Quantitative descriptors for identifying plant species of urban landscape vegetation

doi:10.1007/s11707-010-0128-z

Front Earth Sci Chin

2010, Vol. 4

Issue (4) : 457-462 https://doi.org/10.1007/s11707-010-0128-z

RESEARCH ARTICLE

Quantitative descriptors for identifying plant species of urban landscape vegetation

Jianhua ZHOU¹(

), Yifan ZHOU²

1. GIScience Key Lab., Chinese Ministry of Education, East China Normal University, Shanghai 200062, China; 2. School of Earth Sciences, Stanford University, CA 94305-2220, USA

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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

Corresponding Author(s): ZHOU Jianhua,Email:jhzhou@geo.ecnu.edu.cn

Issue Date: 05 December 2010

Cite this article:

Jianhua ZHOU,Yifan ZHOU. Quantitative descriptors for identifying plant species of urban landscape vegetation[J]. Front Earth Sci Chin, 2010, 4(4): 457-462.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-010-0128-z
https://academic.hep.com.cn/fesci/EN/Y2010/V4/I4/457

Tab.1 New descriptors of identifying plant species with remotely sensed images

Fig.1 Comparison of the capabilities of segmenting three plant species by , , , and

Fig.2 An instance of clustering with a combination of four descriptors. (a) Distribution of ; (b) grade chart of ; (c) output of clustering

Fig.3 An instance of extracting supplemental vegetation pixels in the low red saturation. (a) Original image; (b) = >0.18; (c) ∪; (d)

Fig.4 Comparison of identification accuracy with two different property combinations.(a)Original image; (b)combination of , , and ; (c) combination of spectrographic brightness. Z—camphor; S—metasequoia; G—magnolia; X—cedar; N— privet; 0—cell having<10% vegetation cover

Tab.2 Comparison of identification accuracy

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 doi: 10.1093/jxb/erl231
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 doi: 10.1016/j.jaridenv.2008.12.015
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 doi: 10.1016/S0034-4257(00)00150-4
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 doi: 10.1016/0034-4257(88)90106-X
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 doi: 10.1016/S0034-4257(03)00013-0
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 doi: 10.1016/S0034-4257(98)00035-2
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 doi: 10.1016/j.rse.2002.12.001
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

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