Land Cover Dynamics Monitoring With Landsat Data in Kunming, China: A Cost-Effective Sampling and Modelling Scheme Using Google Earth Imagery and Random Forests

Document Type

Article

Journal/Book Title/Conference

Geocarto International

Volume

30

Issue

2

Publisher

Taylor & Francis

Publication Date

3-31-2014

First Page

186

Last Page

201

Abstract

Changes in forest composition impact ecological services, and are considered important factors driving global climate change. A hybrid sampling method along with a modelling approach to map current and past land cover in Kunming, China is reported. MODIS land cover (2001–2011) data-sets were used to detect pixels with no apparent change. Around 3000 ‘no change points’ were systematically selected and sampled using Google Earth’s high-resolution imagery. Thirty-five per cent of these points were verified and used for training and validation. We used Random forests to classify multi-temporal Landsat imagery. Results show that forest cover has had a net decrease of 14385 ha (1.3% of forest area), which was primary converted to shrublands (11%), urban and barren land (2.7%) and agriculture (2.5%). Our validation indicates an overall accuracy (Kappa) of 82%. Our methodology can be used to consistently map the dynamics of land cover change in similar areas with minimum costs.

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