Land use and land cover (LULC) change is a pattern of alteration of the Earth’s land surface cover by human society and have a significant impact on the terrestrial carbon cycle. Optimizing the distribution of LULC is critical for the redistribution of land resources, the management of carbon storage in terrestrial ecosystems, and global climate change. We integrated the patch-generating land use simulation (PLUS) model and integrated valuation of ecosystem services and trade-offs (InVEST) model to simulate and assess future LULC and ecosystem carbon storage in the Nanjing metropolitan circle in 2030 under four scenarios: natural development (ND), economic development (ED), ecological protection (EP), and collaborative development (CD). The results showed that (1) LULC and carbon storage distribution were spatially heterogenous in the Nanjing metropolitan circle for the different scenarios, with elevation, nighttime lights, and population being the main driving factors of LULC changes; (2) the Nanjing metropolitan circle will experience a carbon increase of 0.50 Tg by 2030 under the EP scenario and losses of 1.74, 3.56, and 0.48 Tg under the ND, ED, and CD scenarios, respectively; and (3) the CD scenario is the most suitable for the development of the Nanjing metropolitan circle because it balances ED and EP. Overall, this study reveals the effects of different development scenarios on LULC and ecosystem carbon storage, and can provide a reference for policymakers and stakeholders to determine the development patterns of metropolitan areas under a dual carbon target orientation.

Terrain positions are widely used to describe the Earth’s topographic features and play an important role in the studies of landform evolution, soil erosion and hydrological modeling. This work develops a new multimodal classification system with enhanced classification performance by integrating different approaches for terrain position identification. The adopted classification approaches include local terrain attribute (LA)-based and regional terrain attribute (RA)-based, rule-based and supervised, and pixel-based and object-oriented methods. Firstly, a double-level definition scheme is presented for terrain positions. Then, utilizing a hierarchical framework, a multimodal approach is developed by integrating different classification techniques. Finally, an assessment method is established to evaluate the new classification system from different aspects. The experimental results, obtained at a Loess Plateau region in northern China on a 5 m digital elevation model (DEM), show reasonably positional relationship, and larger inter-class and smaller intra-class variances. This indicates that identified terrain positions are consistent with the actual topography from both overall and local perspectives, and have relatively good integrity and rationality. This study demonstrates that the current multimodal classification system, developed by taking advantage of various classification methods, can reflect the geographic meanings and topographic features of terrain positions from different levels.

The topographic skeleton is the primary expression and intuitive understanding of topographic relief. This study integrated a topographic skeleton into deep learning for terrain reconstruction. Firstly, a topographic skeleton, such as valley, ridge, and gully lines, was extracted from a global digital elevation model (GDEM) and Google Earth Image (GEI). Then, the Conditional Generative Adversarial Network (CGAN) was used to learn the elevation sequence information between the topographic skeleton and high-precision 5 m DEMs. Thirdly, different combinations of topographic skeletons extracted from 5 m, 12.5 m, and 30 m DEMs and a 1 m GEI were compared for reconstructing 5 m DEMs. The results show the following: (1) from the perspective of the visual effect, the 5 m DEMs generated with the three combinations (5 m DEM + 1 m GEI, 12.5 m DEM + 1 m GEI, and 30 m DEM + 1 m GEI) were all similar to the original 5 m DEM (reference data), which provides a markedly increased level of terrain detail information when compared to the traditional interpolation methods; (2) from the perspective of elevation accuracy, the 5 m DEMs reconstructed by the three combinations have a high correlation (>0.9) with the reference data, while the vertical accuracy of the 12.5 m DEM + 1 m GEI combination is obviously higher than that of the 30 m DEM + 1 m GEI combination; and (3) from the perspective of topographic factors, the distribution trends of the reconstructed 5 m DEMs are all close to the reference data in terms of the extracted slope and aspect. This study enhances the quality of open-source DEMs and introduces innovative ideas for producing high-precision DEMs. Among the three combinations, we recommend the 12.5 m DEM + 1 m GEI combination for DEM reconstruction due to its relative high accuracy and open access. In regions where a field survey of high-precision DEMs is difficult, open-source DEMs combined with GEI can be used in high-precision DEM reconstruction.

Urban road DEM is not only an important basic geographic information data of the city, but also an important element to describe and express the urban topography, and it is an indispensable part of the construction of the smart digital city, urban planning and urban surface process simulation. Previous methods for constructing urban road DEMs do not sufficiently consider the actual morphological characteristics of urban roads, and morphological distortion is evident in the expression of urban roads, seriously affecting the application of urban rainfall flood simulation and urban pipe network design. In response to these problems, this study proposed a considering morphological characteristics fine (CMCF) method of urban road DEM construction, selected a typical urban road area in the Jianye District of Nanjing City in China as the study area, used the 1:500 digital line graphic as data source, hierarchized roads in accordance with different morphological characteristics from the perspective of DEM construction, constructed the corresponding DEMs, and finally merged all levels of road DEMs to produce a complete high-precision urban road DEM. Results showed that the DEM constructed using the CMCF method not only exhibited higher elevation accuracy than the urban road DEM constructed using previous methods, i.e., inverse distance weighting (IDW) and triangulated irregular network (TIN) methods, with a mean error and a root-mean-square error of 0.015 and 0.060, respectively, but it can also accurately express the spatial element composition form and road morphological characteristics of urban roads, avoiding the distorted expression of road morphological characteristics. This study can provide a reference for a new DEM construction method and data support for smart digital city construction and urban surface simulation.

Maintaining topological consistency is a crucial issue for 3D cadastral modeling as this helps to represent the cadastral boundary clearly and accurately. As a result, 3D cadastral data models are mainly built on the basis of topological models that allow topology to be expressed clearly. However, topological models in Euclidean space cannot directly represent objects’ geometric information. As geometric information is important in 3D spatial analysis, 3D cadastral data models based on topological models cannot realize topological calculation and analysis. Previous research has proved that geometric and topological information for cadastral objects can be integrated and represented by conformal geometric algebra (CGA) expressions. This paper aims to realize 3D topological analysis in the cadastral field using CGA’s advantages in geometric relations computation. A calculation framework is designed on the basis of the outer product to achieve the purpose of multidimensional unity for 3D cadastral topological analysis in this paper. A calculation framework of topological relations between a boundary point (or a boundary line) and a cadastral parcel is developed. A total of 13 types of topological relations between a boundary point and a cadastral parcel and 48 types of topological relations between a boundary line and a cadastral parcel are obtained. The study indicates that the advantages of CGA in multidimensional unified representation and calculation can be used to solve problems encountered by topological models in Euclidean space.

Extracting a channel network based on the Digital Elevation Model (DEM) is one of the key research topics in digital terrain analysis. However, when the channel area is wide and flat, it is easy to form parallel channels, which seriously affect the accuracy of channel network extraction. To solve this problem, this study proposes a method to identify and eliminate parallel channels extracted by classical methods. First, the channel level in the study area is marked based on the flow accumulation data, and the parallel channels are then identified using the positional relationship between the different channel levels. Finally, the modification point of the identified parallel channels is determined to eliminate the parallel channels, with the help of the change relationship between the parallel channel and its upper-level channel. In this study, two watersheds in southeast China are selected as examples for method verification and analysis. Experimental results show that the parallel channel identification method proposed in this paper can accurately identify all parallel channels and eliminate the identified parallel channels one by one. The location relationship of the modified channels is consistent with the actual situation, indicating that the proposed method has good application potential in DEM-based channel extraction networks.