Non-Destructive, Laser-Based Individual Tree Aboveground Biomass Estimation in a Tropical Rainforest

The relation between tropical rainforest to the climate variability is very important. This research aims to determine the relationship between aboveground biomass which prefer tree in the tropical rainforest and surrounding temperature. Diameter at breast height (DBH) of ten tree species and surrounding temperature collected data were taken to measure the correlation between the two variables by using statistical test. Furthermore, forest biomass estimation is also important towards the assessment of the productivity, structure and forest condition. The analysis in this research shows that simple linear regression model can be used to predict the future value of DBH for each species. The findings may help the reduction of greenhouse gas emissions with proper conservation and sustainable management.

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A simple non-destructive method for estimating aboveground biomass of emergent aquatic macrophytes

Acta Limnologica Brasiliensia ◽

10.1590/s2179-975x6416 ◽

2017 ◽

Vol 29 (0) ◽

Cited By ~ 2

Author(s):

Laís Samira Correia Nunes ◽

◽

Antonio Fernando Monteiro Camargo ◽

Keyword(s):

Aboveground Biomass ◽

Aquatic Macrophytes ◽

Experimental Studies ◽

River Estuary ◽

Dry Mass ◽

Biomass Estimation ◽

Morphological Variables ◽

The Individual ◽

The Relationship ◽

Non Destructive

Abstract: Aim Non-destructive methods for estimating aquatic macrophytes biomass may be employed by using indirect measurements, especially in experimental studies, thus enabling the conservation of plant samples. It is possible to estimate macrophyte biomass by developing mathematical equations that relate the plants’ dry mass to their morphological variables. The aim of this study was to evaluate the relationship between different morphological variables and biomass in order to determine which variable is easier to be obtained for the emergent aquatic macrophytes Crinum americanum and Spartina alterniflora. Methods We obtained the aboveground area and height of individuals of both species, with different sizes and distinct developmental stages. The samples were collected in the Itanhaém River Estuary (SP, Brazil). The plants were dried in a laboratory oven and weighed so as to obtain their dry mass. Simple linear regression analyses were applied to the morphological variables and the individual dry mass to obtain equations. Results For the both species, the relationship between area and biomass, and the relationship between individual height and biomass presented significant coefficients of determination (p < 0.0001). For the elaboration of models involving the individual height, we used only one morphological measure for each individual, whereas for models involving the individual area it was necessary to obtain more than one hundred morphological measurements per individual. Conclusions The morphological variables chosen are good attributes for estimating the aboveground biomass of C. americanum and S. alterniflora. Considering the models’ adjustment and the consumed time to obtain the measurements, we conclude that the individual height measurement is better for biomass estimation for both species.

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Comparison of individual tree aboveground biomass estimation in community forests using allometric equation and expansion factor in Magetan, East Java, Indonesia

Biodiversitas Journal of Biological Diversity ◽

10.13057/biodiv/d220936 ◽

2021 ◽

Vol 22 (9) ◽

Author(s):

Rahmanta Setiahadi

Keyword(s):

Aboveground Biomass ◽

Allometric Equation ◽

Biomass Estimation ◽

Expansion Factor ◽

Individual Tree ◽

Biomass Expansion Factor ◽

Community Forests ◽

Diameter At Breast Height ◽

Breast Height ◽

Tree Component

Abstract. Setiahadi R. 2021. Comparison of individual tree aboveground biomass estimation in community forests using allometric equation and expansion factor in Magetan, East Java, Indonesia. Biodiversitas 22: 3899-3909. The use of allometric equation and biomass expansion factor can facilitate more efficient tree biomass estimation. This study evaluates the accuracy of the allometric equation and expansion factor for quantifying the individual tree aboveground biomass in community forest tree species. Destructive sampling n on 120 trees from four different species: Falcataria moluccana, Melia azedarach, Swietenia macrophylla, and Tectona grandis. For each tree sample, aboveground biomass measured at every tree component, i.e., stem, branches, and leaves. The allometric equation developed using regression analysis with several predictor variables, such as diameter at breast height (D), squared diameter at breast height combined with tree height (D2H), and D and H separately. On another side, the biomass expansion factor was calculated based on the total aboveground biomass and stem biomass ratio. The results found the highest mean aboveground biomass for all species are M. azedarach (326.36±88.40 kg tree-1), S. macrophylla (244.47±98.73 kg tree-1), T. grandis (173.31±80.97 kg tree-1), and F. moluccana (56.56±23.10 kg tree-1). The most significant average biomass expansion factor observed in M. azedarach (1.78±0.03), adhered by T. grandis (1.66±0.09), S. macrophylla (1.61±0.04), and F. moluccana (1.59±0.06). The equation ln? = lna + b x ln (D) was best for estimating aboveground biomass in each tree component and a total of four species with an accuracy of more than 90%.

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

Silva Gandavensis ◽

10.21825/sg.v63i0.841 ◽

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.

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

Remote Sensing ◽

10.3390/rs13081595 ◽

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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Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

Remote Sensing ◽

10.3390/rs13040581 ◽

2021 ◽

Vol 13 (4) ◽

pp. 581 ◽

Cited By ~ 2

Author(s):

Yuanyuan Fu ◽

Guijun Yang ◽

Xiaoyu Song ◽

Zhenhong Li ◽

Xingang Xu ◽

...

Keyword(s):

Winter Wheat ◽

Least Squares ◽

Aboveground Biomass ◽

Regression Models ◽

Digital Images ◽

Predictive Performance ◽

Estimation Accuracy ◽

Support Vector ◽

Biomass Estimation ◽

High Definition

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

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Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review

Forests ◽

10.3390/f12070914 ◽

2021 ◽

Vol 12 (7) ◽

pp. 914

Author(s):

Adeel Ahmad ◽

Hammad Gilani ◽

Sajid Rashid Ahmad

Keyword(s):

High Resolution ◽

Literature Review ◽

Satellite Imagery ◽

Aboveground Biomass ◽

Vegetation Indices ◽

Biomass Estimation ◽

Independent Variables ◽

Non Linear ◽

Multiple Variables ◽

Forest Aboveground Biomass

This paper provides a comprehensive literature review on forest aboveground biomass (AGB) estimation and mapping through high-resolution optical satellite imagery (≤5 m spatial resolution). Based on the literature review, 44 peer-reviewed journal articles were published in 15 years (2004–2019). Twenty-one studies were conducted across six continents in Asia, eight in North America and Africa, five in South America, and four in Europe. This review article gives a glance at the published methodologies for AGB prediction modeling and validation. The literature review suggested that, along with the integration of other sensors, QuickBird, WorldView-2, and IKONOS satellite images were most widely used for AGB estimations, with higher estimation accuracies. All studies were grouped into six satellite-derived independent variables, including tree crown, image textures, tree shadow fraction, canopy height, vegetation indices, and multiple variables. Using these satellite-derived independent variables, most of the studies used linear regression (41%), while 30% used linear (multiple regression and 18% used non-linear (machine learning) regression, while very few (11%) studies used non-linear (multiple and exponential) regression for estimating AGB. In the context of global forest AGB estimations and monitoring, the advantages, strengths, and limitations were discussed to achieve better accuracy and transparency towards the performance-based payment mechanism of the REDD+ program. Apart from technical limitations, we realized that very few studies talked about real-time monitoring of AGB or quantifying AGB change, a dimension that needs exploration.

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