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Remote Sens. 2014, 6(9), 9086-9103; doi:10.3390/rs6099086

Multi-Level Spatial Analysis for Change Detection of Urban Vegetation at Individual Tree Scale

1
Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
2
Shanghai Botanical Garden, Shanghai 200231, China
*
Author to whom correspondence should be addressed.
Received: 27 May 2014 / Revised: 28 August 2014 / Accepted: 10 September 2014 / Published: 23 September 2014
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Abstract

Spurious change is a common problem in urban vegetation change detection by using multi-temporal remote sensing images of high resolution. This usually results from the false-absent and false-present vegetation patches in an obscured and/or shaded scene. The presented approach focuses on object-based change detection with joint use of spatial and spectral information, referring to it as multi-level spatial analyses. The analyses are conducted in three phases: (1) The pixel-level spatial analysis is performed by adding the density dimension into a multi-feature space for classification to indicate the spatial dependency between pixels; (2) The member-level spatial analysis is conducted by the self-adaptive morphology to readjust the incorrectly classified members according to the spatial dependency between members; (3) The object-level spatial analysis is reached by the self-adaptive morphology involved with the additional rule of sharing boundaries. Spatial analysis at this level will help detect spurious change objects according to the spatial dependency between objects. It is revealed that the error from the automatically extracted vegetation objects with the pixel- and member-level spatial analyses is no more than 2.56%, compared with 12.15% without spatial analysis. Moreover, the error from the automatically detected spurious changes with the object-level spatial analysis is no higher than 3.26% out of all the dynamic vegetation objects, meaning that the fully automatic detection of vegetation change at a joint maximum error of 5.82% can be guaranteed. View Full-Text
Keywords: downtown area; vegetation cover; change detection; spatial dependency downtown area; vegetation cover; change detection; spatial dependency
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Zhou, J.; Yu, B.; Qin, J. Multi-Level Spatial Analysis for Change Detection of Urban Vegetation at Individual Tree Scale. Remote Sens. 2014, 6, 9086-9103.

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