Abstract
Environmental monitoring and assessment of the extent and change of land uses and their renewable natural resources over time is a key element in many international processes and one crucial basis for sustainable management. Remote sensing plays an increasingly important role in these monitoring systems, especially if the interest is in large areas. Integration of remote sensing requires comprehensive and careful preprocessing and a high level of expertise which is not always at hand in all applications. However, easy-to-implement sampling techniques based on visual interpretation are an alternative approach for utilizing remote sensing imagery, including the evolving archives of georeferenced and preprocessed data provided by virtual globes like Google Earth, Bing, and others. The goal of this paper is to propose a simple unified framework that may be used in the context of sampling studies and environmental monitoring from local to global scale. Besides the definition of a sampling design, the observation or plot design, i.e., defining how observations are to be made and recorded, has a strong influence on the precision of estimates as well as the overall efficiency of a sampling exercise. As an example, we present a simulation study focusing on the estimation of forest cover in artificial landscapes with different coverage and degree of fragmentation. The sampling units we compare are point clusters with different configuration and spatial extent.
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References
Achard, F., Defries, R., Eva, H., Hansen, M., Mayaux, P., Stibig, H.J. (2007). Pan-tropical monitoring of deforestation. Environmental Research Letters, 2. https://doi.org/10.1088/1748-9326/2/4/045022.
Achard, F., Eva, H.D., Stibig, H.-J., Mayaux, P., Gallego, J., Richards, T., Malingreau, J. P. (2002). Determination of deforestation rates of the world’s humid tropical forests. Science, 297, 999–1002. https://doi.org/10.1126/science.1070656.
Aune-Lundberg, L., & Strand, G.H. (2014). Comparison of variance estimation methods for use with two-dimensional systematic sampling of land use/land cover data. Environmental Modelling and Software, 61, 87–97. https://doi.org/10.1016/j.envsoft.2014.07.001.
Barrett, F., McRoberts, R.E., Tomppo, E., Cienciala, E., Waser, L.T. (2016). A questionnaire-based review of the operational use of remotely sensed data by national forest inventories. Remote Sensing of Environment, 174, 279–289. http://www.sciencedirect.com/science/article/pii/S0034425715301176.
Bastin, J.-F., Berrahmouni, N., Grainger, A., Maniatis, D., Mollicone, D., Moore, R., Patriarca, C., Picard, N., Sparrow, B., Abraham, E.M., Aloui, K., Atesoglu, A., Attore, F., Bey, A., Garzuglia, M., García-montero, L.G., Groot, N., Guerin, G., Laestadius, L., Lowe, A.J., Mamane, B. (2017). The extent of forest in dryland biomes. Science, 638, 1–5. http://science.sciencemag.org/content/sci/356/6338/635.full.pdf.
Beuchle, R., Eva, H.D., Stibig, H.-J., Bodart, C., Brink, A., Mayaux, P., Johansson, D., Achard, F., Belward, A. (2011). A satellite data set for tropical forest area change assessment. International Journal of Remote Sensing, 32, 7009–7031. https://www.tandfonline.com/doi/full/10.1080/01431161.2011.611186.
Cochran, W.G. (1977). Sampling techniques.
Cracknell, A.P., Kanniah, K.D., Tan, K.P., Wang, L. (2013). Evaluation of MODIS gross primary productivity and land cover products for the humid tropics using oil palm trees in Peninsular Malaysia and Google Earth imagery. International Journal of Remote Sensing, 34, 7400–7423. https://doi.org/10.1080/01431161.2013.820367. http://apps.webofknowledge.com/full_record.do?product=WOS&search_mode=GeneralSearch&qid=2&SID=X1gj212wPM4ZeXzz3lz&page=1&doc=4.
Esseen, P.A., Jansson, K.U., Nilsson, M. (2006). Forest edge quantification by line intersect sampling in aerial photographs. Forest Ecology and Management, 230, 32–42. https://doi.org/10.1016/j.foreco.2006.04.012.
FAO. (2009). The 2010 Global Forest Resources Assessment Remote Sensing Survey: an outline of the objectives, data, methods and approach. Techical Report, FAO. http://www.fao.org/3/a-k7023e.pdf.
FAO. (2010). Global forest resources assessment. Technical Report. FAO. arXiv:0404048. ISBN:978-92-5-106654-6 .
Fattorini, L., Franceschi, S., Pisani, C. (2009). A two-phase sampling strategy for large-scale forest carbon budgets. Journal of Statistical Planning and Inference, 139, 1045–1055. https://doi.org/10.1016/j.jspi.2008.06.014.
Fehrmann, L. (2015). A unified framework for environmental monitoring based on a discrete global sampling grid (GSG) system. In Fehrmann, L., & Kleinn, C (Eds.) Proceedings of the 5th international DAAD workshop (pp. 99–111). Cuvellier Verlag Göttingen.
Fehrmann, L., Seidel, D., Krause, B., Kleinn, C. (2014). Sampling for landscape elements - a case study from Lower Saxony, Germany. Environmental Monitoring and Assessment, 186, 1421–1430. https://doi.org/10.1007/s10661-013-3464-0.
GADM. (2012). Gadm database of global administrative areas, version 2.0. https://www.gadm.org/.
Gibbs, H.K., Brown, S., Niles, J.O., Foley, J.A. (2007). Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environmental Research Letters, 2. https://doi.org/10.1088/1748-9326/2/4/045023.
Gregoire, T., & Valentine, H. (2007). Sampling strategies for natural resources and the environment. Chapman & Hall/CRC Applied Environmental Statistics. Taylor & Francis. https://books.google.de/books?id=1z71MAe3gL0C.
Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., Townshend, J.R.G. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342, 850–853. http://www.sciencemag.org/content/342/6160/850.abstract, https://doi.org/10.1126/science.1244693, arXiv:1011.1669v3.
Hu, Q., Wu, W., Xia, T., Yu, Q., Yang, P., Li, Z., Song, Q. (2013). Exploring the use of Google Earth imagery and object-based methods in land use/cover mapping. Remote Sensing, 5, 6026–6042. https://doi.org/10.3390/rs5116026. http://www.mdpi.com/2072-4292/5/11/6026.
Jr., D.L.S., & Olsen, A.R. (2004). Spatially balanced sampling of natural resources. Journal of the American Statistical Association, 99, 262–278. https://doi.org/10.1198/016214504000000250 .
Kimerling, J.A., Sahr, K., White, D., Song, L. (1999). Comparing geometrical properties of global grids. Cartography and Geographic Information Science, 26, 271–288. http://www.tandfonline.com/doi/abs/10.1559/152304099782294186.
Kleinn, C. (1994). Comparison of the performance of line sampling to other forms of cluster sampling. Forest Ecology and Management, 68, 365–373. https://doi.org/10.1016/0378-1127(94)90057-4 .
Kleinn, C. (1996). Ein vergleich der effizienz von verschiedenen clusterformen in forstlichen großrauminventuren. Forstwissenschaftliches Centralblatt vereinigt mit Tharandter forstliches Jahrbuch, 115, 378–390. https://doi.org/10.1007/BF02738616.
Kleinn, C. (2000). Estimating metrics of forest spatial pattern from large area forest inventory cluster samples. Forest Science, 46, 548–557. https://doi.org/10.1093/forestscience/46.4.548 .
Lister, A.J., & Scott, C.T. (2009). Use of space-filling curves to select sample locations in natural resource monitoring studies. Environmental Monitoring and Assessment, 149, 71–80. https://doi.org/10.1007/s10661-008-0184-y.
Magdon, P., & Kleinn, C. (2013). Uncertainties of forest area estimates caused by the minimum crown cover criterion: - a scale issue relevant to forest cover monitoring. Environmental Monitoring and Assessment, 185, 5345–5360. https://doi.org/10.1007/s10661-012-2950-0.
Magnussen, S., Kurz, W., Leckie, D.G., Paradine, D. (2005). Adaptive cluster sampling for estimation of deforestation rates. European Journal of Forest Research, 124, 207–220. https://doi.org/10.1007/s10342-005-0074-6.
Mandallaz, D. (2007). Sampling techniques for forest inventories. Chapman & Hall/CRC Applied Environmental Statistics. Boca Raton: CRC Press. https://books.google.de/books?id=lCnIGO1rt18C.
Olofsson, P., Stehman, S.V., Woodcock, C.E., Sulla-Menashe, D., Sibley, A.M., Newell, J.D., Friedl, M.A., Herold, M. (2012). A global land-cover validation data set, part I: fundamental design principles. International Journal of Remote Sensing, 33, 5768–5788. https://doi.org/10.1080/01431161.2012.674230.
Pengra, B., Long, J., Dahal, D., Stehman, S.V., Loveland, T.R. (2015). A global reference database from very high resolution commercial satellite data and methodology for application to landsat derived 30m continuous field tree cover data. Remote Sensing of Environment, 165, 234–248. https://doi.org/10.1016/j.rse.2015.01.018. http://www.sciencedirect.com/science/article/pii/S003442571500036X.
Ploton, P., Pélissier, R., Proisy, C., Flavenot, T., Barbier, N., Rai, S.N., Couteron, P. (2012). Assessing aboveground tropical forest biomass using Google Earth canopy images. Ecological Applications : a Publication of the Ecological Society of America, 22, 993–1003. https://doi.org/10.1890/11-1606.1. http://www.ncbi.nlm.nih.gov/pubmed/22645827, http://www.esajournals.org/doi/abs/10.1890/11-1606.1.
Potere, D. (2008). Horizontal positional accuracy of Google Earth’s high-resolution imagery archive. Sensors, 8, 7973–7981. https://doi.org/10.3390/s8127973.
Ramezani, H., & Holm, S. (2011). Sample based estimation of landscape metrics; accuracy of line intersect sampling for estimating edge density and Shannon’s diversity index. Environmental and Ecological Statistics, 18, 109–130. https://doi.org/10.1007/s10651-009-0123-2.
Ramezani, H., Holm, S., Allard, A., Ståhl, G. (2010). Monitoring landscape metrics by point sampling: accuracy in estimating Shannon’s diversity and edge density. Environmental Monitoring and Assessment, 164, 403–421. https://doi.org/10.1007/s10661-009-0902-0. http://link.springer.com/10.1007/s10661-009-0902-0.
Richards, T., Gallego, J., Achard, F. (2000). Sampling for forest cover change assessment at the pan-tropical scale. International Journal of Remote Sensing, 21, 1473–1490. https://doi.org/10.1080/014311600210272.
Saatchi, S.S., Harris, N.L., Brown, S., Lefsky, M., Mitchard, E.T.A., Salas, W., Zutta, B.R., Buermann, W., Lewis, S.L., Hagen, S., Petrova, S., White, L., Silman, M., Morel, A. (2011). Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences, 108, 9899–9904.http://www.pnas.org/content/early/2011/05/24/1019576108.abstract, http://www.pnas.org/content/108/24/9899.abstract%0A, http://www.pnas.org/content/108/24/9899.full.pdf. https://doi.org/10.1073/pnas.1019576108 arXiv:1408.1149.
Sahr, K. (2011). Hexagonal discrete global grid systems for geospatial computing. Archives of Photogrammetry, Cartography and Remote Sensizng, 22, 363–376. https://journals.indexcopernicus.com/search/article?articleId=1435632.
Sahr, K., White, D., Kimerling, A.J. (2003). Geodesic discrete global grid systems. Cartography and Geographic Information Science, 30, 121–134. https://doi.org/10.1559/152304003100011090. http://www.tandfonline.com/doi/abs/10.1559/152304003100011090.
Schabenberger, O., & Gotway, C.A. (2005). Statistical methods for spatial data analysis. USA: Chapman & Hall/CRC.
Schlather, M. (2004). Separate fractal dimension and the hurst effect. SIAM REVIEW, 46, 269–282. https://doi.org/10.1137/S0036144501394387.
Schlather, M., Malinowski, A., Menck, P.J., Oesting, M., Strokorb, K. (2015). Analysis, simulation and prediction of multivariate random fields with package RandomFields. Journal of Statistical Software, 63, 1–25. http://www.jstatsoft.org/v63/i08/.
Sheppard, S.R.J., & Cizek, P. (2009). The ethics of Google Earth: crossing thresholds from spatial data to landscape visualisation. Journal of Environmental Management, 90, 2102–17. https://doi.org/10.1016/j.jenvman.2007.09.012. http://apps.webofknowledge.com/full_record.do?product=WOS&search_mode=Refine&qid=6&SID=X1gj212wPM4ZeXzz3lz&page=1&doc=3.
Song, L., Kimerling, A.J., Sahr, K. (2002). Developing an equal area global grid by small circle subdivision. http://www.ncgia.ucsb.edu/globalgrids-book/song-kimmerling-sahr/.
Stehman, S., Sohl, T., Loveland, T. (2003). Statistical sampling to characterize recent united states land-cover change. Remote Sensing of Environment, 86, 517–529. https://doi.org/10.1016/S0034-4257(03)00129-9 . http://www.sciencedirect.com/science/article/pii/S0034425703001299.
Stehman, S.V. (1999). Basic probability sampling designs for thematic map accuracy assessment. International Journal of Remote Sensing, 20, 2423–2441. https://doi.org/10.1080/014311699212100.
Stehman, S.V., Sohl, T.L., Loveland, T.R. (2005). An evaluation of sampling strategies to improve precision of estimates of gross change in land use and land cover. International Journal of Remote Sensing, 26, 4941–4957. https://doi.org/10.1080/01431160500222632.
Sun, X., Shen, S., Leptoukh, G.G., Wang, P., Di, L., Lu, M. (2012). Development of a Web-based visualization platform for climate research using Google Earth. Computers & Geosciences, 47, 160–168. https://doi.org/10.1016/j.cageo.2011.09.010. http://apps.webofknowledge.com/full_record.do?product=WOS&search_mode=GeneralSearch&qid=3&SID=X1gj212wPM4ZeXzz3lz&page=1&doc=5.
Swinbank, R., & Purser, R.J. (2006). Fibonacci grids: a novel approach to global modelling. Quarterly Journal of the Royal Meteorological Society https://doi.org/10.1256/qj.05.227.
Theobald, D.M., Stevens, D.L., White, D., Urquhart, N.S., Olsen, A.R., Norman, J.B. (2007). Using GIS to generate spatially balanced random survey designs for natural resource applications. Environmental Management, 40, 134–146. https://doi.org/10.1007/s00267-005-0199-x.
White, D., Kimerling, J.A., Overton, S.W. (1992). Cartographic and geometric components of a global sampling design for environmental monitoring. Cartography and Geographic Information Systems, 19, 5–22. https://doi.org/10.1559/152304092783786636.
Wickman, F.E., Elvers, E., Edvarson, K. (1974). A system of domains for global sampling problems. Geografiska Annaler: Series A, Physical Geography, 56, 201–212. https://doi.org/10.1080/04353676.1974.11879899.
Yim, J.S., Shin, M.Y., Son, Y., Kleinn, C. (2015). Cluster plot optimization for a large area forest resource inventory in Korea. Forest Science and Technology, 11, 139–146. https://doi.org/10.1080/21580103.2014.968222.
Youngren, R.W., & Petty, M.D. (2017). A multi-resolution HEALPix data structure for spherically mapped point data. Heliyon https://doi.org/10.1016/j.heliyon.2017.e00332.
Yu, L., & Gong, P. (2012). Google earth as a virtual globe tool for earth science applications at the global scale: progress and perspectives https://doi.org/10.1080/01431161.2011.636081.
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We thank anonymous reviewers for helpful comments and suggestions.
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This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—project no. 273259202.
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Fehrmann, L., Kukunda, C.B., Nölke, N. et al. A unified framework for land cover monitoring based on a discrete global sampling grid (GSG). Environ Monit Assess 191, 46 (2019). https://doi.org/10.1007/s10661-018-7152-y
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DOI: https://doi.org/10.1007/s10661-018-7152-y