Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-02T21:33:28.072Z Has data issue: false hasContentIssue false

Effects of different sampling scales and selection criteria on modelling net primary productivity of Indonesian tropical forests

Published online by Cambridge University Press:  17 October 2013

STEPHAN J. GMUR*
Affiliation:
School of Environmental and Forest Sciences. University of Washington, Box 352100, Seattle, WA 98195-2100, USA
DANIEL J. VOGT
Affiliation:
School of Environmental and Forest Sciences. University of Washington, Box 352100, Seattle, WA 98195-2100, USA
KRISTIINA A. VOGT
Affiliation:
School of Environmental and Forest Sciences. University of Washington, Box 352100, Seattle, WA 98195-2100, USA
ASEP S. SUNTANA
Affiliation:
School of Environmental and Forest Sciences. University of Washington, Box 352100, Seattle, WA 98195-2100, USA Sustainable Terrestrial Management and Integrated Renewable Energy Center (STIREC), Surya University, Gedung SURE Center, Jalan Scientia Boulevard Blok U/7, Gading Serpong, Tangerang 15810, Banten, Indonesia
*
*Correspondence: Mr Stephan Gmur e-mail: [email protected]

Summary

The availability of spatial data sourced from either field-derived or satellite-based systems has created new opportunities to estimate and/or monitor changes in carbon sequestration rates, climate change impacts or the potential habitat alterations occurring across large landscapes. However, an effort to create models is not standardized, in part, due to different needs and data sources available for the models. For example, data may have different spatial resolutions with varying degrees of complexity in regards to inputs and statistical methods. This study determines effects of 20, 15, 10, five and one km sampling resolutions on detection of changes in net primary productivity (NPP), occupancy selection criteria for areas to be included in the sample and identification of significant variables impacting NPP in Indonesia forests. Production forest designated for selective harvest was used to define the sampling areas. Variances explained by predictive models were similar across cell sizes although relative importance of variables was different. Partial dependence plots were used to search for potential thresholds or tipping points of NPP change as affected by an independent variable such as minimum daytime temperature. Applying different cell occupancy selection rules significantly changed the overall distribution of NPP values. The magnitude of those changes within a cell size varied with changes in cell size. The mean estimated NPP for production forests across Indonesia differed significantly at every sampling resolution and occupancy selection criteria. Lows ranged from 1.107 to 1.121 kg C m−2 yr−1 for the 1-km cell size for the three occupancy selection criteria with highs ranging from 1.245 to 1.189 kg C m−2 yr−1 for the 20-km cell size. The difference in NPP values between these two cell sizes for the three occupancy selection criteria extrapolates to a range in annual biomass of 132 × 106 to 66 × 106 t for the total area of production forests in Indonesia.

Type
THEMATIC SECTION: Spatial Simulation Models in Planning for Resilience
Copyright
Copyright © Foundation for Environmental Conservation 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barbosa, J.P.R.A.D., Rambal, S., Soares, A.M., Mouillot, F., Nogueira, J.M.P. & Martins, G.A. (2012) Plant physiological ecology and the global changes. Ciência e Agrotecnologia 36: 253269.CrossRefGoogle Scholar
Bellehumeur, C., Legendre, P. & Marcotte, D. (1997) Variance and spatial scales in a tropical rain forest: changing the size of sampling units. Plant Ecology 130 (1): 8998.CrossRefGoogle Scholar
Berk, R.A. (2011) Statistical Learning From a Regression Perspective. New York, USA and London, UK: Springer.Google Scholar
BIG (2011) Badan Informasi Geospasial [www document]. URL http://www.bakosurtanal.go.id/ Google Scholar
Biro Pusat Statistik (2012) Statistical Yearbook of Indonesia. Jakarta, Indonesia: BPS.Google Scholar
Breiman, L. (2001) Random forests. Machine Learning 45 (1): 532.CrossRefGoogle Scholar
Brown, S. & Lugo, A. (1982) The storage and production of organic matter in tropical forests and their role in the global carbon cycle. Biotropica 14 (3): 161187.CrossRefGoogle Scholar
Carollo, C., Reed, D.J., Ogden, J.C. & Palandro, D. (2009) The importance of data discovery and management in advancing ecosystem-based management. Marine Policy 33 (4): 651653.CrossRefGoogle Scholar
Clark, D.A., Piper, S.C., Keeling, C.D. & Clark, D.B. (2003) Tropical rain forest tree growth and atmospheric carbon dynamics linked to interannual temperature variation during 1984–2000. Proceedings of the National Academy of Sciences USA 100 (10): 58525857.CrossRefGoogle ScholarPubMed
Cleveland, C.C., Townsend, A.R., Taylor, P., Alvarez-Clare, S., Bustamante, M.M.C., Chuyong, G., Dobrowski, S.Z., Grierson, P., Harms, K.E., Houlton, B.Z., Marklein, A., Parton, W., Porder, S., Reed, S.C., Sierra, C.A., Silver, W.L., Tanner, E.V.J. & Wieder, W.R. (2011) Relationships among net primary productivity, nutrients and climate in tropical rain forest: a pan-tropical analysis. Ecology Letters 14 (9): 939947.CrossRefGoogle ScholarPubMed
Cramer, W., Bondeau, A., Woodward, F.I., Prentice, I.C., Betts, R.A., Brovkin, V., Cox, P.M., Fisher, V., Foley, J.A., Friend, A.D., Kucharik, C., Lomas, M.R, Ramankutty, N., Sitch, S., Smith, B., White, A. & Young-Molling, C. (2001) Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Global Change Biology 7 (4): 357373.CrossRefGoogle Scholar
Cramer, W., Kicklighter, D.W., Bondeau, A., Moore, B., Churkina, G., Nemry, B., Ruimy, A. & Schloss, A.L. (1999) Comparing global models of terrestrial net primary productivity (NPP): overview and key results. Global Change Biology 5 (4): 115.CrossRefGoogle Scholar
Cutler, D.R., Edwards, T.C., Beard, K.H., Cutler, A., Hess, K.T., Gibson, J. & Lawler, J.J. (2007) Random forests for classification in ecology. Ecology 88 (11): 27832792.CrossRefGoogle ScholarPubMed
Environmental Systems Research (2013) ESRI GIS & mapping software [www document]. URL http://www.esri.com/ Google Scholar
ESA (2013) GlobCover [www document]. URL http://due.esrin.esa.int/globcover/ Google Scholar
Field, C.B., Randerson, J.T. & Malmstrom, C.M. (1995) Global net primary production. Combining ecology and remote-sensing. Remote Sensing of Environment 51 (1): 7488.CrossRefGoogle Scholar
Garzón, M.B., Blazek, R., Neteler, M., Dios, R.S.d., Ollero, H.S. & Furlanello, C. (2006) Predicting habitat suitability with machine learning models: the potential area of Pinus sylvestris L. in the Iberian Peninsula. Ecological Modelling 197 (3): 383.CrossRefGoogle Scholar
Gmur, S., Vogt, D., Zabowski, D. & Moskal, L.M. (2012) Hyperspectral analysis of soil nitrogen, carbon, carbonate, and organic matter using regression trees. Sensors 12 (12): 1063910658.CrossRefGoogle ScholarPubMed
Gosz, J.R. (1992) Gradient analysis of ecological change in time and space: implications for forest management. Ecological Applications 2 (3): 248261.CrossRefGoogle ScholarPubMed
Hertel, D., Moser, G., Culmsee, H., Erasmi, S., Horna, V., Schuldt, B. & Leuschner, C. (2009) Below- and above-ground biomass and net primary production in a paleotropical natural forest (Sulawesi, Indonesia) as compared to neotropical forests. Forest Ecology and Management 258 (9): 19041912.CrossRefGoogle Scholar
Kementerian Kehutanan (2011) Interactive map index of production forest [www document]. URL http://appgis.dephut.go.id/appgis/petaarahanpemanfaatan2.html Google Scholar
Kitayama, K. & Aiba, S.I. (2002) Ecosystem structure and productivity of tropical rain forests along altitudinal gradients with contrasting soil phosphorus pools on Mount Kinabalu, Borneo. Journal of Ecology 90 (1): 3751.CrossRefGoogle Scholar
Korhonen-Kurki, K., Brockhaus, M., Duchelle, A.E., Atmadja, S. & Thuy, P.T. (2012) Multiple levels and multiple challenges for REDD. Report. Analysing REDD+ 91, Chapter 6. CIFOR, Indonesia.CrossRefGoogle Scholar
Kramer, P.J. & Kozlowski, T.T. (1979) Physiology of Woody Plants. Orlando, FL, USA: Academic Press.Google Scholar
Larcher, W. (1975) Physiological Plant Ecology. Berlin, Germany: Springer-Verlag.CrossRefGoogle Scholar
Levin, S.A. (1993) 2: Concepts of scale at the local level. In: Scaling Physiological Processes, ed. Jacques, R., Ehleringer, J.R. & Field, C.B., pp. 719. San Diego, CA, USA: Academic Press.CrossRefGoogle Scholar
Liaw, A. & Wiener, M. (2002) Classification and regression by randomForest. R News 2 (3): 1822.Google Scholar
Lovejoy, T., Bierregaard, R., Rylands, A., Malcolm, J., Quintela, C., Harper, L., Brown, K., Powell, A., Powell, G., Schubar, H. & Hays, M. (1986) Edge and other effects of isolation on Amazon South America forest fragments. In: Conservation Biology: The Science and Scarcity and Diversity, Sinauer, p. 256. MA, USA: Sunderland.Google Scholar
Melillo, J.M., McGuire, A.D., Kicklighter, D.W., Moore, B., Vorosmarty, C.J. & Schloss, A.L. (1993) Global climate change and terrestrial net primary production. Nature 363 (6426): 234240.CrossRefGoogle Scholar
Moorcroft, P.R., Hurtt, G.C. & Pacala, S.W. (2001) A method for scaling vegetation dynamics: the Ecosystem Demography model (ED). Ecological Monographs 71 (4): 557586.CrossRefGoogle Scholar
Moser, G., Leuschner, C., Hertel, D., Graefe, S., Soethe, N. & Lost, S. (2011) Elevation effects on the carbon budget of tropical mountain forests (S Ecuador): the role of the belowground compartment. Global Change Biology 17 (6): 22112226.CrossRefGoogle Scholar
Naidoo, R., Balmford, A., Costanza, R., Fisher, B., Green, R.E., Lehner, B., Malcolm, T.R. & Ricketts, T.H. (2008) Global mapping of ecosystem services and conservation priorities. Proceedings of the National Academy of Sciences USA 105 (28): 94959500.CrossRefGoogle ScholarPubMed
NASA (2013 a) Earth Observing System Data and Information System [www document]. URL http://reverb.echo.nasa.gov/reverb/ Google Scholar
NASA (2013 b) Shuttle Radar Topography Mission [www document]. URL http://www2.jpl.nasa.gov/srtm/ Google Scholar
NASA (2013 c) Earth Observatory [www document]. URL http://earthobservatory.nasa.gov/ Google Scholar
Parry, M.L. (2007) Climate Change 2007: Impacts, Adaptation and Vulnerability : Contribution of Working Group Ii to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.Google Scholar
Richardson, A.D., Anderson, R.S., Arain, M.A., Barr, A.G., Bohrer, G., Chen, G., Chen, J.M., Ciais, P., Davis, K.J., Desai, A.R., Dietze, M.C., Dragoni, D., Garrity, S.R., Gough, C.M., Grant, R., Hollinger, D.Y., Margolis, H.A., McCaughey, H., Migliavacca, M., Monson, R.K., Munger, J.W., Poulter, B., Raczka, B.M., Ricciuto, D.M., Sahoo, A.K., Schaefer, K., Tian, H., Vargas, R., Verbeeck, H., Xiao, J. & Xue, Y. (2012) Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis. Global Change Biology 18 (2): 566584.CrossRefGoogle Scholar
Running, S.W., Nemani, R., Glassy, J.M. & Thornton, P.E. (1999) MODIS daily photosynthesis (PSN) and annual net primary production (NPP) product (MOD17) Algorithm Theoretical Basis Document. SCF At-Launch Algorithm ATBD Documents, University of Montana, USA [www document]. URL http://www.ntsg.umt.edu/modis/ATBD/ATBD_MOD17_v21.pdf Google Scholar
Running, S.W., Nemani, R.R., Heinsch, F.A., Zhao, M., Reeves, M. & Hashimoto, H. (2004) A continuous satellite-derived measure of global terrestrial primary production. BioScience 54 (6): 547560.CrossRefGoogle Scholar
Solomon, S. (2007) Climate Change 2007: The Physical Science Basis: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.Google Scholar
Suntana, A., Vogt, K., Turnblom, E., Vogt, D. & Upadhye, R. (2013 a) Non-traditional use of biomass at certified forest management units in Indonesia: Forest biomass for energy production and carbon emissions reduction. Journal of International Forest Research (in press).CrossRefGoogle Scholar
Suntana, A.S., Turnblom, E.C. & Vogt, K.A. (2013 b) Addressing unknown variability in seemingly fixed national forest estimates: aboveground forest biomass for renewable energy. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 35 (6): 546555.CrossRefGoogle Scholar
Tan, K.H. (2008) Soils in the Humid Tropics and Monsoon Region of Indonesia. Boca Raton, FL, USA.: CRC Press.CrossRefGoogle Scholar
Turner, D.P., Ritts, W.D., Cohen, W.B., Gower, S.T., Running, S.W., Zhao, M.S., Costa, M.H., Kirschbaum, A.A., Ham, J.M., Saleska, S.R. & Ahl, D.E. (2006) Evaluation of MODIS NPP and GPP products across multiple biomes. Remote Sensing of Environment 102 (3–4): 282292.CrossRefGoogle Scholar
Vogt, K.A., Gordon, J., Wargo, J., Vogt, D., Asbjornsen, H., Palmiotto, P.A., Clark, H., O'Hara, J., Keeton, W.S., Patel-Weynand, T. & Witten, E., with contributions by Larson, B., Tortoriello, D., Perez, J., Marsh, A., Corbett, M., Kaneda, K., Meyerson, F. & Smith, D. (1997) Ecosystems: Balancing Science with Management. New York, NY, USA: Springer-Verlag.CrossRefGoogle Scholar
Vogt, K.A., Patel-Weynand, T., Shelton, M., Vogt, D.J., Gordon, J.C., Mukumoto, C., Suntana, A.S. & Roads, P.A. (2010) Sustainability Unpacked : Food, Energy and Water for Resilient Environments and Societies. London, UK and Washington, DC, USA: Earthscan.Google Scholar
Zar, J.H. (1999) Biostatistical Analysis. Upper Saddle River, NJ, USA: Prentice Hall.Google Scholar
Zhao, M.S., Heinsch, F.A., Nemani, R.R. & Running, SW. (2005) Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment 95 (2): 164176.CrossRefGoogle Scholar
Supplementary material: File

Gmur et al. Supplementary Material

Appendix

Download Gmur et al. Supplementary Material(File)
File 629.3 KB