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Abstract A growing number of Nature-based Solutions (NbS) has been advocated for urban flood risk management (FRM). However, whether NbS for FRM (NbS-FRM) achieves both social and ecological co-benefits remains largely unknown. We here propose and use a conceptual framework with a coupled social-ecological perspective to explore and identify such “win-win” potential in NbS-FRM. Through a scoping-review we find that ecological FRM measures are unevenly distributed around the world, and those solely targeting flood mitigation may have unintended negative consequences for society and ecosystems. In elaborating this framework with evidence from the reviewed studies, we find that NbS-FRM has the potential to provide both social and ecological co-benefits, with remaining gaps including a lack of resilience thinking, inadequate consideration of environmental changes, and limited collaborative efforts to manage trade-offs. The proposed framework shows how to move forward to leverage NbS for equitable and sustainable FRM with improved human well-being and ecosystem health.

Coastal wetlands are important blue carbon ecosystems that play a significant role in the global carbon cycle. However, there is insufficient understanding of the variations in soil organic carbon (SOC) stocks and the mechanisms driving these ecosystems. Here we analyze a comprehensive multi-source dataset of SOC in topsoil (0–20 cm) and subsoil (20–100 cm) across 31 coastal wetlands in China to identify the factors influencing their distribution. Structural equation models (SEMs) reveal that hydrology has the greatest overall effect on SOC in both soil layers, followed by vegetation, soil properties, and climate. Notably, the mechanisms driving SOC density differ between the two layers. In topsoil, vegetation type and productivity directly impact carbon density as primary sources of carbon input, while hydrology, primarily through seawater salinity, exerts the largest indirect influence. Conversely, in subsoil, hydrology has the strongest direct effect on SOC, with seawater salinity also influencing SOC indirectly through soil and vegetation mediation. Soil properties, particularly pH, negatively affect carbon accumulation, while climate influences SOC indirectly via its effects on vegetation and soil, with a diminishing impact at greater depths. Using Random Forest, we generate high-resolution maps (90 m × 90 m) of topsoil and subsoil carbon density (R2 of 0.53 and 0.62, respectively), providing the most detailed spatial distribution of SOC in Chinese coastal wetlands to date. Based on these maps, we estimate that SOC storage to a depth of 1 m in Chinese coastal wetlands totals 74.58 ± 3.85 Tg C, with subsoil carbon storage being 2.5 times greater than that in topsoil. These findings provide important insights into mechanism on driving spatial pattern of blue carbon and effective ways to assess carbon status on a national scale, thus contributing to the advancement of global blue carbon monitoring and management.

Abstract While heat mitigation is crucial to achieving sustainable urban development, an inadequate understanding of the evolution of the urban thermal environment (UTE) and its relationship with socio-ecological systems (SESs) constrains the development of effective mitigation strategies. In this study, we use satellite observations from 2000–2021 to explore the evolving impact of SES interactions on the UTE of 136 Chinese urban areas. The results reveal a nonlinear intensification of the UTE over the period and an indication that an increasing number of urban areas have successfully applied UTE mitigation measures. Spatio-temporal patterns in UTE are shown to be strongly influenced by social and ecological factors and their interactions, whereby the higher the SES status, the stronger the decreasing UTE trend. These findings highlight the need for, and advantages of, developing win-win solutions for urban society and ecology and have important implications in creating integrated strategies for heat mitigation in promoting urban sustainability.

Abstract The 10Be record in laminated travertines is a potential proxy for reconstructing past solar activity down to the annual scale; however, correcting for the potential influence of climatic or environmental variations remains challenging. Here, we present an annually resolved 10Be record using travertines from Baishuitai, China, covering the period from 1510 to 1701 CE, along with environmental proxies, to evaluate climatic influences and implement corrections to accurately reconstruct solar activity. We demonstrate that the 10Be deposition in travertines exhibits two environmental impacts: the transport efficiency of atmospheric 10Be into travertine and the additional 10Be inflow from overland flow associated with rainfall. We show that these impacts can be corrected based on iron and potassium contents. The resulting corrected record agrees with ice‐core and tree‐ring records, demonstrating the feasibility of using such carbonate sediment 10Be records to reconstruct past solar activity.

Abstract Extensive efforts have been dedicated to deciphering the interactions associated with Sustainable Development Goals (SDGs). However, these developments are hampered by a lack of efficient strategies to avoid beneficial synergies being offset by harmful trade-offs. To fill these gaps, we used causal diagnosis and network analysis methods to construct 1302 directed networks of SDGs for 31 provinces in China from 2000 to 2020. We observed a dramatic offsetting effect of SDG synergies and trade-offs in China from 2000 to 2020, with approximately 27% of trade-off indicator pairs turning into synergies and about 25% of the synergy indicator pairs turning into trade-offs. However, our findings suggested that prioritising the progress of high-frequency indicators in virtuous cycles could multiply the positive systemic effects of the SDGs. Moreover, controlling the transition from passive to active in the trade-off network of SDGs remains a challenge in advancing the SDGs holistically.

Abstract Archeological evidence from loess sediments from Shangchen on the southeastern Chinese Loess Plateau indicates a suspension of hominin occupation around the time of the early mid‐Pleistocene climate transition, prompting a re‐assessment of climate‐vegetation‐hominin interactions. Loess deposits with in situ lithic records cover the period of hominin occupation and reveal four distinct climate‐vegetation periods (2.1–1.8, 1.8–1.26, 1.26–0.9, and 0.9–0.6 Ma). Major oscillations in climate superimposed upon an aridification trend and an expansion of C4 herbaceous vegetation from about 1.26 Ma may have driven early humans to move to more hospitable locations in the region. Comparison with the record at Nihewan indicates that large‐scale climate oscillations induced disparate hominin responses due to distinctive local environmental conditions.

Abstract The rapid shrinkage and salinization of the Aral Sea over the last few decades has precipitated an environmental disaster, with widespread implications for people whose livelihoods depend on it. Although debated extensively, few viable strategies have yet been identified for reviving the Aral Sea. Here, we propose a hydro‐eco‐social framework to develop a viable, sustainable solution and explore its feasibility to resolve the Aral Sea problem. Based on eco‐environmental indicators, we contend that it is feasible to raise the Aral Sea by 40 m above the Baltic Sea level, while maintaining salinity levels tolerable for aquatic organisms, simultaneously reducing sandstorm risk by 58%. Basin‐wide water balance under climate change scenarios shows that this level can be supported through management interventions that reduce water usage by 22.0–23.2 km3/year and ensure sufficient recharge into the lake, without compromising socio‐economic opportunities. To implement this solution, we propose establishing a socio‐ecologically aligned water governance network for basin‐scale water management that has potential application for other, similarly declining, major lake systems.

Abstract The COVID-19 pandemic continues to pose substantial challenges to achieving the Sustainable Development Goals (SDGs). Exploring systematic SDG strategies is urgently needed to aid recovery from the pandemic and reinvigorate global SDG actions. Based on available data and comprehensive analysis of the literature, this paper highlights ongoing challenges facing the SDGs, identifies the effects of COVID-19 on SDG progress, and proposes a systematic framework for promoting the achievement of SDGs in the post-pandemic era. Progress towards attaining the SDGs was already lagging behind even before the onset of the COVID-19 pandemic. Inequitable distribution of food–energy–water resources and environmental crises clearly threaten SDG implementation. Evidently, there are gaps between the vision for SDG realization and actual capacity that constrain national efforts. The turbulent geopolitical environment, spatial inequities, and trade-offs limit the effectiveness of SDG implementation. The global public health crisis and socio-economic downturn under COVID-19 have further impeded progress toward attaining the SDGs. Not only has the pandemic delayed SDG advancement in general, but it has also amplified spatial imbalances in achieving progress, undermined connectivity, and accentuated anti-globalization sentiment under lockdowns and geopolitical conflicts. Nevertheless, positive developments in technology and improvement in environmental conditions have also occurred. In reflecting on the overall situation globally, it is recommended that post-pandemic SDG actions adopt a “Classification–Coordination–Collaboration” framework. Classification facilitates both identification of the current development status and the urgency of SDG achievement aligned with national conditions. Coordination promotes domestic/international and inter-departmental synergy for short-term recovery as well as long-term development. Cooperation is key to strengthening economic exchanges, promoting technological innovation, and building a global culture of sustainable development that is essential if the endeavor of achieving the SDGs is to be successful. Systematic actions are urgently needed to get the SDG process back on track.

IntroductionEstuaries are highly vulnerable systems and increasingly exposed to a number of environmental, climatic and human-induced stressors. The Knysna estuary and lagoon complex, on the south coast of South Africa, is regarded as environmentally and economically important, yet faces regional impacts resulting from ongoing urbanisation and land use change as well as the significant global threats of rising sea levels and changing climate. Although the estuary has been reasonably well studied in terms of modern ecological processes, little is known of how the system has responded to changes in the longer term, not least the impact of European colonization and subsequent population growth and economic development.MethodsIn order to address this shortcoming, a series of shallow (

The world is changing at an accelerating pace due to increased human exploitation of the earth's resources and the consequent climate change and biodiversity loss crises. As a transdisciplinary discipline studying the coupled human and nature systems and their interactions, Geography has natural advantages to promote sustainable development. With the aim of stimulating sustainable development in the Anthropocene, the International Geography and Sustainability Workshop 2021 was held virtually during 23–24 November 2021. This editorial briefly reviews the development history of Geography, summarizes the presentations of keynote speakers, outlines the overall research framework, and discusses the future directions by which the discipline of Geography can be harnessed to advance sustainable development. The key outcomes are as follows: (1) The research paradigms of Geography are shifting from basic knowledge acquisition to understanding of coupling patterns and processes, and to the simulation and prediction of complex human-earth systems; (2) Landscape sustainability science and the metacoupling concept are emerging as new comprehensive research perspectives, and the framework of “Pattern—Process—Service—Sustainability” can be used as a basis to underpin Geography's role in sustainability; (3) Geography can support sustainable development in many ways, such as in agricultural development, disaster and risk monitoring and early warning, global climate change mitigation, and in helping to achieve the Sustainable Development Goals (SDGs). Future research directions include: integrated geographical research on climate change and sustainable resource utilization; integrated geographical research on social and economic sustainable development; sustainable cascades of ecosystem structure, functions, services, and human well-being; metacoupling for sustainability; safe and justice space boundaries; the classification–coordination–collaboration approach; and geographical education for sustainable development.

Abstract While the Sustainable Development Goal (SDG) index is a widely employed method of measuring progress in the United Nations (UN) SDGs, as it allows comparisons across countries and regions, it does not usually offer any indication as to how to move the SDGs forward. To address this deficit, we analyzed the impact of the offsetting effects of changes in individual SDG scores on the SDG index from 2015 to 2018 at the provincial level in China. Future priority goals are also revealed by accessing the interactions among the SDGs. Our findings show that both the number of SDGs that experienced an increase or decrease in the score and the degree of change in SDG scores have a major impact on changes in the SDG index score. For example, a decline in individual SDG scores at the national level usually represents a decline in the scores of more than 15 provinces, while an increase in the SDG score may mask the fact that some provinces have experienced a decline in their scores. In addition, changes in SDG scores can show some similarities in the strengths and weaknesses even across provinces in different geographical zones. The analysis of SDG interactions suggests that prioritizing the promotion of SDG7 (Affordable and Clean Energy) and SDG12 (Responsible Production and Consumption) will advance the synergistic development of SDGs in China. Overall, our study highlights the necessity to focus not only on regions with declining aggregated index scores but also on goals with declining scores in order to implement the SDGs as a whole. Meanwhile, leveraging the similarity of SDG changes to inform sustainable management policies will contribute to the overall implementation of the SDGs in different regions.

The Ningbo Plain on the East China coast is an important center of Neolithic culture, and associated settlements were influenced by changing sea levels and the geomorphological and hydrological environments of the palaeo-Ningbo Bay, the details of which are still subject to debate. This study is based on two well-dated sediment cores obtained from the Ningbo Plain, and here we report analyses of their sedimentology and foraminifera to reveal the infilling history of the palaeo-Ningbo Bay and its association with Neolithic occupation. The lithology of the largely muddy sediments and the dominance of euryhaline and brackish water foraminiferal species are indicative of an intertidal to a subtidal environment in the palaeo-bay during the early to mid-Holocene. Abrupt coarsening of sediment grain size and a corresponding increase in the abundance of foraminiferal species of inner and middle shelf environments occurred at ca. 8.8 cal. kyr BP and 7.6 cal. kyr BP, reflecting two major events of strengthened marine transgression that correspond to the rapid global sea-level rise events of Meltwater pulses (MWPs) 1C and 1D, respectively. A marked increase in the relative abundance of Ammonia annectens and Ammonia compressiuscula during ca. 7.5–7.1 cal. kyr BP further indicates frequent storm surges at that time. Between the two rapid transgression events, aggradation of tidal flats prevailed after ca. 8.0 cal. kyr BP, which provided a suitable setting for Neolithic settlements, as indicated by the recently discovered Jingtoushan site. However, the transgression sequence associated with the latter, the MWP-1D event, caused a regional cultural interruption at ca. 7.6 cal. kyr BP. Infilling and coastal marsh development in the palaeo-Ningbo Bay occurred progressively after ca. 7.0 cal. kyr BP and are associated with the emergence of the Hemudu culture.

Globally, the loss of forest vegetation is a significant concern due to the crucial roles that forests play in the Earth’s system, including the provision of ecosystem services, participation in biogeochemical cycles, and support for human well-being. Forests are especially critical in mountains environments, where deforestation can lead to accelerated biodiversity loss, soil erosion, flooding, and reduced agricultural productivity, as well as increased poverty rates. In response to these problems, China has implemented a series of ecological restoration programs aimed at restoring forests. However, there is a lack of knowledge as to whether the forest cover is increasing or decreasing, as well as the relative roles played by natural and human factors in forest change. Here, we aim to address these issues by analyzing the pattern and process of the forest changes in Guizhou province, a typical mountainous karst area with a fragile environment in southwestern China, between 1980 and 2018, and evaluating the extent to which these forest changes were influenced by natural and anthropogenic driving forces. Using a temporal sequence of satellite images and a Markov model, we found that the forest cover increased by 468 km2, and that over 33% of the cropland in Guizhou province was converted into forest between 1980 and 2018, with the most significant increases in the forest cover occurring in Qiandongnan. Through correlation analyses and generalized linear model (GLM) regression, we demonstrate that management factors exerted a more significant positive impact on the forest cover than climate change. While the mean annual precipitation and temperature were mostly stable during the period studied, the effects of population and gross domestic product (GDP) on the forest changes weakened, and the influence of land-use change markedly increased. These findings provide valuable information for resource managers engaging in forest protection, deforestation prevention, and ecological restoration in similar regions.

Increased pressure on the earth's resources has led to what is increasingly referred to as the climate crisis. While a whole range of environmental parameters have been transformed through such pressures, the effect of human activities on the climate is symbolic of the nature of the human footprint upon our planet and makes the lack of any coherent political leadership in most countries even more alarming. The discipline of Geography has a distinct advantage in developing a more holistic understanding of global environmental challenges in that it reaches across all the sciences (including social sciences and humanities). Geographical education therefore represents an important vehicle for citizens of all ages to help them understand the complexity of the sustainability goal and what can (and should) be done to achieve a more sustainable future. In this essay, I reflect on three approaches that are available to individuals and communities towards taking the steps to sustainability. The philosophy embodied by the International Year of Global Understanding (IYGU) is suggested as a particularly valuable tool for geography educators. The activities of the International Geographical Union (IGU) offer important opportunities for geographers to learn from each other and promote best practice in geographical education. As ‘the science for sustainability’, Geography has an increasingly important role to play in developing the knowledge and the skills to equip future generations with the tools to adapt to and mitigate potentially catastrophic global environmental change.

Urban green space (UGS) has gained increasing attention due to its environmental and social functions. However, the compound effects of climate change, population growth and economic development on UGS are largely unknown. We selected 107 medium-sized and large cities in China to investigate dynamics in the spatial pattern of UGS in relation to government policy and other drivers based on remote sensing data for the period 1990 to 2019. To explore the effect of different levels of urbanization on changes in green space, we develop a new Normalized Urban Development Index (NUDI) to classify urban-suburban-rural gradients, viz. Long-term Built-up, New Built-up and Non-Built-up. Then, we analysed changes over time in the annual peak value of fraction of vegetation cover (FVC) for 380,000 cloud-free Landsat images, and regional UGS dynamics were evaluated using the proposed Regional Greenness Dynamic Index (RGDI). Finally, to reveal the major driver(s) of changes in UGS and estimate the extent to which patterns of urban greening are due to differences in economic development, we compared the observed UGS spatio-temporal dynamics with data on several climatic, social-economic and land use related factors for the same period. The NUDI are shown to be highly effective in mapping urban development gradients, with overall accuracy in the identified classes of 89%. Annual maximum FVC analysis indicates that there was significant greening between 1990 and 2019 in both the long-term built up (10,667.52 km2) and the non-built up areas (529,310.47 km2), while there was a major increase in browning (25,110.43 km2) in the newly built-up areas. The RGDI results indicate that 65% (71/107) of long-term built-up areas in cities trended greener over 2010 to 2019 under consideration. At the whole city scale, RGDI is negatively correlated with gross domestic product (GDP), although when considering the long-term built-up areas only, economic growth exhibits a significant positive correlation during 2010 to 2019 (R = 0.62, p

Research on the Himalayan cryosphere has increasingly focused on climate change, considering aspects such as snow cover dynamics, precipitation and temperature trends. The Uttarakhand Himalaya is hydrologically very significant and a more comprehensive understanding of its response to climate change is needed. This paper analyses of elevation-dependent distribution and trends in snow-covered area (SCA), precipitation and temperature for the period 2000–2020 employing MODIS-Terra data, together with reanalysis (CHIRPS) and Landsat-8 data products within the Google Earth Engine (GEE) platform. The study reveals a significant increasing trend in annual, seasonal and monthly precipitation (except November) at different elevations, while SCA and temperatures exhibited more variable trends during the period. Digital elevation and spectral reflectance models reveal the effects of topography and surface attributes on snow cover. Statistical analyses reveal a significant relationship between precipitation and SCA (R2 = 0.78), while seasonal changes in temperature are apparent in an emerging pattern of warmer winters and a cooler pre-monsoon. SCA trends vary markedly between elevation zones in response to precipitation and temperature fluctuations. At higher altitudes, SCA decreased from 2000 to 2020 since, although precipitation increased slightly, marginally higher temperatures led to more snowmelt. Reduced snow cover and increased temperature are also associated with a decline in downstream fluvial during the period for which such data are available (2000 to 2005). The analysis of snow cover dynamics across different elevations improves our understanding of the overall hydrological conditions in the region and enable more reliable flood forecasting and management of water resources.

The ocean delivers many ecosystem services to human society in providing food, livelihoods, and recreation and is crucial for regulating the global climate. Coastal cities, which have become the backbone of national economies, are highly dependent on the ecosystem services supported by the ocean. As a global coastal megacity, Shanghai has benefited enormously from its relationship with the ocean but its burgeoning population and rampant economic development in recent decades have applied great pressures on the associated coastal ecosystems and have reduced the ocean's capacity to provide ecosystem services and, meanwhile, have led to the demand for greater investment in ocean ecosystem restoration. To support the goal of long-term sustainability and facilitate appropriate management decisions, it is essential to assess the current health status of the coastal ecosystems of Shanghai and evaluate potential future risks. Here we apply the Ocean Health Index (OHI) framework, with indicators and reference points adjusted based on the unique coastal environment in Shanghai. The results reveal that the city obtained an overall OHI of 59 (out of 100) for the period 2012 to 2016. Individual indicators for Clean Waters (22) and Fisheries (39) exhibit particularly low values, indicating that the coastal waters around Shanghai are heavily polluted and that marine fishing is unsustainable. The city’s highest OHI scores are in the sectors of Coastal Livelihoods and Economies (93), and Tourism and Recreation (93), indicating that Shanghai’s coastal ecosystems contribute significantly to people’s livelihoods and regional economies, while marine recreational areas and related leisure activities add considerably to the quality of life in the region. This study demonstrates the value of the OHI in assessing ocean health at the city scale and reveals its potential for application in other coastal localities. In so doing, the findings provide a valuable benchmark against which to measure progress towards the sustainable development of Shanghai's oceans.

Kedong County is typical of the black soil region of northeast China in being highly susceptible to accelerated soil erosion by gullying. Using data sourced from Corona satellite imagery for 1965, SPOT5 for 2005 and GF-1 for 2015, the spatial distribution of gullies in the research area was mapped. Land use data for 1965, 2005, and 2015 were obtained from the topographic map of 1954, and from Landsat images for 2005 and 2015. Over the last 50 years, the extent of gully erosion in the study area has increased markedly, most notably on cultivated land, while gully density rose from 2,756.16 m2/km2 to 14,294.19 m2/km2. Cultivating land on slopes, especially on slopes greater than ∼4°, may rapidly aggravate gully erosion. The greatest increases in gully density occurred in situations when cultivated land and other/degraded land were transformed, which gully erosion density increased by 49,526.69 m2/km2. Other/degraded land is the most vulnerable land in the study area, with the highest gully erosion density. In these cases, gully density initially increases and, although the “Grain for Green” project has been implemented, gully erosion density has not always declined in the recent past.

Land transformation is caused by natural as well as anthropogenic driving forces and its impact on the regional environment is a key issue in understanding the relationship between society and the environment. Here, we investigate Land Use Land Cover (LULC) change over four decades, based on Landsat satellite imagery for 1987, 1997, 2007, and 2017, for the Barddhaman district of West Bengal, India. In total, six land use and land cover types have been identified. Over the period in question, there are notable increases in the area under built-up land, plantations and water bodies, whereas there has been a marked decrease in forest cover, agricultural land, and in bare land. The diverse effects of land transformation on the natural environment have been assessed using Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Leaf Area Index (LAI), Effective Roughness Length (ERL), and Surface Albedo (SA). Overall, mean annual LST increased by 2.91°C during the study period, while there were reduced values for vegetation indices and an increase in the water index over the period 1987–2017. LAI and ERL both exhibit notable decreases, although the pattern was not uniform across the study area. For example, LAI values increased over time in the Kalna, Memari, Jamalpur, Ausgram, and Kanksa regions. In Faridpur-Durgapur, Raniganj, Asansol, and Raina, increases in surface albedo and ERL were more marked than in other regions. Negative correlations are found between LST-NDVI and NDVI-NDWI, while there is no correlation between LST and NDWI. During the period 1987–2017, NDVI values have declined, although the NDWI shows no clear trend. LULC change is shown to have had a series of negative impacts on the environment of the Barddhaman district. In response, technological, economic, policy, or legislation measures are needed to restore degraded ecosystem services in the district as well as other areas where similar impacts are experienced.

Fine particulate matter in the lower atmosphere (PM2.5) continues to be a major public health problem globally. Identifying the key contributors to PM2.5 pollution is important in monitoring and managing atmospheric quality, for example, in controlling haze. Previous research has been aimed at quantifying the relationship between PM2.5 values and their underlying factors, but the spatial and temporal dynamics of these factors are not well understood. Based on random forest and Shapley additive explanation (SHAP) algorithms, this study analyses the spatiotemporal variations in selected key factors influencing PM2.5 in Zhejiang Province, China, for the period 2000–2019. The results indicate that, while factors influencing PM2.5 varied significantly during the period studied, SHAP values suggest that there is consistency in their relative importance as follows: meteorological factors (e.g., atmospheric pressure) > socioeconomic factors (e.g., gross domestic product, GDP) > topography and land cover factors (e.g., elevation). The contribution of GDP and transportation factors initially increased but has declined in the recent past, indicating that economic and infrastructural development does not necessarily result in increased PM2.5 concentrations. Vegetation productivity, as indicated by changes in NDVI, is demonstrated to have become more important in improving air quality, and the area of the province over which it constrains PM2.5 concentrations has increased between 2000 and 2019. Mapping of SHAP values suggests that, although the relative importance of industrial emissions has declined during the period studied, the actual area positively impacted by such emissions has actually increased. Despite developments in government policy, greater efforts to conserve energy and reduce emissions are still needed. The study further demonstrates that the combination of random forest and SHAP methods provides a valuable means to identify regional differences in key factors affecting atmospheric PM2.5 values and offers a reliable reference for pollution control strategies.

In regular convolutional neural networks (CNN), fully-connected layers act as classifiers to estimate the probabilities for each instance in classification tasks. The accuracy of CNNs can be improved by replacing fully connected layers with gradient boosting algorithms. In this regard, this study investigates three robust classifiers, namely XGBoost, LightGBM, and Catboost, in combination with a CNN for a land cover study in Hanoi, Vietnam. The experiments were implemented using SPOT7 imagery through (1) image segmentation and extraction of features, including spectral information and spatial metrics, (2) normalization of attribute values and generation of graphs, and (3) using graphs as the input dataset to the investigated models for classifying six land cover classes, namely House, Bare land, Vegetation, Water, Impervious Surface, and Shadow. The results show that CNN-based XGBoost (Overall accuracy = 0.8905), LightGBM (0.8956), and CatBoost (0.8956) outperform the other methods used for comparison. It can be seen that the combination of object-based image analysis and CNN-based gradient boosting algorithms significantly improves classification accuracies and can be considered as alternative methods for land cover analysis.

In recent years, atmospheric PM2.5 pollution in China has become increasingly severe and exploring the relationships among its influencing factors is important in the prevention and control of air pollution. Although previous studies have identified complexity in variations in PM2.5 concentrations and recognized the interaction of multiple factors, little quantitative information is available on the evolution of the relationships among these factors, their spatial heterogeneity, and the multiscale interactions between them. In this study, geographical detector and multiscale geographically weighted regression models have been used to explore the multiscale interactions among natural and socioeconomic factors and PM2.5 concentration in China over the period 2000–2015. The results indicate that the relationship between natural factors and PM2.5 concentration is stronger than that for socioeconomic factors. The type of interaction between each factor is dominated by bivariate and nonlinear enhancement, exhibiting strong interactions between natural factors and anthropogenic factors. Although the effect of each factor on PM2.5 is complex, the relative influence of both human activities and social factors is shown to have gradually increased over time and population, agriculture, urbanization, and socioeconomic activities in general make important contributions to PM2.5. In addition, the scale of effects related to natural factors is smaller and more stable compared to the influence of human activities during the period 2000-2015. There are significant differences in the way natural factors and socioeconomic factors affect PM2.5, and there is strong non-stationarity of spatial relationships. Factors associated with topography, vegetation (NDVI), climate (temperature), natural sources, and agricultural activity are shown to be important determinants of PM2.5 across China and warrant significant attention in terms of managing atmospheric pollution. The study demonstrates that spatial differences in the direction, intensity, and scale of each factor should be accounted for to improve prevention and control measures and alleviate regional PM2.5 pollution.

Telecoupling refers to socioeconomic and environmental interactions between distant places. Telecoupling is becoming even more significant in the increasingly globalized world and it plays a key role in the emergence of major global environmental problems. In particular, it contributes to land degradation and the achievement of the United Nations’ Sustainable Development Goals (SDGs). However, there is a lack of systematic examination of the impacts of telecoupling on land system change, and how to respond to the undesirable impacts. Based on CiteSpace Software, here we analyze the current research status of telecoupled human–land systems, including publications, major scientific research institutions, and research processes. We explore the impacts of telecoupling on land and how to respond to these impacts. Finally, we propose a framework that is composed of impact identification, system integration, and responses to achieve a win-win situation in telecoupled human–land systems. The framework can help to create a sustainable future for telecoupled human–land systems.

Forest fires constitute a major environmental problem in tropical countries, especially in the context of climate change and increasing human populations. This paper aims to identify the causes of frequent forest fires in Son La Province, a fire-prone and forested mountainous region in northwest Vietnam, with a view to constructing a forest fire-related database with multiple layers of natural, social and economic information, extracted largely on the basis of Landsat 7 images. The assessment followed an expert systems approach, applying multi-criteria analysis (MCA) with an analytical hierarchy process (AHP) to determine the weights of the individual parameters related to forest fires. A multi-indicator function with nine parameters was constructed to establish a forest fire risk map at a scale of 1:100,000 for use at the provincial level. The results were verified through regression analysis, yielding R2 = 0.86. A real-time early warning system for forest fire areas has been developed for practical use by the relevant government authorities to provide more effective forest fire prevention planning for Son La Province.

This paper analyses the spatio-temporal trends and variability in annual, seasonal, and monthly rainfall with corresponding rainy days in Bhilangana river basin, Uttarakhand Himalaya, based on stations and two gridded products. Station-based monthly rainfall and rainy days data were obtained from the India Meteorological Department (IMD) for the period from 1983 to 2008 and applied, along with two daily rainfall gridded products to establish temporal changes and spatial associations in the study area. Due to the lack of more recent ground station rainfall measurements for the basin, gridded data were then used to establish monthly rainfall spatio-temporal trends for the period 2009 to 2018. The study shows all surface observatories in the catchment experienced an annual decreasing trend in rainfall over the 1983 to 2008 period, averaging 15.75 mm per decade. Analysis of at the monthly and seasonal trend showed reduced rainfall for August and during monsoon season as a whole (10.13 and 11.38 mm per decade, respectively); maximum changes were observed in both monsoon and winter months. Gridded rainfall data were obtained from the Climate Hazard Infrared Group Precipitation Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). By combining the big data analytical potential of Google Earth Engine (GEE), we compare spatial patterns and temporal trends in observational and modelled precipitation and demonstrate that remote sensing products can reliably be used in inaccessible areas where observational data are scarce and/or temporally incomplete. CHIRPS reanalysis data indicate that there are in fact three significantly distinct annual rainfall periods in the basin, viz. phase 1: 1983 to 1997 (relatively high annual rainfall); phase 2: 1998 to 2008 (drought); phase 3: 2009 to 2018 (return to relatively high annual rainfall again). By comparison, PERSIANN-CDR data show reduced annual and winter precipitation, but no significant changes during the monsoon and pre-monsoon seasons from 1983 to 2008. The major conclusions of this study are that rainfall modelled using CHIRPS corresponds well with the observational record in confirming the decreased annual and seasonal rainfall, averaging 10.9 and 7.9 mm per decade respectively between 1983 and 2008, although there is a trend (albeit not statistically significant) to higher rainfall after the marked dry period between 1998 and 2008. Long-term variability in rainfall in the Bhilangana river basin has had critical impacts on the environment arising from water scarcity in this mountainous region.