A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China

Xie, Yichun; Sha, Zongyao; Yu, Mei; Bai, Yongfei; Zhang, Lei

doi:10.1016/j.ecolmodel.2009.04.025

Cited by 122 publications

(69 citation statements)

References 37 publications

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“…In particular, Landsat images have been the most widely used for forest aboveground biomass (AGB) estimation in the past three decades [5,6,20,24,26,[28][29][30][31][32][33][34][35][36], mainly because they are freely downloadable, have a long history, and have medium spatial resolution. The studies deal with different climate zones and forest ecosystems, from tropical to subtropical, temperate, and boreal forests [4][5][6][7][12][13][14][15]20,28,32,[37][38][39][40][41][42][43]. However, one common problem is the data saturation in Landsat imagery; that is, spectral reflectance values are not sensitive to the change in biomass of dense and multilayer canopy forests, which results in low accuracy of AGB estimation, especially when AGB is high, such as greater than 130 Mg/ha [5,6,29].…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…The precision of the results may be partially due to the method of obtaining information in the field, the characteristics of the sensor (i.e., temporal and spatial resolution) and its age, the type of ecosystem, as well as the statistical analyses carried out. The latter appears to substantially affect the fit of the models between the data from the field and the sensor data [40][41][42][43]. Although some studies have obtained a good fit with the use of a limited number of bands [25,42], others studies [23] have required the interaction of a greater number of bands, indices or synthetic bands to achieve a better fit, as it was the case in this study.…”

Section: Discussionmentioning

confidence: 56%

“…The maps generated for CGC and grass height showed moderately high accuracies that can be representative and used as a reference to help assess current and future land use development, grasslands capacities, animal production potentials and the status of grassland avian habitats [41,[56][57][58].…”

Section: Discussionmentioning

confidence: 99%

Mapping Land Cover and Estimating the Grassland Structure in a Priority Area of the Chihuahuan Desert

Rodríguez-Maturino

Martínez-Guerrero

Chairez-Hernández

et al. 2017

Land

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Abstract:A field characterization of the grassland vegetation structure, represented by the coverage of grass canopy (CGC) and the grass height, was carried out during three years (2009)(2010)(2011) in a priority area for the conservation of grasslands of North America. Landsat Thematic Mapper (TM5) images were selected and the information of reflectance was obtained based on the geographical location of each field-sampling site. Linear models, constructed with field and satellite data, with high coefficients of determination for CGC (R 2 = 0.81, R 2 = 0.81 and R 2 = 0.72) and grass height (R 2 = 0.82, R 2 = 0.79 and R 2 = 0.73) were obtained. The maps showed a good level of CGC (>25%) and grass height (>25 cm), except for the year 2009, which presented the lowest values of grass height in the area. According to the Kappa Index, a moderate concordance among the three CGC maps was presented (0.49-0.59). Conversely, weak and moderate concordances were found among the grass height maps (0.36-0.59). It was observed that areas with a high CGC do not necessarily correspond to areas with greater grass height values. Based on the data analyzed in this study, the grassland areas are highly dynamic, structurally heterogeneous and the spatial distribution of the variables does not show a definite pattern. From the information generated, it is possible to determine those areas that are the most important for monitoring to then establish effective strategies for the conservation of these grasslands and the protection of threatened migratory bird species.

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“…For example, Xie et al [47] analyzed the performance comparison of multiple linear regression (MLR) and ANN for grassland aboveground biomass in Xilingol River Basin, Inner Mongolia. In this work, Landsat ETM+-driven (Normalized Difference Vegetation Index (NDVI), Bands 1, 3, 4, 5 and 7) information was used as input features for training, and ANN (R 2 = 0.817, RMSE = 42.36%) outperformed the MLR (R 2 = 0.591, RMSE = 53.20%).…”

Section: Grassland Biomass Retrievalmentioning

confidence: 99%

Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

et al. 2015

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Abstract:The enormous increase of remote sensing data from airborne and space-borne platforms, as well as ground measurements has directed the attention of scientists towards new and efficient retrieval methodologies. Of particular importance is the consideration of the large extent and the high dimensionality (spectral, temporal and spatial) of remote sensing data. Moreover, the launch of the Sentinel satellite family will increase the availability of data, especially in the temporal domain, at no cost to the users. To analyze these data and to extract relevant features, such as essential climate variables (ECV), specific methodologies need to be exploited. Among these, greater attention is devoted to machine learning methods due to their flexibility and the capability to process large number of inputs and to handle non-linear problems. The main objective of this paper is to provide a review of research that is being carried out to retrieve two critically important terrestrial biophysical quantities (vegetation biomass and soil moisture) from remote sensing data using machine learning methods.

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A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China

Cited by 122 publications

References 37 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

Mapping Land Cover and Estimating the Grassland Structure in a Priority Area of the Chihuahuan Desert

Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

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