Remote Sensing

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ISSN / EISSN : 2072-4292 / 2072-4292
Published by: MDPI AG (10.3390)
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Published: 29 July 2021
by MDPI
Remote Sensing, Volume 13; doi:10.3390/rs13152990

Abstract:
The Korea Meteorological Administration (KMA) has developed many product algorithms including that for soil moisture (SM) retrieval for the geostationary satellite Geo-Kompsat-2A (GK-2A) launched in December 2018. This was developed through a five-year research project owing to the significance of SM information for hydrological and meteorological applications. However, GK-2A’s visible and infrared sensors lack direct SM sensitivity. Therefore, in this study, we developed an SM algorithm based on the conversion relationships between SM and the temperature vegetation dryness index (TVDI) estimated for various land types in the full disk area using two of GK-2A’s level 2 products, land surface temperature (LST) and normalized difference vegetation index (NDVI), and the Global Land Data Assimilation System (GLDAS) SM data for calibration. Methodologically, various coefficients were obtained between TVDI and SM and used to estimate the GK-2A-based SM. The GK-2A SM algorithm was validated with GLDAS SM data during different periods. Our GK-2A SM product showed seasonal and spatial agreement with GLDAS SM data, indicating a dry-wet pattern variation. Quantitatively, the GK-2A SM showed annual validation results with a correlation coefficient (CC) > 0.75, bias < 0.1%, and root mean square error (RMSE) < 4.2–4.7%. The monthly averaged CC values were higher than 0.7 in East Asia and 0.5 in Australia, whereas RMSE and unbiased RMSE values were < 0.5% in East Asia and Australia. Discrepancies between GLDAS and GK-2A TVDI-based SMs often occurred in dry Australian regions during dry seasons due to the high LST sensitivity of GK-2A TVDI. We determined that relationships between TVDI and SM had positive or negative slopes depending on land cover types, which differs from the traditional negative slope observed between TVDI and SM. The KMA is currently operating this GK-2A SM algorithm.
Published: 29 July 2021
by MDPI
Remote Sensing, Volume 13; doi:10.3390/rs13152992

Abstract:
This paper proposes a novel approach for living and missing vine identification and vine characterization in goblet-trained vine plots using aerial images. Given the periodic structure of goblet vineyards, the RGB color coded parcel image is analyzed using proper processing techniques in order to determine the locations of living and missing vines. Vine characterization is achieved by implementing the marker-controlled watershed transform where the centers of the living vines serve as object markers. As a result, a precise mortality rate is calculated for each parcel. Moreover, all vines, even the overlapping ones, are fully recognized providing information about their size, shape, and green color intensity. The presented approach is fully automated and yields accuracy values exceeding 95% when the obtained results are assessed with ground-truth data. This unsupervised and automated approach can be applied to any type of plots presenting similar spatial patterns requiring only the image as input.
Published: 29 July 2021
by MDPI
Remote Sensing, Volume 13; doi:10.3390/rs13152987

Abstract:
Despite an increase in heatwaves and rising air temperatures in the Arctic, little research has been conducted into the temperatures of proglacial lakes in the region. An assumption persists that they are cold and uniformly feature a temperature of 1 °C. This is important to test, given the rising air temperatures in the region (reported in this study) and potential to increase water temperatures, thus increasing subaqueous melting and the retreat of glacier termini from where they are in contact with lakes. Through analysis of ASTER surface temperature product data, we report warm (>4 °C) proglacial lake surface water temperatures (LSWT) for both ice-contact and non-ice-contact lakes, as well as substantial spatial heterogeneity. We present in situ validation data (from problematic maritime areas) and a workflow that facilitates the extraction of robust LSWT data from the high-resolution (90 m) ASTER surface temperature product (AST08). This enables spatial patterns to be analysed in conjunction with surrounding thermal influences, such as parent glaciers and topographies. This workflow can be utilised for the analysis of the LSWT data of other small lakes and crucially allows high spatial resolution study of how they have responded to changes in climate. Further study of the LSWT is essential in the Arctic given the amplification of climate change across the region.
Published: 29 July 2021
by MDPI
Remote Sensing, Volume 13; doi:10.3390/rs13152991

Abstract:
In this paper, we propose a Dirichlet process (DP) mixture model of Gamma distributions, which is an extension of the finite Gamma mixture model to the infinite case. In particular, we propose a novel online nonparametric Bayesian analysis method based on the infinite Gamma mixture model where the determination of the number of clusters is bypassed via an infinite number of mixture components. The proposed model is learned via an online extended variational Bayesian inference approach in a flexible way where the priors of model’s parameters are selected appropriately and the posteriors are approximated effectively in a closed form. The online setting has the advantage to allow data instances to be treated in a sequential manner, which is more attractive than batch learning especially when dealing with massive and streaming data. We demonstrated the performance and merits of the proposed statistical framework with a challenging real-world application namely oil spill detection in synthetic aperture radar (SAR) images.
Published: 29 July 2021
by MDPI
Remote Sensing, Volume 13; doi:10.3390/rs13152986

Abstract:
Semantic segmentation of remote sensing imagery is a fundamental task in intelligent interpretation. Since deep convolutional neural networks (DCNNs) performed considerable insight in learning implicit representations from data, numerous works in recent years have transferred the DCNN-based model to remote sensing data analysis. However, the wide-range observation areas, complex and diverse objects and illumination and imaging angle influence the pixels easily confused, leading to undesirable results. Therefore, a remote sensing imagery semantic segmentation neural network, named HCANet, is proposed to generate representative and discriminative representations for dense predictions. HCANet hybridizes cross-level contextual and attentive representations to emphasize the distinguishability of learned features. First of all, a cross-level contextual representation module (CCRM) is devised to exploit and harness the superpixel contextual information. Moreover, a hybrid representation enhancement module (HREM) is designed to fuse cross-level contextual and self-attentive representations flexibly. Furthermore, the decoder incorporates DUpsampling operation to boost the efficiency losslessly. The extensive experiments are implemented on the Vaihingen and Potsdam benchmarks. In addition, the results indicate that HCANet achieves excellent performance on overall accuracy and mean intersection over union. In addition, the ablation study further verifies the superiority of CCRM.
Published: 29 July 2021
by MDPI
Remote Sensing, Volume 13; doi:10.3390/rs13152993

Abstract:
The long-term estimation of grassland aboveground biomass (AGB) is important for grassland resource management in the Three-River Headwaters Region (TRHR) of China. Due to the lack of reliable grassland AGB datasets since the 1980s, the long-term spatiotemporal variation in grassland AGB in the TRHR remains unclear. In this study, we estimated AGB in the grassland of 209,897 km2 using advanced very high resolution radiometer (AVHRR), MODerate-resolution Imaging Spectroradiometer (MODIS), meteorological, ancillary data during 1982–2018, and 75 AGB ground observations in the growth period of 2009 in the TRHR. To enhance the spatial representativeness of ground observations, we firstly upscaled the grassland AGB using a gradient boosting regression tree (GBRT) model from ground observations to a 1 km spatial resolution via MODIS normalized difference vegetation index (NDVI), meteorological and ancillary data, and the model produced validation results with a coefficient of determination (R2) equal to 0.76, a relative mean square error (RMSE) equal to 88.8 g C m−2, and a bias equal to −1.6 g C m−2 between the ground-observed and MODIS-derived upscaled AGB. Then, we upscaled grassland AGB using the same model from a 1 km to 5 km spatial resolution via AVHRR NDVI and the same data as previously mentioned with the validation accuracy (R2 = 0.74, RMSE = 57.8 g C m−2, and bias = −0.1 g C m−2) between the MODIS-derived reference and AVHRR-derived upscaled AGB. The annual trend of grassland AGB in the TRHR increased by 0.37 g C m−2 (p< 0.05) on average per year during 1982–2018, which was mainly caused by vegetation greening and increased precipitation. This study provided reliable long-term (1982–2018) grassland AGB datasets to monitor the spatiotemporal variation in grassland AGB in the TRHR.
Published: 29 July 2021
by MDPI
Remote Sensing, Volume 13; doi:10.3390/rs13152984

Abstract:
In the last 15 years, the west population of white-naped crane (Antigone vipio) decreased dramatically despite the enhanced conservation actions in both breeding and wintering areas. Recent studies highlighted the importance of protecting the integrity of movement connectivity for migratory birds. Widespread and rapid landcover changes may exceed the adaptive capacity of migrants, leading to the collapse of migratory networks. In this study, using satellite tracking data, we modeled and characterized the migration routes of the white-naped crane at three spatial levels (core area, migratory corridor, and migratory path) based on the utilization distribution for two eras (1990s and 2010s) spanning 20 years. Our analysis demonstrated that the white-naped crane shifted its migratory route, which is supported by other lines of evidences. The widespread loss of wetlands, especially within the stopover sites, might have caused this behavioral adaptation. Moreover, our analysis indicated that the long-term sustainability of the new route is untested and likely to be questionable. Therefore, directing conservation effects to the new route might be insufficient for the long-term wellbeing of this threatened crane and large-scale wetland restorations in Bohai Bay, a critical stopover site in the East Asian-Australasian flyway, are of the utmost importance to the conservation of this species.
Published: 29 July 2021
by MDPI
Remote Sensing, Volume 13; doi:10.3390/rs13152982

Abstract:
An effective blended Sea-Ice Concentration (SIC) product has been developed that utilizes ice concentrations from passive microwave and visible/infrared satellite instruments, specifically the Advanced Microwave Scanning Radiometer-2 (AMSR2) and the Visible Infrared Imaging Radiometer Suite (VIIRS). The blending takes advantage of the all-sky capability of the AMSR2 sensor and the high spatial resolution of VIIRS, though it utilizes only the clear sky characteristics of VIIRS. After both VIIRS and AMSR2 images are remapped to a 1 km EASE-Grid version 2, a Best Linear Unbiased Estimator (BLUE) method is used to combine the AMSR2 and VIIRS SIC for a blended product at 1 km resolution under clear-sky conditions. Under cloudy-sky conditions the AMSR2 SIC with bias correction is used. For validation, high spatial resolution Landsat data are collocated with VIIRS and AMSR2 from 1 February 2017 to 31 October 2019. Bias, standard deviation, and root mean squared errors are calculated for the SICs of VIIRS, AMSR2, and the blended field. The blended SIC outperforms the individual VIIRS and AMSR2 SICs. The higher spatial resolution VIIRS data provide beneficial information to improve upon AMSR2 SIC under clear-sky conditions, especially during the summer melt season, as the AMSR2 SIC has a consistent negative bias near and above the melting point.
Published: 29 July 2021
by MDPI
Remote Sensing, Volume 13; doi:10.3390/rs13152983

Abstract:
Land fragmentation and small plots are the main features of the rural environment of Galicia (NW Spain). Smallholding limits land use management, representing a drawback in local forest planning. This study analyzes the potential use of multitemporal Sentinel-2 images to detect and control forest cuts in very small pine and eucalyptus plots located in southern Galicia. The proposed approach is based on the analysis of Sentinel-2 NDVI time series in 4231 plots smaller than 3 ha (average 0.46 ha). The methodology allowed us to detect cuts, allocate cut dates and quantify plot areas due to different cutting cycles in an uneven-aged stand. An accuracy of approximately 95% was achieved when the whole plot was cut, with an 81% accuracy for partial cuts. The main difficulty in detecting and dating cuts was related to cloud cover, which affected the multitemporal analysis. In conclusion, the proposed methodology provides an accurate estimation of cutting date and area, helping to improve the monitoring system in sustainable forest certifications to ensure compliance with forest management plans.
Published: 29 July 2021
by MDPI
Remote Sensing, Volume 13; doi:10.3390/rs13152985

Abstract:
This research activity, conducted in collaboration with the Aero-Naval Operations Department of the Guardia di Finanza of Bari as part of the Special Commissioner for urgent measures of reclamation, environmental improvements and redevelopment of Taranto’s measurement, is based on the use of a high-resolution airborne sensor, mounted on board a helicopter to identify and map all in operation and abandoned mussel farming in the first and second inlet of Mar Piccolo. In addition, factors able to compromise the environmental status of the Mar Piccolo ecosystem were also evaluated. The methodological workflow developed lets extract significant individual frames from the captured video tracks, improves images by applying five image processing algorithms, georeferences the individual frames based on flight data, and implements the processed data in a thematic Geographical Information System. All mussel farms, in operation and derelict, all partially submerged and/or water-coated invisible to navigation poles and other elements such as illegal fishing nets and marine litter on the seabed up to about 2 m deep, have been identified and mapped. The creation of an instant, high-precision cartographic representation made it possible to identify the anthropogenic pressures on the Mar Piccolo of Taranto and the necessary actions for better management of the area.
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