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A two-scale approach for estimating forest aboveground biomass with optical remote sensing images in a subtropical forest of Nepal

  • Upama A. Koju
  • Jiahua Zhang
  • Shashish Maharjan
  • Sha Zhang
  • Yun Bai
  • Dinesh B. I. P. Vijayakumar
  • Fengmei Yao
Original Paper
  • 11 Downloads

Abstract

Forests account for 80% of the total carbon exchange between the atmosphere and terrestrial ecosystems. Thus, to better manage our responses to global warming, it is important to monitor and assess forest aboveground carbon and forest aboveground biomass (FAGB). Different levels of detail are needed to estimate FAGB at local, regional and national scales. Multi-scale remote sensing analysis from high, medium and coarse spatial resolution data, along with field sampling, is one approach often used. However, the methods developed are still time consuming, expensive, and inconvenient for systematic monitoring, especially for developing countries, as they require vast numbers of field samples for upscaling. Here, we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites. The study was conducted in the Chitwan district of Nepal using GeoEye-1 (0.5 m), Landsat (30 m) and Google Earth very high resolution (GEVHR) Quickbird (0.65 m) images. For the local scale (Kayerkhola watershed), tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images. An overall accuracy of 83% was obtained in the delineation of tree canopy cover (TCC) per plot. A TCC vs. FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots. A coefficient of determination (R2) of 0.76 was obtained in the modelling, and a value of 0.83 was obtained in the validation of the model. To upscale FAGB to the entire district, open source GEVHR images were used as virtual field plots. We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model. Using the multivariate adaptive regression splines machine learning algorithm, we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices. The model was then used to extrapolate FAGB to the entire district. This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution (30 m) and accuracy (R2 = 0.76 and 0.7) with minimal error (RMSE = 64 and 38 tons ha−1) at local and regional scales. This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time. The method is especially applicable for developing countries that have low budgets for carbon estimations, and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation (REDD +) monitoring reporting and verification processes.

Keywords

Forest aboveground biomass Google Earth imagery Multi-scale remote sensing Virtual plot Optical imagery 

Notes

Acknowledgements

The secondary data, as well as field inventory data for the Kayerkhola watershed in the study area, were obtained from the International Centre for Integrated Mountain Development (ICIMOD). We would like to express our humble gratitude for this great contribution. The authors would also like to acknowledge funding from the CAS-TWAS Fellowship Program for the research.

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Copyright information

© Northeast Forestry University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Upama A. Koju
    • 1
    • 2
  • Jiahua Zhang
    • 1
  • Shashish Maharjan
    • 3
  • Sha Zhang
    • 1
    • 2
  • Yun Bai
    • 1
    • 2
  • Dinesh B. I. P. Vijayakumar
    • 4
  • Fengmei Yao
    • 2
  1. 1.Key Laboratory of Digital Earth SciencesInstitute of Remote Sensing and Digital Earth, CASBeijingPeople’s Republic of China
  2. 2.University of Chinese Academy of SciencesBeijingPeople’s Republic of China
  3. 3.International Maize and Wheat Improvement CenterKathmanduNepal
  4. 4.The Laboratory of Forest InventoryThe National Institute of Geographic and Forest InformationNancyFrance

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