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Can tree species diversity be assessed with Landsat data in a temperate forest?

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Abstract

The diversity of forest trees as an indicator of ecosystem health can be assessed using the spectral characteristics of plant communities through remote sensing data. The objectives of this study were to investigate alpha and beta tree diversity using Landsat data for six dates in the Gönen dam watershed of Turkey. We used richness and the Shannon and Simpson diversity indices to calculate tree alpha diversity. We also represented the relationship between beta diversity and remotely sensed data using species composition similarity and spectral distance similarity of sampling plots via quantile regression. A total of 99 sampling units, each 20 m × 20 m, were selected using geographically stratified random sampling method. Within each plot, the tree species were identified, and all of the trees with a diameter at breast height (dbh) larger than 7 cm were measured. Presence/absence and abundance data (tree species number and tree species basal area) of tree species were used to determine the relationship between richness and the Shannon and Simpson diversity indices, which were computed with ground field data, and spectral variables derived (2 × 2 pixels and 3 × 3 pixels) from Landsat 8 OLI data. The Shannon-Weiner index had the highest correlation. For all six dates, NDVI (normalized difference vegetation index) was the spectral variable most strongly correlated with the Shannon index and the tree diversity variables. The Ratio of green to red (VI) was the spectral variable least correlated with the tree diversity variables and the Shannon basal area. In both beta diversity curves, the slope of the OLS regression was low, while in the upper quantile, it was approximately twice the lower quantiles. The Jaccard index is closed to one with little difference in both two beta diversity approaches. This result is due to increasing the similarity between the sampling plots when they are located close to each other. The intercept differences between two investigated beta diversity were strongly related to the development stage of a number of sampling plots in the tree species basal area method. To obtain beta diversity, the tree basal area method indicates better result than the tree species number method at representing similarity of regions which are located close together. In conclusion, NDVI is helpful for estimating the alpha diversity of trees over large areas when the vegetation is at the maximum growing season. Beta diversity could be obtained with the spectral heterogeneity of Landsat data. Future tree diversity studies using remote sensing data should select data sets when vegetation is at the maximum growing season. Also, forest tree diversity investigations can be identified by using higher-resolution remote sensing data such as ESA Sentinel 2 data which is freely available since June 2015.

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References

  • Ahmed, O. S., Franklin, S. E., Wulder, M. A., & White, J. C. (2015). Characterizing stand-level forest canopy cover and height using landsat time series, samples of airborne LiDAR, and the random forest algorithm. ISPRS Journal of Photogrammetry and Remote Sensing, 101, 89–101.

    Article  Google Scholar 

  • Baig, M. H. A., Zhang, L., Shuai, T., & Tong, Q. (2014). Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters, 5, 423–431.

    Article  Google Scholar 

  • Boyd, D. S., & Foody, G. M. (2011). An overview of recent remote sensing and GIS based research in ecological informatics. Ecological Informatics, 6, 25–36.

    Article  Google Scholar 

  • Broge, N. H., & Leblanc, E. (2001). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76, 156–172.

    Article  Google Scholar 

  • Buhk, C., Retzer, V., Beierkuhnlein, C., & Jentsch, A. (2007). Predicting plant species richness and vegetation patterns in cultural landscapes using disturbance parameters. Agriculture, Ecosystems & Environment, 122, 446–452.

    Article  Google Scholar 

  • Cabacinha, C. D., & de Castro, S. S. (2009). Relationships between floristic diversity and vegetation indices, forest structure and landscape metrics of fragments in Brazilian Cerrado. Forest Ecology and Management, 257, 2157–2165.

    Article  Google Scholar 

  • Cade, B. S., & Noon, B. R. (2003). A gentle introduction to quantile regression for ecologists. Frontiers in Ecology and the Environment, 1, 412–420.

    Article  Google Scholar 

  • Callaway, R. M., & Walker, L. R. (1997). Competition and facilitation: a synthetic approach to interactions in plant communities. Ecology, 78, 1958–1965.

    Article  Google Scholar 

  • Carlson, K. M., Asner, G. P., Hughes, R. F., Ostertag, R., & Martin, R. E. (2007). Hyperspectral remote sensing of canopy biodiversity in Hawaiian lowland rainforests. Ecosystems, 10, 536–549.

    Article  Google Scholar 

  • Carroll, S. S. (1998). Modelling abiotic indicators when obtaining spatial predictions of species richness. Environmental and Ecological Statistics, 5, 257–276.

    Article  Google Scholar 

  • Castagneyrol, B., Jactel, H., Vacher, C., Brockerhoff, E. G., & Koricheva, J. (2014). Effects of plant phylogenetic diversity on herbivory depend on herbivore specialization. Journal of Applied Ecology, 51, 134–141.

    Article  Google Scholar 

  • Ceballos, A., Hernández, J., Corvalán, P., & Galleguillos, M. (2015). Comparison of airborne LiDAR and satellite hyperspectral remote sensing to estimate vascular plant richness in deciduous Mediterranean forests of central Chile. Remote Sensing, 7, 2692–2714.

    Article  Google Scholar 

  • Champagne, C. M., Abuelgasim, A., Staenz, K., Monet, S., and White, H. P.. (2004). Ecological restoration from space: the use of remote sensing for monitoring land reclamation in Sudbury. Page 7 Proceedings of the 16th international conference, Society for Ecological Restoration, Victoria, Canada.

  • Chavez, P. S. (1996). Image-based atmospheric corrections-revisited and improved. Photogrammetric Engineering and Remote Sensing, 62, 1025–1035.

    Google Scholar 

  • Clevers, J. (1988). The derivation of a simplified reflectance model for the estimation of leaf area index. Remote Sensing of Environment, 25, 53–69.

    Article  Google Scholar 

  • Crist, E. P., Laurin, R., & Cicone, R. C. (1986). Vegetation and soils information contained in transformed Thematic Mapper data. Proceedings of IGARSS’86 Symposium. European Space Agency Publications Division Paris. P:1465–1470.

  • Colwell, R. K. (2009). Biodiversity: Concepts, patterns, and measurement. The Princeton guide to ecology, 257–263.

  • Congedo, L. (2016). Semi-Automatic Classification Plugin Documentation. Release, 4, 29.

    Google Scholar 

  • Dalmayne, J., Möckel, T., Prentice, H. C., Schmid, B. C., & Hall, K. (2013). Assessment of fine-scale plant species beta diversity using WorldView-2 satellite spectral dissimilarity. Ecological Informatics, 18, 1–9.

    Article  Google Scholar 

  • Dogan, H. M., & Dogan, M. (2006). A new approach to diversity indices—modeling and mapping plant biodiversity of Nallihan (A3-Ankara/Turkey) forest ecosystem in frame of geographic information systems. Biodiversity and Conservation, 15, 855–878.

    Article  Google Scholar 

  • Duro, D. C., Girard, J., King, D. J., Fahrig, L., Mitchell, S., Lindsay, K., & Tischendorf, L. (2014). Predicting species diversity in agricultural environments using Landsat TM imagery. Remote Sensing of Environment, 144, 214–225.

    Article  Google Scholar 

  • Ewijk, K. Y., Randin, C. F., Treitz, P. M., & Scott, N. A. (2014). Predicting fine-scale tree species abundance patterns using biotic variables derived from LiDAR and high spatial resolution imagery. Remote Sensing of Environment, 150, 120–131.

    Article  Google Scholar 

  • Fairbanks, D. H., & McGwire, K. C. (2004). Patterns of floristic richness in vegetation communities of California: regional scale analysis with multi-temporal NDVI. Global Ecology and Biogeography, 13, 221–235.

  • Fallah, C., Mozaffar, S. B., & Hashemi, S. A. (2012). Probability measurement to estimate forest tree diversity using IRS-p6 satellite images in Caspian broad leaved forests. ARPN Journal of Agricultural and Biological Science, 7(4), 238–243.

  • Foody, G. M., & Cutler, M. E. (2003). Tree biodiversity in protected and logged Bornean tropical rain forests and its measurement by satellite remote sensing. Journal of Biogeography, 30, 1053–1066.

    Article  Google Scholar 

  • Foody, G. M., & Cutler, M. E. (2006). Mapping the species richness and composition of tropical forests from remotely sensed data with neural networks. Ecological Modelling, 195, 37–42.

    Article  Google Scholar 

  • Freitas, S. R., Mello, M. C., & Cruz, C. B. (2005). Relationships between forest structure and vegetation indices in Atlantic Rainforest. Forest Ecology and Management, 218, 353–362.

    Article  Google Scholar 

  • Gamfeldt, L., Snäll, T., Bagchi, R., Jonsson, M., Gustafsson, L., Kjellander, P., Ruiz-Jaen, M. C., Fröberg, M., Stendahl, J., Philipson, C. D., et al. (2013). Higher levels of multiple ecosystem services are found in forests with more tree species. Nature Communications, 4, 1340.

    Article  Google Scholar 

  • Gebreslasie, M., Ahmed, F., & Van Aardt, J. A. (2010). Predicting forest structural attributes using ancillary data and ASTER satellite data. International Journal of Applied Earth Observation and Geoinformation, 12, S23–S26.

    Article  Google Scholar 

  • Getzin, S., Wiegand, K., & Schöning, I. (2012). Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles. Methods in Ecology and Evolution, 3, 397–404.

    Article  Google Scholar 

  • Ghahramany, L., Fatehi, P., Ghazanfari, H., et al. (2012). Estimation of basal area in west oak forests of Iran using remote sensing imagery. International Journal of Geosciences, 3, 398.

    Article  Google Scholar 

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

  • Gillespie, T., Saatchi, S., Pau, S., Bohlman, S., Giorgi, A., & Lewis, S. (2009). Towards quantifying tropical tree species richness in tropical forests. International Journal of Remote Sensing, 30, 1629–1634.

  • 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, 203–221.

    Article  Google Scholar 

  • Gould, W. (2000). Remote sensing of vegetation, plant species richness, and regional biodiversity hotspots. Ecological Applications, 10, 1861–1870.

    Article  Google Scholar 

  • GRASS Development Team, G. (2012). Grass Development Team, Geographic Resources Analysis Support System Software. Open Source Geospatial Foundation Project (http://grass.osgeo.org).

  • He, K. S., J. Zhang, and Q. Zhang. 2009. Linking variability in species composition and MODIS NDVI based on beta diversity measurements. acta oecologica 35:14–21.

  • Hernández-Stefanoni, J. L., & Dupuy, J. M. (2007). Mapping species density of trees, shrubs and vines in a tropical forest, using field measurements, satellite multiespectral imagery and spatial interpolation. Biodiversity and Conservation, 16, 3817–3833.

    Article  Google Scholar 

  • Hernández-Stefanoni, J. L., Dupuy, J. M., Johnson, K. D., Birdsey, R., Tun-Dzul, F., Peduzzi, A., Caamal-Sosa, J. P., Sánchez-Santos, G., & López-Merlɩn, D. (2014). Improving species diversity and biomass estimates of tropical dry forests using airborne LiDAR. Remote Sensing, 6, 4741–4763.

    Article  Google Scholar 

  • Hill, M. O. (1973). Diversity and evenness: A unifying notation and its consequences. Ecology, 54, 427–432.

    Article  Google Scholar 

  • Huang, C., Wylie, B., Yang, L., Homer, C., & Zylstra, G. (2002). Derivation of a tasseled cap transformation based on Landsat 7 at-satellite reflectance. International Journal of Remote Sensing, 23, 1741–1748.

    Article  Google Scholar 

  • Huete, A., Liu, H., Batchily, K., & Van Leeuwen, W. (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, 59, 440–451.

    Article  Google Scholar 

  • Jiang, Z., Huete, A. R., Didan, K., & Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112, 3833–3845.

    Article  Google Scholar 

  • John, R., Chen, J., Lu, N., Guo, K., Liang, C., Wei, Y., Noormets, A., Ma, K., & Han, X. (2008). Predicting plant diversity based on remote sensing products in the semi-arid region of Inner Mongolia. Remote Sensing of Environment, 112, 2018–2032.

    Article  Google Scholar 

  • Jones, R. H., Sharitz, R. R., Dixon, P. M., Segal, D. S., & Schneider, R. L. (1994). Woody plant regeneration in four floodplain forests. Ecological Monographs, 345–367.

  • Kalacska, M., Sanchez-Azofeifa, G. A., Rivard, B., Caelli, T., White, H. P., & Calvo-Alvarado, J. C. (2007). Ecological fingerprinting of ecosystem succession: estimating secondary tropical dry forest structure and diversity using imaging spectroscopy. Remote Sensing of Environment, 108, 82–96.

    Article  Google Scholar 

  • Kiran, G. S., & Mudaliar, A. (2012). Remote sensing & geo-informatics technology in evaluation of forest tree diversity. Asian J Plant Sci Res, 2, 237–242.

    Google Scholar 

  • Kirschbaum, M. U., Fischlin, A., Cannell, M., Cruz, R., Galinski, W., Cramer, W., et al. (1995). Climate change impacts on forests. Climate Change, 95–129.

  • Koenker, R., and M. R. Koenker. 2007. The quantreg package.

    Google Scholar 

  • Lassau, S. A., & Hochuli, D. F. (2007). Associations between wasp communities and forest structure: do strong local patterns hold across landscapes? Austral Ecology, 32, 656–662.

    Article  Google Scholar 

  • Levin, N., Shmida, A., Levanoni, O., Tamari, H., & Kark, S. (2007). Predicting mountain plant richness and rarity from space using satellite-derived vegetation indices. Diversity and Distributions, 13, 692–703.

    Article  Google Scholar 

  • Li, P., Jiang, L., & Feng, Z. (2013). Cross-comparison of vegetation indices derived from Landsat-7 enhanced thematic mapper plus (ETM+) and Landsat-8 operational land imager (OLI) sensors. Remote Sensing, 6, 310–329.

    Article  Google Scholar 

  • Markham, B. L., & Helder, D. L. (2012). Forty-year calibrated record of earth-reflected radiance from Landsat: a review. Remote Sensing of Environment, 122, 30–40.

    Article  Google Scholar 

  • Meng, J., Li, S., Wang, W., Liu, Q., Xie, S., & Ma, W. (2016). Estimation of Forest structural diversity using the spectral and textural information derived from SPOT-5 satellite images. Remote Sensing, 8, 125.

    Article  Google Scholar 

  • Mohammadi, J., & Shataee, S. (2010). Possibility investigation of tree diversity mapping using Landsat ETM+ data in the Hyrcanian forests of Iran. Remote Sensing of Environment, 114, 1504–1512.

    Article  Google Scholar 

  • Mohammadi, J., Shataee, S., & Babanezhad, M. (2011). Estimation of forest stand volume, tree density and biodiversity using Landsat ETM+ Data, comparison of linear and regression tree analyses. Procedia Environmental Sciences, 7, 299–304.

    Article  Google Scholar 

  • Morin, X., Fahse, L., Scherer-Lorenzen, M., & Bugmann, H. (2011). Tree species richness promotes productivity in temperate forests through strong complementarity between species. Ecology Letters, 14, 1211–1219. https://doi.org/10.1111/j.1461-0248.2011.01691.x.

    Article  Google Scholar 

  • Morton, D. C., Masek, J. G., Wang, D., Sexton, J. O., Nagol, J. R., Ropars, P., Boudreau, S., et al. (2012). Satellite-based evidence for shrub and graminoid tundra expansion in northern Quebec from 1986 to 2010. Global Change Biology, 18, 2313–2323.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Nagendra, H., Lucas, R., Honrado, J. P., Jongman, R. H., Tarantino, C., Adamo, M., & Mairota, P. (2013). Remote sensing for conservation monitoring: assessing protected areas, habitat extent, habitat condition, species diversity, and threats. Ecological Indicators, 33, 45–59.

    Article  Google Scholar 

  • Nagendra, H., & Rocchini, D. (2008). High resolution satellite imagery for tropical biodiversity studies: the devil is in the detail. Biodiversity and Conservation, 17, 3431–3442.

    Article  Google Scholar 

  • Nagendra, H., Rocchini, D., Ghate, R., Sharma, B., & Pareeth, S. (2010). Assessing plant diversity in a dry tropical forest: comparing the utility of Landsat and IKONOS satellite images. Remote Sensing, 2, 478–496.

    Article  Google Scholar 

  • Nekola, J. C., & White, P. S. (1999). The distance decay of similarity in biogeography and ecology. Journal of Biogeography, 26, 867–878.

    Article  Google Scholar 

  • Oksanen, J., Blanchet, G., Kindt, R., Minchin, P. R., Legendre, P., O’Hara, B., Simpson, G. L., Solymos, P., Stevens, M. H. H. & Wagner, H. (2011). Vegan: Community ecology package. R package Version 2.0–2. Available at: http://cran.r-project.org/. Accessed 5 February 2015.

  • Oldeland, J., Wesuls, D., Rocchini, D., Schmidt, M., & Jürgens, N. (2010). Does using species abundance data improve estimates of species diversity from remotely sensed spectral heterogeneity? Ecological Indicators, 10, 390–396.

    Article  Google Scholar 

  • Palmer, M. W. (2005). Distance decay in an old-growth neotropical forest. Journal of Vegetation Science, 16, 161–166.

    Article  Google Scholar 

  • Pedro, M. S., Rammer, W., & Seidl, R. (2015). Tree species diversity mitigates disturbance impacts on the forest carbon cycle. Oecologia, 177, 619–630.

    Article  Google Scholar 

  • Pettorelli, N. (2013). The normalized difference vegetation index. Oxford University Press.

  • Pommerening, A. (2002). Approaches to quantifying forest structures. Forestry, 75, 305–324.

    Article  Google Scholar 

  • QGIS Development Team (2015). QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org. Accessed 2 February 2015.

  • R Development CoreTeam, R. C. (2015). R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2012). URL: http:// www. R-project. org.

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

    Article  Google Scholar 

  • Rocchini, D. (2007b). Distance decay in spectral space in analysing ecosystem β-diversity. International Journal of Remote Sensing, 28, 2635–2644.

    Article  Google Scholar 

  • Rocchini, D., Ricotta, C., & Chiarucci, A. (2007). Using satellite imagery to assess plant species richness: the role of multispectral systems. Applied Vegetation Science, 10, 325–331.

    Article  Google Scholar 

  • Rocchini, D., Boyd, D. S., Féret, J.-B., Foody, G. M., He, K. S., Lausch. A., Nagendra, H., Wegmann, M., and Pettorelli, N.. (2015). Satellite remote sensing to monitor species diversity: potential and pitfalls. Remote Sensing in Ecology and Conservation.

    Google Scholar 

  • Rocchini, D., Dadalt, L., Delucchi, L., Neteler, M., & Palmer, M. (2014). Disentangling the role of remotely sensed spectral heterogeneity as a proxy for North American plant species richness. Community Ecology, 15, 37–43.

    Article  Google Scholar 

  • Rocchini, D., He, K. S., & Zhang, J. (2009a). Is spectral distance a proxy of beta diversity at different taxonomic ranks? A test using quantile regression. Ecological Informatics, 4, 254–259.

  • Rocchini, D., Nagendra, H., Ghate, R., & Cade, B. S. (2009b). Spectral distance decay. Photogrammetric Engineering & Remote Sensing, 75, 1225–1230.

    Article  Google Scholar 

  • Rocchini, D., Ricotta, C., Chiarucci, A., De Dominicis, V., Cirillo, I., & Maccherini, S. (2009c). Relating spectral and species diversity through rarefaction curves. International Journal of Remote Sensing, 30, 2705–2711.

    Article  Google Scholar 

  • Rocha, A. V., & Shaver, G. R. (2009). Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes. Agricultural and Forest Meteorology, 149, 1560–1563.

    Article  Google Scholar 

  • Rouse Jr., J., Haas, R., Schell, J., & Deering, D. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication, 351, 309.

    Google Scholar 

  • SAGA Development Team, G. (2015). Development Team, 2014. SAGA—System for Automated Geoscientific Analyses/SAGA User Group Association [Electronic resource]. Access mode: http://saga-gis.org.

  • Scaggs, A. K. (2007). New research on forest ecology. Nova Publishers.

  • Schmidtlein, S., & Sassin, J. (2004). Mapping of continuous floristic gradients in grasslands using hyperspectral imagery. Remote Sensing of Environment, 92, 126–138.

    Article  Google Scholar 

  • Shannon, C., & Weaver, W. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423.

    Article  Google Scholar 

  • She, X., Zhang, L., Cen, Y., Wu, T., Huang, C., & Baig, M. H. A. (2015). Comparison of the continuity of vegetation indices derived from Landsat 8 OLI and Landsat 7 ETM+ data among different vegetation types. Remote Sensing, 7, 13485–13506.

    Article  Google Scholar 

  • Simonson, W. D., Allen, H. D., & Coomes, D. A. (2012). Use of an airborne lidar system to model plant species composition and diversity of Mediterranean oak forests. Conservation Biology, 26, 840–850.

    Article  Google Scholar 

  • Solomon, S. (2007). IPCC (2007): Climate Change The Physical Science Basis. Page 01 AGU Fall Meeting Abstracts.

  • Somers, B., G. P. Asner, R. E. Martin, C. B. Anderson, D. E. Knapp, S. J. Wright, and R. Van De Kerchove. 2015. Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy. Remote Sensing of Environment.

    Google Scholar 

  • Tittebrand, A., Spank, U., & Bernhofer, C. (2009). Comparison of satellite-and ground-based NDVI above different land-use types. Theoretical and Applied Climatology, 98, 171–186.

    Article  Google Scholar 

  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127–150.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Viedma, O., Torres, I., Pérez, B., & Moreno, J. M. (2012). Modeling plant species richness using reflectance and texture data derived from QuickBird in a recently burned area of Central Spain. Remote Sensing of Environment, 119, 208–221.

    Article  Google Scholar 

  • Volcani, A., Karnieli, A., & Svoray, T. (2005). The use of remote sensing and GIS for spatio-temporal analysis of the physiological state of a semi-arid forest with respect to drought years. Forest Ecology and Management, 215, 239–250.

    Article  Google Scholar 

  • Vorovencii, I. (2011). Aspects regarding NDVI index calculated for softwood and mixed stands. Bulletin of the Transilvania University of Brasov, Series II. Forestry, Wood Industry, Agricultural Food Engineering, 4(53), 85–90.

  • Wang, R., Gamon, J. A., Montgomery, R. A., Townsend, P. A., Zygielbaum, A. I., Bitan, K., Tilman, D., & Cavender-Bares, J. (2016). Seasonal variation in the NDVI—species richness relationship in a prairie grassland experiment (Cedar Creek). Remote Sensing, 8, 128.

    Article  Google Scholar 

  • Warren, S. D., Alt, M., Olson, K. D., Irl, S. D., Steinbauer, M. J., & Jentsch, A. (2014). The relationship between the spectral diversity of satellite imagery, habitat heterogeneity, and plant species richness. Ecological Informatics, 24, 160–168.

    Article  Google Scholar 

  • Wenting, X., W. Bingfang, T. Yichen, and Z. Yuan. (2004). Mapping plant diversity of broad-leaved forest ecosystem using Landsat TM data. Pages 4598–4600 Geoscience and Remote Sensing Symposium, 2004. IGARSS’04. Proceedings. 2004 I.E. International.. IEEE.

  • Xu, D., & Guo, X. (2014). Compare NDVI extracted from Landsat 8 imagery with that from Landsat 7 imagery. American. Journal of Remote Sensing, 2, 10–14.

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Scientific Research Projects Coordination Unit of 450 Istanbul University (Project Number 25242).

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  1. Department of Forest Engineering, Faculty of Forestry, Istanbul University, 34473 Bahçeköy, Istanbul, Turkey

    Maliheh Arekhi, Osman Yalçın Yılmaz & Yaşar Feyza Akyüz

  2. Ornamental Plants Cultivation Program, Vocational School of Forestry, Faculty of Forestry, Istanbul University, 34473 Bahçeköy, Istanbul, Turkey

    Hatice Yılmaz

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  1. Maliheh Arekhi
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Arekhi, M., Yılmaz, O.Y., Yılmaz, H. et al. Can tree species diversity be assessed with Landsat data in a temperate forest?. Environ Monit Assess 189, 586 (2017). https://doi.org/10.1007/s10661-017-6295-6

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