Subpixel measurement of mangrove canopy closure via spectral mixture analysis

doi:10.1007/s11707-011-0156-3

Front Earth Sci

0, Vol.

Issue () : 130-137 https://doi.org/10.1007/s11707-011-0156-3

RESEARCH ARTICLE

Subpixel measurement of mangrove canopy closure via spectral mixture analysis

Minhe JI^1,2(

), Jing FENG¹

1. The Key Lab of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200062, China; 2. Guangxi-ASEAN Marine Research Center, Guangxi Academy of Science, Nanning 530003, China

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Abstract

Canopy closure can vary spatially within a remotely sensed image pixel, but Boolean logic inherent in traditional classification methods only works at the whole-pixel level. This study attempted to decompose mangrove closure information from spectrally-mixed pixels through spectral mixture analysis (SMA) for coastal wetland management. Endmembers of different surface categories were established through signature selection and training, and memberships of a pixel with respect to the surface categories were determined via a spectral mixture model. A case study involving DigitalGlobe’s Quickbird high-resolution multispectral imagery of Beilun Estuary, China was used to demonstrate this approach. Mangrove canopy closure was first quantified as percent coverage through the model and then further grouped into eight ordinal categories. The model results were verified using Quickbird panchromatic data from the same acquisition. An overall accuracy of 84.4% (Kappa = 0.825) was achieved, indicating good application potential of the approach in coastal resource inventory and ecosystem management.

Keywords spectral mixture analysis (SMA) mangrove canopy closure biomass mixed pixel QuickBird

Corresponding Author(s): JI Minhe,Email:mhji@geo.ecnu.edu.cn

Issue Date: 05 June 2011

Cite this article:

Minhe JI,Jing FENG. Subpixel measurement of mangrove canopy closure via spectral mixture analysis[J]. Front Earth Sci, 0, (): 130-137.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-011-0156-3
https://academic.hep.com.cn/fesci/EN/Y0/V/I/130

Fig.1 The situation of Beilun Estuary (shown on the left) and the peninsula (defined by the small blue frame in the left image) as the study area (shown as a false color composite image on the right)

Tab.1 Eigenvalues of MNF components derived from the Beilun Estuary Quickbird imagery

Fig.2 Eigenimages resulting from the MNF transform of the Quickbird data for Beilun Estuary

Fig.3 N-Dimensional rotating scatterplot showing the PPI of endmembers.

Fig.4 The feature space of first two MNF bands (1-mangrove, 2-water, 3-road, 4-barren)

Fig.5 Fraction images showing abundance (in a 0-100 scale) of mangrove, water, road, and barren of the study area. Pixel brightness is directly proportional to the percent coverage of a surface material within the pixel.

Tab.2 Look-up table for converting percent mangrove of a Quickbird multispectral pixel to the number of pixels identified as being fully mangrove in the Quickbird panchromatic image

Tab.3 Error matrix with the calculated overall accuracy, overall kappa coefficient, and categorical accuracies for the mangrove closure map using 180 sampled validation pixels

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