Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series

Zhu, Xiaolin; Liu, Desheng

doi:10.1016/j.isprsjprs.2014.08.014

Cited by 298 publications

(194 citation statements)

References 44 publications

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“…The difficulty in identifying the best textural images was encountered in this research as different textural images were used for specific vegetation types, depending on the combination of texture measures, window size, and spectral bands. Another potential approach to reducing the data saturation problem is to use different seasonal Landsat images or time series [20,34,48]. This is important because vegetation types such as pine forest, broadleaf forest, and bamboo forest have their own phenology, thus incorporation of different features inherent in vegetation phenology may be beneficial to AGB estimation.…”

Section: Data Saturation Problem In Landsat Imagery and Potential Solmentioning

confidence: 99%

“…Many factors influence the data saturation of Landsat imagery [5,6,[44][45][46][47][48]. The limitation of remote sensing data themselves in spectral, spatial, and radiometric resolutions may result in different saturation values of AGB.…”

Section: Introductionmentioning

confidence: 99%

“…However, high correlation between vegetation indices or spectral bands makes these variables less important in AGB modeling [6,31]. Time series of Landsat imagery is another method to increase AGB estimation accuracy and reduce saturation effects compared to use of a single NDVI [20,34,48].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

et al. 2016

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Abstract:The data saturation problem in Landsat imagery is well recognized and is regarded as an important factor resulting in inaccurate forest aboveground biomass (AGB) estimation. However, no study has examined the saturation values for different vegetation types such as coniferous and broadleaf forests. The objective of this study is to estimate the saturation values in Landsat imagery for different vegetation types in a subtropical region and to explore approaches to improving forest AGB estimation. Landsat Thematic Mapper imagery, digital elevation model data, and field measurements in Zhejiang province of Eastern China were used. Correlation analysis and scatterplots were first used to examine specific spectral bands and their relationships with AGB. A spherical model was then used to quantitatively estimate the saturation value of AGB for each vegetation type. A stratification of vegetation types and/or slope aspects was used to determine the potential to improve AGB estimation performance by developing a specific AGB estimation model for each category. Stepwise regression analysis based on Landsat spectral signatures and textures using grey-level co-occurrence matrix (GLCM) was used to develop AGB estimation models for different scenarios: non-stratification, stratification based on either vegetation types, slope aspects, or the combination of vegetation types and slope aspects. The results indicate that pine forest and mixed forest have the highest AGB saturation values (159 and 152 Mg/ha, respectively), Chinese fir and broadleaf forest have lower saturation values (143 and 123 Mg/ha, respectively), and bamboo forest and shrub have the lowest saturation values (75 and 55 Mg/ha, respectively). The stratification based on either vegetation types or slope aspects provided smaller root mean squared errors (RMSEs) than non-stratification. The AGB estimation models based on stratification of both vegetation types and slope aspects provided the most accurate estimation with the smallest RMSE of 24.5 Mg/ha. Relatively low AGB (e.g., less than 40 Mg/ha) sites resulted in overestimation and higher AGB (e.g., greater than 140 Mg/ha) sites resulted in underestimation. The smallest RMSE was obtained when AGB was 80-120 Mg/ha. This research indicates the importance of stratification in mitigating the data saturation problem, thus improving AGB estimation.

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Section: Data Saturation Problem In Landsat Imagery and Potential Solmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

et al. 2016

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“…Considering the advantages and limitations of different remote sensing images, the medium-resolution (pixel size, 30 m) Landsat series is one of the most widely used for estimating dasometric variables (Agarwal et al 2014, Pflugmacher et al 2014, Dube & Mutanga 2015, Zhu & Liu 2015. The advantages of using the Landsat series are that numerous historical spatiotemporal archives are available and that the sensor is cheaper and more accessible than high resolution sensors, particularly for analysis of large areas (Wu et al 2016).…”

Section: Iforest -Biogeosciences and Forestrymentioning

confidence: 99%

Modelling dasometric attributes of mixed and uneven-aged forests using Landsat-8 OLI spectral data in the Sierra Madre Occidental, Mexico

López-Sánchez

García-Ramírez

Resl

et al. 2017

iForest

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“…Shang et al [47] estimated the AGC of Moso bamboo forests in combination with Landsat and MODIS data, and the estimation accuracy R was 0.70. Zhu et al [79] estimated forest biomass using time series Landsat data and the model accuracy was 0.69. Sandra Eckert [37] used WorldView-2 data estimated forest AGB and model accuracy R is up to 0.93.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China

Han

et al. 2018

Remote Sensing

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China is one of the countries with the most abundant bamboo forest resources in the world, and Zhejiang province is among the top-3 Chinese provinces with richest bamboo forests. For rational bamboo forests management, it is of great significance to study the spatiotemporal dynamic changes of Aboveground Carbon (AGC) stocks of bamboo forest in Zhejiang. In this study, remote sensing variables, such as spectral, vegetation indices and texture features of bamboo forest in Zhejiang, were extracted from 32 Landsat TM and OLI images got from four different years (2000, 2004, 2008 and 2014). These variables were subsequently selected with stepwise regression method to build an estimation model of AGC of the bamboo forests. The results showed that (1) the accuracy of bamboo forest remote sensing information extracted from the four different years was high with a classification accuracy of >76.26% and an accuracy of users of >91.62%. The classification area of bamboo forest was highly consistent with the area from forest resource inventory, and the area accuracy was over 96.50%; (2) the estimation model performed well in predicting the AGC in Zhejiang for different years. The correlation coefficient for estimated and measured AGC was between 63% and 72% with low root mean square error; (3) the derived AGC of the bamboo forests in Zhejiang province increased gradually from 2000 to 2014, with the AGC density of 6.75 Mg·ha −1 , 10.95 Mg·ha −1 , 15.25 Mg·ha −1 and 19.07 Mg·ha −1 respectively, and the average annual growth of 0.88 Mg·ha −1 . The spatiotemporal evolution of bamboo forest AGC in Zhejiang province had a close relationship with the gradual expansion of bamboo forest in the province and the differentiation of management levels in different regions.

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Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series

Cited by 298 publications

References 44 publications

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

Modelling dasometric attributes of mixed and uneven-aged forests using Landsat-8 OLI spectral data in the Sierra Madre Occidental, Mexico

Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China

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