A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing

2013 ◽  
Vol 128 ◽  
pp. 289-298 ◽  
Author(s):  
S.G. Zolkos ◽  
S.J. Goetz ◽  
R. Dubayah
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Meta Analysis ◽  
Biomass Estimation

scholarly journals Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1073 ◽  
Author(s):  
Li ◽  
Li ◽  
Li ◽  
Liu
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Variable Selection ◽  
Aboveground Biomass ◽  
Forest Type ◽  
Learning Algorithms ◽  
Forest Biomass ◽  
Machine Learning Algorithms ◽  
Biomass Estimation ◽  
Landsat 8

Forest biomass is a major store of carbon and plays a crucial role in the regional and global carbon cycle. Accurate forest biomass assessment is important for monitoring and mapping the status of and changes in forests. However, while remote sensing-based forest biomass estimation in general is well developed and extensively used, improving the accuracy of biomass estimation remains challenging. In this paper, we used China’s National Forest Continuous Inventory data and Landsat 8 Operational Land Imager data in combination with three algorithms, either the linear regression (LR), random forest (RF), or extreme gradient boosting (XGBoost), to establish biomass estimation models based on forest type. In the modeling process, two methods of variable selection, e.g., stepwise regression and variable importance-base method, were used to select optimal variable subsets for LR and machine learning algorithms (e.g., RF and XGBoost), respectively. Comfortingly, the accuracy of models was significantly improved, and thus the following conclusions were drawn: (1) Variable selection is very important for improving the performance of models, especially for machine learning algorithms, and the influence of variable selection on XGBoost is significantly greater than that of RF. (2) Machine learning algorithms have advantages in aboveground biomass (AGB) estimation, and the XGBoost and RF models significantly improved the estimation accuracy compared with the LR models. Despite that the problems of overestimation and underestimation were not fully eliminated, the XGBoost algorithm worked well and reduced these problems to a certain extent. (3) The approach of AGB modeling based on forest type is a very advantageous method for improving the performance at the lower and higher values of AGB. Some conclusions in this paper were probably different as the study area changed. The methods used in this paper provide an optional and useful approach for improving the accuracy of AGB estimation based on remote sensing data, and the estimation of AGB was a reference basis for monitoring the forest ecosystem of the study area.

scholarly journals Evaluation of FORMOSAT-2 and PlanetScope Imagery for Aboveground Oil Palm Biomass Estimation in a Mature Plantation in the Congo Basin

2020 ◽  
Vol 12 (18) ◽  
pp. 2926
Author(s):  
Pierre Migolet ◽  
Kalifa Goïta
Keyword(s):  
Remote Sensing ◽  
Oil Palm ◽  
Aboveground Biomass ◽  
Fourier Transforms ◽  
Canopy Structure ◽  
Principal Component ◽  
Congo Basin ◽  
Biomass Estimation ◽  
Mean Square Errors

The present study developed methods using remote sensing for estimation of total dry aboveground biomass (AGB) of oil palm in the Congo Basin. To achieve this, stem diameters at breast height (DBH, 1.3 m) and stem heights were measured in an oil palm plantation located in Gabon (Congo Basin, Central Africa). These measurements were used to determine AGB in situ. The remote sensing approach that was used to estimate AGB was textural ordination (FOTO) based upon Fourier transforms that were applied, respectively, to PlanetScope and FORMOSAT-2 satellite images taken from the area. The FOTO method is based on the combined use of two-dimensional (2D) Fast Fourier Transform (FFT) and Principal Component Analysis (PCA). In the context of the present study, it was used to characterize the variation in canopy structure and to estimate the aboveground biomass of mature oil palms. Two types of equations linking FOTO indices to in situ biomass were developed: multiple linear regressions (MLR); and multivariate adaptive spline regressions (MARS). All best models developed yielded significant results, regardless of whether they were derived from PlanetScope or from FORMOSAT-2 images. Coefficients of determination (R2) varied between 0.80 and 0.92 (p ≤ 0.0005); and relative root mean-square-errors (%RMSE) were less than 10.12% in all cases. The best model was obtained using MARS approach with FOTO indices from FORMOSAT-2 (%RMSE = 6.09%).

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

2014 ◽  
Vol 9 (1) ◽  
pp. 63-105 ◽  
Author(s):  
Dengsheng Lu ◽  
Qi Chen ◽  
Guangxing Wang ◽  
Lijuan Liu ◽  
Guiying Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Forest Ecosystems ◽  
Estimation Methods ◽  
Biomass Estimation

Biomass of understory species of Pinus halepensis Mill. forests in the Kassandra Peninsula - Chalkidiki

1998 ◽  
Vol 63 ◽  
Author(s):  
P. Smiris ◽  
F. Maris ◽  
K. Vitoris ◽  
N. Stamou ◽  
P. Ganatsas
Keyword(s):  
Moisture Content ◽  
Aboveground Biomass ◽  
Quercus Ilex ◽  
Pinus Halepensis ◽  
Biomass Estimation ◽  
Regression Equations ◽  
Pistacia Lentiscus ◽  
Quercus Coccifera ◽  
Understory Species ◽  
Cistus Incanus

This  study deals with the biomass estimation of the understory species of Pinus halepensis    forests in the Kassandra peninsula, Chalkidiki (North Greece). These  species are: Quercus    coccifera, Quercus ilex, Phillyrea media, Pistacia lentiscus, Arbutus  unedo, Erica arborea, Erica    manipuliflora, Smilax aspera, Cistus incanus, Cistus monspeliensis,  Fraxinus ornus. A sample of    30 shrubs per species was taken and the dry and fresh weights and the  moisture content of    every component of each species were measured, all of which were processed  for aboveground    biomass data. Then several regression equations were examined to determine  the key words.

scholarly journals Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Praveen Kumar ◽  
Akhouri P. Krishna ◽  
Thorkild M. Rasmussen ◽  
Mahendra K. Pal
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Test Site ◽  
Forest Area ◽  
Relational Model ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Ground Sample ◽  
Sensing Data

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

scholarly journals Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China

2021 ◽  
Vol 13 (8) ◽  
pp. 1595
Author(s):  
Chunhua Li ◽  
Lizhi Zhou ◽  
Wenbin Xu
Keyword(s):  
Aboveground Biomass ◽  
Carbon Accounting ◽  
Dynamic Monitoring ◽  
Gradient Boosting ◽  
Biomass Estimation ◽  
Floodplain Wetlands ◽  
Red Edge ◽  
Extreme Gradient Boosting ◽  
Ensemble Algorithms ◽  
Sentinel 2

Wetland vegetation aboveground biomass (AGB) directly indicates wetland ecosystem health and is critical for water purification, carbon cycle, and biodiversity conservation. Accurate AGB estimation is essential for the monitoring and supervision of ecosystems, especially in seasonal floodplain wetlands. This paper explored the capability of spectral and texture features from the Sentinel-2 Multispectral Instrument (MSI) for modeling grassland AGB using random forest (RF) and extreme gradient boosting (XGBoost) algorithms in Shengjin Lake wetland (a Ramsar site). We use five-fold cross-validation to verify the model effectiveness. The results indicated that the RF and XGBoost models had a robust and efficient performance (with root mean square error (RMSE) of 126.571 g·m−2 and R2 of 0.844 for RF, RMSE of 112.425 g·m−2 and R2 of 0.869 for XGBoost), and the XGBoost models, by contrast, performed better. Both traditional and red-edge vegetation indices (VIs) obtained satisfactory results of AGB estimation (RMSE = 127.936 g·m−2, RMSE = 125.879 g·m−2 in XGBoost models, respectively), with the red-edge VIs contributed more to the AGB models. Moreover, we selected eight gray-level co-occurrence matrix (GLCM) textures calculated by four processing window sizes using the mean value of four offsets, and further analyzed the results of three analysis sets. Textures derived from traditional and red-edge bands using a 7 × 7 window size performed better in biomass estimation. This finding suggested that textures derived from the traditional bands were as important as the red-edge bands. The introduction of textures moderately improved the accuracy of modeling AGB, whereas the use of textures alo ne was not satisfactory. This research demonstrated that using the Sentinel-2 MSI and the two ensemble algorithms is an effective method for long-term dynamic monitoring and assessment of grass AGB in seasonal floodplain wetlands, which can support sustainable management and carbon accounting of wetland ecosystems.

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