Remote Sensing

Research

18 pages, 9273 KiB

Open AccessArticle

Geomorphological Mapping and Erosion of Abandoned Tailings in the Hiendelaencina Mining District (Spain) from Aerial Imagery and LiDAR Data

by Silvia Martín-Velázquez, Inmaculada Rodríguez-Santalla, Nikoletta Ropero-Szymañska, David Gomez-Ortiz, Tomás Martín-Crespo and Cristina de Ignacio-San José

Remote Sens. 2022, 14(18), 4617; https://doi.org/10.3390/rs14184617 - 15 Sep 2022

Cited by 5 | Viewed by 2374

Abstract

The Hiendelaencina district in Spain was the most important silver producer in Europe during 1844–1925. At the end of the 20th century, with mines having closed, some waste rock dumps were reprocessed, and the sludge from the flotation process was stored in two [...] Read more.

The Hiendelaencina district in Spain was the most important silver producer in Europe during 1844–1925. At the end of the 20th century, with mines having closed, some waste rock dumps were reprocessed, and the sludge from the flotation process was stored in two tailings ponds. When this activity ceased, the residues began to be eroded and disperse. In this study, the state of degradation of both deposits was evaluated using historical mapping and light detection and ranging (LiDAR) data, incorporated into a Geographic Information System. In the aerial images (1946–2018), mine tailings and their main erosive and sedimentary forms were mapped. Geoforms linked to hydrological (channels, gullies, alluvial cones), wind (eolian mantles), hydric–gravitational (colluvium) and anthropic (motorbike tracks) processes which move sludge into the surrounding areas were identified. A net loss of 8849 m³ of sludge, a release of 10.3 t of potentially polluting substances and a high erosion rate of 346 t/ha*year were calculated based on LiDAR data from 2009 and 2014. The ponds show a current high degree of erosion that could increase due to both human activity and the growing frequency of drought and torrential rain periods if stabilization measures are not undertaken. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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15 pages, 4627 KiB

Open AccessArticle

IoT Enabled Deep Learning Based Framework for Multiple Object Detection in Remote Sensing Images

by Imran Ahmed, Misbah Ahmad, Abdellah Chehri, Mohammad Mehedi Hassan and Gwanggil Jeon

Remote Sens. 2022, 14(16), 4107; https://doi.org/10.3390/rs14164107 - 22 Aug 2022

Cited by 17 | Viewed by 3347

Abstract

Advanced collaborative and communication technologies play a significant role in intelligent services and applications, including artificial intelligence, Internet of Things (IoT), remote sensing, robotics, future generation wireless, and aerial access networks. These technologies improve connectivity, energy efficiency, and quality of services of various [...] Read more.

Advanced collaborative and communication technologies play a significant role in intelligent services and applications, including artificial intelligence, Internet of Things (IoT), remote sensing, robotics, future generation wireless, and aerial access networks. These technologies improve connectivity, energy efficiency, and quality of services of various smart city applications, particularly in transportation, monitoring, healthcare, public services, and surveillance. A large amount of data can be obtained by IoT systems and then examined by deep learning methods for various applications, e.g., object detection or recognition. However, it is a challenging and complex task in smart remote monitoring applications (aerial and drone). Nevertheless, it has gained special consideration in recent years and has performed a pivotal role in different control and monitoring applications. This article presents an IoT-enabled smart surveillance solution for multiple object detection through segmentation. In particular, we aim to provide the concept of collaborative drones, deep learning, and IoT for improving surveillance applications in smart cities. We present an artificial intelligence-based system using the deep learning based segmentation model PSPNet (Pyramid Scene Parsing Network) for segmenting multiple objects. We used an aerial drone data set, implemented data augmentation techniques, and leveraged deep transfer learning to boost the system’s performance. We investigate and analyze the performance of the segmentation paradigm with different CNN (Convolution Neural Network) based architectures. The experimental results illustrate that data augmentation enhances the system’s performance by producing good accuracy results of multiple object segmentation. The accuracy of the developed system is 92% with VGG-16 (Visual Geometry Group), 93% with ResNet-50 (Residual Neural Network), and 95% with MobileNet. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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23 pages, 8832 KiB

Open AccessArticle

Mapping Two Decades of New York State Forest Aboveground Biomass Change Using Remote Sensing

by Haifa Tamiminia, Bahram Salehi, Masoud Mahdianpari, Colin M. Beier and Lucas Johnson

Remote Sens. 2022, 14(16), 4097; https://doi.org/10.3390/rs14164097 - 21 Aug 2022

Cited by 4 | Viewed by 3356

Abstract

Forest aboveground biomass (AGB) provides valuable information about the carbon cycle, carbon sink monitoring, and understanding of climate change factors. Remote sensing data coupled with machine learning models have been increasingly used for forest AGB estimation over local and regional extents. Landsat series [...] Read more.

Forest aboveground biomass (AGB) provides valuable information about the carbon cycle, carbon sink monitoring, and understanding of climate change factors. Remote sensing data coupled with machine learning models have been increasingly used for forest AGB estimation over local and regional extents. Landsat series provide a 50-year data archive, which is a valuable source for historical mapping over large areas. As such, this paper proposed a machine learning-based workflow for historical AGB estimation and its change analysis from 2001 to 2019 for the New York State’s forests using Landsat historical imagery, airborne LiDAR, and forest plot data. As the object-based image analysis (OBIA) is able to incorporate spectral, contextual, and textural features into the regression model, the proposed method utilizes an OBIA approach and a random forest (RF) regression model implemented on the Google Earth Engine (GEE) cloud computing platform. Results demonstrated that there is a considerable decrease of 983.79 × 10⁶ Mg/ha in the AGB of deciduous forests from 2001 to 2006, followed by an increase of 618.28 × 10⁶ Mg/ha from 2006 to 2011, continued with an increase of 229.12 × 10⁶ Mg/ha of deciduous forests from 2011–2016. Finally, the results demonstrated a slight change in AGB from 2016 to 2019. The transferability of the proposed framework provides a practical solution for monitoring forests in other states or even on a national scale. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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21 pages, 8563 KiB

Open AccessArticle

PBL Height Retrievals at a Coastal Site Using Multi-Instrument Profiling Methods

by Ioanna Tsikoudi, Eleni Marinou, Ville Vakkari, Anna Gialitaki, Maria Tsichla, Vassilis Amiridis, Mika Komppula, Ioannis Panagiotis Raptis, Anna Kampouri, Vasiliki Daskalopoulou, Nikos Mihalopoulos, Eleni Giannakaki, Maria Tombrou and Helena Flocas

Remote Sens. 2022, 14(16), 4057; https://doi.org/10.3390/rs14164057 - 19 Aug 2022

Cited by 5 | Viewed by 2250

Abstract

The objective of this study was the estimation of the dynamic evolution of the Planetary Boundary Layer (PBL) height, using advanced remote sensing measurements from Finokalia Station, where the Pre-TECT Campaign took place during 1–26 April 2017. PollyXT Raman Lidar and Halo Wind [...] Read more.

The objective of this study was the estimation of the dynamic evolution of the Planetary Boundary Layer (PBL) height, using advanced remote sensing measurements from Finokalia Station, where the Pre-TECT Campaign took place during 1–26 April 2017. PollyXT Raman Lidar and Halo Wind Doppler Lidar profiles were used to study the daily vertical evolution of the PBL. Wavelet Covariance Transform (WCT) and Threshold Method (TM) were performed on different products acquired from Lidars. According to the analysis, all methods and products are able to provide reasonable boundary-layer height estimates, each of them showing assets and barriers under certain conditions. Two cases are presented in detail, indicating the limited daytime evolution of a coastal area, the decisive role of wind speed-direction in the formation of a shallow or high boundary layer and the differences when using aerosols or turbulence as tracers for the PBL height retrieval. Comparison between the observed PBL and ECMWF model results was made, establishing the importance of actual PBL measurements, in coastal regions with complex topography. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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21 pages, 11667 KiB

Open AccessArticle

Simulating the Changes of Invasive Phragmites australis in a Pristine Wetland Complex with a Grey System Coupled System Dynamic Model: A Remote Sensing Practice

by Danlin Yu, Nicholas A. Procopio and Chuanglin Fang

Remote Sens. 2022, 14(16), 3886; https://doi.org/10.3390/rs14163886 - 11 Aug 2022

Cited by 3 | Viewed by 2178

Abstract

Biological invasion has been one of the reasons that coastal wetlands gradually lose their ecological services. The current study investigates the spread of a commonly found invasive species in coastal wetlands in Northeastern US, the Phragmites australis. Within a relatively pristine wetland [...] Read more.

Biological invasion has been one of the reasons that coastal wetlands gradually lose their ecological services. The current study investigates the spread of a commonly found invasive species in coastal wetlands in Northeastern US, the Phragmites australis. Within a relatively pristine wetland complex in coastal New Jersey, we collected high-resolution multispectral remote sensing images for eight years (2011–2018), in both winter and summer seasons. The land cover/land use status in this wetland complex is relatively simple, contains only five identifiable vegetation covers and water. Applying high accuracy machine learning algorithms, we are able to classify the land use/land cover in the complex and use the classified images as the basis for the grey system coupled system dynamics simulative model. The simulative model produces land use land cover change in the wetland complex for the next 25 years. Results suggest that Phragmites australis will increase in coverage in the future, despite the stable intensity of anthropogenic activities. The wetland complex could lose its essential ecological services to serve as an exchange spot for nekton species from the sea. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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32 pages, 15502 KiB

Open AccessArticle

Change Detection Techniques with Synthetic Aperture Radar Images: Experiments with Random Forests and Sentinel-1 Observations

by Pietro Mastro, Guido Masiello, Carmine Serio and Antonio Pepe

Remote Sens. 2022, 14(14), 3323; https://doi.org/10.3390/rs14143323 - 10 Jul 2022

Cited by 36 | Viewed by 6803

Abstract

This work aims to clarify the potential of incoherent and coherent change detection (CD) approaches for detecting and monitoring ground surface changes using sequences of synthetic aperture radar (SAR) images. Nowadays, the growing availability of remotely sensed data collected by the twin Sentinel-1A/B [...] Read more.

This work aims to clarify the potential of incoherent and coherent change detection (CD) approaches for detecting and monitoring ground surface changes using sequences of synthetic aperture radar (SAR) images. Nowadays, the growing availability of remotely sensed data collected by the twin Sentinel-1A/B sensors of the European (EU) Copernicus constellation allows fast mapping of damage after a disastrous event using radar data. In this research, we address the role of SAR (amplitude) backscattered signal variations for CD analyses when a natural (e.g., a fire, a flash flood, etc.) or a human-induced (disastrous) event occurs. Then, we consider the additional pieces of information that can be recovered by comparing interferometric coherence maps related to couples of SAR images collected between a principal disastrous event date. This work is mainly concerned with investigating the capability of different coherent/incoherent change detection indices (CDIs) and their mutual interactions for the rapid mapping of “changed” areas. In this context, artificial intelligence (AI) algorithms have been demonstrated to be beneficial for handling the different information coming from coherent/incoherent CDIs in a unique corpus. Specifically, we used CDIs that synthetically describe ground surface changes associated with a disaster event (i.e., the pre-, cross-, and post-disaster phases), based on the generation of sigma nought and InSAR coherence maps. Then, we trained a random forest (RF) to produce CD maps and study the impact on the final binary decision (changed/unchanged) of the different layers representing the available synthetic CDIs. The proposed strategy was effective for quickly assessing damage using SAR data and can be applied in several contexts. Experiments were conducted to monitor wildfire’s effects in the 2021 summer season in Italy, considering two case studies in Sardinia and Sicily. Another experiment was also carried out on the coastal city of Houston, Texas, the US, which was affected by a large flood in 2017; thus, demonstrating the validity of the proposed integrated method for fast mapping of flooded zones using SAR data. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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15 pages, 6374 KiB

Open AccessArticle

Quantifying the Impacts of the 2020 Flood on Crop Production and Food Security in the Middle Reaches of the Yangtze River, China

by Liang-Chen Wang, Duc Vinh Hoang and Yuei-An Liou

Remote Sens. 2022, 14(13), 3140; https://doi.org/10.3390/rs14133140 - 30 Jun 2022

Cited by 8 | Viewed by 2832

Abstract

This study uses satellite imagery and geospatial data to examine the impact of floods over the main planting areas for double-cropping rice and grain crops in the middle reaches of the Yangtze River. During summer 2020, a long-lasting 62-day heavy rainfall caused record-breaking [...] Read more.

This study uses satellite imagery and geospatial data to examine the impact of floods over the main planting areas for double-cropping rice and grain crops in the middle reaches of the Yangtze River. During summer 2020, a long-lasting 62-day heavy rainfall caused record-breaking floods over large areas of China, especially the Yangtze basin. Through close examination of Sentinel-1/2 satellite imagery and Copernicus Global Land Cover, between July and August 2020, the inundation area reached 21,941 and 23,063 km², and the crop-affected area reached 11,649 and 11,346 km², respectively. We estimated that approximately 4.66 million metric tons of grain crops were seriously affected in these two months. While the PRC government denied that food security existed, the number of Grains and Feeds imported from the U.S. between January to July 2021 increased by 316%. This study shows that with modern remote sensing techniques, stakeholders can obtain critical estimates of large-scale disaster events much earlier than other indicators, such as disaster field surveys or crop price statistics. Potential use could include but is not limited to monitoring floods and land use coverage changes. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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21 pages, 1414 KiB

Open AccessArticle

The Influence of the Signal-to-Noise Ratio upon Radio Occultation Retrievals

by Michael Gorbunov, Vladimir Irisov and Christian Rocken

Remote Sens. 2022, 14(12), 2742; https://doi.org/10.3390/rs14122742 - 7 Jun 2022

Cited by 4 | Viewed by 2236

Abstract

We study the dependence of radio occultation (RO) inversion statistics on the signal-to-noise ratio (SNR). We use observations from four missions: COSMIC, COSMIC-2, METOP-B, and Spire. All data are processed identically using the same software with the same settings for the retrieval of [...] Read more.

We study the dependence of radio occultation (RO) inversion statistics on the signal-to-noise ratio (SNR). We use observations from four missions: COSMIC, COSMIC-2, METOP-B, and Spire. All data are processed identically using the same software with the same settings for the retrieval of bending angles, which are compared with reference analyses of the National Oceanic and Atmospheric Administration (NOAA) Global Forecast System. We evaluate the bias, the standard deviation, and the penetration characterized by the fraction of events reaching a specific height. In order to compare SNRs from the different RO missions, we use the results of our previous study, which defined two types of SNR. The statically normalized SNR is defined in terms of the most probable value of the noise floor for the specific mission and global navigation satellite system. The dynamically normalized SNR uses the noise floor value for the specific profile. This study is based on the dynamical normalization. We also evaluate the latitudinal distributions of occultations for different missions. We show that the dependence of the retrieval statistics on the SNR is not very strong, and it is mostly defined by the variations of latitudinal distributions for different SNR. For Spire, these variations are the smallest, and here, the bias and standard deviation reach saturated values for a relatively low SNR. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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20 pages, 2306 KiB

Open AccessArticle

Dormant Season Vegetation Phenology and Eddy Fluxes in Native Tallgrass Prairies of the U.S. Southern Plains

by Pradeep Wagle, Vijaya G. Kakani, Prasanna H. Gowda, Xiangming Xiao, Brian K. Northup, James P. S. Neel, Patrick J. Starks, Jean L. Steiner and Stacey A. Gunter

Remote Sens. 2022, 14(11), 2620; https://doi.org/10.3390/rs14112620 - 31 May 2022

Cited by 5 | Viewed by 2357

Abstract

Carbon dioxide (CO₂) fluxes and evapotranspiration (ET) during the non-growing season can contribute significantly to the annual carbon and water budgets of agroecosystems. Comparative studies of vegetation phenology and the dynamics of CO₂ fluxes and ET during the dormant season [...] Read more.

Carbon dioxide (CO₂) fluxes and evapotranspiration (ET) during the non-growing season can contribute significantly to the annual carbon and water budgets of agroecosystems. Comparative studies of vegetation phenology and the dynamics of CO₂ fluxes and ET during the dormant season of native tallgrass prairies from different landscape positions under the same climatic regime are scarce. Thus, this study compared the dynamics of satellite-derived vegetation phenology (as captured by the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI)) and eddy covariance (EC)-measured CO₂ fluxes and ET in six differently managed native tallgrass prairie pastures during dormant seasons (November through March). During December–February, vegetation phenology (EVI and NDVI) and the dynamics of eddy fluxes were comparable across all pastures in most years. Large discrepancies in fluxes were observed during March (the time of the initiation of growth of dominant warm-season grasses) across years and pastures due to the influence of weather conditions and management practices. The results illustrated the interactive effects between prescribed spring burns and rainfall on vegetation phenology (i.e., positive and negative impacts of prescribed spring burns under non-drought and drought conditions, respectively). The EVI better tracked the phenology of tallgrass prairie during the dormant season than did NDVI. Similar EVI and NDVI values for the periods when flux magnitudes were different among pastures and years, most likely due to the satellite sensors’ inability to fully observe the presence of some cool-season C₃ species under residues, necessitated a multi-level validation approach of using ground-truth observations of species composition, EC measurements, PhenoCam (digital) images, and finer-resolution satellite data to further validate the vegetation phenology derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) during dormant seasons. This study provides novel insights into the dynamics of vegetation phenology, CO₂ fluxes, and ET of tallgrass prairie during the dormant season in the U.S. Southern Great Plains. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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34 pages, 17083 KiB

Open AccessArticle

Quantifying Water Consumption through the Satellite Estimation of Land Use/Land Cover and Groundwater Storage Changes in a Hyper-Arid Region of Egypt

by Ayihumaier Halipu, Xuechen Wang, Erina Iwasaki, Wei Yang and Akihiko Kondoh

Remote Sens. 2022, 14(11), 2608; https://doi.org/10.3390/rs14112608 - 29 May 2022

Cited by 8 | Viewed by 2851

Abstract

One of the areas that show the most visible effects of human-induced land alterations is also the world’s most essential resource: water. Decision-makers in arid regions face considerable difficulties in providing and maintaining sustainable water resource management. However, developing appropriate and straightforward approaches [...] Read more.

One of the areas that show the most visible effects of human-induced land alterations is also the world’s most essential resource: water. Decision-makers in arid regions face considerable difficulties in providing and maintaining sustainable water resource management. However, developing appropriate and straightforward approaches for quantifying water use in arid/hyper-arid regions is still a formidable challenge. Meanwhile, a better knowledge of the effects of land use land cover (LULC) changes on natural resources and environmental systems is required. The purpose of this study was to quantify the water consumption in a hyper-arid region (New Valley, Egypt) using two different approaches—LULC based on optical remote sensing data and groundwater storage changes based on Gravity Recovery Climate Experiment (GRACE) satellite data—and to compare and contrast the quantitative results of the two approaches. The LULC of the study area was constructed from 1986 to 2021 to identify the land cover changes and investigate the primary water consumption patterns. The analysis of groundwater storage changes utilized two GRACE mascon solutions from 2002 to 2021 in New Valley. The results showed an increase in agricultural areas in New Valley’s oases. They also showed an increased in irrigation water usage and a continuous decrease in the groundwater storage of New Valley. The overall water usage in New Valley for domestic and irrigation was calculated as 18.62 km³ (0.93 km³/yr) based on the LULC estimates. Moreover, the groundwater storage changes of New Valley were extracted using GRACE and calculated to be 19.36 ± 7.96 km³ (0.97 ± 0.39 km³/yr). The results indicated that the water use calculated from LULC was consistent with the depletion in groundwater storage calculated by applying GRACE. This study provides an essential reference for regional sustainability and water resource management in arid/hyper-arid regions. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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18 pages, 3412 KiB

Open AccessArticle

An Investigation on Super- and Sub-Terminal Drops in Two Different Rain Categories and Climate Regimes

by Chandrani Chatterjee, Federico Porcù, Saurabh Das and Alessandro Bracci

Remote Sens. 2022, 14(11), 2515; https://doi.org/10.3390/rs14112515 - 24 May 2022

Cited by 4 | Viewed by 2491

Abstract

The pressing need for accurate and reliable precipitation measurements and forecasting poses theoretical and technological problems. Remote-sensing instruments with increased coverage and sensitivity (such as space-borne and ground-based radar) are available; however, their full exploitation requires physical calibration and validation based on a [...] Read more.

The pressing need for accurate and reliable precipitation measurements and forecasting poses theoretical and technological problems. Remote-sensing instruments with increased coverage and sensitivity (such as space-borne and ground-based radar) are available; however, their full exploitation requires physical calibration and validation based on a deep knowledge of precipitation microphysics. This study reports a detailed analysis of the evidence of non-terminal velocities in a mid-latitude (Bologna, Italy) and a tropical location (Kolkata, India). The data from two identical disdrometers OTT-Parsivel2 were analyzed to shed light on the nature of the raindrops that fall at a velocity that is significantly higher (i.e., super-terminal drops) or lower (sub-terminal drops) than the terminal velocity expected for the raindrop sizes. The results show a significant fraction of super- and sub-terminal drops in both locations. The percentages of both super- and sub-terminal drops were higher in Kolkata. However, the difference was more notable for convective rain. The percentages of both super- and sub-terminal drops were found to be high within a drop diameter of 1 mm. The number of sub-terminal drops seemed to increase with an increase in diameter for drops larger than ~2.5 mm. The natural rain in Bologna showed stronger evidence of drop break-up in correspondence with the evolution of non-terminal velocities. Moreover, this study once again pointed toward the fact that the process of break-up cannot be neglected in natural rain of tropical or mid-latitude locations. We found that 7% and 10% of rain samples in Bologna and Kolkata seemed to be subjected to drop break-up. The results indicate that radar measurements of rain in the tropics or mid-latitude regions, relying on the Gunn–Kinzer relationship between velocity and diameter, should be verified by observations of disdrometers for a high precision QPE. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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41 pages, 15577 KiB

Open AccessArticle

Correcting GEDI Water Level Estimates for Inland Waterbodies Using Machine Learning

by Ibrahim Fayad, Nicolas Baghdadi, Jean-Stéphane Bailly, Frédéric Frappart and Núria Pantaleoni Reluy

Remote Sens. 2022, 14(10), 2361; https://doi.org/10.3390/rs14102361 - 13 May 2022

Cited by 6 | Viewed by 2420

Abstract

The Global Ecosystem Dynamics Investigation (GEDI) LiDAR on the International Space Station has acquired more than 35 billion shots globally in the period between April 2019 and August 2021. The acquired shots could offer a significant database for the measure and monitoring of [...] Read more.

The Global Ecosystem Dynamics Investigation (GEDI) LiDAR on the International Space Station has acquired more than 35 billion shots globally in the period between April 2019 and August 2021. The acquired shots could offer a significant database for the measure and monitoring of inland water levels over the Earth’s surface. Nonetheless, previous and current studies have shown that the provided GEDI elevation estimates are significantly less accurate than any available radar or LiDAR altimeter. Indeed, our analysis of GEDI’s altimetric capabilities to retrieve water levels over the five North American Great Lakes presented estimates with a bias that ranged between 0.26 and 0.35 m and a root mean squared error (RMSE) ranging between 0.54 and 0.68 m. Therefore, our objective in this study is to post-process the original GEDI water level estimates from an error model taking instrumental, atmospheric, and lakes surface state factors as proxies, which affect the physical shape of the waveforms, hence introducing uncertainties on the elevation estimates. The first tested model, namely a random forest regressor (

R F_{I C W}

) with the instrumental, atmospheric, and water surface state factors as inputs, was validated temporally (trained on a given year and validated on another) and spatially (trained on a given lake and validated on the remaining four). The results showed a significant decrease in elevation estimation errors both temporally and spatially. The temporally validated models showed an RMSE on the corrected elevation estimates of 0.18 m. Concerning the spatially validated model, the results varied based on the lake data used for training. Indeed, the most accurate spatially validated model showed an RMSE of 0.17 m, while the least accurate model showed an RMSE of 0.26 m. Finally, given that an elevation correction model using all the factors (instrumental, atmospheric, and water surface state factors) presents a best-case scenario, as water surface state factors are only available over a selected number of lakes globally, three additional models based on random forest were tested. The first,

R F_{I}

, uses only instrumental factors as correction factors,

R F_{I C}

uses both instrumental and atmospheric factors, while the third,

R F_{I W}

, uses instrumental and water surface state factors. The temporal validation of these models showed that the model using instrumental factors, while less accurate than the remaining two models, was capable of correcting the original GEDI elevation estimates by a factor of two across the five lakes. On the other hand, the

R F_{I C}

model was the most accurate between the three, with a slight degradation in comparison to the full model. Indeed, the

R F_{I C}

model showed an RMSE on the estimation of water levels of 0.21 m. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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25 pages, 8658 KiB

Open AccessArticle

Radargrammetric DSM Generation by Semi-Global Matching and Evaluation of Penalty Functions

by Jinghui Wang, Ke Gong, Timo Balz, Norbert Haala, Uwe Soergel, Lu Zhang and Mingsheng Liao

Remote Sens. 2022, 14(8), 1778; https://doi.org/10.3390/rs14081778 - 7 Apr 2022

Cited by 9 | Viewed by 3027

Abstract

Radargrammetry is a useful approach to generate Digital Surface Models (DSMs) and an alternative to InSAR techniques that are subject to temporal or atmospheric decorrelation. Stereo image matching in radargrammetry refers to the process of determining homologous points in two images. The performance [...] Read more.

Radargrammetry is a useful approach to generate Digital Surface Models (DSMs) and an alternative to InSAR techniques that are subject to temporal or atmospheric decorrelation. Stereo image matching in radargrammetry refers to the process of determining homologous points in two images. The performance of image matching influences the final quality of DSM used for spatial-temporal analysis of landscapes and terrain. In SAR image matching, local matching methods are commonly used but usually produce sparse and inaccurate homologous points adding ambiguity to final products; global or semi-global matching methods are seldom applied even though more accurate and dense homologous points can be yielded. To fill this gap, we propose a hierarchical semi-global matching (SGM) pipeline to reconstruct DSMs in forested and mountainous regions using stereo TerraSAR-X images. In addition, three penalty functions were implemented in the pipeline and evaluated for effectiveness. To make accuracy and efficiency comparisons between our SGM dense matching method and the local matching method, the normalized cross-correlation (NCC) local matching method was also applied to generate DSMs using the same test data. The accuracy of radargrammetric DSMs was validated against an airborne photogrammetric reference DSM and compared with the accuracy of NASA’s 30 m SRTM DEM. The results show the SGM pipeline produces DSMs with height accuracy and computing efficiency that exceeds the SRTM DEM and NCC-derived DSMs. The penalty function adopting the Canny edge detector yields a higher vertical precision than the other two evaluated penalty functions. SGM is a powerful and efficient tool to produce high-quality DSMs using stereo Spaceborne SAR images. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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19 pages, 2488 KiB

Open AccessArticle

Mapping Impervious Surface Using Phenology-Integrated and Fisher Transformed Linear Spectral Mixture Analysis

by Linke Ouyang, Caiyan Wu, Junxiang Li, Yuhan Liu, Meng Wang, Ji Han, Conghe Song, Qian Yu and Dagmar Haase

Remote Sens. 2022, 14(7), 1673; https://doi.org/10.3390/rs14071673 - 30 Mar 2022

Cited by 4 | Viewed by 3009

Abstract

The impervious surface area (ISA) is a key indicator of urbanization, which brings out serious adverse environmental and ecological consequences. The ISA is often estimated from remotely sensed data via spectral mixture analysis (SMA). However, accurate extraction of ISA using SMA is compromised [...] Read more.

The impervious surface area (ISA) is a key indicator of urbanization, which brings out serious adverse environmental and ecological consequences. The ISA is often estimated from remotely sensed data via spectral mixture analysis (SMA). However, accurate extraction of ISA using SMA is compromised by two major factors, endmember spectral variability and plant phenology. This study developed a novel approach that incorporates phenology with Fisher transformation into a conventional linear spectral mixture analysis (PF-LSMA) to address these challenges. Four endmembers, high albedo, low albedo, evergreen vegetation, and seasonally exposed soil (H-L-EV-SS) were identified for PF-LSMA, considering the phenological characteristic of Shanghai. Our study demonstrated that the PF-LSMA effectively reduced the within-endmember spectral signature variation and accounted for the endmember phenology effects, and thus well-discriminated impervious surface from seasonally exposed soil, enhancing the accuracy of ISA extraction. The ISA fraction map produced by PF-LSMA (RMSE = 0.1112) outperforms the single-date image Fisher transformed unmixing method (F-LSMA) (RMSE = 0.1327) and the other existing major global ISA products. The PF-LSMA was implemented on the Google Earth Engine platform and thus can be easily adapted to extract ISA in other places with similar climate conditions. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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24 pages, 21662 KiB

Open AccessArticle

Eastern Arctic Sea Ice Sensing: First Results from the RADARSAT Constellation Mission Data

by Hangyu Lyu, Weimin Huang and Masoud Mahdianpari

Remote Sens. 2022, 14(5), 1165; https://doi.org/10.3390/rs14051165 - 26 Feb 2022

Cited by 17 | Viewed by 4283

Abstract

Sea ice monitoring plays a vital role in secure navigation and offshore activities. Synthetic aperture radar (SAR) has been widely used as an effective tool for sea ice remote sensing (e.g., ice type classification, concentration and thickness retrieval) for decades because it can [...] Read more.

Sea ice monitoring plays a vital role in secure navigation and offshore activities. Synthetic aperture radar (SAR) has been widely used as an effective tool for sea ice remote sensing (e.g., ice type classification, concentration and thickness retrieval) for decades because it can collect data by day and night and in almost all weather conditions. The RADARSAT Constellation Mission (RCM) is a new Canadian SAR mission providing several new services and data, with higher spatial coverage and temporal resolution than previous Radarsat missions. As a very deep convolutional neural network, Normalizer-Free ResNet (NFNet) was proposed by DeepMind in early 2021 and achieved a new state-of-the-art accuracy on the ImageNet dataset. In this paper, the RCM data are utilized for sea ice detection and classification using NFNet for the first time. HH, HV and the cross-polarization ratio are extracted from the dual-polarized RCM data with a medium resolution (50 m) for an NFNet-F0 model. Experimental results from Eastern Arctic show that destriping in the HV channel is necessary to improve the quality of sea ice classification. A two-level random forest (RF) classification model is also applied as a conventional technique for comparisons with NFNet. The sea ice concentration estimated based on the classification result from each region was validated with the corresponding polygon of the Canadian weekly regional ice chart. The overall classification accuracy confirms the superior capacity of the NFNet model over the RF model for sea ice monitoring and the sea ice sensing capacity of RCM. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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26 pages, 10061 KiB

Open AccessReview

Scientometric Full-Text Analysis of Papers Published in Remote Sensing between 2009 and 2021

by Timo Balz

Remote Sens. 2022, 14(17), 4285; https://doi.org/10.3390/rs14174285 - 30 Aug 2022

Cited by 5 | Viewed by 2551

Abstract

Covering the full texts of all papers published in MDPI’s Remote Sensing between 2009 and 2021, in-depth scientometric analyses were conducted. Trends in publications show an increase in the overall number of papers. A relative increase in papers using SAR sensors and a [...] Read more.

Covering the full texts of all papers published in MDPI’s Remote Sensing between 2009 and 2021, in-depth scientometric analyses were conducted. Trends in publications show an increase in the overall number of papers. A relative increase in papers using SAR sensors and a relative decrease in papers using optical remote sensing can also be seen. The full-text analyses reveal distinctive styles and writing patterns for papers from different sub-fields of remote sensing and for different countries and even cities. While a slight increase in the readability of abstracts is detected over time, the overall readability of papers is decreasing. Institutional co-authorship analysis reveals the ongoing ‘scientific decoupling’ between China and the USA in remote sensing. Using scientometric full-text analysis, current trends and developments are revealed. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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15 pages, 1787 KiB

Open AccessTechnical Note

Atmospheric Boundary Layer Height: Inter-Comparison of Different Estimation Approaches Using the Raman Lidar as Benchmark

by Donato Summa, Gemine Vivone, Noemi Franco, Giuseppe D’Amico, Benedetto De Rosa and Paolo Di Girolamo

Remote Sens. 2023, 15(5), 1381; https://doi.org/10.3390/rs15051381 - 28 Feb 2023

Cited by 4 | Viewed by 1997

Abstract

This work stems from the idea of improving the capability to measure the atmospheric boundary layer height (ABLH) in variable or unstable weather conditions or in the presence of turbulence and precipitation events. A new approach based on the use of rotational and [...] Read more.

This work stems from the idea of improving the capability to measure the atmospheric boundary layer height (ABLH) in variable or unstable weather conditions or in the presence of turbulence and precipitation events. A new approach based on the use of rotational and roto-vibrational Raman lidar signals is considered and tested. The traditional gradient approach based on the elastic signals at wavelength 532 nm is also considered. Lidar data collected by the University of Basilicata Raman lidar (BASIL) within the Special Observation Period 1 (SOP 1) in Cardillargues (Ceveninnes–CV supersite) during the Hydrological Cycle in the Mediterranean Experiment (HyMeX) were used. Our attention was specifically focused on the data collected during the period 16–21 October 2012. ABLH estimates from the Raman lidar were compared against other innovative methods, such as the recently established Morphological Image Processing Approach (MIPA) and the temperature gradient technique applied to potential temperature obtained from radio-sounding data. For each considered methodology, a statistical analysis was carried out. In general, the results from the different methodologies are in good agreement. Some deviations have been observed in correspondence with quite unstable weather conditions. Full article

(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment)

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