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Based on Innojoy patent search engine, this paper analyzes the patent type, technical fields, innovation activity, life cycle and patent value of green building in China. The development trend of green building in China is summarized. Suggestions were put forward to promote the development of China’s green building industry. It is found that the overall development trend of China’s green building is good, and it is in the growth stage of the technology life cycle, and the development potential is huge. Green building gradually runs through the whole process of construction, and the application scope continues to expand. Technological innovation is the core driving force for the development of green building.

Objective: To compare large‐ and medium‐sized choroidal vascularity and the choriocapillaris (CC) flow area in children with different refractive errors using swept‐source optical coherence tomography angiography (SS‐OCTA). Methods: Forty‐two anisometropic children were enrolled and divided into hyperopic anisometropia (HA) and myopic anisometropia (MA) groups. SS‐OCTA was performed to analyse choroidal vascularity. Mean choroidal thickness (CT), choroidal vascularity volume (CVV), choroidal vascularity index (CVI) and CC flow area were compared between the two eyes. The inter‐ocular differences between the two groups were also determined. Results: Mean CT and CVV were highest in eyes with shorter axial lengths in both refractive groups, and the difference between the two eyes was positively correlated with the difference in axial length at the foveal region. Significant differences in the CVI in the MA group were only found in the parafoveal region. Inter‐ocular differences in the CC were significantly reduced in eyes with longer axial lengths in the foveal and parafoveal regions of the HA and MA groups, respectively. Comparing inter‐ocular differences, CC was significantly greater in the parafoveal region of the MA group than the HA group. Conclusions: All layers of choroidal vasculature were thinner in eyes with longer axial lengths in all groups. The inter‐ocular CC difference was greater in the MA than in the HA group, with similar differences in axial length. This suggests that both medium‐to‐large choroidal vascular and choroidal capillaries may play a role in myopia development. [ABSTRACT FROM AUTHOR]

The advent of the era of big remote sensing data has transformed traditional data management and analysis models, among which visualization analysis has gradually become an effective method, and map tiles for remote sensing data have always played an important role. However, in high-latitude regions, especially in polar regions, the deformation caused by map projection still exists, which lowers the accuracy of global or large-scale visual analysis, as well as the execution efficiency of big data. To solve the above problems, this paper proposes an algorithm called HexTile, which uses a hexagonal discrete global grid system (DGGS) model to effectively avoid problems caused by map projection and ensure global consistency. At the same time, the algorithm was implemented based on the Spark platform, which also has advantages in efficiency. Based on the DGGS model, hierarchical hexagon map tile construction and a visualization algorithm were designed, including hexagonal slicing, merging, and stitching. The above algorithms were parallelized in Spark to improve the big data execution efficiency. Experiments were carried out with Landsat-8, and the results show that the HexTile algorithm can not only guarantee the quality of global data, but also give full play to the advantages of the cluster in terms of efficiency. Additionally, the visualization was conducted with Cesium and OpenLayers to validate the integration and completeness of hexagon tiles. The scheme proposed in this paper could provide a reference for spatiotemporal big data visualization technology. [ABSTRACT FROM AUTHOR]

The straw return practice is essential to soil organic matter (SOM) accumulation in the black soil area with high carbon sequestration potential. However, due to lacking accurate spatial distribution of straw return, few studies took straw return as a variable to carry out rigorous research on the impact of straw return on SOM variation on a regional scale. Based on soil samples and 16 environmental covariates including a 10-meter-resolution straw return amount, the study mapped the spatial distributions of SOM in 2006 and 2018 by random forest (RF) and evaluated the effects of the interaction of soil properties, land use and straw return on SOM spatial-temporal variation. The results show that in the context of the straw returning, the mean SOM content increased from 18.93 g kg−1 to 20.84 g kg−1 during 2006–2018. And 74.49 % of the region had a significant increase (maximum: 24.41 g kg−1) of SOM. The severest SOM loss occurred in the northwest due to the light texture and the transition from paddy fields to dryland. Nevertheless, for areas from paddy fields to dryland, the SOM loss decreased with the increased amount of straw return. The SOM even increased by 1.84 g kg−1 when the straw return amount reached 60–100 %. In addition, soil with higher initial SOM and sand content had a lower response to straw return. The study revealed that straw return is beneficial to carbon sink in farmland and is a better way to prevent a carbon source caused by the change of paddy field to dryland. [ABSTRACT FROM AUTHOR]

BACKGROUND: The timely, rapid, and accurate near real‐time observations are urgent to monitor the damage of corn armyworm, because the rapid expansion of armyworm would lead to severe yield losses. Therefore, the potential of machine learning algorithms for identifying the armyworm infected areas automatically and accurately by multispectral unmanned aerial vehicle (UAV) dataset is explored in this study. The study area is in Beicuizhuang Village, Langfang City, Hebei Province, which is the main corn‐producing area in the North China Plain. RESULTS: Firstly, we identified the optimal combination of image features by Gini‐importance and the comparation of four kinds of machine learning methods including Random Forest (RF), Multilayer Perceptron (MLP), Naive Bayesian (NB) and Support Vector Machine (SVM) was done. And RF was proved to be the most potential with the highest Kappa and OA of 0.9709 and 0.9850, respectively. Secondly, the armyworm infected areas and healthy corn areas were predicted by an optimized RF model in the UAV dataset, and the armyworm incidence levels were classified subsequently. Thirdly, the relationship between the spectral characteristics of different bands and pest incidence levels within the Sentinel‐2 and UAV images were analyzed, and the B3 in UAV images and the B6 in Sentinel‐2 image were less sensitive for armyworm incidence levels. Therefore, the Sentinel‐2 image was used to monitor armyworm in two towns. CONCLUSIONS: The optimized dataset and RF model are effective and reliable, which can be used for identifying the corn damage by armyworm using UAV images accurately and automatically in field‐scale. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Crop classification maps are fundamental data for global change research, regional agricultural regulation, fine production, and insurance services. The key to crop classification is samples, but it is very time-consuming in annual field sampling. Therefore, how to use historical samples in crop classification for future years at a lower cost is a research hotspot. By constructing the spectral feature vector of each historical sample in the historical year and its neighboring pixels in the target year, we produced new samples and classified them in the target year. Specifically, based on environmental similarity, we first calculated the similarities of every two pixels between each historical year and target year and took neighboring pixels with the highest local similarity as potential samples. Then, cluster analysis was performed on those potential samples of the same crop, and the class with more pixels is selected as newly generated samples for classification of the target year. The experiment in Heilongjiang province, China showed that this method can generate new samples with the uniform spatial distribution and that the proportion of various crops is consistent with field data in historical years. The overall accuracy of the target year by the newly generated sample and the real sample is 61.57 and 80.58%, respectively. The spatial pattern of maps obtained by two models is basically the same, and the classification based on the newly generated samples identified rice better. For areas with majority fields having no rotation, this method overcomes the problem of insufficient samples caused by difficulties in visual interpretation and high cost on field sampling, effectively improves the utilization rate of historical samples, and provides a new idea for crop mapping in areas lacking field samples of the target year. [ABSTRACT FROM AUTHOR]

Soil salinity is a significant environmental indicator, directly reflecting the land quality and its productivity. In this work, dual-polarized synthetic aperture radar (PolSAR) data were exploited to retrieve the soil salinity in saline-affected fields based on the deep neural network (DNN) theory. Two polarimetric features gotten from Cloude polarization decomposition, together with six other features, were calculated and selected from ten features according to the feature importance. The DNN regression model for soil salinity (DNNR-S) based on one-dimensional convolutional neural network, quantifying the relationship between the eight feature parameters and soil salinity, was established to retrieve the soil salinity in northeastern China. Compared with other five traditional machine learning regression methods [logistic regression (LOR), random forest regression (RFR), support vector regression (SVR), decision tree regression (DTR), and multilayer perceptron regression (MPR)], DNNR-S is the best-performing model with the performance of root mean square error (RMSE) = 0.28, mean absolute error (MAE) = 0.21, and $r = 0.74$. Using the proposed model, the soil salinity was retrieved, and 52.37% was found to be affected by salinization which was in general agreement with the government report. Due to the training criteria in the proposed model, it was a little more computationally intensive than all the others, but the DNNR-S had the advantage of easy spread to other study areas. [ABSTRACT FROM AUTHOR]

Agricultural drought threatens food security. Numerous remote-sensing drought indices have been developed, but their different principles, assumptions and physical quantities make it necessary to compare their suitability for drought monitoring over large areas. Here, we analyzed the performance of three typical remote sensing-based drought indices for monitoring agricultural drought in two major agricultural production regions in Shaanxi and Henan provinces, northern China (predominantly rain-fed and irrigated agriculture, respectively): vegetation health index (VHI), temperature vegetation dryness index (TVDI) and drought severity index (DSI). We compared the agreement between these indices and the standardized precipitation index (SPI), soil moisture, winter wheat yield and National Meteorological Drought Monitoring (NMDM) maps. On average, DSI outperformed the other indices, with stronger correlations with SPI and soil moisture. DSI also corresponded better with soil moisture and NMDM maps. The jointing and grain-filling stages of winter wheat are more sensitive to water stress, indicating that winter wheat required more water during these stages. Moreover, the correlations between the drought indices and SPI, soil moisture, and winter wheat yield were generally stronger in Shaanxi province than in Henan province, suggesting that remote-sensing drought indices provide more accurate predictions of the impacts of drought in predominantly rain-fed agricultural areas. [ABSTRACT FROM AUTHOR]

The effects of varieties, producing areas, ears, and ear positions of maize on near-infrared (NIR) spectra were investigated to determine the factors causing the differences in NIR fingerprints of maize varieties. A total of 130 inbred lines were grown in two regions in China, and 12,350 kernel samples were analyzed through NIR spectroscopy. Spectral differences among varieties, producing areas, ears, and ear positions were determined and compared on the basis of pretreated spectra. The bands at 1300–1470, 1768–1949, 2010–2064, and 2235–2311 nm were mainly affected by the producing area. Band selection and principal component analysis were applied to improve the influence of variety on NIR spectra by processing the pretreated spectra. The degrees of the influence of varieties, producing areas, ears, and ear positions were calculated, and the percentages of the influence of varieties, producing areas, ears, and ear positions were 45.40%, 42.66%, 8.22%, and 3.72%, respectively. Therefore, genetic differences among maize inbred lines are the main factors accounted for NIR spectral differences. Producing area is a secondary factor. These results could provide a reference for researchers who authenticate varieties, perform geographical origin traceabilities, and conduct maize seed breeding. [ABSTRACT FROM AUTHOR]

Recent advances in laser scanning hardware have allowed rapid generation of high-resolution digital terrain models (DTMs) for large areas. However, the automatic discrimination of ground and non-ground light detection and ranging (lidar) points in areas covered by densely packed buildings or dense vegetation is difficult. In this paper, we introduce a new hierarchical moving curve-fitting filter algorithm that is designed to automatically and rapidly filter lidar data to permit automatic DTM generation. This algorithm is based on fitting a second-degree polynomial surface using flexible tiles of moving blocks and an adaptive threshold. The initial tile size is determined by the size of the largest building in the study area. Based on an adaptive threshold, non-ground points and ground points are classified and labelled step by step. In addition, we used a multi-scale weighted interpolation method to estimate the bare-earth elevation for non-ground points and obtain a recovered terrain model. Our experiments in four study areas showed that the new filtering method can separate ground and non-ground points in both urban areas and those covered by dense vegetation. The filter error ranged from 4.08% to 9.40% for Type I errors, from 2.48% to 7.63% for Type II errors, and from 5.01% to 7.40% for total errors. These errors are lower than those of triangulated irregular network filter algorithms. [ABSTRACT FROM PUBLISHER]

In recent years, well-designed terminal-based methods for collecting index data have gradually replaced traditional pen-and-paper methods and have been extensively used in numerous studies. These new approaches offer users increased accuracy, efficiency, consumption, and data compatibility compared to traditional methods. In general, we find that spatial data content and quality index systems vary widely across different arable land regions. Thus, a system for the investigation of arable land quality indices that has the flexibility to utilize various types of spatial data and quality indices without requiring program modification is needed. This paper presents the framework, the module partition, and the structure of the data exchange interface for a highly flexible mobile GIS-based system, which we call the “arable land quality index data collection system” (ALQIDCS). This system incorporates a series of self-adaptive methods, a data table-driven model and two types of formulas for flexible data collection and processing. We tested our prototype system by investigating arable land quality in the Da Xing District, Beijing and in the Te Da La Qi District, Inner Mongolia, China. The results indicate that the ALQIDCS can effectively adapt to variations in spatial data and quality index systems and meet different objectives. The limitations of the ALQIDCS and suggestions for future work are also presented. [ABSTRACT FROM PUBLISHER]