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For the estimation of global karst carbon sink, a few conventional methods usually require the parameters that are difficult to measure, resulting in the big cost. Moreover, under the constraints of incomplete and timeliness issues in the collection of data over a large region, it has remained a challenge for these methods to study global karst carbon sink. Therefore, this paper proposes estimating the global karst carbon sink, and analyzing the suitability of the response surface methodology and the fluctuating variation of karst carbon sink in global karst regions from 1951 to 2050. This paper shows that the proposed method can reduce the time of numerical calculation and is suitable for application in global weathering models; The global karst carbon sink in the future changes not only displays an upward trend but also exposures its fluctuating trend largely. This fluctuation is probably due to global warming.

Abstract Accurate classification of substation equipment images remains challenging due to various factors such as unexpected illumination, viewing angles, scale variations, shadows, surface contaminants, and different elements sharing similar appearances. This paper presents a novel two‐stage substation equipment classification method based on dual‐scale attention. Leveraging the region proposal technique from Faster‐regions with CNN features (RCNN), the input images are initially decomposed into multiple scales to capture latent features. A dual‐scale attention module is introduced to enhance the precision of feature extraction. Furthermore, a two‐stage network is proposed to address the challenge of classifying closely similar substation equipment. A multi‐layer perceptron performs a coarse classification to categorize the equipment into broad categories. Then, a lightweight classifier is employed for fine‐grained subclassification, further distinguishing equipment within the same broad category. To mitigate the issue of limited training data, a specialized dataset is collected and annotated for the substation equipment classification. Experimental results demonstrate that the proposed method achieves remarkable accuracy, recall, and F1‐score surpassing 0.91, outperforming mainstream approaches in terms of recall and F1 scores. Ablation experiments further validate the significant contributions of both the dual‐scale attention and the two‐stage classification module in improving the overall performance of the classification network.

ABSTRACTArmed conflicts in Africa have serious harmful impacts on human well-being. Existing evidence on whether economic aid (Official Finance, OF) reduced conflicts is diverse and contested. China has become one of the world’s largest overseas financiers. Taking the impacts of China’s OFs on conflict-related mortality risk (CRMR) as a case study is representative of addressing the issue. Combining the data of China’s OFs covering 52 African countries with armed conflict data from 2000 to 2017, we employ the instrumental variable and two-stage least squares estimation to explore the relationship between China’s OFs and CRMR. Our results indicate an 18% decrease (95% confidence interval (CI): 2–34%) in CRMR in a year after the OF commitment for a 1% increase in OF amounts. The OFs in social infrastructures (e.g. health, education and civil society) had greater positive impacts on the CRMR reduction than those in economic and production sectors. More pronounced effects of OFs are observed on small-scale state-based conflicts, in countries with low-level road infrastructures than their counterparts. Our findings give the references for policy-makers to optimize the pathway of the OFs for providing better relief to war-torn regions.

Tidal flats are an important part of coastal wetland systems and play an irreplaceable role in maintaining the health of global coastal ecosystems, resisting natural coastal disasters, and sequestering blue carbon. For the coastal zone around Beibu Gulf, where tidal flats are densely distributed, the spatiotemporal characteristics of tidal flats evolution and its key driving factors remain unclear. This article took the Beibu Gulf as the study area, and constructed an integrated tidal flats extraction algorithm of “maximum spectral index composite (MSIC) + Otsu algorithm (OTSU) + DEM correction” based on the cloud computing platform of Google Earth Engine to realize a large-scope and high-accuracy automatic extraction of tidal flats. The results discovered that 1) the tidal flats extraction method proposed in this study has high accuracy, with the overall accuracy of the confusion matrix reaching 93.9%, and the Kappa coefficient reaching 0.82. The user accuracy and mapping accuracy of the tidal flats are both greater than 85%. 2) In 35 years, the total tidal flats area of the Beibu Gulf has decreased by 683.9 km2, a reduction of 29%, and the average annual rate of change was −19.5 km2/a. 3) The fragmentation state of the tidal flats in Beibu Gulf intensified with time, and the natural driving forces of the tidal flats were mainly the rise in sea level, decrease in the amount of sediment in the river entering the sea, and expansion of mangrove trees. Human-driven forces were mainly coastal economic development and offshore aquaculture.

The loss of feature information during scale propagation in the deep learning method usually causes a big misclassification rate for many complex urban scenes. For this reason, this article presents a new deep learning method, called “Feature combination and promotion network (FCPNet).” This method consists of an end-to-end feature learning layer for obtaining multiscale depth features of point clouds, an external feature combination module for obtaining more fine-grained point cloud features, and a mutiheaded separable self-attention module for learning connections between features to obtain more globally informative features. When compared with PointNet++, the proposed FCPNet improved OA, MIOU, F1-score, and Kappa in the NPM3D dataset by 1.75%, 17.02%, 2.13%, and 0.0263, respectively. When compared with KpConv, the proposed FCPNet improved OA, mIOU, F1-score, and Kappa in the NPM3D dataset by 0.36%, 12.11%, 0.77%, and 0.0085, respectively. Especially, the proposed FCPNet is able to classify the objects with fewer point cloud data, such as pedestrians and cars, whose OA can reach 88.04% and 96.42%, respectively. These experimental results demonstrated the proposed FCPNet has rescued much lost information that happened in the traditional PointNet++. In addition, the adaptability to point cloud density variations for the proposed method is verified as well. The results demonstrated that when the total density of point cloud data decreases from 731.3 to 52.2 ${\text{pcs/}}{{\text{m}}^{2}}$, the OA of classification with the proposed FCPNet method only decreases by 3.07%. This means that the proposed FCPNet method is capable of being adaptive to the point cloud density changes.

The commonly used water depth retrieval models do not consider the influence from instantaneous tidal fluctuations during satellite flying, which causes bathymetric errors. For this reason, the wavelet spline instantaneous tidal correction model, which is applied to calculate accurate tidal heights for any moment of satellite flight, is established for correction of water depth retrieval from satellite multispectral images. Two study areas, Saipan Island and Molokai Island in the USA, are selected, and Landsat 8 images are used to verify the accuracy achieved by the proposed model. Fifteen bathymetric check points and three check lines in each of study area are used to further verify the absolute and relative accuracy achievable for the proposed model. The experimental results from our model demonstrated that the mean absolute errors (relative to reference data) in study area, Saipan, and Molokai are 1.42 and 1.5 m, both of which decrease by 33.33% and 27.54%, respectively. The root mean square errors (RMSEs) are 1.67 and 1.83 m, both of which decrease by 31.84% and 27.09%, respectively. Therefore, it can be concluded that the proposed model can largely increase the accuracy of water depth retrieval by satellite multispectral images.

Recently, security protection is import in many scenarios. Occluded person re-identification (Re-ID) involves identifying obscured pedestrians from images captured by multiple cameras, even when the images are partially or fully occluded. Many state-of-the-art models for occluded Re-ID utilize auxiliary modules such as pose estimation, feature pyramid, and graph matching to address occlusion challenges. However, this approach results in complex models that struggle to generalize to diverse occlusions and may not effectively handle non-occluded pedestrians. Furthermore, real-world Re-ID applications frequently involve both occluded and non-occluded pedestrians, making it difficult to develop versatile models. To tackle these issues, we introduce a novel Re-ID model that learns discriminative features on both local and global scales for occluded pedestrian identification. Our proposed model, the Local-aware Transformer (LAT) for occluded person Re-ID, comprises three modules: a Discriminative Feature Extraction Module (DFEM), a Local Feature Extraction Module (LFEM), and a Global Feature Extraction Module (GFEM). Our experimental results on three occluded and two general Re-ID benchmarks demonstrate that our model surpasses existing state-of-the-art methods and achieves exceptional performance in both occluded and non-occluded Re-ID tasks.

Vegetation is crucial for wetland ecosystems. Human activities and climate changes are increasingly threatening wetland ecosystems. Combining satellite images and deep learning for classifying marsh vegetation communities has faced great challenges because of its coarse spatial resolution and limited spectral bands. This study aimed to propose a method to classify marsh vegetation using multi-resolution multispectral and hyperspectral images, combining super-resolution techniques and a novel self-constructing graph attention neural network (SGA-Net) algorithm. The SGA-Net algorithm includes a decoding layer (SCE-Net) to precisely fine marsh vegetation classification in Honghe National Nature Reserve, Northeast China. The results indicated that the hyperspectral reconstruction images based on the super-resolution convolutional neural network (SRCNN) obtained higher accuracy with a peak signal-to-noise ratio (PSNR) of 28.87 and structural similarity (SSIM) of 0.76 in spatial quality and root mean squared error (RMSE) of 0.11 and R2 of 0.63 in spectral quality. The improvement of classification accuracy (MIoU) by enhanced super-resolution generative adversarial network (ESRGAN) (6.19%) was greater than that of SRCNN (4.33%) and super-resolution generative adversarial network (SRGAN) (3.64%). In most classification schemes, the SGA-Net outperformed DeepLabV3 + and SegFormer algorithms for marsh vegetation and achieved the highest F1-score (78.47%). This study demonstrated that collaborative use of super-resolution reconstruction and deep learning is an effective approach for marsh vegetation mapping.

Kikuchi-Fujimoto disease (KFD) is a benign, self-limiting illness that can progress to systemic lupus erythematosus (SLE) in approximately 30% of cases. Neurological injuries can occur in both diseases, albeit with distinct presentations. Venous sinus thrombosis is a serious cerebrovascular complication in patients with neuropsychiatric SLE but is rarely observed in patients with KFD. The involvement of various antibodies, particularly antiphospholipid antibodies, can cause vascular endothelial cell injury, resulting in focal cerebral ischemia and intracranial vascular embolism in SLE. However, there are cases in which thrombotic pathology occurs without antiphospholipid antibody positivity, attributed to vascular lesions. In this report, we present a case of KFD and lupus encephalopathy featuring cerebral venous sinus thrombosis, despite the patient being negative for antiphospholipid antibody. We also conducted a comparative analysis of C3 and C4 levels in cerebrospinal fluid (CSF) and peripheral blood, along with the protein ratio in CSF and serum, to elucidate the pathological changes and characteristics of lupus encephalopathy.

Previous control methods developed by our research team cannot satisfy the high accuracy requirements of unmanned surface vessel (USV) path-tracking during bathymetric mapping because of the excessive overshoot and slow convergence speed. For this reason, this study developed an adaptive fuzzy integral-derivative line-of-sight (AFIDLOS) method for USV path-tracking control. Integral and derivative terms were added to counteract the effect of the sideslip angle with which the USV could be quickly guided to converge to the planned path for bathymetric mapping. To obtain high accuracy of the look-ahead distance, a fuzzy control method was proposed. The proposed method was verified using simulations and outdoor experiments. The results demonstrate that the AFIDLOS method can reduce the overshoot by 79.85%, shorten the settling time by 55.32% in simulation experiments, reduce the average cross-track error by 10.91% and can ensure a 30% overlap of neighboring strips of bathymetric LiDAR outdoor mapping when compared with the traditional guidance law.

Objectives: Cholangiocarcinoma (CHOL) is a malignant tumor from extrahepatic bile duct with poor prognosis. The critical roles of long non-coding RNAs (lncRNAs) in cancers including CHOL have been unveiled in recent decades. The present study was aimed to investigate the role and mechanism of a certain lncRNA, namely, hepatocellular carcinoma (HCC) associated long non-coding RNA (HANR) in CHOL. Methods: Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was applied for detecting gene expression. Functional assays were done for assessing CHOL cell malignancy and mechanistic assays were conducted for analyzing correlation between HANR and Notch signal pathway, as well as the relation between HANR and Notch intracellular domain (NICD) in CHOL cells. Results: HANR was detected to be significantly overexpressed in CHOL cell lines. HANR silence inhibited cell proliferation, migration and stemness. Besides, HANR could positively regulate the Notch signaling pathway through modulating RBP-JK. HANR could bind to NICD and affect the transcriptional activity of RBP-JK. Furthermore, p-Notch1-NICD-r could wholly countervail the inhibitory effects of HANR silence on CHOL cell proliferation, migration and stemness. Conclusion: HANR could activate Notch pathway by regulating the RBP-JK transcriptional activity, thus contributing to exacerbated malignant behaviors of CHOL cells.

The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called “pixels weighted differential gradient”, to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction.

A few scholars have developed the models for retrieval of water depth from subsurface reflectance of multispectral images to avoid the influences of sun glitter. However, the models are only suitable for case I water. For this reason, this study proposes a bathymetry retrieval model using subsurface reflectance for both case I and case II water. The model first corrects the water surface reflectance image and then converts it into a subsurface reflectance image, and the subsurface reflectance image is used as the water depth retrieval image. Landsat 8 images were taken for experiments in case 1 water and case 2 water, and two water areas, Weizhou Island, Guangxi, China, and Molokai Island, Hawaii, USA, were used to verify the proposed model. The experimental results showed that the proposed model reduced the root-mean-squared error of the retrieved water depth in the Weizhou and Molokai areas from 3.113 to 2.903 m and 4.239 to 3.653 m, respectively, i.e., improve accuracy of water depth at 6.75% and 13.82% for Weizhou and Molokai areas, respectively. Therefore, the results demonstrate that the proposed model using subsurface reflectance can significantly improve the accuracy of bathymetry retrieval via spaceborne multispectral images.

Understanding the changes of wetlands on the Tibetan Plateau (TP) is important for action to ensure ecosystem resilience in Asia. However, mapping long-term changes of wetlands at high resolutions remains challenging. Here, we quantify the spatio-temporal changes of TP wetlands from 1990 to 2019, by combining Landsat imagery with deep learning to map TP wetlands. The deep learning model combined with transfer learning strategies achieves high classification performance using a few class samples. The validation results show that the user’s accuracy is 95.5% and the producer's accuracy is 90.1% for wetland extraction, satisfying with subsequent analysis of wetland spatio-temporal changes. Based on the wetland extraction model, we have created annual wetland map in the TP for the first time. We find that the areal extent of TP wetlands has increased by 31.2 ± 6.6 % over the past 30 years. The growth is particularly noticeable (by 22.5 ± 6.2 %) during 2015–2019. Spatially, the wetland areal extent on the Qiangtang Plateau (in the inner part of TP and as habitats of various birds and rare wild animals) and the source region of Yangtze River show the largest expansions by 55.3 ± 9.3 % and 44.0 ± 8.9 %, respectively. Such rapid wetland expansions are associated with increasing rainfall and temperature which have heterogeneous influences on wetland changes across the TP. Our findings provide evidence for the impact of climate change on wetland area. The marked wetland changes highlight that climate mitigation is a priority for high-latitude ecosystems.

Traditional night light images are black and white with a low resolution, which has largely limited their applications in areas such as high-accuracy urban electricity consumption estimation. For this reason, this study proposes a fusion algorithm based on a dual-transformation (wavelet transform and IHS (Intensity Hue Saturation) color space transform), is proposed to generate color night light remote sensing images (color-NLRSIs). In the dual-transformation, the red and green bands of Landsat multi-spectral images and “NPP-VIIRS-like” night light remote sensing images are merged. The three bands of the multi-band image are converted into independent components by the IHS modulated wavelet transformed algorithm, which represents the main effective information of the original image. With the color space transformation of the original image to the IHS color space, the components I, H, and S of Landsat multi-spectral images are obtained, and the histogram is optimally matched, and then it is combined with a two-dimensional discrete wavelet transform. Finally, it is inverted into RGB (red, green, and blue) color images. The experimental results demonstrate the following: (1) Compared with the traditional single-fusion algorithm, the dual-transformation has the best comprehensive performance effect on the spatial resolution, detail contrast, and color information before and after fusion, so the fusion image quality is the best; (2) The fused color-NLRSIs can visualize the information of the features covered by lights at night, and the resolution of the image has been improved from 500 m to 40 m, which can more accurately analyze the light of small-scale area and the ground features covered; (3) The fused color-NLRSIs are improved in terms of their MEAN (mean value), STD (standard deviation), EN (entropy), and AG (average gradient) so that the images have better advantages in terms of detail texture, spectral characteristics, and clarity of the images. In summary, the dual-transformation algorithm has the best overall performance and the highest quality of fused color-NLRSIs.

The water depth bias of LiDAR point cloud data has to be corrected. The previous models, which used the fixed parameters for an entire water area, cannot efficiently correct the bias due to the different water environments. Therefore, this paper develops an adaptive model for the correction of the water depth bias. A coordinate system, in which the water depth is taken as the X-axis and the water depth bias is taken as the Y-axis, is defined. All of the sample points are normalized, and then projected into the defined coordinate system according to their water depths and water depth biases. Second, the scatter points are clustered into several different clusters using the developed subdivision algorithm. With the clusters, an entire water area is subdivided into several sub-regions. Finally, each sub-region is fitted using a model, which is used to correct the water depth bias. Experimental verification and comparison analysis are conducted in three different environments: an indoor tank, an outdoor pond and the Beihai sea. The experimental results demonstrate that the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) from the adaptive model are reduced by approximately 61% and 59%, respectively, relative to those from the traditional models. Therefore, it can be concluded that the proposed model can adapt to changes of the different water environments and achieve a higher accuracy for water depth bias correction than traditional methods do.

Fractional vegetation coverage (FVC) is an important indicator of ecosystem change. At present, FVC products are mainly concentrated at low and medium spatial resolution and lack high temporal and spatial resolution, which brings certain challenges to the fine monitoring of ecological environments. In this study, we evaluated the accuracy of four remote sensing inversion models for FVC based on high-spatial-resolution Sentinel-2 imagery and unmanned aerial vehicle (UAV) field-measured FVC data in 2019. Then the inversion models were optimized by constructing a multidimensional feature dataset. Finally, the Source Region of the Yellow River (SRYR) FVC product was created using the best inversion model, and the spatial-temporal variation characteristics of the FVC in the region were analyzed. The study’s findings revealed that: (1) The accuracies of the four FVC inversion models were as follows: the Gradient Boosting Decision Tree (GBDT) model (R2 = 0.967, RMSE = 0.045) > Random Forest (RF) model (R2 = 0.962, RMSE = 0.049) > Support Vector Machine (SVM) model (R2 = 0.925, RMSE = 0.072) > Pixel Dichotomy (PD) model (R2 = 0.869, RMSE = 0.097). (2) Constructing a multidimensional feature dataset to optimize the driving data can improve the accuracy of the inversion model. NDVI and elevation are important factors affecting the accuracy of machine learning inversion algorithms, and the visible blue band is the most important feature factor of the GBDT model. (3) The FVC in the SRYR gradually increased from west to east and from north to south. The change trajectories of grassland FVC from 2017 to 2022 were not significant. The areas that tend to improve were mainly distributed in the southeast (1.31%), while the areas that tend to degrade were mainly distributed in the central and northwest (1.89%). This study provides a high-spatial-resolution FVC inversion optimization scheme, which is of great significance for the fine monitoring of alpine grassland ecological environments.

Lunar in-situ resource utilization (ISRU) has been put on the agenda by many countries. Due to the special material nature and low gravity environment, the lunar regolith demonstrates significantly different behavior from terrestrial geomaterials. However, the systematic understanding of its geotechnical behavior is now seriously restricted by the scarcity of lunar regolith and the difficulty in simulating lunar gravity. A new lunar regolith simulant, termed as China University of Mining and Technology Number One (CUMT-1), has been developed to recover properties of the lunar regolith and simulate the lunar gravity by adopting the recently advanced geotechnical magnetic-similitude-gravity model testing (GMMT) method. The CUMT-1 simulant was prepared by reproducing the in-situ formation and fragmentation of the lunar matrix, which plays a key role in the irregular particle morphology. The mineralogical compositions, particle morphology and gradation, specific gravity, bulk density, void ratio, shear strength, and compressibility were determined. After quantifying the magnetization and magnetic-similitude-gravity characteristics, an application of the cone penetration resistance under low gravity was further given. The obtained results are compared to the values known for lunar regolith samples and other simulants, which demonstrates promising characteristics for use in geotechnical engineering-based and scientific-based applications, especially considering the influence of lunar gravity.

As a critical prerequisite for semantic facade reconstruction, accurately separating wall and protrusion points from facade point clouds is required. The performance of traditional separation methods is severely limited by facade conditions, including wall shapes (e.g., nonplanar walls), wall compositions (e.g., walls composed of multiple noncoplanar point clusters), and protrusion structures (e.g., protrusions without regularity, repetitive, or self-symmetric features). This study proposes a more widely applicable wall and protrusion separation method. The major principle underlying the proposed method is to transform the wall and protrusion separation problem as a ground filtering problem and to separate walls and protrusions using ground filtering methods, since the 2 problems can be solved using the same prior knowledge, that is, protrusions (nonground objects) protrude from walls (ground). After transformation problem, cloth simulation filter was used as an example to separate walls and protrusions in 8 facade point clouds with various characteristics. The proposed method was robust to the facade conditions, with a mean intersection over union of 90.7%, and had substantially higher accuracy compared with the traditional separation methods, including region growing-, random sample consensus-, multipass random sample consensus-based, and hybrid methods, with mean intersection over union values of 69.53%, 49.52%, 63.93%, and 47.07%, respectively. Besides, the proposed method was general, since existing ground filtering methods (including the maximum slope, progressive morphology, and progressive triangular irregular network densification filters) can also perform well.

Cretaceous sandstones have weak cementation and high porosity while exhibit a high apparent brittleness. Compression and torsion (C-T) fractures are widely distributed in Cretaceous sandstones due to asymmetric tectonic convergence action. However, studies on C-T fracture formation and the mechanisms causing variability in Cretaceous sandstones containing no oil or gas are rare due to the challenges in sampling intact sandstone cores, despite their significance to mine shaft sinking. Therefore, this study used binder jetting-based 3D printing to prepare artificial Cretaceous sandstone and developed a real-time X-ray computed tomography- (CT-) aided torsion shear apparatus to test them. The test results showed that the 3D printed (3DP) sandstone had characteristic indexes that approached and even exceeded the lower limits of Cretaceous sandstone cores, thereby accurately representing the unavailable cores. Furthermore, the 3DP sandstones had anisotropic properties comparable to the sandstone cores. Under C-T action, the 3DP sandstone exhibited a pronounced strain gradient of 2.0 %/mm perpendicular to fracture inclination. The inclination angles of fractures formed under C-T action tended to increase as the cell pressure increased, and that approached the orientation angles of maximal principal stress. The maximal and minimal principal stresses exerted inclination-slip and width-stretching effects, respectively, on C-T fractures. But the effect of inclination-slip on the C-T fractures was stronger than that of width-stretching. This insight into C-T fracture formation will guide future studies on the fracture evolution and its disaster-dominating mechanisms arisen from disturbances by shaft sinking.

Poverty alleviation is one of the greatest challenges faced by low-income and middle-income countries. China, which had the largest rural poverty-stricken population, has made tremendous efforts in alleviating poverty especially since the implementation of the targeted poverty alleviation (TPA) policy in 2014, and by 2020, all national poverty-stricken counties (NPCs) have been out of poverty. This study combines deep learning with multiple satellite datasets to estimate county-level economic development from 2008 to 2019 and assess the effect of the TPA policy for 592 national poverty-stricken counties (NPCs) at country, provincial and county levels. Per capita gross domestic product (GDP) is used to measure the affluence level. From 2014 through 2019, the 592 NPCs experience an average growth rate of per capita GDP at 7.6%±0.4%, higher than the average growth rate of 310 adjacent non-NPC counties (7.3%±0.4%) and of the whole country (6.3%). We also reveal 42 counties with weak growth recently and that the average affluence level of the NPCs in 2019 is still much lower than the national or provincial averages. The inexpensive, timely and accurate method proposed here can be applied to other low-income and middle-income countries for affluence assessment.

To obtain satisfying results of individual tree detection from LiDAR points, parameters using traditional methods usually need to be adjusted by trials and errors. When encountering complex forest environments, the detection accuracy cannot be satisfied. To resolve this, a multi-level self-adaptive individual tree detection method was presented in this paper. The proposed method can be seen as a hybrid model, which combined the strength of both raster-based and point-based methods. Raster-based strategy was first used for achieving initial trees detection results, while the point-based strategy was adopted for optimizing the clustered trees. In the proposed method, crown width scales were estimated automatically. Meanwhile, multi-scales segmented results were fused together to take advantage of segmented results of both larger and small scales. Six different coniferous forests were adopted for testing. Experimental result shows that this study achieved the lowest omission and commission errors comparing with other three classical approaches. Meanwhile, the average F1 score in this paper is 0.84, which is much highest out of other methods.

The fast development of urban built-up areas in China is causing many problems, such as pollution, congestion, etc. How to effectively evaluate the coordination between urban areas and environmental problems has been attracting many scholars worldwide. This paper intends to discover this “secretary” through investigating the built-up areas and their accompanied economic and environmental factors over almost 30 years (1992 to 2020) in Nanjing, China. DMSP/OLS nighttime lights images from 1992 to 2013 and the NPP/VIIRS nighttime lights images from 2012 to 2022 are used for extraction of built-up areas. A spatiotemporal evolution model is established to evaluate whether the built-up areas have developed in coordination and the relationship between urban built-up areas and various factors, including compactness, the fractal dimension, boundary and shape changes, exhaust emissions, and the production of general industrial solid waste, which was further investigated to ascertain whether there was coordination or not. The investigated results discovered that Nanjing’s built-up areas had maintained continuous growth from 1992 to 2020, with the compactness of built-up areas gradually decreasing from 0.42 to 0.23 and the built-up differentiation dimension changing from 1.31 to 1.39, demonstrating that built-up areas had gradually moved from a loose pattern to a compact pattern and from irregular development to balanced development in all directions. The macro model of the coordination index change trend is 0.847 from 1995 to 2020, which indicates that the coordination between urban built-up areas of development and their environments has been improving; however, the reduction in urban green space, the increase in waste emissions, and the increased production of general industrial solid waste has raised questions regarding sustainable development.

Multi-channel airborne full-waveform LiDAR is widely used for high-precision underwater depth measurement. However, the signal quality of full-waveform data is unstable due to the influence of background light, dark current noise, and the complex transmission process. Therefore, we propose a nonlocal encoder block (NLEB) based on spatial dilated convolution to optimize the feature extraction of adjacent frames. On this basis, a coupled denoising encoder–decoder network is proposed that takes advantage of the echo correlation in deep-water and shallow-water channels. Firstly, full waveforms from different channels are stacked together to form a two-dimensional tensor and input into the proposed network. Then, NLEB is used to extract local and nonlocal features from the 2D tensor. After fusing the features of the two channels, the reconstructed denoised data can be obtained by upsampling with a fully connected layer and deconvolution layer. Based on the measured data set, we constructed a noise–noisier data set, on which several denoising algorithms were compared. The results show that the proposed method improves the stability of denoising by using the inter-channel and multi-frame data correlation.

Alkali metal atomic cells are crucial components of atomic instruments, such as atomic magnetometers, atomic gyroscopes, and atomic clocks. A highly uniform and stable heating structure can ensure the stability of the alkali metal atom density. The vapor cell of an atomic magnetometer that uses laser heating has no magnetic field interference and ease of miniaturization, making it superior to hot air heating and AC electric heating. However, the current laser heating structure suffers from low heating efficiency and uneven temperature distribution inside the vapor cell. In this paper, we designed a non-magnetic heating structure based on the laser heating principle. We studied the temperature distribution of the heating structure using the finite element method (FEM) and analyzed the conversion and transfer of laser energy. We found that the heat conduction between the vapor cell and the heating chips (colored filters) is poor, resulting in uneven temperature distribution and low heating efficiency in the vapor cell. Therefore, the addition of graphite film to the four surfaces of the vapor cell was an important improvement. This addition helped to balance the temperature distribution and improve the conduction efficiency of the heating structure. It was measured that the power of the heating laser remained unchanged. After the addition of the graphite film, the temperature difference coefficient (CVT) used to evaluate the internal temperature uniformity of the vapor cell was reduced from 0.1308 to 0.0426. This research paper is crucial for improving the heating efficiency of the non-magnetic heating structure and the temperature uniformity of the vapor cell.

Mangrove forests are a valuable resource for biological and species diversity, and play a critical role in maintaining biodiversity. However, traditional plant biodiversity survey methods, which rely on labor-intensive field surveys, are not suitable for large-scale continuous spatial observations. To overcome this challenge, we propose an innovative framework for mangrove biodiversity assessment and zoning management based on drone low-altitude remote sensing, integrating data such as vertical structure features and spectral diversity features extracted from on-site measurements, airborne LiDAR, and hyperspectral data. This study focuses on the Maowei Sea mangrove community, located in the estuary of China’s first Pinglu Canal since the founding of the People’s Republic of China. Using the proposed framework, we construct an evaluation index for mangrove biodiversity at the levels of species diversity, ecosystem diversity, and landscape diversity, achieving a quantitative calculation of mangrove biodiversity and an evaluation of spatial distribution patterns. The results show that the biodiversity index of mangroves ranges from 0 to 0.63, with an average value of 0.29, and high-biodiversity areas are primarily concentrated in the southwest of the study area, while low-value areas are mainly located in the north. We also select the elevation and offshore distance of mangrove growth for the spatial zoning of biodiversity. The core area of biodiversity occupies the smallest area, at 2.32%, and is mainly distributed in areas with an elevation of 1.43–1.59 m and an offshore distance of 150.08–204.28 m. Buffer zones and experimental zones account for a significant proportion, with values of 35.99% and 61.69%, respectively. Compared to traditional methods for monitoring mangrove biodiversity, such as community field-sample surveys, the proposed method using unmanned-aerial-vehicle LiDAR and hyperspectral coupling technology to assess mangrove biodiversity and establish a zoning management framework is more conducive to formulating mangrove biodiversity conservation strategies. The study provides a feasible solution for the large-scale biodiversity mapping of mangroves in the Maowei Sea at the estuary of the Pinglu Canal.

Shape segmentation in urban environments forms the foundation for tasks such as classification and reconstruction. Most artificial buildings with complex structures are composed of multiple simple geometric primitives. Based on this assumption, this paper proposes a divisive hierarchical clustering algorithm that uses shape classification and outliers reassignment to segment LiDAR point clouds in order to effectively identify the various shapes of structures that make up buildings. The proposed method adopts a coarse-to-fine strategy. Firstly, based on the geometric properties of different primitives in a Gaussian sphere space, coarse extraction is performed using Gaussian mapping and the DBSCAN algorithm to identify the primary structure of various shapes. Then, the error functions are constructed after parameterizing the recognized shapes. Finally, a minimum energy loss function is built by combining the error functions and binary integer programming (BIP) to redistribute the outlier points. Thereby, the accurate extraction of geometric primitives is achieved. Experimental evaluations on real point cloud datasets show that the indicators of precision, accuracy, and F1 score of our method are 0.98, 0.95, and 0.96 (point assignment) and 0.97, 0.95, and 0.95 (shape recognition), respectively. Compared with other state-of-the-art methods, the proposed method can efficiently segment planar and non-planar structures with higher quality from building point clouds.

Gravity is the most important load source in mining and geotechnical engineering, which causes both the stress level and stress gradient inside geomaterials. Different from the stress level, the influence of gravity-induced stress gradient on the behaviour of the material is still unknown. An in-deep study on it will help to promote the understanding of material behaviour, especially for those cases related to unconventional gravity such as terrestrial ng physical modelling and extraterrestrial resource exploitation (g is the terrestrial gravitational acceleration). In this study, a high-order homogenization for granular materials is proposed at first, in which the stress gradient is drawn into the constitutive representation by adopting a representative volume element (RVE). The consolidation and shear strength behaviour of RVE are then investigated by performing numerical biaxial tests. The results show that all the compressibility, shear strength, shear stiffness, volumetric deformation, and critical state behaviour show a stress gradient dependence. A coupling between stress gradient, stress level, and material properties is also observed. These observations suggest that, besides the stress level, extra attention needs to be paid to material responses related to stress gradient during engineering practices.

Measuring the internal dynamic temperature of alkali metal vapor cells is crucial for enhancing the performance of numerous atomic devices. However, conventional methods of measuring the internal dynamic temperature of the cell are prone to errors. To obtain a more accurate internal dynamic temperature of the alkali metal vapor cell, a temperature measuring method based on the data fusion of the Kalman filter has been proposed. This method combines the indirect temperature measurement signal from a resistance temperature detector with the atomic absorption spectrometric temperature measurement signal. This provides a high-accuracy set of internal dynamic temperatures in the cell. The atomic vapor density calculated from the final fusion results is 37% average lower than that measured by external wall temperature measurements, which is in line with the conclusions reached in many previous studies. This study is highly beneficial to measure the temperature of alkali metal vapor cells.

Tidal flats provide ecosystem services to billions of people worldwide; however, their changing status is largely unknown. Several challenges in the fine extraction of tidal flats using remote sensing techniques, including tide-level and water-edge line changes, exist at present, especially regarding the spatial and temporal distribution of mangroves. This study proposed a tidal flats extraction method using a combination of threshold segmentation and tidal-level correction, considering the influence of mangrove changes. We extracted the spatial distribution of tidal flats in Beibu Gulf, Southwest China, from 1987 to 2021 using time-series Landsat and Sentinel-2 images, and further analyzed the dynamic variation characteristics of the total tidal flats, each coastal segment, and the range of erosion and silting. To quantitatively investigate the interaction between tidal flats and mangroves, this study established a regression model based on multi-temporal tidal flats and mangrove data. The results indicated that the overall accuracy of the tidal flat extraction results was 93.9%, and the kappa coefficient was 0.82. The total area of tidal flats in Beibu Gulf decreased by 130 km2 from 1987 to 2021, with an average annual change of −3.7 km2/a. In addition, a negative correlation between the tidal flat change area and mangrove change area in Shankou, Maowei Sea, and Pearl Bay was observed, with correlation coefficients of −0.28, −0.30 and −0.64, respectively. These results demonstrate that the distribution of tidal flats provides a good environment and expansion space for the rapid growth of mangroves. These results can provide references for tidal flats’ resource conservation, ecological health assessment, and vegetation changes in coastal wetlands in China and other countries in Southeast Asia.

Analysis and prediction of urban ecological risk are crucial means for resolving the dichotomy between ecological preservation and economic development, thereby enhancing regional ecological security and fostering sustainable development. This study uses Nanning, a Chinese landscape garden city, as an example. Based on spatial granularity and extent perspectives, using 30 m land use data, the optimal scale for an ecological risk assessment (ERA) and prediction is confirmed. This study also explores the patterns of spatial and temporal changes in ecological risk in Nanning on the optimal scale. At the same time, the Patch-generating Land Use Simulation model is used to predict Nanning’s ecological risk in 2036 under two scenarios and to propose ecological conservation recommendations in light of the study results. The study results show that: a spatial granularity of 120 m and a spatial extent of 7 km are the best scales for ERA and prediction in Nanning. Although the spatial distribution of ecological risk levels is obviously different, the overall ecological risk is relatively low, and under the scenario of ecological protection in 2036, the area of high ecological risk in Nanning is small. The results can provide theoretical support for ERA and the prediction of landscape cities and ecological civilization construction.

Non-uniformity is a common phenomenon in infrared imaging system, which seriously affects imaging quality. In view of the problems of existing non-uniformity correction of infrared images, such as loss of image details and blurred edge of image, an improved non-uniformity correction method of infrared images based on convolution neural network using long-short connections (LSC-CNN) is proposed. The proposed method designs a long-short connection residual network structure suitable for non-uniformity correction of infrared image.The network depth is increased to fully learn the noise by short connections, image sizes are adjusted to reduce the number of parameters, the long connection is used to solve the problem of image information loss caused by transposed convolution, and a multiply operation is carried out to enhance the contrast of corrected images. Besides, batch normalization is utilized to improve the training speed. The experimental results show that LSC-CNN has excellent performance in non-uniformity correction of infrared images whether qualitative evaluation or quantitative evaluation. LSC-CNN is especially effective in image detail preservation and image edge protection whose average PSNR exceeds 37.5 dB and the average SSIM is greater than 0.98.

Secchi disk depth is a commonly measured parameter representing the optical properties of water bodies. Assessment of water transparency in seas is highly significant to marine-environment monitoring. In this study, an improved Secchi disk depth (ZSD) inversion algorithm was proposed based on the Poole–Atkins model by determining the parameter A in the original model. The Forel–Ule index (FUI) is a water color parameter that can be obtained from remote sensing data. Through the analysis of the International Ocean Color Coordinating Group dataset, it was found that there are strong logarithmic and quadratic correlations between the FUI and parameter A, whose R2 values are 0.929 and 0.925, respectively. Comparing the results derived from MODIS product data with the in situ measured data in the Yellow Sea showed that the RMSE and MRE of the quadratic formula are 1.83 m and 43.74%, respectively, which reflect better performance than the other empirical formulas. Thus, parameter A can be expressed in quadratic form with FUI as a variable. Finally, we mapped the ${Z_{{\rm{SD}}}}$ inversion results for the Yellow Sea and analyzed the spatial changes. This study provides new insight for inverting ZSD transparency algorithms and highlights the value of marine transparency monitoring.

Lake bathymetry plays a pivotal role in environmental monitoring, ecological management, water quality protection, etc. Hyperspectral remote sensing technology can provide large-scale coverage and more detailed spectral information for bathymetry estimation than traditional measurements or multispectral imagery techniques. In this study, a multiband linear model with successive projections algorithm (SPA-MLM) was developed to retrieve the bathymetry of Qinghai Lake, which is the largest inland saltwater lake in China. The three most sensitive spectral bands were first selected by the SPA, and a multiband linear model was established by the least squares method combined with the in situ measured water depth. Zhuhai-1 hyperspectral remotely sensed imagery is employed as the data source. In all, 98 in situ bathymetry measurements matched with the obtained images were obtained during three surveys performed in May, September, and October 2020. The results demonstrated that the established retrieval model can be used to accurately estimate the water depth in the study area, with an accuracy exceeding approximately 90%, which suggests that the proposed model performs better than those used in previous studies employing hyperspectral imagery. The correlation coefficient reaches 0.92, and the root-mean-square error is approximately 1.26 m. This demonstrates that bathymetry estimation obtained using remotely sensed hyperspectral data is an effective detection method and can provide large-scale, rapid monitoring data to the relevant decision-making departments.

The information from the components obtained by waveform decomposition is usually used to inverse topography, and classify tree species, etc. Many efforts on waveform decomposition algorithms have been presented, but they lack comparison analysis and evaluation. Thereby, this article compares and analyzes the performance of five waveform decomposition algorithms, which are Gaussian, Adaptive Gaussian, Weibull, Richardson–Lucy (RL), and Gold, under different topographic conditions such as forests, glaciers, lakes, and residential areas. The experimental results reveal that: first, the Gaussian algorithm causes the biggest fitting error at 9.96 mV in the forested area. It is easy to identify multiple dense peaks as single peaks. Second, there are many misjudged, superimposed, and overlapped waveform components separated by the Weibull algorithm. The Adaptive Gaussian is more capable of fitting complex waveforms but has 122 more outliers than the Weibull algorithm does. Third, the Gold and RL algorithms decompose the largest number of waveform components (272.2k and 265.9k) in the forested area; both RL and Gold algorithms can effectively improve the separability of peaks. Fourth, the RL algorithm is only more effective for the area with sparse vegetation than the Gold algorithm does, i.e., the Gold algorithm is capable of processing data with dense vegetation areas at a lowest false component detection rate of 1.3%, 0.9%, 1.1%, and 0.1% in four areas. Finally, the Gaussian and Gold algorithms have much faster decomposition speed at 1000/s and 2000/s than the other three algorithms do. These results are useful for selecting different algorithms under different environments.

The demand for leaf area index (LAI) retrieval from spaceborne full-waveform LiDAR increases due to its direct sampling of the three-dimensional forest structure at a near-global scale. However, the nonrandomness (i.e., clumping effect) of canopy composition limits the reliability of LAI derived from two common methods. They either assume a homogeneous scene in the footprint or just correct for the large gaps-induced between-crown clumping. The clumping in the crown is still an unaddressed issue. We proposed a method to compensate occlusion (i.e., lower canopy layers are occluded by the upper canopy in the process of LiDAR measurement), through which the vertical canopy profile can be resolved from the waveform. Further, we developed a method of deriving relative path length distribution that can reflect the heterogeneity of the canopy from the occlusion-corrected waveform. In addition to correcting the between-crown clumping, we corrected the within-crown clumping further using the derived relative path length distribution, based on path length distribution (PATH) theory. We used simulated waveform data with known LAI and GLAS data with corresponding field-measured LAI to test the performance of our and the other two common LAI retrieval methods. Results show that the errors of our approach are the lowest (with an error generally below 10% and the maximum error below 20%, compared with up to 69% and 47% for the other two methods), and it is relatively stable in various scenes. This study demonstrated the potential of improving LAI retrieval through full utilization of full-waveform data.

The existing buffers algorithms cannot effectively to meet the demands of high accuracy of buffer analysis in practice although many efforts have been made in the past 60 years. A generalized buffering algorithm (GBA) is presented, which considers the geometric distance and the attribute characteristics of all instances within buffer zone. The proposed algorithm includes three major steps: (1) select and initialize target instance; (2) determine buffer boundary points through mining homogeneous pattern; (3) “smoothly” connect buffer boundary points to generate the generalized buffer zone. The details for the generations of the generalized point buffer (GPIB) zone, the generalized line buffer (GLB) zone, and the generalized polygon buffer (GPLB) zone are discussed. Two dataset are used to validate the performances of the proposed GBA. Six parameters are applied as indexes to evaluate the proposed algorithm. The experimental results discovered that (1) the GBA is close to the tradition buffering algorithm (TBA) when the angle increment ( $\Delta \varphi$ ) in GPIB, line increment ( $\Delta L$ ) in GLB, and arc length increment ( $\Delta S$ ) in GPLB approach to zero, respectively; (2) the proposed GBA can accurately reflect the real situation of the buffering zone, and improve the deficiency and accuracy of TBA in real application.

Quantitative assessment of aboveground biomass (AGB) and spatial distribution pattern of exotic mangrove plants (Sonneratia apetala) is of great significance for blue carbon management and ecological restoration in typical subtropical estuaries in China. Although unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) has certain advantages in the investigation of the vertical three-dimensional structure of mangroves, the existing mangrove investigation results are mainly based on plot investigation method. Few scholars use machine learning (ML) method to estimate AGB of invasive Sonneratia apetala by combining plot investigation and LiDAR data. Therefore, on the basis of the height and intensity variables of UAV-LiDAR data, this study used four different ML algorithms, namely, xgboost regressor (XGBR), catboost regressor (CBR), light gradient boosting regressor (LGBR) and AdaBoost regressor (ABR), to estimate AGB of invasive mangrove. Then, the quantitative relationship between invasive mangrove biomass and hydrological unit was analysed. We found that CBR model had the highest accuracy in estimation of mangrove AGB (R2 = 0.7644, RMSE = 11.1725 Mg/ha), followed by XGBR model (R2 = 0.6759, 13.1053 Mg/ha). However, LGBR model (R2 = 0.3506, RMSE = 18.5510 Mg/ha) had poor fitting effect. The AGB of invasive mangroves showed a spatial distribution pattern of high in northwest and low in southeast, and its value ranged from 7.31 Mg/ha to 114.04 Mg/ha, with an average of 25.57 Mg/ha. The AGB of invasive mangroves was independent of the area size of the hydrological response unit but depended on the elevation of the beach surface and the distance from the main tidal ditch. This study demonstrates the feasibility of UAV-LiDAR remote sensing and CBR model in estimating AGB of invasive mangrove species, which can provide scientific basis and technical support for the assessment of invasive mangrove ecosystem and the protection of local mangrove tree species.

With the increasing needs of and rapid developments in digital and/or smart cities, an effective method for the management of various types of complex and massive buildings for the creation of a three-dimensional (3D) photorealistic city has become crucial for high-quality “digital/smart city” construction. To this end, this paper proposes a data model called the “SCSG-OSM”, which combines spatial constructive solid geometry (SCSG) and the object-based spatial model (OSM). The SCSG-OSM assumes an object can be represented by four element types: point, line, face and body. The SCSG applies an extension of the dimensionally extended nine-intersection model (DE-9IM) to describe the spatial topological relationship. The OSM consists of nine definitions that define the points, lines, faces, and bodies of an object and applies point sets instead of node sets and face sets instead of surface sets to model buildings. Two data sets, depicting Denver, Colorado, USA, and Zurich, Switzerland, are employed to assess the storage space and the time consumption averages during the modelling of 3D buildings using the proposed data model. The experimental results demonstrate that the proposed SCSG-OSM is able to save storage space and reduce the computational time compared with the CSG-BR model, TIN model, POLYGON model, Patch model, SSM and CityGML model. Therefore, it can be concluded that the method proposed in this paper is a simple and effective method for the 3D photorealistic visualization of a large city.

Feature points that are obtained from the combined speeded-up robust feature (SURF) detector and binary robust independent elementary features (BRIEF) descriptor have a highly robust performance. These points are previously considered the ground control points (GCPs) for building a connection between the image coordinates and the corresponding geodetic coordinates. This article proposes a novel architecture to automatically and intelligently extract GCPs based on field programmable gate arrays (FPGAs). The parallelization SURF detector, BRIEF descriptor, and BRIEF matching are implemented in a single Xilinx XC7VX980T FPGA system. Word length reduction, memory-efficient parallel architecture, shift and subtraction strategies, a sliding window for separable convolution, and an optimized multispacer-scale are used to optimize the SURF detector. Improved parallel adder trees are used to accelerate the BRIEF matching. The proposed system achieves 380 frame per second (fps) with a 100 MHz clock frequency, which satisfies the real-time and low-power requirements of embedded devices. The results of the experiment demonstrate that the proposed architecture, when mapped onto a Xilinx Virtex-7 XC7VX980T FPGA device, can select the robust feature points.

The bed separation backfill grouting (BSBG) is commonly used to mitigate the surface subsidence caused by coal mining. The distribution characteristics of bed separation and its dynamic evolving process are crucial for BSBG design. This paper utilizes the continuum-discontinuum element method (CDEM) to study the distribution characteristics of bed separation for a longwall top coal caving (LTCC) panel of Longquan coal mine. Numerical results indicate that in addition to the bed separation below the primary key stratum, several small bed separations may also occurred in the strata between the primary key stratum and the subordinate key stratum. The bed separations in the overburden could be classified into three classes: the upper bed separation, the middle bed separation, and the lower bed separation. The upper bed separation has the longest duration time, and the middle bed separation has the shortest duration time. And the BSBG should be started before the closure of the middle bed separation. Based on the actual geological information, the BSBG scheme for 4203 LTCC panel is proposed to mitigate the surface subsidence by taken the results of numerical simulation into consideration. In addition, the case study of the BSBG is introduced in detail. By using gangue power slurry, BSBG could not only effectively mitigate the surface subsidence but also solve the problems of environmental pollution and land occupation caused by traditional gangue stacking. The present study could provide technical support for surface subsidence mitigation and coal gangue disposal for LTCC mining with similar conditions.

In practical engineering projects, overconsolidation plays an irreplaceable role in the calculation of silt consolidation settlement. Based on a series of systematic consolidation tests conducted on reconstituted soft clay in different cities of the Guangdong-Hong Kong-Macao Greater Bay Area, the overconsolidation behavior of soft clay under different depths was studied. According to these tests, the shape of the curves of the overconsolidation ratio with depth was similar to that of the compression curves. Moreover, the deeper the silt depth is, the smaller the overconsolidation ratio is. Hence, the overconsolidation state eventually changes to the underconsolidation state. On this basis, the depths of normal consolidation were calculated through the fitting curves of the overconsolidation ratio with depth by CurveExpert1.4, which is strongly dependent on both the average thickness of the overfill and the range of water level. Based on the results, a polynomial relationship between overconsolidation ratio (OCR) and sampling depth (h) is established. This study can serve as a basis for predicting the consolidation settlement of the silt layer more accurately in practical engineering applications.

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.

When employing remote sensing images, it is challenging to classify vegetation species and ground objects due to the abundance of wetland vegetation species and the high fragmentation of ground objects. Remote sensing images are classified primarily according to their spatial resolution, which significantly impacts the classification accuracy of vegetation species and ground objects. However, there are still some areas for improvement in the study of the effects of spatial resolution and resampling on the classification results. The study area in this paper was the core zone of the Huixian Karst National Wetland Park in Guilin, Guangxi, China. The aerial images (Am) with different spatial resolutions were obtained by utilizing the UAV platform, and resampled images (An) with different spatial resolutions were obtained by utilizing the pixel aggregation method. In order to evaluate the impact of spatial resolutions and resampling on the classification accuracy, the Am and the An were utilized for the classification of vegetation species and ground objects based on the geographic object-based image analysis (GEOBIA) method in addition to various machine learning classifiers. The results showed that: (1) In multi-scale images, both the optimal scale parameter (SP) and the processing time decreased as the spatial resolution diminished in the multi-resolution segmentation process. At the same spatial resolution, the SP of the An was greater than that of the Am. (2) In the case of the Am and the An, the appropriate feature variables were different, and the spectral and texture features in the An were more significant than those in the Am. (3) The classification results of various classifiers in the case of the Am and the An exhibited similar trends for spatial resolutions ranging from 1.2 to 5.9 cm, where the overall classification accuracy increased and then decreased in accordance with the decrease in spatial resolution. Moreover, the classification accuracy of the Am was higher than that of the An. (4) When vegetation species and ground objects were classified at different spatial scales, the classification accuracy differed between the Am and the An.

Building height is a pivotal factor in studying urban form and understanding the impacts of the vertical characteristics of urban areas on the environment. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) employ photon-counting LiDAR to collect Earth’s surface elevation data globally. However, method at ground and building photon classification and subsequently building height extraction from photon-counting LiDAR data is still missing. This study aimed to develop a methodological framework to retrieve building height from ICESat-2 data under various data acquisition conditions stratified by day/night condition, strong/weak beam. First, the noise removal algorithm adopted by ATL03 product is utilized to filter out noise photons and retain signal photons in the raw dataset. Second, the random sample consensus (RANSAC) algorithm and statistical method are adopted to classify the signal photons into ground and building photons according to their characteristics and spatial neighborhood relationship. The building height is then computed by the elevation of building and ground photons. Finally, estimated heights are evaluated using the reference building height derived from terrestrial laser scanning (TLS) data. The results indicate that the proposed methodological framework can effectively identify building and ground photons under different building types. Strong consistence between estimated and reference building height is obtained by quantifying the estimation precision of building height under different data acquisition conditions, root mean square error (RMSE) ranging between 0.35 m and 0.45 m, indicating that the ATL03 data can be utilized to derive the building height in urban areas for all acquisition times and beam intensity. Further analysis demonstrates that strong/weak beam significantly influences the building height estimation compared to day/night condition. Overall, this study provides a method for estimating building height in urban areas using ICESat-2 data, and the findings approve the strong capability of ICESat-2 data in estimating building height.

Fractional vegetation cover (FVC) products provide essential data support for ecological environmental monitoring and simulation studies. However, the lack of validation efforts of FVC products limits their applications. Based on Sentinel-2 data and intensive multi-scale measured FVC data, the accuracies of two FVC products (GEOV3 and MuSyQ) in alpine grassland ecosystems were validated through direct validation and multi-scale validation. Based on the heterogeneity of the underlying surface (HUS) of the monitoring plots, the impact of the HUS of the monitoring plots on the product validation was analyzed. The results showed that: (1) the measured data directly validated that the GEOV3 FVC product performed better than the MuSyQ FVC product; (2) the multi-scale validation method based on high-resolution reference FVC map of Sentienl-2 satellite images validated the accuracy of these two FVC products, which was higher than the accuracy directly validated by FVC measured data, leading to overestimation of the validation results; and (3) the HUS of the monitored plots has a significant impact on the FVC product validation. By quantifying the HUS of the monitored plots and removing the heterogeneous monitoring plots, the uncertainty of the validation results can be reduced. It is necessary to consider the impact of validation methods and the HUS on the validation results in future product validation.

Traditional bathymetry LiDAR (light detection and ranging) onboard manned and/or unmanned airborne systems cannot operate in the context of narrow rivers in urban areas with high buildings and in mountainous areas with high peaks. Therefore, this study presents a prototype of a lightweight bathymetry LiDAR onboard an unmanned shipborne vehicle (called “GQ-Cor 19”). The GQ-Cor 19 system primarily includes an emitting optical module, a receiving optical module, control module, detection module, high-speed A/D sampling module, and data processing system. Considering that the “GQ-Cor 19” is extremely close to the water surface, various new technical challenges are encountered, such as significant laser scattering energy from the surface of the water, which saturates signals received by the photomultiplier tube detector. Therefore, this study presents various new technical solutions, including (1) an improved Bresenham algorithm, (2) a small and lightweight receiving optical system with a split-field method, and (3) a data acquisition module with a high-speech A/D collector. Following a series of different experimental verifications, the results demonstrate that the new generation of single-band LiDAR onboard an unmanned shipborne vehicle can swiftly measure the underwater depth, and the maximum measurement depth is more than 25 m. The measurement accuracy is better than 30 cm and the weight is less than 12 kg.

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).

The fisheye camera, with its large viewing angle, can acquire more spatial information in one shot and is widely used in many fields. However, a fisheye image contains large distortion, resulting in that many scholars have investigated its accuracy of orthorectification, i.e., generation of digital orthophoto map (DOM). This paper presents an orthorectification method, which first determines the transformation relationship between the fisheye image points and the perspective projection points according to the equidistant projection model, i.e., determines the spherical distortion of the fisheye image; then introduces the transformation relationship and the fisheye camera distortion model into the collinearity equation to derive the fisheye image orthorectification model. To verify the proposed method, high accuracy of the fisheye camera 3D calibration field is established to obtain the interior and exterior orientation parameters (IOPs/EOPs) and distortion parameters of the fisheye lens. Three experiments are used to verify the proposed orthorectification method. The root mean square errors (RMSEs) of the three DOMs are averagely 0.003 m, 0.29 m, and 0.61 m, respectively. The experimental results demonstrate that the proposed method is correct and effective.

The Moon-based Synthetic Aperture Radar (SAR) offers an exceptionally wide spatial coverage and short revisit time for the Earth observation. However, the complex imaging geometry in Moon-based SAR-Earth's target motion, where both the Earth's rotation and lunar revolution exert, poses a great challenge in signal processing and image formation. The Earth's rotation, in particular, plays a dominant role in the aperture synthesis processing of the Moon-based SAR. However, the Earth's rotation, being irregular due to perturbations, induces coordinate drifts and leads to variations in the Moon-based SAR's Doppler parameters. As a result, the image formation of the Moon-based SAR is profoundly impacted by the Earth's irregular rotation. In this paper, we investigate Earth's irregular rotational effects on the imaging performance of the Moon-based SAR through a comprehensive analysis of the relative motion between a Moon-based SAR and Earth's target. Theoretical analyses and numerical simulations show that the effects of the Earth's irregular rotation affect the geometric fidelity and azimuth focusing of the Moon-based SAR; thus accentuating the necessity for special care of the Earth's irregular rotation in light of image focusing.