Multi-date Sentinel1 SAR image textures discriminate perennial agroforests in a tropical forest-savannah transition landscape

Synthetic Aperture Radar (SAR) provides consistent information on target land features; especially in tropical conditions that restrain penetration of optical imaging sensors. Because radar response signal is influenced by geometric and di-electrical properties of surface features’, the different land cover may appear similar in radar images. For discriminating perennial cocoa agroforestry land cover, we compare a multi-spectral optical image from RapidEye, acquired in the dry season, and multi-seasonal C-band SAR of Sentinel 1: A final set of 10 (out of 50) images that represent six dry and four wet seasons from 2015 to 2017. We ran eight RF models for different input band combinations; multi-spectral reflectance, vegetation indices, co-(VV) and cross-(VH) polarised SAR intensity and Grey Level Co-occurrence Matrix (GLCM) texture measures. Following a pixel-based image analysis, we evaluated accuracy metrics and uncertainty Shannon entropy. The model comprising co- and cross-polarised texture bands had the highest accuracy of 88.07 % (95 % CI: 85.52–90.31) and kappa of 85.37; and the low class uncertainty for perennial agroforests and transition forests. The optical image had low classification uncertainty for the entire image; but, it performed better in discriminating non-vegetated areas. The measured uncertainty provides reliable validation for comparing class discrimination from different image resolution. The GLCM texture measures that are crucial in delineating vegetation cover differed for the season and polarization of SAR image. Given the high accuracies of mapping, our approach has value for landscape monitoring; and, an improved valuation of agroforestry contribution to REDD+ strategies in the Congo basin sub-region

Farm rejuvenation-induced changes in tree spatial pattern and live biomass species of Cocoa Agroforests in Central Cameroon : insights for tree conservation incentives in cocoa landscapes

Cocoa agroforests sustain ecosystem services (ESs) to varying degrees. These services are otherwise mostly provided by other non-cocoa shade or companion trees. However, the density of shade trees is associated with services and/or disservices that drive farm-specific tree management successions. Considering the growing impacts of climate crisis on farm productivity and the need for adaptation strategies, the ESs are increasingly provisional and contingent on the prevailing vegetation, land tenure, and management successions, amongst others social and ecological factors. To assess the temporal changes in shade management, we surveyed an age gradient of "family farms" in cocoa agroforests created from forest (fCAFS) and savannah (sCAFS) land cover. We evaluated the temporal changes in farm structure, relative tree abundance, and live aboveground biomass of the major canopy strata. We used a spatial point process and linear mixed effect analysis to assess the contributions of associated perennial trees (AsT) on farm rejuvenation patterns. The density of cocoa trees was inconsistent with farm age; this was significantly high on farms in sCAFS (1544 trees ha 1) with spatially random configuration across farm age. On farms in fCAFS, we observed a transition of the cocoa tree configuration in the order regular, random, and clustering from young (with highest density of 1114 trees ha 1) to old farms. On a temporal scale, there is no clear distinction of farm structure and biomass between fCAFS and sCAFS. However, the cycle of tree species and structural composition of the canopy strata are dissimilar; the live biomass allocation for the considered use groups of tree species was different with farm age. The observed dynamics in canopy tree structure and live biomass provide insights into farmers' temporal allocation of uses and prioritization of different tree species with farm age. We recommend the consideration of such landscape-specific, tree management dynamics in proposing on-farm tree conservation incentives. Our results are also conducive to reliable estimates of the ecosystem services from CAFS in the national implementation of conservation mechanisms such as REDD+