A method for real-time mosaic of video flow acquired by a small low-cost unmanned aerial vehicle (UAV) has been presented in this paper. The basic procedures of real-time mosaic are as follows: (1) ...Each video frame is resampled and orthorectified using a developed mathematical model, which can simultaneously solve the video camera's interior orientation parameters and the exterior orientation parameters of each video frame; (2) each orthorectified video frame is mosaicked at real time. A test field located in Picayune, Mississippi, has been established for testing our method. Sixty-minute video data were collected using the UAV and were processed using the proposed method. The results demonstrated that each video frame can be geo-orthorectified and mosaicked together to produce a 2-D planimetric mapping at near real time. Accuracy of the mosaicked video images (2-D planimetric map) is approximately 1-2 pixels, when compared to 55 checkpoints, which were measured by differential GPS surveying.
As an important parameter in recent and numerous environmental studies, soil moisture (SM) influences the exchange of water and energy at the interface between the land surface and atmosphere. ...Accurate estimate of the spatio-temporal variations of SM is critical for numerous large-scale terrestrial studies. Although microwave remote sensing provides many algorithms to obtain SM at large scale, such as SMOS and SMAP etc., resulting in many data products, they are almost low resolution and not applicable in small catchment or field scale. Estimations of SM from optical and thermal remote sensing have been studied for many years and significant progress has been made. In contrast to previous reviews, this paper presents a new, comprehensive and systematic review of using optical and thermal remote sensing for estimating SM. The physical basis and status of the estimation methods are analyzed and summarized in detail. The most important and latest advances in soil moisture estimation using temporal information have been shown in this paper. SM estimation from optical and thermal remote sensing mainly depends on the relationship between SM and the surface reflectance or vegetation index. The thermal infrared remote sensing methods uses the relationship between SM and the surface temperature or variations of surface temperature/vegetation index. These approaches often have complex derivation processes and many approximations. Therefore, combinations of optical and thermal infrared remotely sensed data can provide more valuable information for SM estimation. Moreover, the advantages and weaknesses of different approaches are compared and applicable conditions as well as key issues in current soil moisture estimation algorithms are discussed. Finally, key problems and suggested solutions are proposed for future research.
Energy and resources including coal,oil,and gas are in demand all over the world.Because these resources near the earth’s surface have been exploited for many years,the extraction depth has ...increased.As mining shafts in the coal extraction process become deeper,especially in western China,an artificial freezing method is used and is concentrated in the fractured rock mass.The frost-heaving pressure(FHP)is directly related to the degree of damage of the fractured rock mass.This paper is focused on FHP during the freezing process,with emphasis on the frost-heaving phenomenon in engineering materials.A review of the frost phenomenon in the geotechnical engineering literature indicates that:(1)During the soil freezing process,the ice content that is influenced by unfrozen water and the freezing rate are the determining factors of FHP;(2)During the freezing process of rock and other porous media,the resulting cracks should be considered because the FHP may damage the crack structure;(3)The FHP in a joint rock mass is analyzed by the joint deformation in field and experimental tests and can be simulated by the equivalent expansion method including water migration and joint deformation.
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 <xref rid="deqn1" ref-type="disp-formula">(1) the GBA is close to the tradition buffering algorithm (TBA) when the angle increment (<inline-formula> <tex-math notation="LaTeX">\Delta \varphi </tex-math></inline-formula>) in GPIB, line increment (<inline-formula> <tex-math notation="LaTeX">\Delta L </tex-math></inline-formula>) in GLB, and arc length increment (<inline-formula> <tex-math notation="LaTeX">\Delta S </tex-math></inline-formula>) in GPLB approach to zero, respectively; <xref rid="deqn2" ref-type="disp-formula">(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.
Although many efforts have been made on the fusion of Light Detection and Ranging (LiDAR) and aerial imagery for the extraction of houses, little research on taking advantage of a building's ...geometric features, properties, and structures for assisting the further fusion of the two types of data has been made. For this reason, this paper develops a seamless fusion between LiDAR and aerial imagery on the basis of aspect graphs, which utilize the features of houses, such as geometry, structures, and shapes. First, 3-D primitives, standing for houses, are chosen, and their projections are represented by the aspects. A hierarchical aspect graph is then constructed using aerial image processing in combination with the results of LiDAR data processing. In the aspect graph, the note represents the face aspect and the arc is described by attributes obtained by the formulated coding regulations, and the coregistration between the aspect and LiDAR data is implemented. As a consequence, the aspects and/or the aspect graph are interpreted for the extraction of houses, and then the houses are fitted using a planar equation for creating a digital building model (DBM). The experimental field, which is located in Wytheville, VA, is used to evaluate the proposed method. The experimental results demonstrated that the proposed method is capable of effectively extracting houses at a successful rate of 93%, as compared with another method, which is 82% effective when LiDAR spacing is approximately 7.3 by 7.3 ft 2 . The accuracy of 3-D DBM is higher than the method using only single LiDAR data.
On the basis of canopy height variables, vegetation index, texture index, and laser point cloud index measured with unmanned aerial vehicle (UAV) low altitude remote sensing, we used eight machine ...learning (ML) models to estimate the aboveground biomass of different species of mangroves in Beibu Gulf and compared the accuracy of different ML models for these estimations. The main species of typical mangrove communities in Kangxiling were Aegiceras corniculata and Sonneratia apetala. The trunks of Sonneratia apetala were thicker, with an average height of 11.82 m, whereas Aegiceras corniculata trees were shorter, with an average height of 2.58 m. The XGBoost regressor (XGBR) model had the highest accuracy in estimating mangrove aboveground biomass (R2 = 0.8319, RMSE = 22.7638 Mg/ha), followed by the random forest regressor model (R2 = 0.7887, RMSE = 25.5193 Mg/ha). Support vector regression, decision tree regressor, and extra trees regressor had poor fitting effects. Mangrove texture index ranked first in importance for the model, followed by the mangrove laser point cloud height index, and the laser point cloud intensity index performed the worst in the model. Mangrove aboveground biomass in the study area is high in the north and low in the south, ranging from 38.23 to 171.80 Mg/ha, with an average value of 94.37 Mg/ha. Generally, the XGBR method can better estimate the aboveground biomass of mangroves based on the measured mangrove plot data and UAV low-altitude remote sensing data.
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•The mangrove community of Sonneratia apetala has been diffused into the Aegiceras corniculata community.•We established the technical method of retrieving mangrove aboveground biomass based on UAV data and plot survey data.•We have pioneered the use of eight ML algorithms to estimate the aboveground biomass of mangrove in South Subtropical China.•The XGBR algorithm in machine learning model can better estimate the aboveground biomass of mangrove.
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.
Oceanic LiDAR (hereafter referred to as O-LiDAR) is an important remote sensing device for measuring the near-coastal water depth and for studying the optical properties of water bodies. With the ...commercialization of LiDAR, the theoretical research on the underwater transmission characteristics of LiDAR has been intensified worldwide. Primary research interests include the simulation and modeling of LiDAR underwater echo signals and the inversion of optical parameters using LiDAR water echo signals. This article provides an overview of the principle of LiDAR echo signal formation, and comprehensively summarizes the LiDAR echo signal simulation modeling methods and the corresponding factors that affect modeling accuracy by focusing on the characteristics of different methods. We found that the current simulation methods of LiDAR underwater transmission echo signals primarily include an analytical method based on the radiation transfer equation and a statistical method based on the Monte Carlo (MC) model. The radiation transport equation needs to be appropriately simplified using the analytical method, usually using the quasi-single-small-angle approximation principle. The analytical method has high calculation efficiency but its accuracy is dependent to the quasi-single small-angle approximation. The statistical method can analyze the influence of various factors on echo signals by controlling the variables, but it has poor calculation efficiency. Finally, the semianalytical MC model was used to quantitatively analyze the three main factors (LiDAR system parameters, water body optical parameters, and environmental parameters) affecting underwater LiDAR transmission characteristics, and summarizes the mechanism and results of different factors.
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 <inline-formula><tex-math notation="LaTeX">{\text{pcs/}}{{\text{m}}^{2}}</tex-math></inline-formula>, 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.
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.
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