Forest Biomass Estimation

Introduction

Nowadays forests resources play an important role in mitigating climate change by sequestering carbon in their biomass. 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.

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. 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.

 

Remote Sensing is considered to be the most precise tool for the quantification and estimation of biomass. Data from Earth observation satellites are readily accessible at a variety of scales, from regional to global, even from a variety of platforms. To quantify ecosystem biomass, three distinct remote sensing methods have emerged: passive optical, radar, and lidar. Several more measurement methodologies exist, ranging from application-driven to technologically cutting-edge.

 

Optical Remote sensing

Due to its high global coverage, consistency, and cost-effectiveness, optical remote sensing is likely to be the best choice for biomass estimation via field sampling. Optical Remote Sensing data is available on a variety of platforms, including IKONOS, Quickbird, Worldview, SPOT, Sentinel, Landsat, and MODIS.

 

Synthetic Aperture Radar (SAR)

SAR is a type of active data collection in which a sensor generates its energy and then measures how much of that energy is reflected after interacting with the Earth. By its ability to penetrate clouds and provide detailed vegetation metadata, radar remote sensing has attracted considerable attention in recent years for terrestrial biomass estimation. While airborne SAR systems are in use for many years, space-borne systems such as Terra-SAR, ALOS, and PALSAR are used.

 

Data from optical sensors or SAR is being used to obtain information about simple and homogeneous forest stand sites. Moreover, optical sensors have major drawbacks, particularly in tropical regions, such as cloud cover, which SAR can resolve, as well as identifying the target saturation and penetration.

Light Detection and Ranging (LiDAR)

LiDAR is the latest technology that has gained prominence in biomass estimation. It can study the vertical distribution of canopy and ground substrates and provide detailed structural information regarding vegetation and more precise estimates of basal area, crown size, tree height, and stem volume.

 

Spatial Biomass modeling

Several factors, such as inadequate sample data, weather patterns, complex biophysical landscapes, the scale of the study site, software availability, spatial resolution of remotely sensed data, or mixed pixels, all can have an impact on remote sensing-based biomass estimation.

Numerous models have been constructed based on different combinations of in situ tree parameters calculated using linear regression (LR) or non-linear regression (NLR) models. Multiple regression analysis is likely the most commonly used method for developing biomass estimation models. The integration of multiple simulations for biomass estimation seems to be an excellent way to enhance accuracy.

 

Vegetation indices along with near-infrared wavelength possess weaker relationships with biomass in tropical forests with complex stand structures than those such as shortwave infrared wavelength. In addition, near-infrared vegetation indices had quite a strong relationship with biomass in areas with poor soil conditions and relatively simple forest stand structures.

Ongoing biomass estimates are mainly obtained from ground-based samples, which are formulated and reported in inventories and ecosystem samples. Folks can scale up the sample values and provide the wall-to-wall mapping of biomass by using remote sensing techniques

Applications of Biomass Estimation

Forest canopy height modeling.

 

Canopy height is the distance between the top of the canopy and the ground. If signals from the surface and vegetation are being differentiated in the aspect of vegetation, the relative heights above the ground of forest canopies can be determined. Even though suitable stem diameter and canopy formation are required to maintain three dimensions, vegetation height is proportional to the volume and thus biomass.

 

Spectral Mixture Analysis (SMA)

To enhance the dry biomass estimations, SMA is often used to eliminate subpixel atmospheric as well as soil reflectance contamination. SMA can be used effectively in vegetation studies to assess land cover fractions and mapping the proportional area of coniferous species.

 

Stand level analysis

This analysis predicts main stand dimensions like top height, canopy cover, tree density, basal area, and volume. The existing features allow forest variables like biomass to be interpreted effectively through the use of lidar measurements, and this analysis is to be executed along with all big forest areas where lidar scope exists.

Aboveground biomass (AGB) and Belowground biomass (BGB)

These are the two types of forest biomass estimation methods used. Although calculating BGB field metrics is difficult, thus many researchers have focussed on computing AGB. The most reliable way to generate AGB data for individual trees is to use field surveys and logarithmic equations. Meanwhile, these methods are difficult to execute since they are time-consuming and labor-intensive. Besides that, even if forests have become a complex and widely spread ecosystem, large-scale implementation of these techniques is extremely expensive.

In AGB used two types of models are employed, ie direct and indirect. The direct models quantify biomass by evaluating the link between spectral data sensitivity and biomass field observations  In indirect models, it is calculated on biophysical parameters or forest basic structural measures.