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Cited by 105Remote Sensing and LiDAR ApplicationsSynthetic Aperture Radar (SAR) Applications and TechniquesRemote Sensing in Agriculture

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Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China

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Shao, Zhenfeng, Zhang, Linjing·2016·Sensors
DOI 10.3390/s16060834PMC PMC4934260
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Abstract

Estimation of forest aboveground biomass is critical for regional carbon policies and sustainable forest management. Passive optical remote sensing and active microwave remote sensing both play an important role in the monitoring of forest biomass. However, optical spectral reflectance is saturated in relatively dense vegetation areas, and microwave backscattering is significantly influenced by the underlying soil when the vegetation coverage is low. Both of these conditions decrease the estimation accuracy of forest biomass. A new optical and microwave integrated vegetation index (VI) was proposed based on observations from both field experiments and satellite (Landsat 8 Operational Land Imager (OLI) and RADARSAT-2) data. According to the difference in interaction between the multispectral reflectance and microwave backscattering signatures with biomass, the combined VI (COVI) was designed using the weighted optical optimized soil-adjusted vegetation index (OSAVI) and microwave horizontally transmitted and vertically received signal (HV) to overcome the disadvantages of both data types. The performance of the COVI was evaluated by comparison with those of the sole optical data, Synthetic Aperture Radar (SAR) data, and the simple combination of independent optical and SAR variables. The most accurate performance was obtained by the models based on the COVI and optical and microwave optimal variables excluding OSAVI and HV, in combination with a random forest algorithm and the largest number of reference samples. The results also revealed that the predictive accuracy depended highly on the statistical method and the number of sample units. The validation indicated that this integrated method of determining the new VI is a good synergistic way to combine both optical and microwave information for the accurate estimation of forest biomass.

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Figure 1 The study area. (a) The location of Inner Mongolia Autonomous Region in China; (b) The administrative boundary of Genhe in Inner Mongolia Autonomous Region, and the location of the study area in Genhe; (c) Landsat 8 OLI image (band 5, 4, 3 false color combination;8% cloud cover) acquired on 25August 2013 of the study area.
Figure 1 The study area. (a) The location of Inner Mongolia Autonomous Region in China; (b) The administrative boundary of Genhe in Inner Mongolia Autonomous Region, and the location of the study area in Genhe; (c) Landsat 8 OLI image (band 5, 4, 3 false color combination;8% cloud cover) acquired on 25August 2013 of the study area.CC BY · PubMed Central
Figure 2 Flow chart of the stepwise predictor exclusion.
Figure 2 Flow chart of the stepwise predictor exclusion.CC BY · PubMed Central
Figure 3 The beanplots illustrate the distribution of the mean RMSE (a); and R2 (b) values from the 100 bootstrapped models obtained by the 5-fold-cross validation for each prediction method and sample size with optical and microwave integrated dataset in Experiment 1 (iii). Furthermore, the median values of the corresponding accuracy measures for each of the four sample size classes are given with the colored horizontal stripes (Class 1 to Class 4 from left to right in colors red, blue, yellow and green).
Figure 3 The beanplots illustrate the distribution of the mean RMSE (a); and R2 (b) values from the 100 bootstrapped models obtained by the 5-fold-cross validation for each prediction method and sample size with optical and microwave integrated dataset in Experiment 1 (iii). Furthermore, the median values of the corresponding accuracy measures for each of the four sample size classes are given with the colored horizontal stripes (Class 1 to Class 4 from left to right in colors red, blue, yellow and green).CC BY · PubMed Central
Figure 4 The beanplots illustrate the distribution of the mean RMSE (a); and R2 (b) values from the 100 bootstrapped models based on the combined optical and microwave optimal variables in Experiment 2 (iii). Explanations follow those from Figure 3.
Figure 4 The beanplots illustrate the distribution of the mean RMSE (a); and R2 (b) values from the 100 bootstrapped models based on the combined optical and microwave optimal variables in Experiment 2 (iii). Explanations follow those from Figure 3.CC BY · PubMed Central
Figure 5 Sensitivity variation (a); and weight variation (b) of OSAVI and HV with increasing biomass.
Figure 5 Sensitivity variation (a); and weight variation (b) of OSAVI and HV with increasing biomass.CC BY · PubMed Central
Figure 6 Wall-to-wall map of mean biomass estimates as obtained from the 100 bootstrapped model runs, using the COVI and residual optical and microwave optimal variables, random forest and largest sample size (models in Experiment 3).
Figure 6 Wall-to-wall map of mean biomass estimates as obtained from the 100 bootstrapped model runs, using the COVI and residual optical and microwave optimal variables, random forest and largest sample size (models in Experiment 3).CC BY · PubMed Central

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Metadata

Authors
Shao, Z., & Zhang, L.
Affiliations
Wuhan University (CN)
Corresponding author
Zhang, Linjing
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2016-06-07
Volume
16
Issue
6
Pages
834
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s16060834
PMC
PMC4934260
OpenAlex
W2414762761
Cited by
105
References
62
Citations by year
2026: 7 · 2025: 9 · 2024: 12 · 2023: 11 · 2022: 8 · 2021: 11 · 2020: 16 · 2019: 17 · 2018: 12 · 2017: 2
FWCI
6.45
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National 973 Basic Research and Development Program project (No. 2012CB719904)
  • Program for New Century Excellent Talents in University (No. NCET-12-0426)
  • Surveying and mapping fund project of National surveying and mapping Geographic Information Bureau ([2015]No.19)
Topics
  • Life Sciences › Agricultural and Biological Sciences › Forestry › Remote Sensing and LiDAR Applications
  • Physical Sciences › Earth and Planetary Sciences › Geophysics › Synthetic Aperture Radar (SAR) Applications and Techniques
  • Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
Keywords
forest aboveground biomass, SAR-optical data fusion, soil-adjusted vegetation index, microwave backscatter, Landsat 8 OLI, RADARSAT-2, random forest, biomass estimation accuracy

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Shao, Z., & Zhang, L. (2016). Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China. Sensors. https://doi.org/10.3390/s16060834