Implementing SLM practices leads to economic benefits for farmers compared to practicing business as usual. Without information and training however, the risk of reversal of the carbon storage through a change in land use during the crediting period is high. Through long-term extension services on various SLM practices, participating farmers are equipped with the knowledge they need to continue the practices throughout the whole project period and potentially beyond and are empowered to estimate consequences of different types of land-use. All participating farmers receive these bi-annual consultations throughout the whole project duration of 20 years. They are independent of their land-size which means that benefits are distributed more equitable and weaker households in terms of carbon sequestration and size even benefit disproportionally. The extension services are provided by community-based organisations, which assure the availability of the services long-term. In addition, organisations that are locally based can build stronger connections to the farmers and thus gain greater trust which is, not only but to a big part, needed to provide assistance when it comes to transferring the farmer’s rights on the stored carbon to the local coordination entity (in this case SCCS) so that it can be sold as a carbon credit.

Through participatory Restoration Opportunities Assessment Methodology (ROAM) processes, TRI was able to outline restoration potential by identifying high-priority restoration opportunities, noting feasible restoration intervention types, and assessing finance and investment options. Activities identified included promoting the production of ecological charcoal, developing a plant production sector, and constructing water points in restoration sites. TRI conducted these ROAM assessments in three pilot sub-national landscapes, Waza, Mbalmayo, and Douala-Edea, where local and national stakeholders have been engaged. Pre-validation workshops occurred in each landscape with representatives of government ministries, local council representatives, local community representatives, and community chiefs, while a final report explaining the findings of the assessments was finalized in October, 2021 through a national validation workshop that included representatives from government ministries, international organizations, as well as local representatives and chiefs. Once finalized, a leaflet with the main findings was shared with stakeholders to quickly disseminate information and ensure those who do not have access to the internet can understand the ROAM assessments.

The implementation of the technical architecture and monitoring programme has ensured the availability of a big database of information both of the species and its environment. Data availability is key to ensure that park staff (managers, rangers, technicians, etc.) makes informed decisions when it comes to territory and species management strategies. Besides, the local design of the monitoring programme and the parameters within it, has increased the capacity of the park staff not only to manage it, but also to improve it and eventually apply it to the monitoring of different species and even other phenomena.

The acoustic component of the project holds special significance, as it played a pivotal role in the automatic detection of over 138 species, with 95 of them being integrated into our pattern matching algorithms. This forms a robust foundation for the continuous monitoring of the region over the upcoming years, allowing us to observe how various environmental factors influence species presence

The symposium was guided by Foreign Affairs Office of Hainan Province, Department of Natural Resources and Planning of Hainan Province, Department of Ecology and Environment of Hainan Province, Forestry Department of Hainan Province; and supported by the big data lab of Research Institute for Eco-civilization, CASS, the research think tank of Research Institute for Eco-civilization, CASS, Institute of Zoology Chinese Academy of Sciences, Xishuangbanna Tropical Botanical Garden Chinese Academy of Sciences, Institute for Carbon Neutrality, Tsinghua University, Advanced Interdisciplinary Institute of Environment and Ecology, Huawei Technologies Co., Ltd., Hainan University, Hainan Normal University, Federation of Hainan Academicians, the Sanya Research Base of the Internation Centre for Bamboo and Rattan.

The two-day symposium focused on the theme of “conservation of the flagship species of tropical rainforests-gibbons” “conservation of tropical rainforests’biodiversity, and was held in a combination of online and offline activities.

Due to the excessive number of features, a 10-fold cross-validated SVM-RFE was used to rank the importance of the features after extracting them, and then the features were added sequentially for LDA classification to record the change in accurancy with the number of features selected, and finally the best number of features was recorded as the input for the subsequent classifications (see Fig. 8). The highest accurancy for LDA classification was 89.2% (pre) / 95.6% (pre + n×mR0).

Since none of the MFCCs extracted with a fixed number of windows achieved better results than the GMM fitting method for LDA classification (6-window: 86.6%; 10-window: 88.5%; 100-window: <80%), we tested the effectiveness of the other classifiers using only the features extracted by the GMM fitting method. In this test, we randomly selected 20% of the data as the test set, and the rest of the data were used to train the classifier, which were repeated 10 times for each kernel function to record the distribution of the accuracy. Among them, the classification effect of GMM is poor when using only pre as MRU, while the effect is generally better than using only pre when using pre + n×mR0 as MRU.