scholarly journals Biomass estimation models and allocation patterns of 14 shrub species in Mountain Luya, Shanxi, China

2017 ◽  
Vol 41 (1) ◽  
pp. 87-92
Author(s):  
LUO Yong-Kai
Keyword(s):  
Biomass Estimation ◽  
Shrub Species ◽  
Allocation Patterns ◽  
Estimation Models

scholarly journals Biomass Estimation Models for Six Shrub Species in Hunshandake Sandy Land in Inner Mongolia, Northern China

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 167
Author(s):  
Xueling Yao ◽  
Guojing Yang ◽  
Bo Wu ◽  
Lina Jiang ◽  
Feng Wang
Keyword(s):  
Water Conservation ◽  
Inner Mongolia ◽  
Estimation Methods ◽  
Biomass Estimation ◽  
Sandy Land ◽  
Shrub Species ◽  
Allometric Models ◽  
Volume Model ◽  
Shrub Biomass ◽  
Estimation Models

Shrub biomass estimation is valuable in assessing ecological health, soil, and water conservation capacity, and carbon storage in arid areas, where trees are scattered, and shrubs are usually dominant. Most shrub biomass estimation models are derived from trees designed for trees, yet shrubs and trees show significant differences in morphology. However, current biomass estimation methods specifically for shrubs are still lacking. This study aimed to test various predictors’ performance in estimating shrub biomass, particularly providing an improved cone frustum volume model as a new predictor. Seven different variables, including three univariates and four composite variables, were selected as predictors in allometric models. Six dominant shrub species of different sizes and morphology in the semi-arid Hunshandake Sandy Land in Inner Mongolia were selected as samples to test the seven predictors’ performances in above-ground biomass estimation. Results showed that the single measurements performed poorly and were not suitable for shrub biomass estimation. The allometric models, including crown-related volumes as predictors, performed much better and were considered ideal for common shrub biomass estimation. The improved cone frustum volume model had more flexible geometric for shrubs of different shapes and sizes, with high fitting accuracy and stability among all the volume predictors. Therefore, we recommend the volume of an inverted cone frustum with a crown diameter and ground diameter as the long and short diameters as an excellent predictor of shrub biomass estimation, especially when studies involve various shrub species, and a general model would be needed.

scholarly journals MANGROVE ABOVE GROUND BIOMASS ESTIMATION USING COMBINATION OF LANDSAT 8 AND ALOS PALSAR DATA

2017 ◽  
Vol 12 (2) ◽  
pp. 85 ◽  
Author(s):  
Gathot Winarso ◽  
Yenni Vetrita ◽  
Anang D. Purwanto ◽  
Nanin Anggraini ◽  
Soni Darmawan ◽  
...  
Keyword(s):  
Carbon Stock ◽  
Vegetation Indices ◽  
Emission Trading ◽  
Remote Sensing Data ◽  
Tree Height ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Alos Palsar ◽  
Sar Data ◽  
Estimation Models

Mangrove ecosystem is important coastal ecosystem, both ecologically and economically. Mangrove provides rich-carbon stock, most carbon-rich forest among ecosystems of tropical forest. It is very important for the country to have a large mangrove area in the context of global community of climate change policy related to emission trading in the Kyoto Protocol. Estimation of mangrove carbon-stock using remote sensing data plays an important role in emission trading in the future. Estimation models of above ground mangrove biomass are still limited and based on common forest biomass estimation models that already have been developed. Vegetation indices are commonly used in the biomass estimation models, but they have low correlation results according to several studies. Synthetic Aperture Radar (SAR) data with capability in detecting volume scattering has potential applications for biomass estimation with better correlation. This paper describes a new model which was developed using a combination of optical and SAR data. Biomass is volume dimension related to canopy and height of the trees. Vegetation indices could provide two dimensional information on biomass by recording the vegetation canopy density and could be well estimated using optical remote sensing data. One more dimension to be 3 dimensional feature is height of three which could be provided from SAR data. Vegetation Indices used in this research was NDVI extracted from Landsat 8 data and height of tree estimated from ALOS PALSAR data. Calculation of field biomass data was done using non-decstructive allometric based on biomass estimation at 2 different locations that are Segara Anakan Cilacap and Alas Purwo Banyuwangi, Indonesia. Correlation between vegetation indices and field biomass with ALOS PALSAR-based biomass estimation was low. However, multiplication of NDVI and tree height with field biomass correlation resulted R2 0.815 at Alas Purwo and R2 0.081 at Segara Anakan.  Low correlation at Segara anakan was due to failed estimation of tree height. It seems that ALOS PALSAR height was not accurate for determination of areas dominated by relative short trees as we found at Segara Anakan Cilacap, but the result was quite good for areas dominated by high trees. To improve the accuracy of tree height estimation, this method still needs validation using more data.

Developing biomass estimation models for above-ground compartments in Eucalyptus dunnii and Corymbia citriodora plantations

2019 ◽  
Vol 130 ◽  
pp. 105353 ◽  
Author(s):  
Lina Garcia_Florez ◽  
Jerome K. Vanclay ◽  
Kevin Glencross ◽  
J. Doland Nichols
Keyword(s):  
Biomass Estimation ◽  
Eucalyptus Dunnii ◽  
Corymbia Citriodora ◽  
Estimation Models

scholarly journals Predicting biomass of hyperdiverse and structurally complex central Amazonian forests – a virtual approach using extensive field data

2016 ◽  
Vol 13 (5) ◽  
pp. 1553-1570 ◽  
Author(s):  
Daniel Magnabosco Marra ◽  
Niro Higuchi ◽  
Susan E. Trumbore ◽  
Gabriel H. P. M. Ribeiro ◽  
Joaquim dos Santos ◽  
...  
Keyword(s):  
Size Distribution ◽  
Tropical Forests ◽  
Floristic Composition ◽  
Data Availability ◽  
Extreme Weather Events ◽  
Biomass Estimation ◽  
Compositional Variation ◽  
Wide Range ◽  
Landscape Level ◽  
Estimation Models

Abstract. Old-growth forests are subject to substantial changes in structure and species composition due to the intensification of human activities, gradual climate change and extreme weather events. Trees store ca. 90 % of the total aboveground biomass (AGB) in tropical forests and precise tree biomass estimation models are crucial for management and conservation. In the central Amazon, predicting AGB at large spatial scales is a challenging task due to the heterogeneity of successional stages, high tree species diversity and inherent variations in tree allometry and architecture. We parameterized generic AGB estimation models applicable across species and a wide range of structural and compositional variation related to species sorting into height layers as well as frequent natural disturbances. We used 727 trees (diameter at breast height  ≥  5 cm) from 101 genera and at least 135 species harvested in a contiguous forest near Manaus, Brazil. Sampling from this data set we assembled six scenarios designed to span existing gradients in floristic composition and size distribution in order to select models that best predict AGB at the landscape level across successional gradients. We found that good individual tree model fits do not necessarily translate into reliable predictions of AGB at the landscape level. When predicting AGB (dry mass) over scenarios using our different models and an available pantropical model, we observed systematic biases ranging from −31 % (pantropical) to +39 %, with root-mean-square error (RMSE) values of up to 130 Mg ha−1 (pantropical). Our first and second best models had both low mean biases (0.8 and 3.9 %, respectively) and RMSE (9.4 and 18.6 Mg ha−1) when applied over scenarios. Predicting biomass correctly at the landscape level in hyperdiverse and structurally complex tropical forests, especially allowing good performance at the margins of data availability for model construction/calibration, requires the inclusion of predictors that express inherent variations in species architecture. The model of interest should comprise the floristic composition and size-distribution variability of the target forest, implying that even generic global or pantropical biomass estimation models can lead to strong biases. Reliable biomass assessments for the Amazon basin (i.e., secondary forests) still depend on the collection of allometric data at the local/regional scale and forest inventories including species-specific attributes, which are often unavailable or estimated imprecisely in most regions.

scholarly journals Above-Ground Biomass Models of Caragana korshinskii and Sophora viciifolia in the Loess Plateau, China

2019 ◽  
Vol 11 (6) ◽  
pp. 1674 ◽  
Author(s):  
Yanxing Dou ◽  
Yang Yang ◽  
Shaoshan An
Keyword(s):  
Loess Plateau ◽  
Leaf Biomass ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
The Loess Plateau ◽  
Ground Biomass ◽  
Caragana Korshinskii ◽  
Canopy Volume ◽  
Branch Biomass ◽  
Estimation Models

The quantification of above-ground biomass is based on the calculation of carbon storage, which is important for the balance of carbon cycling. However, the allometric models of shrubs for calculating the above-ground biomass of shrubs in the Loess Plateau are scarce. In order to solve this issue, this study analyzed some highly correlated variables, including height (H), branch diameters (D), canopy volume (Cv), canopy area (Ca), and then built a regression model to predict the above-ground biomass in two common shrubs (Caragana korshinskii and Sophora viciifolia) in the Loess Plateau, China. The results show that the above-ground biomass of these two shrubs can be accurately predicted by H and D, and then we can use allometric model (y = axb) to calculate shrub above-ground biomass (including leaf biomass and branch biomass). Furthermore, the correlation between leaf biomass and branch biomass in Caragana korshinskii and Sophora viciifolia indicates that the components of above-ground biomass are closely related to each other. In addition, there is a strong linear relationship (p < 0.01) between the observed and estimated biomass values, which confirms the data accuracy of the above-ground biomass estimation models. In summary, these two biomass estimation models provide an accurate way to estimate the quantification of carbon for shrubs in the Loess Plateau.

scholarly journals A biomass equation dataset for common shrub species in China

2021 ◽  
Author(s):  
Yang Wang ◽  
Wenting Xu ◽  
Zhiyao Tang ◽  
Zongqiang Xie
Keyword(s):  
Terrestrial Ecosystem ◽  
Structure And Function ◽  
Biomass Estimation ◽  
Ecosystem Structure ◽  
Biomass Equation ◽  
Shrub Species ◽  
Biomass Equations ◽  
Shrub Biomass ◽  
And Function ◽  
Ecosystem Structure And Function

Abstract. Shrub biomass equations provide an accurate, efficient and convenient method in estimating biomass of shrubland ecosystems and biomass of the shrub layer in forest ecosystems at various spatial and temporal scales. In recent decades, many shrub biomass equations have been reported mainly in journals, books and postgraduate's dissertations. However, these biomass equations are applicable for limited shrub species with respect to a large number of shrub species widely distributed in China, which severely restricted the study of terrestrial ecosystem structure and function, such as biomass, production, and carbon budge. Therefore, we firstly carried out a critical review of published literature (from 1982 to 2019) on shrub biomass equations in China, and then developed biomass equations for the dominant shrub species using a unified method based on field measurements of 738 sites in shrubland ecosystems across China. Finally, we constructed the first comprehensive biomass equation dataset for China’s common shrub species. This dataset consists of 822 biomass equations specific to 167 shrub species and has significant representativeness to the geographical, climatic and shrubland vegetation features across China. The dataset is freely available at https://doi.org/10.11922/sciencedb.00641 for noncommercial scientific applications, and this dataset fills a significant gap in woody biomass equations and provides key parameters for biomass estimation in studies on terrestrial ecosystem structure and function.

scholarly journals PEMETAAN BIOMASSA TEGAKAN HUTAN HUJAN TROPIS DI BUKIT MANDIANGIN MENGGUNAKAN CITRA SENTINEL-2 MSI

2021 ◽  
Vol 9 (3) ◽  
pp. 299
Author(s):  
Mufidah Asy’ari ◽  
Syam’ani Syam’ani ◽  
Trisnu Satriadi
Keyword(s):  
Environmental Sustainability ◽  
Tropical Rainforest ◽  
Vegetation Indices ◽  
Percentage Error ◽  
Biomass Estimation ◽  
Total Biomass ◽  
Average Biomass ◽  
Simple Ratio ◽  
Estimation Models ◽  
Sentinel 2

The preservation of standing biomass is one of the most vital elements for environmental sustainability and the sustainability of the forest itself. One of the actions that can be taken in an effort to maintain the sustainability of forest stand biomass is to map the distribution of biomass, and monitor changes or dynamics of stand biomass from time to time in a sustainable manner. This study aims to build a model based on remote sensing imagery to estimate the total biomass of tropical rainforest stands in Mandiangin Hill, South Kalimantan. The models developed in this study are based on vegetation indices extracted from Sentinel-2 MSI Imagery. A total of ten vegetation indices were tested in this study. For the construction process and validation of stand biomass estimation models, biomass information was measured directly in the field using a number of measuring plots. Stand biomass estimation models were made by correlating stand biomass information from the field with vegetation indices from Sentinel-2 MSI Imagery. The results showed that the most accurate model for estimating the biomass of tropical rainforest stands was 9.5806.exp (0.1454.PSSRa). Where PSSRa is Pigment Specific Simple Ratio. This model has a correlation coefficient (R2) of 0.876, a Mean Absolute Percentage Error (MAPE) of 16.8%, and a Root Mean Square Error (RMSE) of 32.6. The estimation results show that the total biomass of the Bukit Mandiangin tropical rainforest stands is between 11.7 to 998.5 Mg/ha, with an average biomass of 135.8 Mg/ha. Furthermore, the estimation of stand biomass in this study is limited to woody vegetation with a DBH of 10 cm and above. The PSSRa model with various improvements can be used to accurately estimate stand biomass

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