Remote Sensing

22 pages, 3982 KiB

Open AccessArticle

Clouds’ Microphysical Properties and Their Relationship with Lightning Activity in Northeast Brazil

by Lizandro Pereira de Abreu, Weber Andrade Gonçalves, Enrique Vieira Mattos, Pedro Rodrigues Mutti, Daniele Torres Rodrigues and Marcos Paulo Araújo da Silva

Remote Sens. 2021, 13(21), 4491; https://doi.org/10.3390/rs13214491 - 08 Nov 2021

Cited by 2 | Viewed by 2446

Abstract

The Northeast region of Brazil (NEB) has a high rate of deaths from lightning strikes (18% of the country’s total). The region has states, such as Piauí, with high mortality rates (1.8 deaths per million), much higher than the national rate (0.8) and [...] Read more.

The Northeast region of Brazil (NEB) has a high rate of deaths from lightning strikes (18% of the country’s total). The region has states, such as Piauí, with high mortality rates (1.8 deaths per million), much higher than the national rate (0.8) and the NEB rate (0.5). In this sense, the present work analyzes the microphysical characteristics of clouds with and without the occurrence of total lightning. For this purpose, data from the Lightning Imaging Sensor (LIS), TRMM Microwave Imager (TMI) and Precipitation Radar (PR), aboard the Tropical Rainfall Measuring Mission (TRMM) satellite from 1998 to 2013 were used. The TRMM data were analyzed to establish a relationship between the occurrence of lightning and the clouds’ microphysical characteristics, comparing them as a function of lightning occurrence classes, spatial location and atmospheric profiles. A higher lightning occurrence is associated with higher values of ice water path (>38.9 kg m⁻²), rain water path (>2 kg m⁻²), convective precipitation (>5 mm h⁻¹) and surface precipitation (>7 mm h⁻¹), in addition to slightly higher freezing level height values. Reflectivity observations (>36 dBZ) demonstrated typical convective profile curves, with higher values associated with classes with higher lightning densities (class with more than 6.8 flash km⁻² year⁻¹). Full article

(This article belongs to the Special Issue Satellite Microwave Remote Sensing for Severe Storms Detection)

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22 pages, 56903 KiB

Open AccessArticle

Evaluation of Precipitable Water Vapor Retrieval from Homogeneously Reprocessed Long-Term GNSS Tropospheric Zenith Wet Delay, and Multi-Technique

by Hang Su, Tao Yang, Kan Wang, Baoqi Sun and Xuhai Yang

Remote Sens. 2021, 13(21), 4490; https://doi.org/10.3390/rs13214490 - 08 Nov 2021

Cited by 1 | Viewed by 1714

Abstract

Water vapor is one of the most important greenhouse gases in the world. There are many techniques that can measure water vapor directly or remotely. In this work, we first study the Global Positioning System (GPS)- and the Global Navigation Satellite System (GLONASS)-derived [...] Read more.

Water vapor is one of the most important greenhouse gases in the world. There are many techniques that can measure water vapor directly or remotely. In this work, we first study the Global Positioning System (GPS)- and the Global Navigation Satellite System (GLONASS)-derived Zenith Wet Delay (ZWD) time series based on 11 years of the second reprocessing campaign of International Global Navigation Satellite Systems (GNSS) Service (IGS) using 320 globally distributed stations. The amount of measurement, the local environment, and the antenna radome are shown to be the main factors that affect the GNSS ZWDs and the corresponding a posteriori formal errors. Furthermore, antenna radome is able to effectively reduce the systematic bias of ZWDs and a posteriori formal errors between the GPS- and GLONASS-based solutions. With the development of the GLONASS, the ZWD differences between the GPS- and the GLONASS-based solutions have gradually decreased to sub-mm-level after GLONASS was fully operated. As the GPS-based Precipitable Water Vapor (PWV) is usually used as the reference to evaluate the other PWV products, the PWV consistency among several common techniques is evaluated, including GNSSs, spaceborne sensors, and numerical products from the European Center for Medium-Range Weather Forecasts (ECMWF). As an example of the results from a detailed comparison analysis, the long-term global analysis shows that the PWV obtained from the GNSS and the ECMWF have great intra-agreements. Based on the global distribution of the magnitude of the PWV and the PWV drift, most of the techniques showed superior agreement and proved their ability to do climate research. With a detailed study performed for the ZWDs and PWV on a long-term global scale, this contribution provides a useful supplement for future research on the GNSS ZWD and PWV. Full article

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19 pages, 3733 KiB

Open AccessArticle

Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery

by Robert Chancia, Terry Bates, Justine Vanden Heuvel and Jan van Aardt

Remote Sens. 2021, 13(21), 4489; https://doi.org/10.3390/rs13214489 - 08 Nov 2021

Cited by 13 | Viewed by 3089

Abstract

This study aimed to identify the optimal sets of spectral bands for monitoring multiple grapevine nutrients in vineyards. We used spectral data spanning 400–2500 nm and leaf samples from 100 Concord grapevine canopies, lab-analyzed for six key nutrient values, to select the optimal [...] Read more.

This study aimed to identify the optimal sets of spectral bands for monitoring multiple grapevine nutrients in vineyards. We used spectral data spanning 400–2500 nm and leaf samples from 100 Concord grapevine canopies, lab-analyzed for six key nutrient values, to select the optimal bands for the nutrient regression models. The canopy spectral data were obtained with unmanned aerial systems (UAS), using push-broom imaging spectrometers (hyperspectral sensors). The novel use of UAS-based hyperspectral imagery to assess the grapevine nutrient status fills the gap between in situ spectral sampling and UAS-based multispectral imaging, avoiding their inherent trade-offs between spatial and spectral resolution. We found that an ensemble feature ranking method, utilizing six different machine learning feature selection methods, produced similar regression results as the standard PLSR feature selection and regression while generally selecting fewer wavelengths. We identified a set of biochemically consistent bands (606, 641, and 1494 nm) to predict the nitrogen content with an RMSE of 0.17% (using leave-one-out cross-validation) in samples with nitrogen contents ranging between 2.4 and 3.6%. Further studying is needed to confirm the relevance and consistency of the wavelengths selected for each nutrient model, but ensemble feature selection showed promise in identifying stable sets of wavelengths for assessing grapevine nutrient contents from canopy spectra. Full article

(This article belongs to the Special Issue Precision Agriculture Using Hyperspectral Images)

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23 pages, 54251 KiB

Open AccessArticle

Microscale and Mesoscale Aeolian Processes of Sandy Coastal Foredunes from Background to Extreme Conditions

by Bianca R. Charbonneau and Stephanie M. Dohner

Remote Sens. 2021, 13(21), 4488; https://doi.org/10.3390/rs13214488 - 08 Nov 2021

Cited by 4 | Viewed by 2059

Abstract

Aeolian transport affects beach and foredune pre-storm morphologies, which directly contribute to storm responses. However, significant spatiotemporal variation exists within beach-dune systems regarding how biotic and abiotic factors affect topography. There are multiple metrics for quantifying topographic change, with varying pros and cons, [...] Read more.

Aeolian transport affects beach and foredune pre-storm morphologies, which directly contribute to storm responses. However, significant spatiotemporal variation exists within beach-dune systems regarding how biotic and abiotic factors affect topography. There are multiple metrics for quantifying topographic change, with varying pros and cons, but understanding how a system changes across spatiotemporal scales relative to varying forcings is necessary to accurately model and more effectively manage these systems. Beach and foredune micro- and mesoscale elevation changes (Δz) were quantified remotely and in situ across a mid-Atlantic coastal system. The microscale field collections consisted of 27 repeat measurements of 73 elevation pins located in vegetated, transitional, and unvegetated foredune microhabitats over three years (2015 to 2018) during seasonal, event-based, and background wind-condition collections. Unoccupied aerial System (UAS) surveys were collected to link microscale point Δz to mesoscale topographic change. Microscale measurements highlight how Δz varies more pre- to post-event than seasonally or monthly, but regardless of collection type (i.e., seasonal, monthly, or event-based), there was lower Δz in the vegetated areas than in the associated unvegetated and partially vegetated microhabitats. Despite lower Δz values per pin measurement, over the study duration, vegetated pins had a net elevation increase of ≈20 cm, whereas transitional and unvegetated microhabitats had much lower change, near-zero net gain. These results support vegetated microhabitats being more stable and having better sediment retention than unvegetated and transitional areas. Comparatively, mesoscale UAS surfaces typically overestimated Δz, such that variation stemming from vegetation across microhabitats was obscured. However, these data highlight larger mesoscale habitat impacts that cannot be determined from point measurements regarding volumetric change and feature mapping. Changes in features, such as beach access paths, that are associated with increased dynamism are quantifiable using mesoscale remote sensing methods rather than microscale methods. Regardless of the metric, maintaining baseline data is critical for assessing what is captured and missed across spatiotemporal scales and is necessary for understanding the contributors to heterogeneous topographic change in sandy coastal foredunes. Full article

(This article belongs to the Special Issue Geospatial Analysis and Remote Sensing Applications in Coastal Environments)

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18 pages, 4429 KiB

Open AccessArticle

Precise Relative Orbit Determination for Chinese TH-2 Satellite Formation Using Onboard GPS and BDS2 Observations

by Bin Yi, Defeng Gu, Kai Shao, Bing Ju, Houzhe Zhang, Xianping Qin, Xiaojun Duan and Zhiyong Huang

Remote Sens. 2021, 13(21), 4487; https://doi.org/10.3390/rs13214487 - 08 Nov 2021

Cited by 6 | Viewed by 1842

Abstract

TH-2 is China’s first short-range satellite formation system used to realize interferometric synthetic aperture radar (InSAR) technology. In order to achieve the mission goal of InSAR processing, the relative orbit must be determined with high accuracy. In this study, the precise relative orbit [...] Read more.

TH-2 is China’s first short-range satellite formation system used to realize interferometric synthetic aperture radar (InSAR) technology. In order to achieve the mission goal of InSAR processing, the relative orbit must be determined with high accuracy. In this study, the precise relative orbit determination (PROD) for TH-2 based on global positioning system (GPS), second-generation BeiDou navagation satellite system (BDS2), and GPS + BDS2 observations was performed. First, the performance of onboard GPS and BDS2 measurements were assessed by analyzing the available data, code multipath errors and noise levels of carrier phase observations. The differences between the National University of Defense Technology (NDT) and the Xi’an Research Institute of Surveying and Mapping (CHS) baseline solutions exhibited an RMS of 1.48 mm outside maneuver periods. The GPS-based orbit was used as a reference orbit to evaluate the BDS2-based orbit and the GPS + BDS2-based orbit. It is the first time BDS2 has been applied to the PROD of low Earth orbit (LEO) satellite formation. The results showed that the root mean square (RMS) of difference between the PROD results using GPS and BDS2 measurements in 3D components was 2.89 mm in the Asia-Pacific region. We assigned different weights to geostationary Earth orbit (GEO) satellites to illustrate the impact of GEO satellites on PROD, and the accuracy of PROD was improved to 7.08 mm with the GEO weighting strategy. Finally, relative orbits were derived from the combined GPS and BDS2 data. When BDS2 was added on the basis of GPS, the average number of visible navigation satellites from TH-2A and TH-2B improved from 7.5 to 9.5. The RMS of the difference between the GPS + BDS2-based orbit and the GPS-based orbit was about 1.2 mm in 3D. The overlap comparison results showed that the combined orbit consistencies were below 1 mm in the radial (R), along-track (T), and cross-track (N) directions. Furthermore, when BDS2 co-worked with GPS, the average of the ambiguity dilution of precision (ADOP) reduced from 0.160 cycle to 0.153 cycle, which was about a 4.4% reduction. The experimental results indicate that millimeter-level PROD results for TH-2 satellite formation can be obtained by using onboard GPS and BDS2 observations, and multi-GNSS can further improve the accuracy and reliability of PROD. Full article

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24 pages, 3297 KiB

Open AccessReview

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

by Ildar Rakhmatulin, Andreas Kamilaris and Christian Andreasen

Remote Sens. 2021, 13(21), 4486; https://doi.org/10.3390/rs13214486 - 08 Nov 2021

Cited by 44 | Viewed by 13333

Abstract

Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted [...] Read more.

Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted from classical image processing and machine learning techniques to modern artificial intelligence (AI) and deep learning (DL) methods. Based on large training datasets and pre-trained models, DL-based methods have proven to be more accurate than previous traditional techniques. Machine vision has wide applications in agriculture, including the detection of weeds and pests in crops. Variation in lighting conditions, failures to transfer learning, and object occlusion constitute key challenges in this domain. Recently, DL has gained much attention due to its advantages in object detection, classification, and feature extraction. DL algorithms can automatically extract information from large amounts of data used to model complex problems and is, therefore, suitable for detecting and classifying weeds and crops. We present a systematic review of AI-based systems to detect weeds, emphasizing recent trends in DL. Various DL methods are discussed to clarify their overall potential, usefulness, and performance. This study indicates that several limitations obstruct the widespread adoption of AI/DL in commercial applications. Recommendations for overcoming these challenges are summarized. Full article

(This article belongs to the Topic Information Sensing Technology for Intelligent/Driverless Vehicle)

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19 pages, 5131 KiB

Open AccessArticle

Monitoring Mining Activities Using Sentinel-1A InSAR Coherence in Open-Pit Coal Mines

by Lili Wang, Liao Yang, Weisheng Wang, Baili Chen and Xiaolin Sun

Remote Sens. 2021, 13(21), 4485; https://doi.org/10.3390/rs13214485 - 08 Nov 2021

Cited by 15 | Viewed by 4162

Abstract

Long-term continuous monitoring of the mining activities in open-pit coal mines is conducive to planning and management of the mining operations. Additionally, this faciliatates assessment on their environmental impact and supervises illegal mining behaviors. Interferometric Synthetic Aperture Radar (InSAR) technology can be effectively [...] Read more.

Long-term continuous monitoring of the mining activities in open-pit coal mines is conducive to planning and management of the mining operations. Additionally, this faciliatates assessment on their environmental impact and supervises illegal mining behaviors. Interferometric Synthetic Aperture Radar (InSAR) technology can be effectively applied in the monitoring of open-pit mines where vegetation is sparse and land cover is dominated by bare rock. The main objective of this study is to monitor the mining activities of four open-pit coal mines in the Wucaiwan mining area in China from 2018 to 2020, namely No. 1, No. 2 (containing two mining areas), and No. 3. We use the normalized differential activity index (NDAI) based on the coherence coefficient as an indicator of the mine activity due to its robustness to temporal and spatial decorrelation. After analyzing and removing the decorrelation caused by rain and snow weather, 70 NDAI images in 12-day intervals are obtained from Sentinel-1A InSAR coherence images. Then, the annually-averaged NDAI images are applied to an RGB composite technique (red for 2018, green for 2019, blue for 2020) to express the interannual variation of the mining activities. Points of interest are then selected for NDAI time series analysis. The RGB composite results indicated that No. 1 and 3 open-pit coal mines were continuously mined during the three years; whereas, the two mining areas of No. 2 were mainly active in 2018. The 12-day NDAI time-series graphs of No. 2 open-pit coal mine also indicate that the coal piles located in the coal transferring area of the first mining area were not completely removed until April 2019. It is also seen that the second mining area was decommissioned in November 2018 and became rehabilitated in July 2019. Results were validated using the Sentinel-2A images and related background information confirming the efficiency of the proposed approach for monitoring the mining activity in open-pit mines. Full article

(This article belongs to the Special Issue Remote Sensing Solutions for Mapping Mining Environments)

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19 pages, 5848 KiB

Open AccessArticle

Spatiotemporal Characterization of the Urban Expansion Patterns in the Yangtze River Delta Region

by Ziqi Yu, Longqian Chen, Long Li, Ting Zhang, Lina Yuan, Ruiyang Liu, Zhiqiang Wang, Jinyu Zang and Shuai Shi

Remote Sens. 2021, 13(21), 4484; https://doi.org/10.3390/rs13214484 - 08 Nov 2021

Cited by 14 | Viewed by 2615

Abstract

Characterizing urban expansion patterns is of great significance to planning and decision-making for urban agglomeration development. This study examined the urban expansion in the entire Yangtze River Delta Region (YRDR) with its land-use data of six years (1995, 2000, 2005, 2010, 2015, and [...] Read more.

Characterizing urban expansion patterns is of great significance to planning and decision-making for urban agglomeration development. This study examined the urban expansion in the entire Yangtze River Delta Region (YRDR) with its land-use data of six years (1995, 2000, 2005, 2010, 2015, and 2018). On the basis of traditional methods, we comprehensively considered the four aspects of urban agglomeration: expansion speed, expansion difference, expansion direction, and landscape pattern, as well as the interconnection of and difference in the expansion process between each city. The spatiotemporal heterogeneity of urban expansion development in this region was investigated by using the speed and differentiation indices of urban expansion, gravity center migration, landscape indices, and spatial autocorrelations. The results show that: (1) over the 23 years, the expansion of built-up land in the Yangtze River Delta Region was significant, (2) the rapidly expanding cities were mainly located along the Yangtze River and coastal areas, while the slowly expanding cities were mainly located in the inland areas, (3) the expansion direction of each city varied and the gravity center of the urban agglomeration moved toward the southwest, and (4) the spatial structure of the region became more clustered, the shape of built-up land turned simpler, and fragmentation decreased. This study unravels the spatiotemporal change of urban expansion patterns in this large urban agglomeration, and more importantly, can serve as a guide for formulating urban agglomeration development plans. Full article

(This article belongs to the Special Issue Remote Sensing Image and Urban Information Visualization)

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17 pages, 5342 KiB

Open AccessTechnical Note

Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

by W. Gareth Rees, Jack Tomaney, Olga Tutubalina, Vasily Zharko and Sergey Bartalev

Remote Sens. 2021, 13(21), 4483; https://doi.org/10.3390/rs13214483 - 08 Nov 2021

Cited by 9 | Viewed by 3029

Abstract

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower [...] Read more.

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data. Full article

(This article belongs to the Special Issue Remote Sensing of the Russian Boreal Forest)

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20 pages, 2662 KiB

Open AccessArticle

Vector Fuzzy c-Spherical Shells (VFCSS) over Non-Crisp Numbers for Satellite Imaging

by Iman Abaspur Kazerouni, Hadi Mahdipour, Gerard Dooly and Daniel Toal

Remote Sens. 2021, 13(21), 4482; https://doi.org/10.3390/rs13214482 - 08 Nov 2021

Cited by 2 | Viewed by 1473

Abstract

The conventional fuzzy c-spherical shells (FCSS) clustering model is extended to cluster shells involving non-crisp numbers, in this paper. This is achieved by a vectorized representation of distance, between two non-crisp numbers like the crisp numbers case. Using the proposed clustering method, named [...] Read more.

The conventional fuzzy c-spherical shells (FCSS) clustering model is extended to cluster shells involving non-crisp numbers, in this paper. This is achieved by a vectorized representation of distance, between two non-crisp numbers like the crisp numbers case. Using the proposed clustering method, named vector fuzzy c-spherical shells (VFCSS), all crisp and non-crisp numbers can be clustered by the FCSS algorithm in a unique structure. Therefore, we can implement FCSS clustering over various types of numbers in a unique structure with only a few alterations in the details used in implementing each case. The relations of VFCSS applied to crisp and non-crisp (containing symbolic-interval, LR-type, TFN-type and TAN-type fuzzy) numbers are presented in this paper. Finally, simulation results are reported for VFCSS applied to synthetic LR-type fuzzy numbers; where the application of the proposed method in real life and in geomorphology science is illustrated by extracting the radii of circular agricultural fields using remotely sensed images and the results show better performance and lower cost computational complexity of the proposed method in comparison to conventional FCSS. Full article

(This article belongs to the Special Issue Digital Image Processing)

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30 pages, 13181 KiB

Open AccessArticle

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

by Juan Sandino, Frederic Maire, Peter Caccetta, Conrad Sanderson and Felipe Gonzalez

Remote Sens. 2021, 13(21), 4481; https://doi.org/10.3390/rs13214481 - 08 Nov 2021

Cited by 18 | Viewed by 5613

Abstract

Recent advances in autonomy of unmanned aerial vehicles (UAVs) have increased their use in remote sensing applications, such as precision agriculture, biosecurity, disaster monitoring, and surveillance. However, onboard UAV cognition capabilities for understanding and interacting in environments with imprecise or partial observations, for [...] Read more.

Recent advances in autonomy of unmanned aerial vehicles (UAVs) have increased their use in remote sensing applications, such as precision agriculture, biosecurity, disaster monitoring, and surveillance. However, onboard UAV cognition capabilities for understanding and interacting in environments with imprecise or partial observations, for objects of interest within complex scenes, are limited, and have not yet been fully investigated. This limitation of onboard decision-making under uncertainty has delegated the motion planning strategy in complex environments to human pilots, which rely on communication subsystems and real-time telemetry from ground control stations. This paper presents a UAV-based autonomous motion planning and object finding system under uncertainty and partial observability in outdoor environments. The proposed system architecture follows a modular design, which allocates most of the computationally intensive tasks to a companion computer onboard the UAV to achieve high-fidelity results in simulated environments. We demonstrate the system with a search and rescue (SAR) case study, where a lost person (victim) in bushland needs to be found using a sub-2 kg quadrotor UAV. The navigation problem is mathematically formulated as a partially observable Markov decision process (POMDP). A motion strategy (or policy) is obtained once a POMDP is solved mid-flight and in real time using augmented belief trees (ABT) and the TAPIR toolkit. The system’s performance was assessed using three flight modes: (1) mission mode, which follows a survey plan and used here as the baseline motion planner; (2) offboard mode, which runs the POMDP-based planner across the flying area; and (3) hybrid mode, which combines mission and offboard modes for improved coverage in outdoor scenarios. Results suggest the increased cognitive power added by the proposed motion planner and flight modes allow UAVs to collect more accurate victim coordinates compared to the baseline planner. Adding the proposed system to UAVs results in improved robustness against potential false positive readings of detected objects caused by data noise, inaccurate detections, and elevated complexity to navigate in time-critical applications, such as SAR. Full article

(This article belongs to the Special Issue Rapid Processing and Analysis for Drone Applications)

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26 pages, 67940 KiB

Open AccessArticle

Spatiotemporal Monitoring of a Grassland Ecosystem and Its Net Primary Production Using Google Earth Engine: A Case Study of Inner Mongolia from 2000 to 2020

by Renjie Ji, Kun Tan, Xue Wang, Chen Pan and Liang Xin

Remote Sens. 2021, 13(21), 4480; https://doi.org/10.3390/rs13214480 - 08 Nov 2021

Cited by 15 | Viewed by 3206

Abstract

Grassland ecosystems are a significant part of the global ecosystem and support the livelihoods of millions of people. The Inner Mongolia grassland is the largest temperate grassland in the world, and an important ecological barrier for China, but due to human activities and [...] Read more.

Grassland ecosystems are a significant part of the global ecosystem and support the livelihoods of millions of people. The Inner Mongolia grassland is the largest temperate grassland in the world, and an important ecological barrier for China, but due to human activities and climate change it has been faced with an ecological crisis in recent years. In this study, a modified Carnegie-Ames-Stanford approach (CASA) model based on the Google Earth Engine platform was used to determine the net primary production (NPP) in the Inner Mongolia grassland from 2000 to 2020. The results show that the average annual NPP of the Inner Mongolia grassland is 278.63 g C/m², and 83.22% of the total area has shown an increasing trend during the study period. We also analyzed the impact of land-use/cover change (LUCC) and climatic factors on NPP. We found that: (1) the total area of grassland increased from 2000 to 2010 and then decreased from 2010 to 2020. During the whole study period, although the grassland area increased slightly by 4728.69 km² because of LUCC, the overall effect of LUCC on grassland NPP was negative, with a loss of 17.63 Tg C compared to an increase of 16.38 Tg C. (2) The main meteorological factor affecting the NPP of the Inner Mongolia grassland is precipitation, followed by sunshine duration and temperature. About 97.06% of the grassland shows a positive correlation between NPP and precipitation. (3) The results for NPP and its changing trends are not completely consistent in the long- and short-term study periods. Considering the instability of grassland growth, it is necessary to take the periodic variation of precipitation into account when studying NPP. These results could provide basic information for policy formulation and scientific research into the ecological environment management of grassland areas in the future. Full article

(This article belongs to the Section Ecological Remote Sensing)

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18 pages, 5091 KiB

Open AccessArticle

Sentinel-2 and Landsat-8 Observations for Harmful Algae Blooms in a Small Eutrophic Lake

by Miao Liu, Hong Ling, Dan Wu, Xiaomei Su and Zhigang Cao

Remote Sens. 2021, 13(21), 4479; https://doi.org/10.3390/rs13214479 - 08 Nov 2021

Cited by 14 | Viewed by 3655

Abstract

Widespread harmful cyanobacterial bloom is one of the most pressing concerns in lakes and reservoirs, resulting in a lot of negative ecological consequences and threatening public health. Ocean color instruments with low spatial resolution have been used to monitor cyanobacterial bloom in large [...] Read more.

Widespread harmful cyanobacterial bloom is one of the most pressing concerns in lakes and reservoirs, resulting in a lot of negative ecological consequences and threatening public health. Ocean color instruments with low spatial resolution have been used to monitor cyanobacterial bloom in large lakes; however, they cannot be applied to small water bodies well. Here, the Multi-Spectral Instrument (MSI) onboard Sentinel-2A and -2B and the Operational Landsat Imager (OLI) onboard Landsat-8 were employed to assemble the virtual constellation and to track spatial and seasonal variations in floating algae blooms from 2016 to 2020 in a small eutrophic plateau lake: Lake Xingyun in China. The floating algae index (FAI) was calculated using Rayleigh-corrected reflectance in the red, near-infrared, and short-wave infrared bands. The MSI-derived FAI had a similar pattern to the OLI-derived FAI, with a mean absolute percentage error of 19.98% and unbiased percentage difference of 17.05%. Then, an FAI threshold, 0.0693, was determined using bimodal histograms of FAI images for floating algae extraction. The floating algae had a higher occurrence in the northern region than the southern region in this lake, whilst the occurrence of floating algae in summer and autumn was higher than that in spring and winter. Such a spatial and seasonal pattern was related to the variability in air temperature, wind speed and direction, and nutrients. The climatological annual mean occurrence of floating algae from 2016 to 2020 in Lake Xingyun exhibited a significant decrease, which was related to decreases in nutrients, resulting from efficient ecological restoration by the local government. This research highlighted the application of OLI-MSI virtual constellation on monitoring floating algae in a small lake, providing a practical and theoretical reference to monitor aquatic environments in small water bodies. Full article

(This article belongs to the Special Issue Remote Sensing of Water Quality in Relatively Small and Medium-Sized Inland Waters)

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10 pages, 3503 KiB

Open AccessCommunication

Earthquake Magnitude Estimation from High-Rate GNSS Data: A Case Study of the 2021 Mw 7.3 Maduo Earthquake

by Zhiyu Gao, Yanchuan Li, Xinjian Shan and Chuanhua Zhu

Remote Sens. 2021, 13(21), 4478; https://doi.org/10.3390/rs13214478 - 08 Nov 2021

Cited by 14 | Viewed by 2838

Abstract

Peak ground displacement (PGD) and peak ground velocity (PGV) are critical parameters during earthquake early warning, as they can provide rapid magnitude estimation before rupture end. In this study, we used the high-rate Global Navigation Satellite System (GNSS) data from 55 continuous stations [...] Read more.

Peak ground displacement (PGD) and peak ground velocity (PGV) are critical parameters during earthquake early warning, as they can provide rapid magnitude estimation before rupture end. In this study, we used the high-rate Global Navigation Satellite System (GNSS) data from 55 continuous stations to estimate the magnitude of the 2021 Maduo earthquake in western China. We used the relative positioning method and variometric approach to acquire real-time GNSS displacement and velocity waveforms, respectively. The results showed the amplitude of displacement and velocity waveforms gradually decreased with increasing hypocentral distance. Our results showed that the fluctuation of PGD magnitudes over time is smaller than that of PGV magnitudes. Nonetheless, the earthquake magnitudes estimated from both methods were consistent with their counterparts (Mw 7.3) reported by the United States Geological Survey (USGS). The final magnitude estimated from the PGD and PGV methods were Mw 7.25 and Mw 7.31, respectively. In addition, our results highlighted how the number of high-rate GNSS stations could influence the stability and convergence time of magnitude estimation. Full article

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14 pages, 3433 KiB

Open AccessTechnical Note

Landsat TM/OLI-Based Ecological and Environmental Quality Survey of Yellow River Basin, Inner Mongolia Section

by Wenlong Gao, Shengwei Zhang, Xinyu Rao, Xi Lin and Ruishen Li

Remote Sens. 2021, 13(21), 4477; https://doi.org/10.3390/rs13214477 - 08 Nov 2021

Cited by 31 | Viewed by 3268

Abstract

The monitoring and maintenance of the Inner Mongolia section of the Yellow River Basin is of great significance to the safety and development of China’s Yellow River Economic Belt and to the protection of the Yellow River ecology. In this study, we calculated [...] Read more.

The monitoring and maintenance of the Inner Mongolia section of the Yellow River Basin is of great significance to the safety and development of China’s Yellow River Economic Belt and to the protection of the Yellow River ecology. In this study, we calculated diagnostic values from a total of 520 Landsat OLI/TM remote sensing images of the Yellow River Basin of Inner Mongolia from 2001 to 2020. Using the RSEI and the GEE Cloud Computing Jigsaw, we analyzed the spatial and temporal distribution of diagnostic values representative of the basin’s ecological status. Further, Mantel and Pearson correlations were used to analyze the significance of environmental factors in affecting the ecological quality of cities along the Yellow River within the study area. The results indicated that the overall mean of RSEI values rose at first and then fell. The RSEI grade to land area ratio was calculated to be highest in 2015 (excellent) and worst in 2001. From 2001 to 2020, ecological quality monitoring process of main cities in the Inner Mongolia region of the Yellow River Basin. Hohhot, Baotou, and Linhe all have an RSEI score greater than 0.5, considered average. However, Dongsheng had its best score (0.60, good) in 2005, which then declined and increased to an average rating in 2020. The RSEI value for Wuhai reached excellent in 2010 but then became poor in 2020, dropping to 0.28. The analysis of ecological quality in the city shows that the greenness index (NDVI) carried the most significant impact on the ecological environment, followed by the humidity index (Wet), the dryness index (NDBSI), the temperature index (Lst), land use, and then regional gross product (RGP). The significance of this study is to provide a real-time, accurate, and rapid understanding of trends in the spatial and temporal distribution of ecological and environmental quality along the Yellow River, thereby providing a theoretical basis and technical support for ecological and environmental protection and high-quality development of the Yellow River Basin. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)

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26 pages, 5108 KiB

Open AccessArticle

Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques

by Adama Traore, Syed Tahir Ata-Ul-Karim, Aiwang Duan, Mukesh Kumar Soothar, Seydou Traore and Ben Zhao

Remote Sens. 2021, 13(21), 4476; https://doi.org/10.3390/rs13214476 - 08 Nov 2021

Cited by 5 | Viewed by 3097

Abstract

The equivalent water thickness (EWT) is an important biophysical indicator of water status in crops. The effective monitoring of EWT in wheat under different nitrogen and water treatments is important for irrigation management in precision agriculture. This study aimed to investigate the performances [...] Read more.

The equivalent water thickness (EWT) is an important biophysical indicator of water status in crops. The effective monitoring of EWT in wheat under different nitrogen and water treatments is important for irrigation management in precision agriculture. This study aimed to investigate the performances of machine learning (ML) algorithms in retrieving wheat EWT. For this purpose, a rain shelter experiment (Exp. 1) with four irrigation quantities (0, 120, 240, 360 mm) and two nitrogen levels (75 and 255 kg N/ha), and field experiments (Exps. 2–3) with the same irrigation and rainfall water levels (360 mm) but different nitrogen levels (varying from 75 to 255 kg N/ha) were conducted in the North China Plain. The canopy reflectance was measured for all plots at 30 m using an unmanned aerial vehicle (UAV)-mounted multispectral camera. Destructive sampling was conducted immediately after the UAV flights to measure total fresh and dry weight. Deep Neural Network (DNN) is a special type of neural network, which has shown performance in regression analysis is compared with other machine learning (ML) models. A feature selection (FS) algorithm named the decision tree (DT) was used as the automatic relevance determination method to obtain the relative relevance of 5 out of 67 vegetation indices (Vis), which were used for estimating EWT. The selected VIs were used to estimate EWT using multiple linear regression (MLR), deep neural network multilayer perceptron (DNN-MLP), artificial neural networks multilayer perceptron (ANN-MLP), boosted tree regression (BRT), and support vector machines (SVMs). The results show that the DNN-MLP with R² = 0.934, NSE = 0.933, RMSE = 0.028 g/cm², and MAE of 0.017 g/cm² outperformed other ML algorithms (ANN-MPL, BRT, and SVM- Polynomial) owing to its high capacity for estimating EWT as compared to other ML methods. Our findings support the conclusion that ML can potentially be applied in combination with VIs for retrieving EWT. Despite the complexity of the ML models, the EWT map should help farmers by improving the real-time irrigation efficiency of wheat by quantifying field water content and addressing variability. Full article

(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management)

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17 pages, 46538 KiB

Open AccessArticle

Research and Analysis of Ecological Environment Quality in the Middle Reaches of the Yangtze River Basin between 2000 and 2019

by Shengqing Zhang, Peng Yang, Jun Xia, Kunlun Qi, Wenyu Wang, Wei Cai and Nengcheng Chen

Remote Sens. 2021, 13(21), 4475; https://doi.org/10.3390/rs13214475 - 08 Nov 2021

Cited by 28 | Viewed by 2782

Abstract

Ecological environment quality is a long-term continuous concept that is affected by various environmental factors. Its assessment has important implications for implementing the planning and protection of dynamic regional ecosystems. Therefore, this study attempted to obtain these indicators (green, dry, wet, heat) through [...] Read more.

Ecological environment quality is a long-term continuous concept that is affected by various environmental factors. Its assessment has important implications for implementing the planning and protection of dynamic regional ecosystems. Therefore, this study attempted to obtain these indicators (green, dry, wet, heat) through the Google Earth Engine (GEE) platform, and then coupled the ecological environment quality index in the middle reaches of the Yangtze River Basin (MYRB) between 2000 and 2019, based on the remote sensing ecological index (RSEI). The major results show that: (1) changes in the four indicators in summer were more obvious than those in winter, and the changes were concentrated in the central and northern regions of the MYRB; (2) both the modified normalized difference water index (MNDWI) and normalized differential build-up and bare soil index (NDBI) in summer and winter have higher weighting ratios, implying that water body changes and human activities had a greater impact on the ecological environment; and (3) ecological environment quality in the MYRB between 2000 and 2019 was relatively flat. The ecological conditions began to deteriorate in 2008, and substantial ecological degradation was noted in some areas between 2008 and 2019 (18.7% in the central region, 16.0% in the eastern region). The MYRB has an important position in the Yangtze River economic belt and is an important part of the Yangtze River protection. This research could provide a theoretical basis and decision support for the development and protection of the Yangtze River Basin (YRB) green economy. Full article

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23 pages, 8513 KiB

Open AccessArticle

Resilience of the Central Indian Forest Ecosystem to Rainfall Variability in the Context of a Changing Climate

by Beependra Singh, Chockalingam Jeganathan, Virendra Singh Rathore, Mukunda Dev Behera, Chandra Prakash Singh, Parth Sarathi Roy and Peter M. Atkinson

Remote Sens. 2021, 13(21), 4474; https://doi.org/10.3390/rs13214474 - 08 Nov 2021

Cited by 7 | Viewed by 3569

Abstract

Understanding the spatio-temporal pattern of natural vegetation helps decoding the responses to climate change and interpretation on forest resilience. Satellite remote sensing based data products, by virtue of their synoptic and repetitive coverage, offer to study the correlation and lag effects of rainfall [...] Read more.

Understanding the spatio-temporal pattern of natural vegetation helps decoding the responses to climate change and interpretation on forest resilience. Satellite remote sensing based data products, by virtue of their synoptic and repetitive coverage, offer to study the correlation and lag effects of rainfall on forest growth in a relatively longer time scale. We selected central India as the study site. It accommodates tropical natural vegetation of varied forest types such as moist and dry deciduous and evergreen and semi-evergreen forests that largely depend on the southwest monsoon. We used the MODIS derived NDVI and CHIRPS based rainfall datasets from 2001 to 2018 in order to analyze NDVI and rainfall trend by using Sen’s slope and standard anomalies. The study observed a decreasing rainfall trend over 41% of the forests, while the rest of the forest area (59%) demonstrated an increase in rainfall. Furthermore, the study estimated drought conditions during 2002, 2004, 2009, 2014 and 2015 for 98.2%, 92.8%, 89.6%, 90.1% and 95.8% of the forest area, respectively; and surplus rainfall during 2003, 2005, 2007, 2011, 2013 and 2016 for 69.5%, 63.9%, 71.97%, 70.35%, 94.79% and 69.86% of the forest area, respectively. Hence, in the extreme dry year (2002), 93% of the forest area showed a negative anomaly, while in the extreme wet year (2013), 89% of forest cover demonstrated a positive anomaly in central India. The long-term vegetation trend analysis revealed that most of the forested area (>80%) has a greening trend in central India. When we considered annual mean NDVI, the greening and browning trends were observed over at 88.65% and 11.35% of the forested area at 250 m resolution and over 93.01% and 6.99% of the area at 5 km resolution. When we considered the peak-growth period mean NDVI, the greening and browning trends were as follows: 81.97% and 18.03% at 250 m and 88.90% and 11.10% at 5 km, respectively. The relative variability in rainfall and vegetation growth at five yearly epochs revealed that the first epoch (2001–2005) was the driest, while the third epoch (2011–2015) was the wettest, corresponding to the lowest vegetation vigour in the first epoch and the highest in the third epoch during the past two decades. The study reaffirms that rainfall is the key climate variable in the tropics regulating the growth of natural vegetation, and the central Indian forests are dominantly resilient to rainfall variation. Full article

(This article belongs to the Special Issue Geostatistics and Spatial Data Mining for Ecological Climatology)

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12 pages, 6411 KiB

Open AccessTechnical Note

Partial Shape Recognition for Sea Ice Motion Retrieval in the Marginal Ice Zone from Sentinel-1 and Sentinel-2

by Mingfeng Wang, Marcel König and Natascha Oppelt

Remote Sens. 2021, 13(21), 4473; https://doi.org/10.3390/rs13214473 - 08 Nov 2021

Cited by 2 | Viewed by 1977

Abstract

We present an algorithm for computing ice drift in the marginal ice zone (MIZ), based on partial shape recognition. With the high spatial resolution of Sentinel-1 and Sentinel-2 images, and the low sensitivity to atmospheric influences of Sentinel-1, a considerable quantity of ice [...] Read more.

We present an algorithm for computing ice drift in the marginal ice zone (MIZ), based on partial shape recognition. With the high spatial resolution of Sentinel-1 and Sentinel-2 images, and the low sensitivity to atmospheric influences of Sentinel-1, a considerable quantity of ice floes is identified using a mathematical morphology method. Hausdorff distance is used to measure the similarity of segmented ice floes. It is tolerant to perturbations and deficiencies of floe shapes, which enhances the density of retrieved sea ice motion vectors. The PHD algorithm can be applied to sequential images from different sensors, and was tested on two combined image mosaics consisting of Sentinel-1 and Sentinel-2 data acquired over the Fram Strait; the PHD algorithm successfully produced pairs of matched ice floes. The matching result has been verified using shape and surface texture similarity of the ice floes. Moreover, the present method can naturally be extended to the problem of multi-source sea ice image registration. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Arctic Environments)

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21 pages, 9386 KiB

Open AccessArticle

Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification

by Tianyu Zhang, Cuiping Shi, Diling Liao and Liguo Wang

Remote Sens. 2021, 13(21), 4472; https://doi.org/10.3390/rs13214472 - 07 Nov 2021

Cited by 10 | Viewed by 2183

Abstract

Convolutional neural networks (CNNs) have been widely used in hyperspectral image classification in recent years. The training of CNNs relies on a large amount of labeled sample data. However, the number of labeled samples of hyperspectral data is relatively small. Moreover, for hyperspectral [...] Read more.

Convolutional neural networks (CNNs) have been widely used in hyperspectral image classification in recent years. The training of CNNs relies on a large amount of labeled sample data. However, the number of labeled samples of hyperspectral data is relatively small. Moreover, for hyperspectral images, fully extracting spectral and spatial feature information is the key to achieve high classification performance. To solve the above issues, a deep spectral spatial inverted residuals network (DSSIRNet) is proposed. In this network, a data block random erasing strategy is introduced to alleviate the problem of limited labeled samples by data augmentation of small spatial blocks. In addition, a deep inverted residuals (DIR) module for spectral spatial feature extraction is proposed, which locks the effective features of each layer while avoiding network degradation. Furthermore, a global 3D attention module is proposed, which can realize the fine extraction of spectral and spatial global context information under the condition of the same number of input and output feature maps. Experiments are carried out on four commonly used hyperspectral datasets. A large number of experimental results show that compared with some state-of-the-art classification methods, the proposed method can provide higher classification accuracy for hyperspectral images. Full article

(This article belongs to the Special Issue New Insights into Hyperspectral Image Processing Methods in Remote Sensing)

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17 pages, 4411 KiB

Open AccessArticle

Comparitive Study of the Geomorphological Characteristics of Valley Networks between Mars and the Qaidam Basin

by Lu Chen, Yi Xu and Bo Li

Remote Sens. 2021, 13(21), 4471; https://doi.org/10.3390/rs13214471 - 07 Nov 2021

Cited by 3 | Viewed by 2445

Abstract

The complex valley networks that cross the Martian surface offer geomorphologic evidence of the presence of liquid water at some point in its history. However, the derivation of both temporal and hydrological dimensions of this climate phase is far from settled. Studies comparing [...] Read more.

The complex valley networks that cross the Martian surface offer geomorphologic evidence of the presence of liquid water at some point in its history. However, the derivation of both temporal and hydrological dimensions of this climate phase is far from settled. Studies comparing terrestrial fluvial networks of known formation environments with those on Mars can be used as a key to unlock the past. This work represents an analogy study and comparison between the river networks in the Qaidam Basin and those on Mars. As the Martian valley networks formed in different geologic periods with characteristic and unique features, three cases from the Noachian to the Amazonian were selected to be compared with streams in the Mangya area, where the climate is extremely arid. In terms of the maturity of the dendritic river system, shape, concave index, and branching angle (BA), the valley network in the Mangya area is comparable to Naktong Vallis, dated to the Hesperian. We also calculated throughout the valley networks on Mars the parameters of the BA and the concave index, both of which are important climatic indicators. The results show that the climate on Mars became progressively more arid, starting from the Noachian up to the Amazonian. Full article

(This article belongs to the Special Issue Mars Remote Sensing)

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25 pages, 726 KiB

Open AccessReview

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

by Teo Nguyen, Benoît Liquet, Kerrie Mengersen and Damien Sous

Remote Sens. 2021, 13(21), 4470; https://doi.org/10.3390/rs13214470 - 07 Nov 2021

Cited by 8 | Viewed by 6497

Abstract

Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a [...] Read more.

Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy. Full article

(This article belongs to the Special Issue Computer Vision and Machine Learning for Remote Sensing Solutions Applied to Environmental Challenges)

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25 pages, 4238 KiB

Open AccessArticle

Spatial and Temporal Analysis of Surface Urban Heat Island and Thermal Comfort Using Landsat Satellite Images between 1989 and 2019: A Case Study in Tehran

by Faezeh Najafzadeh, Ali Mohammadzadeh, Arsalan Ghorbanian and Sadegh Jamali

Remote Sens. 2021, 13(21), 4469; https://doi.org/10.3390/rs13214469 - 07 Nov 2021

Cited by 21 | Viewed by 4497

Abstract

Mapping and monitoring the spatio-temporal variations of the Surface Urban Heat Island (SUHI) and thermal comfort of metropolitan areas are vital to obtaining the necessary information about the environmental conditions and promoting sustainable cities. As the most populated city of Iran, Tehran has [...] Read more.

Mapping and monitoring the spatio-temporal variations of the Surface Urban Heat Island (SUHI) and thermal comfort of metropolitan areas are vital to obtaining the necessary information about the environmental conditions and promoting sustainable cities. As the most populated city of Iran, Tehran has experienced considerable population growth and Land Cover/Land Use (LULC) changes in the last decades, which resulted in several adverse environmental issues. In this study, 68 Landsat-5 and Landsat-8 images, collected from the Google Earth Engine (GEE), were employed to map and monitor the spatio-temporal variations of LULC, SUHI, and thermal comfort of Tehran between 1989 and 2019. In this regard, planar fitting and Gaussian Surface Model (GSM) approaches were employed to map SUHIs and derive the relevant statistical values. Likewise, the thermal comfort of the city was investigated by the Urban Thermal Field Variance Index (UTFVI). The results indicated that the SUHI intensities have generally increased throughout the city by an average value of about 2.02 °C in the past three decades. The most common reasons for this unfavorable increase were the loss of vegetation cover (i.e., 34.72%) and massive urban expansions (i.e., 53.33%). Additionally, the intra-annual investigations in 2019 revealed that summer and winter, with respectively 8.28 °C and 4.37 °C, had the highest and lowest SUHI magnitudes. Furthermore, the decadal UTFVI maps revealed notable thermal comfort degradation of Tehran, by which in 2019, approximately 52.35% of the city was identified as the region with the worst environmental condition, of which 59.94% was related to human residents. Additionally, the relationships between various air pollutants and SUHI intensities were appraised, suggesting positive relationships (i.e., ranging between 0.23 and 0.43) that can be used for establishing possible two-way mitigations strategies. This study provided analyses of spatio-temporal monitoring of SUHI and UTFVI throughout Tehran that urban managers and policymakers can consider for adaption and sustainable development. Full article

(This article belongs to the Special Issue Usage of Remote Sensing Data and Machine Learning Methods for Sustainable Urban Planning)

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19 pages, 11719 KiB

Open AccessArticle

Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks

by Yufang He, Guangzong Zhang, Hermann Kaufmann and Guochang Xu

Remote Sens. 2021, 13(21), 4468; https://doi.org/10.3390/rs13214468 - 07 Nov 2021

Cited by 3 | Viewed by 2282

Abstract

The small baseline subset of spaceborne interferometric synthetic aperture radar (SBAS-InSAR) technology has become a classical method for monitoring slow deformations through time series analysis with an accuracy in the centimeter or even millimeter range. Thereby, the selection of high-quality interferograms calculated is [...] Read more.

The small baseline subset of spaceborne interferometric synthetic aperture radar (SBAS-InSAR) technology has become a classical method for monitoring slow deformations through time series analysis with an accuracy in the centimeter or even millimeter range. Thereby, the selection of high-quality interferograms calculated is one of the key operations for the method, since it mainly determines the credibility of the deformation information. Especially in the era of big data, the demand for an automatic and effective selection method of high-quality interferograms in SBAS-InSAR technology is growing. In this paper, a deep convolutional neural network (DCNN) for automatichigh-quality interferogram selection is proposed that provides more efficient image feature extraction capabilities and a better classification performance. Therefore, the ResNet50 (a kind of DCNN) is used to identify and delete interferograms that are severely contaminated. According to simulation experiments and calculated Sentinel-1A data of Shenzhen, China, the proposed approach can significantly separate interferograms affected by turbulences in the atmosphere and by the decorrelation phase. The remarkable performance of the DCNN method is validated by the analysis of the standard deviation of interferograms and the local deformation information compared with the traditional selection method. It is concluded that DCNN algorithms can automatically select high quality interferogram for the SBAS-InSAR method and thus have a significant impact on the precision of surface deformation monitoring. Full article

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17 pages, 3113 KiB

Open AccessArticle

Including Leaf Traits Improves a Deep Neural Network Model for Predicting Photosynthetic Capacity from Reflectance

by Guangman Song and Quan Wang

Remote Sens. 2021, 13(21), 4467; https://doi.org/10.3390/rs13214467 - 06 Nov 2021

Cited by 6 | Viewed by 2245

Abstract

Accurate knowledge of photosynthetic capacity is critical for understanding the carbon cycle under climate change. Despite the fact that deep neural network (DNN) models are increasingly applied across a wide range of fields, there are very few attempts to predict leaf photosynthetic capacity [...] Read more.

Accurate knowledge of photosynthetic capacity is critical for understanding the carbon cycle under climate change. Despite the fact that deep neural network (DNN) models are increasingly applied across a wide range of fields, there are very few attempts to predict leaf photosynthetic capacity (indicated by maximum carboxylation rate, Vcmax, and maximum electron transport rate, Jmax) from reflected information. In this study, we have built a DNN model that uses leaf reflected spectra, alone or together with other leaf traits, for the reliable estimation of photosynthetic capacity, accounting for leaf types and growing periods in cool–temperate deciduous forests. Our results demonstrate that even though DNN models using only the reflectance spectra are capable of estimating both Vcmax and Jmax acceptably, their performance could nevertheless be improved by including information about other leaf biophysical/biochemical traits. The results highlight the fact that leaf spectra and leaf biophysical/biochemical traits are closely linked with leaf photosynthetic capacity, providing a practical and feasible approach to tracing functional traits. However, the DNN models developed in this study should undergo more extensive validation and training before being applied in other regions, and further refinements in future studies using larger datasets from a wide range of ecosystems are also necessary. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Vegetation Functions)

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25 pages, 118279 KiB

Open AccessArticle

Disturbance Mapping in Arctic Tundra Improved by a Planning Workflow for Drone Studies: Advancing Tools for Future Ecosystem Monitoring

by Isabell Eischeid, Eeva M. Soininen, Jakob J. Assmann, Rolf A. Ims, Jesper Madsen, Åshild Ø. Pedersen, Francesco Pirotti, Nigel G. Yoccoz and Virve T. Ravolainen

Remote Sens. 2021, 13(21), 4466; https://doi.org/10.3390/rs13214466 - 06 Nov 2021

Cited by 11 | Viewed by 3902

Abstract

The Arctic is under great pressure due to climate change. Drones are increasingly used as a tool in ecology and may be especially valuable in rapidly changing and remote landscapes, as can be found in the Arctic. For effective applications of drones, decisions [...] Read more.

The Arctic is under great pressure due to climate change. Drones are increasingly used as a tool in ecology and may be especially valuable in rapidly changing and remote landscapes, as can be found in the Arctic. For effective applications of drones, decisions of both ecological and technical character are needed. Here, we provide our method planning workflow for generating ground-cover maps with drones for ecological monitoring purposes. The workflow includes the selection of variables, layer resolutions, ground-cover classes and the development and validation of models. We implemented this workflow in a case study of the Arctic tundra to develop vegetation maps, including disturbed vegetation, at three study sites in Svalbard. For each site, we generated a high-resolution map of tundra vegetation using supervised random forest (RF) classifiers based on four spectral bands, the normalized difference vegetation index (NDVI) and three types of terrain variables—all derived from drone imagery. Our classifiers distinguished up to 15 different ground-cover classes, including two classes that identify vegetation state changes due to disturbance caused by herbivory (i.e., goose grubbing) and winter damage (i.e., ‘rain-on-snow’ and thaw-freeze). Areas classified as goose grubbing or winter damage had lower NDVI values than their undisturbed counterparts. The predictive ability of site-specific RF models was good (macro-F1 scores between 83% and 85%), but the area of the grubbing class was overestimated in parts of the moss tundra. A direct transfer of the models between study sites was not possible (macro-F1 scores under 50%). We show that drone image analysis can be an asset for studying future vegetation state changes on local scales in Arctic tundra ecosystems and encourage ecologists to use our tailored workflow to integrate drone mapping into long-term monitoring programs. Full article

(This article belongs to the Special Issue Earth Observation (EO), Remote Sensing (RS), and Geoinformation (GI) Applications in Svalbard)

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19 pages, 13488 KiB

Open AccessArticle

Fusing Geostationary Satellite Observations with Harmonized Landsat-8 and Sentinel-2 Time Series for Monitoring Field-Scale Land Surface Phenology

by Yu Shen, Xiaoyang Zhang, Weile Wang, Ramakrishna Nemani, Yongchang Ye and Jianmin Wang

Remote Sens. 2021, 13(21), 4465; https://doi.org/10.3390/rs13214465 - 06 Nov 2021

Cited by 14 | Viewed by 2915

Abstract

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) [...] Read more.

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover. Full article

(This article belongs to the Special Issue Advancing Land Surface Phenological Analysis with High Spatial Resolution Imagery)

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27 pages, 10668 KiB

Open AccessArticle

Evaluation of Surface Upward Longwave Radiation in the CMIP6 Models with Ground and Satellite Observations

by Jiawen Xu, Xiaotong Zhang, Chunjie Feng, Shuyue Yang, Shikang Guan, Kun Jia, Yunjun Yao, Xianhong Xie, Bo Jiang, Jie Cheng and Xiang Zhao

Remote Sens. 2021, 13(21), 4464; https://doi.org/10.3390/rs13214464 - 06 Nov 2021

Cited by 4 | Viewed by 2070

Abstract

Surface upward longwave radiation (SULR) is an indicator of thermal conditions over the Earth’s surface. In this study, we validated the simulated SULR from 51 Coupled Model Intercomparison Project (CMIP6) general circulation models (GCMs) through a comparison with ground measurements and satellite-retrieved SULR [...] Read more.

Surface upward longwave radiation (SULR) is an indicator of thermal conditions over the Earth’s surface. In this study, we validated the simulated SULR from 51 Coupled Model Intercomparison Project (CMIP6) general circulation models (GCMs) through a comparison with ground measurements and satellite-retrieved SULR from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF). Moreover, we improved the SULR estimations by a fusion of multiple CMIP6 GCMs using multimodel ensemble (MME) methods. Large variations were found in the monthly mean SULR among the 51 CMIP6 GCMs; the bias and root mean squared error (RMSE) of the individual CMIP6 GCMs at 133 sites ranged from −3 to 24 W m⁻² and 22 to 38 W m⁻², respectively, which were higher than those found between the CERES EBAF and GCMs. The CMIP6 GCMs did not improve the overestimation of SULR compared to the CMIP5 GCMs. The Bayesian model averaging (BMA) method showed better performance in simulating SULR than the individual GCMs and simple model averaging (SMA) method, with a bias of 0 W m⁻² and an RMSE of 19.29 W m⁻² for the 133 sites. In terms of the global annual mean SULR, our best estimation for the CMIP6 GCMs using the BMA method was 392 W m⁻² during 2000–2014. We found that the SULR varied between 386 and 393 W m⁻² from 1850 to 2014, exhibiting an increasing tendency of 0.2 W m⁻² per decade (p < 0.05). Full article

(This article belongs to the Special Issue Advance on Net Surface Radiation Inversion Using Remote Sensing Technology)

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22 pages, 1713 KiB

Open AccessArticle

A SINS/SAR/GPS Fusion Positioning System Based on Sensor Credibility Evaluations

by Maoyou Liao, Jiacheng Liu, Ziyang Meng and Zheng You

Remote Sens. 2021, 13(21), 4463; https://doi.org/10.3390/rs13214463 - 06 Nov 2021

Cited by 7 | Viewed by 2526

Abstract

A reliable framework for SINS/SAR/GPS integrated positioning systems is proposed for the case that sensors are in critical environments. Credibility is used to describe the difference between the true error and the initial setting standard deviation. Credibility evaluation methods for inertial measurement unit [...] Read more.

A reliable framework for SINS/SAR/GPS integrated positioning systems is proposed for the case that sensors are in critical environments. Credibility is used to describe the difference between the true error and the initial setting standard deviation. Credibility evaluation methods for inertial measurement unit (IMU), synthetic aperture radar (SAR), and global positioning system (GPS) are presented. In particular, IMU credibility is modeled by noises and constant drifts that are accumulated with time in a strapdown inertial navigation system (SINS). The quality of the SAR image decides the credibility of positioning based on SAR image matching. In addition, a cumulative residual chi-square test is used to evaluate GPS credibility. An extended Kalman filter based on a sensor credibility evaluation is introduced to integrate the measurements. The measurement of a sensor is either discarded when its credibility value is below a threshold or the variance matrix for the estimated state is otherwise adjusted. Simulations show that the final fusion positioning accuracy with credibility evaluation can be improved by 1–2 times compared to that without evaluation. In addition, the derived standard deviation correctly indicates the value of the position error with credibility evaluation. Moreover, the experiments on an unmanned ground vehicle partially verify the proposed evaluation method of GPS and the fusion framework in the actual environment. Full article

(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)

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24 pages, 19382 KiB

Open AccessArticle

A Multidisciplinary Approach for the Mapping, Automatic Detection and Morphometric Analysis of Ancient Submerged Coastal Installations: The Case Study of the Ancient Aegina Harbour Complex

by Nikos Georgiou, Xenophon Dimas, Elias Fakiris, Dimitris Christodoulou, Maria Geraga, Despina Koutsoumpa, Kalliopi Baika, Pari Kalamara, George Ferentinos and George Papatheodorou

Remote Sens. 2021, 13(21), 4462; https://doi.org/10.3390/rs13214462 - 06 Nov 2021

Cited by 5 | Viewed by 3028

Abstract

The documentation of underwater cultural heritage (UCH) is the basis for sustainable maritime development including its protection, preservation, and incorporation in coastal zone management plans. In this study, we present a multidisciplinary, non-intrusive downscale approach for the documentation of UCH implemented on the [...] Read more.

The documentation of underwater cultural heritage (UCH) is the basis for sustainable maritime development including its protection, preservation, and incorporation in coastal zone management plans. In this study, we present a multidisciplinary, non-intrusive downscale approach for the documentation of UCH implemented on the coastal area of Aegina Island, Greece, where a unique submerged harbour complex is preserved. This approach succeeded in obtaining information that serves both geomorphological and archaeological purposes in a time- and cost-effective way, while obtaining information of centimeters to millimeters scale. The geomorphology of the area was mapped in detail through marine geophysical means while ancient submerged conical rubble structures and breakwaters were documented using automatic seafloor segmentation techniques, revealing previously unknown sites of archaeological interest. The structures’ parameters were extracted from the acoustic data to analyze their morphometry, while photogrammetry was realized using a Remotely Operated Vehicle to expose their micro-structure. The spatial distribution of the structures revealed the construction of a well-planned harbour complex with multiple passages and different possible functionalities. Finally, through the structures’ morphometric analysis (geometry and terrain statistical parameters) their preservation status was revealed, demonstrating the anthropogenic impact on the submerged ancient structures due to the modern harbor activity. Full article

(This article belongs to the Special Issue Documenting and Monitoring Underwater Cultural Heritage Using Remote Sensing)

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Remote Sens., Volume 13, Issue 21 (November-1 2021) – 301 articles

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