Quantitative descriptors for identifying plant species of urban landscape vegetation

Abstract

This paper discusses the ideas and methods of designing effective descriptors for identifying plant species of urban landscape vegetation. Fourteen of such descriptors induced from image spectrum, texture, and shape properties were designed. These descriptors were intended to meet such requirements as possessing a true physical or geometric implication relating to ecological significance, having a relatively steady segmentation threshold and being less sensitive to image types or environmental conditions during image acquisition. This study used decision trees to combine four selected descriptors for plant species identification, and the experiment was able to reach an error rate of 5.8% compared 25.9% by merely using the conventional pixel brightness values in plant species identification.

Keywords

urban landscape vegetation / plant species / machine discerning / descriptor

References

[1] Baret F, Houlès V, Guérif M (2007). Quantification of plant stress using remote sensing observations and crop models: the case of nitrogen management. J Exp Bot, 58(4): 869–880

[2] Buyantuyev A, Wu J (2009). Urbanization alters spatiotemporal patterns of ecosystem primary production: A case study of the Phoenix metropolitan region, USA. J Arid Environ, 73(4-5): 512–520

[3] Gao X, Huete A R, Ni W, Miura T (2000). Optical-biophysical relationships of vegetation spectra without background contamination. Remote Sens Environ, 74(3): 609–620

[4] Gong P, Pu R L, Yu B (1998). Recognition and analysis of conifer species in various seasons and time with hyperspectral data. Journal of Remote Sensing, 2(3): 211–217 (in Chinese with English abstract)

[5] Huete A R (1988). A soil-adjusted vegetation index (SAVI). Remote Sens Environ, 25(3): 295–309

[6] Leckie D G, Gougeon F A, Walsworth N, Paradine D (2003). Stand delineation and composition estimation using semi-automated individual tree crown analysis. Remote Sens Environ, 85(3): 355–369

[7] Liu C F, Zhao S, Li L, Li X M, He X Y, Chen W (2008). Difference analysis of carbon fixation and pollution removal of urban forest in Shenyang. Journal of Northwest Forestry University, 23(4): 56–61 (in Chinese with English abstract)

[8] Martin M E, Newman S D, Aber J D, Congalton R G (1998). Determining forest species composition using high spectral resolution remote sensing data. Remote Sens Environ, 65(3): 249–254

[9] Santos J R, Freitas C C, Araujo L S, Dutra L V, Mura J C, Gama F F, Soler L S, Sant'Anna S J S (2003). Airborne P-band SAR applied to the aboveground biomass studies in the Brazilian tropical rainforest. Remote Sens Environ, 87(4): 482–493

[10] Tan B X, Li Z Y, Chen E X, Pang Y (2005). Forest type recognition with hyperspectral and multi-spectral remote sensing data. Journal of Northeast Forestry University, 33(S): 61–64 (in Chinese with English abstract)

[11] Zhou J H (2001). Theory and practice on database of three-dimensional vegetable quantity. Acta Geogr Sin, 56(1): 14–23 (in Chinese with English abstract)

[12] Zhou J H, Sun T Z (1995). Measuring model of three-dimensional green biomass through remote sensing and estimating method of environmental benefits of urban vegetation. Journal of Remote Sensing, 10(3): 162–174

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg