Estimation of carbon sequestration by using vegetation indices

Chang, Kuan-Tsung; Liang, Long-Shin; Yiu, F. G.; Wang, Ruei-Yuan

doi:10.1109/igarss.2012.6352718

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…BPNN is one of the most popular and proven neural networks [ 52 , 53 ]. This machine-learning algorithm has great potential for estimating vegetation parameters due to its ability to find and learn complex linear and nonlinear relationships between radiometric data and vegetation parameters [ 52 , 54 ]. Zheng et al [ 53 ] found that BPNN was better at predicting forest growing stock volume compared to LMSTEP based on Landsat Thematic Mapper (TM) and field data.…”

Section: Methodsmentioning

confidence: 99%

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

Shao

Zhang

2016

Sensors

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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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Section: Methodsmentioning

confidence: 99%

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

Shao

Zhang

2016

Sensors

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“…The evaluation method for C and CO 2 sinks per hectare usually uses the principle of IPCC. Initially, the mean specific weight of timber volume is converted into organism quantity of timber volume, and next converted into the coefficient of C content to evaluate C or CO 2 sinks, shown as formula (9) (Wang et al 2011;Chang et al 2012):…”

Section: Rs-kbdss Frameworkmentioning

confidence: 99%

A decision support system for Taiwan’s forest resource management using Remote Sensing Big Data

Wang

Lin

Chu

et al. 2021

Enterprise Information Systems

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This study aims to incorporate the application of RS technology with management strategies and proposes the Remote Sensing Knowledge-Based Decision Support System (RS-KBDSS) framework. It is not only focusing on considering RS technology for discussing the accuracy of estimation but also conducting strategic thinking that is needed for keeping the inventory and monitoring task running by obtaining knowledge and decision support. This study offers more flexible and accurate estimation models for different resolution images and different kinds of models. The results obtained by the study is satisfied with the practical application.

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Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass

Shao

Zhang

Wang

2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Estimation of carbon sequestration by using vegetation indices

Cited by 3 publications

References 3 publications

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

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

A decision support system for Taiwan’s forest resource management using Remote Sensing Big Data

Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass

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