bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluating Stan’s Variational Bayes Algorithm for Estimating Multidimensional IRT Models
Authors: Ulitzsch Esther , Nestler Steffen
Published: February 2022

Bayesian estimation of multidimensional item response theory (IRT) models in large data sets may come with impractical computational burdens when general-purpose Markov chain Monte Carlo (MCMC) samplers are employed. Variational Bayes (VB)—a method for approximating the posterior distribution—poses a potential remedy. Stan’s general-purpose VB algorithms have drastically improved the accessibility of VB methods for a wide psychometric audience. Using marginal maximum likelihood (MML) and MCMC as benchmarks, the present simulation study investigates the utility of Stan’s built-in VB function for estimating multidimensional IRT models with between-item dimensionality. VB yielded a marked speed-up in comparison to MCMC, but did not generally outperform MML in terms of run time. VB estimates were trustworthy only for item difficulties, while bias in item discriminations depended on the model’s dimensionality. Under realistic conditions of non-zero correlations between dimensions, VB correlation estimates were subject to severe bias. The practical relevance of performance differences is illustrated with data from PISA 2018. We conclude that in its current form, Stan’s built-in VB algorithm does not pose a viable alternative for estimating multidimensional IRT models.
Synthesis and Reactivity of Cyclic Oxonium Derivatives of nido-Carborane: A Review
Authors: Stogniy Marina Yu. , Sivaev Igor B.
Published: February 2022

Nucleophilic ring-opening reactions of cyclic oxonium derivatives of anionic boron hydrides are a convenient method of their modification which opens practically unlimited prospects for their incorporation into various macro- and biomolecules. This contribution provides an overview of the synthesis and reactivity of cyclic oxonium derivatives of nido-carborane as well as half-sandwich complexes based on it.
N-4 Alkyl Cytosine Derivatives Synthesis: A New Approach
Authors: De Nisco Mauro , Di Maio Antonio , Manfra Michele , Ostacolo Carmine , Bertamino Alessia , Campiglia Pietro , Gomez-Monterrey Isabel M. , Pedatella Silvana
Published: February 2022

The selective N-4 alkylation of cytosine plays a critical role in the synthesis of biologically active molecules. This work focuses on the development of practical reaction conditions toward a regioselective synthesis of N-4-alkyl cytosine derivatives. The sequence includes a direct and selective sulfonylation at the N-1 site of the cytosine, followed by the alkylation of the amino site using KHMDS in CH2Cl2/THF mixture, providing a fast and efficient approach consistent with pyrimidine-based drug design.
Thematic Presentations in Indonesian Qur’anic Commentaries
Authors: Azizy Jauhar , Syarifuddin Mohammad Anwar , Ubaidah Hani Hilyati
Published: February 2022

The study of thematic interpretation (tafsīr mawḍū‘ī) in Indonesia focuses primarily on the products of interpretations written in the 2000s, with little attention paid to the origins of thematic interpretations in Indonesia. This article will look at different ways of presenting thematic interpretations in the Indonesian commentary literature prior to the 2000s. This article will also investigate whether the development of the form of interpretation in the Middle East, particularly Egypt, has had any impact on the form of Indonesian thematic interpretation. The methodology used in this study is a literature review based on thematic interpretations (mawḍū‘ī) of several Egyptian commentators, including Amīn al-Khūlī (d. 1966), Mahmūd Shaltut (d. 1963), Bint Shāti’ (d. 1998), ‘Abd al-Hayy al-Farmawī (d. 2017), Hassan Hanafī (d. 2021), and Mustafā Muslim (d. 2021). The authors also use content analysis to examine some of the Indonesian commentary literature. The conclusion of this article demonstrates that thematic interpretation discourse in Egypt had a significant influence on the development of thematic interpretation in Indonesia, particularly interpretation literature published in the 1990s. This influence can be seen in the presence of a glossary and an index of discussion topics, complete with Qur’anic verses and arranged alphabetically or chronologically. This is in keeping with the spirit of Amīn al-Khūlī (d. 1966), who emphasized the importance of thematic discussions in determining the Qur’anic viewpoint on specific issues.
Buddhism and Cultural Heritage in the Memorialization of the Hiroshima Bombing: The Art and Activism of Hirayama Ikuo
Authors: Stortini Paride
Published: February 2022

Debates on the memorialization of the atomic bombing of Hiroshima have played an essential role in the construction of postwar Japanese identity, public memory, and historical consciousness. Religion, often conceived beyond traditional terms through concepts such as “spirituality” and “heritage”, was part of this process. This article examines the role of Buddhism in the autobiographical and visual narratives of the atomic bomb survivor Hirayama Ikuo, who expressed his personal trauma through art, turning it into a call for peace and for the preservation of the cultural heritage of the Silk Road, associated with the spread of Buddhism. Using recent critical approaches to heritage studies, I will show how the heritagization of Buddhism in Hirayama’s work does not preclude the sacralization of aspects of Silk Road heritage. Placing Hirayama’s approach to the nuclear bombing in the context of postwar discourses on Japan as a peaceful “nation of culture”, I will also problematize his view of Buddhism and the Silk Road by showing how similar views were used in support of imperialism in the prewar period.
The Buddhist Philanthropist: The Life and Times of Lee Choon Seng
Authors: Hue Guan Thye , Tang Chang , Choo Juhn Khai Klan
Published: February 2022

This article examines the life and times of Lee Choon Seng (李俊承), exploring his role as a prominent lay Buddhist leader in Singapore and the broader Chinese Buddhist world. Lee Choon Seng’s influence in society, as well as his adherence to Chinese Mahayana Buddhism, laid an important foundation for him to carry out philanthropic activities within the Buddhist community in Singapore. Before World War II, Lee Choon Seng acted as a patron of the Dharma, sponsoring Buddhist activities and advocating for the idea of revitalizing China in the spirit of Buddhism. During the Japanese Occupation, Lee Choon Seng initiated and led the Buddhist philanthropy movement, which converged into a huge developmental trend in society. In the postwar period, Lee Choon Seng established and served as the inaugural chairman of the Singapore Buddhist Federation, making him a key figure in unifying the Buddhist community in Singapore. As this article demonstrates, Lee Choon Seng’s socio-religious model, which was comprised of a combination of Mahayana Buddhism with social charity and welfare, was an important factor for the promotion of Buddhism among the Chinese community in Singapore.
“I Have Come Not to Abolish but to Fulfil”: Reflections on Understanding Christianity as Fulfilment without Presupposing Supersessionism
Authors: Svartvik Jesper
Published: February 2022

This article explores the interrelationship between the two major issues that the theologians of earliest Christianity were pondering, i.e., how to explain the suspension of the eschaton, and how to understand the relation between Christ believers and the people of Israel. Whereas within a couple of decades a number of answers were already given to the former question in order to appease the questioners and marginalize the topic, the answer to the latter question has been not only formative for all Christian thinking for two millennia, but also utterly destructive for Jewish–Christian relations throughout the ages. This article recognizes that after two thousand years of supersessionism, it takes a lot of work to articulate a Christian theology which is fulfilment without supersessionism—but not as much work as the first generations of Christ believers had to devote to de-eschatologizing their message in order to be able to hand it over to future generations of Christians. They prevailed the challenge of their time—will we?
A New Concept of Crisis Water Management in Urban Areas Based on the Risk Maps of Lack of Water Supply in Response to European Law
Authors: Piegdoń Izabela
Published: February 2022

The paper presents issues related to ensuring the safety of water supply, based on the analysis and assessment of the risk of the lack of water supply to consumers using risk maps, in accordance with the current global trend recommended by guidelines and procedures, compliance with which should be a priority activity in the struggle against adverse events, related to the functioning of every collective water supply system (CWSS). The work contribution is the development of an original methodology of risk analysis and assessment for the needs of mapping the risk of a lack of water supply in the normal operation of the water supply network, as well as during a crisis. The presented methods take into account the quantitative aspect of the lack of water supply, with the possibility of extending the proposed solutions with a qualitative aspect. The article will contribute to the development of knowledge in the area of analysis and assessment of the risk of a water supply failure in PsDyW, and will determine new standards in its graphical presentation. It is a response to the World Health Organization (WHO) requirements of providing protection for the critical infrastructure and ensuring access to water for all citizens in the European Union.
Temperature Variation and Climate Resilience Action within a Changing Landscape
Authors: Marajh Leah , He Yuhong
Published: February 2022

Temperature change can have profound impacts on livelihood activities and human well-being. Specific factors such as land transitions and climate knowledge can influence temperature variation and actions for adaptation. In addition to meteorological data, this study integrates land surface temperature (LST) derived from satellite imagery and local temperature perceptions obtained through interviews to advance a deeper understanding of spatial temperature and its impacts, which is not often seen within climate studies. This study examines local temperature across three different land types (rural mountains, rural agricultural lowlands, urban areas) in the Greater Angkor Region of Cambodia to highlight important insights about temperature and climate resilience action. The results revealed that changes in temperature were most pronounced in Phnom Kulen National Park (rural mountain) and in the rural agricultural lowlands, where residents discussed direct impacts and disruptions to their lives. Temperature, in both the LST results and through local perceptions, demonstrated a strong correlation to ground features, where areas with low vegetation exhibited high temperatures and areas with high vegetation observed low temperatures. While climate action in the form of tree planting and forest conservation are major climate mitigation strategies being undertaken in this region, social awareness and the ability to adapt to changes in temperature was revealed to be uneven across the landscape, suggesting that local entities should mobilize around gaining more education and training for all residents.
Spatiotemporal Variations in Satellite-Derived Vegetation Phenological Parameters in Northeast China
Authors: Guo Jinting , Hu Yuanman
Published: February 2022

Vegetation phenology does not only serve as a key index of terrestrial ecosystem response to worldwide climate change but also has a major influence on plant productivity and the carbon cycle. In the current research, the change of vegetation phenological parameters was studied and the impact exerted by climate change on phenological phases in northeast China for 1982–2014 was explored using the latest edition of the Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index (GIMMS NDVI3g) dataset. The results showed that the start of the growing season (SOS) slightly advanced, the end of the growing season (EOS) showed a significant delay, and the length of the growing season (LOS) exhibited a significant prolonging at the regional scale. At the different vegetation types scale, there existed diverse responses of vegetation phenological phases to climate change for forest, grassland, and cultivated land. Significant decreasing trends in the SOS occupied 19.1% of the entire research area, whereas pixels with significantly increasing trends in the SOS accounted for 13.1%. The EOS was delayed in most of the study region (approximately 72.1%). As the result of the variations of SOS and EOS, the LOS was obviously enhanced (p < 0.05) in 29.7% of the research area. According to the correlation of vegetation phenology with climate factors, the SOS had a significant negative relationship with the average temperature in springtime, while the EOS was notably negatively connected to summer total precipitation at the regional scale. At the pixel scale, the correlation of phenological parameters with climate variables showed strong spatial heterogeneities. This study contributes to the comprehension of the responses of vegetation phenology to climate change.
Mismatching Removal for Feature-Point Matching Based on Triangular Topology Probability Sampling Consensus
Authors: He Zaixing , Shen Chentao , Wang Quanyou , Zhao Xinyue , Jiang Huilong
Published: February 2022

Feature-point matching between two images is a fundamental process in remote-sensing applications, such as image registration. However, mismatching is inevitable, and it needs to be removed. It is difficult for existing methods to remove a high ratio of mismatches. To address this issue, a robust method, called triangular topology probability sampling consensus (TSAC), is proposed, which combines the topology network and resampling methods. The proposed method constructs the triangular topology of the feature points of two images, quantifies the mismatching probability for each point pair, and then weights the probability into the random process of RANSAC by calculating the optimal homography matrix between the two images so that the mismatches can be detected and removed. Compared with the state-of-the-art methods, TSAC has superior performances in accuracy and robustness.
SRSe-Net: Super-Resolution-Based Semantic Segmentation Network for Green Tide Extraction
Authors: Cui Binge , Zhang Haoqing , Jing Wei , Liu Huifang , Cui Jianming
Published: February 2022

Due to the phenomenon of mixed pixels in low-resolution remote sensing images, the green tide spectral features with low Enteromorpha coverage are not obvious. Super-resolution technology based on deep learning can supplement more detailed information for subsequent semantic segmentation tasks. In this paper, a novel green tide extraction method for MODIS images based on super-resolution and a deep semantic segmentation network was proposed. Inspired by the idea of transfer learning, a super-resolution model (i.e., WDSR) is first pre-trained with high spatial resolution GF1-WFV images, and then the representations learned in the GF1-WFV image domain are transferred to the MODIS image domain. The improvement of remote sensing image resolution enables us to better distinguish the green tide patches from the surrounding seawater. As a result, a deep semantic segmentation network (SRSe-Net) suitable for large-scale green tide information extraction is proposed. The SRSe-Net introduced the dense connection mechanism on the basis of U-Net and replaces the convolution operations with dense blocks, which effectively obtained the detailed green tide boundary information by strengthening the propagation and reusing features. In addition, the SRSe-Net reducs the pooling layer and adds a bridge module in the final stage of the encoder. The experimental results show that a SRSe-Net can obtain more accurate segmentation results with fewer network parameters.
Spectral-Based Monitoring of Climate Effects on the Inter-Annual Variability of Different Plant Functional Types in Mediterranean Cork Oak Woodlands
Authors: Soares Cristina , Silva João M. N. , Boavida-Portugal Joana , Cerasoli Sofia
Published: February 2022

Using remotely sensed data to estimate the biophysical properties of vegetation in woodlands is a challenging task due to their heterogeneous nature. The objective of this study was to assess the biophysical parameters of different vegetation types (cork oak trees, shrubs and herbaceous vegetation) in cork oak woodland through the analysis of temporal trends in spectral vegetation indices (VIs). A seven-year database (from 2011 until 2017) of in situ observations collected with a field spectroradiometer with a monthly basis was used and four VIs were derived, considered as proxies for several biophysical properties of vegetation such as biomass (Normalized Difference Vegetation Index—NDVI); chlorophyll content (MERIS Terrestrial Chlorophyll Index-MTCI), tissue water content (Normalized Difference Water Index—NDWI) and the carotenoid/chlorophyll ratio (Photochemical Reflectance Index—PRI). During the analyzed period, some key meteorological data (precipitation, temperature, relative air humidity and global radiation) were collected for the study site, aggregated at three different time-lags (short period (30 d), medium period (90 d) and hydrological period (HIDR)), and their relationship with VIs was analyzed. The results showed different trends for each vegetation index and vegetation type. In NDVI and NDWI, herbaceous vegetation showed a highly marked seasonal trend, whereas for MTCI, it was the cork oak and Cistus salvifolius, and for PRI, it was Ulex airensis that showed the marked seasonal trend. Shrubs have large differences depending on the species: the shallow-rooted Cistus salvifolius showed a higher seasonal variability than the deep-rooted Ulex airensis. Our results revealed the importance of temperature and precipitation as the main climatic variables influencing VI variability in the four studied vegetation types. This study sets up the relationships between climate and vegetation indices for each vegetation type. Spectral vegetation indices are useful tools for assessing the impact of climate on vegetation, because using these makes it easier to monitor the amount of “greenness”, biomass and water stress of vegetation than assessing the photosynthetic efficiency. Proximal remote sensing measurements are fundamental for the correct use of remote sensing in monitoring complex agroforest ecosystems, largely used to inform policies to improve resilience to drought, particularly in the Mediterranean region.
Effects of Environmental Factors on the Changes in MODIS NPP along DEM in Global Terrestrial Ecosystems over the Last Two Decades
Authors: Wang Zhaoqi , Wang Hong , Wang Tongfang , Wang Lina , Huang Xiaotao , Zheng Kai , Liu Xiang
Published: February 2022

Global warming has exerted widespread impacts on the terrestrial ecosystem in the past three decades. Vegetation is an important part of the terrestrial ecosystem, and its net primary productivity (NPP) is an important variable in the exchange of materials and energy in the terrestrial ecosystem. However, the effect of climate variation on the spatial pattern of zonal distribution of NPP has remained unclear over the past two decades. Therefore, we analyzed the spatiotemporal patterns and trends of MODIS NPP and environmental factors (temperature, radiation, and soil moisture) derived from three sets of reanalysis data. The moving window method and digital elevation model (DEM) were used to explore their changes along elevation gradients. Finally, we explored the effect of environmental factors on the changes in NPP and its elevation distribution patterns. Results showed that nearly 60% of the global area exhibited an increase in NPP with increasing elevation. Soil moisture has the largest uncertainty either in the spatial pattern or inter-annual variation, while temperature has the smallest uncertainty among the three environmental factors. The uncertainty of environmental factors is also reflected in its impact on the elevation distribution of NPP, and temperature is still the main dominating environmental factor. Our research results imply that the carbon sequestration capability of vegetation is becoming increasingly prominent in high-elevation regions. However, the quantitative evaluation of its carbon sink (source) functions needs further research under global warming.
Estimating the Horizontal and Vertical Distributions of Pigments in Canopies of Ginkgo Plantation Based on UAV-Borne LiDAR, Hyperspectral Data by Coupling PROSAIL Model
Authors: Yin Shiyun , Zhou Kai , Cao Lin , Shen Xin
Published: February 2022

Pigments are the biochemical material basis for energy and material exchange between vegetation and the external environment, therefore quantitative determination of pigment content is crucial. Unmanned Aerial Vehicle (UAV)-borne remote sensing data coupled with radiative transfer models (RTM) provide marked strengths for three-dimensional (3D) visualization, as well as accurate determination of the distributions of pigment content in forest canopies. In this study, Light Detection and Ranging (LiDAR) and hyperspectral images acquired by a multi-rotor UAV were assessed with the PROSAIL model (i.e., PROSPECT model coupled with 4SAIL model) and were synthetically implemented to estimate the horizontal and vertical distribution of pigments in canopies of Ginkgo plantations in a study site within coastal southeast China. Firstly, the fusion of LiDAR point cloud and hyperspectral images was carried out in the frame of voxels to obtain fused hyperspectral point clouds. Secondly, the PROSAIL model was calibrated using specific model parameters of Ginkgo trees and the corresponding look-up tables (LUTs) of leaf pigment content were constructed and optimally selected. Finally, based on the optimal LUTs and combined with the hyperspectral point clouds, the horizontal and vertical distributions of pigments in different ages of ginkgo trees were mapped to explore their distribution characteristics. The results showed that 22-year-old ginkgo trees had higher biochemical pigment content (increase 3.37–55.67%) than 13-year-old ginkgo trees. Pigment content decreased with the increase of height, whereas pigment content from the outer part of tree canopies showed a rising tendency as compared to the inner part of canopies. Compared with the traditional vegetation index models (R2 = 0.25–0.46, rRMSE = 16.25–19.37%), the new approach developed in this study exhibited significant higher accuracies (R2 = 0.36–0.60, rRMSE = 13.53–16.86%). The results of this study confirmed the effectiveness of coupling the UAV-borne LiDAR and hyperspectral image with the PROSAIL model for accurately assessing pigment content in ginkgo canopies, and the developed estimation methods can also be adopted to other regions under different conditions, providing technical support for sustainable forest management and precision silvicuture for plantations.
Identifying Dike-Pond System Using an Improved Cascade R-CNN Model and High-Resolution Satellite Images
Authors: Ma Yintao , Zhou Zheng , She Xiaoxiong , Zhou Longyu , Ren Tao , Liu Shishi , Lu Jianwei
Published: February 2022

The dike-pond system (DPS) is the integration of a natural or man-made pond and crop cultivation on dikes, widely distributed in the Pearl River Delta and Jianghan plain in China. It plays a key role in preserving biodiversity, enhancing the nutrient cycle, and increasing crop production. However, DPS is rarely mapped at a large scale with satellite data, due to the limitations in the training dataset and traditional classification methods. This study improved the deep learning algorithm Cascade Region Convolutional Neural Network (Cascade R-CNN) algorithm to detect the DPS in Qianjiang City using high-resolution satellite data. In the proposed mCascade R-CNN, the regular convolution layer in the backbone was modified into the deformable convolutional layer, which was more suitable for learning the features of DPS with variable shapes and orientations. The mCascade R-CNN yielded the most accurate detection of DPS, with an average precision (AP) value that was 2.71% higher than Cascade R-CNN and 11.84% higher than You Look Only Once-v4 (YOLOv4). The area of oilseed rape growing on the dikes accounted for 3.42% of the total oilseed rape planting area. This study demonstrates the potential of the deep leaning methods combined with high-resolution satellite images in detecting integrated agriculture systems.
Comparative Analysis of Several Typical Landsat 8 OLI Cloud Detection Methods
Authors: Sui Songman , Sun Lin
Published: February 2022

The Landsat 8 Operational Land Imager (OLI) is a high-resolution satellite sensor that is carried on the next-generation Landsat which was launched by the National Aeronautics and Space Administration (NASA) in 2013. Compared to the previous land observation satellites in this series, Landsat 8 OLI has been further optimized in regard to its band setting and data acquisition frequency. The application scope of the data and the ability of information extraction have been further expanded and enhanced. However, the existence of clouds reduces the efficiency and the quality of satellite data. Therefore, in order to identify cloud pixels with a high level of accuracy and efficiency, a variety of cloud detection methods have been developed for Landsat 8 OLI sensors. Distinct treatment methods have been adopted for identifying different surface backgrounds and various types of cloud characteristics in order to improve the accuracy of cloud detection. The present study compares and analyzes the detection results from five typical cloud detection algorithms for different cloud types over different ground types. In addition, it discusses the various cloud detection algorithms used for different types of adaptive backgrounds and clouds in order to shed light upon the comprehensive application of different methods. Subsequently, these findings may provide a reference for the development of an algorithm with a higher precision of cloud detection.
Evaluating the Use of Lidar to Discern Snag Characteristics Important for Wildlife
Authors: Stitt Jessica M. , Hudak Andrew T. , Silva Carlos A. , Vierling Lee A. , Vierling Kerri T.
Published: February 2022

Standing dead trees (known as snags) are historically difficult to map and model using airborne laser scanning (ALS), or lidar. Specific snag characteristics are important for wildlife; for instance, a larger snag with a broken top can serve as a nesting platform for raptors. The objective of this study was to evaluate whether characteristics such as top intactness could be inferred from discrete-return ALS data. We collected structural information for 198 snags in closed-canopy conifer forest plots in Idaho. We selected 13 lidar metrics within 5 m diameter point clouds to serve as predictor variables in random forest (RF) models to classify snags into four groups by size (small (<40 cm diameter) or large (≥40 cm diameter)) and intactness (intact or broken top) across multiple iterations. We conducted these models first with all snags combined, and then ran the same models with only small or large snags. Overall accuracies were highest in RF models with large snags only (77%), but kappa statistics for all models were low (0.29–0.49). ALS data alone were not sufficient to identify top intactness for large snags; future studies combining ALS data with other remotely sensed data to improve classification of snag characteristics important for wildlife is encouraged.
A Geospatial Platform to Manage Large-Scale Individual Mobility for an Urban Digital Twin Platform
Authors: Lee Ahyun , Lee Kang-Woo , Kim Kyong-Ho , Shin Sung-Woong
Published: February 2022

Urban digital twin (UDT) technology can be used to digitize physical urban spaces. Previous UDT or smart city research reconstructed the three-dimensional topography of urban spaces, buildings, and facilities. They collected various multimodal sensor data from cities and monitored conditions such as temperature, humidity, fine dust, and real-time road traffic. However, these studies lacked ways to manage individual mobility data, such as those of the vehicles and pedestrians, which constitute major components of a city. Here, we propose a geospatial platform based on the universal game engine Unity3D, which manages large-scale individual mobility data for a UDT platform. The proposed platform stores and manages individual vehicles or pedestrians using information from public closed-circuit television. It also allows the generation of long-term route information for a unique vehicle based on its license plate. We also propose methods to anonymize license plates, to ensure the security of individuals, and to compress individual mobility data. Unique UDT models with individual mobility functionalities can be built and visualized using our proposed geospatial platform.
Assessment of Different Object Detectors for the Maturity Level Classification of Broccoli Crops Using UAV Imagery
Authors: Psiroukis Vasilis , Espejo-Garcia Borja , Chitos Andreas , Dedousis Athanasios , Karantzalos Konstantinos , Fountas Spyros
Published: February 2022

Broccoli is an example of a high-value crop that requires delicate handling throughout the growing season and during its post-harvesting treatment. As broccoli heads can be easily damaged, they are still harvested by hand. Moreover, human scouting is required to initially identify the field segments where several broccoli plants have reached the desired maturity level, such that they can be harvested while they are in the optimal condition. The aim of this study was to automate this process using state-of-the-art Object Detection architectures trained on georeferenced orthomosaic-derived RGB images captured from low-altitude UAV flights, and to assess their capacity to effectively detect and classify broccoli heads based on their maturity level. The results revealed that the object detection approach for automated maturity classification achieved comparable results to physical scouting overall, especially for the two best-performing architectures, namely Faster R-CNN and CenterNet. Their respective performances were consistently over 80% mAP@50 and 70% mAP@75 when using three levels of maturity, and even higher when simplifying the use case into a two-class problem, exceeding 91% and 83%, respectively. At the same time, geometrical transformations for data augmentations reported improvements, while colour distortions were counterproductive. The best-performing architecture and the trained model could be tested as a prototype in real-time UAV detections in order to assist in on-field broccoli maturity detection.
Monitoring Marine Aquaculture and Implications for Marine Spatial Planning—An Example from Shandong Province, China
Authors: Wang Jun , Yang Xiaomei , Wang Zhihua , Ge Dazhuan , Kang Junmei
Published: February 2022

Offshore marine aquaculture can not only provide a large amount of high-quality food for humans, but also effectively relieve the pressure on land space. However, it is difficult for traditional statistical data to reflect changes in spatial dynamics in marine aquaculture. It is also difficult to effectively manage marine space development given the current status of spatial planning regarding land–sea integration. This study used multiphase satellite remote sensing images of Shandong Province together with an automatic extraction algorithm for aquaculture to obtain spatial distribution data of marine aquaculture (surface of seawater visible by remote sensing, types of rafts, and cage aquaculture). GIS spatial overlay analysis technology was used to superimpose marine functional zoning (2010–2020) data for comparative analysis to evaluate implementation effectiveness and existing problems in marine functional zoning. Results showed that the critical time regarding substantial change in marine aquaculture area was around 2010, concurrent with the implementation of the current round of marine functional areas. The aquaculture area in the agricultural and fishery planning area increased from 228.33 km2 in 2010 to 344.6 km2 in 2018, and the overall proportion of aquaculture increased from 65.53% to 70.48%. This indicated that marine function planning can exert a guiding influence. The port area and protection area were found to be other major areas for the expansion of marine aquaculture. We also used field investigations in uncovering the phenomenon of the combined marine functions of marine aquaculture and tourism. On this basis, the role of spatial information technology in marine spatial planning was analyzed, which revealed the importance of coordinated integration of land–sea space for effective control of marine development.
Hybrid Variability Aware Network (HVANet): A Self-Supervised Deep Framework for Label-Free SAR Image Change Detection
Authors: Wang Jian , Wang Yinghua , Liu Hongwei
Published: February 2022

Synthetic aperture radar (SAR) image change detection (CD) aims to automatically recognize changes over the same geographic region by comparing prechange and postchange SAR images. However, the detection performance is usually subject to several restrictions and problems, including the absence of labeled SAR samples, inherent multiplicative speckle noise, and class imbalance. More importantly, for bitemporal SAR images, changed regions tend to present highly variable sizes, irregular shapes, and different textures, typically referred to as hybrid variabilities, further bringing great difficulties to CD. In this paper, we argue that these internal hybrid variabilities can also be used for learning stronger feature representation, and we propose a hybrid variability aware network (HVANet) for completely unsupervised label-free SAR image CD by taking inspiration from recent developments in deep self-supervised learning. First, since different changed regions may exhibit hybrid variabilities, it is necessary to enrich distinguishable information within the input features. To this end, in shallow feature extraction, we generalize the traditional spatial patch (SP) feature to allow for each pixel in bitemporal images to be represented at diverse scales and resolutions, called extended SP (ESP). Second, with the carefully customized ESP features, HVANet performs local spatial structure information extraction and multiscale–multiresolution (MS-MR) information encoding simultaneously through a local spatial stream and a scale-resolution stream, respectively. Intrinsically, HVANet projects the ESP features into a new high-level feature space, where the change identification becomes easier. Third, to train the framework effectively, a self-supervision layer is attached to the top of the HVANet to enable the two-stream feature learning and recognition of changed pixels in the corresponding feature space, in a self-supervised manner. Experimental results on three low/medium-resolution SAR datasets demonstrate the effectiveness and superiority of the proposed framework in unsupervised SAR CD tasks.
Mapping South America’s Drylands through Remote Sensing—A Review of the Methodological Trends and Current Challenges
Authors: Ganem Khalil Ali , Xue Yongkang , Rodrigues Ariane de Almeida , Franca-Rocha Washington , Oliveira Marceli Terra de , Carvalho Nathália Silva de , Cayo Efrain Yury Turpo , Rosa Marcos Reis , Dutra Andeise Cerqueira , Shimabukuro Yosio Edemir
Published: February 2022

The scientific grasp of the distribution and dynamics of land use and land cover (LULC) changes in South America is still limited. This is especially true for the continent’s hyperarid, arid, semiarid, and dry subhumid zones, collectively known as drylands, which are under-represented ecosystems that are highly threatened by climate change and human activity. Maps of LULC in drylands are, thus, essential in order to investigate their vulnerability to both natural and anthropogenic impacts. This paper comprehensively reviewed existing mapping initiatives of South America’s drylands to discuss the main knowledge gaps, as well as central methodological trends and challenges, for advancing our understanding of LULC dynamics in these fragile ecosystems. Our review centered on five essential aspects of remote-sensing-based LULC mapping: scale, datasets, classification techniques, number of classes (legends), and validation protocols. The results indicated that the Landsat sensor dataset was the most frequently used, followed by AVHRR and MODIS, and no studies used recently available high-resolution satellite sensors. Machine learning algorithms emerged as a broadly employed methodology for land cover classification in South America. Still, such advancement in classification methods did not yet reflect in the upsurge of detailed mapping of dryland vegetation types and functional groups. Among the 23 mapping initiatives, the number of LULC classes in their respective legends varied from 6 to 39, with 1 to 14 classes representing drylands. Validation protocols included fieldwork and automatic processes with sampling strategies ranging from solely random to stratified approaches. Finally, we discussed the opportunities and challenges for advancing research on desertification, climate change, fire mapping, and the resilience of dryland populations. By and large, multi-level studies for dryland vegetation mapping are still lacking.
The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication
Authors: Demattê José A. M. , Paiva Ariane Francine da Silveira , Poppiel Raul Roberto , Rosin Nícolas Augusto , Ruiz Luis Fernando Chimelo , Mello Fellipe Alcantara de Oliveira , Minasny Budiman , Grunwald Sabine , Ge Yufeng , Ben Dor Eyal , Gholizadeh Asa , Gomez Cecile , Chabrillat Sabine , Francos Nicolas , Ayoubi Shamsollah , Fiantis Dian , Biney James Kobina Mensah , Wang Changkun , Belal Abdelaziz , Naimi Salman , Hafshejani Najmeh Asgari , Bellinaso Henrique , Moura-Bueno Jean Michel , Silvero Nélida E. Q.
Published: February 2022

Although many Soil Spectral Libraries (SSLs) have been created globally, these libraries still have not been operationalized for end-users. To address this limitation, this study created an online Brazilian Soil Spectral Service (BraSpecS). The system was based on the Brazilian Soil Spectral Library (BSSL) with samples collected in the Visible–Near–Short-wave infrared (vis–NIR–SWIR) and Mid-infrared (MIR) ranges. The interactive platform allows users to find spectra, act as custodians of the data, and estimate several soil properties and classification. The system was tested by 500 Brazilian and 65 international users. Users accessed the platform (besbbr.com.br), uploaded their spectra, and received soil organic carbon (SOC) and clay content prediction results via email. The BraSpecS prediction provided good results for Brazilian data, but performed variably for other countries. Prediction for countries outside of Brazil using local spectra (External Country Soil Spectral Libraries, ExCSSL) mostly showed greater performance than BraSpecS. Clay R2 ranged from 0.5 (BraSpecS) to 0.8 (ExCSSL) in vis–NIR–SWIR, but BraSpecS MIR models were more accurate in most situations. The development of external models based on the fusion of local samples with BSSL formed the Global Soil Spectral Library (GSSL). The GSSL models improved soil properties prediction for different countries. Nevertheless, the proposed system needs to be continually updated with new spectra so they can be applied broadly. Accordingly, the online system is dynamic, users can contribute their data and the models will adapt to local information. Our community-driven web platform allows users to predict soil attributes without learning soil spectral modeling, which will invite end-users to utilize this powerful technique.
Solar Cycle Signal in Climate and Artificial Neural Networks Forecasting
Authors: Tzanis Chris G. , Benetatos Charilaos , Philippopoulos Kostas
Published: February 2022

Natural climate variability is partially attributed to solar radiative forcing. The purpose of this study is to contribute to a better understanding of the influence of solar variability on the Earth’s climate system. The object of this work is the estimation of the variation of multiple climatic parameters (temperature, zonal wind, relative and specific humidity, sensible and latent surface heat flux, cloud cover and precipitable water) in response to solar cycle forcing. An additional goal is to estimate the response of the climate system’s parameters to short-term solar variability in multiple forecasting horizons and to evaluate the behavior of the climate system in shorter time scales. The solar cycle is represented by the 10.7 cm solar flux, a measurement collected by terrestrial radio telescopes, and is provided by NOAA/NCEI/STP, whereas the climatic data are provided by the NCEP/NCAR reanalysis 1 project. The adopted methodology includes the development of a linear regression statistical model in order to calculate the climatic parameters’ feedback to the 11-year solar cycle on a monthly scale. Artificial Neural Networks (ANNs) have been employed to forecast the solar indicator time series for up to 6 months in advance. The climate system’s response is further forecasted using the ANN’s estimated values and the regression equations. The results show that the variation of the climatic parameters can be partially attributed to solar variability. The solar-induced variation of each of the selected parameters, averaged globally, was of an order of magnitude of 10−1–10−3, and the corresponding correlation coefficients (Pearson’s r) were relatively low (−0.5–0.5). Statistically significant areas with relatively high solar cycle signals were found at multiple pressure levels and geographical areas, which can be attributed to various mechanisms.
Fine-Scale Improved Carbon Bookkeeping Model Using Landsat Time Series for Subtropical Forest, Southern China
Authors: Wang Xinyu , Li Runhao , Ding Hu , Fu Yingchun
Published: February 2022

Subtropical forests easily suffer anthropogenic disturbance, including deforestation and reforestation management, which both highly affect the carbon pools. This study proposes spatial-temporal tracking of the carbon density dynamics to improve bookkeeping in the carbon model and applied to subtropical forest activities in Guangzhou, southern China, during the period of 1995 to 2014. Based on the overall accuracy of 87.5% ± 1.7% for forest change products using Landsat time series (LTS), we found that this is a typical period of deforestation conversion to reforestation activity accompanied with urbanization. Additionally, linear regression, random forest regression and allometric growth fitting were proposed by using forest field plots to obtain reliable per-pixel carbon density estimations. The cross-validation (CV) of random forest with LTS-derived parameters reached the highest accuracy of R2 and RMSE of 0.763 and 7.499 Mg ha−1. The RMES of the density estimation ranged between 78 and 84% of the mean observed biomass in the study area, which outperformed previous studies. Over the 20-year period, the study results showed that the explicit carbon emissions were (6.82 ± 0.26) × 104 Mg C yr−1 from deforestation; emissions increased to (1.02 ± 0.04) × 105 Mg C yr−1 given the implicit carbon not yet released to the atmosphere in the form of decomposing slash and wood products. In addition, a carbon uptake of about 1.91 ± 0.73 × 105 Mg C yr−1, presented as the net carbon pool. Based on the continuous detection capability, biennial reforestation activity has increased carbon density by a growth rate of 1.55 Mg ha−1, and the emission factors can be identified with LTS-derived parameters. In general, the study realizes the spatiotemporal improvement of carbon density and flux dynamics tracking, including the abrupt and graduate change based on fine-scale forest activity. It can provide more comprehensive and detailed feedback on the carbon source and sink change process of forest activities and disturbances.
Comparative Assessment of Pixel and Object-Based Approaches for Mapping of Olive Tree Crowns Based on UAV Multispectral Imagery
Authors: Šiljeg Ante , Panđa Lovre , Domazetović Fran , Marić Ivan , Gašparović Mateo , Borisov Mirko , Milošević Rina
Published: February 2022

Pixel-based (PB) and geographic-object-based (GEOBIA) classification approaches allow the extraction of different objects from multispectral images (MS). The primary goal of this research was the analysis of UAV imagery applicability and accuracy assessment of MLC and SVM classification algorithms within PB and GEOBIA classification approaches. The secondary goal was to use different accuracy assessment metrics to determine which of the two tested classification algorithms (SVM and MLC) most reliably distinguishes olive tree crowns and which approach is more accurate (PB or GEOBIA). The third goal was to add false polygon samples for Correctness (COR), Completeness (COM) and Overall Quality (OQ) metrics and use them to calculate the Total Accuracy (TA). The methodology can be divided into six steps, from data acquisition to selection of the best classification algorithm after accuracy assessment. High-quality DOP (digital orthophoto) and UAVMS were generated. A new accuracy metric, called Total Accuracy (TA), combined both false and true positive polygon samples, thus providing a more comprehensive insight into the assessed classification accuracy. The SVM (GEOBIA) was the most reliable classification algorithm for extracting olive tree crowns from UAVMS imagery. The assessment carried out indicated that application of GEOBIA-SVM achieved a TACOR of 0.527, TACOM of 0.811, TAOQ of 0.745, Overall Accuracy (OA) of 0.926 or 0.980 and Area Under Curve (AUC) value of 0.904 or 0.929. The calculated accuracy metrics confirmed that the GEOBIA approach (SVM and MLC) achieved more accurate olive tree crown extraction than the PB approach (SVM and MLC) if applied to classifying VHR UAVMS imagery. The SVM classification algorithm extracted olive tree crowns more accurately than MLC in both approaches. However, the accuracy assessment has proven that PB classification algorithms can also achieve satisfactory accuracy.
Long-Term Changes and Factors That Influence Changes in Thermal Discharge from Nuclear Power Plants in Daya Bay, China
Authors: Zhang Zhihua , Wang Difeng , Cheng Yinhe , Gong Fang
Published: February 2022

Thermal discharge (i.e., warm water) from nuclear power plants (NPPs) in Daya Bay, China, was analyzed in this study. To determine temporal and spatial patterns as well as factors affecting thermal discharge, data were acquired by the Landsat series of remote-sensing satellites for the period 1993–2020. First, sea surface temperature (SST) data for waters off NPPs were retrieved from Landsat imagery using a radiative transfer equation in conjunction with a split-window algorithm. Then, retrieved SST data were used to analyze seasonal and interannual changes in areas affected by NPP thermal discharge, as well as the effects of NPP installed capacity, tides, and wind field on the diffusion of thermal discharge. Analysis of interannual changes revealed an increase in SST with an increase in NPP installed capacity, with the area affected by increased drainage outlet temperature increasing to different degrees. Sea surface temperature and NPP installed capacity were significantly linearly related. Both flood tides (peak spring and neap) and ebb tides (peak spring and neap) affected areas of warming zones, with ebb tides having greater effects. The total area of all warming zones in summer was approximately twice that in spring, regardless of whether winds were favorable (i.e., westerly) or adverse (i.e., easterly). The effects of tides on areas of warming zones exceeded those of winds.