Estimation of the stand ages of tropical secondary forests after shifting cultivation based on the combination of WorldView-2 and time-series Landsat images

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• 作者：Shogoro Fujiki^a ; ^{fujiki5636@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Kei-ichi Okada^a ; ^b ; Shogo Nishio^a ; Kanehiro Kitayama^a
• 关键词：Object-based image analysis ; Change detection analysis ; Multi-resolution analysis ; Borneo ; Montane tropical secondary forest ; Matrix
• 刊名：ISPRS Journal of Photogrammetry and Remote Sensing
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：119
• 期：Complete
• 页码：280-293
• 全文大小：4784 K

文摘

We developed a new method to estimate stand ages of secondary vegetation in the Bornean montane zone, where local people conduct traditional shifting cultivation and protected areas are surrounded by patches of recovering secondary vegetation of various ages. Identifying stand ages at the landscape level is critical to improve conservation policies. We combined a high-resolution satellite image (WorldView-2) with time-series Landsat images. We extracted stand ages (the time elapsed since the most recent slash and burn) from a change-detection analysis with Landsat time-series images and superimposed the derived stand ages on the segments classified by object-based image analysis using WorldView-2. We regarded stand ages as a response variable, and object-based metrics as independent variables, to develop regression models that explain stand ages. Subsequently, we classified the vegetation of the target area into six age units and one rubber plantation unit (1–3 yr, 3–5 yr, 5–7 yr, 7–30 yr, 30–50 yr, >50 yr and ‘rubber plantation’) using regression models and linear discriminant analyses. Validation demonstrated an accuracy of 84.3%. Our approach is particularly effective in classifying highly dynamic pioneer vegetation younger than 7 years into 2-yr intervals, suggesting that rapid changes in vegetation canopies can be detected with high accuracy. The combination of a spectral time-series analysis and object-based metrics based on high-resolution imagery enabled the classification of dynamic vegetation under intensive shifting cultivation and yielded an informative land cover map based on stand ages.