Remote Sensing Concepts and Their Applicability in REDD+ Monitoring

Abstract

Th e Tier app ro ach introd uced by the ntergovernmental Panel on Climate Change (IPCC) has been used to describe levels of methodological quality and complexity in the measurement reporting and verification (MRV) assessment system. Although guidelines do not explicitly state accuracy requirements, it has been asserted in previous studies that remote sensing missions should entail biomass errors that remain within 20 % of stand level estimates. The biomass of the target forest can be almost directly measured using LiDAR techniques when other sensors have rather low capacity to penetrate through canopy and response signals are not too noisy to detect changes in closed forests. Optical or radar satellite data, in relative terms, are only a tenth of the price of LiDAR. Taking into account cost-effectiveness within a tropical context, it is proposed that Landsat-like data can be useful for obtaining land cover and economical estimates for large areas. Typically, the role of airborne LiDAR is to provide ground truth type of information covering large areas. The reducing emissions from deforestation and forest degradation measurement, reporting and verification (REDD MRV) will need good ground sample for basal area and canopy cover change detection. LiDAR will be also feasible for forest management planning maps and planning operational REDD activities, but cost-effective mapping of carbon stocks for regional-and national-scale assessments will rely on a combination of satellite imagery and ground-based inventory samples. The distribution of tree species and different forest types need to be measured from the field and information can be generalised using satellite data with 1-30-m resolution. The global scale maps with the ground resolution 100 m-1 km are mainly for visualisation purposes and can be used to extrapolate distribution of information collected by 1-30-m remote sensing sensors and field plots.

LiDAR-Assisted Multi-Source Program (LAMP) for Measuring Above Ground Biomass and Forest Carbon

“…Such large-scale measurement efforts can, when well designed, be accurate and deliver on verifiability ( [1,2,41,76,77] and references therein), but will fall short either on effectiveness, if plots are laid out too sparsely, or affordability, if plots are assigned to, say, every hectare. Methods based purely on remote sensing will, on the other hand, fall short on accuracy since without ground truth there is no way to guarantee that they contain no systematic error.…”

Optical data-driven multi-source forest inventory setups for boreal and tropical forests

“…Forest above-ground biomass (AGB) is a key variable for the characterization of the forest state and its disturbance status, as well tracking its dynamics over time (Häme et al 2013b). The Tier approach introduced by the Intergovernmental Panel on Climate Change (IPCC) has been used to describe levels of methodological quality and complexity in the measurement reporting and verification (MRV) assessment system (see Tokola 2015). In this way, the United Nations Framework Convention on Climate Change (UNFCCC) policy on Reduced Emissions from Deforestation and forest Degradation (REDD+) has provided guidelines to assist countries in developing carbon assessment methodologies (e.g.…”

“…In this way, the United Nations Framework Convention on Climate Change (UNFCCC) policy on Reduced Emissions from Deforestation and forest Degradation (REDD+) has provided guidelines to assist countries in developing carbon assessment methodologies (e.g. Asner 2009;Tokola 2015). For estimating aboveground carbon in the REDD+ monitoring setups, calculation chains are conventionally implemented using coefficients such as the biomass expansion factors discussed by Tokola (2015).…”

“…For estimating aboveground carbon in the REDD+ monitoring setups, calculation chains are conventionally implemented using coefficients such as the biomass expansion factors discussed by Tokola (2015). Notably, Tokola (2015) emphasised that remote sensing-based applications are definitively key tools for REDD+ MRV, and the good practices applied in traditional forest inventories (such as a proper sample and fieldwork design) ought to be followed in allocating field plots and carrying out field measurements also in tropical conditions (see Olofsson et al 2014). The challenges in the fields of forest degradation and the REDD+ framework have been discussed, for instance by Morales-Barquero et al (2014: see also Peres et al 2006).…”

“…The role of LiDAR is to provide a ground truth type of information over large areas. The innermost REDD+ concept still lies in the realm of forest inventory (Tokola 2015). A review of approaches for LiDAR sampling over large areas was presented by Wulder et al (2012, see p. 207), and it is noted that an evident way to generate measures in support of REDD+ programs is through the integration of optical imagery and samples of LiDAR data.…”