvariance explained. The first 20 components (99.9% variance explained) were used toreconstruct the series using inverse PCA (Gillespie 1992), removing the 0.1% varianceconsidered to be noise.

Step 2: seasonal trend analysis

The inter-annual trends in seasonal (annual and semi-annual) patterns of de-noisedNDVI data were analysed using STA, which is a two-stage process. In stage 1, win-dowed harmonic regression, with a temporal window of 1 year, was performed onthe 26-year NDVI time series to extract an annual sequence of overall greenness andthe amplitude and phase of the annual and semi-annual cycles (Eastman et al. 2009).Amplitude 0 represents overall greenness, or the mean annual NDVI for each year.Amplitude 1 represents the peak of annual greenness, defined as the difference betweenthe magnitude of peak of sine wave for annual greenness and the mean annual NDVI.Phase 1 refers to the timing of annual peak greenness, represented by the position of

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Seasonal trends analysis of NDVI in Yucatán Peninsula 437

the starting point of the representative sine wave of annual greenness. An increas-ing or decreasing phase angle indicates a shift to an earlier or later time of the year,respectively. Phase image values potentially range from 0◦ to 359◦ such that each30◦ represents a shift of approximately one calendar month (Eastman et al. 2009).Henceforth, this suite of harmonics is referred to as greenness parameters.

Temporal changes in greenness parameter coefficients are indicative of change inland cover characteristics (Jakubauskas et al. 2002). These changes can be estimatedby the second stage of the STA procedure, which involves calculating the temporaltrend in the greenness parameters using a Theil–Sen (TS) slope procedure. The TSprocedure calculates the linear trend at each pixel using all pairwise combinations ofimages in time and takes the median of all slopes to create a slope image for each of thegreenness parameters (Hoaglin et al. 2000). This TS slope represents the overall trendof the series and is not affected by outlier values, as long as the number of outliersis less than 0.29 times the total number of observations in the series (Hoaglin et al.2000, p. 160). For example, a conversion of forest to urban land cover within one8 × 8 km AVHRR-NDVI pixel would register as potential change if dominant (in thiscase decreasing) trends of NDVI values have persisted for at least 29% (∼8 out of 26observations) of total observations in the time series.

Step 3: significance testing and mapping seasonal trends

The existence of a trend in greenness parameters can be substantiated systematicallyby analysing the statistical significance of each pixel to identify locations where trendsoccur unequivocally. The CMK statistic (Neeti and Eastman in press), an extensionof the original Mann–Kendall test (Mann 1945, Kendall 1975), was used to analysethe statistical significance of the trends obtained using the TS slope. CMK incorpo-rates local information to detect trends that makes intuitive sense from a geographicalperspective in relatively short time series. Similar to the TS slope procedure, the Mann–Kendall test is non-parametric and robust to outliers; therefore, these measures havebeen used together in the literature (Douglas et al. 2000). Stage 1 of CMK removesserial correlation without affecting the trend in the greenness parameters using a pre-whitening method. Stage 2 evaluates the trend at a regional scale consisting of the3 × 3 neighbourhood around each pixel. Stage 3 uses the same 3 × 3 window to calcu-late the spatial autocorrelation using local covariance for the kernel, which is used toadjust the variance in significance estimation. The results of this procedure are imagesof the statistical significance that correspond to each of the greenness parameters, withvalues representing the p-value of the trend and the degree of confidence to which thepatterns can be seen as non-random.

Step 4: visualizing seasonal curves in significant change locations

The STA image results were examined further at locations with substantial regionalchange by generating the 5-year mean NDVI curves from the de-noised data for thebeginning (1982–1986) and end (2003–2007) of the series. Locations were sampledfrom areas with a minimum of five contiguous pixels (i.e. 320 km2) in the maps pro-duced from the previous step (figures 1 and 2 for sample locations). Differences inthe shapes of the two curves indicate changes in the coefficients of the greennessparameters that collectively describe the annual phenological curve.

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Negatively significant at 99.9%

Negatively significant at 99%

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Figure 2. Yucatán maps illustrating the results of contextual Mann–Kendall statistical signifi-cance for (a) overall greenness, (b) magnitude of peak annual greenness and (c) timing of annualpeak greenness.

5. Results

The locations of statistically significant trends (p-values < 0.05, 0.01 or 0.001) of over-all greenness (amplitude 0), peak of annual greenness (amplitude 1) and timing ofgreenness events (phase 1) are presented in figure 2. Land use activity and/or precip-itation variability can impact each of these greenness parameters independently. Thecentre of Yucatán State, southern Quintana Roo and adjacent parts of Campeche,including the Calakmul Biosphere Reserve, exhibit significant increase in overall peakannual greenness. Decreases in overall greenness occur in the vicinity of Cancún andsouthwards along the Caribbean coast – the Maya Riviera – to the northern border ofthe Sian Ka’an Bioreserve. A widespread decrease in overall greenness is apparent inwest and south-west Campeche. Significant delay in the peak annual greenness occursin central and southern Quintana Roo. Correlation values between average monthlyprecipitation and average monthly NDVI, in significant locations, were low (i.e. < 0.2),and there was negative correlation between the two in the western region of Campeche.

Six locations with significant trend patterns (figure 2) were selected to visualize theseasonal curves of mean NDVI for the first 5 years and the last 5 years of the 26-yeartime series (figure 3). Location a corresponds to the development of Cancún as a majorurban centre (Torres and Momsen 2005) and its sprawl into former forest and agricul-tural lands. This registers a significant decrease in overall greenness in all months, withno significant change in the magnitude and timing of peak greenness. The maximumdifference between the two curves occurs in March and April, the driest months, and

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Figure 3. Seasonal curve of NDVI (5-year average) for six NDVI change locations: (a) east-ern border of Quintana Roo near Cancun; (b) south-west border of Campeche; (c) CalakmulBiosphere Reserve; (d) central Yucatán state; (e) north-east Quintana Roo; and (f ) centralQuintana Roo.

the minimum difference in May and June as the wet season starts to peak. The nearestclimate station, Ria Lagartos, exhibits a significant increase in precipitation.

Location b, in western Campeche, represents a significant decrease in overall green-ness for all months, with no significant change in the other greenness parameters. Themaximum difference between curves occurs in April, whereas the minimum differencein June. This change is caused by the expansive reach of cultivated cattle pasture, espe-cially in the late 1980s (Mas et al. 2004, Velázquez et al. 2010). The nearest climatestation, Escarcega, exhibits a significant increase in precipitation.

Location c, the Calakmul Biosphere Reserve in south-eastern Campeche adjacentto Quintana Roo, registers significant increase in overall greenness with no significantchanges in the other two greenness variables. The maximum difference between thecurves occurs in January/April, while the minimum difference occurs over several wetseason months. The reserve, founded in 1989, restricts agricultural expansion in itscore zone and attempts to control the same in the surrounding buffer zone (Turneret al. 2001). Changes in the regional economy have also spurred forest regrowth during

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the past decade, while sugarcane production is increasingly common to the south-east of the reserve (Vester et al. 2007, Rueda 2010). The Zoh-Laguna climate stationexhibits a significant decrease in precipitation.

Location d in central Yucatán State exhibits a significant increase in overall green-ness for all months except March, with no changes in the other greenness parameters.The maximum difference in the curves in the series occurs in the peak of theagricultural growing season, June/July, and also in December/January, associatedwith winter cropping. The minimum difference occurs during the fallow season inMarch/April. This change corresponds to the expansion of intensive agriculture inthe area (Wyman et al. 2007). The nearest climate station in Ria Lagartos exhibits asignificant increase in precipitation.

Location e in the north-eastern Quintana Roo exhibits a significant decrease in peakannual greenness with no changes in the overall timing of peak greenness. The max-imum difference in curves between the beginning and end of the series is in the wetseason, and the minimum difference is in the dry season. This change is associated withthe deforestation of evergreen forest for cultivated pasture (Mas et al. 2004, Velázquezet al. 2010). The nearest climate station, Ria Lagartos, exhibits a significant increasein precipitation.

Location f in the central and southern Quintana Roo exhibits a significant shift inthe rate and timing of peak green-up, with non-significant changes in overall peakgreenness. The points of 50% green-up and 50% green-down (White et al. 1997) bothoccur approximately 1 month later (i.e. May–June green-up, and January–Februarygreen-down). The minimum difference between the curves occurs in June, July andNovember, and the maximum difference occurs in January. This change correspondsto agricultural conversion from dry forest to cultivated pasture and a variety of agri-cultural activities in the region (Velázquez et al. 2010). The nearest climate station,Chetumal, exhibits a significant decrease in precipitation.

6. Conclusions

This article analysed inter- and intra-annual changes in NDVI patterns to understandthe implications of different land use trajectories and precipitation variability acrossthe Yucatán Peninsula. Substantial change in seasonal patterns in NDVI is indicated,affecting the overall annual greenness, the range of greenness and the timing of green-ness events, and corresponding to the type of land use activities and changes in eachlocation. Generally, forest conversion to different agriculture practices has led to anoverall decrease in greenness. However, other shifts in greenness parameters point tointensive agricultural activities and cattle grazing. Changes in overall greenness vastlyoutnumber changes in peak and/or timing of greenness. Campeche State experiencedthe highest land change associated with pasture cultivation, and Quintana Roo andYucatán State experienced extensive urban and different types of agricultural expan-sion. STA maps correspond well to land conversion information from existing maps.In contrast, greenness change mostly does not correspond to precipitation trends.Overall, the NDVI change phenomena appear to be entirely human-driven, thoughfuture work will test the relationship between greenness and precipitation using alarger number of climate stations. This study demonstrates the utility of STA in con-junction with the CMK test to identify statistically significant locations of land changethat are affecting the regional patterns of vegetative variability and should proveeffective in other study areas and land change scenarios.

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AcknowledgementsThis research was supported by the Gordon and Betty Moore Foundation underGrant #1697. The authors thank Nicholas Giner (Graduate School of Geography) forhis thoughtful comments on this work, and the staff of Clark Labs, Worcester (MA,USA) for facilitating this work using Idrisi® software (Clark Labs, Clark University,Worcester, MA, USA).

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