An investigation on the best-fit models for sugarcane biomass estimation by linear mixedeffect modelling on unmanned aerial vehicle-based multispectral images:A case study of Australia

Due to the worldwide population growth and the increasing needs for sugar-based products,accurate estimation of sugarcane biomass is critical to the precise monitoring of sugarcane growth.This research aims to find the imperative predictors correspond to the random and fixed effects to improve the accuracy of wet and dry sugarcane biomass estimations by integrating ground data and multi-temporal images from Unmanned Aerial Vehicles(UAVs).The multispectral images and biomass measurements were obtained at different sugarcane growth stages from 12 plots with three nitrogen fertilizer treatments.Individual spectral bands and different combinations of the plots,growth stages,and nitrogen fertilizer treatments were investigated to address the issue of selecting the correct fixed and random effects for the modelling.A model selection strategy was applied to obtain the optimum fixed effects and their proportional contribution.The results showed that utilizing Green,Blue,and Near Infrared spectral bands on models rather than all bands improved model performance for wet and dry biomass estimates.Additionally,the combination of plots and growth stages outperformed all the candidates of random effects.The proposed model outperformed the Multiple Linear Regression(MLR),Generalized Linear Model(GLM),and Generalized Additive Model(GAM)for wet and dry sugarcane biomass,with coefficients of determination(R2)of 0.93 and 0.97,and Root Mean Square Error(RMSE)of 12.78 and 2.57 t/ha,respectively.This study indicates that the proposed model can accurately estimate sugarcane biomasses without relying on nitrogen fertilizers or the saturation/senescence problem of Vegetation Indices(VIs)in mature growth stages.

Allometric equations for aboveground biomass estimation of Olea europaea L.subsp.cuspidata in Mana Angetu Forest

Introduction:African wild olive,Olea europaea L.subsp.cuspidata(Wall.ex G.Don)Cif.,L‘Olivicoltore is widely distributed in dry forest and forest margins,often with Juniperus procera,in east Africa and Ethiopia.It reaches southern Africa,also India and China,ranging from tall trees to stunted shrubs.Does best in good forest soil,but hardy and drought resistant once established,even in poor soils.It is used for firewood,charcoal,poles,posts,timber(furniture,carving,floors,and paneling),medicine(stem,bark,and leaves),bee forage,milk flavoring(smoking wood),toothbrushes(twigs),and walking sticks.Although the species has many economic and ecological functions,its environmental uses like carbon storage and climate change mitigation are less assessed.Therefore,the objective of the study was to develop species-specific allometric equations for O.europaea L.subsp.cuspidata using semi-destructive method and evaluate allometric models for estimating the aboveground biomass(AGB).Results and Discussions:After all the necessary biomass calculations were done,seven AGB equations were developed.These regression equations relate AGB with diameter at breast height(DBH),height(H),and density(ρ)individually and in combination.Out of seven,four allometric equations were chosen based on goodness-of-fit statistics and three were rejected.The selected models were tested for accuracy based on observed data.The best models selected have higher R2-adj and lower residual standard error and Akaike information criterion than rejected equations.The relations for all selectedmodels are significant(p<0.000),which showed strong correlation of AGB with selected dendrometric variables.Accordingly,the AGB was strongly correlated with DBH and was not significantly correlated with wood density and height individually in O.europaea L.subsp.cuspidata allometric equation development.In combination,AGB was strongly correlated with DBH and height;DBH and wood density;and the combination of DBH,height,and wood density.Species-specific equations are used for better carbon assessment than general equations.

Estimation of Crop Biomass Using GF-3 Polarization SAR Data Based on Genetic Algorithm Feature Selection

In recent years,Polarization SAR(PolSAR)has been widely used in the filed of crop biomass estimation.However,high dimensional features extracted from PolSAR data will lead to information redundancy which will result in low accuracy and poor transfer ability of the estimation model.Aiming at this problem,we proposed a estimation method of crop biomass based on automatic feature selection method using genetic algorithm(GA).Firstly,the backscattering coefficient,the polarization parameters and texture features were extracted from PolSAR data.Then,these features were automatically pre-selected by GA to obtain the optimal feature subset.Finally,based on this subset,a support vector regression machine(SVR)model was applied to estimate crop biomass.The proposed method was validated using the GaoFen-3(GF-3)QPSΙ(C-band,quad-polarization)SAR data.Based on wheat and rape biomass samples acquired from a synchronous field measurement campaign,the proposed method achieve relative high validation accuracy(over 80%)in both crop types.For further analyzing the improvement of proposed method,validation accuracies of biomass estimation models based on several different feature selection methods were compared.Compared with feature selection based on linear correlation,GA method has increased by 5.77%in wheat biomass estimation and 11.84%in rape biomass estimation.Compared with the method of recursive feature elimination(RFE)selection,the proposed method has improved crops biomass estimation accuracy by 3.90%and 5.21%,respectively.

Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.

Morphometry of leaf and shoot variables to assess aboveground biomass structure and carbon sequestration by different varieties of white mulberry(Morus alba L.)

Mulberry is economically important and can also play a pivotal role in mitigating greenhouse gases.Leaf and shoot traits were measured for Morus alba var.Kanmasi,M.alba var.Karyansuban,M.alba var.Latifolia,and M.alba var.PFI-1 to assess aboveground biomass(AGB)and carbon sequestration.Variety-specific and multivariety allometric AGB models were developed using the equivalent diameter at breast height(EDBH)and plant height(H).The completeharvest method was used to measure leaf and shoot traits and biomass,and the ash method was used to measure organic carbon content.The results showed significant(p<0.01)varietal differences in leaf and shoot traits,AGB and carbon sequestration.PFI-1 variety had the greatest leaf density(mean±SE:1828.3±0.3 leaves tree^(-1)),Karyansuban had the largest mean leaf area(185.94±8.95 cm^(2)).A diminishing return was found between leaf area and leaf density.Latifolia had the highest shoot density per tree(46.6±1.83 shoots tree^(-1)),total shoot length(264.1±2.32 m),dry biomass(16.69±0.58 kg tree^(-1)),carbon sequestration(9.99±0.32 kg tree^(-1))and CO_(2) mitigation(36.67±1.16 kg).The variety-specific AGB models b(EDBH)and b(EDBH)2 showed good fit and reasonable accuracy with a coefficient of determination(R^(2))=0.98-0.99,standard error of estimates(SEE)=0.1125-0.3130 and root mean square error(RMSE)=0.1084-0.3017.The multivariety models bln(EDBH)and(EDBH)0.756 showed good-fitness and accuracy with R^(2)=0.85-0.86,SEE=1.6231-1.6445 and RMSE=1.609-1.630.On the basis of these findings,variety Latifolia has good potential for biomass production,and allometric equations based on EDBH can be used to estimate AGB with a reasonable accuracy.

Variations in the biomass of Eucalyptus plantations at a regional scale in Southern China

We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method（BEF） versus estimates obtained from a local biomass model,based on large-scale empirical field inventory sampling data.The sources and relative contributions of deviations between the two models were analyzed by the boosted regression trees method.Relative to the local model,BEF overestimated accumulative biomass by 22.12%.The predominant sources of the total deviation （70.94%） were stand-structure variables.Stand age and diameter at breast height are the major factors.Compared with biotic variables,abiotic variables had a smaller overall contribution （29.06%）,with elevation and soil depth being the most important among the examined abiotic factors.Large deviations in regional forest biomass and carbon stock estimates are likely to be obtained with BEF relative to estimates based on local data.To minimize deviations,stand age and elevation should be included in regional forest-biomass estimation.

Bayesian meta-analysis of regional biomass factors for Quercus mongolica forests in South Korea

Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these factors. Although intensive studies have been conducted to quantify biomass factors, each study typically covers a limited geographic area. The goal of this study was to employ a meta-analysis approach to develop regional bio- mass factors for Quercus mongolica forests in South Korea. The biomass factors of interest were biomass conversion and expansion factor （BCEF）, biomass expansion factor （BEF） and root-to-shoot ratio （RSR）. Our objectives were to select probability density functions （PDFs） that best fitted the three biomass factors and to quantify their means and uncertainties. A total of 12 scientific publications were selected as data sources based on a set of criteria. Fromthese publications we chose 52 study sites spread out across South Korea. The statistical model for the meta- analysis was a multilevel model with publication （data source） as the nesting factor specified under the Bayesian framework. Gamma, Log-normal and Weibull PDFs were evaluated. The Log-normal PDF yielded the best quanti- tative and qualitative fit for the three biomass factors. However, a poor fit of the PDF to the long right tail of observed BEF and RSR distributions was apparent. The median posterior estimates for means and 95 % credible intervals for BCEF, BEF and RSR across all 12 publica- tions were 1.016 （0.800-1.299）, 1.414 （1.304-1.560） and 0.260 （0.200-0.335）, respectively. The Log-normal PDF proved useful for estimating carbon stock of Q. mongolica forests on a regional scale and for uncertainty analysis based on Monte Carlo simulation.

Forest aboveground biomass estimates in a tropical rainforest in Madagascar: new insights from the use of wood specific gravity data

To generate carbon credits under the Reducing Emissions from Deforestation and forest Degradation program（REDD＋）, accurate estimates of forest carbon stocks are needed. Carbon accounting efforts have focused on carbon stocks in aboveground biomass（AGB）.Although wood specific gravity（WSG） is known to be an important variable in AGB estimates, there is currently a lack of data on WSG for Malagasy tree species. This study aimed to determine whether estimates of carbon stocks calculated from literature-based WSG values differed from those based on WSG values measured on wood core samples. Carbon stocks in forest biomass were assessed using two WSG data sets：（i） values measured from 303 wood core samples extracted in the study area,（ii） values derived from international databases. Results suggested that there is difference between the field and literaturebased WSG at the 0.05 level. The latter data set was on average 16 % higher than the former. However, carbon stocks calculated from the two data sets did not differ significantly at the 0.05 level. Such findings could be attributed to the form of the allometric equation used which gives more weight to tree diameter and tree height than to WSG. The choice of dataset should depend on the level of accuracy（Tier II or III） desired by REDD＋. As higher levels of accuracy are rewarded by higher prices, speciesspecific WSG data would be highly desirable.

Additive predictions of aboveground stand biomass in commercial logs and harvest residues for rotation age Pinus radiata plantations in New South Wales,Australia

Two systems of additive equations were developed to predict aboveground stand level biomass in log products and harvest residue from routinely measured or predicted stand variables for Pinus radiata plantations in New South Wales,Australia.These plantations were managed under three thinning regimes or stand types before clear-felling at rotation age by cut-to-length harvesters to produce sawlogs and pulpwood.The residue material following a clear-fell operation mainly consisted of stumps,branches and treetops,short off-cut and waste sections due to stem deformity,defects,damage and breakage.One system of equations did not include dummy variables for stand types in the model specification and was intended for more general use in plantations where stand density management regimes were not the same as the stand types in our study.The other system that incorporated dummy variables was for stand type-specific applications.Both systems of equations were estimated using 61 plot-based estimates of biomass in commercial logs and residue components that were derived from systems of equations developed in situ for predicting the product and residue biomass of individual trees.To cater for all practical applications,two sets of parameters were estimated for each system of equations for predicting component and total aboveground stand biomass in fresh and dry weight respectively.The two sets of parameters for the system of equations without dummy variables were jointly estimated to improve statistical efficiency in parameter estimation.The predictive performances of the two systems of equations were benchmarked through a leave-one-plot-out cross validation procedure.They were generally superior to the performance of an alternative two-stage approach that combined an additive system for major components with an allocative system for sub-components.As using forest harvest residue biomass for bioenergy has increasingly become an integrated part of forestry,reliable estimates of product and residue biomass will assist harvest and management planning for clear-fell operations that integrate cut-to-length log production with residue harvesting.

Assessment of the exploitable biomass of thread herring(Opisthonema spp.)in northwestern Mexico

In recent years,the small pelagic fishery on the Pacific northwest coast of Mexico has significantly increased fishing pressure on thread herring Opisthonema spp.This fishery is regulated using a precautionary approach(acceptable biological catch(ABC)and minimum catch size).However,due to fishing dynamics,fish aggregation habits and increased fishing mortality,periodic biomass assessments are necessary to estimate ABC and assess the resource status.The Catch-MSY approach was used to analyze historical series of thread herring catches off the western Baja California Sur(BCS,1981–2018)and the Gulf of California(GC,1972–2018)to estimate exploitable biomass and target reference points in order to obtain catch quotas.According to the results,in GC,the maximum biomass reached in 1972(at the beginning of fishery)and minimum biomass reached in 2015;the estimated exploitable biomass for 2019 was 42.2×10^(4) t;and the maximum sustainable yield(MSY)was 15.4×10^(4) t.In the western BCS coast,the maximum biomass was reached in 1981(at the beginning of fishery)and minimum biomass was reached in 2017;the estimated exploitable biomass for 2019 was 3.2×10^(4) t;and the MSY was 1.2×10^(4) t.Both stocks showed a decrease in biomass over the past years and were currently near to point of full exploitation.The results suggest that the use of the Catch-MSY method is suitable to obtain annual biomass estimates,in order to establish an ABC,to know the current state of the resource,and to avoid overcoming the potential recovery of the stocks.

An estimate of fish biomass at the bottom layer of the Bohai Sea

The biomasses of fishes at the bottom layer of the Bohai Sea are extimated by analysing the data on samples (447 hauls,more than 0.2 million in number or 20 t in weight of fishes) collected monthly by our institute from April, 1982 to May, 1983 by a pair of bottom trawls through the Bohai Sea. The exploitation problems are also discussed in this paper.

Estimation of aboveground biomass using in situ hyperspectral measurements in five major grassland ecosystems on the Tibetan Plateau

Aims There are numerous grassland ecosystem types on the Tibetan Plateau.These include the alpine meadow and steppe and degraded alpine meadow and steppe.This study aimed at developing a method to estimate aboveground biomass(AGB)for these grasslands from hyperspectral data and to explore the feasibility of applying air/satellite-borne remote sensing techniques to AGB estimation at larger scales.Methods We carried out a field survey to collect hyperspectral reflectance and AGB for five major grassland ecosystems on the Tibetan Plateau and calculated seven narrow-band vegetation indices and the vegetation index based on universal pattern decomposition(VIUPD)from the spectra to estimate AGB.First,we investigated correlations between AGB and each of these vegetation indices to identify the best estimator of AGB for each ecosystem type.Next,we estimated AGB for the five pooled ecosystem types by developing models containing dummy variables.At last,we compared the predictions of simple regression models and the models containing dummy variables to seek an ecosystem type-independent model to improve prediction of AGB for these various grassland ecosystems from hyperspectral measurements.Important findings When we considered each ecosystem type separately,all eight vegetation indices provided good estimates of AGB,with the best predictor of AGB varying among different ecosystems.When AGB of all the five ecosystems was estimated together using a simple linear model,VIUPD showed the lowest prediction error among the eight vegetation indices.The regression models containing dummy variables predicted AGB with higher accuracy than the simple models,which could be attributed to the dummy variables accounting for the effects of ecosystem type on the relationship between AGB and vegetation index(VI).These results suggest that VIUPD is the best predictor of AGB among simple regression models.Moreover,both VIUPD and the soil-adjusted VI could provide accurate estimates of AGB with dummy variables integrated in regression models.Therefore,ground-based hyperspectral measurements are useful for estimating AGB,which indicates the potential of applying satellite/airborne remote sensing techniques to AGB estimation of these grasslands on the Tibetan Plateau.