bluuebunny/crossref_metadata_2025 huggingface dataset:
Extraction of Abandoned Land in Hilly Areas Based on the Spatio-Temporal Fusion of Multi-Source Remote Sensing Images
Authors: He Shan , Shao Huaiyong , Xian Wei , Zhang Shuhui , Zhong Jialong , Qi Jiaguo
Published: October 2021

Hilly areas are important parts of the world’s landscape. A marginal phenomenon can be observed in some hilly areas, leading to serious land abandonment. Extracting the spatio-temporal distribution of abandoned land in such hilly areas can protect food security, improve people’s livelihoods, and serve as a tool for a rational land plan. However, mapping the distribution of abandoned land using a single type of remote sensing image is still challenging and problematic due to the fragmentation of such hilly areas and severe cloud pollution. In this study, a new approach by integrating Linear stretch (Ls), Maximum Value Composite (MVC), and Flexible Spatiotemporal DAta Fusion (FSDAF) was proposed to analyze the time-series changes and extract the spatial distribution of abandoned land. MOD09GA, MOD13Q1, and Sentinel-2 were selected as the basis of remote sensing images to fuse a monthly 10 m spatio-temporal data set. Three pieces of vegetation indices (VIs: ndvi, savi, ndwi) were utilized as the measures to identify the abandoned land. A multiple spatio-temporal scales sample database was established, and the Support Vector Machine (SVM) was used to extract abandoned land from cultivated land and woodland. The best extraction result with an overall accuracy of 88.1% was achieved by integrating Ls, MVC, and FSDAF, with the assistance of an SVM classifier. The fused VIs image set transcended the single source method (Sentinel-2) with greater accuracy by a margin of 10.8–23.6% for abandoned land extraction. On the other hand, VIs appeared to contribute positively to extract abandoned land from cultivated land and woodland. This study not only provides technical guidance for the quick acquirement of abandoned land distribution in hilly areas, but it also provides strong data support for the connection of targeted poverty alleviation to rural revitalization.
Vessel Target Detection in Spaceborne–Airborne Collaborative SAR Images via Proposal and Polarization Fusion
Authors: Zhu Dong , Wang Xueqian , Cheng Yayun , Li Gang
Published: October 2021

This paper focuses on vessel detection through the fusion of synthetic aperture radar (SAR) images acquired from spaceborne–airborne collaborative observations. The vessel target detection task becomes more challenging when it features inshore interferences and structured and shaped targets. We propose a new method, based on target proposal and polarization information exploitation (TPPIE), to fuse the spaceborne–airborne collaborative SAR images for accurate vessel detection. First, a new triple-state proposal matrix (TSPM) is generated by combining the normed gradient-based target proposal and the edge-based morphological candidate map. The TSPM can be used to extract the potential target regions, as well as filtering out the sea clutter and inshore interference regions. Second, we present a new polarization feature, named the absolute polarization ratio (APR), to exploit the intensity information of dual-polarization SAR images. In the APR map, the vessel target regions are further enhanced. Third, the final fused image with enhanced targets and suppressed backgrounds (i.e., improved target-to-clutter ratio; TCR) is attained by taking the Hadamard product of the intersected TSPM from multiple sources and the composite map exploiting the APR feature. Experimental analyses using Gaofen-3 satellite and unmanned aerial vehicle (UAV) SAR imagery indicate that the proposed TPPIE fusion method can yield higher TCRs for fused images and better detection performance for vessel targets, compared to commonly used image fusion approaches.
A Simple and Efficient Method for Correction of Basin-Scale Evapotranspiration on the Tibetan Plateau
Authors: Feng Yuqing , Kuang Xingxing , Liang Sihai , Liu Suning , Yao Yingying , Xie Yueqing , Zheng Chunmiao
Published: October 2021

Evapotranspiration (ET) is one of the important components of the global hydrologic cycle, energy exchange, and carbon cycle. However, basin scale actual ET (hereafter ETa) is difficult to estimate accurately. We present an evaluation of four actual ET products (hereafter ETp) in seven sub-basins in the Tibetan Plateau. The actual ET calculated by the water balance method (hereafter ETref) was used as the reference for correction of the different ETp. The ETref and ETp show obvious seasonal cycles, but the ETp overestimated or underestimated the ET of the sub-basins in the Tibetan Plateau. A simple and effective method was proposed to correct the basin-scale ETp. The method was referred to as ratio bias correction, and it can effectively remove nearly all biases of the ETp. The proposed method is simpler and more effective in correcting the four ETp compared with the gamma distribution bias correction method. The reliability of the ETp is significantly increased after the ratio bias correction. The ratio bias correction method was used to correct the ETp in the seven sub-basins in the Tibetan Plateau, and regional ET was significantly improved. The results may help improve estimation of the ET of the Tibetan Plateau and thereby contribute to a better understanding of the hydrologic cycle of the plateau.
The Grain for Green Program Intensifies Trade-Offs between Ecosystem Services in Midwestern Shanxi, China
Authors: Hu Baoan , Zhang Zhijie , Han Hairong , Li Zuzheng , Cheng Xiaoqin , Kang Fengfeng , Wu Huifeng
Published: October 2021

Ecological engineering is a widely used strategy to address environmental degradation and enhance human well-being. A quantitative assessment of the impacts of ecological engineering on ecosystem services (ESs) is a prerequisite for designing inclusive and sustainable engineering programs. In order to strengthen national ecological security, the Chinese government has implemented the world’s largest ecological project since 1999, the Grain for Green Program (GFGP). We used a professional model to evaluate the key ESs in Lvliang City. Scenario analysis was used to quantify the contribution of the GFGP to changes in ESs and the impacts of trade-offs/synergy. We used spatial regression to identify the main drivers of ES trade-offs. We found that: (1) From 2000 to 2018, the contribution rates of the GFGP to changes in carbon storage (CS), habitat quality (HQ), water yield (WY), and soil conservation (SC) were 140.92%, 155.59%, −454.48%, and 92.96%, respectively. GFGP compensated for the negative impacts of external environmental pressure on CS and HQ, and significantly improved CS, HQ, and SC, but at the expense of WY. (2) The GFGP promotes the synergistic development of CS, HQ, and SC, and also intensifies the trade-off relationships between WY and CS, WY and HQ, and WY and SC. (3) Land use change and urbanization are significantly positively correlated with the WY–CS, WY–HQ, and WY–SC trade-offs, while increases in NDVI helped alleviate these trade-offs. (4) Geographically weighted regression explained 90.8%, 94.2%, and 88.2% of the WY–CS, WY–HQ, and WY–SC trade-offs, respectively. We suggest that the ESs’ benefits from the GFGP can be maximized by controlling the intensity of land use change, optimizing the development of urbanization, and improving the effectiveness of afforestation. This general method of quantifying the impact of ecological engineering on ESs can act as a reference for future ecological restoration plans and decision-making in China and across the world.
Evaluating the Territorial Impact of Built-Up Area Expansion in the Surroundings of Bucharest (Romania) through a Multilevel Approach Based on Landsat Satellite Imagery
Authors: Stoica Ilinca-Valentina , Zamfir Daniela , Vîrghileanu Marina
Published: October 2021

Assessing the relentless expansion of built-up areas is one of the most important tasks for achieving sustainable planning and supporting decision-making on the regional and local level. In this context, techniques based on remote sensing can play a crucial role in monitoring the fast rhythm of urban growth, allowing the regular appraisal of territorial dynamics. The main aim of the study is to evaluate, in a multi-scalar perspective, the built-up area expansion and the spatio–temporal changes in Ilfov County, which overlaps the surroundings of Bucharest, capital of Romania. Our research focuses on processing multi-date Landsat satellite imagery from three selected time references (2000, 2008, 2018) through the supervised classification process. Further on, the types of built-up area dynamics are explored using LDTtool, a landscape metrics instrument. The results reveal massive territorial restructuring in the 18 years, as the new built-up developments occupy a larger area than the settlements’ surface in 2000. The rhythm of the transformations also changed over time, denoting a significant acceleration after 2008, when 75% of the new development occurred. At the regional level, the spatial pattern has become more and more complex, in a patchwork of spatial arrangements characterized by the proliferation of low density areas interspersed with clusters of high density developments and undeveloped land. At the local level, a comparative assessment of the administrative territorial units’ pathway was conducted based on the annual growth of built-up areas, highlighting the most attractive places and the main territorial directions of development. In terms of the specific dynamics of built-up areas, the main change patterns are “F—NP increment by gain”, followed by “G—Aggregation by gain”, both comprising around 80% of the total number of cells. The first type was prevalent in the first period (2000–2008), while the second is identified only after 2008, when it became the most represented, followed in the hierarchy by the previously dominant category. The spatial pattern differentiations were further explored in three complementary case studies investigated in correlation with socioeconomic data, revealing a heterogeneous landscape.
Monitoring Cyanobacteria Bloom in Dianchi Lake Based on Ground-Based Multispectral Remote-Sensing Imaging: Preliminary Results
Authors: Zhao Huan , Li Junsheng , Yan Xiang , Fang Shengzhong , Du Yichen , Xue Bin , Yu Kai , Wang Chen
Published: October 2021

Some lakes in China have undergone serious eutrophication, with cyanobacterial blooms occurring frequently. Dynamic monitoring of cyanobacterial blooms is important. At present, the traditional lake-survey-based cyanobacterial bloom monitoring is spatiotemporally limited and requires considerable human and material resources. Although satellite remote sensing can rapidly monitor large-scale cyanobacterial blooms, clouds and other factors often mean that effective images cannot be obtained. It is also difficult to use this method to dynamically monitor and manage aquatic environments and provide early warnings of cyanobacterial blooms in lakes and reservoirs. In contrast, ground-based remote sensing can operate under cloud cover and thus act as a new technical method to dynamically monitor cyanobacterial blooms. In this study, ground-based remote-sensing technology was applied to multitemporal, multidirectional, and multiscene monitoring of cyanobacterial blooms in Dianchi Lake via an area array multispectral camera mounted on a rotatable cloud platform at a fixed station. Results indicate that ground-based imaging remote sensing can accurately reflect the spatiotemporal distribution characteristics of cyanobacterial blooms and provide timely and accurate data for salvage treatment and early warnings. Thus, ground-based multispectral remote-sensing data can operationalize the dynamic monitoring of cyanobacterial blooms. The methods and results from this study can provide references for monitoring such blooms in other lakes.
Evaluation of the Continuous Monitoring of Land Disturbance Algorithm for Large-Scale Mangrove Classification
Authors: Awty-Carroll Katie , Bunting Pete , Hardy Andy , Bell Gemma
Published: October 2021

Mangrove forests are of high biological, economic, and ecological importance globally. Growing within the intertidal zone, they are particularly vulnerable to the effects of climate change in addition to being threatened on local scales by over-exploitation and aquaculture expansion. Long-term monitoring of global mangrove populations is therefore highly important to understanding the impact of these threats. However, data availability from satellites is often limited due to cloud cover. This problem can be mitigated using a season-trend modelling approach such as Continuous Monitoring of Land Disturbance (COLD). COLD operates by using every available observation on a pixel-wise basis, removing the need for whole cloud free images. The approach can be used to better classify land cover by taking into account the underlying seasonal variability, and can also be used to extrapolate between data points to obtain more accurate long term trends. To demonstrate the utility of COLD for global mangrove monitoring, we applied it to five study sites chosen to represent a range of mangrove species, forest types, and quantities of available data. The COLD classifier was trained on the Global Mangrove Watch 2010 dataset and applied to 30 years of Landsat data for each site. By increasing the period between model updates, COLD was successfully applied to all five sites (2253 scenes) in less than four days. The method achieved an overall accuracy of 92% with a User’s accuracy of 77% and a Dice score of 0.84 for the mangrove class. The lowest User’s accuracy was for North Kalimantan (49.9%) due to confusion with mangrove palms. However, the method performed extremely well for the Niger Delta from the 2000s onwards (93.6%) despite the absence of any Landsat 5 data. Observation of trends in mangrove extent over time suggests that the method was able to accurately capture changes in extent caused by the 2014/15 mangrove die-back event in the Gulf of Carpentaria and highlighted a net loss of mangroves in the Matang Forest Reserve over the last two decades, despite ongoing management. COLD is therefore a promising methodology for global, long-term monitoring of mangrove extent and trends.
A Faster and More Effective Cross-View Matching Method of UAV and Satellite Images for UAV Geolocalization
Authors: Zhuang Jiedong , Dai Ming , Chen Xuruoyan , Zheng Enhui
Published: October 2021

Cross-view geolocalization matches the same target in different images from various views, such as views of unmanned aerial vehicles (UAVs) and satellites, which is a key technology for UAVs to autonomously locate and navigate without a positioning system (e.g., GPS and GNSS). The most challenging aspect in this area is the shifting of targets and nonuniform scales among different views. Published methods focus on extracting coarse features from parts of images, but neglect the relationship between different views, and the influence of scale and shifting. To bridge this gap, an effective network is proposed with well-designed structures, referred to as multiscale block attention (MSBA), based on a local pattern network. MSBA cuts images into several parts with different scales, among which self-attention is applied to make feature extraction more efficient. The features of different views are extracted by a multibranch structure, which was designed to make different branches learn from each other, leading to a more subtle relationship between views. The method was implemented with the newest UAV-based geolocalization dataset. Compared with the existing state-of-the-art (SOTA) method, MSBA accuracy improved by almost 10% when the inference time was equal to that of the SOTA method; when the accuracy of MSBA was the same as that of the SOTA method, inference time was shortened by 30%.
Benchmarking Deep Learning for On-Board Space Applications
Authors: Ziaja Maciej , Bosowski Piotr , Myller Michal , Gajoch Grzegorz , Gumiela Michal , Protich Jennifer , Borda Katherine , Jayaraman Dhivya , Dividino Renata , Nalepa Jakub
Published: October 2021

Benchmarking deep learning algorithms before deploying them in hardware-constrained execution environments, such as imaging satellites, is pivotal in real-life applications. Although a thorough and consistent benchmarking procedure can allow us to estimate the expected operational abilities of the underlying deep model, this topic remains under-researched. This paper tackles this issue and presents an end-to-end benchmarking approach for quantifying the abilities of deep learning algorithms in virtually any kind of on-board space applications. The experimental validation, performed over several state-of-the-art deep models and benchmark datasets, showed that different deep learning techniques may be effectively benchmarked using the standardized approach, which delivers quantifiable performance measures and is highly configurable. We believe that such benchmarking is crucial in delivering ready-to-use on-board artificial intelligence in emerging space applications and should become a standard tool in the deployment chain.
Paddy Rice Mapping in Thailand Using Time-Series Sentinel-1 Data and Deep Learning Model
Authors: Xu Lu , Zhang Hong , Wang Chao , Wei Sisi , Zhang Bo , Wu Fan , Tang Yixian
Published: October 2021

The elimination of hunger is the top concern for developing countries and is the key to maintain national stability and security. Paddy rice occupies an essential status in food supply, whose accurate monitoring is of great importance for human sustainable development. As one of the most important paddy rice production countries in the world, Thailand has a favorable hot and humid climate for paddy rice growing, but the growth patterns of paddy rice are too complicated to construct promising growth models for paddy rice discrimination. To solve this problem, this study proposes a large-scale paddy rice mapping scheme, which uses time-series Sentinel-1 data to generate a convincing annual paddy rice map of Thailand. The proposed method extracts temporal statistical features of the time-series SAR images to overcome the intra-class variability due to different management practices and modifies the U-Net model with the fully connected Conditional Random Field (CRF) to maintain the edge of the fields. In this study, 758 Sentinel-1 images that covered the whole country from the end of 2018 to 2019 were acquired to generate the annual paddy rice map. The accuracy, precision, and recall of the resultant paddy rice map reached 91%, 87%, and 95%, respectively. Compared to SVM classifier and the U-Net model based on feature selection strategy (FS-U-Net), the proposed scheme achieved the best overall performance, which demonstrated the capability of overcoming the complex cultivation conditions and accurately identifying the fragmented paddy rice fields in Thailand. This study provides a promising tool for large-scale paddy rice monitoring in tropical production regions and has great potential in the global sustainable development of food and environment management.
Global Land High-Resolution Cloud Climatology Based on an Improved MOD09 Cloud Mask
Authors: Zhang Shuyan , Ma Yong , Chen Fu , Shang Erping , Yao Wutao , Qiu Yubao , Liu Jianbo
Published: October 2021

Clouds play an important role in the energy and moisture cycle of the earth–atmosphere system, which affects many important processes in nature and human societies. However, there are very few fine-grained and high-precision global cloud climatology data available for high-resolution models. In this paper, we produced a fine-grained (1 km resolution) global land cloud climatology (GLHCC) report based on MOD09 cloud masks from 2001 to 2016, with a temporal resolution of 10 days. The two improvements (short-wave infrared and Band 2/6 ratio threshold method) on the original MOD09 cloud mask have reduced the snow, ice, and bright areas mistakenly classified as clouds. The preliminary cloud products undergo the removal of orbital artifacts by Variational Stationary Noise Remover (VSNR) and the removal of abnormal albedo areas to generate the final cloud climatology data. The new product was directly validated by ground-based cloud observations collected from 3777 global weather stations. PATMOS-X from the Advanced Very High Resolution Radiometer (AVHRR) and MOD/MYD35 served as comparison products for consistency check of GLHCC. The assessment results show that GLHCC demonstrated a strong correlation with ground station observations, MOD/MYD35, and PATMOS-X. When the ground observations were taken as the truth value, GLHCC and MOD/MYD35 displayed higher accuracy than PATMOS-X. In most selected interested areas where the three behave differently, GLHCC matched the facts better than MOD/MYD35 and PATMOS-X. The GLHCC can well represent the cloud distribution over the past 16 years and will play an important role in the fine-grained demands of many aspects of nature and human society.
Sentinel-2 Cloud Removal Considering Ground Changes by Fusing Multitemporal SAR and Optical Images
Authors: Gao Jianhao , Yi Yang , Wei Tang , Zhang Guanhao
Published: October 2021

Publicly available optical remote sensing images from platforms such as Sentinel-2 satellites contribute much to the Earth observation and research tasks. However, information loss caused by clouds largely decreases the availability of usable optical images so reconstructing the missing information is important. Existing reconstruction methods can hardly reflect the real-time information because they mainly make use of multitemporal optical images as reference. To capture the real-time information in the cloud removal process, Synthetic Aperture Radar (SAR) images can serve as the reference images due to the cloud penetrability of SAR imaging. Nevertheless, large datasets are necessary because existing SAR-based cloud removal methods depend on network training. In this paper, we integrate the merits of multitemporal optical images and SAR images to the cloud removal process, the results of which can reflect the ground information change, in a simple convolution neural network. Although the proposed method is based on deep neural network, it can directly operate on the target image without training datasets. We conduct several simulation and real data experiments of cloud removal in Sentinel-2 images with multitemporal Sentinel-1 SAR images and Sentinel-2 optical images. Experiment results show that the proposed method outperforms those state-of-the-art multitemporal-based methods and overcomes the constraint of datasets of those SAR-based methods.
Sentinel-1 and RADARSAT Constellation Mission InSAR Assessment of Slope Movements in the Southern Interior of British Columbia, Canada
Authors: Choe Byung-Hun , Blais-Stevens Andrée , Samsonov Sergey , Dudley Jonathan
Published: October 2021

Landslides are the most common natural hazard in British Columbia. The province has recorded the largest number of historical landslide fatalities in Canada, and damage to infrastructure comes at a great cost. In order to understand the potential impacts of landslides, radar remote sensing has become a cost-effective method for detecting downslope movements. This study investigates downslope movements in the Southern Interior of British Columbia, Canada, with Sentinel-1 and RADARSAT Constellation Mission (RCM) interferometric synthetic aperture radar (InSAR) data. The 2-dimensional time-series analysis with Sentinel-1 ascending and descending InSAR pairs from October 2017 to June 2021 observed distinct earthflow movements of up to ~15 cm/year in the east–west direction. The Grinder Creek, Red Mountain, Yalakom River, and Retaskit Creek earthflows previously documented are still active, with east–west movements of ~30 cm over the past four years. New RCM data acquired from June 2020 to September 2020 with a 4-day revisit capability were compared to 12-day Sentinel-1 InSAR pairs. The 4-day RCM InSAR pairs at higher spatial resolution showed better performance by detecting relatively small-sized slope movements within a few hundred meters, which were not clearly observed by Sentinel-1. The temporal variabilities observed from the RCM InSAR showed great potential for observing detailed slope movements within a narrower time window.
Landscape Structure of Woody Cover Patches for Endangered Ocelots in Southern Texas
Authors: Lombardi Jason V. , Perotto-Baldivieso Humberto L. , Sergeyev Maksim , Veals Amanda M. , Schofield Landon , Young John H. , Tewes Michael E.
Published: October 2021

Few ecological studies have explored landscape suitability using the gradient concept of landscape structure for wildlife species. Identification of conditions influencing the landscape ecology of endangered species allows for development of more robust recovery strategies. Our objectives were to (i) identify the range of landscape metrics (i.e., mean patch area; patch and edge densities; percent land cover; shape, aggregation, and largest patch indices) associated with woody vegetation used by ocelots (Leopardus pardalis), and (ii) quantify the potential distribution of suitable woody cover for ocelots across southern Texas. We used the gradient concept of landscape structure and the theory of slack combined with GPS telemetry data from 10 ocelots. Spatial distribution of high suitable woody cover is comprised of large patches, with low shape-index values (1.07–2.25), patch (27.21–72.50 patches/100 ha), and edge (0–191.50 m/ha) densities. High suitability landscape structure for ocelots occurs in 45.27% of woody cover in southern Texas. Our study demonstrates a new approach for measuring landscape suitability for ocelots in southern Texas. The range of landscape values identified that there are more large woody patches containing the spatial structure used by ocelots than previously suspected, which will aid in evaluating recovery and road planning efforts.
Day–Night Monitoring of Volcanic SO2 and Ash Clouds for Aviation Avoidance at Northern Polar Latitudes
Authors: Krotkov Nickolay , Realmuto Vincent , Li Can , Seftor Colin , Li Jason , Brentzel Kelvin , Stuefer Martin , Cable Jay , Dierking Carl , Delamere Jennifer , Schneider David , Tamminen Johanna , Hassinen Seppo , Ryyppö Timo , Murray John , Carn Simon , Osiensky Jeffrey , Eckstein Nate , Layne Garrett , Kirkendall Jeremy
Published: October 2021

We describe NASA’s Applied Sciences Disasters Program, which is a collaborative project between the Direct Readout Laboratory (DRL), ozone processing team, Jet Propulsion Laboratory, Geographic Information Network of Alaska (GINA), and Finnish Meteorological Institute (FMI), to expedite the processing and delivery of direct readout (DR) volcanic ash and sulfur dioxide (SO2) satellite data. We developed low-latency quantitative retrievals of SO2 column density from the solar backscattered ultraviolet (UV) measurements using the Ozone Mapping and Profiler Suite (OMPS) spectrometers as well as the thermal infrared (TIR) SO2 and ash indices using Visible Infrared Imaging Radiometer Suite (VIIRS) instruments, all flying aboard US polar-orbiting meteorological satellites. The VIIRS TIR indices were developed to address the critical need for nighttime coverage over northern polar regions. Our UV and TIR SO2 and ash software packages were designed for the DRL’s International Planetary Observation Processing Package (IPOPP); IPOPP runs operationally at GINA and FMI stations in Fairbanks, Alaska, and Sodankylä, Finland. The data are produced within 30 min of satellite overpasses and are distributed to the Alaska Volcano Observatory and Anchorage Volcanic Ash Advisory Center. FMI receives DR data from GINA and posts composite Arctic maps for ozone, volcanic SO2, and UV aerosol index (UVAI, proxy for ash or smoke) on its public website and provides DR data to EUMETCast users. The IPOPP-based software packages are available through DRL to a broad DR user community worldwide.
Multi-Sensor, Active Fire-Supervised, One-Class Burned Area Mapping in the Brazilian Savanna
Authors: Pereira Allan A. , Libonati Renata , Rodrigues Julia A. , Nogueira Joana , Santos Filippe L. M. , Oom Duarte , Sanches Waislan , Alvarado Swanni T. , Pereira José M. C.
Published: October 2021

Increasing efforts are being devoted to understanding fire patterns and changes highlighting the need for a consistent database about the location and extension of burned areas (BA). Satellite-derived BA mapping accuracy in the Brazilian savannas is limited by the underestimation of burn scars from small, fragmented fires and high cloudiness. Moreover, systematic mapping of BA is challenged by the need for human intervention in training sample acquisition, which precludes the development of automatic-generated products over large areas and long periods. Here, we developed a multi-sensor, active fire-supervised, one-class BA mapping algorithm to address several of these limitations. Our main objective is to generate a long-term, detailed BA atlas suitable to improve fire regime characterization and validation of coarse resolution products. We use composite images derived from the Landsat satellite to generate end-of-season maps of fire-affected areas for the entire Cerrado. Validation exercises and intercomparison with BA maps from a semi-automatic algorithm and visual photo interpretation were conducted for the year 2015. Our results improve the BA mapping by reducing omission errors, especially where there is high cloud frequency, few active fires are detected, and burned areas are small and fragmented. Finally, our approach represents at least a 45% increase in BA mapped in the Cerrado, in comparison to the annual extent detected by the current coarse global product from MODIS satellite (MCD64), and thus, it is capable of supporting improved regional emissions estimates.
Seasonal Surface Fluctuation of a Slow-Moving Landslide Detected by Multitemporal Interferometry (MTI) on the Huafan University Campus, Northern Taiwan
Authors: Lu Chiao-Yin , Chan Yu-Chang , Hu Jyr-Ching , Tseng Chia-Han , Liu Che-Hsin , Chang Chih-Hsin
Published: October 2021

A slow-moving landslide on the Huafan University campus, which is located on a dip slope in northern Taiwan, has been observed since 1990. However, reliable monitoring data are difficult to acquire after 2018 due to the lack of continuous maintenance of the field measurement equipment. In this study, the multitemporal interferometry (MTI) technique is applied with Sentinel-1 SAR images to monitor the slow-moving landslide from 2014–2019. The slow-moving areas detected by persistent scatterer (PS) pixels are consistent with the range of previous studies, which are based on in situ monitoring data and field surveys. According to the time series of the PS pixels, a long period gravity-induced deformation of the slow-moving landslide can be clearly observed. Moreover, a short period seasonal surface fluctuation of the slow-moving landslide, which has seldom been discussed before, can also be detected in this study. The seasonal surface fluctuation is in-phase with precipitation, which is inferred to be related to the geological and hydrological conditions of the study area. The MTI technique can compensate for the lack of surface displacement data, in this case, the Huafan University campus, and provide information for evaluating and monitoring slow-moving landslides for possible landslide early warning in the future.
Impacts of Spatial Configuration of Land Surface Features on Land Surface Temperature across Urban Agglomerations, China
Authors: Zhang Qiang , Wu Zixuan , Singh Vijay P. , Liu Chunling
Published: October 2021

Booming urbanization triggers a significant modification of surface landscape configuration and hence complex urban climates. Considerable concerns exist regarding impacts of impervious surface area (ISA) and/or urban green space (UGS) on land surface temperature (LST). However, a knowledge gap still exists concerning the influence of urban landscape components and related spatial configuration on LST. To date, case studies have usually focused on individual cities, while few reports have addressed the impacts of urban surface components and relevant spatial configurations on LST within cities of different sizes, at different latitudes, and with different climatic backgrounds. Considering case studies from different latitudes and various climatic backgrounds can assist in obtaining comprehensive viewpoints about impacts of urban surface features on LST in both space and time. In this paper we analyzed data from three urban agglomerations, Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD) and the Pearl River Delta (PRD), over the period 2000–2015. These three regions are densely populated with the most developed socio-economy across China, and are also dominated by booming urbanization. Based on Landsat remotely sensed data, we included the spatial pattern of surface components and related configuration into our analysis, quantifying impacts of spatial configuration of surface components on LST in both space and time. We found generally rising LST over all cities, which can be attributed to continuous urban expansion-induced decreased UGS. Generally, LST over ISA was 0.96–7.96 °C higher than that over UGS. We investigated the impacts of spatial pattern of land surface components on LST and found that the joint effect of the composition and spatial configuration of land surface components had the most significant impact on LST. Specifically, ISA and UGS had higher impact on LST than the impact of geometry of the ISA and UGS on LST. In the future, continuous expansion of ISA and continuous shrinking of UGS will drive the rising tendency of LST. Moreover, a larger rising tendency of LST will be observed in larger sized cities than smaller sized cities.
A New Integrated Approach for Landslide Data Balancing and Spatial Prediction Based on Generative Adversarial Networks (GAN)
Authors: Al-Najjar Husam A. H. , Pradhan Biswajeet , Sarkar Raju , Beydoun Ghassan , Alamri Abdullah
Published: October 2021

Landslide susceptibility mapping has significantly progressed with improvements in machine learning techniques. However, the inventory/data imbalance (DI) problem remains one of the challenges in this domain. This problem exists as a good quality landslide inventory map, including a complete record of historical data, is difficult or expensive to collect. As such, this can considerably affect one’s ability to obtain a sufficient inventory or representative samples. This research developed a new approach based on generative adversarial networks (GAN) to correct imbalanced landslide datasets. The proposed method was tested at Chukha Dzongkhag, Bhutan, one of the most frequent landslide prone areas in the Himalayan region. The proposed approach was then compared with the standard methods such as the synthetic minority oversampling technique (SMOTE), dense imbalanced sampling, and sparse sampling (i.e., producing non-landslide samples as many as landslide samples). The comparisons were based on five machine learning models, including artificial neural networks (ANN), random forests (RF), decision trees (DT), k-nearest neighbours (kNN), and the support vector machine (SVM). The model evaluation was carried out based on overall accuracy (OA), Kappa Index, F1-score, and area under receiver operating characteristic curves (AUROC). The spatial database was established with a total of 269 landslides and 10 conditioning factors, including altitude, slope, aspect, total curvature, slope length, lithology, distance from the road, distance from the stream, topographic wetness index (TWI), and sediment transport index (STI). The findings of this study have shown that both GAN and SMOTE data balancing approaches have helped to improve the accuracy of machine learning models. According to AUROC, the GAN method was able to boost the models by reaching the maximum accuracy of ANN (0.918), RF (0.933), DT (0.927), kNN (0.878), and SVM (0.907) when default parameters used. With the optimum parameters, all models performed best with GAN at their highest accuracy of ANN (0.927), RF (0.943), DT (0.923) and kNN (0.889), except SVM obtained the highest accuracy of (0.906) with SMOTE. Our finding suggests that RF balanced with GAN can provide the most reasonable criterion for landslide prediction. This research indicates that landslide data balancing may substantially affect the predictive capabilities of machine learning models. Therefore, the issue of DI in the spatial prediction of landslides should not be ignored. Future studies could explore other generative models for landslide data balancing. By using state-of-the-art GAN, the proposed model can be considered in the areas where the data are limited or imbalanced.
Assessing a Prototype Database for Comprehensive Global Aquatic Land Cover Mapping
Authors: Xu Panpan , Tsendbazar Nandin-Erdene , Herold Martin , Clevers Jan G. P. W.
Published: October 2021

The monitoring of Global Aquatic Land Cover (GALC) plays an essential role in protecting and restoring water-related ecosystems. Although many GALC datasets have been created before, a uniform and comprehensive GALC dataset is lacking to meet multiple user needs. This study aims to assess the effectiveness of using existing global datasets to develop a comprehensive and user-oriented GALC database and identify the gaps of current datasets in GALC mapping. Eight global datasets were reframed to construct a three-level (i.e., from general to detailed) prototype database for 2015, conforming with the United Nations Land Cover Classification System (LCCS)-based GALC characterization framework. An independent validation was done, and the overall results show some limitations of current datasets in comprehensive GALC mapping. The Level-1 map had considerable commission errors in delineating the general GALC distribution. The Level-2 maps were good at characterizing permanently flooded areas and natural aquatic types, while accuracies were poor in the mapping of temporarily flooded and waterlogged areas as well as artificial aquatic types; vegetated aquatic areas were also underestimated. The Level-3 maps were not sufficient in characterizing the detailed life form types (e.g., trees, shrubs) for aquatic land cover. However, the prototype GALC database is flexible to derive user-specific maps and has important values to aquatic ecosystem management. With the evolving earth observation opportunities, limitations in the current GALC characterization can be addressed in the future.
Robust Kalman Filter Soil Moisture Inversion Model Using GPS SNR Data—A Dual-Band Data Fusion Approach
Authors: Jing Lili , Yang Lei , Yang Wentao , Xu Tianhe , Gao Fan , Lu Yilin , Sun Bo , Yang Dongkai , Hong Xuebao , Wang Nazi , Ruan Hongliang , Darrozes José
Published: October 2021

This article aims to attempt to increase the number of satellites that can be used for monitoring soil moisture to obtain more precise results using GNSS-IR (Global Navigation Satellite System-Interferometric Reflectometry) technology to estimate soil moisture. We introduce a soil moisture inversion model by using GPS SNR (Signal-to-Noise Ratio) data and propose a novel Robust Kalman Filter soil moisture inversion model based on that. We validate our models on a data set collected at Lamasquère, France. This paper also compares the precision of the Robust Kalman Filter model with the conventional linear regression method and robust regression model in three different scenarios: (1) single-band univariate regression, by using only one observable feature such as frequency, amplitude, or phase; (2) dual-band data fusion univariate regression; and (3) dual-band data fusion multivariate regression. First, the proposed models achieve higher accuracy than the conventional method for single-band univariate regression, especially by using the phase as the input feature. Second, dual-band univariate data fusion achieves higher accuracy than single-band and the result of the Robust Kalman Filter model correlates better to the in situ measurement. Third, multivariate variable fusion improves the accuracy for both models, but the Robust Kalman Filter model achieves better improvement. Overall, the Robust Kalman Filter model shows better results in all the scenarios.
Acquisition and Processing of Brain Signals
Authors: Bizzego Andrea , Esposito Gianluca
Published: October 2021

We live within a context of unprecedented opportunities for brain research, with a flourishing of novel sensing technologies and methodological approaches [...]
Synthesis and Characterization of N-Isopropylacrylamide Microspheres as pH Sensors
Authors: Lavine Barry K. , Kaval Necati , Oxenford Leah , Kim Mariya , Dahal Kaushalya Sharma , Perera Nuwan , Seitz Rudolf , Moulton James T. , Bunce Richard A.
Published: October 2021

Swellable polymer microspheres that respond to pH were prepared by free radical dispersion polymerization using N-isopropylacrylamide (NIPA), N,N′-methylenebisacrylamide (MBA), 2,2-dimethoxy-2-phenylacetylphenone, N-tert-butylacrylamide (NTBA), and a pH-sensitive functional comonomer (acrylic acid, methacrylic acid, ethacrylic acid, or propacrylic acid). The diameter of the microspheres was between 0.5 and 1.0 μm. These microspheres were cast into hydrogel membranes prepared by mixing the pH-sensitive swellable polymer particles with aqueous polyvinyl alcohol (PVA) solutions followed by crosslinking with glutaric dialdehyde for use as pH sensors. Large changes in the turbidity of the PVA membrane were observed as the pH of the buffer solution in contact with the membrane was varied. These changes were monitored by UV–visible absorbance spectroscopy. Polymer swelling of many NIPA copolymers was reversible and independent of the ionic strength of the buffer solution in contact with the membrane. Both the degree of swelling and the apparent pKa of the polymer microspheres increased with temperature. Furthermore, the apparent pKa of the polymer particles could be tuned to respond sharply to pH in a broad range (pH 4.0–7.0) by varying the amount of crosslinker (MBA) and transition temperature modifier (NTBA), and the amount, pKa, and hydrophobicity of the pH-sensitive functional comonomer (alkyl acrylic acid) used in the formulation. Potential applications of these polymer particles include fiber optic pH sensing where the pH-sensitive material can be immobilized on the distol end of an optical fiber.
Artificial Neural Networks to Solve the Singular Model with Neumann–Robin, Dirichlet and Neumann Boundary Conditions
Authors: Nisar Kashif , Sabir Zulqurnain , Asif Zahoor Raja Muhammad , Ag Ibrahim Ag Asri , J. P. C. Rodrigues Joel , Refahy Mahmoud Samy , Chowdhry Bhawani Shankar , Gupta Manoj
Published: October 2021

The aim of this work is to solve the case study singular model involving the Neumann–Robin, Dirichlet, and Neumann boundary conditions using a novel computing framework that is based on the artificial neural network (ANN), global search genetic algorithm (GA), and local search sequential quadratic programming method (SQPM), i.e., ANN-GA-SQPM. The inspiration to present this numerical framework comes through the objective of introducing a reliable structure that associates the operative ANNs features using the optimization procedures of soft computing to deal with such stimulating systems. Four different problems that are based on the singular equations involving Neumann–Robin, Dirichlet, and Neumann boundary conditions have been occupied to scrutinize the robustness, stability, and proficiency of the designed ANN-GA-SQPM. The proposed results through ANN-GA-SQPM have been compared with the exact results to check the efficiency of the scheme through the statistical performances for taking fifty independent trials. Moreover, the study of the neuron analysis based on three and 15 neurons is also performed to check the authenticity of the proposed ANN-GA-SQPM.
Averting BER Floor with Iterative Source and Channel Decoding for Layered Steered Space-Time Codes
Authors: Minallah Nasru , Ahmed Ishtiaque , Frnda Jaroslav , Khattak Khurram S.
Published: October 2021

The widespread development in wireless technologies and the advancements in multimedia communication have brought about a positive impact on the performance of wireless transceivers. We investigate the performance of our three-stage turbo detected system using state-of-the-art high efficiency video coding (HEVC), also known as the H.265 video standard. The system makes use of sphere packing (SP) modulation with the combinational gain technique of layered steered space-time code (LSSTC). The proposed three-stage system is simulated for the correlated Rayleigh fading channel and the bit-error rate (BER) curve obtained after simulation is free of any floor formation. The system employs low complexity source-bit coding (SBC) for protecting the H.265 coded stream. An intermediate recursive unity-rate code (URC) with an infinite impulse response is employed as an inner precoder. More specifically, the URC assists in the prevention of the BER floor by distributing the information across the decoders. There is an observable gain in the BER and peak signal-to-noise ratio (PSNR) performances with the increasing value of minimum Hamming distance (dH,min) using the three-stage system. Convergence analysis of the proposed system is investigated through an extrinsic information transfer (EXIT) chart. Our proposed system demonstrates better performance of about 22 dB than the benchmarker utilizing LSSTC-SP for iterative source-channel detection, but without exploiting the optimized SBC schemes.
Optimized Design of Neural Networks for a River Water Level Prediction System
Authors: Lineros Miriam López , Luna Antonio Madueño , Ferreira Pedro M. , Ruano Antonio E.
Published: October 2021

In this paper, a Multi-Objective Genetic Algorithm (MOGA) framework for the design of Artificial Neural Network (ANN) models is used to design 1-step-ahead prediction models of river water levels. The design procedure is a near-automatic method that, given the data at hand, can partition it into datasets and is able to determine a near-optimal model with the right topology and inputs, offering a good performance on unseen data, i.e., data not used for model design. An example using more than 11 years of water level data (593,178 samples) of the Carrión river collected at Villoldo gauge station shows that the MOGA framework can obtain low-complex models with excellent performance on unseen data, achieving an RMSE of 2.5 × 10−3, which compares favorably with results obtained by alternative design.