This paper assesses quantification methods for carbon leakage from forestry activities for their suitability in leakage accounting in a future Reducing Emissions from Deforestation and Forest Degradation (REDD) mechanism. To that end, we first conducted a literature review to identify specific pre-requisites for leakage assessment in REDD. We then analyzed a total of 34 quantification methods for leakage emissions from the Clean Development Mechanism (CDM), the Verified Carbon Standard (VCS), the Climate Action Reserve (CAR), the CarbonFix Standard (CFS), and from scientific literature sources. We screened these methods for the leakage aspects they address in terms of leakage type, tools used for quantification and the geographical scale covered. Results show that leakage methods can be grouped into nine main methodological approaches, six of which could fulfill the recommended REDD leakage requirements if approaches for primary and secondary leakage are combined. The majority of methods assessed, address either primary or secondary leakage; the former mostly on a local or regional and the latter on national scale. The VCS is found to be the only carbon accounting standard at present to fulfill all leakage quantification requisites in REDD. However, a lack of accounting methods was identified for international leakage, which was addressed by only two methods, both from scientific literature.

Detailed information on tree cover structure is critical for research and monitoring programs targeting African woodlands, including agroforestry parklands. High spatial resolution satellite imagery represents a potentially effective alternative to field-based surveys, but requires the development of accurate methods to automate information extraction. This study presents a method for tree crown mapping based on Geographic Object Based Image Analysis (GEOBIA) that use spectral and geometric information to detect and delineate individual tree crowns and crown clusters. The method was implemented on a WorldView-2 image acquired over the parklands of Saponé, Burkina Faso, and rigorously evaluated against field reference data. The overall detection rate was 85.4% for individual tree crowns and crown clusters, with lower accuracies in areas with high tree density and dense understory vegetation. The overall delineation error (expressed as the difference between area of delineated object and crown area measured in the field) was 45.6% for individual tree crowns and 61.5% for crown clusters. Delineation accuracies were higher for medium (35–100 m²) and large (≥100 m²) trees compared to small (<35 m²) trees. The results indicate potential of GEOBIA and WorldView-2 imagery for tree crown mapping in parkland landscapes and similar woodland areas.

Mapping of tree height is of great importance for management, planning, and research related to agroforestry parklands in Africa. In this paper, we investigate the potential of spotlight-mode data from the interferometric synthetic aperture radar (InSAR) satellite system TanDEM-X (TDM) for mapping of tree height in Saponé, Burkina Faso, a test site characterised by a low average canopy cover (~15%) and a mean tree height of 9.0 m. Seven TDM acquisitions from January–April 2018 are used jointly to create high-resolution (~3 m) maps of interferometric phase height and mean canopy elevation, the latter derived using a new, model-based processing approach compensating for some effects of the side-looking geometry of SAR. Compared with phase height, mean canopy elevation provides a more accurate representation of tree height variations, a better tree positioning accuracy, and better tree height estimation performance when assessed using 915 trees inventoried in situ and representing 15 different species/genera. We observe and discuss two bias effects, and we use empirical models to compensate for these effects. The best-performing model using only TDM data provides tree height estimates with a standard error (SE) of 2.8 m (31% of the average height) and a correlation coefficient of 75%. The estimation performance is further improved when TDM height data are combined with in situ measurements; this is a promising result in view of future synergies with other remote sensing techniques or ground measurement-supported monitoring of well-known trees.