Remote sensing-based mapping of senescent leaf C:N ratio in the sundarbans reserved forest using machine learning techniques

Md Mizanur Rahman, Xunh Zhang, Imran Ahmed, Zaheer Iqbal, Mojtaba Zeraatpisheh, Mamoru Kanzaki, Ming Xu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Carbon to nitrogen ratio (C:N) of senescent leaf is a crucial functional trait and indicator of litter quality that affects belowground carbon and nitrogen cycles, especially soil decomposition. Although mapping the C:N ratio of fresh mature canopies has been attempted, few studies have attempted to map the C:N ratio of senescent leaves, particularly in mangroves. In this study, four machine learning models (Stochastic Gradient Boosting, SGB; Random Forest, RF; Support Vector Machine, SVM; and Partial Least Square Regression, PLSR) were compared for testing the predictability of using the LandsatTM5 (LTM5) and Landsat 8 to map spatial and temporal distribution of C:N ratio of senescent leaves in Sundarbans Reserved Forest (SRF), Bangladesh. Surface reflectance of bands, texture metrics of bands and vegetation indices of LTM5 and Landsat 8 yearly composite images were extracted using Google Earth Engine for 2009-2010 and 2019. We found SGB, RF and SVM were significant different from PLSR based on MAE, RMSE, and R2 (p < 0.05). Our results indicate that remote sensing data, such as Landsat TM data, can be used to map the C:N ratio of senescent leaves in mangroves with reasonable accuracy. We also found that the mangroves had a high spatial variation of C:N ratio and the C:N ratio map developed in the current study can be used for improving the biogeochemical and ecosystem models in the mangroves.

Original languageEnglish (US)
Article number1375
JournalRemote Sensing
Volume12
Issue number9
DOIs
StatePublished - May 1 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)

Keywords

  • Functional trait
  • Litter quality
  • Machine learning
  • Mangrove
  • Remote sensing
  • Spatial modeling

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