Growth Analysis and Biomass Estimation

Keywords

Relative growth rate, unit leaf rate, net assimilation rate, specific leaf area, leaf weight fraction, leaf area ratio, allometry, GEDI, LiDAR, forest biomass, regression, non-parametric models, pixel-based approach, sentinal-2, terrestrial carbon stock, uncertainty mapping, carbon accounting, climate change, field survey, geostatistics, remote sensing technique, scale, uncertainty.

Introduction

Growth analysis is an explanatory, holistic and integrative approach of interpreting plant functional or structural significance. The type of growth analysis requires measurement of plant biomass and assimilatory area (leaf area) and methods of computing certain parameters that describe growth. Growth is an irreversible increase in plant size accompanied by a quantitative change in biomass (weight).

In natural environments, growth and development cycles must be completed within a time frame dictated by environmental conditions where light, moisture and nutrients often limit expression of genetic potential. Adaptive features that counter such constraints and help sustain relative growth rate can be revealed via growth analysis under contrasting conditions. Researchers are yet to explore plant growth and reproductive development in quantitative terms.

Biomass estimation and mapping is a key element of global climate change impact assessment, carbon stock quantification, site suitability for bioprocessing plants, investigating the terrestrial ecosystem's carbon cycle, and assessing fuel energy for forest fires. Forest aboveground biomass (AGB) plays an important role in the study of the carbon cycle and climate change in the global terrestrial ecosystem.

Forests play a vital role in maintaining the global carbon balance. Biomass (B) is an assessment of how much living tissue mass is present in a population at one point in time. The health and environmental conditions of a forest ecosystem are reflected in biomass. AGB estimation based on remote sensing is the most precise tool for the quantification and estimation of biomass. The use of synthetic aperture radar (SAR) for remote sensing of forest vegetation and biomass has great potential for mapping and insights into forest ecology.

Absolute Growth Rate (AGR)

In the case, the plant size was determined more than one occasion, the increase in size over a given period can be determined as Absolute Growth Rate (AGR), given by:

$$\mathrm{AGR=M_{2}-M_{1}}$$

$$\mathrm{t_{2}-t_{1}}$$

where M₂ and M₁ are the mass of the plant at time t₂ and t₁, respectively. Absolute size at the end of an experiment then depends on seed mass, germination time, and the integration of AGR over all time steps measured. In plant biology, size is often measured as dry mass of whole plants (M), or the above-ground part of it.

The most useful and widely used analysis is the concept of relative growth rate (RGR) and the simple RGR equation, which derives from the growth of cell populations with unrestricted resources such as where light, space and nutrient supply are not limiting.

Growth models developed from populations of single cells can be extended mathematically to cover complex multicellular organisms where whole-plant growth is expressed in terms of leaf area and nutrient resources. These concepts of the early 1900s have proved increasingly useful for studies of growth and developmental responses in natural and managed environments.

Technologies Used in Biomass Estimation

As energy plants are characterized by a large net accumulation of biomass. The estimate methods, including allometric equation, mean biomass density, biomass expansion factor, geostatistics, etc.

There are different technologies that are used to detect biomass estimation. They are Optical Remote sensing, Synthetic Aperture Radar (SAR), Light Detection and Ranging (LiDAR), and Spatial Biomass modeling. AGB is modelled into two scenarios: extensive national forest inventory (NFI), and airborne Light Detection and Ranging (LiDAR) as reference data.

Measurement of forest aboveground biomass is critical to account for carbon budgeting, carbon flux monitoring, biodiversity health monitoring, and climate change studies. Global Ecosystem Dynamic Investigations (GEDI) mission LiDAR data combined with field-measured biomass and geospatial analysis to estimate forest aboveground woody biomass (AGB) in the managed forest. LiDAR can be used as an extension to NFI, for example for areas that are difficult or not possible to access.

Applications of Biomass Estimation

• Forest canopy height modeling.
• Spectral Mixture Analysis (SMA)
• Stand level analysis.
• Aboveground biomass (AGB) and Belowground biomass (BGB)

Highlights

• Importance of GEDI-LiDAR data filtering before biomass model development.
• Forest AGB model development based on relative height metrics and ground truth value.
• Conversion of GEDI LiDAR footprints into biomass footprints and computation of forest compartment-wise biomass.
• Forest AGB compartment wise map and validation.
• Machine Learning techniques were used to estimate Above-Ground Biomass (AGB).
• Wet season Sentinel-2 imagery was found sensitive to changes in AGB.
• Multiple band combinations act as better predictor variables than single band.
• Uncertainty maps help to identify regions with robust biomass prediction.
• Easily replicable framework, applicable to other dry deciduous tropical forests.

Challenges and Prospects

• Global field biomass dataset.
• Upscaling in biomass estimation.
• High-accuracy biomass estimate needs remote sensing.
• Geographical variation of allometric equation.

Conclusion

Quantitative growth analysis will be essential in developing new plants or improving management practices for higher yields in both optimal and suboptimal environments. Growth rates measure how quickly variables increase or decrease, showing the net change in value over some period.

To measure the bio-productivity of a natural ecosystem or agricultural crop, the component of immediate interest is the net primary production or total yield. In simple terms, plant-growth analysis requires little more than a balance, photosensitive paper, and a calculator for detailed studies of quantitative aspects of dry-matter production.

Plant allometry is the theoretical basis of vegetation biomass estimation. Generally, the use of allometric equations is indispensable to estimate biomass for both tree and forest. By reviewing the methods used for estimating forest biomass, we can conclude that each estimation method has its advantages and disadvantages, and none of these methods mentioned is always the best from individual to large scales. Newly developed techniques such as geostatistics and remote sensing technique (e.g., LIDAR) would be the key tools to improve forest biomass estimation with a high accuracy.

However, prior to this, spatial variation in forest biomass at various levels should be explored using multi-source data and multi-approaches.