A remote sensing-based approach to estimating montado canopy density using the FCD model: a contribution to identifying HNV farmlands in southern Portugal

Abstract

Mapping the land-cover pattern dominated by complex Mediterranean silvo-pastoral systems with an accuracy that enables precise monitoring of changing tree-cover density is still an open challenge. The main goal of this paper is to demonstrate the implementation and effectiveness of the Forest Canopy Density (FCD) model in producing a remote sensing-based and detailed map of montado canopy density over a large territory in southern Portugal. This map will make a fundamental contribution to accurately identifying and assessing High Nature Value farmland in montado areas. The results reveal that the FCD model is an effective approach to estimating the density classes of montado canopy (overall accuracy = 78.0 %, kappa value = 0.71). The study also shows that the FCD approach generated good user’s and producer’s accuracies for the three montado canopy-density classes. Globally, the results obtained show that biophysical indices such as the advanced vegetation index, the bare soil index, the shadow index and the thermal index are suitable for estimating and mapping montado canopy-density classes. These results constitute the first remote sensing-based product for mapping montado canopy density that has been developed using the FCD model. This research clearly demonstrates that this approach can be used in the context of Mediterranean agro-forestry systems.

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References

  1. AFN 2010 Relatório Final do 5.º Inventário Florestal Nacional (IFN5). Autoridade Florestal Nacional, Lisboa, Portugal

  2. Almeida M, Guerra C, Pinto-Correia T (2013) Unfolding relations between land cover and farm management: high nature value assessment in complex silvo-pastoral systems. Geogr Tidsskr-Dan J Geogr 113(2):97–108. doi:10.1080/00167223.2013.848611

    Article  Google Scholar 

  3. Andersen E, Baldock D, Bennett H et al (2003) Developing a high nature value farming area indicator. Report for the European Environment Agency, Copenhagen

    Google Scholar 

  4. Aronson J, Santos Pereira J, Pausas JG (eds) (2009) Cork oak woodlands on the edge: ecology, adaptative management, and restoration. Island Press, Washington

  5. Baynes J (2004) Assessing forest canopy density in a highly variable landscape using landsat data and FCD mapper software. Aust For 67:247–253. doi:10.1080/00049158.2004.10674942

    Article  Google Scholar 

  6. Berberoglu S, Lloyd CD, Atkinson PM, Curran PJ (2000) The integration of spectral and textural information using neural networks for land cover mapping in the Mediterranean. Comput Geosci 26:385–396

    Article  Google Scholar 

  7. Berberoglu S, Curran PJ, Lloyd CD, Atkinson PM (2007) Texture classification of Mediterranean land cover. Int J Appl Earth Obs 9:322–334

    Article  Google Scholar 

  8. Bernstein LS, Adler-Golden SM, Sundberg RL, Levine RY, Perkins TC, Berk A (2005) Validation of the quick atmospheric correction (QUAC) algorithm for VNIR-SWIR multi—and hyperspectral imagery. In: Shen SS, Lewis PE (eds) SPIE proceeding algorithms and technologies for multispectral, hyperspectral and ultraspectral imagery XI. 5806: 668–678

  9. Bhandari S, Phinn S, Gill T (2012) Preparing landsat image time series (LITS) for monitoring changes in vegetation phenology in Queensland, Australia. Remote Sens 4:1856–1886. doi:10.3390/rs4061856

    Article  Google Scholar 

  10. Billeter R, Liira J et al (2008) Indicators for biodiversity in agricultural lansdcapes: a pan-European study. J Appl Ecol 45:141–150

    Article  Google Scholar 

  11. Boyd DS, Foody GM, Ripple WJ (2002) Evaluation of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing. Appl Geogr 22:375–392

    Article  Google Scholar 

  12. Branco O, Bugalho M, Silva LN, Barreira R, Vaz PG, Dias F (2010) Hotspot areas for biodiversity and ecosystem services in montados, technical report, world wide fund for nature (WWF) Mediterranean in Portugal & Centre for Applied Ecology Prof. Baeta Neves (CEABN). http://awsassets.panda.org/downloads/habeas_report2010.pdf

  13. Bugalho M, Plieninger T, Aronson J, Ellatifi M, Crespo DG (2009) Open woodlands: a diversity of uses (and overuses). In: Aronson J, Pereira JS, Pausas JG (eds) Cork oak woodlands on the edge, ecology, adaptive management, and restoration, Society for Ecological Restoration International, 1st edn. Island Press, Washington, pp 33–45

    Google Scholar 

  14. Carreiras JMB, Pereira JMC, Pereira JS (2006) Estimation of tree canopy cover in evergreen oak woodlands using remote sensing. For Ecol Manag 223:45–53

    Article  Google Scholar 

  15. Chandrashekhar MB, Saran S, Raju PLN, Roy PS (2005) Forest canopy density stratification: How relevant is biophysical spectral response modelling approach? Geocarto Int 20(1):15–21

    Article  Google Scholar 

  16. Congalton RG, Green K (2009) Assessing the accuracy of remotely sensed data: principles and practices, 2nd edn. CRC Press, Boca Raton

    Google Scholar 

  17. Costa JC, Aguiar C, Capelo J, Lousã M, Neto C (1998) Biogeografia de Portugal Continental. Quercetea 0:5-56

  18. Cross AM, Settle JJ, Drake NA, Paivinen RTM (1991) Subpixel measurement of tropical forest cover using AVHRR data. Int J Remote Sens 12:1119–1129

    Article  Google Scholar 

  19. Defries R, Hansen M, Steininger M, Dubayah R, Sohlberg R, Townshend J (1997) Subpixel forest cover in central Africa from multisensor, multitemporal data. Remote Sens Environ 60:228–246

    Article  Google Scholar 

  20. Deka J, Tripathi OP, Khan ML (2013) Implementation of forest canopy density model to monitor tropical deforestation. J Indian Soc Remote Sens 41:469–475. doi:10.1007/s12524-012-0224-5

    Article  Google Scholar 

  21. Díaz M, Campos P, Pulido FJ (1997) The Spanish dehesas: a diversity of land use and wildlife. In: Pain D, Pienkowski M (eds) Farming and birds in Europe: the common agricultural policy and its implications for bird conservation. Academic Press, London, pp 178–209

    Google Scholar 

  22. Díaz M, Pulido FJ, Marañón T (2003) Diversidad biológica y sostenibilidad ecológica y económica de los sistemas adehesados. Ecosistemas 2003

  23. Dorren LK, Maier AB, Seijmonsbergen AC (2003) Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification. For Ecol Manag 183:31–46

    Article  Google Scholar 

  24. Doxa A, Bas Y, Paracchini ML, Pointereau P, Terres JM, Jiguet F (2010) Low-intensity agriculture increases farmland bird abundances in France. J Appl Ecol 47:1348–1356

    Article  Google Scholar 

  25. EEA/UNEP (2004) High nature value farmland. European environmental agency and UNEP regional office for Europe. Office for official publications of the European communities, Luxembourg. http://www.ieep.eu/assets/215/highnaturefarming.pdf

  26. Fischer J, Zergerc A, Gibbons P, Stott J, Law BS (2010) Tree decline and the future of Australian farmland biodiversity. Proc Natl Acad Sci. USA 107(45):19597–19602

  27. Franklin SE, Hall RJ, Moskal LM, Maudie AJ, Lavigne MB (2000) Incorporating texture into classification of forest species composition from airborne multispectral images. Int J Remote Sens 21:61–79

    Article  Google Scholar 

  28. Godinho C, Rabaça JE (2011) Birds like it Corky: the influence of habitat features and management of ‘montados’ in breeding bird communities. Agrofor Syst 82:183–195. doi:10.1007/s10457-010-9345-4

    Article  Google Scholar 

  29. Godinho S, Guiomar N, Machado R, Santos P, Sá-Sousa P, Fernandes JP, Neves N, Pinto-Correia T (2014) Assessment of environment, land management, and spatial variables on recent changes in montado land cover in southern Portugal. Agrofor Syst. doi:10.1007/s10457-014-9757-7

    Google Scholar 

  30. Gong P, Miller JR, Spanner M (1994) Forest canopy closure from classification and spectral unmixing of scene components-multisensor evaluation of an open canopy. IEEE Trans Geosci Remote Sens 32(5):1067–1080

    Article  Google Scholar 

  31. Harvey CA et al (2006) Patterns of animal diversity in different forms of tree cover in agricultural landscapes. Ecol Appl 16:1986–1999

    Article  PubMed  Google Scholar 

  32. Hazeu G, Milenov P, Pedroli B, Samoungi V, Eupen MV, Vassilev V (2014) High nature value farmland identification from satellite imagery, a comparison of two methodological approaches. Int J Appl Earth Obs 30: 98–112

  33. Jamalabad MS, Abkar AA (2004) Forest canopy density monitoring, using satellite images. XXth ISPRS Congress, Istanbul 12–23 July. Turkey, Commission 7:244. http://www.isprs.org/proceedings/xxxv/congress/comm7/papers/48.pdf

  34. James AW, Randall SM (2012) Canopy cover estimates for individual tree attributes. Moving from status to trends: Forest inventory and analysis symposium 2012. 248–253. http://www.nrs.fs.fed.us/pubs/gtr/gtr-nrs-p-105papers/39westfall-p-105.pdf

  35. Jennings SB, Brown ND, Sheil D (1999) Assessing forest canopies and understory illumination: canopy closure, canopy cover and other measures. Forestry 72:59–74

    Article  Google Scholar 

  36. Joffre R, Rambal S (1993) How tree cover influences the water balance of Mediterranean rangelands. Ecology 74:570–582

    Article  Google Scholar 

  37. Joshi C, Leeuw JD, Skidmore AK, Duren ICV, Oosten HV (2006) Remotely sensed estimation of forest canopy density: a comparison of the performance of four methods. Int J Appl Earth Obs 8:84–95

    Article  Google Scholar 

  38. Levesque J, King DJ (2003) Spatial analysis of radiometric fractions from high-resolution multispectral imagery for modelling individual tree crown and forest canopy structure and health. Remote Sens Environ 84:589–602

    Article  Google Scholar 

  39. Manning AD, Fischer J, Lindenmayer DB (2006) Scattered trees are keystone structures—implications for conservation. Biol Conserv 132:311–321

    Article  Google Scholar 

  40. Marañón T, Pugnaire FI, Callaway RM (2009) Mediterranean-climate oak savannas: the interplay between abiotic environment and species interactions. Web Ecol 9:30–43

    Article  Google Scholar 

  41. Mesquita S, Sousa AJ (2009) Bioclimatic mapping using geostatistical approaches: application to mainland Portugal. Int J Climatol 29:2156–2170

    Article  Google Scholar 

  42. Mon MS, Mizoue N, Htun NZ, Kajisa T, Yoshida S (2012) Estimating forest canopy density of tropical mixed deciduous vegetation using Landsat data: a comparison of three classification approaches. Int J Remote Sens 33(4):1042–1057

    Article  Google Scholar 

  43. Morelli F, Jerzak L, Tryjanowski P (2014) Birds as useful indicators of high nature value (HNV) farmland in Central Italy. Ecol Indic 38:236–242

    Article  Google Scholar 

  44. Nandy S, Joshi PK, Das KK (2003) Forest canopy density stratification using biophysical modeling. J Indian Soc Remote Sens 31(4):291–297

    Article  Google Scholar 

  45. Oppermann R, Beaufoy G, Jones G (2012) High nature value farming in Europe. Verlag Regionalkultur, Ubstadt-Weiher

    Google Scholar 

  46. Panta M, Kim K, Joshi C (2008) Temporal mapping of deforestation and forest degradation in Nepal: applications to forest conservation. For Ecol Manag 256:1587–1595

    Article  Google Scholar 

  47. Paracchini ML, Petersen JE, Hoogeveen Y, Bamps C, Burfield I, van Swaay C (2008) High nature value farmland in Europe: an estimate of the distribution patterns on the basis of land cover and biodiversity data. European commission, joint research centre, institute for environment and sustainability, office for official publications of the European communities, Luxembourg. http://agrienv.jrc.it/publications/pdfs/HNV_Final_Report.pdf

  48. Pinto-Correia T, Carvalho-Ribeiro S (2012) High nature value farming in Portugal. In: Beaufoy G, Oppermann R, Herzog F (eds) High nature value farmland in Europe. Verlag Regionalkultur, Ubstad-Weiher, pp 336–345

    Google Scholar 

  49. Pinto-Correia T, Ribeiro N, Sá-Sousa P (2011) Introducing the montado, the cork and holm oak agroforestry system of Southern Portugal. Agrofor Syst 82:99–104

    Article  Google Scholar 

  50. Plieninger T (2006) Habitat loss, fragmentation, and alteration—quantifying the impact of land-use changes on a Spanish dehesa landscape by use of aerial photography and GIS. Landsc Ecol 21:91–105

    Article  Google Scholar 

  51. Plieninger T, Bieling C (2013) Resilience-based perspectives to guiding high-nature-value farmland through socioeconomic change. Ecol Soc 18(4):20

    Google Scholar 

  52. Plieninger T, Modolell JM, Konold W (2004) Land manager attitudes toward management, regeneration, and conservation of Spanish holm oak savannas (dehesas). Landsc Urban Plan 66:185–198

    Article  Google Scholar 

  53. Rikimaru A, Roy PS, Miyatake S (2002) Tropical forest cover density mapping. Trop Ecol 43(1):39–47

    Google Scholar 

  54. Rodríguez-Galiano VF, Chica-Olmo M (2012) Land cover change analysis of Mediterranean area in Spain using different sources of data: multi-seasonal Landsat images, land surface temperature, digital terrain models and texture. Appl Geogr 35:208–218

    Article  Google Scholar 

  55. Rogan J, Chen D (2004) Remote sensing technology for mapping and monitoring land-cover and land-use change. Prog Plann 61:301–325

    Article  Google Scholar 

  56. Stoate C, Báldi A, Beja P, Boatman ND, Herzon I, van Doorn AM, Snoo GR, Rakosy L, Ramwell C (2009) Ecological impacts of early 21st century agricultural change in Europe—a review. J Environ Manag 91:22–46

    Article  CAS  Google Scholar 

  57. Tomé M (2006) Current status of cork and holm oak forests in Portugal. Seminar the vitality of cork and holm oaks stands, current situation, state of knowledge and actions to take. Évora, 25th and 26th October

  58. van Doorn AM, Pinto-Correia T (2007) Differences in land cover interpretation in landscapes rich in cover gradients: reflections based on the montado of South Portugal. Agrofor Syst 70(2):169–183. doi:10.1007/s10457-007-9055-8

    Article  Google Scholar 

  59. Vicente AM, Alés RF (2006) Long term persistence of dehesas. Evidences from history. Agrofor Syst 67:19–28. doi:10.1007/s10457-005-1110-8

    Article  Google Scholar 

  60. Vinué A, Gómez M (2012) ArcFUEL density map based on the FCD model. Case study: Sierra de las Nieves (Spain). 3rd international conference on modelling, monitoring and management of forest fires 22–24 May 2012, New Forest, UK.http://www.arcfuel.eu/index.php/en/broadcasting/2013-02-16-09-55-39.html?start=9

  61. Xie Y, Sha Z, Yu M (2008) Remote sensing imagery in vegetation mapping: a review. J Plant Ecol 1:9–23

    Article  Google Scholar 

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Acknowledgments

This study was funded by FEDER as part of the Operational Programme for Competitiveness Factors—COMPETE and also received National Funding from the FCT—Foundation for Science and Technology as part of the PEst-C/AGR/UI0115/2011 strategic project. Research associated with this paper was also funded by the FCT as part of the POPH-QREN-Tipologia 4.1 Programme: scholarship SFRH/BD/77897/2011 was awarded to S. Godinho. This research has also been developed on the behalf of a Post-Doctoral (M3.1.7/F/005/2011) Research Project lead by A. Gil and supported by the FRC—Regional Fund for Science (Azorean Regional Government). N. Guiomar was also funded by the Doctoral FCT scholarship with the reference SFRH/BD/35848/2007.

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Correspondence to Sérgio Godinho.

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Godinho, S., Gil, A., Guiomar, N. et al. A remote sensing-based approach to estimating montado canopy density using the FCD model: a contribution to identifying HNV farmlands in southern Portugal. Agroforest Syst 90, 23–34 (2016). https://doi.org/10.1007/s10457-014-9769-3

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Keywords

  • Canopy density
  • FCD
  • Agroforestry
  • Montado
  • Dehesa
  • Advanced vegetation index