Spatial Consistencies Research Articles

Downscaling Land Surface Temperature via Assimilation of LandSat 8/9 OLI and TIRS Data and Hypersharpening

Land surface temperature (LST) plays a pivotal role in many environmental sectors. Unfortunately, thermal bands produced by instruments that are onboard satellites have limited spatial resolutions; this seriously impairs their potential usefulness. In this study, we propose an automatic procedure for the spatial downscaling of the two 100 m thermal infrared (TIR) bands of LandSat 8/9, captured by the TIR spectrometer (TIRS), by exploiting the bands of the optical instrument. The problem of fusion of heterogeneous data is approached as hypersharpening: each of the two sharpening images is synthesized following data assimilation concepts, with the linear combination of 30 m optical bands and the 15 m panchromatic (Pan) image that maximizes the correlation with each thermal channel at its native 100 m scale. The TIR bands resampled at 15 m are sharpened, each by its own synthetic Pan. On two different scenes of an OLI-TIRS image, the proposed approach is compared with 100 m to 15 m pansharpening, carried out uniquely by means of the Pan image of OLI and with the two high-resolution assimilated thermal images that are used for hypersharpening the two TIRS bands. Besides visual evaluations of the temperature maps, statistical indexes measuring radiometric and spatial consistencies are provided and discussed. The superiority of the proposed approach is highlighted: the classical pansharpening approach is radiometrically accurate but weak in the consistency of spatial enhancement. Conversely, the assimilated TIR bands, though adequately sharp, lose more than 20% of radiometric consistency. Our proposal trades off the benefits of its counterparts in a unique method.

An Evaluation of Evapotranspiration Products over the Tibetan Plateau

Abstract In situ observations from 13 sites located over the Tibetan Plateau (TP) are used to evaluate evapotranspiration (ET) products, including remote sensing–based, land surface–modeled, and reanalysis products. It is found that the Global Land Surface Satellite (GLASS) product, the Global Land Evaporation Amsterdam Model (GLEAM) product, and the simulations by the Community Land Model–dynamic global vegetation model [biogeochemical dynamic vegetation (CLM–BGCDV)] are the top-ranked products measured by the percentage bias, root-mean-square error, and correlation coefficient against in situ observations. The evaluated data are then used to examine the consistency in spatial and temporal variability of summer ET and its controlling factors on the TP and the three-river source region (TRSR). All products show consistently that precipitation in the central semiarid part of TP is the dominant factor influencing summer ET, while air temperature plays a certain role in the southeastern and eastern TP. Uncertainties exist in the western TP, possibly due to the lack of observations or gaps in the satellite data. Some suggestions for improving ET product development based on models and satellite retrievals, particularly for the cold and complex surface of the TP, are also given. Significance Statement Evapotranspiration (ET) is a crucial water cycle component, but accurately assessing it remains difficult. In situ measurements are scarce, while other products have large uncertainties, such as satellite remote sensing retrievals, land surface model simulations, and reanalyses. This is particularly a problem for the Tibetan Plateau (TP). With some considerable effort, ET fluxes from 13 sites and reasonable length in the TP have become available. These in situ data are used to evaluate gridded ET products. Based on an overall score, they are ranked as the Global Land Surface Satellite (GLASS) product, the Global Land Evaporation Amsterdam Model (GLEAM) product, and the model simulation by the Community Land Model–dynamic global vegetation model [biogeochemical dynamic vegetation (CLM–BGCDV)]. Then, we used these top-ranked products to detect the spatial and trend consistencies and the environmental impacts to further discuss the product conformance. It was shown that all products show precipitation dominated the summer ET variation in the semiarid region in the central TP, while air temperature had more impact on the relatively humid region in the east and southeast of the TP. Uncertainties exist in the west TP possibly due to the lack of observations in the assimilation product or missing gaps in the satellite product. Specifically, we conclude that dynamic vegetation could play an increasing role under the background of climate warming, which should be considered in the ET model–based product generation. The suggestions for ET generation based on models and satellite retrievals, particularly for the special cold and complex surface of the TP, are also provided.

The novel triangular spectral indices for characterizing winter wheat drought

Agricultural drought threatens food security and agricultural sustainable development. There have been numerous spectral indices from remote sensing images developed for monitoring crop drought. However, most present spectral indices are focusing on crop growth and Land Surface Temperature (LST), and the crop canopy water content are in less consideration simultaneously. Additionally, the Normalized Difference Vegetation Index (NDVI) is used for characterizing crop growth in almost all spectral drought indices, with the spectral saturation problem of NDVI for closed crop canopy. When vegetation cover is high, NDVI values tend to saturate, which makes them insensitive to further changes in crop health. Therefore, the NDVI saturation phenomenon may lead to an underestimation of the extent of crop drought, as it is not effective in identifying subtle changes in crops under high-density vegetation conditions. Hence, we propose three novel triangular spectral indices for characterizing winter wheat drought using three features including LAI, Land Surface Water Index (LSWI) and LST. For validating the proposed spectral indices, we compared the agreement between these indices with measured Relative Soil Moisture (RSM) and Volumetric Water Content (VWC) of soil in agricultural meteorological station and present popular indices including Crop Water Stress Index (CWSI), Temperature-Vegetation Drought Index (TVDI), and Vegetation Health Index (VHI). The results revealed that our proposed indices including Euclidean distance Crop Health Index (ECHI), Difference Crop Health Index (DCHI) and Perpendicular Water Stress Index (PWSI) outperformed the popular CWSI, TVDI and VHI, with stronger correlations with measured RSM and VWC in agricultural meteorological station. Secondly, there are spatial consistencies for characterizing winter wheat drought between proposed ECHI, DCHI and PWSI with popular CWSI, TVDI and VHI. In addition, our proposed ECHI, DCHI and PWSI have achieved good performance of drought monitoring both in irrigated and rainfed croplands. All these results suggest that our proposed indices have great potential in crop drought monitoring.

Time-series China urban land use mapping (2016–2022): An approach for achieving spatial-consistency and semantic-transition rationality in temporal domain

The global urbanization trend is geographically manifested through city expansion and the renewal of internal urban structures and functions. Time-series urban land use (ULU) maps are vital for capturing dynamic land changes in the urbanization process, giving valuable insights into urban development and its environmental consequences. Recent studies have mapped ULU in some cities with a unified model, but ignored the regional differences among cities; and they generated ULU maps year by year, but ignored temporal correlations between years; thus, they could be weak in large-scale and long time-series ULU monitoring. Accordingly, we introduce an temporal-spatial-semantic collaborative (TSS) mapping framework to generating accurate ULU maps with considering regional differences and temporal correlations. Firstly, to support model training, a large-scale ULU sample dataset based on OpenStreetMap (OSM) and Sentinel-2 imagery is automatically constructed, providing a total number of 56,412 samples with a size of 512 × 512 which are divided into six sub-regions in China and used for training different classification models. Then, an urban land use mapping network (ULUNet) is proposed to recognize ULU. This model utilizes a primary and an auxiliary encoder to process noisy OSM samples and can enhance the model's robustness under noisy labels. Finally, taking the temporal correlations of ULU into consideration, the recognized ULU are optimized, whose boundaries are unified by a time-series co-segmentation, and whose categories are modified by a knowledge-data driven method. To verify the effectiveness of the proposed method, we consider all urban areas in China (254,566 km2), and produce a time-series China urban land use dataset (CULU) at a 10-m resolution, spanning from 2016 to 2022, with an overall accuracy of CULU is 82.42%. Through comparison, it can be found that CULU outperforms existing datasets such as EULUC-China and UFZ-31cities in data accuracies, spatial boundaries consistencies and land use transitions logicality. The results indicate that the proposed method and generated dataset can play important roles in land use change monitoring, ecological-environmental evolution analysis, and also sustainable city development.

Exploring Multi-modal Spatial-Temporal Contexts for High-performance RGB-T Tracking.

In RGB-T tracking, there exist rich spatial relationships between the target and backgrounds within multi-modal data as well as sound consistencies of spatial relationships among successive frames, which are crucial for boosting the tracking performance. However, most existing RGB-T trackers overlook such multi-modal spatial relationships and temporal consistencies within RGB-T videos, hindering them from robust tracking and practical applications in complex scenarios. In this paper, we propose a novel Multi-modal Spatial-Temporal Context (MMSTC) network for RGB-T tracking, which employs a Transformer architecture for the construction of reliable multi-modal spatial context information and the effective propagation of temporal context information. Specifically, a Multi-modal Transformer Encoder (MMTE) is designed to achieve the encoding of reliable multi-modal spatial contexts as well as the fusion of multi-modal features. Furthermore, a Quality-aware Transformer Decoder (QATD) is proposed to effectively propagate the tracking cues from historical frames to the current frame, which facilitates the object searching process. Moreover, the proposed MMSTC network can be easily extended to various tracking frameworks. New state-of-the-art results on five prevalent RGB-T tracking benchmarks demonstrate the superiorities of our proposed trackers over existing ones.

Which land cover product provides the most accurate land use land cover map of the Yellow River Basin?

Precise land use land cover (LULC) data are essential for understanding the landscape structure and spatial pattern of land use/cover in the Yellow River Basin (YRB) to regulate scientific and rational territorial spatial planning and support sustainable development. However, differences in the multiple sets of LULC products in portraying the land composition of the YRB limit our understanding of the land cover composition in this region. To address this issue, this study chose five sets of open and high spatiotemporal LULC data in 2020, namely, CLCD, LSV10, ESRI10, CLC_FCS30, and Globeland30, to evaluate the accuracy and consistency of classification in the YRB. Our results show that: (1) The LULC composition of the YRB in 2020 was mapped consistently by the five datasets. Grasslands, croplands, and woodlands constitute the major LULC types, accounting for 96% of the total area of the study area. (2) The correlation coefficients of the LULC types of any two of the five datasets ranged from 0.926 to 0.998, showing high land compositional consistency. However, among the five datasets, there were considerable differences in the areas of a single LULC type. (3) The classification consistencies of croplands, woodlands, grasslands, and water bodies were higher than 60% in any two datasets. The spatial consistencies of grasslands, croplands, and woodlands were higher than those of other LULC types. An area with better consistency can reach more than 50% of the average area of the corresponding land types, but grasslands were mixed with other LULC types in ESRI10 and GLC_FCS30. (4) According to the accuracy assessments, LSV10 data have the highest overall classification accuracy, 79.32%, and the classification accuracy of major land types is also higher than 70%; GLC_FCS30 data have the lowest overall accuracy, 70.14%. Based on these results, LSV10 can more accurately demonstrate LULC than the other four datasets. This study can be used as a reference for selecting land cover data, and it also highlights that the necessary assessments of consistency and accuracy are essential when conducting land use/cover change studies in a specific region.

Quantifying spatio-temporal consistency in the trophic ecology of two sympatric flatfishes.

Sympatric flatfish predators may partition their resources in coastal environments to reduce competition and maximise foraging efficiency. However, the degree of spatial and temporal consistency in their trophic ecology is not well understood because dietary studies tend to overlook the heterogeneity of consumed prey. Increasing the spatial and temporal scale of dietary analyses can thus help to resolve predator resource use. We applied a stomach content and multi-tissue (liver and muscle) stable isotope (δ13 C, δ15 N and δ34 S) approach to investigate the feeding habits of two co-occurring flatfish predators, common dab (Limanda limanda) and European plaice (Pleuronectes platessa), across four bays on the Northumberland coast (UK) over short (hours), medium (days) and long (months) temporal scales. Stomach content analyses showed spatial consistencies in predator resource use, whereas stable isotope mixing models revealed considerable inter-bay diet variability. Stomach contents also indicated high dietary overlap between L. limanda and P. platessa, whilethe stable isotope data yielded low to moderate levels of overlap, with cases of complete niche separation. Furthermore, individual specialisation metrics indicated consistently low levels of specialisation among conspecifics over time. We document changes in resource partitioning in space and time, reflecting diet switching in response to local and temporal fluctuations of patchily distributed prey. This study highlights how trophic tracers integrated at multiple temporal and spatial scales (within tens of kilometres) provide a more integrative approach for assessing the trophic ecology of sympatric predators in dynamic environments.

Examining effect of super-resolution on AVIRIS-NG data: A precursor to generation of large-scale urban material and natural cover maps

The two-phase Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) campaign over India has generated datasets of numerous natural and artificial surroundings with high signal-to-noise ratios and higher spatial and spectral consistencies. However, the first phase AVIRIS-NG datasets of urban areas have 8.1 m spatial resolution, which is insufficient for producing level-4 urban material and natural cover maps required to model Earth’s processes at the micro-scale or constantly monitor urban processes. The current work addresses this drawback by demonstrating a part of the proposed proof-of-concept approach on the AVIRIS-NG dataset of Ahmedabad, India, wherein super-resolution (SR) is proposed to enhance the hyperspectral image’s spatial resolution and preserve its rich spectral content at the targeted spatial scale before utilizing it for producing detailed urban land cover maps. A step-wise account of the SR process has been provided herein to aid the reader in reproducing these experiments. Super-resolved products generated using four SR algorithms have been compared and validated using a composite framework based on processing time, qualitative visual investigation of selected image patches and spectral profiles, re-interpretation of Wald’s reduced resolution protocol and full-reference image quality metrics. Results suggest the AVIRIS-NG dataset’s successful spectral and spatial fidelity at a larger spatial scale, albeit varying in each super-resolved output. Sparse regression and natural prior (SRP), and anchored neighbourhood regression (ANR) produce the best and poorest super-resolved outputs among the tested SR algorithms. Although gradient profile prior (GPP) generated super-resolved product is the second best, it is also the most computationally expensive algorithm. An additional yardstick in the form of a spectral similarity analysis of randomly selected unknown spectral signatures on super-resolved output against a reference spectral library has been proposed to ascertain spectral content preservation further before utilizing the super-resolved product for urban information extraction. This exercise’s findings reveal the presence of the target urban land cover feature at the unknown pixels with matching scores of greater than 0.5 in both best and worst super-resolved products, thereby advocating the extension of the proposed proof-of-concept to recently launched spaceborne hyperspectral missions’ datasets.

Developing an integrated approach to validate 3D ownership spaces in complex multistorey buildings

3D geospatial data are being progressively adopted in urban land administration to represent 3D ownership rights in multistorey buildings. The integrity of urban land administration highly depends on the validity and quality of cadastral data. However, a large portion of research deals with the conceptual principles of internal and external spatial consistencies of 3D cadastral data. This article integrates the principles in practice and develops methods to check the validity of 3D parcels and their relationships in complex ownership settings. To evaluate the methods, this research adopts a case study using an 18-floor multistorey building with 248 lots and three types of common properties. The ownership rights of this building are delineated as polyhedral surface models, including non-2-manifold and non-simple polyhedra, and evaluated by implemented principles. The results reveal that the integrated methods can identify 3D cadastral errors, overlaps and gaps. The developed integrated approach can significantly advance urban land administration, as it facilitates complex 3D cadastral data validation.

Double U-Net CycleGAN for 3D MR to CT image synthesis.

CycleGAN and its variants are widely used in medical image synthesis, which can use unpaired data for medical image synthesis. The most commonly used method is to use a Generative Adversarial Network (GAN) model to process 2D slices and thereafter concatenate all of these slices to 3D medical images. Nevertheless, these methods always bring about spatial inconsistencies in contiguous slices. We offer a new model based on the CycleGAN to work out this problem, which can achieve high-quality conversion from magnetic resonance (MR) to computed tomography (CT) images. To achieve spatial consistencies of 3D medical images and avoid the memory-heavy 3D convolutions, we reorganized the adjacent 3 slices into a 2.5D slice as the input image. Further, we propose a U-Net discriminator network to improve accuracy, which can perceive input objects locally and globally. Then, the model uses Content-Aware ReAssembly of Features (CARAFE) upsampling, which has a large field of view and content awareness takes the place of using a settled kernel for all samples. The mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) for double U-Net CycleGAN generated 3D image synthesis are 74.56±10.02, 27.12±0.71 and 0.84±0.03, respectively. Our method achieves preferable results than state-of-the-art methods. The experiment results indicate our method can realize the conversion of MR to CT images using ill-sorted pair data, and achieves preferable results than state-of-the-art methods. Compared with 3D CycleGAN, it can synthesize better 3D CT images with less computation and memory.

Unsupervised learning of light field depth estimation with spatial and angular consistencies

Learning-based depth estimation from light fields has made significant advances in recent years, however, most of these work abandon the traditional non-learning based formulations and start over with an end-to-end deep network framework. Actually traditional methods have also presented many effective and evident depth cues or constraints for light field depth estimation. In this paper, we try to combine the advantages of the learning and non-learning strategies, and incorporate the traditional light field constraints into an unsupervised framework by using a learnable approximation scheme to yield differentiable unsupervised loss functions. Specifically, we first propose an unsupervised coarse-to-fine network architecture for light field depth estimation, and then design an adaptive spatio-angular consistency loss combined with the differentiable versions of modified traditional constraints. Comparative experiments demonstrate that our method is superior to the state-of-the-art unsupervised methods.

The Relationship Between Illusory Heaviness Sensation and the Motion Speed of Visual Feedback in Gesture-Based Touchless Inputs.

Interaction systems with gesture-based touchless inputs are becoming more common. Nevertheless, perceptual properties of the visual feedback used in the system have not been well documented. We investigated whether the speed of motion shown in visual feedback used in gesture-based touchless inputs could be a cue for the heaviness sensation of an object even when other incidental cues, such as changes in object size and spatial consistencies in direction between gestures and feedback, were eliminated from the stimuli. Participants were asked to make a gesture to grasp and raise/lower disks shown on a horizontal display. The disk’s diameter changed in accordance with the vertical position of the participant’s hand. The results showed that the rate of change in diameter determined the heaviness sensation. When the disks were replaced with concentric gratings having sinusoidal radial intensity and thus the cue of size change was eliminated from the stimuli, the heaviness sensation was dependent on the speed of phase shift (that is, motion) in the grating. It was also found that spatial consistency between the direction of gestures and phase shift was not a critical condition for the heaviness sensation. Finally, the speed of motion served as a critical determinant of the heaviness sensation even when another visual feature (i.e., frame rate) was modulated in a single session, which indicates that the effect of the speed of motion on the heaviness sensation was unlikely due to demanded characteristics. The results indicate that the heaviness sensation for visual feedback of gesture-based touchless inputs is based purely on the speed of the visual feedback motion.

Comparison of Three Ten Meter Land Cover Products in a Drought Region: A Case Study in Northwestern China

The ecological and natural conditions in drought regions are harsh. Water shortages and land desertification are prominent features of these regions. The land cover in these regions has a large impact on global climate change, as well as on ecological protection and construction. To make rational and sustainable use of land resources, it is crucial to quickly grasp the accuracy and spatial distribution differences of multi-source remote sensing land cover products in drought regions. Therefore, taking northwestern China as the study area, in this study, the accuracy and spatial pattern distribution differences of three high-resolution (10 m) land cover products, namely, the Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC), European Space Agency (ESA), and Environmental Systems Research Institute (ESRI) products, were compared and analyzed via area composition similarity, spatial pattern consistency, and absolute accuracy assessment for three validation samples. The results show that the FROM-GLC product had the highest overall accuracy, ranging from 53.81% to 73.45%. The ESRI product had the lowest overall accuracy, ranging from 35.90% to 64.16%. The spatial consistencies of the three products were low, accounting for 46.26% of the total area, and they were mostly distributed in a single area (mainly bare land and forest). The low accuracy for grassland, bare land, shrubland, and other vegetation types was the primary reason for the large differences between the three products. Future research should focus on improving the mapping accuracy for these vegetation types. Accuracies for water and cropland of the three products were consistent, and, thus, the FROM-GLC, ESA, and ESRI products can be used as auxiliary data in research related to water resources and cropland resources in drought regions.

Halophytes Differ in Their Adaptation to Soil Environment in the Yellow River Delta: Effects of Water Source, Soil Depth, and Nutrient Stoichiometry.

The Yellow River Delta is water, salt, and nutrient limited and hence the growth of plants depend on the surrounding factors. Understanding the water, salt, and stoichiometry of plants and soil systems from the perspective of different halophytes is useful for exploring their survival strategies. Thus, a comprehensive investigation of water, salt, and stoichiometry characteristics in different halophytes and soil systems was carried out in this area. Results showed that the oxygen isotopes (δ18O) of three halophytes were significantly different (P < 0.05). Phragmites communis primarily used rainwater and soil water, while Suaeda salsa and Limonium bicolor mainly used soil water. The contributions of rainwater to three halophytes (P. communis, S. salsa, and L. bicolor) were 50.9, 9.1, and 18.5%, respectively. The carbon isotope (δ13C) analysis showed that P. communis had the highest water use efficiency, followed by S. salsa and L. bicolor. Na+ content in the aboveground and underground parts of different halophytes was all followed an order of S. salsa > L. bicolor > P. communis. C content and N:P in leaves of P. communis and N content of leaves in L. bicolor were significantly positively correlated with Na+. Redundancy analysis (RDA) between plants and each soil layer showed that there were different correlation patterns in the three halophytes. P. communis primarily used rainwater and soil water with low salt content in 60–80 cm, while the significant correlation indexes of C:N:P stoichiometry between plant and soil were mainly in a 20–40 cm soil layer. In S. salsa, the soil layer with the highest contribution of soil water and the closest correlation with the C:N:P stoichiometry of leaves were both in 10–20 cm layers, while L. bicolor were mainly in 40–80 cm soil layers. So, the sources of soil water and nutrient of P. communis were located in different soil layers, while there were spatial consistencies of soils in water and nutrient utilization of S. salsa and L. bicolor. These results are beneficial to a comprehensive understanding of the adaptability of halophytes in the Yellow River Delta.

Video Frame Interpolation via Generalized Deformable Convolution

Video frame interpolation aims at synthesizing intermediate frames from nearby source frames while maintaining spatial and temporal consistencies. The existing deep-learning-based video frame interpolation methods can be roughly divided into two categories: flow-based methods and kernel-based methods. The performance of flow-based methods is often jeopardized by the inaccuracy of flow map estimation due to oversimplified motion models, while that of kernel-based methods tends to be constrained by the rigidity of kernel shape. To address these performance-limiting issues, a novel mechanism named generalized deformable convolution is proposed, which can effectively learn motion information in a data-driven manner and freely select sampling points in space-time. We further develop a new video frame interpolation method based on this mechanism. Our extensive experiments demonstrate that the new method performs favorably against the state-of-the-art, especially when dealing with complex motions. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhshi0816/GDConvNet</uri> .

Quality assurance and assessment framework for land cover maps validation in the Copernicus Hot Spot Monitoring activity

ABSTRACT The Copernicus High-Resolution Hot Spot Monitoring activity (C-HSM) delivers a global dataset of Key Landscapes for Conservation (KLC), which are characterized by pronounced anthropogenic pressures that require high mapping accuracy. Detailed land cover and land cover change map products are freely available through the activity and include extensive map production accuracy assessments. Without a complete understanding of the map products’ spatial, temporal and logical consistencies, quality or quantified confidence levels, usability is reduced and can affect stakeholder decision-making and the implementation of sustainable solutions. For the quantitative accuracy assessment, a stratified random sampling approach was implemented where special emphasis was placed on (i) allocation of sampling units for rare land cover change categories; (ii) effective and accurate labelling of large numbers of sampling units; (iii) accuracy and area estimation in one consistent approach; and (iv) derivation of confidence intervals for all accuracy measures and area estimates. To handle correlations, large uncertainties, and complex probability density functions, bootstrapping was applied instead of analytical equations, which are based on normality assumptions. This paper presents the Quality Assurance and Quality Control framework applied to validate all the C-HSM thematic map products. The Kundelungu-Upemba KLC product results are presented as our use case.

Foraging ecology of masked boobies (Sula dactylatra) in the world\u2019s largest \u201coceanic desert\u201d

The South Pacific Gyre has the most hyper-oligotrophic waters in the world and is considered the largest “oceanic desert.” Rapa Nui (Easter Island), located within the South Pacific Gyre, is a breeding ground for masked boobies (Sula dactylatra), which are seabirds with a foraging range that effectively confines them within the gyre. The foraging ecology of this species in the gyre was examined by attaching GPS and time-depth devices to chick-rearing adult birds (9 and 14 birds in 2016 and 2017, respectively) and by collecting regurgitates (18 and 15 samples in 2016 and 2017, respectively). In addition, the birds’ foraging ecology between years was compared. Masked boobies traveled in various directions, dived at unspecific locations, and explored areas < 110 km from the colony. Local environmental conditions were not significantly different between years, and differences in foraging parameters (maximum foraging range, trip duration, and dive depth) were greater among individuals than between years. The foraging characteristics of masked boobies suggest that resources were ephemerally distributed around the colony, with similar abundances across years. Under these conditions, traveling to unspecific locations may increase the area covered and the probability of prey encounter. The spatial and temporal consistencies in environmental conditions explain the uniformity of foraging parameters between years. The ability of masked boobies to exploit ephemerally distributed resources in seascapes like Rapa Nui may help explain its pantropical distribution.

Fingerprint of rice paddies in spatial\u2013temporal dynamics of atmospheric methane concentration in monsoon Asia

Agriculture (e.g., rice paddies) has been considered one of the main emission sources responsible for the sudden rise of atmospheric methane concentration (XCH4) since 2007, but remains debated. Here we use satellite-based rice paddy and XCH4 data to investigate the spatial–temporal relationships between rice paddy area, rice plant growth, and XCH4 in monsoon Asia, which accounts for ~87% of the global rice area. We find strong spatial consistencies between rice paddy area and XCH4 and seasonal consistencies between rice plant growth and XCH4. Our results also show a decreasing trend in rice paddy area in monsoon Asia since 2007, which suggests that the change in rice paddy area could not be one of the major drivers for the renewed XCH4 growth, thus other sources and sinks should be further investigated. Our findings highlight the importance of satellite-based paddy rice datasets in understanding the spatial–temporal dynamics of XCH4 in monsoon Asia.

Efficient Outdoor Video Semantic Segmentation Using Feedback-Based Fully Convolution Neural Network

In this article, we focus on efficient semantic segmentation problem from sequential two-dimensional images, in which all pixels are classified into certain classes for scene understanding. Such problem is challenging because it involves constraints of both spatial and temporal consistencies, which have large difficulties in explicitly determining such structural constraints. Traditionally, such a problem is tackled using structured prediction method, such as conditional random field (CRF). However, pure CRF method suffers from very high complexity in computing high-order potentials and slow performance during inference step, which is unsuitable for efficient video segmentation in real scenario. In this article, a novel feedback-based deep fully convolutional neural network (CNN) is proposed to inherently incorporate spatial context through appending output feedback mechanism. The proposed method has the following contributions: 1) spatial context in images are easily captured through iterative feedback refinement, without the expensive postprocess step such as CRF refinement; 2) easily integrated with generic deep CNN structure; and 3) the inference time is greatly reduced for efficient image segmentation. Compared to current state-of-the-art methods, our proposed method was shown to provide up to 14% better accuracy on semantic segmentation task in challenging Camvid and Cityscapes datasets, while taking up to relatively 980% shorter inference time. The proposed method also shows its effectiveness for real-time road detection task of autonomous driving.

Spatiotemporal Comparison and Validation of Three Global-Scale Fractional Vegetation Cover Products

Fractional vegetation cover (FVC) is an important parameter for many environmental and ecological models. Large-scale and long-term FVC products are critical for various applications. Currently, several global-scale FVC products have been generated with remote sensing data, such as VGT bioGEOphysical product Version 2 (GEOV2), PROBA-V bioGEOphysical product Version 3 (GEOV3) and Global LAnd Surface Satellite (GLASS) FVC products. However, studies comparing and validating these global-scale FVC products are rare. Therefore, in this study, the performances of three global-scale time series FVC products, including the GEOV2, GEOV3, and GLASS FVC products, are investigated to assess their spatial and temporal consistencies. Furthermore, reference FVC data generated from high-spatial-resolution data are used to directly evaluate the accuracy of these FVC products. The results show that these three FVC products achieve general agreements in terms of spatiotemporal consistencies over most regions. In addition, the GLASS and GEOV2 FVC products have reliable spatial and temporal completeness, whereas the GEOV3 FVC product contains much missing data over high-latitude regions, especially during wintertime. Furthermore, the GEOV3 FVC product presents higher FVC values than GEOV2 and GLASS FVC products over the equator. The main differences between the GEOV2 and GLASS FVC products occur over deciduous forests, for which the GLASS product presents slightly higher FVC values than the GEOV2 product during wintertime. Finally, temporal profiles of the GEOV2 and GLASS FVC products show better consistency than the GEOV3 FVC product, and the GLASS FVC product presents more reliable accuracy (R2 = 0.7878, RMSE = 0.1212) compared with the GEOV2 (R2 = 0.5798, RMSE = 0.1921) and GEOV3 (R2 = 0.7744, RMSE = 0.2224) FVC products over these reference FVC data.