A new GIS-compatible methodology for visibility analysis in digital surface models of earth sites

As a GIS tool,visibility analysis is used in many areas to evaluate both visible and non-visible places.Visibility analysis builds on a digital surface model describing the terrain morphology,including the position and shapes of all objects that can sometimes act as visibility barriers.However,some barriers,for example vegetation,may be permeable to a certain degree.Despite extensive research and use of visibility analysis in different areas,standard GIS tools do not take permeability into account.This article presents a new method to calculate visibility through partly permeable obstacles.The method is based on a quasi-Monte Carlo simulation with 100 iterations of visibility calculation.Each iteration result represents 1%of vegetation permeability,which can thus range from 1%to 100%visibility behind vegetation obstacles.The main advantage of the method is greater accuracy of visibility results and easy implementation on any GIS software.The incorporation of the proposed method in GIS software would facilitate work in many fields,such as architecture,archaeology,radio communication,and the military.

An Adaptive and Image-guided Fusion for Stereo Satellite Image Derived Digital Surface Models

The accuracy of Digital Surface Models(DSMs)generated using stereo matching methods varies due to the varying acquisition conditions and configuration parameters of stereo images.It has been a good practice to fuse these DSMs generated from various stereo pairs to achieve enhanced,in which multiple DSMs are combined through computational approaches into a single,more accurate,and complete DSM.However,accurately characterizing detailed objects and their boundaries still present a challenge since most boundary-ware fusion methods still struggle to achieve sharpened depth discontinuities due to the averaging effects of different DSMs.Therefore,we propose a simple and efficient adaptive image-guided DSM fusion method that applies k-means clustering on small patches of the orthophoto to guide the pixel-level fusion adapted to the most consistent and relevant elevation points.The experiment results show that our proposed method has outperformed comparing methods in accuracy and the ability to preserve sharpened depth edges.

Using UAVs for detection of trees from digital surface models

A difficult problem in forestry is tree inventory.In this study, a GoProHero attached to a small unmanned aerial vehicle was used to capture images of a small area covered by pinus pinea trees. Then, a digital surface model was generated with image matching. The elevation model representing the terrain surface, a ‘digital terrain model’,was extracted from the digital surface model using morphological filtering. Individual trees were extracted by analyzing elevation flow on the digital elevation model because the elevation reached the highest value on the tree peaks compared to the neighborhood elevation pixels. The quality of the results was assessed by comparison with reference data for correctness of the estimated number of trees. The tree heights were calculated and evaluated with ground truth dataset. The results showed 80% correctness and 90% completeness.

Primary safe criterion of earth-brushing flight for flying vehicle over digital surface model

In modern terrain-following guidance it is an important index for flight vehicle to cruise about safely and normally. On the basis of a constructing method of digital surface model (DSM), the definition, classification and scale analysis of an isolated obstacle threatening flight safety of terrain-following guidance are made. When the interval of vertical-and cross-sections on DSM is 12. 5 m, the proportion of isolated obstacles to the data amount of DSM model to be loaded is optimal. The main factors influencing the lowest flying height in terrain-following guidance are analyzed, and a primary safe criterion of the lowest flying height over DSM model is proposed. According to their test errors, the lowest flying height over 1:10 000 DSM model can reach 40. 5 m^45. 0 m in terrain-following guidance. It is shown from the simulation results of a typical urban district that the proposed models and methods are reasonable and feasible.

Digital surface model applied to unmanned aerial vehicle based photogrammetry to assess potential biotic or abiotic effects on grapevine canopies

Accurate data acquisition and analysis to obtain crop canopy information are critical steps to understand plant growth dynamics and to assess the potential impacts of biotic or abiotic stresses on plant development.A versatile and easy to use monitoring system will allow researchers and growers to improve the follow-up management strategies within farms once potential problems have been detected.This study reviewed existing remote sensing platforms and relevant information applied to crops and specifically grapevines to equip a simple Unmanned Aerial Vehicle(UAV)using a visible high definition RGB camera.The objective of the proposed Unmanned Aerial System(UAS)was to implement a Digital Surface Model(DSM)in order to obtain accurate information about the affected or missing grapevines that can be attributed to potential biotic or abiotic stress effects.The analysis process started with a three-dimensional(3D)reconstruction from the RGB images collected from grapevines using the UAS and the Structure from Motion(SfM)technique to obtain the DSM applied on a per-plant basis.Then,the DSM was expressed as greyscale images according to the halftone technique to finally extract the information of affected and missing grapevines using computer vision algorithms based on canopy cover measurement and classification.To validate the automated method proposed,each grapevine row was visually inspected within the study area.The inspection was then compared to the digital assessment using the proposed UAS in order to validate calculations of affected and missing grapevines for the whole studied vineyard.Results showed that the percentage of affected and missing grapevines was 9.5%and 7.3%,respectively from the area studied.Therefore,for this specific study,the abiotic stress that affected the experimental vineyard(frost)impacted a total of 16.8%of plants.This study provided a new method for automatically surveying affected or missing grapevines in the field and an evaluation tool for plant growth conditions,which can be implemented for other uses such as canopy management,irrigation scheduling and other precision agricultural applications.

Assessment of canopy vigor information from kiwifruit plants based on a digital surface model from unmanned aerial vehicle imagery

Information about canopy vigor and growth are critical to assess the potential impacts of biotic or abiotic stresses on plant development.By implementing a Digital Surface Model(DSM)to imagery obtained using Unmanned Aerial Vehicles(UAV),it is possible to filter canopy information effectively based on height,which provides an efficient method to discriminate canopy from soil and lower vegetation such as weeds or cover crops.This paper describes a method based on the DSM to assess canopy growth(CG)as well as missing plants from a kiwifruit orchard on a plant-by-plant scale.The DSM was initially extracted from the overlapping RGB aerial imagery acquired over the kiwifruit orchard using the Structure from Motion(SfM)algorithm.An adaptive threshold algorithm was implemented using the height difference between soil/lower plants and kiwifruit canopies to identify plants and extract canopy information on a non-regular surface.Furthermore,a customized algorithm was developed to discriminate single kiwifruit plants automatically,which allowed the estimation of individual canopy cover fractions(fc).By applying differential fc thresholding,four categories of the CG were determined automatically:(i)missing plants;(ii)low vigor;(iii)moderate vigor;and(iv)vigorous.Results were validated by a detailed visual inspection on the ground,which rendered an overall accuracy of 89.5%for the method proposed to assess CG at the plant-by-plant level.Specifically,the accuracies for CG category(i)-(iv)were 94.1%,85.1%,86.7%,and 88.0%,respectively.The proposed method showed also to be appropriate to filter out weeds and other smaller non-plant materials which are extremely difficult to be distinguished by common colour thresholding or edge identification methods.

A Method to Identify Flight Obstacles on Digital Surface Model

In modern low-altitude terrain-following guidance, a constructing method of the digital surface model (DSM) is presented in the paper to reduce the threat to flying vehicles of tall surface features for safe flight. The relationship between an isolated obstacle size and the intervals of vertical- and cross-section in the DSM model is established. The definition and classification of isolated obstacles are proposed, and a method for determining such isolated obstacles in the DSM model is given. The simulation of a typical urban district shows that when the vertical- and cross-section DSM intervals are between 3 m and 25 m, the threat to terrain-following flight at low-altitude is reduced greatly, and the amount of data required by the DSM model for monitoring in real time a flying vehicle is also smaller. Experiments show that the optimal results are for an interval of 12.5 m in the vertical- and cross-sections in the DSM model, with a 1:10 000 DSM scale grade.

Issues in the application of Digital Surface Model data to correct the terrain illumination effects in Landsat images

The accuracy of topographic correction of Landsat data based on a Digital Surface Model(DSM)depends on the quality,scale and spatial resolution of the DSM data used and the co-registration between the DSM and the satellite image.A physics-based bidirectional reflectance distribution function(BRDF)and atmospheric correction model in conjunction with a 1-second DSM was used to conduct the analysis in this paper.The results show that for the examples used from Australia,the 1-second DSM,can provide an effective product for this task.However,it was found that some remaining artefacts in the DSM data,originally due to radar shadow,can still cause significant local errors in the correction.Where they occur,false shadows and over-corrected surface reflectance factors can be observed.More generally,accurate co-registration between satellite images and DSM data was found to be critical for effective correction.Mis-registration by one or two pixels could lead to large errors of retrieved surface reflectance factors in gully and ridge areas.Using low-resolution DSM data in conjunction with high-resolution satellite images will also fail to correct significant terrain components where they occur at the finer scales of the satellite images.DSM resolution appropriate to the resolution of satellite image and the roughness of the terrain is needed for effective results,and the rougher the terrain,the more critical will be the accurate registration.

Analysis of Remotely Sensed Imagery and Architecture Environment for Modelling 3D Detailed Buildings Using Geospatial Techniques

The use of three-dimensional maps is more effective than two-dimensional maps in representing the Earth’s surface. However, the traditional methods used to create digital surface models are not efficient for capturing the details of Earth’s features. This is because they represent only three-dimensional objects in a single texture and do not provide a realistic representation of the real world. Additionally, there is a growing demand for up-to-date and accurate geo-information, particularly in urban areas. To address this challenge, a new technique is proposed in this study that involves integrating remote sensing, Geographic Information System, and Architecture Environment software to generate a highly-detailed three-dimensional model. The method described in this study includes several steps such as acquiring high-resolution satellite imagery, gathering ground truth data, performing radiometric and geometric corrections during image preprocessing, producing a 2D map of the region of interest, constructing a digital surface model by extending the building outlines, and transforming the model into multi-patch layers to create a 3D model for each object individually. The research findings indicate that the digital surface model obtained with comprehensive information is suitable for different purposes, such as environmental research, urban development and expansion planning, and shape recognition tasks.

Integrating Highly Spatial Satellite Image for 3D Buildings Modelling Using Geospatial Algorithms and Architecture Environment

The growing demand for current and precise geographic information that pertains to urban areas has given rise to a significant interest in digital surface models that exhibit a high level of detail. Traditional methods for creating digital surface models are insufficient to reflect the details of earth’s features. These models only represent three-dimensional objects in a single texture and fail to offer a realistic depiction of the real world. Furthermore, the need for current and precise geographic information regarding urban areas has been increasing significantly. This study proposes a new technique to address this problem, which involves integrating remote sensing, Geographic Information Systems (GIS), and Architecture Environment software environments to generate a detailed three-dimensional model. The processing of this study starts with: 1) Downloading high-resolution satellite imagery; 2) Collecting ground truth datasets from fieldwork; 3) Imaging nose removing; 4) Generating a Two-dimensional Model to create a digital surface model in GIS using the extracted building outlines; 5) Converting the model into multi-patch layers to construct a 3D model for each object separately. The results show that the 3D model obtained through this method is highly detailed and effective for various applications, including environmental studies, urban development, expansion planning, and shape understanding tasks.

2-D delineation of individual citrus trees from UAV-based dense photogrammetric surface models

One of the challenges of remote sensing and computer vision lies in the three-dimensional(3-D)reconstruction of individual trees by using automated methods through very high-resolution(VHR)data sets.However,a successful and complete 3-D reconstruction relies on precise delineation of the trees in two dimensions.In this paper,we present an original approach to detect and delineate citrus trees using unmanned aerial vehicles based on photogrammetric digital surface models(DSMs).The symmetry of the citrus trees in a DSM is handled by an orientationbased radial symmetry transform which is computed in a unique way.Next,we propose an efficient strategy to accurately build influence regions of each tree,and then we delineate individual citrus trees through active contours by taking into account the influence region of each canopy.We also present two efficient strategies to filter out erroneously detected canopy regions without having any height thresholds.Experiments are carried out on eight test DSMs composed of different types of citrus orchards with varying densities and canopy sizes.Extensive comparisons to the state-of-the-art approaches reveal that our proposed approach provides superior detection and delineation performances through supporting a nice balance between precision and recall measures.

Updating digital elevation models via change detection and fusion of human and remote sensor data in urban environments

OpenStreetMap(OSM)currently represents the most popular project of Volunteered Geographic Information(VGI):geodata are collected by common people and made available for public use.Airborne Laser Scanning(ALS)enables the acquisition of high-resolution digital elevation models that are used for many applications.This study combines the advantages of both ALS and OSM,offering a promising new approach that enhances data quality and allows change detection:the mainly up-to-date 2D data of OSM can be combined with the high-resolution–but rarely updated–elevation information provided by ALS.This case study investigates building objects of OSM and ALS data of the city of Bregenz,Austria.Data quality of OSM is discerned by the comparison of building footprints using different true positive definitions(e.g.overlapping area).High quality of OSM data is revealed,yet also limitations of each method with respect to heterogeneous regions and building outlines are identified.For the first time,an up-to-date Digital Surface Model(DSM)combining 2D OSM and ALS data is achieved.A multitude of applications such as flood simulations and solar potential assessments can directly benefit from this data combination,since their value and reliability strongly depend on an up-to-date DSM.

Geomorphologic map of the 1st Mutnaya River,Southeastern Kamchatka,Russia

The Kamchatka Peninsula – situated in the Pacific "Ring of Fire" – has 29 active and over 400 extinct volcanoes. Since it is situated in the northeastern extremity of Russia, in subarctic climate,the volcanic landforms are overprinted by the 446 glaciers. This research focuses on the 1^(st) Mutnaya catchment which drains the southern slopes of two active volcanoes: Avachinsky and Koryaksky. Those volcanoes are a permanent threat for the cities of Petropavlovsk and Elizovo, which are the 2 of 3 cities of the peninsula. Hence, most of the studies carried out in the area dealt with the natural hazards and only few focus on landscape evolution. Thus, the purpose of this study was to elaborate a cartographic approach which integrates classic geomorphology with state of the art GIS and remote sensing techniques. As result,different landforms and related processes have been analysed and included in the first general geomorphologic map of the 1^(st) Mutnaya catchment.

A Survey of Sediment Fineness and Moisture Content in the Soyang Lake Floodplain Using GPS Data

Soyang Lake is the largest lake in Republic of Korea bordering Chuncheon,Yanggu,and Inje in Gangwon Province.It is widely used as an environmental resource for hydropower,flood control,and water supply.Therefore,we conducted a survey of the floodplain of Soyang Lake to analyze the sediments in the area.We used global positioning system(GPS)data and aerial photography to monitor sediment deposits in the Soyang Lake floodplain.Data from three GPS units were compared to determine the accuracy of sampling location measurement.Sediment samples were collected at three sites:two in the eastern region of the floodplain and one in the western region.A total of eight samples were collected:Three samples were collected at 10 cm intervals to a depth of 30 cm from each site of the eastern sampling point,and two samples were collected at depths of 10 and 30 cm at the western sampling point.Samples were collected and analyzed for vertical and horizontal trends in particle size and moisture content.The sizes of the sediment samples ranged from coarse to very coarse sediments with a negative slope,which indicate eastward movement from the breach.The probability of a breach was indicated by the high water content at the eastern side of the floodplain,with the eastern sites showing a higher probability than the western sites.The results of this study indicate that analyses of grain fineness,moisture content,sediment deposits,and sediment removal rates can be used to understand and predict the direction of breach movement and sediment distribution in Soyang Lake.

Precision Assessment of UAVRS Based DSM in Disaster Emergency

Ground control point (GCP) is important for georeferencing remotely sensed images and topographic model. However, considering that GCP collection is sometimes a difficult, time-consuming and expensive task with high resolution (HR) data in remote and harsh environments, today unmanned aerial vehicle based remote sensing (UAVRS) is frequently used in geological disaster emergency monitoring and rescuing for its great advantage in collecting timely onsite images. In this paper, for evaluating the feasibility of the UAVRS in disaster emergency and high cut slope safety monitoring, the digital surface model (DSM) without GCPs based on Structure from Motion (SfM) is accessed, and results showed that the geometric accuracy of DSM was smaller than 1 percent, which prove the usefulness of DSM based on UAVRS in emergency. Comparing to normal disaster emergency, the method without GCPs can be more efficient and save the disaster emergency time by neglecting GCPs measurement.

Prediction accuracy of reservoir break flood simulation model using finite volume method and UAV

Methods for producing high-resolution digital topographic maps using an unmanned aerial vehicle(UAV),and 3D fluid dynamics simulation to estimate the flooded areas caused by a collapsed reservoir,were proposed in this paper.The UAV flight path for photographing damaged areas was divided into two sections considering the drone flight time and overlapping range of the images in the x-and y-directions.The metadata taken by the drone were transferred into world coordinates by tracking the key features of the photographs of nearby areas using a 3D rotation matrix.The point cloud data with a 3D space were extracted from the registered images,and a digital surface map(DSM)was produced using a point cloud classification geometric mapping technique.To amend the serious elevation errors caused by natural or artificial obstacles,a kriging interpolation method was used to reproduce the DSM.A transient computational simulation that considers both the complex geometric topology and hydrodynamic energy of flowing water was conducted using FLOW-3D software to deal with an renormalization group(RNG)turbulence model.The flooded areas calculated through visual reading using images taken by the UAV were compared with the 3D simulation results for verification.The flooded areas estimated through the simulation were approximately 18.3%larger than those found by visual reading.Turbulent flows were mainly observed in obstacles or curved areas of the stream,and the differences in the water depth could be further increased.However,the villagers confirmed that the flooded areas were much greater than what was seen through the visual reading.Therefore,the combination of UAV surveying and the 3D simulation method based on the RNG turbulence model is recommended to accurately estimate flooded areas,and it will support an administrative policy aimed at minimizing the economic costs of damage caused by future reservoir collapses.

Height estimation from single aerial imagery using contrastive learning based multi-scale refinement network

Height map estimation from a single aerial image plays a crucial role in localization,mapping,and 3D object detection.Deep convolutional neural networks have been used to predict height information from single-view remote sensing images,but these methods rely on large volumes of training data and often overlook geometric features present in orthographic images.To address these issues,this study proposes a gradient-based self-supervised learning network with momentum contrastive loss to extract geometric information from non-labeled images in the pretraining stage.Additionally,novel local implicit constraint layers are used at multiple decoding stages in the proposed supervised network to refine high-resolution features in height estimation.The structural-aware loss is also applied to improve the robustness of the network to positional shift and minor structural changes along the boundary area.Experimental evaluation on the ISPRS benchmark datasets shows that the proposed method outperforms other baseline networks,with minimum MAE and RMSE of 0.116 and 0.289 for the Vaihingen dataset and 0.077 and 0.481 for the Potsdam dataset,respectively.The proposed method also shows around threefold data efficiency improvements on the Potsdam dataset and domain generalization on the Enschede datasets.These results demonstrate the effectiveness of the proposed method in height map estimation from single-view remote sensing images.

Geometric-constrained multi-view image matching method based on semi-global optimization

Targeting at a reliable image matching of multiple remote sensing images for the generation of digital surface models,this paper presents a geometric-constrained multi-view image matching method,based on an energy minimization framework.By employing a geometrical constraint,the cost value of the energy function was calculated from multiple images,and the cost value was aggregated in an image space using a semi-global optimization approach.A homography transform parameter calculation method is proposed for fast calculation of projection pixel on each image when calculation cost values.It is based on the known interior orientation parameters,exterior orientation parameters,and a given elevation value.For an efficient and reliable processing of multiple remote sensing images,the proposed matching method was performed via a coarse-to-fine strategy through image pyramid.Three sets of airborne remote sensing images were used to evaluate the performance of the proposed method.Results reveal that the multi-view image matching can improve matching reliability.Moreover,the experimental results show that the proposed method performs better than traditional methods.