Skip to main content

Advertisement

SpringerLink
Log in

Land Use and Environmental Variability Impacts on the Phenology of Arid Agro-Ecosystems

  • Published:
Environmental Management Aims and scope Submit manuscript

Abstract

The overexploitation of water resources in arid environments often results in abandonment of large extensions of agricultural lands, which may (1) modify phenological trends, and (2) alter the sensitivity of specific phenophases to environmental triggers. In Mexico, current governmental policies subsidize restoration efforts, to address ecological degradation caused by abandonments; however, there is a need for new approaches to assess their effectiveness. Addressing this, we explore a method to monitor and assess (1) land surface phenology trends in arid agro-ecosystems, and (2) the effect of climatic factors and restoration treatments on the phenology of abandoned agricultural fields. We used 16-day normalized difference vegetation index composites from the moderate resolution imaging spectroradiometer from 2000 to 2009 to derive seasonal phenometrics. We then derived phenoclimatic variables and land cover thematic maps, to serve as a set of independent factors that influence vegetation phenology. We conducted a multivariate analysis of variance to analyze phenological trends among land cover types, and developed multiple linear regression models to assess influential climatic factors driving phenology per land cover analyzed. Our results suggest that the start and length of the growing season had different responses to environmental factors depending on land cover type. Our analysis also suggests possible establishment of arid adapted species (from surrounding ecosystems) in abandoned fields with longer times since abandonment. Using this approach, we were able increase our understanding on how climatic factors influence phenology on degraded arid agro-ecosystems, and how this systems evolve after disturbance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Aelst S (2014) Outliers. Statistics Reference Online John Wiley & Sons Ltd, Wiley StatsRef

    Book  Google Scholar 

  • Andrews RW (1981) Salt-water-intrusion in the Costa de Hermosillo, Mexico: A numerical analysis of water management proposals. Ground Water 19:635–647

  • Avery TE, Berlin GL (1992) Fundamentals of remote Sensing and airphoto interpretation, 5th edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Breiman L, Friedman JH, Olshen RA, Stone CG (1984) Classification and regression trees. Wadsworth International group, Belmont

    Google Scholar 

  • Carl M (2014) Encyclopedia of quality of life and well-being research. Springer, Dordrecht

    Google Scholar 

  • Castellanos AE, Martinez MJ, Llano JM, Halvorson WL, Espiricueta M, Espejel I (2005) Successional trends in Sonoran Desert abandoned agricultural fields in northern Mexico. J Arid Environ 60:437–455

    Article  Google Scholar 

  • Cerda A (1997) Soil erosion after land abandonment in a semiarid environment of southeastern Spain. Arid Soil Res Rehabil 11:163–176

    Article  Google Scholar 

  • Chuine I, Kramer K, Hanninen H (2003) Plant development models. In: Schwartz M (ed) Phenology: an integrative environmental science. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  • Cleland EE, Chuine I, Menzel A, Mooney HA, Schwartz MD (2007) Shifting plant phenology in response to global change. Trends Ecol Evol 22:357–365

    Article  Google Scholar 

  • Cleland EE, Allen J, Crimmins T, Dunne J, Pau S, Travers S, Zavaleta E, Wolkovich E (2012) Phenological tracking enables positive species responses to climate change. Ecology 93:1765–1771

    Article  Google Scholar 

  • Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37:35–46

    Article  Google Scholar 

  • Crimmins T, Crimmins M, Bertelsen D (2010) Complex responses to climate drivers in onset of spring flowering across a semi-arid elevation gradient. J Ecol 98:1042–1051

    Article  Google Scholar 

  • Davison JE, Breshears DD, van Leeuwen WJD, Casady GM (2011) Remotely sensed vegetation phenology and productivity along a climatic gradient: on the value of incorporating the dimension of woody plant cover. Glob Ecol Biogeogr 20:101–113. doi:10.1111/j.1466-8238.2010.00571.x

    Article  Google Scholar 

  • De Fries RS, Hansen M, Townshend JRG, Sohlberg R (1998) Global land cover classifications at 8 km spatial resolution: the use of training data derived from Landsat imagery in decision tree classifiers. Int J Remote Sens 19:3141–3168

    Article  Google Scholar 

  • Doi H, Katano I (2008) Phenological timings of leaf budburst with climate change in Japan. Agr Forest Meteorol 148:512–516

  • Dregne HE (2002) Land degradation in the drylands. Arid Land Res Manag 16:99–132. doi:10.1080/153249802317304422

    Article  Google Scholar 

  • Eidenshink JC, Haas RH (1992) Analyzing vegetation dynamics of land systems with satellite data. Geocarto Int 7:53–61

    Article  Google Scholar 

  • Elgabaly MM (1977) Water in arid agriculture: salinity and waterlogging in the near-east region. Ambio 6:36–39. doi:10.2307/4312240

    Google Scholar 

  • Farrar DE, Glauber RR (1967) Multicollinearity in regression analysis: the problem revisited. Rev Econ Stat 49:92–107

    Article  Google Scholar 

  • Friedl MA et al (2002) Global land cover mapping from MODIS: algorithms and early results. Remote Sens Environ 83:287–302

    Article  Google Scholar 

  • Gamon JA et al (1995) Relationships between NDVI canopy structure, and photosynthesis in three Californian vegetation types. Ecol Appl 5:28–41

    Article  Google Scholar 

  • Geist HJ, Lambin EF (2004) Dynamic causal patterns of desertification. BioScience 54:817–829. doi:10.1641/0006-3568

    Article  Google Scholar 

  • Goetz SJ, Prince SD, Small J, Gleason ACR (2000) Interannual variability of global terrestrial primary production: results of a model driven with satellite observations. J Geophys Res 105:20077–20091

    Article  Google Scholar 

  • Halvorson WL, Castellanos AE, Murrieta-Saldivar J (2003) Sustainable land use requires attention to ecological signals. Environ Manage 32:551–558. doi:10.1007/s00267-003-2889-6

    Article  Google Scholar 

  • Hansen M, Dubayah R, DeFries R (1996) Classification trees: an alternative to traditional land cover classifiers. Int J Remote Sens 17:1075–1081

    Article  Google Scholar 

  • Idso SB, Jackson RD, Reginato RJ (1978) Extending the “degree day” concept of plant phenological development to include water stress effects. Ecology 59:431–433

    Article  Google Scholar 

  • Jensen JR (2005) Introductory digital image processing: a remote sensing perspective, 3rd edn. Pearson Prentice Hall, Upper Saddle River

    Google Scholar 

  • Jönsson P, Eklundh L (2002) Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans Geosci Remote Sens 40:1824–1832

    Article  Google Scholar 

  • Jönsson P, Eklundh L (2003) Seasonality extraction from satellite sensor data. Frontiers of Remote Sensing Information Processing. World Scientific Publishing, Singapore, pp 487–500

    Chapter  Google Scholar 

  • Jönsson P, Eklundh L (2004) TIMESAT a program for analyzing time-series of satellite sensor data. Comput Geosci 30:833–845

    Article  Google Scholar 

  • Justice CO, Townshend JRG, Holben BN, Tucker CJ (1985) Analysis of the phenology of global vegetation using meteorological satellite data. Int J Remote Sens 6:1271–1318

    Article  Google Scholar 

  • Kariyeva J, van Leeuwen W (2011) Environmental drivers of NDVI-based vegetation phenology in central Asia. Remote Sens 3:203–246

    Article  Google Scholar 

  • Kemp PR (1983) Phenological patterns of chihuahuan desert plants in relation to the timing of water availability. J Ecol 71:427–436

    Article  Google Scholar 

  • Keselman HJ (2014) Sphericity test statistics reference online. Statistics Reference Online John Wiley & Sons, Ltd, Wiley StatsRef

    Google Scholar 

  • Knapp AK, Smith MD (2001) Variation among biomes in temporal dynamics of aboveground primary production. Science 291:481–484. doi:10.1126/science.291.5503.481

    Article  CAS  Google Scholar 

  • Lambin EF et al (2001) The causes of land-use and land-cover change: moving beyond the myths. Glob Environ Change 11:261–269

    Article  Google Scholar 

  • Loik ME, Breshears DD, Lauenroth WK, Belnap J (2004) A multi-scale perspective of water pulses in dryland ecosystems: climatology and ecohydrology of the western USA. Oecologia 141:269–281

    Article  Google Scholar 

  • Lowry J et al (2007) Mapping moderate-scale land-cover over very large geographic areas within a collaborative framework: a case study of the Southwest Regional Gap Analysis Project (SWReGAP). Remote Sens Environ 108:59–73

    Article  Google Scholar 

  • Matson PA, Parton WJ, Power AG, Swift MJ (1997) Agricultural intensification and ecosystem properties. Science 277:504–509

    Article  CAS  Google Scholar 

  • McGinnies WG (1981) Discovering the dessert. The University of Arizona Press, Tucson

    Google Scholar 

  • Menzel A (2000) Trends in phenological phases in Europe between 1951 and 1996. Int J Biometeorol 44:76–81

    Article  CAS  Google Scholar 

  • Menzel A (2003) Plant Phenological “Fingerprints”. In: Schwartz M (ed) Phenology: an integrative environmental science. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  • Menzel A, Estrella N, Fabian P (2001) Spatial and temporal variability of the phenological seasons in Germany from 1951 to 1996. Glob Change Biol 7:657–666

    Article  Google Scholar 

  • Menzel A et al (2006) European phenological response to climate change matches the warming pattern. Glob Change Biol 12:1969–1976

    Article  Google Scholar 

  • Mooney HA, Gulmon SL, Rundel PW, Ehleringer J (1980) Further observations on the water relations of Prosopis tamarugo of the northern Atacama desert. Oecologia 44:177–180

    Article  Google Scholar 

  • Nilsen ET, Sharifi MR, Rundel PW, Jarrell WM, Virginia RA (1983) Diurnal and seasonal water relations of the desert phreatophyte Prosopis Glandulosa (honey mesquite) in the Sonoran Desert of California. Ecology 64:1381–1393

    Article  Google Scholar 

  • Noy-Meir I (1973) Desert Ecosystems: environment and Producers. Annu Rev Ecol Syst 4:25–51. doi:10.1146/annurev.es.04.110173.000325

    Article  Google Scholar 

  • Pal M, Mather PM (2003) An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens Environ 86:554–565

    Article  Google Scholar 

  • Prince SD, Justice CO, Los SO (1990) Remote sensing of the sahelian environment. A review of the current status and future prospects. Technical Centre for Agricultural and Rural Cooperation and the Commission of the European Communities, Brussels

  • Raich JW et al (1991) Potential net primary productivity in South-America—application of a global model. Ecol Appl 1:399–429. doi:10.2307/1941899

    Article  Google Scholar 

  • Ramsey FL, Schafer DW (2002) The statistical sleuth: a course in methods of data analysis. Duxbury, Pacific Grove

    Google Scholar 

  • Rangel Medina M, Monreal Saavedra R, Morales Montaño M, Castillo Gurrola J (2002) Vulnerabilidad a la Intrusion Marina de Acuiferos Costeros en el Pacifico Norte Mexicano; un caso, el Acuifero Costa de Hermosillo, Sonora, Mexico Revista Latino-Americana de Hirdrogeologia, pp 31–51

  • Reed BC, Brown JF, VanderZee D, Loveland TR, Merchant JW, Ohlen DO (1994) Measuring phenological variability from satellite imagery. J Veg Sci 5:703–714

    Article  Google Scholar 

  • Reed BC, Schwartz MD, Xiao X, Noormets A (2009) Remote Sensing Phenology. Phenology of Ecosystem Processes. Springer, New York, pp 231–246

    Chapter  Google Scholar 

  • Rogan J, Franklin J, Roberts DA (2002) A comparison of methods for monitoring multitemporal vegetation change using thematic mapper imagery. Remote Sens Environ 80:143–156

    Article  Google Scholar 

  • Romo-Leon JR, van Leeuwen WJD, Castellanos-Villegas A (2014) Using remote sensing tools to assess land use transitions in unsustainable arid agro-ecosystems. J Arid Environ 106:27–35

    Article  Google Scholar 

  • Schwartz M (2003a) Phenoclimatic Measures. In: Schwartz M (ed) Phenology: an integrative environmental science. Kluwer Academic Publishers, Dordrecht

    Chapter  Google Scholar 

  • Schwartz M (2003b) Phenology: an integrative environmental science. Kluwer Academic Publishers, Dordrecht

    Book  Google Scholar 

  • Schwinning S, Sala OE (2004) Hierarchy of responses to resource pulses in arid and semi-arid ecosystems. Oecologia 141:211–220

    Article  Google Scholar 

  • Shahina AG (1997) The phenology of desert plants: a 3-year study in a gravel desert wadi in northern Oman. J Arid Environ 35:407–417

    Article  Google Scholar 

  • Sherry RA et al (2007) Divergence of reproductive phenology under climate warming. Proc Natl Acad Sci 104:198–202

    Article  CAS  Google Scholar 

  • Shreve F, Wiggins IL (1964) Vegetation and Flora of the Sonoran Desert. Stanford University Press, Stanford

    Google Scholar 

  • Small NJH (2014) Multivariate normality testing: overview. Statistics Reference Online John Wiley & Sons Ltd, Wiley StatsRef

    Google Scholar 

  • Smith MJ, Goodchild MF, Longley PA (2009) Geospatial analysis, 3rd edn. Spatial Literacy in Teaching, Matador

    Google Scholar 

  • Story MH (1986) Accuracy assessment: a user’s perspective. Photogr EngRemote Sens 52:397–399

    Google Scholar 

  • Tateishi R, Ebata M (2004) Analysis of phenological change patterns using 1982–2000 advanced very high resolution radiometer (AVHRR) data. Int J Remote Sens 25:2287–2300

    Article  Google Scholar 

  • Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150

    Article  Google Scholar 

  • Tucker CJ, Newcomb WW, Los SO, Prince SD (1991) Mean and inter-year variation of growing-season normalized difference vegetation index for the Sahel 1981–1989. Int J Remote Sens 12:1133–1135

    Article  Google Scholar 

  • van Leeuwen WJD (2008) Monitoring the effects of forest restoration treatments on post-fire vegetation recovery with MODIS multitemporal data. Sensors 8:2017–2042

    Article  Google Scholar 

  • van Leeuwen WJD, Huete AR, Laing TW (1999) MODIS vegetation index compositing approach: a prototype with AVHRR data. Remote Sens Environ 69:264–280

    Article  Google Scholar 

  • van Leeuwen WJD, Orr BJ, Marsh SE, Herrmann SM (2006) Multi-sensor NDVI data continuity: uncertainties and implications for vegetation monitoring applications. Remote Sens Environ 100:67–81

    Article  Google Scholar 

  • van Leeuwen WJD et al (2010a) Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain USA and Israel. Int J Wildland Fire 19:75–93. doi:10.1071/WF08078

    Article  Google Scholar 

  • van Leeuwen WJD, Davison JE, Casady GM, Marsh SE (2010b) Phenological characterization of desert sky island vegetation communities with remotely sensed and climate time series data. Remote Sens 2:388–415

    Article  Google Scholar 

  • Villarreal ML, Van Leeuwen WJD, Romo-Leon JR (2011) Mapping and monitoring riparian vegetation distribution, structure and composition with regression tree models and post-classification change metrics. Int J Remote Sens 33:4266–4290. doi:10.1080/01431161.2011.644594

    Article  Google Scholar 

  • Virginia RA, Jarrell WM (1982) Soil properties in a mesquite-dominated sonoran desert ecosystem. Soil Sci Soc Am J 47:138–144. doi:10.2136/sssaj1983.03615995004700010028x

    Article  Google Scholar 

  • Wezel A, Rath T (2002) Resource conservation strategies in agro-ecosystems of semi-arid West Africa. J Arid Environ 51:383–400. doi:10.1006/jare.2001.0968

    Article  Google Scholar 

  • White MA, Thornton PE, Running SW (1997) A continental phenology model for monitoring vegetation responses to interannual climatic variability. Glob Biogeochem Cycles 11:217–234

    Article  CAS  Google Scholar 

  • Xiao X, Wang Y, Jiang S, Ojima DS, Bonham CD (1995) Interannual variation in the climate and above-ground biomass of Leymus chinense steppe and Stipa grandis steppe in the Xilin river basin Inner Mongolia, China. J Arid Environ 31:283–299

    Article  Google Scholar 

  • Xiao X, Zhang J, Yan H, Wu W, Biradar C, Noormets A (2009) Phenology of Ecosystem Processes. Land Surface Phenology. Springer, New York, pp 247–270

    Google Scholar 

  • Young DR, Nobel PS (1986) Predictions of soil-water potentials in the North-Western sonoran desert. J Ecol 74:143–154

    Article  Google Scholar 

  • Zhang X et al (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 84:471–475

    Article  Google Scholar 

  • Zhang XY, Friedl MA, Schaaf CB, Strahler AH (2005) Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments. J Geophys Res 110:1–14

    Google Scholar 

Download references

Acknowledgments

The authors wish to thank the support for this research provided by the Arizona Remote Sensing Center, University of Arizona, Tucson, AZ, USA. Also we wish to recognize the assistance with fieldwork provided by the Departamento de Investigaciones Cientificas y Tecnologicas (DICTUS) of the Universidad de Sonora. MODIS data were obtained through the online Data Pool at the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) http://lpdaac.usgs.gov/. Landsat TM data were obtained through the online USGS/Earth Resources Observation and Science (EROS) Earth Explorer website http://edcsns17.cr.usgs.gov/NewEarthExplorer.

Author information

Authors and Affiliations

  1. School of Natural Resources and the Environment, Arizona Remote Sensing Center, The University of Arizona, 1955 E. Sixth Street, Tucson, AZ, 85721, USA

    Jose Raul Romo-Leon, Willem J. D. van Leeuwen & Alejandro Castellanos-Villegas

  2. Departamento de Investigaciones Cientificas y Tecnologicas, Universidad de Sonora, Luis Donaldo Colosio s/n, entre Sahuaripa y Reforma Colonia Centro, C.P. 83000, Hermosillo, Sonora, Mexico

    Jose Raul Romo-Leon & Alejandro Castellanos-Villegas

  3. School of Geography and Development, The University of Arizona, Tucson, AZ, 85721, USA

    Willem J. D. van Leeuwen

Authors
  1. Jose Raul Romo-Leon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Willem J. D. van Leeuwen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alejandro Castellanos-Villegas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jose Raul Romo-Leon.

Ethics declarations

Conflict of Interest

The authors declare that there are no conflict of interest related to the work reported in this manuscript.

Ethical Standard

The authors declare that every aspect related to the research presented in this work does not violate any of the current laws of the country where the studies were conducted.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Romo-Leon, J.R., van Leeuwen, W.J.D. & Castellanos-Villegas, A. Land Use and Environmental Variability Impacts on the Phenology of Arid Agro-Ecosystems. Environmental Management 57, 283–297 (2016). https://doi.org/10.1007/s00267-015-0617-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00267-015-0617-7

Keywords

Search

Navigation