Biomass Distribution and Development of Allometric Equations for Non-Destructive Estimation of Carbon Sequestration in Grafted Mango Trees

Ganeshamurthy, A. N.; Ravindra, V.; Venugopalan, R.; Mathiazhagan, Malarvizhi; Bhat, Ravi

doi:10.5539/jas.v8n8p201

Cited by 12 publications

(18 citation statements)

References 25 publications

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“…The later process is known as allometric measurements and requires destructive methods to scan specific area of total leaves per plant or tree in the case of LAI [25]. Indirect validation methods are based on proximal instrumentation using the same or similar spectral instrumentation to assess georeferenced sentinel plants at the same angle as the aerial platforms.…”

Section: Vegetation Indices and Validation Processmentioning

confidence: 99%

Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

2017

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Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications. These indices have been widely implemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned Aerial Vehicles (UAV). Up to date, there is no unified mathematical expression that defines all VIs due to the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used. Therefore, customized algorithms have been developed and tested against a variety of applications according to specific mathematical expressions that combine visible light radiation, mainly green spectra region, from vegetation, and nonvisible spectra to obtain proxy quantifications of the vegetation surface. In the real-world applications, optimization VIs are usually tailored to the specific application requirements coupled with appropriate validation tools and methodologies in the ground. The present study introduces the spectral characteristics of vegetation and summarizes the development of VIs and the advantages and disadvantages from different indices developed. This paper reviews more than 100 VIs, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision. Predictably, research, and development of VIs, which are based on hyperspectral and UAV platforms, would have a wide applicability in different areas.

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Section: Vegetation Indices and Validation Processmentioning

confidence: 99%

Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

2017

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“…The use of regression models has become a very precise non-destructive method that allows the real leaf area of plants to be estimated from linear measurements of leaf blades (length and width), which are directly correlated with leaf surface area (ZHANG; PAN, 2011). Such methods have been used to estimate leaf area of both cultivated (GANESHAMURTHY et al, 2016;CARVALHO et al, 2017;OLIVEIRA et al, 2017) and forest species (ASSIS et al, 2015;KERAMATLOU et al, 2015;RIBEIRO et al, 2018b;RIBEIRO et al, 2019a;RIBEIRO et al, 2019b).…”

Section: Introductionmentioning

confidence: 99%

A NON-DESTRUCTIVE METHOD FOR ESTIMATING LEAF AREA OF Ceiba glaziovii (Kuntze) K. Schum.

Ribeiro

Figueiredo

Coêlho

et al. 2019

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The determination of leaf area is of fundamental importance in studies involving ecological and ecophysiological aspects of forest species. The objective of this research was to adjust an equation to determine the leaf area of Ceiba glaziovii as a function of linear measurements of leaves. Six hundred healthy leaf limbs were collected in different matrices, with different shapes and sizes, in the Mata do Pau-Ferro State Park, Areia, Paraíba state, Northeast Brazil. The maximum length (L), maximum width (W), product between length and width (L.W), and leaf area of the leaf limbs were calculated. The regression models used to construct equations were: linear, linear without intercept, quadratic, cubic, power and exponential. The criteria for choosing the best equation were based on the coefficient of determination (R²), Akaike information criterion (AIC), root mean square error (RMSE), Willmott concordance index (d) and BIAS index. All the proposed equations satisfactorily estimate the leaf area of C. glaziovii, due to their high determination coefficients (R² ≥ 0.851). The linear model without intercept, using the product between length and width (L.W), presented the best criteria to estimate the leaf area of the species, using the equation 0.4549*LW.

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“…The fruit accounted for less than 20% of the tree's dry matter at this time, with an increasing investment in the trunk and branches. Oguntunde et al (2011) and Ganeshamurthy et al (2016) explored the changes in tree growth over time for orchards in Nigeria and India and found similar relationships as the ones documented by Davie and Stassen (1997) in South Africa. In Nigeria, the changes in tree canopy volume and leaf area per tree followed sigmoid patterns in trees from two-to thirty-three years of age (Fig.…”

Section: Relationship Between Productivity Tree Growth and Light Intmentioning

confidence: 66%

“…5). The studies of Oguntunde et al (2011) and Ganeshamurthy et al (2016) also demonstrated the strong relationship between leaf area or leaf dry weight per tree and tree canopy volume. Unfortunately, no data on fruit production were mentioned in these investigations.…”

Section: Relationship Between Productivity Tree Growth and Light Intmentioning

confidence: 99%

Can the productivity of mango orchards be increased by using high-density plantings?

Menzel¹,

Lagadec²

2017

Scientia Horticulturae

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a b s t r a c tMango (Mangifera indica) trees are traditionally established at about 100-200 trees per ha and eventually grow into large specimens 10 m tall or more, making spraying and harvesting difficult. It also takes a long time to recover the initial costs of establishing and maintaining the orchard. There has been considerable interest in planting orchards up to 4000 trees per ha to take advantage of early production and to increase economic returns. However, trees planted at high density soon begin to crowd and shade each other and production falls. We reviewed the performance of high-density orchards in different growing areas, and the role of dwarfing cultivars and rootstocks, tree canopy management and the growth regulator, paclobutrazol to control tree growth. There has been no general agreement on the optimum planting density for commercial orchards which vary from 200-4000 trees per ha in different experiments. Some potential dwarfing material has been developed in India and elsewhere, but these cultivars and rootstocks have not been widely integrated into high-density orchards. Canopy management needs to take into account the effect of pruning on the regrowth of the shoots and branches, light distribution through the canopy and the loss of the leaves that support the developing crop. Pruning must also take into account the effect of vegetative growth on flower initiation. Annual light pruning usually provides better fruit production than more severe pruning conducted less regularly. There have only been a few cases where it has been demonstrated that paclobutrazol can counteract the negative effect of pruning on flowering and fruit production. There are also concerns with residues of this chemical in export markets and contamination of ground waters. The future development of high-density plantings in this crop is dependent on the use of dwarfing cultivars and/or rootstocks and better canopy management strategies. Dwarfing cultivars and rootstocks should provide small-to medium-sized trees with medium to large yields. This can readily be identified in experiments by examining the relationship between yield and tree growth. Research on canopy management should assess the impact of pruning on flowering, light distribution within the canopy and the leaf area supporting the developing crop. The productivity of mango is not likely to be increased by the use of high-density plantings without extensive efforts in plant breeding and canopy management.Crown

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Biomass Distribution and Development of Allometric Equations for Non-Destructive Estimation of Carbon Sequestration in Grafted Mango Trees

Cited by 12 publications

References 25 publications

Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

A NON-DESTRUCTIVE METHOD FOR ESTIMATING LEAF AREA OF Ceiba glaziovii (Kuntze) K. Schum.

Can the productivity of mango orchards be increased by using high-density plantings?

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