The Laboratory for Remote Sensing and Environmental Change (LRSEC)
The Laboratory for Remote Sensing and Environmental Change (LRSEC)
An Interdisciplinary Research Group Using Remote Sensing and Geospatial Science to Understand Landscape Change
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    • Dr. Gang Chen
    • Chenyu Xing
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Latest News in LRSEC

  • Welcome Austin Barbee to join LRSEC October 3, 2024
  • Welcome Justin Erlick to join the lab September 16, 2024
  • Welcome Rachel to join the lab March 21, 2024

Contact Lab Director

Dr. Gang Chen
Address: McEniry 446, 9201 University City Blvd, Charlotte, NC 28223, USA (35°18'26"N 80°43'48"W)
Email: Gang.Chen 'at' charlotte.edu

Links

  • Department of Earth, Environmental and Geographical Sciences
  • College of Humanities & Earth and Social Sciences
  • University of North Carolina at Charlotte

New paper accepted by ISPRS Journal of Photogrammetry and Remote Sensing

July 10, 2018 by Gang Chen
Categories: Updates

Title: Stand age estimation of rubber (Hevea brasiliensis) plantations using an integrated pixel- and object-based tree growth model and annual Landsat time series

Abstract: 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.


Fig. 1. Image-objects (yellow boundaries) capturing three stages of rubber tree growth, young (2005), middle-age (2010), and mature (2015). Three green grid cells represent pixels covering the same part of a rubber tree stand over years.

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