Skip to main content

Estimating impacts of plantation forestry on plant biodiversity in southern Chile—a spatially explicit modelling approach

Environmental Monitoring and Assessment volume 188, Article number: 564 (2016) Cite this article

Abstract

Monitoring the impacts of land-use practices is of particular importance with regard to biodiversity hotspots in developing countries. Here, conserving the high level of unique biodiversity is challenged by limited possibilities for data collection on site. Especially for such scenarios, assisting biodiversity assessments by remote sensing has proven useful. Remote sensing techniques can be applied to interpolate between biodiversity assessments taken in situ. Through this approach, estimates of biodiversity for entire landscapes can be produced, relating land-use intensity to biodiversity conditions. Such maps are a valuable basis for developing biodiversity conservation plans. Several approaches have been published so far to interpolate local biodiversity assessments in remote sensing data. In the following, a new approach is proposed. Instead of inferring biodiversity using environmental variables or the variability of spectral values, a hypothesis-based approach is applied. Empirical knowledge about biodiversity in relation to land-use is formalized and applied as ascription rules for image data. The method is exemplified for a large study site (over 67,000 km2) in central Chile, where forest industry heavily impacts plant diversity. The proposed approach yields a coefficient of correlation of 0.73 and produces a convincing estimate of regional biodiversity. The framework is broad enough to be applied to other study sites.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

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

References

  • Aguayo, M., Pauchard, A., Azocar, G., & Parra, O. (2009). Land use change in the south Central Chile at the end of the twentieth century: understanding the spatio-temporal dynamics of the landscape. Revista Chilena de la Historia Natural, 82(3), 361–374.

    Article  Google Scholar 

  • Altamirano, A., Field, R., Cayuela, L., Aplin, P., Lara, A., & Rey-Benayas, J. M. (2010). Woody species diversity in temperate Andean forests: the need for new conservation strategies. Biological Conservation, 143(9), 2080–2091.

    Article  Google Scholar 

  • Barbier, E. B., Burgess, J. C., & Folke, C. (1994). Paradise lost? The ecological economics of biodiversity. London: Earthscan, Routledge.

    Google Scholar 

  • Braun, A. C. (2013). Eine fernerkundungsgestützte geoökologische Prozessanalyse zum Risikozusammenhang zwischen Landnutzung und Biodiversität an einem Beispiel aus Chile. Karlsruher Institute of Technology, KIT, Karlsruhe: PhD-Thesis.

    Google Scholar 

  • Braun, A. C., Weidner, U., & Hinz, S. (2010). Support vector machines for vegetation classification–a revision. Photogrammetrie-Fernerkundung-Geoinformation, 2010(4), 273–281.

    Article  Google Scholar 

  • Braun, A. C., Weidner, U., Jutzi, B., & Hinz, S. (2011). Integrating model knowledge into SVM classification–Fusing hyperspectral and laserscanning data by kernel composition. High-resolution earth imaging for geospatial information.–International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(4/W19).

  • Braun, A. C., Weidner, U., Jutzi, B., & Hinz, S. (2012). Kernel composition with the one-against-one Cascade for integrating external knowledge into SVM classification. Photogrammetrie-Fernerkundung-Geoinformation, 2012(4), 371–384.

    Article  Google Scholar 

  • Braun, A. C., Rojas, C., Echeverria, C., Rottensteiner, F., Bähr, H. P., Niemeyer, J., Aguayo, M., Kosov, S., Hinz, S., & Weidner, U. (2014). Design of a spectral–spatial pattern recognition framework for risk assessments using Landsat data—a case study in Chile. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(3), 917–928.

    Article  Google Scholar 

  • Brook, B. W., Sodhi, N. S., & Ng, P. K. L. (2003). Catastrophic extinctions follow deforestation in Singapore. Nature, 424(6947), 420–426.

    CAS  Article  Google Scholar 

  • Brooks, T. M., Mittermeier, R. A., da Fonseca, G. A. B., Gerlach, J., Hoffmann, M., Lamoreux, J. F., Mittermeier, C. G., Pilgrim, J. D., & Rodrigues, A. S. L. (2006). Global biodiversity conservation priorities. Science, 313(5783), 58–61.

    CAS  Article  Google Scholar 

  • Bustamante, R. O., & Castor, C. (1998). The decline of an endangered temperate ecosystem: the ruil (Nothofagus alessandrii) forest in Central Chile. Biodiversity & Conservation, 7(12), 1607–1626.

    Article  Google Scholar 

  • Butchart, S. H. M., Walpole, M., Collen, B., van Strien, A., Scherlemann, J. P. W., Almond, R. E. A., Baillie, J. W. M., Bomhard, B., Brown, C., Bruno, J., Carpenter, K. E., Carr, G. M., Chanson, J., Chenery, A. M., Csirke, J., Davidson, N. C., Dentener, F., Foster, M., Galli, A., Galloway, J. N., Genovesi, P., Greory, R. D., Hockings, M., Kapos, V., Lamarque, J. F., Leverington, F., Loh, J., McGeoch, M. A., McRae, L., Minasyan, A., Hernandez Morcillo, M., Oldfield, T. E. E., Pauly, D., Quader, S., Revenga, C., Sauer, J. R., Skolnik, B., Spear, D., Stanwell-Smith, D., Stuart, S. N., Symes, A., Tierney, M., Tyrrell, T. D., Vie, J. C., & Watson, R. (2001). Global biodiversity: indicators of recent declines. Science, 328(5982), 1164–1168.

    Article  Google Scholar 

  • Camathias, L., Bergamini, A., Küchler, M., Stofer, S., & Baltensweiler, A. (2013). High-resolution remote sensing data improves models of species richness. Applied Vegetation Science, 16(4), 539–551.

    Article  Google Scholar 

  • Camus, P. (2003). Federico Albert: Artifice de la Gestion de los Bosques de Chile. Revista de Geografia Norte Grande, 30(2003), 55–63.

    Google Scholar 

  • Cayuela, L., Rey-Benayas, J. M., & Echeverria (2006). Clearance and fragmentation of tropical montane forests in the highlands of Chiapas, Mexico (1975–2000). Forest Ecology and Management, 226(1), 208–218.

    Article  Google Scholar 

  • Cincotta, R. P., Wisnewski, J., & Engelmann, R. (2000). Human population in the biodiversity hotspots. Nature, 404(6781), 990–992.

    CAS  Article  Google Scholar 

  • Clapp, R. A. (1995a). The unnatural history of the Monterey pine. Geographical Review, 85(1), 1–19.

    Article  Google Scholar 

  • Clapp, R. A. (1995b). Creating competitive advantage: forest policy as industrial policy in Chile. Economic Geography, 71(3), 273–296.

    Article  Google Scholar 

  • Comaniciu, D., & Meer, P. (2002). Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 603–619.

    Article  Google Scholar 

  • Dirzo, R., & Raven, P. H. (2003). Global state of biodiversity and loss. Annual Review of Environment and Resources, 28(1), 137–167.

    Article  Google Scholar 

  • Echeverria, C., Coomes, D., Salas, J., Rey-Benayas, J. M., Lara, A., & Newton, A. (2006). Rapid deforestation and fragmentation of Chilean temperate forests. Biological Conservation, 130(4), 481–494.

    Article  Google Scholar 

  • Fitzherbert, E. B., Struebig, M. J., Morel, A., Danielsen, F., Brühl, C. A., Donald, P. F., & Phalan, B. (2008). How will oil palm expansion affect biodiversity? Trends in Ecology & Evolution, 23(10), 538–545.

    Article  Google Scholar 

  • Freemantle, T. P., Wacher, T., Newby, H., & Pettorelli, N. (2013). Earth observation: overlooked potential to support species reintroduction programmes. African Journal of Ecology, 51(3), 482–492.

    Article  Google Scholar 

  • Fujisada, H., Bailey, G. B., Kelly, G. G., Hara, S., & Abrams, M. J. (2005). ASTER DEM performance. IEEE Transactions on Geoscience and Remite Sensing, 43(12), 2707–2714.

    Article  Google Scholar 

  • Ghiyamat, A., & Shafri, H. Z. (2010). A review on hyperspectral remote sensing for homogeneous and heterogeneous forest biodiversity assessment. International Journal of Remote Sensing, 31(7), 1837–1856.

    Article  Google Scholar 

  • Gillespie, T. W., Foody, G. M., Rocchini, D., Giorgi, A. P., & Saatchi, S. (2008). Measuring and modelling biodiversity from space. Progress in Physical Geography, 32(2), 203–221.

    Article  Google Scholar 

  • Gould, W. (2001). Remote sensing of vegetation, plant species richness, and regional biodiversity hotspots. Ecological Applications, 10(6), 1861–1870.

    Article  Google Scholar 

  • Guerrero, P. C., & Bustamante, R. O. (2007). Can native tree species regenerate in Pinus radiata plantations in Chile?: evidence from field and laboratory experiments. Forest Ecology and Management, 253(1), 97–102.

    Article  Google Scholar 

  • Haines-Young, R. (2009). Land use and biodiversity relationships. Land Use Policy, 26(2009), 178–186.

    Article  Google Scholar 

  • He, C. (2012). Advances in the research on hyperspectral remote sensing in biodiversity and conservation. Spectroscopy and Spectral Analysis, 32(6), 1628–1632.

    Google Scholar 

  • Hernandez-Stefanoni, J. L., & Ponce-Hernandez, R. (2004). Mapping the spatial distribution of plant diversity indices in a tropical forest using multi-spectral satellite image classification and field measurements. Biodiversity & Conservation, 13(14), 2599–2621.

    Article  Google Scholar 

  • Hernandez-Stefanoni, J. L., & Ponce-Hernandez, R. (2006). Mapping the spatial variability of plant diversity in a tropical forest: comparison of spatial interpolation methods. Environmental Monitoring and Assessment, 117(1–3), 307–334.

    Article  Google Scholar 

  • Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15), 1965–1978.

    Article  Google Scholar 

  • Hirano, A., Welch, R., & Lang, H. (2003). Mapping from ASTER stereo image data: DEM validation and accuracy assessment. ISPRS Journal of Photogrammetry and Remote Sensing, 57(5), 356–370.

    Article  Google Scholar 

  • Holmgren, M., Aviles, R., Sierralta, L., Segura, A. M., & Fuentes, E. R. (2000). Why have European herbs so successfully invaded the Chilean matorral? Effects of herbivory, soil nutrients, and fire. Journal of Arid Environments, 44(2), 197–211.

    Article  Google Scholar 

  • Innes, J. L., & Koch, B. (1998). Forest biodiversity and its assessment by remote sensing. Global Ecology & Biogeography Letters, 7(6), 397–419.

    Article  Google Scholar 

  • Instituto Nacional de Estadisticas (2002). Censo 2002. Sintesis de Resultados. Comision Nacional Del XVII Censo de Poblacion. http://www.ine.cl/cd2002/sintesiscensal.pdf. Accessed 18 November 2014.

  • Jha, S., & Bawa, K. S. (2009). Population growth, human development, and deforestation in biodiversity hotspots. Conservation Biology, 20(3), 906–912.

    Article  Google Scholar 

  • Kamp, U., Bolch, T. and Olsenholler, J. (2003). DEM generation from ASTER satellite data for geomorphometric analysis of Cerro Sillajhuay, Chile/Bolivia, In Proceedings: ASPRS 2003 Annual Conference, Anchorage, Alaska.

  • Kerr, J. T., & Ostrovsky, M. (2003). From space to species: ecological applications for remote sensing. Trends in Ecology & Evolution, 18(6), 299–305.

    Article  Google Scholar 

  • Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Journal, In Proceedings: International Joint Conference of Artificial Intelligence 1995, Montreal, Quebec, Canada.

  • Langdon, B., Pauchard, A., & Aguayo, M. (2010). Pinus contorta Invasion in the Chilean Patagonia: local patterns in a global context. Biological Invasions, 12(12), 3961–3971.

    Article  Google Scholar 

  • Mattison, E. H., & Norris, K. (2005). Bridging the gaps between agricultural policy, land-use and biodiversity. Trends in Ecology & Evolution, 20(11), 610–616.

    Article  Google Scholar 

  • Miller, M. E., Hui, S. L., & Tierney, W. M. (1991). Validation techniques for logistic regression models. Statistic in. Medicine, 10(8), 1213–1226.

    CAS  Google Scholar 

  • Ministerio de Agricultura (1974). Fija regimen legal de los terrenos forestales o preferentemente aptos para la forestacion, y establece normas de foment sobre la materia. http://www.leychile.cl/Navegar?idNorma=6294&buscar=DL+701. Accessed 11 November 2014.

  • Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B., & Kent, J. (2000). Biodiversity hotspots for conservation priorities. Nature, 403, 853–858.

    CAS  Article  Google Scholar 

  • Nagendra, H. (2001). Using remote sensing to assess biodiversity. International Journal of Remote Sensing, 22(12), 2377–2400.

    Article  Google Scholar 

  • Nahuelhual, L., Carmona, A., Lara, A., Echeverria, C., & Gonzalez, M. E. (2012). Land-cover change to forest plantations: proximate causes and implications for the landscape in south-Central Chile. Landscape and Urban Planning, 107(1), 12–20.

    Article  Google Scholar 

  • O’Sullivan, F., & Wahba, G. (1985). A cross validated Bayesian retrieval algorithm for nonlinear remote sensing experiments. Journal of Computational Physics, 59(3), 441–455.

    Article  Google Scholar 

  • Oliver, I., & Beattie, A. J. (1993). A possible method for the rapid assessment of biodiversity. Conservation Biology, 7(3), 562–568.

    Article  Google Scholar 

  • Oliver, I., & Beattie, A. J. (1996). Designing a cost-effective invertebrate survey: a test of methods for rapid assessment of biodiversity. Ecological Applications, 6(2), 594–607.

    Article  Google Scholar 

  • Palmer, M. W., Earls, P. G., Hoagland, B. W., White, P. A., & Wohlgemuth, T. (2002). Quantitative tools for perfecting species lists. Environmetrics, 13(2), 121–137.

    Article  Google Scholar 

  • Paritsis, J., & Aizen, M. A. (2008). Effects of exotic conifer plantations on the biodiversity of understory plants, epigeal beetles and birds in Nothofagus dombeyi forests. Forest Ecology and Management, 255(5), 1575–1583.

    Article  Google Scholar 

  • Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007). Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences, 4(2), 439–473.

    Article  Google Scholar 

  • Pradhan, B. (2010). Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Advances in Space Research, 45(10), 1244–1256.

    CAS  Article  Google Scholar 

  • Rapport, D. J. (1998). Biodiversity and saving the earth. Environmental Monitoring and Assessment, 49(2–3), 169–175.

    Article  Google Scholar 

  • Reidsma, P., Tekelenburg, T., van den Berg, M., & Alkemade, R. (2006). Impacts of land-use change on biodiversity: an assessment of agricultural biodiversity in the European Union. Agriculture, Ecosystems & Environment, 114(1), 86–102.

    Article  Google Scholar 

  • Rocchini, D. (2007). Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery. Remote Sensing of Environment, 111(4), 423–434.

    Article  Google Scholar 

  • Rocchini, D., Chiarucci, A., & Loiselle, S. A. (2004). Testing the spectral variation hypothesis by using satellite multispectral images. Acta Oecologica, 26(2), 117–120.

    Article  Google Scholar 

  • Rocchini, D., Balkenhol, N., Carter, G. A., Foody, G. M., Gillespie, T. W., He, K. S., Kark, S., Levin, N., Lucas, K., Luoto, M., Nagendra, H., Oldeland, J., Ricotta, C., Southworth, J., & Neteler, M. (2010). Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges. Ecological Informatics, 5(5), 318–329.

    Article  Google Scholar 

  • Rojas, C., Vivanco, M., Sergio, O., Peters, S., & Villaroel, C. (2013). Pre and post earthquake land use and land cover identification in conception. In J. M. Krisp, L. Meng, R. Pail, & U. Stilla (Eds.), Earth observation of global changes (pp. 223–234). Heidelberg: Springer.

    Chapter  Google Scholar 

  • Sala, O. E., Chapin, F. S., Armesto, J. J., Berlow, E., Bloomfield, J., Dirzo, R., Huber-Sanwald, E., Huenneke, L. F., Jackson, R. B., Kinzig, A., Leemans, R., Lodge, D. M., Mooney, H. A., Oesterheld, M., LeRoy Poff, N., Sykes, M. T., Walker, B. H., Walker, M., & Wall, D. H. (2001). Global biodiversity scenarios for the year 2100. Science, 287(5459), 1770–1774.

    Article  Google Scholar 

  • Schumacher, M., Holländer, N., & Sauerbrei, W. (1997). Resampling and cross-validation techniques: a tool to reduce bias caused by model building? Statistics in Medicine, 16(24), 2813–2827.

    CAS  Article  Google Scholar 

  • Shahzad, F., & Gloaguen, R. (2011a). TecDEM: a MATLAB based toolbox for tectonic geomorphology, part 1: drainage network preprocessing and stream profile analysis. Computers & Geoscience, 37(2), 250–260.

    Article  Google Scholar 

  • Shahzad, F., & Gloaguen, R. (2011b). TecDEM: a MATLAB based toolbox for tectonic geomorphology, part 2: surface dynamics and basin analysis. Computers & Geoscience, 37(2), 261–271.

    Article  Google Scholar 

  • Smith, R. S., & Doyle, J. C. (2001). Model validation: a connection between robust control and identification. IEEE Transactions on Automatic Control, 37(7), 942–952.

    Article  Google Scholar 

  • Smith-Ramirez, C. (2004). The Chilean coastal range: a vanishing center of biodiversity and endemism in south American temperate rainforests. Biological Conservation, 13(2), 373–393.

    Google Scholar 

  • Stoms, D. M., & Estes, J. E. (1993). A remote sensing research agenda for mapping and monitoring biodiversity. International Journal of Remote Sensing, 14(10), 1839–1860.

    Article  Google Scholar 

  • Stork, N. E. (1997). Measuring global biodiversity and its decline. Washington, DC: Joseph Henry Press.

    Google Scholar 

  • Swanson, T., & Myers, N. (1998). Global action for biodiversity. Environmental Conservation, 25(2), 175–185.

    Google Scholar 

  • Tuomisto, H. (2010a). A diversity of beta diversities: straightening up a concept gone awry. Part 1. Defining beta diversity as a function of alpha and gamma diversity. Ecography, 33(1), 2–22.

    Article  Google Scholar 

  • Tuomisto, H. (2010b). A diversity of beta diversities: straightening up a concept gone awry. Part 2. Quantifying beta diversity and related phenomena. Ecography, 33(1), 23–45.

    Article  Google Scholar 

  • Turner, W., Spector, S., Gardiner, N., Fladeland, M., Sterling, R., & Steininger, M. (2003). Remote sensing for biodiversity science and conservation. Trends in Ecology & Evolution, 18(6), 306–314.

    Article  Google Scholar 

Download references

Acknowledgments

Acknowledgments are given to scientists of University of Concepción (UdeC), Concepción, Chile for assistance during the fieldwork. These persons are Dr. Rafael Garcia, Helmuth Puschmann, Dr. Cristian Echeverria, Dr. Mauricio Aguayo, Dr. Carolina Rojas and Dr. Gunhild Hansen-Rojas.

Furthermore, the authors acknowledge the financial support for the fieldwork through Ph.D. students scholarships granted by Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany and the German Aerospace Centre (DLR) which have financed the in situ sampling and remote sensing methods development of this contribution.

Author information

Affiliations

  1. Institute of Regional Science (IfR), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131, Karlsruhe, Germany

    Andreas Christian Braun

  2. Chair of Remote Sensing and Landscape Information Systems (FeLis), University of Freiburg, Tennenbacherstr. 4, 79106, Freiburg im Breisgau, Germany

    Barbara Koch

Authors
  1. Andreas Christian Braun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Barbara Koch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andreas Christian Braun.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Braun, A.C., Koch, B. Estimating impacts of plantation forestry on plant biodiversity in southern Chile—a spatially explicit modelling approach. Environ Monit Assess 188, 564 (2016). https://doi.org/10.1007/s10661-016-5547-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-016-5547-1

Keywords

  • Biodiversity hotspot
  • Chile
  • Plantation forestry
  • Deforestation
  • Spatially-explicit prediction
  • Remote sensing