Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass

Li, Le; Guo, Qinghua; Tao, Shengli; Kelly, Maggi; Xu, Guangcai

doi:10.1016/j.isprsjprs.2015.02.007

Cited by 51 publications

(39 citation statements)

References 66 publications

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“…The difference between the plot size and the resolution of prediction variables may be another source of error in the final result. Airborne LiDAR has been recognized as an important method to extrapolate plot biomass into grids with high accuracy [45]. In the future, it would be a practical option to first use airborne LiDAR to extrapolate plot measurements into regional scale and then use the airborne LiDAR derived AGB as the ground truth input of the proposed method in this study.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, we collected global MOD13A2 data during 2004 from the Land Processes Distributed Active Archive Center (LP DAAC) data pool. Since cumulative NDVI from a time-series can increase the AGB estimation accuracy compared with the use of NDVI data from a single time period [45], we computed the cumulative NDVI from the sum of all NDVI data in the growing season of 2004 and used this as a predictor in forest AGB estimations.…”

Section: Ndvi Datamentioning

confidence: 99%

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Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data

et al. 2016

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Abstract:As a large carbon pool, global forest ecosystems are a critical component of the global carbon cycle. Accurate estimations of global forest aboveground biomass (AGB) can improve the understanding of global carbon dynamics and help to quantify anthropogenic carbon emissions. Light detection and ranging (LiDAR) techniques have been proven that can accurately capture both horizontal and vertical forest structures and increase the accuracy of forest AGB estimation. In this study, we mapped the global forest AGB density at a 1-km resolution through the integration of ground inventory data, optical imagery, Geoscience Laser Altimeter System/Ice, Cloud, and Land Elevation Satellite data, climate surfaces, and topographic data. Over 4000 ground inventory records were collected from published literatures to train the forest AGB estimation model and validate the resulting global forest AGB product. Our wall-to-wall global forest AGB map showed that the global forest AGB density was 210.09 Mg/ha on average, with a standard deviation of 109.31 Mg/ha. At the continental level, Africa (333.34˘63.80 Mg/ha) and South America (301.68˘67.43 Mg/ha) had higher AGB density. The AGB density in Asia, North America and Europe were 172. 28˘94.75, 166.48˘84.97, and 132.97˘50.70 Mg/ha, respectively. The wall-to-wall forest AGB map was evaluated at plot level using independent plot measurements. The adjusted coefficient of determination (R 2 ) and root-mean-square error (RMSE) between our predicted results and the validation plots were 0.56 and 87.53 Mg/ha, respectively. At the ecological zone level, the R 2 and RMSE between our map and Intergovernmental Panel on Climate Change suggested values were 0.56 and 101.21 Mg/ha, respectively. Moreover, a comprehensive comparison was also conducted between our forest AGB map and other published regional AGB products. Overall, our forest AGB map showed good agreements with these regional AGB products, but some of the regional AGB products tended to underestimate forest AGB density.

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Section: Discussionmentioning

confidence: 99%

Section: Ndvi Datamentioning

confidence: 99%

Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data

et al. 2016

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“…In Marabel-García and Álvarez-Taboada (2014) a comparison between two different methods (Partial Least Square Regression -PLSR-and linear regression) were applied in order to find the best estimate of the aboveground biomass, recommending the use of linear regression due to its simplicity with respect to PLSR. In Li et al (2015) several regression models (linear, exponential growth, support vector regression and neural network) were compared for aboveground biomass estimation, concluding that linear regression achieved the best performance. In other cases (Hyyppä et al, 2000;Erdody and Moskal, 2010), a comparison of regression methods using different data sources (imagery, LiDAR or imagery + LiDAR) is performed.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of regression methods to predict forest structure and canopy fuel variables from LiDAR full-waveform data

2016

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Regression methods are widely employed in forestry to predict and map structure and canopy fuel variables. We present a study where several regression models (linear, non-linear, regression trees and ensemble) were assessed. Independent variables were calculated using metrics extracted from full-waveform LiDAR data, while the reference data used to generate the dependent variables for the prediction models were obtained from fieldwork in 78 plots of 16 m radius. Transformations of dependent and independent variables with feature selection were carried out to assess their influence in the prediction of response variables. In order to evaluate significant differences and rank regression models we used the non-parametric tests Wilcoxon and Friedman, and post-hoc analysis or post-hoc pairwise multiple comparison tests, such as Nemenyi, for Friedman test. Regressions using transformation of the dependent variable, like square-root or logarithmic, or the independent variable, increased R 2 up to 6% with respect to linear regression using unprocessed response variables. CART (Classification and Regression Tree) method provided poor results, but it may be interesting for categorisation purposes. Square-root transformation of the dependent variable is the method having the best overall results, except for stand volume. However, not always has a significant improvement with respect to other regression methods.Key words: regression models, Random Forest, CART, M5, Wilcoxon, Friedman, forest structure, canopy fuel, LiDAR full-waveform. Estudio comparativo de métodos de regresión para la predicción de variables de estructura y combustibilidad a partir de datos LiDAR full-waveformResumen: Los métodos de regresión se utilizan ampliamente en el ámbito forestal para la predicción y el cartografiado de las variables de estructura y combustibilidad. En este artículo se evalúan diferentes modelos de regresión (lineal, no lineal, árboles de regresión y ensemble). Como variables independientes se utilizaron métricas extraídas de datos LiDAR full-waveform, mientras que los valores de las variables dependientes se generaron a partir de modelos basados en datos de campo obtenidos para 78 parcelas de 16 m de radio. Se llevaron a cabo transformaciones de las variables dependientes e independientes con selección de atributos para evaluar su influencia en la predicción de la variable respuesta. Con el fin de verificar diferencias significativas y ordenar los modelos de regresión se emplearon los tests no paramétricos de Wilcoxon y Friedman, y el análisis post-hoc o los tests de comparación post-hoc por pares, como el de Nemenyi, para el test de Friedman. Las regresiones basadas en la transformación de la variable dependiente,

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“…The cumulative normalized difference vegetation index (NDVI) and maximum value composite (MVC) were calculated from the time-series TM imagery. The use of cumulative NDVI can increase the estimation accuracy of forest parameters compared with the use of NDVI data from a single time period [52]. MVC imagery has been demonstrated to be highly related to green-vegetation dynamics [53], and is capable of minimizing the problems common to single-date optical imagery, such as cloud contamination, atmospheric attenuation, surface directional reflectance, and view and illumination geometry [53].…”

Section: Landsat Tm Imagerymentioning

confidence: 99%

SRTM DEM Correction in Vegetated Mountain Areas through the Integration of Spaceborne LiDAR, Airborne LiDAR, and Optical Imagery

Guo

et al. 2015

Remote Sensing

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Abstract:The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is one of the most complete and frequently used global-scale DEM products in various applications. However, previous studies have shown that the SRTM DEM is systematically higher than the actual land surface in vegetated mountain areas. The objective of this study is to propose a procedure to calibrate the SRTM DEM over large vegetated mountain areas. Firstly, we developed methods to estimate canopy cover from aerial imagery and tree height from multi-source datasets (i.e., field observations, airborne light detection and ranging (LiDAR) data, Geoscience Laser Altimeter System (GLAS) data, Landsat TM imagery, climate surfaces, and topographic data). Then, the airborne LiDAR derived DEM, covering ~5% of the study area, was used to evaluate the accuracy of the SRTM DEM. Finally, a regression model of the SRTM DEM error depending on tree height, canopy cover, and terrain slope was developed to calibrate the SRTM DEM. Our results show that the proposed procedure can significantly improve the accuracy of the SRTM DEM over vegetated mountain areas. The mean difference between the SRTM DEM and the LiDAR DEM decreased from 12.15 m to −0.82 m, and the standard deviation dropped by 2 m.

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Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass

Cited by 51 publications

References 66 publications

Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data

Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data

A comparative study of regression methods to predict forest structure and canopy fuel variables from LiDAR full-waveform data

SRTM DEM Correction in Vegetated Mountain Areas through the Integration of Spaceborne LiDAR, Airborne LiDAR, and Optical Imagery

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