Stand Age Estimation of Rubber (Hevea brasiliensis) Plantations Using an Integrated Pixel- and Object-based Tree Growth Model and Annual Landsat Time Series
Stand age estimation, Rubber plantation, Geographic object-based image analysis, Landsat time series, Tree growth model
Digital Object Identifier (DOI)
Rubber (Hevea brasiliensis) plantations are a rapidly increasing source of land cover change in mainland Southeast Asia. Stand age of rubber plantations obtained at fine scales provides essential baseline data, informing the pace of industrial and smallholder agricultural activities in response to the changing global rubber markets, and local political and socioeconomic dynamics. In this study, we developed an integrated pixel- and object-based tree growth model using Landsat annual time series to estimate the age of rubber plantations in a 21,115 km2 tri-border region along the junction of China, Myanmar and Laos. We produced a rubber stand age map at 30 m resolution, with an accuracy of 87.00% for identifying rubber plantations and an average error of 1.53 years in age estimation. The integration of pixel- and object-based image analysis showed superior performance in building NDVI yearly time series that reduced spectral noises from background soil and vegetation in open-canopy, young rubber stands. The model parameters remained relatively stable during model sensitivity analysis, resulting in accurate age estimation robust to outliers. Compared to the typically weak statistical relationship between single-date spectral signatures and rubber tree age, Landsat image time series analysis coupled with tree growth modeling presents a viable alternative for fine-scale age estimation of rubber plantations.
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Citation / Publisher Attribution
ISPRS Journal of Photogrammetry and Remote Sensing, v. 144, p. 94-104
Scholar Commons Citation
Chen, Gang; Thill, Jean-Claude; Anantsuksomsri, Sutee; Tontisirin, Nij; and Tao, Ran, "Stand Age Estimation of Rubber (Hevea brasiliensis) Plantations Using an Integrated Pixel- and Object-based Tree Growth Model and Annual Landsat Time Series" (2018). School of Geosciences Faculty and Staff Publications. 1278.