Impacts of Plot Location Errors on Accuracy of Mapping and Scaling Up Aboveground Forest Carbon Using Sample Plot and Landsat TM Data

Zhang, Maozhen; Lin, Hui; Si-qi, Zeng; Li, Jiping; Shi, Junnan; Wang, Guangxing

doi:10.1109/lgrs.2013.2260719

Cited by 18 publications

(14 citation statements)

References 13 publications

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“…Methods such as correlation coefficient analysis and stepwise regression analysis can be used to determine these variables (Lu 2005). Another group of parametric-based methods is spatial co-simulation algorithms where spatial interpolation of forest biomass/carbon is conducted based on sample plot data and remotely sensed images using conditional simulation such as sequential Gaussian simulation (Wang et al 2009(Wang et al , 2011aZhang et al 2013). These co-simulation algorithms are based on spatial autocorrelation of forest biomass/carbon and its spatial crosscorrelation with spectral variables from remotely sensed images.…”

Section: Parametric-based Algorithmsmentioning

confidence: 99%

“…In addition, Wang et al (2011a) and Zhang et al (2013) investigated the effects of location errors of sample plots on the accuracy of forest biomass/carbon estimates by randomly perturbing the east and north coordinates of sample plots and found that the location errors did not lead to significant bias in population mean estimates. However, the perturbations significantly decreased correlation between forest carbon and Landsat TM spectral variables and changed the pixel level spatial distribution of forest carbon estimates.…”

Section: International Journal Of Digital Earth 21mentioning

confidence: 99%

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A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

Chen

Wang

et al. 2014

International Journal of Digital Earth

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Remote sensing-based methods of aboveground biomass (AGB) estimation in forest ecosystems have gained increased attention, and substantial research has been conducted in the past three decades. This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issuescollection of field-based biomass reference data, extraction and selection of suitable variables from remote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure. Additionally, we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure. Although optical sensor and radar data have been primary sources for AGB estimation, data saturation is an important factor resulting in estimation uncertainty. LIght Detection and Ranging (lidar) can remove data saturation, but limited availability of lidar data prevents its extensive application. This literature survey has indicated the limitations of using single-sensor data for biomass estimation and the importance of integrating multi-sensor/scale remote sensing data to produce accurate estimates over large areas. More research is needed to extract a vertical vegetation structure (e.g. canopy height) from interferometry synthetic aperture radar (InSAR) or optical stereo images to incorporate it into horizontal structures (e.g. canopy cover) in biomass estimation modeling.

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Section: Parametric-based Algorithmsmentioning

confidence: 99%

Section: International Journal Of Digital Earth 21mentioning

confidence: 99%

A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

Chen

Wang

et al. 2014

International Journal of Digital Earth

Self Cite

584

511

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“…This could become more serious as the texture measures from the windows of 3ˆ3 pixels, 5ˆ5 pixels, etc., are employed. Wang and Zhang [78,79] studied the uncertainty due to error of plot geolocalization and mismatch of sample plots with image pixels and found that, as the distance of the mismatch increased, the estimation accuracy of forest AGB or carbon density obviously decreased.…”

Section: Uncertainties Due To Sample Plotsmentioning

confidence: 99%

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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“…The more fragmented land cover maps produce the greater effect on the change detection error owing to positional error [25]. Aggregation-based or object-based change detection methods have been suggested to reduce the impact of the position on the change detection accuracy [26][27][28][29]. Studies of misresgistration issues in change detection analysis and soft classification have therefore provided the impetus for this paper.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Positional Errors on Soft Classification Accuracy Assessment: A Simulation Analysis

Congalton

Pan

2015

Remote Sensing

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Abstract:Validating or accessing the accuracy of soft classification maps has rapidly developed over the past few years. This assessment employs a soft error matrix as generalized from the traditional, hard classification error matrix. However, the impact of positional error on the soft classification is uncertain and whether the well-accepted half-pixel registration accuracy is suitable for the soft classification accuracy assessment is unknown. In this paper, a simulation analysis was conducted to examine the influence of positional error on the overall accuracy (OA) and kappa in soft classification accuracy assessment under different landscape conditions (i.e., spatial characteristics and spatial resolutions). Results showed that with positional error ranging from 0 to 3 soft pixels, the OA-error varied from 0 to 44.6 percent while the kappa-error varied from 0 to 93.7 percent. Landscape conditions with smaller mean patch size (MPS) and greater fragmentation produced greater positional error impact on the accuracy measures at spatial resolutions of 1 and 2 unit distances. However, this trend did not hold for spatial resolutions of 5 and 10 unit distances. A half of a pixel was not sufficient to keep the overall accuracy error and kappa error under 10 percent. The results indicate that for soft classification accuracy assessment the requirement for registration accuracy is higher and depends greatly on the landscape characteristics. There is a great need to consider positional error for validating soft classification maps of different spatial resolutions. OPEN ACCESSRemote Sens. 2015, 7 580

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Impacts of Plot Location Errors on Accuracy of Mapping and Scaling Up Aboveground Forest Carbon Using Sample Plot and Landsat TM Data

Cited by 18 publications

References 13 publications

A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

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

The Impact of Positional Errors on Soft Classification Accuracy Assessment: A Simulation Analysis

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