Land Carbon Stocks Research Articles

Predicting land cover changes and carbon stock fluctuations in Fuzhou, China: A deep learning and InVEST approach

Predicting the spatiotemporal dynamics of land cover and its carbon stock holds significant importance in guiding regional sustainable development, enhancing regional carbon stocks, and addressing global climate change. However, there is insufficient research on the quantitative relationships between various land cover types and carbon stock changes, as well as their future spatial predictions. Focusing on the core area of Fuzhou City, China, this study constructs a streamlined framework by coupling deep learning and the InVEST model to predict urban land cover and carbon stock changes in 2025 and 2035. The results show that: (1) The prediction model for land cover change has high applicability and can produce the simulated images with high accuracy. Impervious surface is expected to increase by 53 km2 in 2025 and 131 km2 in 2035 compared to 2020, resulting in considerable reductions in forest and cropland. (2) Carbon stocks of the study area are expected to decrease by 1.68 × 106t in 2035 compared to 2020 due to large amounts of high-carbon-density forests and croplands being converted into low-carbon-density impervious surfaces. (3) Multiple regression analysis reveals that forests have the largest impact on carbon stocks in the area, with a magnitude 5.25 times greater than impervious surfaces and 11.5 times greater than cropland, whereas impervious surfaces are the second most influential land cover type on carbon stock changes. Therefore, expanding forest areas becomes an essential initiative as forests could offset the carbon stock loss caused by impervious surface growth. This study provides scientific references for optimizing land-use planning and formulating policies for the development of low-carbon cities.

Land Use Change and Carbon Stocks in the Toari Watershed

Land Use Change and Carbon Stocks in the Toari Watershed

Changes in Wuhan’s Carbon Stocks and Their Spatial Distributions in 2050 under Multiple Projection Scenarios

Urbanization in the 21st century has reshaped carbon stock distributions through the expansion of cities. By using the PLUS and InVEST models, this study predicts land use and carbon stocks in Wuhan in 2050 using three future scenarios. Employing local Moran’s I, we analyze carbon stock clustering under these scenarios, and the Getis–Ord Gi* statistic identifies regions with significantly higher and lower carbon-stock changes between 2020 and 2050. The results reveal a 2.5 Tg decline in Wuhan’s carbon stock from 2000 to 2020, concentrated from the central to the outer city areas along the Yangtze River. By 2050, the ecological conservation scenario produced the highest carbon stock prediction, 77.48 Tg, while the economic development scenario produced the lowest, 76.4 Tg. High-carbon stock-change areas cluster in the north and south, contrasting with low-change area concentrations in the center. This research provides practical insights that support Wuhan’s sustainable development and carbon neutrality goals.

Spatially-explicit land use change emissions and carbon payback times of biofuels under the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA)

The Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) requires airlines to offset their greenhouse gas (GHG) emissions above 2019 levels by either buying carbon offsets or using Sustainable Aviation Fuels (SAFs). These are drop-in jet fuels made from biomass or other renewable resources that reduce GHG emissions by at least 10 % compared to kerosene and meet certain sustainability criteria. This study assesses the direct land use change (DLUC) emissions of SAF, i.e., GHG emissions from on-site land conversion from previous uses (excluding primary forests, peatlands, wetlands, and protected and biodiversity-rich areas) into alternative feedstocks, considering spatial variability in global yields and land carbon stocks. The results provide DLUC values and carbon payback times at 0.5-degree resolution for six SAF pathways, with and without irrigation and a medium-input intensity, according to CORSIA sustainability criteria. When excluding CORSIA non-compliant areas, soybean SAF shows the highest mean DLUC factor (31.9 ± 20.7 gCO2/MJ), followed by reed canary grass and maize. Jatropha SAF shows the lowest mean DLUC factor (3.6 ± 31.4 gCO2/MJ), followed by miscanthus and switchgrass. The latter feedstocks show potential for reducing GHG emissions over large areas but with relatively greater variability. Country-average DLUC values are higher than accepted ILUC ones for all pathways except for maize. To ensure the GHG benefits of CORSIA, feedstocks must be produced in areas where not only carbon stocks are relatively low but also where attainable yields are sufficiently high. The results help identify locations where the combination of these two factors may be favourable for low-DLUC SAF production. Irrigated miscanthus offers the highest SAF production potential (2.75 EJ globally) if grown on CORSIA-compliant cropland and grassland areas, accounting for ∼1/5 of the total kerosene used in 2019. Quantifying other environmental impacts of SAFs is desirable to understand sustainability trade-offs and financial constraints that may further limit production potentials.

Impact of Land Use Change on Carbon Storage in Urban Agglomerations in the Guanzhong Plain

It is important to study the impact of land use change on terrestrial ecosystem carbon stocks in urban agglomerations for the optimization of land use structure and sustainable development in urban agglomerations. Based on the patch-generating land use simulation (PLUS) model and integrated valuation of ecosystem services and trade-offs (InVEST) model, a simulation was developed that predicted the land use change and carbon stock of the Guanzhong Plain urban agglomeration in 2040 under different scenarios and further analyzed the impact of land use change on carbon stock. The results showed that:① The land use types of the Guanzhong Plain urban agglomeration were mainly cultivated land, forest land, and grassland, which accounted for more than 90 % of the total study area. ② From 2000 to 2020, the carbon stock in the Guanzhong Plain showed a continuous downward trend, with cropland, woodland, and grassland being the main sources of carbon stock in the Guanzhong Plain, and the overall carbon stock declined by 15.12×106 t, with the spatial distribution presenting the distribution characteristics of "high in the north and south and low in the middle." ③ By 2040, the carbon stock would decrease the most under the urban development scenario, with a total reduction of 27.08×106 t, and the least under the ecological development scenario, with a total reduction of 4.14×106t. The research results can provide data support for the high-quality development and rational land use planning of the Guanzhong Plain urban agglomeration.

Spatial–Temporal Pattern Analysis and Development Forecasting of Carbon Stock Based on Land Use Change Simulation: A Case Study of the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration, China

The spatial–temporal distribution and evolution characteristics of carbon stock under the influence of land use changes are crucial to the scientific management of environmental resources and the optimization of land spatial layout. Taking the Xiamen–Zhangzhou–Quanzhou urban agglomeration in the southeastern coastal region of China as an example, based on seven land use types from 1990 to 2020, including cultivated land, woodland, and construction land, we quantitatively investigate the spatial–temporal patterns of carbon stock development and the spatial correlation of carbon stock distribution. Additionally, two scenarios for the development of urban and ecological priorities in 2060 are established to investigate the effects of land use changes on carbon stock. The results indicate that (1) the research area has formed a land use spatial pattern centered around urban construction in the eastern bay area, with the western forest area and coastal forest belt serving as ecological barriers. Carbon stock is influenced by land use type, and the distribution of total carbon stock exhibits a spatial aggregation phenomenon characterized by “low in the southeast, high in the north, and medium in the center”. (2) Distance of trunk and secondary roads, elevation, slope, watershed borders, population size, and gross domestic product (GDP) factors are the main drivers of the growth of land use types. The primary causes of the reduction in carbon stock are the widespread conversion of cultivated land, woodland, and grassland into construction land, as well as water and unused land. (3) In 2060, there will be a decrease of 41,712,443.35 Mg in the urban priority development scenario compared to 2020, and a decrease of 29,577,580.48 Mg in the ecological priority development scenario. The estimated carbon stock under the two scenarios varies by 12,134,862.88 Mg. The average carbon storage of Zhangpu County, Quangang County, and Jimei County is expected to rise by one level under the ecological protection scenario, indicating that the vast forest area can become a potential area to maintain carbon stock. It is crucial to encourage the coordinated development of peri-urban agroforestry and ecological barriers, as well as to establish a harmonious spatial pattern of land use and carbon stock at the scale of urban agglomerations.

Forest cover change and its carbon dynamic of the karst area in Bulusaraung, South Sulawesi, Indonesia

Information on land forest cover changes and carbon stocks in karst areas is vital for management planning. Therefore, an analysis of the dynamics of land forest cover, carbon stocks of secondary karst forests, and their future projections is necessary. This study aimed to analyze the carbon stocks of secondary forests, forest cover change, and their projections in karst areas. We used 43 sample plots of 20 m x 20 m to measure Above Ground Biomass (AGB) and soil organic carbon stocks. Forest cover data were collected using remote sensing and GIS tools. Data analysis of the ABG of the secondary forest for the karst plain and karst tower was conducted using the Chave equation. Land-cover change was analyzed by delineating the 2011, 2016, and 2021 land-cover data from satellite imagery and testing the accuracy using survey data fields and high-resolution images. Land-cover projections for 2026 and 2031 were obtained using Cellular Automata Markov (CA-Markov) analysis. The results revealed ten land cover types in the karst area of the Bulusaraung Forest Management Unit (FMU). Secondary dryland karst forests (tower and plain karst) were the dominant forest types (60%) in the Maros-Pangkep essential karst ecosystem area. Both karst types of secondary forests are relatively high carbon stores. Data projection shows that the mining area will grow extensively by 2031. Secondary dryland karst forests will be degraded by 15.5% in 20 years due to increased mining activities, conversion to paddy fields, dryland agriculture, and mixed dryland agriculture. Karst ecosystems are vital for carbon and water storage. Therefore, some strategies are required for the sustainable management of Maros-Pangkep Karst are as follows: (i) controlling the opening of mining areas according to sustainable environmental principles, (ii) encouraging the implementation of post-mining land rehabilitation, (iii) increasing public awareness of the importance of preserving karst areas, and (iv) implementing soil conservation rules by prioritizing agroforestry systems in dryland farming.

Analysis and Simulation of Land Use Changes and Their Impact on Carbon Stocks in the Haihe River Basin by Combining LSTM with the InVEST Model

The quantitative analysis and prediction of spatiotemporal patterns of land use in Haihe River Basin are of great significance for land use and ecological planning management. To reveal the changes in land use and carbon stock, the spatial–temporal pattern of land use data in the Haihe River Basin from 2000 to 2020 was studied via Mann–Kendall (MK) trend analysis, the transfer matrix, and land use dynamic attitude. Through integrating the models of the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) and the Long Short-Term Memory (LSTM), the results of the spatial distribution of land use and carbon stock were obtained and compared with Cellular Automation (CA-Markov), and then applied to predict the spatial distribution in 2025. The results show the following: (1) The land use and land cover (LULC) changes in the Haihe River Basin primarily involve an exchange between cultivated land, forest, and grassland, as well as the conversion of cultivated land to built-up land. This transformation contributes to the overall decrease in carbon storage in the basin, which declined by approximately 1.20% from 2000 to 2020. (2) The LULC prediction accuracy of LSTM is nearly 2.00% higher than that of CA-Markov, reaching 95.01%. (3) In 2025, the area of grassland in Haihe River Basin will increase the most, while the area of cultivated land will decrease the most. The spatial distribution of carbon stocks is higher in the northwest and lower in the southeast, and the changing areas are scattered throughout the study area. However, due to the substantial growth of grassland and forest, the carbon stocks in the Haihe River Basin in 2025 will increase by about 10 times compared with 2020. The research results can provide a theoretical basis and reference for watershed land use planning, ecological restoration, and management.

Assessment of land use cover changes, carbon sequestration and carbon stock in dry temperate forests of Chilas watershed, Gilgit-Baltistan

Abstract Land use and land cover change are affecting the global environment and ecosystems of the different biospheres. Monitoring, reporting and verification (MRV) of these changes is of utmost importance as they often results in several global environmental consequences such as land degradation, mass erosion, habitat deterioration as well as micro and macro climate of the regions. The advance technologies like remote sensing (RS) and geographical information system (GIS) are helpful in determining/ identifying these changes. In the current study area, changes in carbon stocks, notably in forest areas, are resulting in considerable dynamics of carbon stocks as a result of climate change and carbon sequestration. This study was carried out in the Diamer district of the Gilgit Baltistan (GB) Pakistan to investigate the change in cover change/land use change (particularly Forest Land use) as well as carbon sequestration potential of the forests in the district during almost last 25years. The land cover, temporal Landsat data (level 1, LIT) were downloaded from the USGS EROS (2016), for 1979-1989, 1990-2000 and 2001-2012. Change in land uses, particularly forest cover was investigated using GIS techniques. Forest inventory was carried out using random sampling techniques. A standard plot of size 0.1 ha (n=80) was laid out to determine the tree density, volume, biomass and C stocks. Simulation of C stocks was accomplished by application of the CO2FIX model with the data input from inventory. Results showed a decrease in both forest and snow cover in the region from 1979-2012. Similarly decrease was seen in tree volume, tree Biomass, dynamics of C Stocks and decrease was in occur tree density respectively. It is recommended we need further more like project such as BTAP (Billion Tree Afforestation Project) and green Pakistan project to increase the forest cover, to control on land use change, protect forest ecosystem and to protect snow cover.

Mind the gap: reconciling tropical forest carbon flux estimates from earth observation and national reporting requires transparency

BackgroundThe application of different approaches calculating the anthropogenic carbon net flux from land, leads to estimates that vary considerably. One reason for these variations is the extent to which approaches consider forest land to be “managed” by humans, and thus contributing to the net anthropogenic flux. Global Earth Observation (EO) datasets characterising spatio-temporal changes in land cover and carbon stocks provide an independent and consistent approach to estimate forest carbon fluxes. These can be compared against results reported in National Greenhouse Gas Inventories (NGHGIs) to support accurate and timely measuring, reporting and verification (MRV). Using Brazil as a primary case study, with additional analysis in Indonesia and Malaysia, we compare a Global EO-based dataset of forest carbon fluxes to results reported in NGHGIs.ResultsBetween 2001 and 2020, the EO-derived estimates of all forest-related emissions and removals indicate that Brazil was a net sink of carbon (− 0.2 GtCO2yr−1), while Brazil’s NGHGI reported a net carbon source (+ 0.8 GtCO2yr−1). After adjusting the EO estimate to use the Brazilian NGHGI definition of managed forest and other assumptions used in the inventory’s methodology, the EO net flux became a source of + 0.6 GtCO2yr−1, comparable to the NGHGI. Remaining discrepancies are due largely to differing carbon removal factors and forest types applied in the two datasets. In Indonesia, the EO and NGHGI net flux estimates were similar (+ 0.6 GtCO2 yr−1), but in Malaysia, they differed in both magnitude and sign (NGHGI: -0.2 GtCO2 yr−1; Global EO: + 0.2 GtCO2 yr−1). Spatially explicit datasets on forest types were not publicly available for analysis from either NGHGI, limiting the possibility of detailed adjustments.ConclusionsBy adjusting the EO dataset to improve comparability with carbon fluxes estimated for managed forests in the Brazilian NGHGI, initially diverging estimates were largely reconciled and remaining differences can be explained. Despite limited spatial data available for Indonesia and Malaysia, our comparison indicated specific aspects where differing approaches may explain divergence, including uncertainties and inaccuracies. Our study highlights the importance of enhanced transparency, as set out by the Paris Agreement, to enable alignment between different approaches for independent measuring and verification.

Assessment and simulation of ecosystem carbon storage in rapidly urbanizing areas based on land use cover: a case study of the southern Jiangsu urban agglomeration, China

Since China’s reform and opening-up period, the southern Jiangsu urban agglomeration has been one of the fastest urbanizing regions in the country. This rapid urbanization has led to dramatic changes in land use cover that have been the primary drivers of carbon stock changes in the terrestrial ecosystem. In this study, we utilize the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model and a patch-generating land use simulation (PLUS) model to analyze the land use changes and carbon stocks in the southern Jiangsu urban agglomeration over the past 30 years. We then simulate the carbon stock changes in the study area in the year 2050 under natural growth, cultivated land conservation, and ecological conservation scenarios. The results showed that 1) over the past 30 years, the urban area has increased by 2.98 times, reaching 7,408.42 km2 by 2020. In contrast, the area of cultivated and forested land has continued to decrease with rapid urbanization. 2) Between 1990 and 2020, the carbon stock of the urban agglomeration in southern Jiangsu decreased by 5.34%. The changes in the spatial distribution of carbon stocks are consistent with the changes in land use. 3) By 2050, the carbon stock loss was the largest under the natural growth scenario at 10.49 mt, while the carbon stock loss was the smallest under the cultivated land protection scenario at 0.97 mt. Under the ecological protection scenario, the carbon stock loss was 9.9 mt. The results indicate that the adoption of cultivated land and ecological protection measures can effectively control the reduction of carbon stock in rapidly urbanizing areas. 4) The conversion of cultivated land and forest land to urban land was the primary reason for the carbon stock reduction in the study area, which was primarily located in the urban outward expansion area. This study provides a reference- and data-based support for the management, decision-making, and planning in rapidly urbanizing areas.

The Impact of Land Use Changes on Carbon Flux in the World’s 100 Largest Cities

Urbanization has become an important player in the global carbon cycle, and land use change is the second largest source of carbon emissions. However, despite great advances in remote sensing and satellite imagery, there is no reliable estimate of the impact of land use change on changes in land carbon stock in global cities. This paper quantified the impact of land use change on land carbon flux in the world’s 100 largest cities by using annual land cover data based on LandSat 8 images and land carbon stock parameters provided by the IPCC (Intergovernmental Panel on Climate Change). It was found that significant urban expansion could be observed in 83 cities, while 29 cities showed a deforestation trend, and croplands in 42 cities have shrunk. Carbon stock reduced by more than 112 million tons in the 100 selected cities from 2013 to 2022 due to land cover change. A total of 39 cities showed significant negative trends in land carbon stock that were mainly caused by urban sprawl and shrinkage in forest or cropland, among which Kolkata, Chongqing, Seoul, Guangzhou, and Hefei showed the greatest decline. Because of the growth of forest and cropland, or reduction in barren land and grassland, 28 cities showed clear positive trends in land carbon stock. In order to increase urban land carbon stock, the urban planning of most cities should focus on the protection of forests or afforestation that replace barren land or grassland and should avoid mindless urban expansion.

Impact of ecological conservation policies on land use and carbon stock in megacities at different stages of development

Urban expansion, especially the construction of megacities, increases carbon emissions and adversely affects the carbon storage of terrestrial ecosystems. However, scientific land-use management policies can increase carbon storage. This study takes two megacities at different stages of development, Beijing and Tianjin, as examples to explore the impact of different ecological conservation scenarios on both urban land use and carbon storage to provide recommendations for the construction planning of large cities with low-carbon development as the goal. Furthermore, we coupled the patch-generating land use simulation (PLUS) model with the integrated valuation of ecosystem services and tradeoffs (InVEST) model to simulate land use and carbon storage under a natural development scenario, a planned ecological protection scenario (PEPS), and a policy-based ecological restoration scenario (PERS). From 2000 to 2020, both cities had different degrees of construction land expansion and carbon loss, and Tianjin's dynamic degree of construction land was 0.94% higher than Beijing's, with a carbon loss 183,536.19 Mg higher than Beijing's; this trend of reducing carbon reserves will continue under the natural development scenario (NDS). Under the PEPS and PERS, the carbon stock of both cities increases, and the impact on Tianjin is greater, with an increase of 4.51% and 8.04%, respectively. Under PERS, the carbon stock increases the most, but the dynamic degree of construction land use is negative for both cities. Beijing's carbon stock is 0.40% lower than Tianjin's, which deviates slightly from the trend of urban economic development. Megacities in the rapid development stage can refer to Tianjin, strictly following the ecological protection land planning scope and vigorously implementing ecological restoration policies to effectively increase regional carbon stock. Megacities in the mature stage of development can refer to Beijing, and flexibly implement ecological restoration policies to increase regional carbon stock without affecting the city's economic development.

Future land-use change and its impact on terrestrial ecosystem carbon pool evolution along the Silk Road under SDG scenarios

Sustainable development goals (SDGs) in the United Nations 2030 Agenda call for action by all nations to promote economic prosperity while protecting the planet. Projection of future land-use change under SDG scenarios is a new attempt to scientifically achieve the SDGs. Herein, we proposed four scenario assumptions based on the SDGs, including the sustainable economy (ECO), sustainable grain (GRA), sustainable environment (ENV), and reference (REF) scenarios. We forecasted land-use change along the Silk Road (resolution: 300 m) and compared the impacts of urban expansion and forest conversion on terrestrial carbon pools. There were significant differences in future land use change and carbon stocks, under the four SDG scenarios, by 2030. In the ENV scenario, the trend of decreasing forest land was mitigated, and forest carbon stocks in China increased by approximately 0.60% compared to 2020. In the GRA scenario, the decreasing rate of cultivated land area has slowed down. Cultivated land area in South and Southeast Asia only shows an increasing trend in the GRA scenario, while it shows a decreasing trend in other SDG scenarios. The ECO scenario showed highest carbon losses associated with increased urban expansion. The study enhances our understanding of how SDGs can contribute to mitigate future environmental degradation via accurate simulations that can be applied on a global scale.

Spatial and temporal evolution of carbon stocks in Dongting Lake wetlands based on remote sensing data

Wetlands are one of the important carbon reservoirs on earth, assessing the spatial and temporal dynamics of wetland carbon stocks and their driving mechanisms is critical for stabilizing and enhancing the carbon sink function of wetlands. In this study, the carbon stocks of Dongting Lake wetland during 1995–2020 were estimated using the Integrate Valuation of Ecosystem Services and Tradeoffs Tool (InVEST) model and land use/cover data derived from remote sensing data. Subsequently, the geographically weighted regression (GWR) model was utilized to explore the driving mechanisms of the spatial and temporal dynamics of carbon stocks based on natural and socio-economic factors. Finally, evolution of land use status and carbon stocks was simulated based on patch-generating land use simulation (PLUS) model during 2030–2050 under different scenarios. The results showed that: 1) over the past 25 years, carbon stocks have been fluctuating in a ‘decrease–increase–decrease’ pattern, with an average decrease of 39.70 × 106 t per year; 2) compared with socio-economic factors, climate conditions have a greater influence on the change of carbon stocks distribution; 3) under the natural variation scenario, the carbon stocks in 2030 is 1345.36 × 106 t, which is increased compared with the year of 2020. However, the carbon stocks in 2040–2050 started to show a decreasing trend. Under the ecological conservation scenario, the increase in forest area leads to a significant increase in carbon stocks from 2030 to 2050, which showed a continuous growth trend and obviously enhanced the carbon sequestration capacity of the Ecological and Economic Circle around Dongting Lake. The results of this study are expected to provide a scientific basis for carbon balance, land use restructuring and wetland resource management planning in the ecological and economic circle around Dongting Lake. Highlights The carbon stocks of Dongting Lake wetland during 1995–2020 were estimated using the InVEST model. Over the past 25 years, carbon stocks of Dongting Lake wetlands have been fluctuating in a ‘decrease–increase–decrease’ pattern. Climate conditions have a greater influence on the change of carbon stocks distribution compared with socio-economic factors.

Land-based climate change mitigation measures can affect agricultural markets and food security.

Earlier studies have noted potential adverse impacts of land-related emissions mitigation strategies on food security, particularly due to food price increases-but without distinguishing these strategies' individual effects under different conditions. Using six global agroeconomic models, we show the extent to which three factors-non-CO2 emissions reduction, bioenergy production and afforestation-may change food security and agricultural market conditions under 2 °C climate-stabilization scenarios. Results show that afforestation (often simulated in the models by imposing carbon prices on land carbon stocks) could have a large impact on food security relative to non-CO2 emissions policies (generally implemented as emissions taxes). Respectively, these measures put an additional 41.9 million and 26.7 million people at risk of hunger in 2050 compared with the current trend scenario baseline. This highlights the need for better coordination in emissions reduction and agricultural market management policies as well as better representation of land use and associated greenhouse gas emissions in modelling.

Settlement Land Cover and Carbon Stocks by Land Use and Parcel Size in Ontario, Canada

Settlement Land Cover and Carbon Stocks by Land Use and Parcel Size in Ontario, Canada

Temporal and Spatial Change of Carbon Storage in Alara Forest Planning Unit

Aim of study: Land use and carbon stock changes of Alara Forest Planning Unit (FPU) between the years of 1997 and 2017 were investigated.
 Area of study: Alara Forest Planning Unit (FPU) of Alanya Forest Enterprise (FE) within the boundaries of Antalya province, southwest Turkey.
 Material and methods: Data obtained from the forest management plans for the periods of 1997-2006, 2008-2017 and 2018-2037 were used in the study. Biomass conversion and expansion factors (BCEF), root to shoot ratios (R) and carbon factors (CF) were used for carbon stock estimation.
 Main results: Total forest area of the Alara FPU decreased from 7497 ha to 7344 ha, while total carbon stock of the forest lands increased by 12.7%, from 1997 to 2017.
 Highlights: Despite the reduction of total forest area from 1997 to 2017, carbon stocks of all biomass pools in forest ecosystem raised with the increase of productive forest lands and growing stock.

LPDynR: A new tool to calculate the land productivity dynamics indicator

The United Nations Sustainable Development Goal 15 (Life on Land), adopted the indicator 15.3.1 to measure the Land Degradation Neutrality. This indicator is based on three sub-indicators: (1) Trends in Land Cover, (2) Land Productivity and (3) Carbon Stocks. The Land Productivity sub-indicator refers to the total above-ground Net Primary Production and reflects changes in health and productive capacity of the land. It can be calculated using the Land Productivity Dynamics approach, which performs a combined assessment of the long term tendency of change of land productivity and its current level relative to homogeneous land areas.Here, we present the R-based tool LPDynR, which implements the Land Productivity Dynamics approach for the calculation of the Land Productivity sub-indicator. LPDynR ingests vegetation-related indices derived from time series of remote sensed imagery. The final indicator is a 5-class map, ranging from declining to increasing land productivity. As an example of LPDynR functionalities and applicability, we present a case study for Europe. First, we show the general way to calculate the indicator for the entire time series (2000–2019), explained in a step by step process. Secondly, we show how to alternatively calculate the indicator based only on the long term tendency of change, but we evidence the added value of including the current level of productivity to refine the final indicator. Finally, we present some code for the calculation of “partial indicators” in terms of time scale along the observation period, which may help the user to understand the land productivity dynamics within the time series, as well as to assess the stability of the final product. While the indicator shows a general positive dynamics across Europe during the period 2000–2019, some of the partial maps show more negative trends, demonstrating the highly fluctuating character of vegetation.

Impact of merging of historical and future climate data sets on land carbon cycle projections for South America

AbstractEarth System Models (ESMs) project climate change, but they often contain biases in their estimates of contemporary climate that propagate into simulated futures. Land models translate climate projections into surface impacts, but these will be inaccurate if ESMs have substantial errors. Bias concerns are relevant for terrestrial physiological processes which often respond non‐linearly (i.e. contain threshold responses) and are therefore sensitive to absolute environmental conditions as well as changes. We bias‐correct the UK Met Office ESM, HadGEM2‐ES, against the CRU–JRA observation‐based gridded estimates of recent climate. We apply the derived bias corrections to future projections by HadGEM2‐ES for the RCP8.5 scenario of future greenhouse gas concentrations. Focusing on South America, the bias correction includes adjusting for ESM estimates that, annually, are approximately 1 degree too cold, for comparison against 21st Century warming of around 4 degrees. Locally, these values can be much higher. The ESM is also too wet on average, by approximately 1 mm·day−1, which is substantially larger than the mean predicted change. The corrected climate fields force the Joint UK Land Environment Simulator (JULES) dynamic global vegetation model to estimate land surface changes, with an emphasis on the carbon cycle. Results show land carbon sink reductions across South America, and in some locations, the net land–atmosphere CO2 flux becomes a source to the atmosphere by the end of this century. Transitions to a CO2 source is where increases in plant net primary productivity are offset by larger enhancements in soil respiration. Bias‐corrected simulations estimate the rise in South American land carbon stocks between pre‐industrial times and the end of the 2080s is ∼12 GtC lower than that without climate bias removal, demonstrating the importance of merging historical observational meteorological forcing with ESM diagnostics. We present evidence for a substantial climate‐induced role of greater soil decomposition in the fate of the Amazon carbon sink.