scholarly journals Biomass estimation equations for mesquite trees in the Americas

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6782 ◽  
Author(s):  
Jose Navar ◽  
Felipa de Jesus Rodriguez-Flores ◽  
Julio Rios-Saucedo
Keyword(s):  
Aboveground Biomass ◽  
Biomass Estimation ◽  
Data Sets ◽  
South American ◽  
Biomass Equation ◽  
Northern Mexico ◽  
Biomass Equations ◽  
Semi Empirical ◽  
North And South ◽  
Semi Arid

Mesquite trees are the preferred dendroenergy sources in arid and semi-arid forests. In spite of their relative importance, regional aboveground biomass (AGB) equations for mesquite trees are scarce in the scientific literature. For that reason, the aims of this study were to: (a) harvest trees and develop regional biomass equations; (b) contrast measured data with equations developed previously; and (c) test the applicability of the fitted equation for mesquite trees in the arid and semi-arid forests of the Americas. We harvested 206 new mesquite trees from arid and semi-arid forests in northern Mexico (Coahuila, Nuevo Leon, and Tamaulipas) in addition to using two other previously compiled data sets from Mexico (N = 304) to develop a regional equation. To test the validity of this equation, for biomass equations reported for the rest of the country, as well as for North and South American mesquite trees, we contrasted AGB measurements with predictions of fitted equations. Statistical analysis revealed the need for a single, regional, semi-empirical equation as together the three data sets represented the variability of the aboveground biomass of mesquite trees across northern Mexico, as well as mesquite trees in America’s arid and semiarid regions. Due to the large quantity of mesquite trees harvested for sampling and their variability, the regional biomass equation developed encompasses all other North and South American equations, and is representative of mesquite trees throughout the arid and semi-arid forests of the Americas.

Allometric equations for aboveground biomass estimation by size class for Pinus brutia Ten. trees growing in North and South Aegean Islands, Greece

2010 ◽  
Vol 130 (2) ◽  
pp. 145-160 ◽  
Author(s):  
Dimitris Zianis ◽  
Gavriil Xanthopoulos ◽  
Kostas Kalabokidis ◽  
George Kazakis ◽  
Dany Ghosn ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Size Class ◽  
Allometric Equations ◽  
Biomass Estimation ◽  
Pinus Brutia ◽  
Aegean Islands ◽  
North And South

scholarly journals A review of biomass equations for China's tree species

2020 ◽  
Vol 12 (1) ◽  
pp. 21-40 ◽  
Author(s):  
Yunjian Luo ◽  
Xiaoke Wang ◽  
Zhiyun Ouyang ◽  
Fei Lu ◽  
Liguo Feng ◽  
...  
Keyword(s):  
Tree Species ◽  
Carbon Accounting ◽  
Sub Saharan Africa ◽  
Background Information ◽  
Vegetation Coverage ◽  
Biomass Estimation ◽  
Tree Biomass ◽  
Biomass Equation ◽  
Geographical Regions ◽  
Biomass Equations

Abstract. Tree biomass equations are the most commonly used method to estimate tree and forest biomasses at various spatial and temporal scales because of their high accuracy, efficiency and conciseness. For decades, many tree biomass equations have been reported in diverse types of literature (e.g., journals, books and reports). These scattered equations are being compiled, and tree biomass equation datasets are currently available for many geographical regions (e.g., Europe, North America and sub-Saharan Africa) and countries (e.g., Australia, Indonesia and Mexico). However, one important country stands out as an area where a large number of biomass equations have not yet been reviewed and inventoried extensively: China. Therefore, in this study, we carried out a broad survey and critical review of the literature (from 1978 to 2013) on biomass equations in China and compiled a normalized tree biomass equation dataset for China. This dataset consists of 5924 biomass equations for nearly 200 tree species and their associated background information (e.g., geographical location, climate and stand description), showing sound geographical, climatic and forest vegetation coverage across China. The dataset is freely available at https://doi.org/10.1594/PANGAEA.895244 (Luo et al., 2018) for noncommercial scientific applications, and this dataset fills an important regional gap in global biomass equations and provides key parameters for biomass estimation in forest inventory and carbon accounting studies in China.

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 ChinAllomeTree 1.0: China's normalized tree biomass equation dataset

2019 ◽  
Author(s):  
Yunjian Luo ◽  
Xiaoke Wang ◽  
Zhiyun Ouyang ◽  
Fei Lu ◽  
Liguo Feng ◽  
...  
Keyword(s):  
Geographical Location ◽  
Allometric Equation ◽  
Carbon Accounting ◽  
Sub Saharan Africa ◽  
Background Information ◽  
Biomass Estimation ◽  
Tree Biomass ◽  
Biomass Equation ◽  
Geographical Regions ◽  
Biomass Equations

Abstract. The tree biomass equation, which is also called the tree allometric equation, is the most commonly used method to estimate tree and forest biomass at various spatial-temporal scales because of its high accuracy, efficiency and conciseness. For decades, many tree biomass equations have been reported in diverse types of literature (e.g., journals, books and reports). These scattered equations are being compiled, and tree biomass equation datasets are currently available for many geographical regions (e.g., Europe, North America and Sub-Saharan Africa) and countries (e.g., Australia, Indonesia and Mexico) except for in an important region of the world, Eastern Asia, specifically China. Therefore, in this study, we carried out an extensive survey and critical review of the literature (from 1978–2013) on biomass equations conducted in China and developed China’s normalized tree biomass equation dataset (ChinAllomeTree version 1.0). This dataset consists of 5,924 biomass component equations for nearly 200 species and their associated background information (e.g., geographical location, climate and stand description), showing sound geographical, climatic and forest vegetation coverages across China. The dataset is freely available at https://doi.pangaea.de/10.1594/PANGAEA.895244 for noncommercial scientific applications, which fills an important regional gap in global biomass datasets and provides key parameters for biomass estimation in forest inventory and carbon accounting in China.

scholarly journals POLINSAR BASED SCATTERING INFORMATION RETRIEVAL FOR FOREST ABOVEGROUND BIOMASS ESTIMATION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1913-1920
Author(s):  
N. Agrawal ◽  
S. Kumar ◽  
V. A. Tolpekin
Keyword(s):  
Aboveground Biomass ◽  
Cloud Model ◽  
Accuracy Assessment ◽  
Forest Biomass ◽  
Orientation Angle ◽  
Coefficient Of Determination ◽  
Biomass Estimation ◽  
Biophysical Parameters ◽  
Empirical Modelling ◽  
Semi Empirical

<p><strong>Abstract.</strong> Forests play a crucial role in storing carbon and are of paramount importance in maintaining global carbon cycle. Assessment of forest biomass at regional and global level is vital for understanding and monitoring health of both tree species and entire cover. Changes in forest biomass are caused by human activities, natural factors and variations in climate. Forest biomass measurement is necessary for gauging the changes in forest ecosystems. Remote sensing is indispensable for mapping forest biophysical parameters. Microwaves are capable of collecting data even in case of cloud cover as the microwaves are of long wavelength. Microwaves help in retrieving scattering information of target. The goal of this research was to map aboveground biomass (AGB) over Barkot forest range in Dehradun, India. The current work focuses on the retrieval of PolInSAR based scattering information for the estimation of aboveground biomass. Radarsat-2 fully Polarimetric C-band data was used for the estimation of AGB in Barkot forest area. A semi-empirical model, which is Extended Water Cloud Model (EWCM) was utilized for AGB estimation. EWCM considers ground-stem interactions. Due to overestimation of volume scattering, polarization orientation angle shift correction was implemented on the PolInSAR pair. Field biomass data was utilized for accuracy assessment. The results show that coefficient of determination (R<sup>2</sup>) value of 0.47, Root Mean Square Error (RMSE) of 56.18 (t&amp;thinsp;ha<sup>&amp;minus;1</sup>) and accuracy of 72% was obtained between modelled biomass against field measured biomass. Hence, it can be inferred from the obtained results that PolInSAR technique, in combination with semi-empirical modelling approach, can be implemented for estimating forest biomass.</p>

Regional aboveground biomass equations for North American arid and semi-arid forests

2013 ◽  
Vol 97 ◽  
pp. 127-135 ◽  
Author(s):  
José Návar ◽  
Julio Ríos-Saucedo ◽  
Gustavo Pérez-Verdín ◽  
F. de Jesús Rodríguez-Flores ◽  
Pedro A. Domínguez-Calleros
Keyword(s):  
North American ◽  
Aboveground Biomass ◽  
Biomass Equations ◽  
Semi Arid

scholarly journals A new species of the genus Hyalella (Crustacea, Amphipoda) from northern Mexico

ZooKeys ◽  
2020 ◽  
Vol 942 ◽  
pp. 1-19
Author(s):  
Aurora Marrón-Becerra ◽  
Margarita Hermoso-Salazar ◽  
Gerardo Rivas
Keyword(s):  
New Species ◽  
Costa Rica ◽  
South American ◽  
Northern Mexico ◽  
The North ◽  
Dorsal Process ◽  
North And South ◽  
A New Species

A new species, Hyalella tepehuanasp. nov., is described from Durango state, Mexico, a region where studies on Hyalella have been few. This species differs from most species of the North and South American genus Hyalella in the number of setae on the inner plate of maxilla 1 and maxilla 2, characters it shares with Hyalella faxoni Stebbing, 1903. Nevertheless, H. faxoni, from the Volcan Barva in Costa Rica, lacks a dorsal process on pereionites 1 and 2. Also, this new species differs from other described Hyalella species in Mexico by the shape of the palp on maxilla 1, the number of setae on the uropods, and the shape of the telson.

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