Approaches of Satellite Remote Sensing for the Assessment of Above-Ground Biomass across Tropical Forests: Pan-tropical to National Scales

Abstract

Tropical forests are acknowledged for providing important ecosystem services and are renowned as “the lungs of the planet Earth” due to their role in the exchange of gasses—particularly inhaling CO2 and breathing out O2—within the atmosphere. Overall, the forests provide 50% of the total plant biomass of the Earth, which accounts for 450–650 PgC globally. Understanding and accurate estimates of tropical forest biomass stocks are imperative in ascertaining the contribution of the tropical forests in global carbon dynamics. This article provides a review of remote-sensing-based approaches for the assessment of above-ground biomass (AGB) across the tropical forests (global to national scales), summarizes the current estimate of pan-tropical AGB, and discusses major advancements in remote-sensing-based approaches for AGB mapping. The review is based on the journal papers, books and internet resources during the 1980s to 2020. Over the past 10 years, a myriad of research has been carried out to develop methods of estimating AGB by integrating different remote sensing datasets at varying spatial scales. Relationships of biomass with canopy height and other structural attributes have developed a new paradigm of pan-tropical or global AGB estimation from space-borne satellite remote sensing. Uncertainties in mapping tropical forest cover and/or forest cover change are related to spatial resolution; definition adapted for ‘forest’ classification; the frequency of available images; cloud covers; time steps used to map forest cover change and post-deforestation land cover land use (LCLU)-type mapping. The integration of products derived from recent Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) satellite missions with conventional optical satellite images has strong potential to overcome most of these uncertainties for recent or future biomass estimates. However, it will remain a challenging task to map reference biomass stock in the 1980s and 1990s and consequently to accurately quantify the loss or gain in forest cover over the periods. Aside from these limitations, the estimation of biomass and carbon balance can be enhanced by taking account of post-deforestation forest recovery and LCLU type; land-use history; diversity of forest being recovered; variations in physical attributes of plants (e.g., tree height; diameter; and canopy spread); environmental constraints; abundance and mortalities of trees; and the age of secondary forests. New methods should consider peak carbon sink time while developing carbon sequestration models for intact or old-growth tropical forests as well as the carbon sequestration capacity of recovering forest with varying levels of floristic diversity.

Terrestrial carbon cycle: tipping edge of climate change between the atmosphere and biosphere ecosystems

“…RS facilities researchers with the synoptic view of the attributed enigmatic terrestrial ecosystems, which are inaccessible, with multiple temporal (diurnal to decadal) and spatial (regional to global) scale data sets 7,89,133 . To date, the spatial viability of RS platform ranges with coarser-resolution: higher than 250m 118,134 (e.g., MODIS, AVHRR), mediumresolution: between 10 -200m 94 (e.g., Landsat, Sentinel), finerresolution: less than 10m 15,89,135 (e.g., QuickBird, IKONOS). Monitoring carbon fluxes are predominant in quantifying the terrestrial carbon cycle, met by the RS with high spatial heterogenetic consistent and repeated cycles 15,83,120 .…”

Monitoring green biomass utilizing remote sensing techniques for agriculture and forest areas in East Malaysia

“…Equation 5 was developed from a similar study conducted in Central Kalimantan, Indonesia, that employs NDVI as an indicator of vegetation health and vigor by Sugiarto & June (2008), as follows: fAPAR = 1.25 x NDVI -0.025 … (5) Estimating the fAPAR is crucial as it uses NDVI derived from remote sensing in this equation, which depends on the availability of satellite data. We chose this equation because it can indicate solar radiation energy that is used in the photosynthetic process.…”

“…Sentinel and Worldview are the recent satellite data sets that have enabled NPP evaluation, allowing forest plantation management to estimate green vegetation biomass in plantation areas. To date, approaches of using satellite remote sensing in assessing biomass have been demonstrated in the tropical forest of pan-tropical to national scales using Synthetic Aperture Radar (SAR) [5]. This is because remote sensing data is temporal and best suited for most ecological applications by using biophysical parameters derived from satellite data.…”

Green Energy Sources Assessment Using Sentinel-1 Satellite Remote Sensing

“…C (S-1) and X-band satellite data are used in many studies to remotely measure plants and grasslands (McNairn et al, 2009), although in many cases L-band satellite data are also used for these studies (Yekkehkhany et al, 2014). The results of studies show that SAR backscatters satellites (P and L bands) can evaluate and assess the main parameters of forest biomass as well, for example, tree age (Lucas et al, 2020), tree height (Abbas et al, 2020), DBH (Morin et al, 2019), and tree dryness (Hernández-Stefanoni et al, 2020). Fast methods with more accurate tool measurements of these parameters by SAR satellites can help to manage and sustainably develop how and when to properly manage these sources.…”