Under the background of green economic transformation, the sustainable utilization of ecological resources has become a trend, and bamboo all-for-one tourism has become a new development direction for bamboo-resource-rich areas. Based on the all-for-one tourism model and characteristics of bamboo resources, this paper puts forward a bamboo all-for-one tourism model, which shows the relationship between resources, products, and markets, and elaborates on the joint effect mechanism of industrial environment, governance environment, and external environment. Taking Yibin City, Sichuan Province as an example, this paper also analyzes existing problems of developing bamboo all-for-one tourism and then proposes suggestions to provide effective analytical ideas and reference, such as establishing a market-oriented all-product development model, introducing the sustainable development concept of bamboo management, establishing the management concept of sharing by all people, and driving all industries developing in a coordinated way.

Fire hazard is often mapped as a static conditional probability of fire characteristics’ occurrence. We developed a dynamic product for operational risk management to forecast the probability of occurrence of fire radiative power in the locally possible near-maximum fire intensity range. We applied standard machine learning techniques to remotely sensed data. We used a block maxima approach to sample the most extreme fire radiative power (FRP) MODIS retrievals in free-burning fuels for each fire season between 2001 and 2020 and associated weather, fuel, and topography features in northwestern south America. We used the random forest algorithm for both classification and regression, implementing the backward stepwise repression procedure. We solved the classification problem predicting the probability of occurrence of near-maximum wildfire intensity with 75% recall out-of-sample in ten annual test sets running time series cross validation, and 77% recall and 85% ROC-AUC out-of-sample in a twenty-fold cross-validation to gauge a realistic expectation of model performance in production. We solved the regression problem predicting FRP with 86% r² in-sample, but out-of-sample performance was unsatisfactory. Our model predicts well fatal and near-fatal incidents reported in Peru and Colombia out-of-sample in mountainous areas and unimodal fire regimes, the signal decays in bimodal fire regimes.

Natural forests and abandoned agricultural lands are increasingly replaced by monospecific forest plantations that have poor capacity to support biodiversity and ecosystem services. Natural forests harbour plants belonging to different mycorrhiza types that differ in their microbiome and carbon and nutrient cycling properties. Here we describe the MycoPhylo field experiment that encompasses 116 woody plant species from three mycorrhiza types and 237 plots, with plant diversity and mycorrhiza type diversity ranging from one to four and one to three per plot, respectively. The MycoPhylo experiment enables us to test hypotheses about the plant species, species diversity, mycorrhiza type, and mycorrhiza type diversity effects and their phylogenetic context on soil microbial diversity and functioning and soil processes. Alongside with other experiments in the TreeDivNet consortium, MycoPhylo will contribute to our understanding of the tree diversity effects on soil biodiversity and ecosystem functioning across biomes, especially from the mycorrhiza type and phylogenetic conservatism perspectives.

Cocoa is important for the economy and rural development of Ghana. However, small-scale cocoa production is the leading agricultural product driver of deforestation in Ghana. Uncertain tree tenure disincentivizes farmers to retain and nurture trees on their farms. There is therefore the call for structures that promote tree retention and management within cocoa farming. We examined tenure barriers and governance for tree resources on cocoa farms. Data was collected from 200 cocoa farmers from two regions using multistage sampling technique. Information was gathered on tree ownership and fate of tree resources on cocoa farms, tree felling permit acquisition and associated challenges and illegal logging and compensation payments on cocoa farms. Results suggest 62.2% of farmers own trees on their farms. However, these farmers may or may not have ownership rights over the trees depending on the ownership of their farmlands. More than half of the farmers indicated they require felling permits to harvest trees on their farms, indicative of the awareness of established tree harvesting procedures. Seventy percent of the farmers have never experienced illegal logging on their farms. There is however the need to educate the remaining 30% on their rights and build their compensation negotiation powers for destructions to their cocoa crops. This study has highlighted ownership and governance issues with cocoa farming and it is important for the sustainability of on-farm tree resources and Ghana’s forest at large.

The effects of climate change are already being felt, including the failure to harvest several agricultural products. On the other hand, peatland requires good management because it is a high carbon store and is vulnerable as a contributor to high emissions if it catches fire. This study aims to determine the potential for livelihood options through land management with an agroforestry pattern in peatlands. The methods used are field observation and in-depth interviews. The research location is in Kuburaya Regency, West Kalimantan, Indonesia. Several land use scenarios are presented using additional secondary data. The results show that agroforestry provides more livelihood options than monoculture farming or wood. The economic contribution is very important so that people reduce slash-and-burn activities that can increase carbon emissions and threaten the sustainability of peatland.

Remote sensing technologies have revolutionized forestry analysis by providing valuable information about forest ecosystems on a large scale. This review article explores the latest advancements in remote sensing tools that leverage optical, thermal, RADAR, and LiDAR data, along with state-of-the-art methods of data processing and analysis. We investigate how these tools, combined with artificial intelligence (AI) techniques and cloud-computing facilities, enhance the analytical outreach and offer new insights in the fields of remote sensing and forestry disciplines. The article aims to provide a comprehensive overview of these advancements, discuss their potential applications, and highlight the challenges and future directions. Through this examination, we demonstrate the immense potential of integrating remote sensing and AI to revolutionize forest management and conservation practices.