Albedo/reflectance is of great importance for glaciers’ mass balance and energy budget. Elevation could be a major factor of influence for glacier reflectance, and therefore when studying glacier ...reflectance, the altitude ranges should be considered. However, due to the limitations of traditional earth observation systems, conventional analyses usually consider the spatial and temporal patterns of the reflectance average, which is severely restricted. The launch of Landsat-8 gives us the opportunity to study the seasonal glacier reflectance–elevation relationship. We have obtained the monthly near-nadir reflectance per 100 m for twelve glaciers in western China based on 372 scenes of Landsat 8 images acquired from April 2013 to December 2015. Variations of monthly broadband reflectance, reflectance–elevation relationships and reflectance gradients are analyzed and discussed. The results show that the linear trend of the reflectance–elevation relationship (when the altitude is less than 6100 m) is very significant; elevation has greater influence than location on seasonal reflectance variations; and the level of glacier reflectance gradient may relate with its climate. This may be the first work that has used remote-sensing data to analyze seasonal glacier reflectance–elevation patterns.
The spatial distribution of forest stands is one of the fundamental properties of forests. Timely and accurately obtained stand distribution can help people better understand, manage, and utilize ...forests. The development of remote sensing technology has made it possible to map the distribution of tree species in a timely and accurate manner. At present, a large amount of remote sensing data have been accumulated, including high-spatial-resolution images, time-series images, light detection and ranging (LiDAR) data, etc. However, these data have not been fully utilized. To accurately identify the tree species of forest stands, various and complementary data need to be synthesized for classification. A curve matching based method called the fusion of spectral image and point data (FSP) algorithm was developed to fuse high-spatial-resolution images, time-series images, and LiDAR data for forest stand classification. In this method, the multispectral Sentinel-2 image and high-spatial-resolution aerial images were first fused. Then, the fused images were segmented to derive forest stands, which are the basic unit for classification. To extract features from forest stands, the gray histogram of each band was extracted from the aerial images. The average reflectance in each stand was calculated and stacked for the time-series images. The profile curve of forest structure was generated from the LiDAR data. Finally, the features of forest stands were compared with training samples using curve matching methods to derive the tree species. The developed method was tested in a forest farm to classify 11 tree species. The average accuracy of the FSP method for ten performances was between 0.900 and 0.913, and the maximum accuracy was 0.945. The experiments demonstrate that the FSP method is more accurate and stable than traditional machine learning classification methods.
Spatiotemporal patterns of global forest net primary productivity (NPP) are pivotal for us to understand the interaction between the climate and the terrestrial carbon cycle. In this study, we use ...Google Earth Engine (GEE), which is a powerful cloud platform, to study the dynamics of the global forest NPP with remote sensing and climate datasets. In contrast with traditional analyses that divide forest areas according to geographical location or climate types to retrieve general conclusions, we categorize forest regions based on their NPP levels. Nine categories of forests are obtained with the self-organizing map (SOM) method, and eight relative factors are considered in the analysis. We found that although forests can achieve higher NPP with taller, denser and more broad-leaved trees, the influence of the climate is stronger on the NPP; for the high-NPP categories, precipitation shows a weak or negative correlation with vegetation greenness, while lacking water may correspond to decrease in productivity for low-NPP categories. The low-NPP categories responded mainly to the La Niña event with an increase in the NPP, while the NPP of the high-NPP categories increased at the onset of the El Niño event and decreased soon afterwards when the warm phase of the El Niño-Southern Oscillation (ENSO) wore off. The influence of the ENSO changes correspondingly with different NPP levels, which infers that the pattern of climate oscillation and forest growth conditions have some degree of synchronization. These findings may facilitate the understanding of global forest NPP variation from a different perspective.
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•After ultrasound treatment, the changes of muscle fiber structure and T22 are intensified.•Protein conformation changed and lipid oxidation increased under ultrasound ...treatment.•200 W ultrasound group has the less fishy (Hexanal, 1-Pentene-3-ol, 1-Octane-3-ol).•Umami taste amino peptide (γ-Glu-Met etc.) are increased under ultrasound treatment (200 W and 300 W).
This study investigated the effects of ultrasound treatment on the quality of salted Culter alburnus fish. The results showed that with the increasing ultrasound power, the structural degradation of muscle fibers was intensified, and the conformation of myofibrillar protein was significantly changed. The high-power ultrasound treatment group (300 W) had relatively higher thiobarbiturate reactive substance content (0.37 mg malondialdehyde eq/kg) and peroxidation value (0.63 mmol/kg). A total of 66 volatile compounds were identified with obvious differences among groups. The 200 W ultrasound group exhibited fewer fishy substances (Hexanal, 1-Pentene-3-ol, and 1-Octane-3-ol). Compared with control group, ultrasound groups (200, 300 W) contained more umami taste-related amino peptides such as γ-Glu-Met, γ-Glu-Ala, and Asn-pro. In the ultrasound treatment group, L-isoleucine and L-methionine, which may be used as flavor precursors, were significantly down-regulated, while carbohydrates and its metabolites were up-regulated. Amino acid, carbohydrate, and FA (fatty acyls) metabolism products in salted fish were enriched by ultrasound treatment, and those products might ultimately be related to the taste and flavor of salted fish.
Building extraction plays an important role in urban information analysis, which is helpful for urban planning and disaster monitoring. With the improvement of SAR resolution, rich detailed ...information in urban areas is revealed, but the discretized features also pose challenges for object detection. This paper addresses the problem of individual high-rise building extraction based on single high-resolution SAR image. Different from previous methods that require building facades to be presented in specific appearances, the proposed method is suitable for extraction of various types of high-rise buildings. After analyzing the SAR images of many types of high-rise buildings, we establish a unified high-rise building part model, on the basis of a scattering mechanism of building structures, to describe the facade characteristics of high-rise buildings, including a facade regularity part, facade bright line part, double bounce part, and their spatial topological relationships. A three-level high-rise building extraction framework, named HRBE-PM, is proposed based on the high-rise building part model. At the pixel level, a modified spot filter is used to extract bright spots and bright lines of different scales simultaneously to obtain salient features. At the structure level, building parts are generated based on the salient features according to semantic information. At the object level, spatial topological information between parts is introduced to generate building hypotheses. We define two indicators, completeness and compactness, to comprehensively evaluate each building hypothesis and select the optimal ones. After postprocessing, the final high-rise building extraction results are obtained. Experiments on two TerraSAR-X images show that the high-rise building extraction precision rate of the HRBE-PM method is above 85.29%, the recall rate is above 82.95%, and the F1-score is above 0.87. The results indicate that the HRBE-PM method can accurately extract individual high-rise buildings higher than 24 m in most dense scenes, regardless of building types.
Accumulating literature and evidence has highlighted the cancer stem-like cell (CSC) model as a cellular mechanism responsible for the phenotypic heterogeneity observed in various types of cancers, ...including cervical cancer. Long non-coding RNAs (lncRNAs) have been implicated in the retention of stem cell-like traits in cancer cells. However, the role of lncRNAs in the acquisition and maintenance of CSCs in cervical cancer remains largely unknown. Hence, the current study identified that LINC00337 knockdown diminished the CSC-like properties of CD44
/CD24
SFCs, evidenced by a decline in the generation of tumorospheres and colonies, a reduction in multi-drug resistance gene-1 (MDR-1), Nanog, Sox2, and Oct4 expression, along with an enhancement in cell apoptosis. RNA pull-down assays and RNA immunoprecipitation revealed the role of LINC00337 as a competing endogenous RNA (ceRNA) of microRNA-145 (miR-145). Furthermore, the miR-145 mRNA target, Kruppel-like factor 5 (KLF5), was decreased in CD44
/CD24
SFCs upon LINC00337 knockdown. The
results were reproduced in
studies, which provided verification attesting that LINC00337 knockdown attenuated the tumorigenicity of CD44
/CD24
SFCs in nude mice. Taken together, the key findings of the current study demonstrate that LINC00337 acts as an oncogenic lncRNA in cervical cancer and exerts its influence on the expression of KLF5 and the maintenance of cancer stem cell-like properties by means of downregulating miR-145.
In polar regions, vegetation is especially sensitive to climate dynamics and thus can be used as an indicator of the global and regional environmental change. However, in Antarctica, there is very ...little information on vegetation distribution and growth status. To fill this gap, we evaluated the ability of both linear and nonlinear spectral mixture analysis (SMA) models, including a group of newly developed modified Nascimento’s models for Antarctic vegetated areas (MNM-AVs), in estimating the abundance of major Antarctic vegetation types, i.e., mosses and lichens. The study was conducted using WorldView-2 satellite data and field measurements over the Fildes Peninsula and its surroundings, which are representative vegetated areas in Antarctica. In MNM-AVs, we introduced secondary scattering components for vegetation and its background to account for the sparsity of vegetation cover and reassigned their coefficients. The new models achieved improved performances, among which MNM-AV3 achieved the lowest error for mosses (lichens) abundance estimation with RMSE = 0.202 (0.213). Compared with MNM-AVs, the linear model performed particularly poor for lichens (RMSE = 0.322), which is in contrast to the case of mosses (RMSE = 0.212), demonstrating that spectral signals of lichens are more prone to mix with their backgrounds. Abundance maps of mosses and lichens, as well as a map of moss health status for the entire study area, were then obtained based on MNM-AV3 with around 80% overall accuracy. Moss areas account for 0.7695 km2 in Fildes and 0.3259 km2 in Ardley Island; unhealthy mosses amounted to 40% (49%) of the area in the summer of 2018 (2019), indicating considerable environmental stress.
Currently, numerous studies have reported that the invasion of Cassytha filiformis has affected both above and below ground communities, resulting in difficulties in the growth of original ...vegetation. Meanwhile, Cassytha filiformis was observed on the Xisha Islands in recent years which brings up the importance of monitoring its invasion to protect the biodiversity of the island. Nonetheless, to effectively monitor Cassytha filiformis at finer regional scales, there is a pressing need for centimeter-level resolution, a level of precision that current satellite sensors find challenging to attain in a consistent manner. Therefore, we adopted a DJI Phantom 4 unmanned aerial vehicle with five multispectral bands and centimeter-level spatial resolution to overcome this problem. An advanced deep learning network is employed to identify the invasion in Xisha Islands for three different time periods. Results show that the area of Cassytha filiformis on Bei Island increased from 211.8 m 2 in April 2020 to 458.6 m 2 in April 2021, and dropped to 112.8 m 2 in July 2021, while that on Ganquan Island changed from 1996.9 m 2 in April 2021 (dry season) to 1275.9 m 2 in July 2021 (wet season). By incorporating climatic indicators, we further found that Cassytha filiformis in both Bei Island and Ganquan Island favors dry climate and its large area invasion in 2021 was possibly caused by a drought event.
A reasonable and balanced diet is essential for maintaining good health. With advancements in deep learning, an automated nutrition estimation method based on food images offers a promising solution ...for monitoring daily nutritional intake and promoting dietary health. While monocular image-based nutrition estimation is convenient, efficient and economical, the challenge of limited accuracy remains a significant concern. To tackle this issue, we proposed DPF-Nutrition, an end-to-end nutrition estimation method using monocular images. In DPF-Nutrition, we introduced a depth prediction module to generate depth maps, thereby improving the accuracy of food portion estimation. Additionally, we designed an RGB-D fusion module that combined monocular images with the predicted depth information, resulting in better performance for nutrition estimation. To the best of our knowledge, this was the pioneering effort that integrated depth prediction and RGB-D fusion techniques in food nutrition estimation. Comprehensive experiments performed on Nutrition5k evaluated the effectiveness and efficiency of DPF-Nutrition.
The Yellow River Delta (YRD), known for its vast and diverse wetland ecosystem, is the largest estuarine delta in China. However, human activities and climate change have significantly degraded the ...wetland ecosystem in recent decades in the YRD. Therefore, an understanding of the land use modifications is essential for the efficient management and preservation of ecosystems in this region. This study utilized time series of remote sensing data and the extreme gradient boosting method to generate land use maps of the YRD from 2000 to 2020. Several methods, including transition matrix, land use dynamic degree, and standard deviation ellipse, were employed to explore the characteristics of land use transitions. The results underscore significant spatial variations in land use over the past two decades. The most rapid increase was observed in built-up area, followed by terrestrial water and tidal flats, while unutilized land experienced the fastest decrease, followed by forest–grassland. The spatial distribution patterns of agricultural land, built-up area, terrestrial water, and forest–grassland demonstrated stronger directionality compared to other land use types. The wetlands have expanded in size and improved in structure. Unutilized land has been converted into artificial wetlands comprising ponds, reservoirs, salt ponds, shrimp and crab ponds, and natural wetlands featuring mudflats and forest–grassland. The wetland conservation efforts after 2008 have proven very effective, playing a positive role in ecological and environmental preservation, as well as in regional sustainable development.
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