The High Carbon Stock (HCS) approach is increasingly being promoted as a land-use planning tool of choice to demarcate conservation priority areas based on carbon value. The methodology seeks to conserve biodiverse and ecologically functional forest networks within agricultural concessions by directing conversion towards heavily degraded land of low carbon value. This is achieved by stratifying land into discrete classes according to vegetation density and structure, which are then adopted as proxies for above-ground carbon stocks and assumed to support varying levels of biodiversity. These strata are subsequently validated using field-derived above-ground carbon estimates, before land parcels are prioritised for conversion based on area and connectivity. Applications of such approaches primarily rely on the interpretation of optical, multispectral very high-resolution imagery, which is known to have limited capacity to detect variation in forest density and 3D structure. There is some evidence that HCS forests provide benefits for biodiversity, although existing validations focus on medium-sized non-volant mammals.
The project will (1) explore how the integration of radar and lidar data with multispectral high resolution data impact HCS classification; (2) assess the degree to which HCS categories derived from various satellite remote sensing approaches relate to biodiversity, focusing in particular on the distribution of functional diversity; (3) identify the factors associated with the conversion of high carbon stock area in the region, and use this information to develop an early warning system enabling the prioritisation of conservation efforts on the ground.