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In the face of increasingly diverse demands from tourists, traditional methods for scenic route planning often struggle to meet these varied needs. To address this challenge and enhance the overall service quality of tourist destinations, as well as to better understand individualized preferences of visitors, this study proposes a novel approach to scenic route planning and itinerary customization based on multi-layered mixed hypernetwork optimization. Firstly, an adaptive multi-route feature extraction method is introduced to capture personalized demands of tourists. Subsequently, a personalized tourist inference method based on a multi-layered mixed network is presented, utilizing the extracted personalized features to infer the true intentions of the tourists. Lastly, we propose a hypernetwork optimized route planning method, incorporating the inference results and personalized features to tailor the optimal touring paths for visitors. The results of our experiments underscore the efficacy of our methodology, attaining an accuracy score of 0.877 and an mAP score of 0.881 and outperforming strong competitors and facilitating the design of optimal paths for tourists.

High-altitude lakes in the Tibetan Plateau (TP) showed strong spatio-temporal variability during past decades. The lake dynamics could be associated with several important factors including lake type, supply of glacial meltwater, local climate variations. It is important to differentiate these factors when analyzing the driving forces of lakes dynamics. With a focus on lakes over the Tanggula Mountains of the central TP, this study investigates the temporal evolution patterns of lake area and water level of different types: glacier-fed closed lake, non-glacier-fed closed lake and upstream lake (draining into closed lakes). We collected all available Landsat archive data and quantified the inter-annual variability of lake extents. Results reveal accelerated expansions of both glacier-fed and non-glacier-fed lakes during 1970s–2013, and different temporal patterns of the two types of lakes: the non-glacier-fed lakes displayed a batch-wise growth pattern, with obvious growth in 2002, 2005 and 2011 and slight changes in other years, while glacier-fed lakes showed steady expanding tendency. The contrasting patterns are confirmed by distinct lake level changes between the two groups derived from satellite altimetry during 2003–2013. The upstream lakes remained basically stable due to natural drainage regulation. The intermittent expansions for non-glacier-fed lakes are found to be related to excessive precipitation events and positive “precipitation–evaporation”. In contrast, glacier-fed lake changes showed weak correlations with precipitation variations, which implies a joint contribution from glacial meltwater to water budgets. Our study suggests that glacial meltwater supply may have an equivalent influence on lake growth with precipitation/evaporation in the study area.

The Surface Water and Ocean Topography (SWOT) mission promises quasi‐global monitoring of glacial lakes, yet the elevation difference arising from its Ka‐band radar penetrating lake snow/ice cover remains unquantified. This poses a challenge to assessing their level changes. We present the first quantification of this difference by intercomparing SWOT and ICESat‐2 surface elevations over 260 glacial lakes (>1 km2) globally. Given ICESat‐2’s laser measures the snow/ice surface, we leverage near‐synchronous observations to treat the elevation difference as an empirical measure of the snow/ice penetration effect of SWOT. We find a globally consistent negative difference (SWOT minus ICESat‐2) of −0.25 m over the 1‐year period. This difference is dependent on season, amplifying by more than double from −0.15 m (warm) to −0.34 m (cold), with peak monthly differences nearly reaching −1 m. This study provides a framework for reconciling SWOT and ICESat‐2 observations, enabling long‐term, high‐resolution records of glacial lake variability.

Rivers are among the most diverse, dynamic, and productive ecosystems on Earth. River flow regimes are constantly changing, but characterizing and understanding such changes have been challenging from a long-term and global perspective. By analyzing water extent variations observed from four-decade Landsat imagery, we here provide a global attribution of the recent changes in river regime to morphological dynamics (e.g., channel shifting and anabranching), expansion induced by new dams, and hydrological signals of widening and narrowing. Morphological dynamics prevailed in ~20% of the global river area. Booming reservoir constructions, mostly skewed in Asia and South America, contributed to ~32% of the river widening. The remaining hydrological signals were characterized by contrasting hotspots, including prominent river widening in alpine and pan-Arctic regions and narrowing in the arid/semi-arid continental interiors, driven by varying trends in climate forcing, cryospheric response to warming, and human water management. Our findings suggest that the recent river extent dynamics diverge based on hydroclimate and socio-economic conditions, and besides reflecting ongoing morphodynamical processes, river extent changes show close connections with external forcings, including climate change and anthropogenic interference. Rivers are among the most diverse, dynamic, and productive ecosystems on Earth. Here, using Landsat imagery, the authors provide a global attribution of the recent changes in river regime to morphological dynamics, dam-induced widening, and hydrological signals.

With rapid population growth and socioeconomic development over the last century, a great number of dams/reservoirs have been constructed globally to meet various needs. China has strong economical and societal demands for constructing dams and reservoirs. The official statistics reported more than 98 000 dams/reservoirs in China, including nearly 40 % of the world's largest dams. Despite the availability of several global-scale dam/reservoir databases (e.g., the Global Reservoir and Dam database (GRanD), the GlObal geOreferenced Database of Dams (GOODD), and the Georeferenced global Dams And Reservoirs (GeoDAR)), these databases have insufficient coverage of the reservoirs in China, especially for small or newly constructed ones. The lack of reservoir information impedes the estimation of water budgets and the evaluation of dam impacts on hydrologic and nutrient fluxes for China and its downstream countries. Therefore, we presented the China Reservoir Dataset (CRD), which contains 97 435 reservoir polygons and fundamental attribute information (e.g., name and storage capacity) based on existing dam/reservoir products, national basic geographic datasets, multi-source open map data, and multi-level governmental yearbooks and databases. The reservoirs compiled in the CRD have a total maximum water inundation area of 50 085.21 km2 and a total storage capacity of about 979.62 km3 (924.96–1060.59 km3). The quantity of reservoirs decreases from the southeast to the northwest, and the density hotspots mainly occur in hilly regions and large plains, with the Yangtze River basin dominating in reservoir count, area, and storage capacity. We found that these spatial accumulations of reservoirs are closely related to China's socioeconomic development and the implementation of major policies. Finally, we presented the comparison of the CRD with GOODD, GeoDAR, and GRanD databases. The CRD has significantly increased the reservoir count, area, and storage capacity in China, especially for reservoirs smaller than 1 km2. The CRD database provides more comprehensive reservoir spatial and attribute information and is expected to benefit water resources managements and the understanding of ecological and environmental impacts of dams across China and its affected transboundary basins. The CRD database is publicly available at https://doi.org/10.5281/zenodo.6984619 (Song et al., 2022).

Urban lakes provide important ecological services to local communities, such as flood mitigation, biodiversity, and recreation. With rapid urbanization, urban lakes are significantly affected by socio-economic development and urgently need attention. Yet there is still a lack of datasets that include tiny urban lakes on a global or national scale. This study aims to produce a high-resolution circa-2020 map of urban lakes (≥0.001 km 2 ) in China. The 10-m-resolution Sentinel-2 imagery and a simple but robust water extraction method was used to generate waterbodies. The accuracy of this national-scale dataset was evaluated by comparing it with manually sampled urban units, with the average accuracy of 81.85% in area and 93.35% in count. The database totally inventories 1.11 × 10 6 urban lakes in China, with a net area of ~2.13 × 10 3  km 2 . Overall, the spatial distribution of urban lakes in China showed strongly heterogeneous characteristics. This dataset will enhance our understanding of the distribution pattern of China’s urban lakes and contribute to better ecological and environmental management as well as sustainable urban development planning.

Land use and land cover (LULC) is a key variable of the Earth’s system and has become an important indicator to evaluate the impact of human activities on the Earth’s ecosystems. With the increasing demand of mine resources, widespread opencast mining has led to significant changes in LULC and caused substantial damage to the environment. An efficient approach of detecting mining activities at large scales is of critical importance in mitigating their potential impacts on downstream settlements and in assessing LULC characteristics. In this study, we present a novel approach for enabling large-scale automatic detection of opencast mining areas by integrating multitemporal digital elevation models (DEMs, including the SRTM DEM and the recently released TanDEM-X DEM) and multispectral imagery in object-based image analysis and random forest (RF) algorithms. A sequence of data preparation, image segmentation, threshold analysis, calculation of metrics, and influence factor regulation was developed and tested on the Landsat 8 sample dataset in Inner Mongolia in China, which is a mineral-rich area. Aside from spectral metrics, such as brightness and reflectance value, introduced topographical features enhanced the modeling and classification significantly, and the overall performance is greatly influenced by feature selection (the out-of-bag error rate in the RF algorithm is 7.54% for the integrated DEM method in comparison with 12.70% for the only-optical images method). The integrated use of spectral imagery and multitemporal DEMs reveals that the identified mining area is about 1100 km2 in the study area and period, and the topographic changes of opencast mining in terms of elevation difference is between −258 and 162 m. The results show that the method can map the locations and extents of mining areas automatically from spectral and DEM data and can potentially be applied to larger areas.

Glacier area changes on the Tibetan Plateau were studied in different drainage basins based on Landsat satellite images from three epochs: 263 in the mid-1970s, 150 in 1999–2002 and 148 in 2013/14. Three mosaics (M1976, M2001 and M2013) with minimal cloud and snow cover were constructed, and the uncertainty due to each epoch having a finite span was accounted for. Glacier outlines (TPG1976, TPG2001 and TPG2013) were digitized manually with guidance from the SRTM DEM v4.1 and Google Earth imagery. To achieve complete multi-temporal coverage in a reasonable time, only debris-free ice was delineated. Area mapping uncertainty was evaluated at three study sites, Mount Qomolangma (Everest), Mount Naimona'Nyi, Mount Geladandong, where the largest differences between present and earlier measurements were within ~±4%. Area differences with previous inventories ranged from −19.6% (TPG1976 minus the first Chinese Glacier Inventory) to −3.6% and −1.1% (TPG2013 and TPG2001, respectively minus the second Chinese Glacier Inventory), while the difference TPG2001 minus the GAMDAM Glacier Inventory was +10.4%. Glacier area on the plateau decreased from 44 366 ± 2827 km2 (1.7% of the study area) in the 1970s to 42 210 ± 1621 km2 in 2001 and 41 137 ± 1616 km2 in 2013. Shrinkage was faster in external drainage basins of the southeast than in the interior basins of the northwest, from a maximum of −0.43% a−1 (−1.60% a−1 during 1994–2013) in the Mekong catchment down to a minimum of −0.12% a−1 in the Tarim interior drainage.

Grounded in a typological framework, this study utilizes design-based research to systematically examine urban furniture in Macau, specifically interrogating the mechanisms of morphological continuity and spatial evolution. Through extensive field surveys and iterative design practices, the research identifies pronounced deficiencies in existing urban furniture, particularly concerning cultural resonance, functional versatility, and environmental synergy. To resolve these issues, the study proposes a typological design methodology predicated on cultural identification, archetypal restoration, and morphological innovation. This framework is articulated through three strategic pillars: (1) reconfiguring the structural logic of vernacular architecture via analogical synthesis; (2) recontextualizing architectural symbols to facilitate the contemporary translation of cultural heritage; and (3) calibrating ergonomic performance with aesthetic cohesion through rigorous proportional reasoning. Empirical validation confirms that this approach substantially augments both the cultural legibility and spatial fluidity of urban furniture. Ultimately, this research establishes a refined theoretical and methodological template for urban design in culturally heterogeneous contexts.

The provision of precise and dependable information regarding the extent and distribution of cropland is imperative for the evaluation of food security, agricultural planning, and resource management. Cropland is an important component of land cover type and is offered in multiple existing global/regional land cover products. However, global-scale accuracy evaluation may not be representative of class-specific or local-area accuracy, such as in Northeast China, which is an important grain-producing region of China and has various types of cultivated land (e.g., wheat, rice) and diverse terrains. It poses a great challenge in generating precise cropland classification by automated mapping. Thus, it is indispensable to evaluate the accuracy and reliability of these various land cover datasets before using them. In this study, we collected thirteen sets of global or national-scale land cover datasets. Through the visual interpretation of high-resolution images, ground “truth” samples were collected to evaluate the data accuracy across Northeast China. The overall accuracy (OA) evaluation results in Phase-2020 show that CLCD has the highest value with 0.914, followed by GlobeLand30 (0.906), GLC_FCS30 (0.902), and Esri (0.896) for cropland classification in Northeast China. CGLS-LC100 has the lowest OA (0.710). For the commission and omission errors of six datasets in Phase-2020, CGLS-LC100 has an obvious overestimation (larger commission error), while the two national-scale datasets (CLCD and CLUDs) perform relatively better. In terms of spatial consistency, high spatial agreement among the nine Phase-2015 datasets or in the six Phase-2020 datasets could be discovered in traditional agricultural regions like the Sanjiang–Songnen–Liaohe Plain, and low agreement is found in the transition areas of mountains (hills) and plains with the mixed landscape of forest (grassland) and farmland. In the aspect of comparison pairwise data, CLCD is in good agreement with GLC_FCS30, GlobeLand30, and Esri, while CGLS-LC100 is in the poorest agreement with any other dataset. The comparison and evaluation results are expected to provide a reference on which aspects and to what extent these land cover products may be consistent and guide the cropland data product selection for Northeast China.