Non-flat Areas Research Articles

The phenological soil water balance: a proposed model for estimating water resources for an entire watershed using crop coefficients

The aim of the research was to develop a model that would allow the application, at a watershed scale, of the hydrological soil water balance model proposed by the FAO for the dosing of irrigation water in agriculture, which uses crop coefficients (Kc) for the calculation of potential crop evapotranspiration (ETc). To be able to assess the water resources of a territory in which there are land uses other than agricultural ones, the application of the proposed model has made it necessary to determine the crop coefficients of the latter. Since crop coefficients vary according to phenological stage, this model was termed ‘phenological soil water balance’. A correction factor for precipitation and potential evapotranspiration, using an acclivity coefficient (i.e., the ratio between the actual area and the projected area), has also been proposed to obtain accurate results even in non-flat areas, which allowed us to consider the actual area of the territory instead of the projected one. The model was applied daily for 7 consecutive years (from 2013 to 2019) in the Santa Maria degli Angeli watershed (Urbino, central Italy) whose area is about 14 km2. The calibration and validation of the model were conducted by comparing the deep percolation computed by the model with baseflow values of the Santa Maria degli Angeli stream obtained by flow measurements made at the closing section of the sample watershed. The results of the model showed that the total values of deep percolation and measured baseflow only differed by 3% in the whole period considered; thus the phenological soil water balance model can be used to accurately estimate water resources and can be applied at different time intervals (daily, monthly, annual, etc.). The structure of the model makes it suitable for application in both small and large watersheds and territories.

Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China.

Landslides are one of the major geohazards threatening human society. The objective of this study was to conduct a landslide hazard susceptibility assessment for Ruijin, Jiangxi, China, and to provide technical support to the local government for implementing disaster reduction and prevention measures. Machine learning approaches, e.g., random forests (RFs) and support vector machines (SVMs) were employed and multiple geo-environmental factors such as land cover, NDVI, landform, rainfall, lithology, and proximity to faults, roads, and rivers, etc., were utilized to achieve our purposes. For categorical factors, three processing approaches were proposed: simple numerical labeling (SNL), weight assignment (WA)-based and frequency ratio (FR)-based. Then 19 geo-environmental factors were respectively converted into raster to constitute three 19-band datasets, i.e., DS1, DS2, and DS3 from three different processes. Then, 155 observed landslides that occurred in the past decades were vectorized, among which 70% were randomly selected to compose a training set (TS1) and the remaining 30% to form a validation set (VS1). A number of non-landslide (no-risk) samples distributed in the whole study area were identified in low slope (<1–3°) zones such as urban areas and croplands, and also added to the TS1 and VS1 in the same ratio. For comparison, we used the FR approach to identify the no-risk samples in both flat and non-flat areas, and merged them into the field-observed landslides to constitute another pair of training and validation sets (TS2 and VS2) using the same ratio of 7:3. The RF algorithm was applied to model the probability of the landslide occurrence using DS1, DS2, and DS3 as predictive variables and TS1 and TS2 for training to obtain the SNL-based, WA-based, and FR-based RF models, respectively. Verified against VS1 and VS2, the three models have similar overall accuracy (OA) and Kappa coefficient (KC), which are 89.61%, 91.47%, and 94.54%, and 0.7926, 0.8299, and 0.8908, respectively. All of them are much better than the three models obtained by SVM algorithm with OA of 81.79%, 82.86%, and 83%, and KC of 0.6337, 0.655, and 0.660. New case verification with the recent 26 landslide events of 2017–2020 revealed that the landslide susceptibility map from WA-based RF modeling was able to properly identify the high and very high susceptibility zones where 23 new landslides had occurred, and performed better than the SNL-based and FR-based RF modeling, though the latter has a slightly higher OA and KC. Hence, we concluded that all three RF models achieve reasonable risk prediction, but WA-based and FR-based RF modeling deserves a recommendation for application elsewhere. The results of this study may serve as reference for the local authorities in prevention and early warning of landslide hazards.

Biogenic supported lipid bilayers as a tool to investigate nano-bio interfaces

Hypothesis: Extracellular Vesicles (EVs) are natural nanosized lipid vesicles involved in most intercellular communication pathways. Given their nature, they represent natural cell membrane models, with intermediate complexity between real and synthetic lipid membranes. Here we compare EVs-derived (EVSLB) and synthetic Supported Lipid Bilayers (SLBs) in the interaction with cationic superparamagnetic iron oxide nanoparticles (SPIONs). The aim is twofold: (i) exploit SPIONs as nanometric probes to investigate the features of EVSLBs as novel biogenic platforms; (ii) contribute at improving the knowledge on the behavior of SPIONs with biological interfaces. Experiments: Quartz Crystal Microbalance, X-ray Reflectivity, Grazing-incidence Small-angle X-ray Scattering, Atomic Force Microscopy, Confocal Microscopy data on SPIONs-EVSLB were systematically compared to those on SPIONs challenging synthetic SLBs, taken as references. Findings: The ensemble of experimental results highlights the much stronger interaction of SPIONs with EVSLBs with respect to synthetic SLBs. This evidence strongly supports the hypotheses on the peculiar structure of EVSLBs, with cushioned non-flat areas and extended exposed surface; in addition, it suggests that these features are relevant in the response of biogenic membranes to nano-objects. These findings contribute to the fundamental knowledge on EVSLBs, key for their development both as biomimetic membranes, or as platforms for biomedical applications.

An image denoising approach based on adaptive nonlocal total variation

In the nonlocal total variation (NLTV) model the constant regularization parameter λ cannot adaptively control the balance between the regularization term and the fidelity term, which may results in over-smoothing and the more losing image details in non-flat areas when λ is small, or insufficient noise removal in flat areas when λ is large. It is better that λ has different values according to the characteristics of image areas. In this paper, we introduce an adaptive regularization parameter λ(x) which can recognize flat areas and non-flat areas of an image and propose an improved NLTV model by replacing regularization parameter λ in NLTV model with the function λ(x). In addition, we calculate the similarity weight function of our model from the pre-filtered image to reduce the influence of noise on it. Experimental results demonstrate our approach outperforms some existing methods in terms of objective criteria and subjective visual perception.

Integrating Geophysical and Multispectral Data to Delineate Homogeneous Management Zones within a Vineyard in Northern Italy.

Soil electrical conductivity (EC) maps obtained through proximal soil sensing (i.e., geophysical data) are usually considered to delineate homogeneous site-specific management zones (SSMZ), used in Precision Agriculture to improve crop production. The recent literature recommends the integration of geophysical soil monitoring data with crop information acquired through multispectral (VIS-NIR) imagery. In non-flat areas, where topography can influence the soil water conditions and consequently the crop water status and the crop yield, considering topography data together with soil and crop data may improve the SSMZ delineation. The objective of this study was the fusion of EC and VIS-NIR data to delineate SSMZs in a rain-fed vineyard located in Northern Italy (Franciacorta), and the assessment of the obtained SSMZ map through the comparison with data acquired by a thermal infrared (TIR) survey carried out during a hot and dry period of the 2017 agricultural season. Data integration is performed by applying multivariate statistical methods (i.e., Principal Component Analysis). The results show that the combined use of soil, topography and crop information improves the SSMZ delineation. Indeed, the correspondence between the SSMZ map and the CWSI map derived from TIR imagery was enhanced by including the NDVI information.

Deep Reconstruction of Least Significant Bits for Bit-Depth Expansion.

Bit-depth expansion (BDE) is important for displaying a low bit-depth image in a high bit-depth monitor. Current BDE algorithms often utilize traditional methods to fill the missing least significant bits and suffer from multiple kinds of perceivable artifacts. In this paper, we present a deep residual network-based method for BDE. Based on the different properties of flat and non-flat areas, two channels are proposed to reconstruct these two kinds of areas, respectively. Moreover, a simple yet efficient local adaptive adjustment preprocessing is presented in the flat-area-channel. By combining the benefits of both the traditional debanding strategy and network-based reconstruction, the proposed method can further promote the subjective quality of the flat area. Experimental results on several image sets demonstrate that the proposed BDE network can obtain favorable visual quality as well as decent quantitative performance.

Original and modified technique of tie-over dressing: Method and application in burn patients

In burn patients, meshed split thickness skin grafts are commonly used on large and non-flat areas whom immobilization is difficult to achieve. The frequent mobilizations of burn patients can make the graft slip and prevent the revascularization and therefore the taking of the skin graft. In order to prevent this pitfall, we modified and adapted the tie-over dressing procedure. The giant running tie-over dressing enables large skin grafts to be applied to their wound bed and therefore helps revascularization. Some cautions are necessary in order to avoid any infection. This original and easy-to-perform procedure answers to the difficulties of large split-thickness skin grafts in burn patients.

Significant effect of topographic normalization of airborne LiDAR data on the retrieval of plant area index profile in mountainous forests

As an important metric for describing vertical forest structure, the plant area index (PAI) profile is used for many applications including biomass estimation and wildlife habitat assessment. PAI profiles can be estimated with the vertically resolved gap fraction from airborne LiDAR data. Most research utilizes a height normalization algorithm to retrieve local or relative height by assuming the terrain to be flat. However, for many forests this assumption is not valid. In this research, the effect of topographic normalization of airborne LiDAR data on the retrieval of PAI profile was studied in a mountainous forest area in Germany. Results show that, although individual tree height may be retained after topographic normalization, the spatial arrangement of trees is changed. Specifically, topographic normalization vertically condenses and distorts the PAI profile, which consequently alters the distribution pattern of plant area density in space. This effect becomes more evident as the slope increases. Furthermore, topographic normalization may also undermine the complexity (i.e., canopy layer number and entropy) of the PAI profile. The decrease in PAI profile complexity is not solely determined by local topography, but is determined by the interaction between local topography and the spatial distribution of each tree. This research demonstrates that when calculating the PAI profile from airborne LiDAR data, local topography needs to be taken into account. We therefore suggest that for ecological applications, such as vertical forest structure analysis and modeling of biodiversity, topographic normalization should not be applied in non-flat areas when using LiDAR data.

DISTRIBUTED DIMENSONALITY-BASED RENDERING OF LIDAR POINT CLOUDS

Abstract. Mobile Mapping Systems (MMS) are now commonly acquiring lidar scans of urban environments for an increasing number of applications such as 3D reconstruction and mapping, urban planning, urban furniture monitoring, practicability assessment for persons with reduced mobility (PRM)... MMS acquisitions are usually huge enough to incur a usability bottleneck for the increasing number of non-expert user that are not trained to process and visualize these huge datasets through specific softwares. A vast majority of their current need is for a simple 2D visualization that is both legible on screen and printable on a static 2D medium, while still conveying the understanding of the 3D scene and minimizing the disturbance of the lidar acquisition geometry (such as lidar shadows). The users that motivated this research are, by law, bound to precisely georeference underground networks for which they currently have schematics with no or poor absolute georeferencing. A solution that may fit their needs is thus a 2D visualization of the MMS dataset that they could easily interpret and on which they could accurately match features with their user datasets they would like to georeference. Our main contribution is two-fold. First, we propose a 3D point cloud stylization for 2D static visualization that leverages a Principal Component Analysis (PCA)-like local geometry analysis. By skipping the usual and error-prone estimation of a ground elevation, this rendering is thus robust to non-flat areas and has no hard-to-tune parameters such as height thresholds. Second, we implemented the corresponding rendering pipeline so that it can scale up to arbitrary large datasets by leveraging the Spark framework and its Resilient Distributed Dataset (RDD) and Dataframe abstractions.

Self‐adaptive 3D pairwise view registration by surface‐fitting and hill‐climbing method

A self‐adaptive 3D pairwise view registration method is presented. Coarse registration of neighboring views is achieved according to their common continuous nonflat areas. These areas are calculated by local least‐square (LS) surface fitting. Then, the principle of point‐to‐surface projection is used to search for the corresponding points, where the local moving least‐square (MLS) surface is employed. Finally, multidimensional optimization based on the technique of hill‐climbing is performed to refine the coarse registration. Experimental results show that this method gives good noise immunity and requires simple computation process and very few manual interventions. Thus it is suitable for large‐scale free‐form surfaces lacking obvious geometric characteristics. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.

Relationship between atmospheric corrections and training-site strategy with respect to accuracy of greenhouse detection process from very high resolution imagery

Frequently, satellite images that are acquired to extract a target surface are atmospherically corrected prior to the detection process. Thus, the unification of measure units is achieved, and atmospheric effects are removed from various imagery sources or taken at different dates. In this paper, four increasing levels of atmospheric corrections are applied (Top-Of-Atmosphere transformation: TOA; Apparent Reflectance Model: ARM; Flat Areas Model: FAM; Non-Flat Areas Model: NFAM). Then, the classification process is carried out using two strategies of training-site definitions (statistically purified and crude training sites) and two satellite imagery sources (QuickBird and Ikonos). Three-way Analysis of Variance (ANOVA) tests and Fisher's least-significant difference tests are included in quality classification assessment, based on four accuracy indexes. Two images from both remote sensors are orthorectified, and then it is checked that all selected atmospheric correction levels have significantly different influences on the statistics of both orthoimages. Taking into account the conditions established in this work, it is concluded that a lower atmospheric correction level would be preferred because it does not present significantly worse results than other levels considered. Training sites would not be statistically purified, and QuickBird or Ikonos would be chosen, depending on the aspect of the greenhouse detection accuracy preferred.

Digital Video Watermarking in P-Frames With Controlled Video Bit-Rate Increase

Most video watermarking algorithms embed the watermark in I-frames, but refrain from embedding in P- and B-frames, which are highly compressed by motion compensation. However, P-frames appear more frequently in the compressed video and their watermarking capacity should be exploited, despite the fact that embedding the watermark in P-frames can increase the video bit rate significantly. This paper gives a detailed overview of a common approach for embedding the watermark in I-frames. This common approach is adopted to use P-frames for video watermarking. We show that by limiting the watermark to nonzero-quantized AC residuals in P-frames, the video bit-rate increase can be held to reasonable values. Since the nonzero-quantized AC residuals in P-frames correspond to nonflat areas that are in motion, temporal and texture masking are exploited at the same time. We also propose embedding the watermark in nonzero quantized AC residuals with spatial masking capacity in I-frames. Since the locations of the nonzero-quantized AC residuals is lost after decoding, we develop a watermark detection algorithm that does not depend on this knowledge. Our video watermark detection algorithm has controllable performance. We demonstrate the robustness of our proposed algorithm to several different attacks.

地上据置型レーザスキャナを用いた地形計測のためのフィルタリング手法に関する研究

Laser scanner has been receiving more attention as a useful tool for real-time 3D data acquisition, and many applications such as city modeling, DTM generation, monitoring electrical power lines and detection of forest areas were proposed. However, effective filtering for distinguish on- and off-terrain points from point cloud 3D data collected by airborne laser scanner is still issues.The paper describes a robust filtering method from a view point of topographic surveying using a terrestrial laser scanner. The method is based on flatness within 0.3×0.3m. Flat area (ground surface, wall of buildings, etc.) and non-flat area (trees, bushes, windows, sky, etc.) are classified using surface flatness, and non-flat areas are interpolated using morphological procedure. The filtering methods show very robust results, and the most remarkable point of this filtering method is its ability to obtain break-line.

GPS and GIS to assess the accuracy of land cover maps in mountain areas

The creation of thematic maps by photointerpretation of aerial photographs of non-flat areas should rely on orthocorrected images. When the application of orthocorrection is not possible (lack of elevation data or suitable software) the question is posed as to which alternative method minimises the effect of relief in the displacement of the category boundaries. The work described here applies a method for assessing the spatial accuracy of linear features in thematic maps to assess the performances, in mountain areas, of two mapping methods which do not include orthocorrection. The test was performed on two study areas in order to determine the best method for producing land cover maps on other 9 study areas. The first method included the rectification of aerial photographs with a rubber sheet transformation, on-screen photointerpretation and digitising. The second method included traditional photointerpretation, tracing the boundaries on base maps and digitising. GPS in kinematic differential mode was used to collect reference data for the assessment of spatial accuracy. The assessment showed that the second method resulted more accurate in both study areas. Transactions on Information and Communications Technologies vol 18, © 1998 WIT Press, www.witpress.com, ISSN 1743-3517