Above-ground biomass estimates for invasive trees and shrubs in the Pantanal of Mato Grosso, Brazil

ABSTRACTEvidence from southern African semi-arid savannas suggests that high elephant densities in nature reserves cause the over-utilization of woodlands. Northern Botswana, with its prolific elephant population, is expected to follow such a pattern unless the densities of elephants which could be sustained by indigenous woodlands are determined and maintained at carrying capacity. Above ground biomass production was estimated for mopane (Colophospermum mopane) woodlands, a principal food source for elephants, which grows over much of northern Botswana. Densities of trees and shrubs, dimensions of plants and elephant densities were recorded within stratified plots situated according to a regional rainfall gradient. Mean estimates of above ground biomass (foliage and twigs) were 9.41 and 7.83 t ha−1 for shrubs and trees respectively. A logistic model described the maximum levels of biomass removal from plants before over-utilization of mopane occurred. Variables incorporated in the model included above ground biomass of mopane shrubs and trees, growth rates of plants and expected off-take by elephants. The model predicted a complete biomass regain within 10 y if no elephant browsing occurs. Intensive elephant browsing in woodlands containing 15 elephants km−2, however, can suppress biomass production if growth rates of plants fall below 70% of the maximum annual rate. Nonetheless, there was no substantial evidence to suggest that elephants will reduce the biomass of mopane woodlands in northern Botswana below a sustainable level if their numbers are allowed to increase considerably beyond the current estimate.

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Estimation of above-ground biomass of trees and shrubs in a Eucalyptus populnea F. Muell. woodland by regression of mass on trunk diameter and plant height

Australian Journal of Botany ◽

10.1071/bt9790135 ◽

1979 ◽

Vol 27 (2) ◽

pp. 135 ◽

Cited By ~ 54

Author(s):

G Harrington

Keyword(s):

Standard Error ◽

Correlation Coefficients ◽

Common Species ◽

Regression Equations ◽

Above Ground Biomass ◽

Trunk Diameter ◽

Ground Biomass ◽

Acacia Aneura ◽

Trees And Shrubs ◽

Eucalyptus Populnea

An estimate of the above-ground biomass of leaf and wood in the shrubs and trees of Eucalyptus populnea woodland near Cobar, N.S.W., was made, by regression of leaf and wood weight on height or trunk diameter of the most common species. A new double regression technique was used for E. populnea to avoid destructively sampling whole trees. The mass of branches was regressed on primary branch diameter and these equations were used to estimate the total mass of crowns of trees. The estimated crown mass was then regressed on trunk diameters. Regression equations for estimation of mass of wood and leaf are provided for Geijera parvifora, Cassia nemophila, Dodonaea viscosa, Eremophila mitchellii, E. sturtii, E. bowmanii, Myoporum deserti and young Acacia aneura. The error of the estimates varied between 3 and 18% for different species. Log transformation of the data usually improved the correlation coefficients obtained but sometimes increased the standard error of the estimate. Estimates based on canopy measurements were rarely more accurate than those from height or trunk diameter. The overall estimate of biomass was 3.4 t of leaf and 51.4 t of wood per ha with a standard error of < 15%.

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ESTIMATION OF ABOVE GROUND BIOMASS OF THE THREE SITES (GRAZED SITE, PROTECTED SITE AND SEED SOWN SITE) FOR COMPARING THEIR PRODUCTIVITY IN KASHMIR VALLEY

FLORA AND FAUNA ◽

10.33451/florafauna.v23i2pp259-262 ◽

2017 ◽

Vol 23 (2) ◽

Author(s):

AFSHAN ANJUM BABA ◽

SYED NASEEM UL-ZAFAR GEELANI ◽

ISHRAT SALEEM ◽

MOHIT HUSAIN ◽

PERVEZ AHMAD KHAN ◽

...

Keyword(s):

Protected Areas ◽

Aboveground Biomass ◽

Plant Biomass ◽

Summer Season ◽

Above Ground Biomass ◽

Spring Season ◽

Kashmir Valley ◽

Ground Biomass ◽

Maximum Value ◽

Protected Site

The plant biomass for protected areas was maximum in summer (1221.56 g/m2) and minimum in winter (290.62 g/m2) as against grazed areas having maximum value 590.81 g/m2 in autumn and minimum 183.75 g/m2 in winter. Study revealed that at Protected site (Kanidajan) the above ground biomass ranged was from a minimum (1.11 t ha-1) in the spring season to a maximum (4.58 t ha-1) in the summer season while at Grazed site (Yousmarag), the aboveground biomass varied from a minimum (0.54 t ha-1) in the spring season to a maximum of 1.48 t ha-1 in summer seasonandat Seed sown site (Badipora), the lowest value of aboveground biomass obtained was 4.46 t ha-1 in spring while as the highest (7.98 t ha-1) was obtained in summer.

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The climatic imprint of bimodal distributions in vegetation cover for western Africa

Biogeosciences ◽

10.5194/bg-13-3343-2016 ◽

2016 ◽

Vol 13 (11) ◽

pp. 3343-3357 ◽

Cited By ~ 4

Author(s):

Zun Yin ◽

Stefan C. Dekker ◽

Bart J. J. M. van den Hurk ◽

Henk A. Dijkstra

Keyword(s):

Land Cover ◽

Congo Basin ◽

Shortwave Radiation ◽

Mean Annual Precipitation ◽

Above Ground Biomass ◽

Precipitation Regime ◽

Ground Biomass ◽

Western Africa ◽

Precipitation Regimes ◽

Woody Cover

Abstract. Observed bimodal distributions of woody cover in western Africa provide evidence that alternative ecosystem states may exist under the same precipitation regimes. In this study, we show that bimodality can also be observed in mean annual shortwave radiation and above-ground biomass, which might closely relate to woody cover due to vegetation–climate interactions. Thus we expect that use of radiation and above-ground biomass enables us to distinguish the two modes of woody cover. However, through conditional histogram analysis, we find that the bimodality of woody cover still can exist under conditions of low mean annual shortwave radiation and low above-ground biomass. It suggests that this specific condition might play a key role in critical transitions between the two modes, while under other conditions no bimodality was found. Based on a land cover map in which anthropogenic land use was removed, six climatic indicators that represent water, energy, climate seasonality and water–radiation coupling are analysed to investigate the coexistence of these indicators with specific land cover types. From this analysis we find that the mean annual precipitation is not sufficient to predict potential land cover change. Indicators of climate seasonality are strongly related to the observed land cover type. However, these indicators cannot predict a stable forest state under the observed climatic conditions, in contrast to observed forest states. A new indicator (the normalized difference of precipitation) successfully expresses the stability of the precipitation regime and can improve the prediction accuracy of forest states. Next we evaluate land cover predictions based on different combinations of climatic indicators. Regions with high potential of land cover transitions are revealed. The results suggest that the tropical forest in the Congo basin may be unstable and shows the possibility of decreasing significantly. An increase in the area covered by savanna and grass is possible, which coincides with the observed regreening of the Sahara.

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Above-ground biomass estimation of Indian tropical forests using X band Pol-InSAR and Random Forest

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2020.100462 ◽

2021 ◽

Vol 21 ◽

pp. 100462

Author(s):

Sadhana Yadav ◽

Hitendra Padalia ◽

Sanjiv K. Sinha ◽

Ritika Srinet ◽

Prakash Chauhan

Keyword(s):

Random Forest ◽

Tropical Forests ◽

Biomass Estimation ◽

Above Ground Biomass ◽

Ground Biomass ◽

X Band

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Comparison of Capability of SAR and Optical Data in Mapping Forest above Ground Biomass Based on Machine Learning

Environmental Sciences Proceedings ◽

10.3390/iecg2020-07916 ◽

2020 ◽

Vol 5 (1) ◽

pp. 13

Author(s):

Negar Tavasoli ◽

Hossein Arefi

Keyword(s):

Machine Learning ◽

Carbon Stock ◽

Vegetation Indices ◽

Forest Biomass ◽

Principal Component ◽

Optical Data ◽

Above Ground Biomass ◽

Ground Biomass ◽

Texture Characteristics ◽

Sentinel 2

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and Sentinel-1 data, and Sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

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