Quantitative Estimation of Biomass of Alpine Grasslands Using Hyperspectral Remote Sensing

Kong, Bo; Yu, Huan; Du, Rongxiang; Wang, Qing

doi:10.1016/j.rama.2018.10.005

Cited by 37 publications

(22 citation statements)

References 29 publications

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“…Regardless of the RS data sources, there are no RS techniques that are capable of providing a direct measurement of biomass. As a result, biomass prediction accuracy increases when combined with field-sampled data, especially when using machine learning approaches to build biomass models [4,[24][25][26]. Machine learning algorithms allow one to analyze a large number of predictor variables from remote sensing data, thereby filling in the missing data and reducing the error of the prediction models [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Aboveground Biomass of an Evergreen Broadleaf Forest in Xuan Lien Nature Reserve, Thanh Hoa, Vietnam, Using SPOT-6 Data and the Random Forest Algorithm

Kappas

2020

International Journal of Forestry Research

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Forest biomass is an important ecological indicator for the sustainable management of forests. The aim of this study was to estimate forest aboveground biomass (AGB) by integrating SPOT-6 data with field-based measurements using the random forest (RF) algorithm. In total, 52 remote sensing variables, including spectral bands, vegetation indices, topography data, and textures, were extracted from SPOT-6 images to predict the forest AGB of Xuan Lien Nature Reserve, Vietnam. To determine the optimal predictor variables for AGB estimation, 10 different RF models were built. To evaluate these models, 10-fold cross-validation was applied. We found that a combination of spectral and vegetation indices and topography variables offer the highest prediction results (Radj2 = 0.74 and RMSE = 61.24 Mg ha−1). Adding texture features into the predictor variables did not improve the model performance. In addition, the SPOT-6 sensor has the potential to predict forest AGB using the RF algorithm.

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Section: Introductionmentioning

confidence: 99%

Estimating the Aboveground Biomass of an Evergreen Broadleaf Forest in Xuan Lien Nature Reserve, Thanh Hoa, Vietnam, Using SPOT-6 Data and the Random Forest Algorithm

Kappas

2020

International Journal of Forestry Research

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“…As spectral characteristics of land cover show great differences [28][29][30][31][32], it is one of the important links in current research to accurately quantify various indicators of forest resources [33,34]. Moreover, studies have found that remote sensing data and its derived bands have good practicability for simulating forest AGC [35][36][37]; this is especially the case when combined with machine learning algorithms that allow for large scale automated analysis of high dimensional data from satellites [38]. The machine learning approach can derive rich information from remote sensing data as the input data, and continuously optimize the algorithm's performance via empirical learning to make the results more feasible and credible [39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Zhang

Zhou

et al. 2019

Forests

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Dynamic monitoring of carbon storage in forests resources is important for tracking ecosystem functionalities and climate change impacts. In this study, we used multi-year Landsat data combined with a Random Forest (RF) algorithm to estimate the forest aboveground carbon (AGC) in a forest area in China (Hang-Jia-Hu) and analyzed its spatiotemporal changes during the past two decades. Maximum likelihood classification was applied to make land-use maps. Remote sensing variables, such as the spectral band, vegetation indices, and derived texture features, were extracted from 20 Landsat TM and OLI images over five different years (2000, 2004, 2010, 2015, and 2018). These variables were subsequently selected according to their importance and subsequently used in the RF algorithm to build an estimation model of forest AGC. The results showed the following: (1) Verification of classification results showed maximum likelihood can extract land information effectively. Our land cover classification yielded overall accuracies between 86.86% and 89.47%. (2) Additionally, our RF models showed good performance in predicting forest AGC, with R2 from 0.65 to 0.73 in the training and testing phase and a RMSE range between 3.18 and 6.66 Mg/ha. RMSEr in the testing phase ranged from 20.27 to 22.27 with a low model error. (3) The estimation results indicated that forest AGC in the past two decades increased with density at 10.14 Mg/ha, 21.63 Mg/ha, 26.39 Mg/ha, 29.25 Mg/ha, and 44.59 Mg/ha in 2000, 2004, 2010, 2015, and 2018. The total forest AGC storage had a growth rate of 285%. (4) Our study showed that, although forest area decreased in the study area during the time period under study, the total forest AGC increased due to an increment in forest AGC density. However, such an effect is overridden in the vicinity of cities by intense urbanization and the loss of forest covers. Our study demonstrated that the combined use of remote sensing data and machine learning techniques can improve our ability to track the forest changes in support of regional natural resource management practices.

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“…These spectral regions are known for their sensitivity to physiological attributes and, thereby, for vegetation biomass yields. Therefore, visible and near infrared spectra are commonly used in indices to predict biomass yields (Rouse et al, 1974; Silleos et al, 2006; Xue and Su, 2017; Kong et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Remote sensing data fusion as a tool for biomass prediction in extensive grasslands invaded by L. polyphyllus

Schulze‐Brüninghoff

Wachendorf

Astor

2020

Remote Sens Ecol Conserv

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Remote sensing data fusion is a powerful tool to gain information of quantitative and qualitative vegetation properties on field level. The aim of this study was to develop prediction models from sensor data fusion for fresh and dry matter yield (FMY/DMY) in extensively managed grasslands with variable degree of invasion by L. polyphyllus. Therefore, a terrestrial 3d laser scanner (TLS) and a drone-based hyperspectral camera was used to collect high resolution 3d point clouds and hyperspectral aerial orthomosaics of four extremely heterogenous grasslands. From 3d point clouds multiple features (vegetation height, sum of voxel, point density and surface structure) were extracted and combined with hyperspectral data to develop an optimized biomass model from random forest regression algorithm to predict FMY and DMY (n train = 130, n test = 33). Models from hyperspectral data solitarily had the lowest prediction performance (FMY: R 2 = 0.61, nRMSE r = 17.14; DMY: R 2 = 0.59, nRMSE r = 19.37). Higher performance was gained by models derived from 3d laser data (FMY: R 2 = 0. 76, nRMSE r = 13.3; DMY: R 2 = 0. 74, nRMSE r = 15.1). A fusion of both sensor systems increased the FMY prediction performance up to R 2 = 0.8; nRMSE r = 12.02 and the DMY prediction performance to R 2 = 0.81 and nRMSE r = 12.06. The fusion of complementary sensor systems can increase the power to predict biomass yields of heterogenous and extensively managed grasslands. It is a novel alternative to labour-intensive, traditional biomass prediction methods and to remote sensing methods using only single sensor data.

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Quantitative Estimation of Biomass of Alpine Grasslands Using Hyperspectral Remote Sensing

Cited by 37 publications

References 29 publications

Estimating the Aboveground Biomass of an Evergreen Broadleaf Forest in Xuan Lien Nature Reserve, Thanh Hoa, Vietnam, Using SPOT-6 Data and the Random Forest Algorithm

Estimating the Aboveground Biomass of an Evergreen Broadleaf Forest in Xuan Lien Nature Reserve, Thanh Hoa, Vietnam, Using SPOT-6 Data and the Random Forest Algorithm

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Remote sensing data fusion as a tool for biomass prediction in extensive grasslands invaded by L. polyphyllus

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