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Derivation of vegetation density and land-use type pattern in mountain regions of Jordan using multi-seasonal SPOT images

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Abstract

The mountainous region represents the most important agricultural and biodiversity haven in Jordan. The objective of this study is to characterize the seasonal pattern of land use and vegetation using multi-temporal SPOT images. Multi-temporal SPOT images were analyzed to characterize the land use and cropping pattern in the mountain regions of Jordan. The images were radiometrically corrected using invariant objects located on the image, and a linear inter-calibration method was used to calibrate the other images. A hybrid classification approach was used in the classification; the spectral signatures of the land-use classes were derived in an iterative procedure using the ISODATA and field survey data. Then, the maximum likelihood classification was applied on all images to classify the class signatures into thematic land-use types. The hybrid classification approach gives more accurate classification accuracy especially for the multi-seasonal image classification. The overall accuracy of the multi-temporal data set was achieved with 87.9%, while classification accuracy for single-date classifications were 61.3, 76.8, 72.2, and 65.5 for months of October, February, April, and June, respectively. In addition, the scene combinations that were derived from February and April were classified the land-use types almost as well as those combinations including more scenes. Regarding the classification details, the multi-temporal images enable higher level of classification for land-use types such as Anderson level 2, and produce accurate boundaries for the different cropping and farming systems.

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References

  • Ahl D, Gower S, Burrows S, Shabanovm N, Myneni R, Knyazikhin Y (2006) Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS. Remote Sens Environ 104:88–95

    Article  Google Scholar 

  • Alrababh MA, Alahmad MN (2006) Land use/cover classification of arid and semi-arid Mediterranean landscapes using Landsat ETM. Int J Remote Sens 27(13):2703–2718

    Article  Google Scholar 

  • Anderson JR, Hardy EE, Roach JT, Witmer RE (1976) A land use/cover classification system for use with remote sensor data. US Geological Survey Professional paper 964, Sioux Falls

  • Arora VK, Boer GJ (2005) A parameterization of leaf phenology for the terrestrial ecosystem component of climate models. Glob Change Biol 11:39–59

    Article  Google Scholar 

  • Badhwer GD, Carnes JG, Austin WW (1982) Use of Landsat-derived temporal profiles for corn-soybean feature extraction and classification. Remote Sens Environ 12:57–79

    Article  Google Scholar 

  • Bauman B, Kropff M, Tuong T, Wopereis MM, Berge H, Laar H (2001) ORYZA2000: modeling lowland rice. IRRI, Manila

    Google Scholar 

  • Beck PSA, Jeonsson P, Hogda KA, Karlsenm SR, Eklundh L, Skidmore AK (2007) A ground validated NDVI dataset for monitoring vegetation dynamics and mapping phenology in Fennoscandia and the Kola Peninsula. Int J Remote Sens 28(19):4311–4330

    Article  Google Scholar 

  • Calera Belmonte A, Jochum AM, Cuestagarcia A (2003) Space-assisted irrigation management: towards user-friendly products. In: ICID workshop on remote sensing of crop evapotranspiration, 17 September, Montpellier

  • Cihlar J, Guindon B, beaubien J, Latifovic R, Peddle D, Wulder M, Fernandes R, Kerr J (2003) From need to product: a methodology for completing a land cover map of Canada with Landsat data. Can J Remote Sens 29:171–186

    Article  Google Scholar 

  • Congalton RG, Green K (1998) Assessing the accuracy of remotely sensed data: principles and practices. Lewis, Boca Raton

    Book  Google Scholar 

  • Dawbin KW, Evans JC (1998) Large area crop classification in New South Wales, Australia, using Landsat data. Int J Remote Sens 9:295–301

    Article  Google Scholar 

  • Delbart N, Toan TL, Kergoat TL, Fedotova V (2006) Remote sensing of spring phenology in boreal regions: a free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982–2004). Remote Sens Environ 101:52–62

    Article  Google Scholar 

  • Digkuhn M, Gal P (1996) Effect of drainage date on yield and dry matter partitioning in irrigated rice. Field Crops Res 46:117–126

    Article  Google Scholar 

  • Dmour T, Vaughan R (1998) Detection of urban growth towards agricultural areas in western Amman area of Jordan using remotely sensed data. In: Gudmandsen PE (ed) Proceedings of the 17th EARSel symposium on future trends in remote sensing 17–19 June 1997, Lyngby. Balkema, Rotterdam, pp 207–211

    Google Scholar 

  • Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80:185–201

    Article  Google Scholar 

  • Grignetti A, Salvator R, Casacchia R, Manes F (1997) Mediterranean vegetation analysis by multi-temporal satellite sensor data. Int J Remote Sens 18:1307–1318

    Article  Google Scholar 

  • Hunsaker DJ, Pinter PJJR., Barnes EM, Kimball BA (2003) Estimating cotton evapotranspiration crop coefficients with a multispectral vegetation index. Irrig Sci 22:95–105

    Article  Google Scholar 

  • Jewell N (1989) An evaluation of multi-date SPOT data for agriculture and land use mapping in the United Kingdom. Int J Remote Sens 10:939–951

    Article  Google Scholar 

  • Kamusoko C, Aniya M (2009) Hybrid classification of Landsat data and GIS for land use/cover change analysis of the Bindura district, Zimbabwe. Int J Remote Sens 30:97–115

    Article  Google Scholar 

  • Kimball J, Mcdonald K, Running S, Frolking S (2004) Satellite radar remote sensing of seasonal growing seasons for boreal and subalpine evergreen forests. Remote Sens Environ 90:243–258

    Article  Google Scholar 

  • Kuemmerle T, Radeloff V, Perzanowski K, Hostert P (2006) Cross-border comparison of land cover and landscape pattern in Eastern Europe using a hybrid classification technique. Remote Sens Environ 103:449–464

    Article  Google Scholar 

  • Lanjeri S, Melia J, Segarra D (2001) A multi-temporal masking classification method for vineyard monitoring in central Spain. Int J Remote Sens 22:3167–3186

    Article  Google Scholar 

  • Latifovic R, Zhu ZL, Cihlar J, Girl C, Olthof I (2004) Land cover mapping of north and Central America—global land cover 2000. Remote Sens Environ 89:116 – 127

    Article  Google Scholar 

  • Leudeke MKB, Kanecek A, Kohlmaier GH (1991) Modelling the seasonal CO2 uptake by land vegetation using the global vegetation index. Tellus 43B:188–196

    Article  Google Scholar 

  • Levin N, Mcalpine C, Phinn S, Price B, Pullar D, Kavanagh RP, Law BS (2009) Mapping forest patches and scattered trees from SPOT images and testing their ecological importance for woodland birds in a fragmented agricultural landscape. Int J Remote Sens 30(12):3147–3169

    Article  Google Scholar 

  • Lhermitte S, Verbesselt J, Jonckheere I, Nackaerts K, Van Aardt J, Verstraeten W, Coppin P (2008) Hierarchical image segmentation based on similarity of NDVI time series. Remote Sens Environ 112:506–521

    Article  Google Scholar 

  • Lloyd D (1990) A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. Int J Remote Sens 11:2269–2279

    Article  Google Scholar 

  • Lo CP, Choi J (2004) A hybrid approach to urban land use/cover mapping using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images. Int J Remote Sens 25:2687–2700

    Article  Google Scholar 

  • Makhamreh Z (2005) Optical remote sensing and Geo-spatial analysis for assessing and monitoring of land degradation in the northern Jordan, Ph.D. thesis. Remote Sensing Department, University of Trier, Trier

    Google Scholar 

  • Makhamreh Z, Hill J (2004) Spectral mixture analysis for characterization of seasonal vegetation dynamics in northern Jordan. Ist Göttingen GIS and Remote Sensing Days, Environmental Studies, 7–8 October, 2004, Göttingen (Göttinger Geographische Abhandlungen, Heft 113), pp 94–100

  • Makhamreh Z, Hill J (2005) Detection of sensitive areas for degradation risk by analyzing of seasonal vegetation density along climatic gradient (International Conference on Remote Sensing and Geoinformation Processing in the Assessment and Monitoring of Land Degradation and Desertification: state of the art and operational perspectives. September 7th to 9th, 2005, Trier, Germany), pp 511–518

  • Mccleary AL, Crews-Meyer KA, Young KR (2008) Refining forest classifications in the western Amazon using an intra-annual multi-temporal approach. Int J Remote Sens, 29:991–1006

    Article  Google Scholar 

  • MoA (1995) The soils of Jordan. Report of the National Soil Map and Land Use Project, Undertaken by Ministry of Agriculture, Huntings Technical Services Ltd. and European Commission. Level One, Level Two, Level Three and JOSCIS Manual

  • Murakami T, Ogawa S, Ishitsuka N, Kumagai K, Saito G (2001) Crop discrimination with multi-temporal SPOT/HRV data in the Saga Plains, Japan. Int J Remote Sens 22:1335 – 1348

    Article  Google Scholar 

  • Oetter DR, Cohen WB, Berterretche M, Maiersperger TK, Kennedy RE (2000) Land cover mapping in an agricultural setting using multi-seasonal thematic mapped data. Remote Sens Environ 76:139 – 155

    Article  Google Scholar 

  • Pasqualini V, Pergent-Martini C, Pergent G, Agreil M, Skoufas G, Sourbes L, Tsirika A (2005) Use of SPOT 5 for mapping sea grasses: an application to Posidonia oceanica. Remote Sens Environ 94:39–45

    Article  Google Scholar 

  • Propastin PA, Kappas M, Erasmi S, Muratova NR (2007) Remote sensing based study on intra-annual dynamics of vegetation and climate in drylands of Kazakhstan. Basic Appl Dryland Res,12:138–154

  • Richards JA, Jia X (1999) Remote sensing digital image analysis: an introduction, 3rd edn. Springer, New York

    Book  Google Scholar 

  • Rosenfield GH, Fitzpatrick-Lins K (1986) A coefficient of agreement as a measure of thematic classification accuracy. Photogramm Eng Remote Sens 52:223–227

    Google Scholar 

  • Sakamoto T, Yokozawa M, Toritani H, Shibayama M, Ishitsuka N, Ohno H (2005) Crop phenology detection method using time-series MODIS data. Remote Sens Environ 96:366–374

    Article  Google Scholar 

  • Simonneaux V, Duchemin B, Helson D, Er-Raki S, Olioso A, Chehbouni AG (2008) The use of high-resolution image time series for crop classification and evapotranspiration estimate over an irrigated area in central Morocco. Int J Remote Sens 29:95–116

    Article  Google Scholar 

  • Stabach J, Dabek L, Jensen R, Wang Y (2009) Discrimination of dominant forest types for Matschie’s tree kangaroo conservation in Papua New Guinea using high-resolution remote sensing data. Int J Remote Sens 30:405–422

    Article  Google Scholar 

  • Tao F, Yokozawa M, Zhang Z, Hayashi Y, Ishigooka Y (2008) Land surface phenology dynamics and climate variations in the North East China Transect (NECT), 1982–2000. Int J Remote Sens 29:5461–5478

    Article  Google Scholar 

  • Tennakoon SB, Murty VVN, Eiumnoh A (1992) Estimation of cropped area and grain yield of rice using remote sensing data. Int J Remote Sens 13:427–439

    Article  Google Scholar 

  • Treitz P, Rogan J (2004) Remote sensing for mapping and monitoring land-cover and land-use change. Prog Plan 61:267–279

    Article  Google Scholar 

  • Tucker CJ, Gatlin JA, Schneider SR (1984) Monitoring vegetation in the Nile delta with NOAA-6 and NOAA-7 AVHRR imagery. Photogramm Eng Remote Sens 50:53–61

    Google Scholar 

  • Udelhoven T, Stellmes M, Del Barrio G, Hill J (2009) Assessment of rainfall and NDVI anomalies in Spain (1989–1999) using distributed lag models. Int J Remote Sens 30:1961–1976

    Article  Google Scholar 

  • Wardlow B, Egbert S, Kastens J (2007) Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sens Environ 108:290–310

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Wulder MA, Franklin SE, White JC (2004) Sensitivity of hyper clustering and labelling land cover classes to Landsat image acquisition date. Int J Remote Sens 25:5337–5344

    Article  Google Scholar 

  • Zhang X, Friedl MA, Schaaf CB, Strahiler AH, Hodges JCF, Gao F (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 84:471–475

    Article  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the financial funds provided by the Deanship of Scientific Research Faculty at the University of Jordan, which facilitated the fieldwork analysis. Also thanks for Dr. Vincent (MEDRAB Project) for offering the SPOT satellite image for the analysis.

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Correspondence to Zeyad Makhamreh.

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Makhamreh, Z. Derivation of vegetation density and land-use type pattern in mountain regions of Jordan using multi-seasonal SPOT images. Environ Earth Sci 77, 384 (2018). https://doi.org/10.1007/s12665-018-7534-z

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