Nation-wide mapping of tree-level aboveground carbon stocks in Rwanda

Major overlap in plant and soil organic carbon hotspots across Africa

Terrestrial plant and soil organic carbon stocks are critical for regulating climate change, enhancing soil fertility, and supporting biodiversity. While a global-scale decoupling between plant and soil organic carbon has been documented, the hotspots and interconnections between these two carbon compartments across Africa, the second-largest continent on the planet, have been significantly overlooked. Here, we have compiled over 10,000 existing soil organic carbon observations to generate a high-resolution map, illustrating the distribution pattern of soil organic carbon in Africa. We then showed that above- and below-ground plant carbon are significantly and positively correlated with soil organic carbon across Africa. Both soil and plant carbon compartments shared major hotspots in the tropical regions. Our study provides critical insights into the spatial distribution of carbon hotspots across Africa, essential for soil conservation and safeguarding terrestrial carbon stocks amidst the challenges of climate change.

Perspectives on Advancing Multimodal Learning in Environmental Science and Engineering Studies

The environment faces increasing anthropogenic impacts, resulting in a rapid increase in environmental issues that undermine the natural capital essential for human wellbeing. These issues are complex and often influenced by various factors represented by data with different modalities. While machine learning (ML) provides data-driven tools for addressing the environmental issues, the current ML models in environmental science and engineering (ES&E) often neglect the utilization of multimodal data. With the advancement in deep learning, multimodal learning (MML) holds promise for comprehensive descriptions of the environmental issues by harnessing data from diverse modalities. This advancement has the potential to significantly elevate the accuracy and robustness of prediction models in ES&E studies, providing enhanced solutions for various environmental modeling tasks. This perspective summarizes MML methodologies and proposes potential applications of MML models in ES&E studies, including environmental quality assessment, prediction of chemical hazards, and optimization of pollution control techniques. Additionally, we discuss the challenges associated with implementing MML in ES&E and propose future research directions in this domain.

Mapping every adult baobab (Adansonia digitata L.) across the Sahel and relationships to rural livelihoods

The baobab tree (Adansonia digitata L.) is an integral part of rural livelihoods throughout the African continent. However, the combined effects of climate change and increasing global demand for baobab products are currently exerting pressure on the sustainable utilization of these resources. Here we use sub-metre-resolution satellite imagery to identify the presence of nearly 2.8 million (underestimation bias 27.1%) baobab trees in the Sahel, a dryland region of 2.4 million km2. This achievement is considered an essential step towards an improved management and monitoring system of valuable woody species. Using Senegal as a case country, we find that 94% of rural buildings have at least one baobab tree in their immediate surroundings and that the abundance of baobabs is associated with a higher likelihood of people consuming a highly nutritious food group: dark green leafy vegetables. The generated database showcases the feasibility of mapping the location of single tree species at a sub-continental scale, providing vital information in times when deforestation and climate change cause the extinction of numerous tree species.

A new ChatGPT-empowered, easy-to-use machine learning paradigm for environmental science

The quantity and complexity of environmental data show exponential growth in recent years. High-quality big data analysis is critical for performing a sophisticated characterization of the complex network of environmental pollution. Machine learning (ML) has been employed as a powerful tool for decoupling the complexities of environmental big data based on its remarkable fitting ability. Yet, due to the knowledge gap across different subjects, ML concepts and algorithms have not been well-popularized among researchers in environmental sustainability. In this context, we introduce a new research paradigm-"ChatGPT + ML + Environment", providing an unprecedented chance for environmental researchers to reduce the difficulty of using ML models. For instance, each step involved in applying ML models to environmental sustainability, including data preparation, model selection and construction, model training and evaluation, and hyper-parameter optimization, can be easily performed with guidance from ChatGPT. We also discuss the challenges and limitations of using this research paradigm in the field of environmental sustainability. Furthermore, we highlight the importance of "secondary training" for future application of "ChatGPT + ML + Environment".

Forest demography and biomass accumulation rates are associated with transient mean tree size vs. density scaling relations

Linking individual and stand-level dynamics during forest development reveals a scaling relationship between mean tree size and tree density in forest stands, which integrates forest structure and function. However, the nature of this so-called scaling law and its variation across broad spatial scales remain unquantified, and its linkage with forest demographic processes and carbon dynamics remains elusive. In this study, we develop a theoretical framework and compile a broad-scale dataset of long-term sample forest stands (n = 1,433) from largely undisturbed forests to examine the association of temporal mean tree size vs. density scaling trajectories (slopes) with biomass accumulation rates and the sensitivity of scaling slopes to environmental and demographic drivers. The results empirically demonstrate a large variation of scaling slopes, ranging from -4 to -0.2, across forest stands in tropical, temperate, and boreal forest biomes. Steeper scaling slopes are associated with higher rates of biomass accumulation, resulting from a lower offset of forest growth by biomass loss from mortality. In North America, scaling slopes are positively correlated with forest stand age and rainfall seasonality, thus suggesting a higher rate of biomass accumulation in younger forests with lower rainfall seasonality. These results demonstrate the strong association of the transient mean tree size vs. density scaling trajectories with forest demography and biomass accumulation rates, thus highlighting the potential of leveraging forest structure properties to predict forest demography, carbon fluxes, and dynamics at broad spatial scales.

Scattered tree death contributes to substantial forest loss in California

In recent years, large-scale tree mortality events linked to global change have occurred around the world. Current forest monitoring methods are crucial for identifying mortality hotspots, but systematic assessments of isolated or scattered dead trees over large areas are needed to reduce uncertainty on the actual extent of tree mortality. Here, we mapped individual dead trees in California using sub-meter resolution aerial photographs from 2020 and deep learning-based dead tree detection. We identified 91.4 million dead trees over 27.8 million hectares of vegetated areas (16.7-24.7% underestimation bias when compared to field data). Among these, a total of 19.5 million dead trees appeared isolated, and 60% of all dead trees occurred in small groups ( ≤ 3 dead trees within a 30 × 30 m grid), which is largely undetected by other state-level monitoring methods. The widespread mortality of individual trees impacts the carbon budget and sequestration capacity of California forests and can be considered a threat to forest health and a fuel source for future wildfires.

Tree-level landscape transitions and changes in carbon storage throughout the mine life cycle

Opencast mining activities destroy native vegetation, directly impacting the carbon sequestration capacity of the regional ecosystem. Restoring tree species have significant impacts on carbon storage. However, changes in carbon storage across tree-level landscape and the impact of tree-level landscape transitions on carbon storage remain poorly described in the literature, and this information is urgently needed to support management decisions. In this study, we combined field data and remote sensing techniques to create field data-driven maps of the tree-level landscape. This enabled the assessment of carbon storage and quantification of the impact of tree-level landscape transitions on carbon storage. We founded that carbon storage rises in initial/stable stages, decreases in development stage during mining expansion and reclamation. The choice of restoration tree species significantly influenced carbon storage. Pinus tabuliformis-R. pseudoacacia accumulated more carbon storage, making it a more suitable model for ecological reclamation of Pingshuo opencast mine. Furthermore, changes in carbon storage are influenced by land-use policies. Land-use policies and reclamation efforts counterbalance carbon loss associated with construction. Various tree-level landscape transitions were examined, with Pinus tabuliformis transitions notably affecting carbon storage, offering insights for ecological reclamation planning. Our research provides a reference for carbon storage assessment in opencast mining areas, enhances understanding of carbon storage changes in mining areas, assists in formulating ecological reclamation plans, and contributes to the "dual‑carbon" goals and climate change mitigation.

The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe

Trees are an integral part in European landscapes, but only forest resources are systematically assessed by national inventories. The contribution of urban and agricultural trees to national-level carbon stocks remains largely unknown. Here we produced canopy cover, height and above-ground biomass maps from 3-meter resolution nanosatellite imagery across Europe. Our biomass estimates have a systematic bias of 7.6% (overestimation; R = 0.98) compared to national inventories of 30 countries, and our dataset is sufficiently highly resolved spatially to support the inclusion of tree biomass outside forests, which we quantify to 0.8 petagrams. Although this represents only 2% of the total tree biomass, large variations between countries are found (10% for UK) and trees in urban areas contribute substantially to national carbon stocks (8% for the Netherlands). The agreement with national inventory data, the scalability, and spatial details across landscapes, including trees outside forests, make our approach attractive for operational implementation to support national carbon stock inventory schemes.