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Integration of high-resolution optical and SAR satellite remote sensing datasets for aboveground biomass estimation in subtropical pine forest, Pakistan

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Abstract

In this study, we investigate stand-alone and combined Pleiades high-resolution passive optical and ALOS PALSAR active Synthetic Aperture Radar (SAR) satellite imagery for aboveground biomass (AGB) estimation in subtropical mountainous Chir Pine (Pinus roxburghii) forest in Murree Forest Division, Punjab, Pakistan. Spectral vegetation indices (NDVI, SAVI, etc.) and sigma nought HV-polarization backscatter dB values are derived from processing optical and SAR datasets, respectively, and modeled against field-measured AGB values through various regression models (linear, nonlinear, multi-linear). For combination of multiple spectral indices, NDVI, TNDVI, and MSAVI2 performed the best with model R2/RMSE values of 0.86/47.3 tons/ha. AGB modeling with SAR sigma nought dB values gives low model R2 value of 0.39. The multi-linear combination of SAR sigma nought dB values with spectral indices exhibits more variability as compared with the combined spectral indices model. The Leave-One-Out-Cross-Validation (LOOCV) results follow closely the behavior of the model statistics. SAR data reaches AGB saturation at around 120–140 tons/ha, with the region of high sensitivity around 50–130 tons/ha; the SAR-derived AGB results show clear underestimation at higher AGB values. The models involving only spectral indices underestimate AGB at low values (< 60 tons/ha). This study presents biomass estimation maps of the Chir Pine forest in the study area and also the suitability of optical and SAR satellite imagery for estimating various biomass ranges. The results of this work can be utilized towards environmental monitoring and policy-level applications, including forest ecosystem management, environmental impact assessment, and performance-based REDD+ payment distribution.

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Acknowledgments

This work is part of the PhD research of the first author, and the supervisors and research collaborators are included as co-authors. The authors thank Divisional Forest Officer (DFO) Murree for his support in conducting this research. Range Forest Officer and Foresters/Block Officers of the Development Working Plan Circle are thanked for assistance in field data collection.

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Authors and Affiliations

  1. College of Earth & Environmental Sciences, University of the Punjab, Quaid-e-Azam Campus, Lahore, Punjab, 54590, Pakistan

    Aqeela M. Akhtar & Sajid Rashid Ahmad

  2. Development Working Plan Circle, Punjab Forest Department, 108 Ravi Road, Lahore, Punjab, Pakistan

    Aqeela M. Akhtar

  3. Geospatial Research & Education Lab (GREL), Department of Space Science, Institute of Space Technology, Islamabad, 44000, Pakistan

    Waqas A. Qazi & Hammad Gilani

  4. Department of Space Science, University of the Punjab, Quaid-e-Azam Campus, Lahore, Punjab, 54590, Pakistan

    Syed Amer Mahmood

  5. Green Pakistan Program, Punjab Forest Department, Lahore, Punjab, Pakistan

    Ansir Rasool

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  1. Aqeela M. Akhtar
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Correspondence to Hammad Gilani.

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Akhtar, A.M., Qazi, W.A., Ahmad, S.R. et al. Integration of high-resolution optical and SAR satellite remote sensing datasets for aboveground biomass estimation in subtropical pine forest, Pakistan. Environ Monit Assess 192, 584 (2020). https://doi.org/10.1007/s10661-020-08546-1

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