Water bodies are a fundamental element of urban ecosystems, and water mapping is critical for urban and landscape planning and management. Remote sensing has increasingly been used for water mapping in rural areas; however, when applied to urban areas, this spatially- explicit approach is a challenging task due to the fact that the water bodies are often of a small size and spectral confusion is common between water and the complex features in the urban environment. Water indexes are the most common method of water extraction at the pixel level. More recently, spectral mixture analysis (SMA) has been widely employed in analyzing the urban environment at the subpixel level. The objective of this study is to develop an automatic subpixel water mapping method (ASWM) which can achieve a high accuracy in urban areas. Specifically, we first apply a water index for the automatic extraction of mixed land-water pixels, and the pure water pixels that are generated in this process are exported as the final result. Secondly, the SMA technique is applied to the mixed land-water pixels for water abundance estimation. As for obtaining the most representative endmembers, we propose an adaptive iterative endmember selection method based on the spatial similarity of adjacent ground surfaces. One classical water index method (the modified normalized difference water index (MNDWI)), a pixel-level target detection method (constrained energy minimization (CEM)), and two widely used SMA methods (fully constrained least squares (FCLS) and multiple endmember spectral mixture analysis (MESMA)) were chosen for the water mapping comparison in the experiments. The results indicate that the proposed ASWM was able to detect water pixels more efficiency than other unsupervised water extraction methods, and the water fractions estimated by the proposed ASWM method correspond closely to the reference fractions with the slopes of 0.97, 1.02, 1.04, and 0.98 and the R-squared values of 0.9454, 0.9486, 0.9665, and 0.9607 in regression analysis corresponding to different test regions. In the quantitative accuracy assessment, the ASWM method shows the best performance in water mapping with the mean kappa coefficient of 0.862, mean producer’s accuracy of 82.8%, and mean user’s accuracy of 91.8% for test regions.

/pdf/automated-subpixel-surface-water-mapping-from-heterogeneous-29m5s6qq16.pdf

Automated Subpixel Surface Water Mapping from Heterogeneous Urban Environments Using Landsat 8 OLI Imagery

Over the past decade, the incorporation of spatial information has drawn increasing attention in multispectral and hyperspectral data analysis. In particular, the property of spatial autocorrelation among pixels has shown great potential for improving understanding of remotely sensed imagery. In this paper, we provide a comprehensive review of the state-of-the-art techniques in incorporating spatial information in image classification and spectral unmixing. For image classification, spatial information is accounted for in the stages of pre-classification, sample selection, classifiers, post-classification, and accuracy assessment. With regards to spectral unmixing, spatial information is discussed in the context of endmember extraction, selection of endmember combinations, and abundance estimation. Finally, a perspective on future research directions for advancing spatial-spectral methods is offered.

https://tandfonline.com/doi/pdf/10.1080/01431161.2016.1204032

A survey of methods incorporating spatial information in image classification and spectral unmixing

Abstract The estimation of water storage variations in lakes is essential for water resource management activities in a region. In areas of ungauged or poorly gauged water bodies, satellite altimetry acts as a powerful tool to measure changes in surface water level. Remote sensing provides images of temporal coastline variations, and a combination of both measurement techniques can indicate a change in water volume. In this study variations of the water level of the Aral Sea were computed for the period 2002–2011 from the combination of radar and laser satellite altimetry data sets over the lake. The estimated water levels were analyzed in combination with coastline changes from Landsat images in order to obtain a comprehensive picture of the lake water changes. In addition to these geometrical observations temporal changes of water storage in the lake and its surrounding were computed from GRACE satellite gravimetry. With respect to its temporal evolution the GRACE results agree very well with the geometrical changes determined from altimetry and Landsat. The advancing desiccation until the beginning of 2009 and a subsequent abrupt gain of water in 2009–2010 due to exceptional discharge from Amu Darya can clearly be identified in all data sets.

Inter-annual Water Storage Changes in the Aral Sea from Multi-mission Satellite Altimetry, Remote Sensing, and GRACE Satellite Gravimetry

. Timely and accurate monitoring of disruptions to the electricity grid, including the magnitude, spatial extent, timing, and duration of net power losses, is needed to improve situational awareness of disaster response and long-term recovery efforts. Satellite-derived Nighttime Lights (NTL) provide an indication of human activity patterns and have been successfully used to monitor disaster-related power outages. The global 500 m spatial resolution National Aeronautics and Space Administration (NASA) Black Marble NTL daily standard product suite (VNP46) is generated from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the NASA/National Oceanic and Atmospheric Administration (NOAA) Suomi National Polar-orbiting Partnership (Suomi- NPP) satellite, which began operations on 19 January 2012. With its improvements in product accuracy (including critical atmospheric and BRDF correction routines), the VIIRS daily Black Mable product enables systematic monitoring of outage conditions across all stages of the disaster management cycle.

/pdf/monitoring-disaster-related-power-outages-using-nasa-black-3pmqu63kks.pdf

Monitoring Disaster-Related Power Outages Using NASA Black Marble Nighttime Light Product

Information on the temporal variation of surface water area of reservoirs is fundamental for water resource management and is often monitored by satellite remote sensing. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery is an attractive data source for the routine monitoring of reservoirs, however, the accuracy is often limited due to the negative impacts associated with its coarse spatial resolution and the effects of cloud contamination. Methods have been proposed to solve these two problems independently but it remains challenging to address both problems simultaneously. To overcome this, this paper proposes a new approach that aims to monitor reservoir surface water area variations accurately and timely from daily MODIS images by exploring sub-pixel scale information. The proposed approach used estimates of reservoir water areas obtained from cloud-free and relatively fine spatial resolution Landsat images and water fraction images by spectral unmixing of coarse MODIS imagery as reference data. For each MODIS pixel, these reference reservoir water areas and their corresponding pixel water fractions were used to construct a linear regression equation, which in turn may be applied to predict the time series of reservoir water areas from daily MODIS water fraction images. The proposed approach was assessed with 21 reservoirs, where the correlation coefficients between reservoir water areas predicted by the common pixel-based analysis method and altimetry water levels were all less than 0.5. With the proposed sub-pixel analysis method, the resultant correlation coefficients were much improved, with eleven values larger than 0.5 including six values larger than 0.8 and the highest value of 0.94. The results show that the proposed sub-pixel analysis method is superior to the pixel based analysis method. The proposed method makes it possible to directly estimate the whole reservoir water area from, potentially, an individual cloud-free MODIS pixel, and is a promising way to improve the accuracy in the usability of MODIS images for the monitoring of reservoir surface water area variations.

Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information

Locally adaptive unmixing method for lake-water area extraction based on MODIS 250 m bands

The Hapke two-layer medium model is an efficient way of simulating the spectra of dusty leaves. However, the simulation accuracy is low when the amount of dustfall is small. To solve this problem, we introduced the dust coverage factor and the linear spectral mixing model, to improve the accuracy of the Hapke two-layer medium model. Firstly, based on the assumption of spherical dust particles, the arrangement and accumulation mode of the particles were set, and the coverage factor and accumulation thickness of particles in the leaf area were calculated. Then, the coverage factor was used as an abundance. Endmembers were the spectra of dust-free leaves (measured) and dust-covered leaves (simulated by model), and the final simulated spectra were calculated using linear spectral mixing theory. This study presents the following findings: (1) When the coverage factor was calculated using the exponential model, the maximum difference between the corrected simulated spectra and the measured spectra was 3.4%, and the maximum difference between the original simulated spectra and the measured spectra was 15.2%. The accuracy of the corrected spectra is much higher than that of the original simulated spectra. (2) In this study, the physical thickness and optical thickness calculated by the Hapke two-layer medium model are equivalent, which is quite different from the actual dust accumulation. When the linear spectral mixing model is introduced, to modify the simulation value when the number of dust particles accumulated is less than one layer, the spectral endmember value of the simulated dust leaf is replaced by the simulation spectrum when the number of dust particles accumulated is exactly one layer. The calculated correction spectrum has high rationality and credibility. This finding may be beneficial for monitoring amounts of dustfall accurately using remote sensing in mining areas.

/pdf/spectral-simulation-and-error-analysis-of-dusty-leaves-by-1nhsb0ag.pdf

Spectral Simulation and Error Analysis of Dusty Leaves by Fusing the Hapke Two-Layer Medium Model and the Linear Spectral Mixing Model

Path planning is widely used in many domains, and it is crucial for the advancement of map navigation, autonomous driving, and robot path planning. However, existing path planning methods have certain limitations for complex field scenes with undulating terrain and diverse landcover types. This paper presents an energy-efficient 3D path planning algorithm based on an improved A* algorithm and the particle swarm algorithm in complex field scenes. The evaluation function of the A* algorithm was improved to be suitable for complex field scenes. The slope parameter and friction coefficient were respectively used in the evaluation function to represent different terrain features and landcover types. The selection of expanding nodes in the algorithm depends not only on the minimum distance but also on the minimum consumption cost. Furthermore, the turning radius factor and slope threshold factor of vehicles were added to the definition of impassable points in the improved A* algorithm, so that the accessibility of path planning could be guaranteed by excluding some bends and steep slopes. To meet the requirements for multi-target path planning, the improved A* algorithm was used as the fitness function of the particle swarm algorithm to solve the traveling salesman problem. The experimental results showed that the proposed algorithm is capable of multi-target path planning in complex field scenes. Furthermore, the path planned by this algorithm is more passable and more energy efficient. In this experimental environment model, the average energy-saving efficiency of the path planned by the improved algorithm is 14.7% compared to the traditional A* algorithm. This would be beneficial to the development of ecotourism and geological exploration.

https://www.mdpi.com/2220-9964/12/2/82/pdf?version=1676951712

Energy-Efficient 3D Path Planning for Complex Field Scenes Using the Digital Model with Landcover and Terrain

Unmanned aerial vehicle (UAV) oblique photography technology is widely used in a variety of fields because of its excellent efficiency, realism, and low cost of manufacturing. However, due to the influence of lighting, occlusion, weak textures, and other factors in aerial images, the modeling results can have the problem of an incorrect structure that is inconsistent with the real scene. The edge line of a building is the main external expression of its structure. Whether the edge line is straight or not will directly affect the realism of the building, so the restoration of the edge line can improve the realism of the building. In this study, we proposed and developed a method for the restoration of the edge line of a 3D building model based on triangular mesh cutting. Firstly, the feature line of the edge line was drawn using human–computer interaction, and axis-aligned bounding box (AABB) collision detection was carried out around the feature line to determine the triangular patches to be cut. Then, the triangular cutting algorithm was used to cut the triangular patches projected onto the plane. Finally, the structure and texture of the 3D building model were reconstructed. This method allowed us to actualize the physical separation of continuous triangulation; the triangulation around the edge line was cut, and the plane was fitted. This method was able to improve cutting accuracy and edge flatness and enhance the edge features of buildings and the rendering quality of models. The experimental results showed that the edge restoration method proposed in this paper is reliable and that it can effectively improve the building rendering effect of a 3D building model based on UAV oblique photography and can also enhance the realism of the model.

https://www.mdpi.com/2076-3417/12/24/12911/pdf?version=1671524527

Edge Restoration of a 3D Building Model Based on Oblique Photography

A Physics-Constrained Data-Driven Workflow for Predicting Bottom Hole Pressure Using a Hybrid Model of Artificial Neural Network and Particle Swarm Optimization

Baodong Ma

Papers

Locally adaptive unmixing method for lake-water area extraction based on MODIS 250 m bands

Spectral Simulation and Error Analysis of Dusty Leaves by Fusing the Hapke Two-Layer Medium Model and the Linear Spectral Mixing Model

Energy-Efficient 3D Path Planning for Complex Field Scenes Using the Digital Model with Landcover and Terrain

Edge Restoration of a 3D Building Model Based on Oblique Photography

A Physics-Constrained Data-Driven Workflow for Predicting Bottom Hole Pressure Using a Hybrid Model of Artificial Neural Network and Particle Swarm Optimization