Digital/true orthoimage maps (D/TOMs) are one of the most important forms of national spatial data infrastructure (NSDI). The traditional generation of D/TOM is to orthorectify an aerial image into its upright and correct position by deleting displacements on and distortions of imagery. This results in the generated D/TOM having no building façade texture when the D/TOM superimposes on the digital building model (DBM). This phenomenon is no longer tolerated for certain applications, such as micro-climate investigation. For this reason, this paper presents the generation of a true² orthoimage map (T²OM), which is radically different from the traditional D/TOM. The basic idea for the T²OM generation of a single building is to orthorectify the DBM-based building roof from up to down, the building façade from front to back, from back to front, from left side to right side, and from right side to left side, as well as complete a digital terrain model (DTM)-based T²OM, of which a superpixel is proposed to store building ID, texture ID, the elevation of each pixel, and gray information. Two study areas are applied to verify the methods. The experimental results demonstrate that the T²OM not only maintains the traditional characteristics of D/TOM, but also displays building façade texture and three-dimensional (3D) coordinates (XYZ) measurable at any point, and the accuracy of 3D measurement on a T²OM can achieve 0.025 m (0.3 pixel).

Due to the façade visibility, intuitive expression, and multi-view redundancy, oblique photogrammetry can provide optional data for large-scale urban LoD-2 reconstruction. However, the inherent noise in oblique photogrammetric point cloud resulting from the image-dense matching limits further model reconstruction applications. Thus, this paper proposes a novel method for the efficient reconstruction of LoD-2 building models guided by façade structures from an oblique photogrammetric point cloud. First, a building planar layout is constructed combined with footprint data and the vertical planes of the building based on spatial consistency constraints. The cells in the planar layout represent roof structures with a distinct altitude difference. Then, we introduce regularity constraints and a binary integer programming model to abstract the façade with the best-fitting monotonic regularized profiles. Combined with the planar layout and regularized profiles, a 2D building topology is constructed. Finally, the vertices of building roof facets can be derived from the 2D building topology, thus generating a LoD-2 building model. Experimental results using real datasets indicate that the proposed method can generate reliable reconstruction results compared with two state-of-the-art methods.

Precipitation water vapor (PWV) is an important parameter in numerical weather forecasting and climate research. However, existing PWV adjustment models lack comprehensive consideration of seasonal and geographic factors. This study utilized the General Regression Neural Network (GRNN) algorithm and Global Navigation Satellite System (GNSS) PWV in China to construct and evaluate European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5) PWV adjustment models for various seasons and subregions based on meteorological parameters (GMPW model) and non-meteorological parameters (GFPW model). A linear model (GLPW model) was established for model accuracy comparison. The results show that: (1) taking GNSS PWV as a reference, the Bias and root mean square error (RMSE) of the GLPW, GFPW, and GMPW models are about 0/1 mm, which better weakens the systematic error of ERA5 PWV. The overall Bias of the GLPW, GFPW, and GMPW models in the Northwest (NWC), North China (NC), Tibetan Plateau (TP), and South China (SC) subregions is approximately 0 mm after adjustment. The adjusted overall RMSE of the GLPW, GFPW, and GMPW models of the four subregions are 0.81/0.71/0.62 mm, 1.15/0.95/0.77 mm, 1.66/1.26/1.05 mm, and 2.11/1.35/0.96 mm, respectively. (2) The accuracy of the three models is tested using GNSS PWV, which is not involved in the modeling. The adjusted overall RMSE of the GLPW, GFPW, and GMPW models in the four subregions are 0.89/0.85/0.83 mm, 1.61/1.58/1.27 mm, 2.11/1.75/1.68 mm and 3.65/2.48/1.79 mm, respectively. As a result, the GFPW and GMPW models have better accuracy in adjusting ERA5 PWV than the linear model GLPW. Therefore, the GFPW and GMPW models can effectively contribute to water vapor monitoring and the integration of multiple PWV datasets.

Identifying suitable areas for ecosystem services (ES) development is essential for balancing economic growth with environmental sustainability in ecologically fragile regions. However, existing studies often neglect integrating future climate and socioeconomic drivers into ES optimization, hindering the design of robust strategies for sustainable resource management. In this study, we propose a novel framework integrating the System Dynamics (SD) model, the Patch-based Land Use Simulation (PLUS) model, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and the Dynamic Bayesian Network (DBN) to optimize ES patterns in the Sanjiangyuan region under three climate scenarios (SSP126, SSP245, and SSP585) from 2030 to 2060. Our results show the following: (1) Ecological land (forest) expanded by 0.86% under SSP126, but declined by 11.54% under SSP585 due to unsustainable land use intensification. (2) SSP126 emerged as the optimal scenario for ES sustainability, increasing carbon storage and sequestration, habitat quality, and water conservation by 3.2%, 1%, and 1.4%, respectively, compared to SSP585. (3) The central part of the Sanjiangyuan region, characterized by gentle topography and adequate rainfall, was identified as a priority zone for ES development. This study provides a transferable framework for aligning ecological conservation with low-carbon transitions in global biodiversity hotspots.

Night-light remote sensing imagery (NLRSI) effectively reflects urban economic and human activities and has important value in the field of remote sensing. However, the applications are limited by their low spatial resolution. In recent years, multisource remote sensing image fusion has become an important method for enhancing the spatial and spectral resolution of single-image data. To address the low-resolution limitation of NLRSI, this study proposes a multisource remote sensing image fusion framework based on the two-branch convolutional neural network (TbCNN), which fuses Landsat-8 and NPP/VIIRS data to generate high-resolution color night-light remote sensing imagery (CNLRSI). First, TbCNN features a deep two-branch structure for multiscale feature extraction, yielding richer spatial texture features. The framework also integrates a multilevel feature fusion module and a residual learning mechanism, further improving the fusion performance of CNLRSI. Quantitative evaluations demonstrate TbCNNs superiority over other methods, achieving optimal values in objective evaluation metrics. Second, in the built-up area extraction experiment, CNLRSI better identifies the true morphology of urban built-up areas compared with NPP/VIIRS data, reducing the overestimation of central urban areas and the underestimation of suburban areas in NPP/VIIRS data. Finally, the enhanced classification capability of CNLRSI is quantitatively validated through confusion matrix analysis, achieving a higher Kappa coefficient (0.814 versus 0.774) than NPP/VIIRS in urban pixel recognition.