The Qilian Mountains (QLM) act as an “ecological security barrier” in western China, impacting the downstream ecosystems and water resource utilization. However, the hydrological evolution of the QLM during the last millennium remains controversial, and their ecological response to climate change is poorly understood. We present a pH record based on the brGDGTs (branched glycerol dialkyl glycerol tetraethers) of a 14C-dated sediment core from Lake Maozangtianchi in the QLM. We combined this record with element contents determined by scanning XRF and grain size to reconstruct the summer monsoon precipitation variability over the last 900 years. We also reconstructed the history of eco-environmental changes from the total n-alkane contents. On centennial scales, local precipitation exhibited peaks during the intervals of 1100‒1300 CE and 1750‒2000 CE, as well as between 1400‒1750 CE. Additionally, abrupt decreases in precipitation occurred during the transition from the Medieval Warm Period (MWP) to the Little Ice Age (LIA) (1300‒1400 CE). This pattern coherent with other hydroclimatic records from the monsoonal margin of northern China, likely resulted from the combined impact of the El Niño‒Southern Oscillation on tropical Pacific sea-surface temperatures and the meridional shift of the Intertropical Convergence Zone. In addition, a coupled relationship between plant biomass in the Lake Maozangtianchi watershed and fluctuations in monsoon precipitation was observed, with higher plant biomass during 1100‒1200 CE, 1750‒1900 CE, and 1950‒2000 CE, and lower biomass during 1200‒1400 CE and 1900‒1950 CE. However, during 1400‒1750 CE, plant biomass exhibited a minor increasing trend, deviating from its usual correlation with monsoon precipitation. Despite precipitation usually being the primary climatic factor influencing plant biomass in the QLM, during the LIA, nutrients transported by dust and decreased evapotranspiration became pivotal in bolstering plant growth. Our research emphasizes the significant moderating effects of exogenic dust on vegetation changes in alpine ecosystems.

Most urban tree inventories depend on resource-intensive, field-based assessments, which are unevenly distributed in space and time. Recently, these inventories have been conducted using field inventories combined with airborne multispectral, hyperspectral, LiDAR, and spaceborne multispectral remote sensing. Significant advances have been made in urban tree GIS databases and remote sensing methods, which include delineating individual tree crowns, extracting tree species metrics, and employing classification techniques. Generally, remote sensing methods distinguish individual urban trees using either pixel-based or object-based methods, while image classification procedures are typically divided into parametric (e.g., regression-based classification, Bayesian, and principal component analysis) and non-parametric approaches such as machine learning (e.g., random forests support vector machines) and deep learning (e.g., convolutional neural networks). Our synthesis of the current state of science suggests sensors with the highest spatial (m), spectral (bands), and temporal (repeat time) resolutions result in the most accurate tree species identification. Combining airborne LiDAR/hyperspectral or airborne LiDAR/spaceborne high-resolution multispectral sensors yields the highest accuracy for the most diverse urban forests. An object-based non-parametric approach, like a fully convolutional neural network, scores higher in accuracy assessments than pixel-based parametric approaches. Future studies can leverage global/regional GIS field inventory databases to expand the scope of studies within and across multiple cities, utilizing LiDAR and spaceborne sensors.

Water exhibits various politico-economic dynamic. Water scarcity can lead to conflicts, and it lies at the core of Iran’s environmental crises. The literature on Iran’s water crisis indicates the effects of this issue in terms of multidimensional environmental degradation, community disintegration, and state-society and intercommunal conflict. Approximately 28 million of Iran’s 85 million residents reside in water-stressed areas, a situation identified as ‘water bankruptcy’. The water shortage is experienced differently across the country. The plateau’s central regions, home to Iran’s major industries, are where the worst water deficit is occurring. However, regions with abundant water resources have also been impacted. These regions – known as ‘donor basins’ – due to intensive and disproportional inter-basin water transfer and other engineering interventions deployed by the Government to deal with the water shortage of the central regions, suffer from a different form of water crisis. A condition of asymmetrical and conflictual power relations between the state and subaltern communities in Iran’s peripheral regions has been created. This paper argues that this constitutes environmental racism, characterised by multilayered impoverishment and unsustainable development among communities in the donor regions.

Ecological value (EV) is a term used to characterize the biotic or abiotic elements of a landscape, excluding human influence. Significant criteria for EV estimation can be grouped into two categories: ecological properties (biodiversity and vulnerability) and functional/structural features (fragmentation, connectivity, and resilience). While various methodological frameworks exist for estimating these criteria, few studies integrate all five criteria, and even fewer compare their results with fieldwork data. The objective of this study was to devise a novel spatial modelling tool for EV estimation based on biodiversity, vulnerability, fragmentation, connectivity, and resilience, utilizing data from Neotropical montane forests in west-central Mexico. The model incorporated data on (i) biodiversity and vulnerability estimated through ecological niche models, (ii) fragmentation and connectivity using landscape spatial patterns, and (iii) resilience estimated through the inverse of the vegetation sensitivity index. The results were then compared with fieldwork data. Natural protected areas within the Neotropical montane forests of west-central Mexico exhibited high EVs; however, a substantial portion of these forests lack legal protection. In terms of vegetation types, cloud and semideciduous forests exhibited the highest EV, emphasizing the urgent need for legal protection of these vital ecosystems. The comparison process demonstrated a moderate to high correlation in some criteria between the spatial and fieldwork data, indicating that the spatial model robustly captured the landscape spatial patterns. The spatial modelling tool proposed in this study is not only practical but also reproducible and applicable globally. Its efficacy lies in combining ecological properties with the functional and structural features of the landscape, making it particularly suitable for delineating protected natural areas and contributing to landscape planning efforts.

Remote sensing plays an important role for modern geography and environmental science. At the same time, it often stands on a weak epistemological foundation. Remote sensing results are mostly treated as strictly objective, context-independent artifacts. This vastly ignores the human practices that led to these results. Thus, remote sensing data are uncritically incorporated into (environmental) policy decision-making processes without understanding exactly how they were generated. Recent research has been critical of this. In a previous study, I showed that the accuracy of land use results can be increased by class aggregation, while the geographic or environmental meaning of the results suffers. I called this provocatively the “more accurate, less meaningful (MALM)” effect and showed that it exists regardless of the technical level of classification. In this study, I discuss the extent to which MALM can be remedied by choosing an appropriate quality indicator. I show that, to the largest extent conceivable, the quality indicator does not and cannot unveil the effects of socio-technical practices, which are materially inscribed into land use maps. Hence, quality indicators are unable to objectivize the effects of practices and values by the researchers. Consequently, they do not solve the MALM problem. On the contrary, I show that the explicit inclusion of geographic knowledge in quality addresses the MALM effect to the largest extent possible. This reinforces my claim that more attention needs to be paid to considering the values and practices behind remote sensing information. I discuss the results in a broad context and argue that and why critical remote sensing based on critical (physical) geography and science-and-technology studies is vital to better incorporate such results into policymaking.

Investigators seeking to solve the mystery of rock varnish formation have yet to embrace hypothesis testing. Thus, this paper presents nine tests that assess the validity of the eight hypotheses proposed over the last four decades to explain varnish formation. An almost singular focus on manganese (Mn)-enrichment by many in varnish research may have led to six of the eight hypotheses failing to explain iron (Fe)-enrichment, as well as why clay minerals dominate the composition of rock varnish. Many varnish researchers displayed a sampling bias by collecting samples only from hot and dry deserts; thus, it should be of no surprise that four hypotheses failed a test of explaining varnishes in different climates; five hypotheses failed to explain varnishes in subsurface locations; and seven hypotheses failed to explain differences in varnish growth rates in hot deserts versus wetter locations. In the end, seven of eight proposed hypotheses to explain varnish formation failed more than five tests, any one of which would falsify the hypothesis. Only one hypothesis “passed” all nine tests.

Fine particulate matter (PM2.5) is a major source of air pollution and exerts serious impacts on human health. The 3D urban landscape patterns can significantly affect the diffusion and emissions of PM2.5. However, studies on the relationships between 3D urban landscape patterns and PM2.5 pollution across different seasons remain understudied. With the ground-level air pollutants estimated by the remote sensing and fine-scale building information, this study applied the multiscale geographically weighted regression model to explore such relationships. Wuhan, the largest metropolis in Central China, was selected as the study area for the application of our methodology. The results showed that the direction, degree, and scale of the effect of 3D urban landscape patterns on PM2.5 pollution varied across seasons. For building height, the standard deviation of building height had a significant positive correlation with PM2.5 all year round. For building density, the building count density showed a significant positive correlation with PM2.5 in general, with the bandwidth in winter and autumn smaller than in spring and summer. The building plan area fraction exerted both positive and negative influences on PM2.5, dependent on season and location. The bandwidth of it gradually increased from spring to winter, with the effect changing from local to regional scale. For building volume, the floor area ratio showed a significant negative correlation with PM2.5 in winter and autumn, and a localized effect was found, especially in winter. The findings of this study provide practical implications for urban planning and policy making to mitigate PM2.5 pollution in the rapidly urbanizing regions.

A consensus has not yet been reached on effects of climate change and driving mechanisms between the Tibetan Plateau (TP) and adjacent monsoonal areas during the Marine Isotope Stage 3 (MIS 3). Loess–paleosol sequences from the TP provide valuable information about the MIS 3 environmental history. Detailed color index and a diffuse reflectance spectral (DRS) analysis of Zhouqu (ZQ) loess from the Western Qinling Mountains were conducted to investigate climate change on the eastern margin of the TP during the MIS 3. Our results show that the variations in color index and iron oxide content in ZQ loess are mainly caused by the pedogenesis and climate conditions. The lightness (L*) value and hematite (Hm) content were used to reconstruct the precipitation history and temperature changes, respectively. The reconstructed records revealed that climate change during the MIS 3 was characterized by high frequency and large amplitude, with millennial-scale changes superimposed on orbital-scale changes. Warm–humid climate occurred in the late MIS 3, while the early climate of MIS 3 was relatively cold–dry. The Indian summer monsoon (ISM) and temperature variations during the MIS 3 mainly occurred due to obliquity and precession. The North Atlantic cooling led to the southward movement of the Intertropical Convergence Zone, and the downstream cooling of the atmosphere by the westerly jet could result in events on a millennial-scale in the eastern margin of the TP. The interhemispheric forcing may play an important role in driving the strong summer monsoon in the late MIS 3.