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Since 24 February 2022, Ukraine has experienced full-scale military aggression initiated by the Russian Federation. The war has had a major negative impact on vegetation cover of war-affected regions. We explored interactions between pre-war forest management and the impacts of military activities in three of the most forested Ukrainian areas of interest (AOI), affected by the war. These were forests lying between Kharkiv and Luhansk cities (AOI ‘East’), forests along the Dnipro River delta (AOI ‘Kherson’), and those of the Chornobyl Exclusion Zone (AOI CEZ). We used Sentinel satellite imagery to create damaged forest cover masks for the year 2022. We mapped forests with elevated fire hazard, which was defined as a degree of exposure to the fire-supporting land use (mostly an agricultural land, a common source of ignitions in Ukraine). We evaluated the forest disturbance rate in 2022, as compared to pre-war rates. We documented significant increases in non-stand replacing disturbances (low severity fires and non-fire disturbances) for all three of the AOIs. Damaged forest cover varied among the AOIs (24,180 ± 4,715 ha, or 9.3% ± 1.8% in the ‘East’ AOI; 7,293 ± 1,925 ha, or 15.7% ± 4.1% in the ‘Kherson’ AOI; 7,116 ± 1,274 ha, or 5.0% ± 0.9% in the CEZ AOI). Among the forests damaged in 2022, the ‘Kherson’ AOI will likely have the highest proportion of an area with elevated fire hazard in the coming decades, as compared to other regions (89% vs. 70% in the ‘East’ and CEZ AOIs respectively). Future fire risks and extensive war-related disturbance of forest cover call for forest management to develop strategies explicitly addressing these factors. [ABSTRACT FROM AUTHOR]

Here we present a geographically diverse, temporally consistent, and nationally relevant land cover (LC) reference dataset collected by visual interpretation of very high spatial resolution imagery, in a national-scale crowdsourcing campaign (targeting seven generic LC classes) and a series of expert workshops (targeting seventeen detailed LC classes) in Indonesia. The interpreters were citizen scientists (crowd/non-experts) and local LC visual interpretation experts from different regions in the country. We provide the raw LC reference dataset, as well as a quality-filtered dataset, along with the quality assessment indicators. We envisage that the dataset will be relevant for: (1) the LC mapping community (researchers and practitioners), i.e., as reference data for training machine learning algorithms and map accuracy assessment (with appropriate quality-filters applied), and (2) the citizen science community, i.e., as a sizable empirical dataset to investigate the potential and limitations of contributions from the crowd/non-experts, demonstrated for LC mapping in Indonesia for the first time to our knowledge, within the context of complementing traditional data collection by expert interpreters. Measurement(s) land cover Technology Type(s) visual interpretation of satellite imagery Sample Characteristic - Location Indonesia [ABSTRACT FROM AUTHOR]

Since the collapse of the Soviet Union and transition to a new forest inventory system, Russia has reported almost no change in growing stock (+ 1.8%) and biomass (+ 0.6%). Yet remote sensing products indicate increased vegetation productivity, tree cover and above-ground biomass. Here, we challenge these statistics with a combination of recent National Forest Inventory and remote sensing data to provide an alternative estimate of the growing stock of Russian forests and to assess the relative changes in post-Soviet Russia. Our estimate for the year 2014 is 111 ± 1.3 × 109 m3, or 39% higher than the value in the State Forest Register. Using the last Soviet Union report as a reference, Russian forests have accumulated 1163 × 106 m3 yr-1 of growing stock between 1988–2014, which balances the net forest stock losses in tropical countries. Our estimate of the growing stock of managed forests is 94.2 × 109 m3, which corresponds to sequestration of 354 Tg C yr-1 in live biomass over 1988–2014, or 47% higher than reported in the National Greenhouse Gases Inventory. [ABSTRACT FROM AUTHOR]

In recent decades, global oil palm production has shown an abrupt increase, with almost 90% produced in Southeast Asia alone. To understand trends in oil palm plantation expansion and for landscape-level planning, accurate maps are needed. Although different oil palm maps have been produced using remote sensing in the past, here we use Sentinel 1 imagery to generate an oil palm plantation map for Indonesia, Malaysia and Thailand for the year 2017. In addition to location, the age of the oil palm plantation is critical for calculating yields. Here we have used a Landsat time series approach to determine the year in which the oil palm plantations are first detected, at which point they are 2 to 3 years of age. From this, the approximate age of the oil palm plantation in 2017 can be derived. Measurement(s) 2-D extent • age Technology Type(s) Classification Factor Type(s) input dataset Sample Characteristic - Organism Elaeis guineensis Sample Characteristic - Environment oil palm plantation Sample Characteristic - Location Southeast Asia Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13899713 [ABSTRACT FROM AUTHOR]

We present a study about the integrated impact of adaptive management and climate change using spatially explicit tools at first time for assessing changes in forest ecosystem services in South Korea. The aims of this study were to (i) project potential changes among forest ecosystem services under various scenarios, (ii) assess the impact of forest management (Sc1, controlled; Sc2, business as usual; and Sc3, adaptive management) and climate change through comparison among them, and (iii) find insight for strategies to implementing both sustainable society and environment. For this purpose, the integrated tool was applied to analyze the changes in five ecosystem services: forest carbon storage, carbon sequestration, industrial wood production, freshwater supply, and forest recreation. The simulated results show that the ratio of these five ecosystem services of Sc3-to-Sc2 in the 2040s was estimated as 88.4%, 114.7%, 105.4%, 228.2%, and 86.5%, respectively. These results showed clear trade-offs between industrial wood production and freshwater supply on one side and forest recreation and forest carbon storage on the other side. In the case of carbon sequestration, the harvest activity might provide a negative effect in the short term, but it with a longer term perspective could be positive through reforestation activity. In addition, this study showed that future climate change until 2050 in Korea could have a generally negative influence on forest carbon sequestration but that these negative effects could be partly offset through harvest management. Therefore, the plans of the spatial distribution of management activities for the equilibrium of demand and supply in ecosystem services should be required. [ABSTRACT FROM AUTHOR]

Climate change mitigation policies for the land use, land use change, and forestry (LULUCF) sector are commonly assessed based on marginal abatement cost curves (MACC) derived from optimization models or engineering approaches. Yet, little is known about the space of validity of MACCs and how they are influenced by changes in main underlying drivers. In this study, we apply the Global Forest Model (G4M) to explore the sensitivity of MACCs to variation of socioeconomic drivers of deforestation, afforestation, and forest management activities. Particularly, three key factors are considered: (I) wood price, as an indicator of timber market developments; (II) agricultural land price, as a proxy representing the developments on agricultural markets; and (III) corruption coefficient, representing the progress in institutional development and measuring abatement costs use efficiency. The results indicate that the MACCs are more sensitive to the corruption coefficient than to agricultural land price and wood price. Furthermore, we find that the MACCs are more robust with high carbon dioxide (CO2) price and that the sensitivity of the MACCs is higher at low CO2 prices. In general, it can be concluded that when assessing medium-term mitigation policies characterized by low CO2 prices, MACCs need to be developed in-line with institutions currently in place. When designing long-term mitigation policy characterized by high CO2 prices, the role of the analyzed drivers in MACCs estimation is less important. [ABSTRACT FROM AUTHOR]

This study identifies the hotspots of land use cover change (LUCC) under two socioeconomic and climate change scenarios [business as usual (BAU) and a pessimistic scenario] at the national level for Mexico for three-time periods. Modelling suggests that by 2050 grassland and tropical evergreen forest will be the most endangered ecosystems, having lost 20-33% (BAU) or 43-46% (pessimistic scenario) of their extent in comparison to 1993. Agricultural expansion would be the major driver of LUCC, increasing from 24.4% of the country in 1993 to 30% (BAU) or 34% (pessimistic) in 2050. The most influential variables were distance from roads and human settlements, slope, aridity, and evapotranspiration. The hotspots of LUCC were influenced by environmental constraints and socioeconomic activities more than by climate change. These findings could be used to build proposals to reduce deforestation, including multiple feedbacks among urbanization, industrialization and food consumption. [ABSTRACT FROM AUTHOR]

Net carbon sinks capable of avoiding dangerous perturbation of the climate system and preventing ocean acidification have been identified, but they are likely to be limited by resource constraints (Nature 463:747-756, 2010). Land scarcity already creates tension between food security and bioenergy production, and this competition is likely to intensify as populations and the effects of climate change expand. Despite research into microalgae as a next-generation energy source, the land-sparing consequences of alternative sources of livestock feed have been overlooked. Here we use the FeliX model to quantify emissions pathways when microalgae is used as a feedstock to free up to 2 billion hectares of land currently used for pasture and feed crops. Forest plantations established on these areas can conceivably meet 50% of global primary energy demand, resulting in emissions mitigation from the energy and LULUC sectors of up to 544 ± 107 PgC by 2100. Further emissions reductions from carbon capture and sequestration (CCS) technology can reduce global atmospheric carbon concentrations close to preindustrial levels by the end of the present century. Though previously thought unattainable, carbon sinks and climate change mitigation of this magnitude are well within the bounds of technological feasibility. [ABSTRACT FROM AUTHOR]

The authors comment on the effectiveness of bioenergy carbon capture and storage in the removal of atmospheric carbon dioxide. The authors states that the credibility of the method is still unproven. Moreover, they warned that using this technique in the stabilization of the climate can be dangerous.

It is critical to study how different forest management practices affect forest carbon sequestration under global climate change regime. Previous researches focused on the stand-level forest carbon sequestration with rare investigation of forest carbon stocks influenced by forest management practices and climate change at regional scale. In this study, a general integrative approach was used to simulate spatial and temporal variations of woody biomass and harvested biomass of forest in China during the 21st century under different scenarios of climate and CO concentration changes and management tasks by coupling Integrated Terrestrial Ecosystem Carbon budget (InTEC) model with Global Forest Model (G4M). The results showed that forest management practices have more predominant effects on forest stem stocking biomass than climate and CO concentration change. Meanwhile, the concurrent future changes in climate and CO concentration will enhance the amounts of stem stocking biomass in forests of China by 12%-23% during 2001-2100 relative to that with climate change only. The task for maximizing stem stocking biomass will dramatically enhance the stem stocking biomass from 2001-2100, while the task for maximum average increment will result in an increment of stem stocking biomass before 2050 then decline. The difference of woody biomass responding to forest management tasks was owing to the current age structure of forests in China. Meanwhile, the sensitivity of long-term woody biomass to management practices for different forest types (coniferous forest, mixed forest and deciduous forest) under changing climate and CO concentration was also analyzed. In addition, longer rotation length under future climate change and rising CO concentration scenario will dramatically increase the woody biomass of China during 2001-2100. Therefore, our estimation indicated that taking the role of forest management in the carbon cycle into the consideration at regional or national level is very important to project the forest carbon sequestration under future climate change and rising atmospheric CO concentration. [ABSTRACT FROM AUTHOR]

In this paper we provide an analytical framework to estimate the joint production of biomass and carbon sequestration from afforestation and reforestation activities. The analysis is based on geographical explicit information on a half-degree resolution. For each grid-cell the model estimates forest growth using a global vegetation model and chooses forest management rules. Land prices, cost of forest production and harvesting are determined as a function of grid specific site productivity, population density and estimates of economic wealth. The sensitivity of the results due to scenario storylines is assessed using different population and economic growth assumptions, which are consistent with B1 and A2 of the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios (IPCC-SRES) marker scenarios. Considerable differences in the economic supply schedules are found. However, technical potentials seem to converge given constancy in other underlying assumptions of the model. [ABSTRACT FROM AUTHOR]

The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems., (© 2024. The Author(s).)

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)