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Remote Sens., Volume 15, Issue 5 (March-1 2023) – 303 articles

Cover Story (view full-size image): The Echus-Kasei region on Mars has been exposed to different episodic volcanic, fluvial, and glacial events in the Amazonian epoch. This work uses remote sensing tools to investigate the Echus-Kasei region and map well-preserved subterranean layers beneath a lava fan that formed about 59 ± 4 Ma over the Echus Chasma region. Analysing observations from the SHAllow RADar (SHARAD) instrument aboard the Mars Reconnaissance Orbiter (MRO), we discovered the presence of subterranean reflectors at a depth of 30 to 79 m, a long chain of pits formed by the collapse of a lava tube and lava vents. These potentially enclosed environments may be suitable for life and the future human exploration of Mars, as they offer shelter from radiation and thermal extremes and may provide access to preserved water reservoirs. View this paper
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20 pages, 51152 KiB  
Article
Tropical Dry Forest Dynamics Explained by Topographic and Anthropogenic Factors: A Case Study in Mexico
by Yan Gao, Jonathan V. Solórzano, Ronald C. Estoque and Shiro Tsuyuzaki
Remote Sens. 2023, 15(5), 1471; https://doi.org/10.3390/rs15051471 - 06 Mar 2023
Cited by 1 | Viewed by 1748
Abstract
Tropical dry forest is one of the most threatened ecosystems, and it is disappearing at an alarming rate. Shifting cultivation is commonly cited as a driver of tropical dry forest loss, although it helps to maintain the forest coverage but with less density. [...] Read more.
Tropical dry forest is one of the most threatened ecosystems, and it is disappearing at an alarming rate. Shifting cultivation is commonly cited as a driver of tropical dry forest loss, although it helps to maintain the forest coverage but with less density. We investigated tropical dry forest dynamics and their contributing factors to find out if there is an equilibrium between these two processes. We classified multi-temporal Sentinel-2A images with machine learning algorithms and used a logistic regression model to associate topographic, anthropogenic, and land tenure variables as plausible factors in the dynamics. We carried out an accuracy assessment of the detected changes in loss and gain considering the imbalance in area proportion between the change classes and the persistence classes. We estimated a 1.4% annual loss rate and a 0.7% annual gain rate in tropical dry forest and found that the topographic variable of slope and the anthropogenic variable of distance to roads helped explain the occurrence probability of both tropical forest loss and tropical forest gain. Since the area estimation yielded a wide confidence interval for both tropical forest loss and gain despite the measures that we took to counterbalance the disproportion in areas, we cannot conclude that the loss process was more intense than the gain process, but rather that there was an equilibrium in tropical dry forest dynamics under the influence of shifting cultivation. Full article
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19 pages, 8582 KiB  
Article
Recognition of Area without Understory Vegetation Based on the RGB-UAV Ultra-High Resolution Images in Red Soil Erosion Area
by Chunming Han, Jia Liu, Yixing Ding, Peng Chai and Xiaolin Bian
Remote Sens. 2023, 15(5), 1470; https://doi.org/10.3390/rs15051470 - 06 Mar 2023
Cited by 1 | Viewed by 1491
Abstract
Understory vegetation plays an important ecological role in maintaining the diversity of the ecosystem, the stability of ecosystem services, and the accumulation of nutrient elements, as an important part of a forest ecosystem. In this study, a new method of recognizing areas without [...] Read more.
Understory vegetation plays an important ecological role in maintaining the diversity of the ecosystem, the stability of ecosystem services, and the accumulation of nutrient elements, as an important part of a forest ecosystem. In this study, a new method of recognizing areas without understory vegetation is proposed. The method makes full use of the advantages of spectral characteristics, spatial structure information and temporal resolution of UAV images, and can quickly and simply distinguish understory, without vegetation cover. Combined with fractional vegetation coverage (FVC) and vegetation dispersion, understory, with no vegetation area, can be successfully recognized, and the Pr, Re and F1 are all above 85%. The proportion of bare soil under forest in our study area is 20.40%, 19.98% and even 41.69%. The study area is located in Changting County, Fujian Province, which is a typical red soil area in China where serious soil erosion is taking place in the forest. The method provides a promising, quick and economic way of estimating understory vegetation coverage with high spatial accuracy. Full article
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31 pages, 14235 KiB  
Article
Effects of Wind Wave Spectra, Non-Gaussianity, and Swell on the Prediction of Ocean Microwave Backscatter with Facet Two-Scale Model
by Yuqi Wu, Chenqing Fan, Qiushuang Yan, Junmin Meng, Tianran Song and Jie Zhang
Remote Sens. 2023, 15(5), 1469; https://doi.org/10.3390/rs15051469 - 06 Mar 2023
Cited by 2 | Viewed by 1363
Abstract
The image intensity of high-resolution synthetic aperture radar (SAR) is closely related to the facet scattering distribution. In this paper, the effects of wind wave spectra, non-Gaussianity of the sea surface, and swell on the distribution of the facet normalized radar cross section [...] Read more.
The image intensity of high-resolution synthetic aperture radar (SAR) is closely related to the facet scattering distribution. In this paper, the effects of wind wave spectra, non-Gaussianity of the sea surface, and swell on the distribution of the facet normalized radar cross section (NRCS) simulated by the facet two-scale model (TSM) are analyzed by comparing the simulated results with the Sentinel-1 SAR data, the Advanced Scatterometer (ASCAT) data, and the geophysical model function (GMF) at the wind speed range of 3–16 m/s, the wind direction range of 0°–360°, and the incidence angle range of 30°–50° under VV and HH polarizations. The results show that the Apel spectrum achieves a more consistent mean NRCS and NRCS distribution with the reference data at low incidence angles, while the composite spectra perform better at high incidence angles under VV polarization. Under HH polarization, the Apel spectrum always has a better performance. The upwind–downwind asymmetry of backscattering can be predicted well by the modified TSM, which is constructed by incorporating bispectrum correction into the conventional TSM. The distribution of the scattering simulated by the modified TSM deviates from the Gaussian distribution significantly, which is in good agreement with the Sentinel-1 data. Additionally, the introduction of swell widens the spread of the NRCS distribution, and the fluctuation range of the NRCS profile considering swell is larger than that without swell. All these changes caused by the introduction of swell make the distribution of the facet scattering more consistent with the Sentinel-1 data. Moreover, the scattering image patterns and scattering image spectrum of the Sentinel-1 data can be successfully simulated at various sea states with the consideration of swell. Full article
(This article belongs to the Section Ocean Remote Sensing)
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16 pages, 12527 KiB  
Technical Note
Analysis of Seismic Impact on Hailuogou Glacier after the 2022 Luding Ms 6.8 Earthquake, China, Using SAR Offset Tracking Technology
by Weile Li, Junyi Chen, Huiyan Lu, Congwei Yu, Yunfeng Shan, Zhigang Li, Xiujun Dong and Qiang Xu
Remote Sens. 2023, 15(5), 1468; https://doi.org/10.3390/rs15051468 - 06 Mar 2023
Cited by 3 | Viewed by 1654
Abstract
An Ms 6.8 earthquake struck Luding County, Ganzi Prefecture, Sichuan Province on 5 September 2022, with the epicenter about 10 km away from Hailuogou Glacier. How Hailuogou Glacier was affected by the earthquake was of major concern to society. Sentinel-1 SAR satellite imaging [...] Read more.
An Ms 6.8 earthquake struck Luding County, Ganzi Prefecture, Sichuan Province on 5 September 2022, with the epicenter about 10 km away from Hailuogou Glacier. How Hailuogou Glacier was affected by the earthquake was of major concern to society. Sentinel-1 SAR satellite imaging was used to monitor the glacier surface velocity during different periods before and after the Luding earthquake based on pixel offset tracking (POT) technology, which applies a feature-tracking algorithm to overcome the phase co-registration problems commonly encountered in large displacement monitoring. The results indicated that the velocity had a positive correlation with the average daily maximum temperature and the slope gradient on the small-slope surfaces. The correlation was not apparent on the steeper surfaces, which corresponded spatially with the identified ice avalanche region in the Planet images. It was deduced that this may be because of the occurrence of ice avalanches on surfaces steeper than 25°, or that the narrower front channel impeded the glacier’s movement. The Luding earthquake did not cause a significant increase in the velocity of Hailuogou Glacier within a large range, but it disturbed the front area of the ice cascade, where the maximum velocity reached 2.5 m/d. Although the possibility of directly-induced destruction by ice avalanches after the earthquake was low, and the buffering in the downstream glacier tongue further reduced the risk of ice avalanches, the risk of some secondary hazards such as debris flow increased. The proposed method in this study might be the most efficient in monitoring and evaluating the effects of strong earthquakes on glaciers because it would not be limited by undesirable weather or traffic blockage. Full article
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16 pages, 8000 KiB  
Article
Spatial and Temporal Evolution Characteristics of the Salween River Delta from 1973 to 2021
by Aoyang He, Jiangcheng Huang, Zhengbao Sun, Jingyi Zhou and Cheng Yang
Remote Sens. 2023, 15(5), 1467; https://doi.org/10.3390/rs15051467 - 06 Mar 2023
Cited by 2 | Viewed by 3085
Abstract
We obtained sixteen clear-sky remote sensing images of Landsat series data from 1973 to 2021 and extracted continental and island coastlines of the Salween River Delta based on the Modified Normalized Difference Water Index (MNDWI) and visual interpretation correction. We determined the overall [...] Read more.
We obtained sixteen clear-sky remote sensing images of Landsat series data from 1973 to 2021 and extracted continental and island coastlines of the Salween River Delta based on the Modified Normalized Difference Water Index (MNDWI) and visual interpretation correction. We determined the overall evolution of coastlines with statistical and superposition analysis and applied the Digital Shoreline Analysis System (DSAS) to summarize the spatial and temporal evolution process and characteristics in the past 50 years. Experimental results show that (1) the overall change of the coastline was more rapid on the island than on the continent, and on the Indian Ocean side than on the continental side, (2) the total area of the island increased by 91.16 km2 from 1973 to 2021, the area of Bilu Island increased by 50.38 km2, the length of the continental coastline decreased by 0.39 km, and the length of the coastline of the Bilu Island increased by 6.43 km, (3) the Linear Regression Rate (LRR) were: 4.69 m/yr for the total coastline, 1.06 and −2.07 m/yr, respectively, for the western and southern branches of the continental coastline, and 0.83 and 21.52 m/yr, respectively, for the continental and Indian Ocean sides of Bilu Island, and (4) the dominant process in the Salween River Delta was accretion, with an overall accretion area of about 10 km2, and an unstable accretion rate. Full article
(This article belongs to the Special Issue Earth Observation of Study on Coastal Geomorphic Evolution)
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4 pages, 203 KiB  
Editorial
Editorial for Special Issue: “Remote Sensing of Hydrological Processes: Modelling and Applications”
by Sandra G. García-Galiano, Fulgencio Cánovas-García and Juan Diego Giraldo-Osorio
Remote Sens. 2023, 15(5), 1466; https://doi.org/10.3390/rs15051466 - 06 Mar 2023
Cited by 1 | Viewed by 1014
Abstract
Improvements in satellite remote sensing techniques have allowed the development of several platforms that are able to capture multitemporal data with a wide range of spatial and temporal resolutions [...] Full article
(This article belongs to the Special Issue Remote Sensing of Hydrological Processes: Modelling and Applications)
21 pages, 45090 KiB  
Article
Evaluating Characteristics of an Active Coastal Spreading Area Combining Geophysical Data with Satellite, Aerial, and Unmanned Aerial Vehicles Images
by Emanuele Colica, Luciano Galone, Sebastiano D’Amico, Adam Gauci, Roberto Iannucci, Salvatore Martino, Davide Pistillo, Peter Iregbeyen and Gianluca Valentino
Remote Sens. 2023, 15(5), 1465; https://doi.org/10.3390/rs15051465 - 06 Mar 2023
Cited by 9 | Viewed by 1600
Abstract
The northern region of the Maltese archipelago is experiencing lateral spreading landslide processes. This region is characterized by cliffs with a hard coralline limestone outcropping layer sitting on a thick layer of clay. Such a geological configuration causes coastal instability that results in [...] Read more.
The northern region of the Maltese archipelago is experiencing lateral spreading landslide processes. This region is characterized by cliffs with a hard coralline limestone outcropping layer sitting on a thick layer of clay. Such a geological configuration causes coastal instability that results in lateral spreading which predispose to rockfalls and topplings all over the cliff slopes. The aim of this research was to develop a methodology for evaluating cliff erosion/retreat using the integration of geomatics and geophysical techniques. Starting from a 3D digital model of the Selmun promontory, generated by unmanned aerial vehicle (UAV) photogrammetry, it was possible to map the fractures and conduct geophysical measurements such as electrical resistivity tomography and ground penetrating radar for the identification and mapping of vertical fractures affecting the hard coralline limestone plateau, and to create a 3D geological model of the study area. In addition to this, high-accuracy orthophotos from UAV that were captured between 1957 and 2021 were georeferenced into a GIS and compared to aerial and satellite images. The movement and evolution of boulders and cracks in rocks were then vectorized to highlight, track and quantify the phenomenon through time. The results were used to derive a qualitative assessment of the coastal variations in the geometric properties of the exposed discontinuity surfaces to evaluate the volumes and the stop points of the observed rockfalls. The outcomes of this research were finally imported in a GIS which offers an easy approach for the collection and processing of coastal monitoring data. In principle, such a system could help local authorities to address social, economic and environmental issues of pressing importance as well as facilitate effective planning in view of a risk mitigation strategy. Full article
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18 pages, 10560 KiB  
Article
Local Convergence Index-Based Infrared Small Target Detection against Complex Scenes
by Siying Cao, Jiakun Deng, Junhai Luo, Zhi Li, Junsong Hu and Zhenming Peng
Remote Sens. 2023, 15(5), 1464; https://doi.org/10.3390/rs15051464 - 06 Mar 2023
Cited by 5 | Viewed by 1742
Abstract
Infrared small target detection (ISTD) plays a crucial role in precision guidance, anti-missile interception, and military early-warning systems. Existing approaches suffer from high false alarm rates and low detection rates when detecting dim and small targets in complex scenes. A robust scheme for [...] Read more.
Infrared small target detection (ISTD) plays a crucial role in precision guidance, anti-missile interception, and military early-warning systems. Existing approaches suffer from high false alarm rates and low detection rates when detecting dim and small targets in complex scenes. A robust scheme for automatically detecting infrared small targets is proposed to address this problem. First, a gradient weighting technique with high sensitivity was used for extracting target candidates. Second, a new collection of features based on local convergence index (LCI) filters with a strong representation of dim or arbitrarily shaped targets was extracted for each candidate. Finally, the collective set of features was inputted to a random undersampling boosting classifier (RUSBoost) to discriminate the real targets from false-alarm candidates. Extensive experiments on public datasets NUDT-SIRST and NUAA-SIRST showed that the proposed method achieved competitive performance with state-of-the-art (SOTA) algorithms. It is also important to note that the average processing time was as low as 0.07 s per frame with low time consumption, which is beneficial for practical applications. Full article
(This article belongs to the Special Issue Computer Vision and Machine Learning Application on Earth Observation)
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17 pages, 5493 KiB  
Article
Individual Tree-Crown Detection and Species Identification in Heterogeneous Forests Using Aerial RGB Imagery and Deep Learning
by Mirela Beloiu, Lucca Heinzmann, Nataliia Rehush, Arthur Gessler and Verena C. Griess
Remote Sens. 2023, 15(5), 1463; https://doi.org/10.3390/rs15051463 - 06 Mar 2023
Cited by 12 | Viewed by 22076
Abstract
Automatic identification and mapping of tree species is an essential task in forestry and conservation. However, applications that can geolocate individual trees and identify their species in heterogeneous forests on a large scale are lacking. Here, we assessed the potential of the Convolutional [...] Read more.
Automatic identification and mapping of tree species is an essential task in forestry and conservation. However, applications that can geolocate individual trees and identify their species in heterogeneous forests on a large scale are lacking. Here, we assessed the potential of the Convolutional Neural Network algorithm, Faster R-CNN, which is an efficient end-to-end object detection approach, combined with open-source aerial RGB imagery for the identification and geolocation of tree species in the upper canopy layer of heterogeneous temperate forests. We studied four tree species, i.e., Norway spruce (Picea abies (L.) H. Karst.), silver fir (Abies alba Mill.), Scots pine (Pinus sylvestris L.), and European beech (Fagus sylvatica L.), growing in heterogeneous temperate forests. To fully explore the potential of the approach for tree species identification, we trained single-species and multi-species models. For the single-species models, the average detection accuracy (F1 score) was 0.76. Picea abies was detected with the highest accuracy, with an average F1 of 0.86, followed by A. alba (F1 = 0.84), F. sylvatica (F1 = 0.75), and Pinus sylvestris (F1 = 0.59). Detection accuracy increased in multi-species models for Pinus sylvestris (F1 = 0.92), while it remained the same or decreased slightly for the other species. Model performance was more influenced by site conditions, such as forest stand structure, and less by illumination. Moreover, the misidentification of tree species decreased as the number of species included in the models increased. In conclusion, the presented method can accurately map the location of four individual tree species in heterogeneous forests and may serve as a basis for future inventories and targeted management actions to support more resilient forests. Full article
(This article belongs to the Section Forest Remote Sensing)
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22 pages, 3805 KiB  
Review
Satellite Navigation Signal Authentication in GNSS: A Survey on Technology Evolution, Status, and Perspective for BDS
by Xiao Chen, Ruidan Luo, Ting Liu, Hong Yuan and Haitao Wu
Remote Sens. 2023, 15(5), 1462; https://doi.org/10.3390/rs15051462 - 05 Mar 2023
Cited by 4 | Viewed by 3779
Abstract
As the Global Navigation Satellite System (GNSS) is widely used in all walks of life, the signal structure of satellite navigation is open, and the vulnerability to spoofing attacks is also becoming increasingly prominent, which will seriously affect the credibility of navigation, positioning, [...] Read more.
As the Global Navigation Satellite System (GNSS) is widely used in all walks of life, the signal structure of satellite navigation is open, and the vulnerability to spoofing attacks is also becoming increasingly prominent, which will seriously affect the credibility of navigation, positioning, and timing (PNT) services. Satellite navigation signal authentication technology is an emerging technical means of improving civil signal anti-spoofing on the satellite navigation system side, and it is also an important development direction and research focus of the GNSS. China plans to carry out the design and development of the next-generation Beidou navigation satellite system (BDS), and one of its core goals is to provide more secure and credible PNT services. This paper first expounds on the principles and technical architecture of satellite navigation signal authentication, then clarifies the development history of satellite navigation signal authentication, and finally proposes the BDS authentication service system architecture. It will provide technical support for the construction and development of the follow-up Beidou authentication service. Full article
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19 pages, 24478 KiB  
Article
Mapping Maize Tillage Practices over the Songnen Plain in Northeast China Using GEE Cloud Platform
by Jian Li, Weilin Yu, Jia Du, Kaishan Song, Xiaoyun Xiang, Hua Liu, Yiwei Zhang, Weijian Zhang, Zhi Zheng, Yan Wang and Yue Sun
Remote Sens. 2023, 15(5), 1461; https://doi.org/10.3390/rs15051461 - 05 Mar 2023
Cited by 4 | Viewed by 2261
Abstract
As the population grows, the development of conservation tillage offers a means of promoting the sustainability of agricultural engineering. Remote sensing images with high spatial and temporal resolutions enable the accurate monitoring of conservation tillage on a broad spatial scale, further promoting conservation [...] Read more.
As the population grows, the development of conservation tillage offers a means of promoting the sustainability of agricultural engineering. Remote sensing images with high spatial and temporal resolutions enable the accurate monitoring of conservation tillage on a broad spatial scale, further promoting conservation tillage research. This paper describes using streamlined time series Sentinel-2 images based on the Google Earth Engine (GEE) cloud platform for mapping maize tillage practices in the Songnen Plain region of Northeast China. Based on the correlation with the normalized difference tillage index (NDTI) and maize residue coverage (MRC) data, the optimal time series and streamlining functions in the GEE cloud platform are determined. Estimates of MRC and the mapping of tillage practices in the Songnen Plain for 2019–2022 are then determined using GEE and a previous model. Geostatistical analysis using ArcGIS is applied to analyze the spatial and temporal distribution characteristics of MRC and conservation tillage over the Songnen Plain. The results show that time series images from 20–30 May achieve an r value of 0.902 and an R2 value of 0.8136 when using the median streamlining function. The mean MRC for the study area in 2022 is 2.3%, and an overall upward trend in conservation tillage is observed (from 0.08% in 2019 to 0.25% in 2022). Our analysis shows that MRC monitoring and conservation tillage mapping can be performed over a broad spatial scale using remote sensing technology based on the GEE cloud platform. Spatial and temporal information on farm practices provides a theoretical basis for agricultural development planning efforts, which can promote sustainable agricultural development. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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24 pages, 5246 KiB  
Article
A Principal Component Analysis Methodology of Oil Spill Detection and Monitoring Using Satellite Remote Sensing Sensors
by Niyazi Arslan, Meysam Majidi Nezhad, Azim Heydari, Davide Astiaso Garcia and Georgios Sylaios
Remote Sens. 2023, 15(5), 1460; https://doi.org/10.3390/rs15051460 - 05 Mar 2023
Cited by 4 | Viewed by 2861
Abstract
Monitoring, assessing, and measuring oil spills is essential in protecting the marine environment and in efforts to clean oil spills. One of the most recent oil spills happened near Port Fourchon, Louisiana, caused by Hurricane Ida (Category 4), that had a wind speed [...] Read more.
Monitoring, assessing, and measuring oil spills is essential in protecting the marine environment and in efforts to clean oil spills. One of the most recent oil spills happened near Port Fourchon, Louisiana, caused by Hurricane Ida (Category 4), that had a wind speed of 240 km/h. In this regard, Earth Observation (EO) Satellite Remote Sensing (SRS) images can effectively highlight oil spills in marine areas as a “fast and no-cost” technique. However, clouds and the sea surface spectral signature complicate the interpretation of oil spill areas in the optical images. In this study, Principal Component Analysis (PCA) has been applied of Landsat-8 and Sentinel-2 SRS images to improve information from the optical sensor bands. The PCA produces an output unrelated to the main bands, making it easier to distinguish oil spills from clouds and seawater due to the spectral diversity between oil, clouds, and the seawater surface. Then, an additional step has been applied to highlight the oil spill area using PCAs with different band combinations. Furthermore, Sentinel-1 (SAR), Sentinel-2 (optical), and Landsat-8 (optical) SRS images have been analyzed with cross-sections to suppress the “look-alike” effect of marine oil spill areas. Finally, mean and high-pass filters were used for Land Surface Temperature (LST) SRS images estimated from the Landsat thermal band. The results show that the seawater value is about −17.5 db and the oil spill area shows a value between −22.5 db and −25 db; the Landsat 8 satellites thermal band 10, depicting contrast at some areas for oil spill, can be determined by the 3 × 3 and 5 × 5 Kernel High pass and the 3 × 3 Mean filter. The results demonstrate that the SRS images should be used together to improve oil spill detection studies results. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing - Part 2)
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19 pages, 13419 KiB  
Article
The Ground-Level Particulate Matter Concentration Estimation Based on the New Generation of FengYun Geostationary Meteorological Satellite
by Lin Tian, Lin Chen, Peng Zhang, Bo Hu, Yang Gao and Yidan Si
Remote Sens. 2023, 15(5), 1459; https://doi.org/10.3390/rs15051459 - 05 Mar 2023
Cited by 1 | Viewed by 1570
Abstract
The new-generation FengYun geostationary meteorological satellite has a high spatial and temporal resolution, which is advantageous in environmental assessments and air pollution monitoring. This study researched the ground-level particulate matter concentration estimation, based on satellite-observed radiations. The radiation of ground-level particulate matter is [...] Read more.
The new-generation FengYun geostationary meteorological satellite has a high spatial and temporal resolution, which is advantageous in environmental assessments and air pollution monitoring. This study researched the ground-level particulate matter concentration estimation, based on satellite-observed radiations. The radiation of ground-level particulate matter is separate from the apparent radiation observed by satellites. The positive correlation between PM2.5 and PM10 is also considered to improve the accuracy of inversion results and the interpretability of the estimation model. Then, PM2.5 and PM10 concentrations were estimated synchronously every 5 min in mainland China based on FY-4A satellite directly observed radiations. The validation results showed that the improved model estimated results were close to the ground site measured results, with a high determination coefficient (R2) (0.89 for PM2.5, and 0.90 for PM10), and a small Root Mean Squared Error (RMSE) (4.69 μg/m3 for PM2.5 concentrations, and 13.77 μg/m3 for PM10 concentrations). The estimation model presented a good performance in PM2.5 and PM10 concentrations during typical haze and dust storm cases, indicating that it is applicable in different weather conditions and regions. Full article
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21 pages, 15902 KiB  
Article
Bridge Deformation Analysis Using Time-Differenced Carrier-Phase Technique
by María Jesús Jiménez-Martínez, Nieves Quesada-Olmo, José Julio Zancajo-Jimeno and Teresa Mostaza-Pérez
Remote Sens. 2023, 15(5), 1458; https://doi.org/10.3390/rs15051458 - 05 Mar 2023
Cited by 3 | Viewed by 1577
Abstract
Historically, monitoring possible deformations in suspension bridges has been a crucial issue for structural engineers. Therefore, to understand and calibrate models of the “load-structure-response”, it is essential to implement suspension bridge monitoring programs. In this work, due to increasing GNSS technology development, we [...] Read more.
Historically, monitoring possible deformations in suspension bridges has been a crucial issue for structural engineers. Therefore, to understand and calibrate models of the “load-structure-response”, it is essential to implement suspension bridge monitoring programs. In this work, due to increasing GNSS technology development, we study the movement of a long-span bridge structure using differenced carrier phases in adjacent epochs. Many measurement errors can be decreased by a single difference between consecutive epochs, especially from receivers operating at 10 Hz. Another advantage is not requiring two receivers to observe simultaneously. In assessing the results obtained, to avoid unexpected large errors, the outlier and cycle-slip exclusion are indispensable. The final goal of this paper is to obtain the relative positioning and associated standard deviations of a stand-alone geodetic receiver. Short-term movements generated by traffic, tidal current, wind, or earthquakes must be recoverable deformations, as evidenced by the vertical displacement graphs obtained through this approach. For comparison studies, three geodetic receivers were positioned on the Assut de l’Or Bridge in València, Spain. The associated standard deviation for the north, east, and vertical positioning values was approximately 0.01 m. Full article
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13 pages, 2916 KiB  
Technical Note
Machine Learning in the Classification of Soybean Genotypes for Primary Macronutrients’ Content Using UAV–Multispectral Sensor
by Dthenifer Cordeiro Santana, Marcelo Carvalho Minhoto Teixeira Filho, Marcelo Rinaldi da Silva, Paulo Henrique Menezes das Chagas, João Lucas Gouveia de Oliveira, Fábio Henrique Rojo Baio, Cid Naudi Silva Campos, Larissa Pereira Ribeiro Teodoro, Carlos Antonio da Silva Junior, Paulo Eduardo Teodoro and Luciano Shozo Shiratsuchi
Remote Sens. 2023, 15(5), 1457; https://doi.org/10.3390/rs15051457 - 05 Mar 2023
Cited by 6 | Viewed by 1969
Abstract
Using spectral data to quantify nitrogen (N), phosphorus (P), and potassium (K) contents in soybean plants can help breeding programs develop fertilizer-efficient genotypes. Employing machine learning (ML) techniques to classify these genotypes according to their nutritional content makes the analyses performed in the [...] Read more.
Using spectral data to quantify nitrogen (N), phosphorus (P), and potassium (K) contents in soybean plants can help breeding programs develop fertilizer-efficient genotypes. Employing machine learning (ML) techniques to classify these genotypes according to their nutritional content makes the analyses performed in the programs even faster and more reliable. Thus, the objective of this study was to find the best ML algorithm(s) and input configurations in the classification of soybean genotypes for higher N, P, and K leaf contents. A total of 103 F2 soybean populations were evaluated in a randomized block design with two repetitions. At 60 days after emergence (DAE), spectral images were collected using a Sensefly eBee RTK fixed-wing remotely piloted aircraft (RPA) with autonomous take-off, flight plan, and landing control. The eBee was equipped with the Parrot Sequoia multispectral sensor. Reflectance values were obtained in the following spectral bands (SBs): red (660 nm), green (550 nm), NIR (735 nm), and red-edge (790 nm), which were used to calculate the vegetation index (VIs): normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), green normalized difference vegetation index (GNDVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI), modified chlorophyll absorption in reflectance index (MCARI), enhanced vegetation index (EVI), and simplified canopy chlorophyll content index (SCCCI). At the same time of the flight, leaves were collected in each experimental unit to obtain the leaf contents of N, P, and K. The data were submitted to a Pearson correlation analysis. Subsequently, a principal component analysis was performed together with the k-means algorithm to define two clusters: one whose genotypes have high leaf contents and another whose genotypes have low leaf contents. Boxplots were generated for each cluster according to the content of each nutrient within the groups formed, seeking to identify which set of genotypes has higher nutrient contents. Afterward, the data were submitted to machine learning analysis using the following algorithms: decision tree algorithms J48 and REPTree, random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR, used as control). The clusters were used as output variables of the classification models used. The spectral data were used as input variables for the models, and three different configurations were tested: using SB only, using VIs only, and using SBs+VIs. The J48 and SVM algorithms had the best performance in classifying soybean genotypes. The best input configuration for the algorithms was using the spectral bands as input. Full article
(This article belongs to the Special Issue High-Throughput Phenotyping in Plants Using Remote Sensing)
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17 pages, 4542 KiB  
Article
Exploring Contrastive Representation for Weakly-Supervised Glacial Lake Extraction
by Hang Zhao, Shuang Wang, Xuebin Liu and Fang Chen
Remote Sens. 2023, 15(5), 1456; https://doi.org/10.3390/rs15051456 - 05 Mar 2023
Cited by 2 | Viewed by 1663
Abstract
Against the background of the ongoing atmospheric warming, the glacial lakes that are nourished and expanded in High Mountain Asia pose growing risks of glacial lake outburst floods (GLOFs) hazards and increasing threats to the downstream areas. Effectively extracting the area and consistently [...] Read more.
Against the background of the ongoing atmospheric warming, the glacial lakes that are nourished and expanded in High Mountain Asia pose growing risks of glacial lake outburst floods (GLOFs) hazards and increasing threats to the downstream areas. Effectively extracting the area and consistently monitoring the dynamics of these lakes are of great significance in predicting and preventing GLOF events. To automatically extract the lake areas, many deep learning (DL) methods capable of capturing the multi-level features of lakes have been proposed in segmentation and classification tasks. However, the portability of these supervised DL methods need to be improved in order to be directly applied to different data sources, as they require laborious effort to collect the labeled lake masks. In this work, we proposed a simple glacial lake extraction model (SimGL) via weakly-supervised contrastive learning to extend and improve the extraction performances in cases that lack the labeled lake masks. In SimGL, a Siamese network was employed to learn similar objects by maximizing the similarity between the input image and its augmentations. Then, a simple Normalized Difference Water Index (NDWI) map was provided as the location cue instead of the labeled lake masks to constrain the model to capture the representations related to the glacial lakes and the segmentations to coincide with the true lake areas. Finally, the experimental results of the glacial lake extraction on the 1540 Landsat-8 image patches showed that our approach, SimGL, offers a competitive effort with some supervised methods (such as Random Forest) and outperforms other unsupervised image segmentation methods in cases that lack true image labels. Full article
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23 pages, 18629 KiB  
Article
Infrasound and Low-Audible Acoustic Detections from a Long-Term Microphone Array Deployment in Oklahoma
by Trevor C. Wilson, Christopher E. Petrin and Brian R. Elbing
Remote Sens. 2023, 15(5), 1455; https://doi.org/10.3390/rs15051455 - 05 Mar 2023
Cited by 2 | Viewed by 2767
Abstract
A three-microphone acoustic array (OSU1), with microphones that have a flat response from 0.1 to 200 Hz, was deployed for 6 years (2016–2022) at Oklahoma State University (OSU) in Stillwater, Oklahoma, and sampled at 1000 Hz. This study presents a new dataset of [...] Read more.
A three-microphone acoustic array (OSU1), with microphones that have a flat response from 0.1 to 200 Hz, was deployed for 6 years (2016–2022) at Oklahoma State University (OSU) in Stillwater, Oklahoma, and sampled at 1000 Hz. This study presents a new dataset of acoustic measurements in a high interest region (e.g., study of tornado infrasound), provides a broad overview of acoustic detections and the means to identify them, and provides access to these recordings to the broader scientific community. A wide variety of infrasound and low-audible sources were identified and characterized via analysis of time traces, power spectral densities, spectrograms, and beamforming. Low, median, and high noise models were compared with global noise models. Detected sources investigated include natural (microbaroms, bolides, earthquakes, and tornadoes) and anthropomorphic (fireworks, airplanes, and munition detonations) phenomena. Microbarom detections showed consistency with literature (~0.2 Hz with peak amplitude in the winter) and evidence that the frequency was inversely related to the amplitude. Fireworks and airplanes served as verified local events for the evaluation of data quality and processing procedures. Infrasound from munition detonations, that occur nearly daily at a location 180 km southeast of OSU1, matched the available ground truth on days with favorable propagation to OSU1. A clear bolide detection with an estimated position of approximately 300 km from OSU1 was shown. Most detected earthquakes were seismic arrivals due to sensor vibrations; however, the largest earthquake in Oklahoma history showed an acoustic arrival. Finally, data from multiple tornadoes are discussed, including a previously unpublished quasi-linear convective system tornado. Full article
(This article belongs to the Special Issue Infrasound, Acoustic-Gravity Waves, and Atmospheric Dynamics)
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36 pages, 8036 KiB  
Review
A Comprehensive Survey on SAR ATR in Deep-Learning Era
by Jianwei Li, Zhentao Yu, Lu Yu, Pu Cheng, Jie Chen and Cheng Chi
Remote Sens. 2023, 15(5), 1454; https://doi.org/10.3390/rs15051454 - 05 Mar 2023
Cited by 9 | Viewed by 3611
Abstract
Due to the advantages of Synthetic Aperture Radar (SAR), the study of Automatic Target Recognition (ATR) has become a hot topic. Deep learning, especially in the case of a Convolutional Neural Network (CNN), works in an end-to-end way and has powerful feature-extracting abilities. [...] Read more.
Due to the advantages of Synthetic Aperture Radar (SAR), the study of Automatic Target Recognition (ATR) has become a hot topic. Deep learning, especially in the case of a Convolutional Neural Network (CNN), works in an end-to-end way and has powerful feature-extracting abilities. Thus, researchers in SAR ATR also seek solutions from deep learning. We review the related algorithms with regard to SAR ATR in this paper. We firstly introduce the commonly used datasets and the evaluation metrics. Then, we introduce the algorithms before deep learning. They are template-matching-, machine-learning- and model-based methods. After that, we introduce mainly the SAR ATR methods in the deep-learning era (after 2017); those methods are the core of the paper. The non-CNNs and CNNs, that is, those used in SAR ATR, are summarized at the beginning. We found that researchers tend to design specialized CNN for SAR ATR. Then, the methods to solve the problem raised by limited samples are reviewed. They are data augmentation, Generative Adversarial Networks (GAN), electromagnetic simulation, transfer learning, few-shot learning, semi-supervised learning, metric leaning and domain knowledge. After that, the imbalance problem, real-time recognition, polarimetric SAR, complex data and adversarial attack are also reviewed. The principles and problems of them are also introduced. Finally, the future directions are conducted. In this part, we point out that the dataset, CNN architecture designing, knowledge-driven, real-time recognition, explainable and adversarial attack should be considered in the future. This paper gives readers a quick overview of the current state of the field. Full article
(This article belongs to the Special Issue Ship Detection and Maritime Monitoring Based on SAR Data)
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24 pages, 5835 KiB  
Article
Open Source Data-Based Solutions for Identifying Patterns of Urban Earthquake Systemic Vulnerability in High-Seismicity Areas
by Andra-Cosmina Albulescu
Remote Sens. 2023, 15(5), 1453; https://doi.org/10.3390/rs15051453 - 05 Mar 2023
Cited by 1 | Viewed by 1653
Abstract
Urban settlements located in high-seismicity areas should benefit from comprehensive vulnerability analyses, which are essential for the proper implementation of vulnerability modelling actions. Alas, many developing countries face a shortage of knowledge on seismic vulnerability, particularly concerning its systemic component, as a consequence [...] Read more.
Urban settlements located in high-seismicity areas should benefit from comprehensive vulnerability analyses, which are essential for the proper implementation of vulnerability modelling actions. Alas, many developing countries face a shortage of knowledge on seismic vulnerability, particularly concerning its systemic component, as a consequence of a combination of data scarcity and a lack of interest from authorities. This paper aims to identify primary time-independent spatial patterns of earthquake systemic vulnerability based on the accessibility of key emergency management facilities (e.g., medical units, fire stations), focusing on the urban settlements located in the high-seismicity area nearby the Vrancea Seismogenic Zone in Romania. The proposed methodological framework relies on open source data extracted from OpenStreetMap, which are processed via GIS techniques and tools (i.e., Network Analyst, Weighted Overlay Analysis), to compute the service areas of emergency management centres, and to map earthquake systemic vulnerability levels. The analysis shows that accessibility and systemic vulnerability patterns are significantly impacted by a synergy of factors deeply rooted in the urban spatial layout. Although the overall accessibility was estimated to be medium-high, and the overall systemic vulnerability to be low-medium, higher systemic vulnerability levels in certain cities (e.g., Bacău, Onești, Tecuci, Urziceni). The presented findings have multi-scalar utility: they aid in the development of improved, locally tailored seismic vulnerability reduction plans, as well as the allocation of financial and human resources required to manage earthquake-induced crises at regional scale. Further to that, the paper provides a transparent methodological framework that can be replicated to put cities in high-seismicity areas on the map of systemic vulnerability assessments, laying the groundwork for positive change in countries where the challenges associated with high-level seismic risk are often overlooked. Full article
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17 pages, 33523 KiB  
Article
Landslide Detection Using Time-Series InSAR Method along the Kangding-Batang Section of Shanghai-Nyalam Road
by Yaning Yi, Xiwei Xu, Guangyu Xu and Huiran Gao
Remote Sens. 2023, 15(5), 1452; https://doi.org/10.3390/rs15051452 - 05 Mar 2023
Cited by 3 | Viewed by 3413
Abstract
Due to various factors such as urban development, climate change, and tectonic movements, landslides are a common geological phenomenon in the Qinghai–Tibet Plateau region, especially on both sides of a road, where large landslide hazards often result in traffic disruptions and casualties. Identifying [...] Read more.
Due to various factors such as urban development, climate change, and tectonic movements, landslides are a common geological phenomenon in the Qinghai–Tibet Plateau region, especially on both sides of a road, where large landslide hazards often result in traffic disruptions and casualties. Identifying the spatial distribution of landslides and monitoring their stability are essential for predicting landslide occurrence and implementing prevention measures. In this study, taking the Kangding-Batang section of Shanghai-Nyalam Road as the study area, we adopted a semi-automated time-series interferometric synthetic aperture radar (InSAR) method to identify landslides and monitor their activity. A total of 446 Sentinel-1 ascending and descending SAR images from January 2018 to December 2021 were thus collected and processed by using open-source InSAR processing software. After a series of error corrections, we obtained surface deformation maps covering the study area, and a total of 236 potential landslides were subsequently identified and classified into three categories, namely slow-sliding rockslides, debris flows, and debris avalanches, by combining deformation maps, optical images, and a digital elevation model (DEM). For a typical landslide, we performed deformation decomposition and analyzed the relationship between its deformation and rainfall, revealing the contribution of rainfall to the landslide. In addition, we discussed the effect of SAR geometric distortion on landslide detection, highlighting the importance of joint ascending and descending observations in mountainous areas. We analyzed the controlling factors of landslide distribution and found that topographic conditions are still the dominant factor. Our results may be beneficial for road maintenance and disaster mitigation. Moreover, the entire processing is semi-automated based on open-source tools or software, which provides a paradigm for landslide-related studies in other mountainous regions of the world. Full article
(This article belongs to the Special Issue Rockfall Hazard Analysis Using Remote Sensing Techniques)
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23 pages, 24008 KiB  
Article
PolSAR Image Building Extraction with G0 Statistical Texture Using Convolutional Neural Network and Superpixel
by Mei Li, Qikai Shen, Yun Xiao, Xiuguo Liu and Qihao Chen
Remote Sens. 2023, 15(5), 1451; https://doi.org/10.3390/rs15051451 - 04 Mar 2023
Cited by 2 | Viewed by 1509
Abstract
Polarimetric synthetic aperture radar (PolSAR) has unique advantages in building extraction due to its sensitivity to building structures and all-time/all-weather imaging capabilities. However, the structure of buildings is complex, and buildings are easily confused with other objects in polarimetric SAR images. The speckle [...] Read more.
Polarimetric synthetic aperture radar (PolSAR) has unique advantages in building extraction due to its sensitivity to building structures and all-time/all-weather imaging capabilities. However, the structure of buildings is complex, and buildings are easily confused with other objects in polarimetric SAR images. The speckle noise of SAR images will affect the accuracy of building extraction. This paper proposes a novel building extraction approach from PolSAR images with statistical texture and polarization features by using a convolutional neural network and superpixel. A feature space that is sensitive to building, including G0 statistical texture and PualiRGB features, is constructed and used as CNN input. Considering that the building boundary of the CNN classification result is inaccurate due to speckle noise, the simple linear iterative cluster (SLIC) superpixel is utilized to constrain the building extraction result. Finally, the effectiveness of the proposed method has been verified by experimenting with PolSAR images from three different sensors, including ESAR, GF-3, and RADARSAT-2. Experiment results show that compared with the other five PolSAR building extraction methods including threshold, SVM, RVCNN, and PFDCNN, our method without superpixel constraint, the F1-score of this method is the highest, reaching 84.22%, 91.24%, and 87.49%, respectively. The false alarm rate of this method is at least 10% lower and the F1 index is at least 6% higher when the building extraction accuracy is comparable. Further, the discussion and method parameter analysis results show that increasing the use of G0 statistical texture parameters can improve building extraction accuracy and reduce false alarms, and the introduction of superpixel constraints can further reduce false alarms. Full article
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19 pages, 4504 KiB  
Article
Application of a Fusion Model Based on Machine Learning in Visibility Prediction
by Maochan Zhen, Mingjian Yi, Tao Luo, Feifei Wang, Kaixuan Yang, Xuebin Ma, Shengcheng Cui and Xuebin Li
Remote Sens. 2023, 15(5), 1450; https://doi.org/10.3390/rs15051450 - 04 Mar 2023
Cited by 3 | Viewed by 3096
Abstract
To improve the accuracy of atmospheric visibility (V) prediction based on machine learning in different pollution scenarios, a new atmospheric visibility prediction method based on the stacking fusion model (VSFM) is established in this paper. The new method uses the stacking strategy to [...] Read more.
To improve the accuracy of atmospheric visibility (V) prediction based on machine learning in different pollution scenarios, a new atmospheric visibility prediction method based on the stacking fusion model (VSFM) is established in this paper. The new method uses the stacking strategy to fuse two base learners—eXtreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM)—to optimize prediction accuracy. Furthermore, seasonal feature importance evaluations and feature selection were utilized to optimize prediction accuracy in different seasons with different pollution sources. The new VSFM was applied to 1-year environmental and meteorological data measured in Qingdao, China. Compared to other traditional non-stacking models, the new VSFM improved precision during different seasons, especially in extremely low-visibility scenarios (V< 2 km). The TS score of the VSFM was significantly better than that of other models. For extremely low-visibility scenarios, the VSFM had a threat score (TS) of 0.5, while the best performance of other models was less than 0.27. The new method is promising for atmospheric visibility prediction under complex urban pollution conditions. The research results can also improve our understanding of the factors that influence urban visibility. Full article
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25 pages, 4062 KiB  
Article
Irrigation Timing Retrieval at the Plot Scale Using Surface Soil Moisture Derived from Sentinel Time Series in Europe
by Michel Le Page, Thang Nguyen, Mehrez Zribi, Aaron Boone, Jacopo Dari, Sara Modanesi, Luca Zappa, Nadia Ouaadi and Lionel Jarlan
Remote Sens. 2023, 15(5), 1449; https://doi.org/10.3390/rs15051449 - 04 Mar 2023
Cited by 5 | Viewed by 2224
Abstract
The difficulty of calculating the daily water budget of irrigated fields is often due to the uncertainty surrounding irrigation amounts and timing. The automated detection of irrigation events has the potential to greatly simplify this process, and the combination of high-resolution SAR (Sentinel-1) [...] Read more.
The difficulty of calculating the daily water budget of irrigated fields is often due to the uncertainty surrounding irrigation amounts and timing. The automated detection of irrigation events has the potential to greatly simplify this process, and the combination of high-resolution SAR (Sentinel-1) and optical satellite observations (Sentinel-2) makes the detection of irrigation events feasible through the use of a surface soil moisture (SSM) product. The motivation behind this study is to utilize a large irrigation dataset (collected during the ESA Irrigation + project over five sites in three countries over three years) to analyze the performance of an established algorithm and to test potential improvements. The study’s main findings are (1) the scores decrease with SSM observation frequency; (2) scores decrease as irrigation frequency increases, which was supported by better scores in France (more sprinkler irrigation) than in Germany (more localized irrigation); (3) replacing the original SSM model with the force-restore model resulted in an improvement of about 6% in the F-score and narrowed the error on cumulative seasonal irrigation; (4) the Sentinel-1 configuration (incidence angle, trajectory) did not show a significant impact on the retrieval of irrigation, which supposes that the SSM is not affected by these changes. Other aspects did not allow a definitive conclusion on the irrigation retrieval algorithm: (1) the lower scores obtained with small NDVI compared to large NDVI were counter-intuitive but may have been due to the larger number of irrigation events during high vegetation periods; (2) merging different runs and interpolating all SSM data for one run produced comparable F-scores, but the estimated cumulative sum of irrigation was around −20% lower compared to the reference dataset in the best cases. Full article
(This article belongs to the Special Issue Irrigation Estimates and Management from EO Data)
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20 pages, 5791 KiB  
Article
How Sensitive Is Thermal Image-Based Orchard Water Status Estimation to Canopy Extraction Quality?
by Livia Katz, Alon Ben-Gal, M. Iggy Litaor, Amos Naor, Aviva Peeters, Eitan Goldshtein, Guy Lidor, Ohaliav Keisar, Stav Marzuk, Victor Alchanatis and Yafit Cohen
Remote Sens. 2023, 15(5), 1448; https://doi.org/10.3390/rs15051448 - 04 Mar 2023
Cited by 2 | Viewed by 2372
Abstract
Accurate canopy extraction and temperature calculations are crucial to minimizing inaccuracies in thermal image-based estimation of orchard water status. Currently, no quantitative comparison of canopy extraction methods exists in the context of precision irrigation. The accuracies of four canopy extraction methods were compared, [...] Read more.
Accurate canopy extraction and temperature calculations are crucial to minimizing inaccuracies in thermal image-based estimation of orchard water status. Currently, no quantitative comparison of canopy extraction methods exists in the context of precision irrigation. The accuracies of four canopy extraction methods were compared, and the effect on water status estimation was explored for these methods: 2-pixel erosion (2PE) where non-canopy pixels were removed by thresholding and morphological erosion; edge detection (ED) where edges were identified and morphologically dilated; vegetation segmentation (VS) using temperature histogram analysis and spatial watershed segmentation; and RGB binary masking (RGB-BM) where a binary canopy layer was statistically extracted from an RGB image for thermal image masking. The field experiments occurred in a four-hectare commercial peach orchard during the primary fruit growth stage (III). The relationship between stem water potential (SWP) and crop water stress index (CWSI) was established in 2018. During 2019, a large dataset of ten thermal infrared and two RGB images was acquired. The canopy extraction methods had different accuracies: on 12 August, the overall accuracy was 83% for the 2PE method, 77% for the ED method, 84% for the VS method, and 90% for the RGB-BM method. Despite the high accuracy of the RGB-BM method, canopy edges and between-row weeds were misidentified as canopy. Canopy temperature and CWSI were calculated using the average of 100% of canopy pixels (CWSI_T100%) and the average of the coolest 33% of canopy pixels (CWSI_T33%). The CWSI_T33% dataset produced similar SWP–CWSI models irrespective of the canopy extraction method used, while the CWSI_T100% yielded different and inferior models. The results highlighted the following: (1) The contribution of the RGB images is not significant for canopy extraction. Canopy pixels can be extracted with high accuracy and reliability solely with thermal images. (2) The T33% approach to canopy temperature calculation is more robust and superior to the simple mean of all canopy pixels. These noteworthy findings are a step forward in implementing thermal imagery in precision irrigation management. Full article
(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)
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18 pages, 27567 KiB  
Article
Studying the Water Supply System of the Roman Villa of Pisões (Beja, Portugal) Using Ground-Penetrating Radar and Geospatial Methods
by Rui Jorge Oliveira, Pedro Trapero Fernández, Bento Caldeira, José Fernando Borges and André Carneiro
Remote Sens. 2023, 15(5), 1447; https://doi.org/10.3390/rs15051447 - 04 Mar 2023
Viewed by 1245
Abstract
The Roman villa of Pisões (Beja, Portugal) was part of the Lusitanian colony of Pax Iulia. This place stands out for the predominance of the water element in several structures of the villa, highlighting the balneum and the large natatio, one of [...] Read more.
The Roman villa of Pisões (Beja, Portugal) was part of the Lusitanian colony of Pax Iulia. This place stands out for the predominance of the water element in several structures of the villa, highlighting the balneum and the large natatio, one of the largest known in Roman Hispania. The records of the initial excavations that took place beginning in 1967 do not allow the establishment of clear functionalities of the villa. The University of Évora, the owner of the site, conceived an action plan for the requalification and enhancement of the archaeological site. One of the tasks aims to investigate the site using applied geophysics. This work analyses the landscape directly related to the villa, given that it is in the flooded area of a river with a Roman containment dam. It is uncertain whether the water supply comes from this structure or other nearby springs. The use of ground-penetrating radar, combined with unmanned aerial vehicles, all integrated in a geographic information system, allows us to determine the location of underground water connections and create a topographic model with high resolution. Considering all the information, we propose a model for water transport inside the villa and estimate the location of the water supply. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Cultural Heritage Research)
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22 pages, 7579 KiB  
Article
Modeling Historical and Future Forest Fires in South Korea: The FLAM Optimization Approach
by Hyun-Woo Jo, Andrey Krasovskiy, Mina Hong, Shelby Corning, Whijin Kim, Florian Kraxner and Woo-Kyun Lee
Remote Sens. 2023, 15(5), 1446; https://doi.org/10.3390/rs15051446 - 04 Mar 2023
Cited by 4 | Viewed by 2981
Abstract
Climate change-induced heat waves increase the global risk of forest fires, intensifying biomass burning and accelerating climate change in a vicious cycle. This presents a challenge to the response system in heavily forested South Korea, increasing the risk of more frequent and large-scale [...] Read more.
Climate change-induced heat waves increase the global risk of forest fires, intensifying biomass burning and accelerating climate change in a vicious cycle. This presents a challenge to the response system in heavily forested South Korea, increasing the risk of more frequent and large-scale fire outbreaks. This study aims to optimize IIASA’s wildFire cLimate impacts and Adaptation Model (FLAM)—a processed-based model integrating biophysical and human impacts—to South Korea for projecting the pattern and scale of future forest fires. The developments performed in this study include: (1) the optimization of probability algorithms in FLAM based on the national GIS data downscaled to 1 km2 with additional factors introduced for national specific modeling; (2) the improvement of soil moisture computation by adjusting the Fine Fuel Moisture Code (FFMC) to represent vegetation feedbacks by fitting soil moisture to daily remote sensing data; and (3) projection of future forest fire frequency and burned area. Our results show that optimization has considerably improved the modeling of seasonal patterns of forest fire frequency. Pearson’s correlation coefficient between monthly predictions and observations from national statistics over 2016–2022 was improved from 0.171 in the non-optimized to 0.893 in the optimized FLAM. These findings imply that FLAM’s main algorithms for interpreting biophysical and human impacts on forest fire at a global scale are only applicable to South Korea after the optimization of all modules, and climate change is the main driver of the recent increases in forest fires. Projections for forest fire were produced for four periods until 2100 based on the forest management plan, which included three management scenarios (current, ideal, and overprotection). Ideal management led to a reduction of 60–70% of both fire frequency and burned area compared to the overprotection scenario. This study should be followed by research for developing adaptation strategies corresponding to the projected risks of future forest fires. Full article
(This article belongs to the Special Issue Latest Advances in Remote Sensing-Based Environmental Dynamic Models)
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17 pages, 5133 KiB  
Article
Spatio-Temporal Evolution and Prediction of Carbon Storage in Guilin Based on FLUS and InVEST Models
by Yunlin He, Jiangming Ma, Changshun Zhang and Hao Yang
Remote Sens. 2023, 15(5), 1445; https://doi.org/10.3390/rs15051445 - 04 Mar 2023
Cited by 15 | Viewed by 3095
Abstract
In the context of sustainable development and dual-carbon construction, to quantify the carbon storage and its spatial-temporal distribution characteristics of Guilin City and predict the carbon storage of Guilin City in 2035 under different future scenarios, this study set four future scenarios based [...] Read more.
In the context of sustainable development and dual-carbon construction, to quantify the carbon storage and its spatial-temporal distribution characteristics of Guilin City and predict the carbon storage of Guilin City in 2035 under different future scenarios, this study set four future scenarios based on SDGs and the sustainable development plan of Guilin City: natural development, economic priority, ecological priority, and sustainable development. At the same time, FLUS and InVEST models and GeoDa 1.20and ArcGIS software were used to establish a coupling model of land use change and ecosystem carbon storage to simulate and predict the distribution and change of ecosystem carbon storage based on land use change in the future. The results showed that: (1) From 2005 to 2020, forest land was the main type of land use in Guilin, and cropland and impervious continued to expand. In 2035, the forest land under four different future scenarios will be an important transformation type; (2) From 2005 to 2020, the carbon storage in the northwest of Guilin was relatively high, and the carbon loss area was larger than the carbon increase area. The carbon storage in the ecological priority scenario in 2035 is the highest, reaching 874.76 × 106 t. The aboveground carbon storage (ACG) is the main carbon pool in Guilin. Most of the regions with high carbon storage are located in the northwest and northeast of Guilin. No matter what scenario, the carbon storage in the main urban area is maintained at a low level; (3) In 2035, the distribution of carbon storage in Guilin has a strong spatial positive correlation, with more hot spots than cold spots. The high-value areas of carbon storage are concentrated in the northwest and east, whereas the low-value areas are concentrated in the urban area of Guilin. Full article
(This article belongs to the Special Issue Remote Sensing for Advancing Nature-Based Climate Solutions)
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22 pages, 21321 KiB  
Article
Ground Deformation Monitoring over Xinjiang Coal Fire Area by an Adaptive ERA5-Corrected Stacking-InSAR Method
by Yuxuan Zhang, Yunjia Wang, Wenqi Huo, Feng Zhao, Zhongbo Hu, Teng Wang, Rui Song, Jinglong Liu, Leixin Zhang, José Fernández, Joaquin Escayo, Fei Cao and Jun Yan
Remote Sens. 2023, 15(5), 1444; https://doi.org/10.3390/rs15051444 - 04 Mar 2023
Viewed by 1414
Abstract
Underground coal fire is a global geological disaster that causes the loss of resources as well as environmental pollution. Xinjiang, China, is one of the regions suffering from serious underground coal fires. The accurate monitoring of underground coal fires is critical for management [...] Read more.
Underground coal fire is a global geological disaster that causes the loss of resources as well as environmental pollution. Xinjiang, China, is one of the regions suffering from serious underground coal fires. The accurate monitoring of underground coal fires is critical for management and extinguishment, and many remote sensing-based approaches have been developed for monitoring over large areas. Among them, the multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been recently employed for underground coal fires-related ground deformation monitoring. However, MT-InSAR involves a relatively high computational cost, especially when the monitoring area is large. We propose to use a more cost-efficient Stacking-InSAR technique to monitor ground deformation over underground coal fire areas in this study. Considering the effects of atmosphere on Stacking-InSAR, an ERA5 data-based estimation model is employed to mitigate the atmospheric phase of interferograms before stacking. Thus, an adaptive ERA5-Corrected Stacking-InSAR method is proposed in this study, and it is tested over the Fukang coal fire area in Xinjiang, China. Based on original and corrected interferograms, four groups of ground deformation results were obtained, and the possible coal fire areas were identified. In this paper, the ERA5 atmospheric delay products based on the estimation model along the LOS direction (D-LOS) effectively mitigate the atmospheric phase. The accuracy of ground deformation monitoring over a coal fire area has been improved by the proposed method choosing interferograms adaptively for stacking. The proposed Adaptive ERA5-Corrected Stacking-InSAR method can be used for efficient ground deformation monitoring over large coal fire areas. Full article
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24 pages, 20344 KiB  
Article
High-Precision Single Building Model Reconstruction Based on the Registration between OSM and DSM from Satellite Stereos
by Yong He, Wenting Liao, Hao Hong and Xu Huang
Remote Sens. 2023, 15(5), 1443; https://doi.org/10.3390/rs15051443 - 04 Mar 2023
Viewed by 1675
Abstract
For large-scale 3D building reconstruction, there have been several approaches to utilizing multi-view satellite imagery to produce a digital surface model (DSM) for height information and extracting building footprints for contour information. However, limited by satellite resolutions and viewing angles, the corresponding DSM [...] Read more.
For large-scale 3D building reconstruction, there have been several approaches to utilizing multi-view satellite imagery to produce a digital surface model (DSM) for height information and extracting building footprints for contour information. However, limited by satellite resolutions and viewing angles, the corresponding DSM and building footprints are sometimes of a low accuracy, thus generating low-accuracy building models. Though some recent studies have added GIS data to refine the contour of the building footprints, the registration errors between the GIS data and satellite images are not considered. Since OpenStreetMap (OSM) provides a high level of precision and complete building polygons in most cities worldwide, this paper proposes an automatic single building reconstruction method that utilizes a DSM from high-resolution satellite stereos, as well as building footprints from OSM. The core algorithm accurately registers the building polygons from OSM with the rasterized height information from the DSM. To achieve this goal, this paper proposes a two-step “coarse-to-fine registration” algorithm, with both steps being formulated into the optimization of energy functions. The coarse registration is optimized by separately moving the OSM polygons at fixed steps with the constraints of a boundary gradient, an interior elevation mean, and variance. Given the initial solution of the coarse registration, the fine registration is optimized by a genetic algorithm to compute the accurate translations and rotations between the DSM and OSM. Experiments performed in the Beijing/Shanghai region show that the proposed method can significantly improve the IoU (intersection over union) of the registration results by 69.8%/26.2%, the precision by 41.0%/15.5%, the recall by 41.0%/16.0%, and the F1-score by 42.7%/15.8%. For the registration, the method can reduce the translation errors by 4.656 m/2.815 m, as well as the rotation errors by 0.538°/0.228°, which indicates its great potential in smart 3D applications. Full article
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25 pages, 9022 KiB  
Article
Evaluating Carbon Sink Potential of Forest Ecosystems under Different Climate Change Scenarios in Yunnan, Southwest China
by Fucheng Lü, Yunkun Song and Xiaodong Yan
Remote Sens. 2023, 15(5), 1442; https://doi.org/10.3390/rs15051442 - 04 Mar 2023
Cited by 2 | Viewed by 2237
Abstract
Nature-based Solutions (NbS) can undoubtedly play a significant role in carbon neutrality strategy. Forests are a major part of the carbon budget in terrestrial ecosystems. The possible response of the carbon balance of southwestern forests to different climate change scenarios was investigated through [...] Read more.
Nature-based Solutions (NbS) can undoubtedly play a significant role in carbon neutrality strategy. Forests are a major part of the carbon budget in terrestrial ecosystems. The possible response of the carbon balance of southwestern forests to different climate change scenarios was investigated through a series of simulations using the forest ecosystem carbon budget model for China (FORCCHN), which clearly represents the influence of climate factors on forest carbon sequestration. Driven by downscaled global climate model (GCM) data, the FORCCHN evaluates the carbon sink potential of southwestern forest ecosystems under different shared socioeconomic pathways (SSPs). The results indicate that, first, gross primary productivity (GPP), ecosystem respiration (ER) and net primary productivity (NPP) of forest ecosystems are expected to increase from 2020 to 2060. Forest ecosystems will maintain a carbon sink, but net ecosystem productivity (NEP) will peak and begin to decline in the 2030s. Second, not only is the NEP in the SSP1-2.6 scenario higher than in the other climate change scenarios for 2025–2035 and 2043–2058, but the coefficient of variation of the NEP is also narrower than in the other scenarios. Third, in terms of spatial distribution, the carbon sequestration potential of northwest and central Yunnan is significantly higher than that of other regions, with a slight upward trend in NEP in the future. Finally, GPP and ER are significantly positively correlated with temperature and insignificantly correlated with precipitation, and the increasing temperature will have a negative and unstable impact on forest carbon sinks. This study provides a scientific reference for implementing forest management strategies and achieving sustainable development. Full article
(This article belongs to the Special Issue Forest Biomass/Carbon Monitoring towards Carbon Neutrality)
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