Peatland Heterogeneity Impacts on Regional Carbon Flux and Its Radiative Effect Within a Boreal Landscape

Peatlands, with high spatial variability in ecotypes and microforms, constitute a significant part of the boreal landscape and play an important role in the global carbon (C) cycle. However, the effects of this peatland heterogeneity within the boreal landscape are rarely quantified. Here, we use field-based measurements, high-resolution land cover classification, and biogeochemical and atmospheric models to estimate the atmosphere-ecosystem C fluxes and the corresponding radiative effect (RE) for a boreal landscape (Kaamanen) in northern Finland. Our result shows that the Kaamanen catchment currently functioned as a sink of carbon dioxide (CO2) and a source of methane (CH4). Peatlands (26% of the area) contributed 22% of the total CO2 uptake and 89% of CH4 emissions; forests (61%) accounted for 78% of CO2 uptake and offset 6% of CH4 emissions; water bodies (13%) offset 7% of CO2 uptake and contributed 11% of CH4 emissions. The heterogeneity of peatlands accounted for 11%, 88%, and 75% of the area-weighted variability (deviation from the area-weighted mean among different land cover types (LCTs) within the catchment) in CO2 flux, CH4 flux, and the combined RE of CO2 and CH4 exchanges over the 25-year time horizon, respectively. Aggregating peatland LCTs or misclassifying them as nonpeatland LCTs can significantly (p < 0.05) bias the regional CH4 exchange and RE estimates, while differentiating between drier noninundated and wetter inundated peatlands can effectively reduce the bias. Current land cover products lack such details in peatland heterogeneity, which would be needed to better constrain boreal C budgets and global C-climate feedbacks. Plain Language Summary Peatlands form part of the boreal landscapes exhibiting diverse types and microforms that have different characteristics of topography, hydrology, vegetation, and soil. Our understanding is still limited concerning how boreal peatlands, especially their inherent heterogeneities, affect the regional biosphere-atmosphere exchange of carbon and related climate effects, and what level of detail is needed to characterize them in land cover maps. By combining remote sensing information, field measurements, and biogeochemical modeling, we showed that, among different land cover types, peatlands played a dominant role in the variability of methane (CH4) flux (88%) and the combined radiative climate effect due to carbon dioxide and CH4 exchanges (75% over the 25-year time horizon). Possible aggregation and misclassification of peatland types could induce significant biases in the regional CH4 balances and radiative effect estimates, but the distinction of noninundated drier and inundated wetter peatland types could reduce these biases effectively.Peer reviewe

Climatic factors dominate the spatial patterns of urban green space coverage in the contiguous United States

Urban green space (UGS) is directly or indirectly related to the human well-being of urban inhabitants, information on the availability or quantity of UGS is thus very fundamental for policy-makers to conduct sustainable land management. Analysis of UGS patterns and their influencing factors at large scales using high-resolution remotely sensed imagery is still understudied. Our study aimed to map the spatial patterns of UGS coverage (UGSC) in all (i.e., 3,535) urban areas of the contiguous US (CONUS) and uncover the main influencing factors that dominate the spatial patterns. We mapped the UGS cover of each urban area using the one-meter high-resolution remote sensing images provided by the National Agriculture Imagery Program (NAIP) of the US on the Google Earth Engine platform. Then we calculated the UGSC of each urban area and quantified the spatial patterns of UGSC for the CONUS urban areas. We established a random forests model to quantify the impact of the influencing factors on UGSC. The results showed that: (1) UGSC in the CONUS urban areas varied largely from 2.2% in Kayenta, AZ to 89.36% in Ocala Estates, FL, with a mean UGSC of 39.43% (SD = 18.19%); (2) UGSC in humid Eastern US was much higher than that in urban areas with hyper-arid or arid climate classes in Western or Central regions of the US. Yet, UGSC of urban areas with different city sizes dose not vary largely; (3) the climatic factors were the main influencing factors that dominate the spatial patterns of UGSC in urban areas of the CONUS, while the socio-economic and terrain factors play relatively less important roles in shaping the UGSC pattern

Quantifying urban expansion from 1985 to 2018 in large cities worldwide

The world has experienced widely distributed urbanization from local to global scales in the past decades. Urban expansion can bring multiple benefits for urban inhabitants, however it can also result in a series of environmental and ecological issues, such as carbon emission, air pollution, and biodiversity loss. Therefore, it is urgently required to conduct timely and quantitative monitoring of the dynamics of urban areas worldwide to support sustainable policy making for urban management and development, such as guiding rational urbanization in developing countries, uncovering inequality of global urbanization, and understanding the consistency of urbanization and population growth. However, we still lack high resolution information on the characteristics of global urban dynamics over the past decades. To fill this gap, we quantified spatiotemporal patterns of urban expansion from 1985 to 2018 in 501 large cities worldwide based on the 30 m fine resolution Global annual Artificial Impervious Area dataset. We then assessed the spatiotemporal patterns of urban expansion using the annual growth rate indicator. Our results showed that: (1) The total (mean) impervious surface area in these cities increased obviously from 50,778 (101.35) km2 in 1985 to 150,145 (299.69) km2 in 2018, i.e. the total (mean) impervious surface area tripled from 1985 to 2018. (2) cities in developed countries or high-income countries (e.g. North American and European cities) have a much higher impervious surface area than cities in developing countries or low-income countries (e.g. Asian and African cities). (3) cities in North America and Europe that are dominated by developed or high-income and upper-middle-income countries have lower annual growth rate of impervious surface than cities in Africa and Asia, which are dominated by low-income and lower-middle-income countries. In addition, cities in China and India experienced very high urban expansion rates during the past decades based on the annual growth rate indicator. The findings of this study could provide supporting information for land management and sustainable development for cities around the world

Assessing Impacts of Urban Form on Landscape Structure of Urban Green Spaces in China Using Landsat Images Based on Google Earth Engine

The structure of urban green spaces (UGS) plays an important role in determining the ecosystem services that they support. Knowledge of factors shaping landscape structure of UGS is imperative for planning and management of UGS. In this study, we assessed the influence of urban form on the structure of UGS in 262 cities in China based on remote sensing data. We produced land cover maps for 262 cities in 2015 using 6673 scenes of Landsat ETM+/OLI images based on the Google Earth Engine platform. We analyzed the impact of urban form on landscape structure of UGS in these cities using boosted regression tree analysis with the landscape and urban form metrics derived from the land cover maps as response and prediction variables, respectively. The results showed that the three urban form metrics&mdash;perimeter area ratio, road density, and compound terrain complexity index&mdash;were all significantly correlated with selected landscape metrics of UGS. Cities with high road density had less UGS area and the UGS in those cities was more fragmented. Cities with complex built-up boundaries tended to have more fragmented UGS. Cities with high terrain complexity had more UGS but the UGS were more fragmented. Our results for the first time revealed the importance of urban form on shaping landscape structure of UGS in 262 cities at a national scale

Green Spaces as an Indicator of Urban Health: Evaluating Its Changes in 28 Mega-Cities

Urban green spaces can yield considerable health benefits to urban residents. Assessing these health benefits is a key step for managing urban green spaces for human health and wellbeing in cities. In this study, we assessed the change of health benefits generated by urban green spaces in 28 megacities worldwide between 2005 and 2015 by using availability and accessibility as proxy indicators. We first mapped land covers of 28 megacities using 10,823 scenes of Landsat images and a random forest classifier running on Google Earth Engine. We then calculated the availability and accessibility of urban green spaces using the land cover maps and gridded population data. The results showed that the mean availability of urban green spaces in these megacities increased from 27.63% in 2005 to 31.74% in 2015. The mean accessibility of urban green spaces increased from 65.76% in 2005 to 72.86% in 2015. The increased availability and accessibility of urban green spaces in megacities have brought more health benefits to their residents

The Effects of Climate Change on the Development of Tree Plantations for Biodiesel Production in China

Biodiesel produced from woody oil plants is a promising form of renewable energy but a combination of tree plantations’ long cultivation time and rapid climate change may put large-scale production at risk. If plantations are located in future-unsuitable places, plantations may fail or yield may be poor, then significant financial, labor, and land resources invested in planting programs will be wasted. Incorporating climate change information into the planning and management of forest-based biodiesel production therefore can increase its chances of success. However, species distribution models, the main tool used to predict the influence of future climate–species distribution modeling, often contain considerable uncertainties. In this study we evaluated how these uncertainties could affect the assessment of climate suitability of the long-term development plans for forest-based biodiesel in China by using Sapindus mukorossi Gaertn as an example. The results showed that only between 59% and 75% of the planned growing areas were projected suitable habitats for the species, depending on the set-up of simulation. Our results showed the necessity for explicitly addressing the uncertainty of species distribution modeling when using it to inform forest-based bioenergy planning. We also recommend the growing area specified in China’s national development plan be modified to lower the risk associated with climate change

Satellite observed recent rising water levels of global lakes and reservoirs

Monitoring global lake/reservoir water level changes is needed to understand the global water cycle and investigate its potential drivers. The existing global water level products only cover lakes/reservoirs with large sizes (>100 km ^2 ). Here, Ice, Cloud, and land Elevation Satellite (ICESat) and ICESat-2 altimetry data with small footprints are employed to examine global water level changes for 22 008 lakes/reservoirs greater than 1 km ^2 . We report that 77.56% of them exhibited rising water levels over 2003–2021. Across the globe, 78.84% of lakes exhibit a rising water level, while the proportion for reservoirs is only 56.01%. Global lake/reservoir is estimated to experience a median water level change rate of +0.02 ± 0.02 m yr ^−1 over 2003–2021, and lakes have a larger water level rise (+0.02 ± 0.02 m yr ^−1 ) than reservoirs (+0.008 ± 0.14 m yr ^−1 ). We detect large-scale rising water levels in the Tibetan Plateau, the Mississippi River basin, and high-latitude regions of the Northern Hemisphere. Our calculation also suggests a negative relationship between the percentage of water level rise in lakes/reservoirs and population density for global river basins ( r = −0.41, p -value < 0.05) and 11 hotspots ( r = −0.48, p -value < 0.05). Our result suggests that inland water level has tended to rise in recent years under natural processes while human activities (i.e. with higher population density) can balance the water level rise via reservoir regulation. We find the existing datasets underestimated global water level rise, which may be caused by the exclusion of numerous small lakes/reservoirs. Our estimated global water level change rates (that include numerous small lakes with areas of 1–10 km ^2 ) can improve the understanding of global hydrological cycle and water resource management under the double pressure of climate warming and human activities

Unraveling the Causal Mechanisms for Non-Grain Production of Cultivated Land: An Analysis Framework Applied in Liyang, China

The excessive use of cultivated land for non-grain production activities is considered a threat to grain security. This study presents an analysis framework on unraveling the causal mechanisms for non-grain production of cultivated land. We apply the analysis framework in Liyang, which is located in the Yangtze River Delta and is also an important “national grain base” county of China. We first determine four non-grain production categories as immediately recoverable (IMR), simple-engineering recoverable (SER), engineering recoverable (ENR), and irrecoverable (IR) based on the effect of non-grain activities on the degree of soil damage of the cultivated land, especially the difficulty of restoring the capacity for grain production. Then, we analyze the spatial pattern features for non-grain production of four given categories at the village scale. Furthermore, we reveal the mechanisms of the four categories using multiple linear regression modeling with geophysical, demographic, economic, and policy variables. The results show that the total non-grain area of cultivated land in Liyang is 28,158.38 hectares, and the non-grain rate is 48.09%, ranging from 10.59% to 96.75% among villages. The IMR, SER, ER, and IR rates are 11.81%, 17.76%, 15.07%, and 3.45%, respectively. There is also a significant neighborhood effect among the four categories, indicating that non-grain production activities have a stimulating effect on the surrounding operators of cultivated land. Farming conditions such as the proportion of irrigated farmland and economic variables such as the tourism scale have stronger effects on non-grain production than demographic variables. Policy variables, especially the cultivated land transfer policy, neither inhibit nor promote non-grain production. Based on these findings, we make policy suggestions for reducing non-grain production activities and protecting cultivated land. This analysis framework contributes to a new perspective for unraveling the causal mechanisms and making categorical governance decisions of non-grain production on cultivated land at the village level