Root zone soil moisture (RZSM) has a direct impact on ecosystem function, vegetation growth and food security, and plays a vital role in global climate system, water and carbon cycles. However, large variations and uncertainties still exist in RZSM across the globe under the warming climate. In this study, we applied comparison map profile (CMP), Theil-Sen regression and partial correlation analysis to investigate the spatial and temporal changes of RZMS and its driving factors from 1981 to 2017 by using three soil moisture products-ERA5, GLDAS and MERRA-2. Results showed that RZSM derived from three products presented a similar spatial pattern that the highest RZSM values occurred in tropical forest and cold areas, followed by subtropical, while the relatively low RZSM values were observed in arid and semiarid regions. Globally, RZSM decreased in all of three datasets with a rate of -0.14 x 10(-3) m(3) m(-3) yr(-1) on average (p < 0.001), which was largely correlated with temperature anomalies. Spatially, the RZSM trends greatly varied, with 21-31% of global land areas experiencing a significant decreasing trend and 7-24% for an increasing trend, respectively, confirming their different sensitivities to climate change. Temperature-driven RZSM dominated 19-29% of global land areas and was primarily distributed in northern high-latitude areas. The areas dominated by evapotranspiration were mainly in arid and semiarid areas, accounting for 29-44% of global land areas. Precipitation dominates the remaining 36-45% of global land areas mainly in eastern America and Europe, suggesting variations in the dominance of environmental factors on the spatial patterns of RZSM trend. Our findings will deepen our understanding of the impacts of climate change on the long-term trend of global soil moisture, and will be greatly critical to global soil water resource protection and management under the warming climate.

Water is the major guarantee for ecological restoration and a virtuous ecological cycle. This study is based on spatial information processing technology and mathematical modeling. The results demonstrate that the overall trend in regional modulus of eco-water conservation (MEC) and quantity of eco-water conservation (QEC) was dropped dramatically and then gradually increased from 2007 to 2017. The restoration process of eco-water conservation capacity cannot match both in time and space. Moreover, there are significant differences in MEC between different land types. Serious destruction of forestland are the leading factors of the 14.27% decrease in MEC of the study area after earthquake. Besides, there is an apparently positive correlation between MEC distribution and slope changes on the whole (p < 0.05), so the earthquake has the greatest impact on MEC if the surface slope is between 15° and 35°. Furthermore, the average MEC in areas below 3,000 m above sea level is higher than in other areas, and the MEC reduction rate after the earthquake is 31.85% higher than the areas above 3,000 m. In addition, the disturbance of MEC around faults was more often than that in other areas.

The empirical mode decomposition (EMD) is a method that is commonly applied to extract the intrinsic mode functions (IMFs) of a signal by a sifting process, which requires imposing the extended extrema at both ends of the signal (i.e., the end condition). The imposition of extended extrema can cause an error, which is often presented by the changing shapes of original envelopes and distort extracted IMFs, which is described as the end effect. An important issue during the application of the EMD is restricting the end effect. This paper reveals the decisive factors that can restrict the end effect by determining the uniqueness of the envelope, and provides an interpretation of the end effect in terms of the differences between the original envelope and the extended envelope based on the cubic spline theory. Two principles that are important to the design of an end condition method are provided. The first principle is that the domain of the extended envelope needs to cover the original signal; the second is that the ordinate value of the extended local maxima is greater than or equal to that of the extended local minima. Following these two principles, a new end condition method, the cubic spline based method (CSBM), is proposed in this study. The novelty of the CSBM is that the extended envelope maintains the shape of the original envelope in their intersection domain, and the end effect can be restricted in a limited domain during the sifting process of EMD for each different input signal. Six signals are used to demonstrate the performance of the CSBM by comparing them with two other end condition methods, the extreme method and the improved slope based method (ISBM). The six signals include: a damped sinusoid signal, four monovariate signals with various amplitude modulation-frequency modulation (AM-FM) behaviors, and a one-channel functional near-infrared spectroscopy (fNIRS) signal. Results show that the CSBM in general performs better than the other two methods.

全国矿山开发状况和矿山遥感环境监测实施中,监测数据指标的统计计算是监测成果集中展现和应用的主要方法之一,是相关部分执法的依据,同时也为相关矿山政策调整和制定提供数据支撑.传统人工统计计算各项监测指标存在效率低下、人工成本高、工作量大、计算易出错、准确度低、统计标准不一致等问题.通过项目实践提出基于数据流式过滤和多维矩阵计算的矿山遥感监测自动统计方法,快速统计各矿山遥感监测数据的属性信息和空间信息,分别实现按照行政区域和矿山类型统计计算各个分类指标,完成矿山统计指标的快速汇总.实验结果表明,该方法在2018年青海省矿山遥感监测项目中,快速、准确、自动化地完成了统计汇总,节省了大量人力资源和时间,具有较好的应用效果.

The upstream of Minjiang River, which was one of the main water sources for Chengdu Plain and the Yangtze River, was selected as the study area in the paper.With a series of cloud-free Landsat TM/OLI images acquired on June 24, 1994 and June 1, 2014, the vegetation coverage of the study area was calculated based on the vegetation index and dimidiate pixel model.The temporal and spatial changes of vegetation coverage were analyzed with digital elevation model and county area.The average vegetation coverage of the study area was decreased from 68.97% in 1994 to 60.39% in 2014.The analysis results showed that vegetation changes were closely related to topographical characteristics.The proportion of vegetation degradation area increased with the elevation increase and vegetation degradation was the most serious at the elevation range from 3500 m to 4500 m.The vegetation degeneration was the most obvious in Wenchuan County and Songpan County.Geological hazards caused by the earthquake and human disturbance were the main cause of the vegetation degradation and the aridity trend in the study area was another important factor.

With the successful launch of China's GF series satellites, it is more important to study the image data quality, the adaptability of processing method and information extraction method. The panchromatic and multi-spectral data which is based on the GF-2 images data of Chinese sub-meter high-resolution remote sensing satellite is fused by PCA, Pansharp, Gram-Schmidt and NNDiffuse fusion. Then, the quality of the fusion images were evaluated subjectively and objectively. In order to evaluate the applicability of different classification algorithms to the classification, the object-oriented classification algorithm which is based on machine learning algorithm, such as KNN, SVM and Random Trees were used to classify the different GF-2 fusion images. The results showed that: (1) The best visual effect of GF-2 fusion image was the Pansharp fusion image; The quantitative evaluation results showed that the brightness and information retention of Gram-Schmidt fusion image was the best, while the Pansharp fusion image had the highest correlation with the original multi-spectral image; the NNDiffuse fusion image had the highest clarity, and the PCA fusion image quantitative evaluation effect was the worst; (2) According to the applicability analysis of the fusion images based on different classification algorithms with features information extraction, it could be seen that the NNDiffuse fusion method was used for the fusion of GF-2 image data, and the classification of the fusion images was more suitable by using KNN or Random Trees classification algorithm.

Multiscale segmentation is the premise and key step of geographic object-based image analysis (GEOBIA), but scale selection remains a challenge in multiscale segmentation. Over the years, scale selection and evaluation in image segmentation has been extensively explored and many methods have been developed. In these methods, when a scale is chosen or evaluated, all the features are generally extracted from images. In addition, an optimal scale is generally selected based on the pre-estimation of the statistical variance in remote sensing images or determined based on the postsegmentation evaluation of segmented results. In this study, a method was proposed to identify the optimal scale of each segmented object during the segmentation through combining the a priori thematic map knowledge with image features. First, 25 image segmentations were obtained using multiresolution segmentation algorithm of Definiens Professional 9.0 with different scales. A global score (GS) value was assigned to each segmentation based on the calculation results of the weighted variance and global Moran's I and the single-scale optimal segmentation result was determined according to each GS of 25 segmentation scales. Second, the image feature complexity information and the a priori thematic map complexity information of each segmentation object were extracted to calculate complexity values for each object. Third, the optimal scale of each segmentation object was determined through the iterative calculation with the multithreshold method. Finally, the segmentation results of the proposed method were evaluated. The proposed method was applied to process Gaofen-2 (GF-2), GF-1, Korea Multipurpose Satellite (KOMPSAT-2), IKONOS, QuickBird and WorldView-2 high resolution satellite images to obtain the segmentation results and classification results, compared with results obtained of the optimal singlescale segmentation and the unsupervised evaluation method. The experimental results of GEOBIA showed that the method was helpful for generating the segmentation object with the optimal scale. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

Leaf equivalent water thickness (LEWT) is an important parameter in ecological and environmental monitoring. Our study proposes two new indices, the normalized differential projection index (NDPI) and the distance projection ratio index (DPRI), by considering the angle of beta SWIR1 and the length, projection, and relationships among the random forest feature bands at 721, 1466, and 2061 nm, corresponding to NIR, SWIR1, and SWIR2 wavebands, respectively. Single-factor analysis of NDPI and DPRI and multifactor analysis of other vegetation indices are used to build a LEWT linear model whose effectiveness is evaluated using 10-fold cross validation. Despite their simple structure, the NDPI and DPRI can explain the majority of the variation in LEWT. After single-factor analysis, results from DPRI are superior to those from previous vegetation indices or equations with a higher coefficient of determination (R-2 = 0.79) and lower relative root-mean-square error (rRMSE = 9.79%). Multifactor analysis shows that the model (R-2 = 0.81, rRMSE = 9.45%) built using DPRI and the water index is the most accurate. The proposed use of NDPI and DPRI as parameter bands to build such models provides a method for further study of the vegetation water content inversion. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

The Ga’erqiong Cu-Au deposit, which sits on the north side of the Coqên-Xainzamagmatite belt, is a large-scale skarn-type deposit, whose ore body has formed in the skarn zone in the contact part of quartz diorite and marble of Duoai formation or the cracks of quartz diorite. Its mineralization is closely related to quartz diorite. And granite porphyry-related molybdenum ore still exists in its deep part. Currently, there are disputes about the metallogenic dynamics background of this deposit. From previous studies, this paper carried out zircon LA-LCPMS U-Pb dating and petrogeochemistry study for quartz diorite of Ga’erqiong Cu-Au deposit. The testing result indicates: quartz diorite and granite porphyry were formed respectively in 88±2Ma and 83±1Ma, belonging to the magmatic activity of the early stage of Upper Cretaceous; quartz diorite and granite porphyry have geochemical characteristics similar to those of island arc rock of subduction zone and geochemical indexes similar to “adakite.” Combining with the regional tectonic evolution, we think that quartz diorite and granite porphyry were all formed in the extension environment after the collision of Lhasa block and Qiangtang block. Quartz diorite is the result of the migmatization of basic melt and acid melt evoked by asthenosphere material raise caused by lower crustal delamination; the formation of granite porphyry may be crust-mantle material’s partial melting results due to delaminated lower crustal. Therefore, Ga’erqiongskarn-type Cu-Au deposit belongs to the metallogenic response to the collisional orogeny in the closing process of Meso-Tethys.

Bridge deflection is an important indicator to evaluate the status of bridges, thus its monitoring is an important aspect. This paper proposes to use the tilt sensor to detect the deflection linear data of bridges and designs the tilt sensor and its data collection system. Various experiments were carried out for the designed sensor and the observed results suggest that sensor can accurately measure the deflection of the bridge at different tilt angles. The proposed sensor can be applied to the actual bridge deflection measurement. In addition, it was observed that the temperature compensation can improve the measurement accuracy of the tilt sensor at different temperatures.

Abstract. Vegetation carbon use efficiency (CUE) is a key measure of carbon (C) transfer from the atmosphere to terrestrial biomass, and indirectly reflects how much C is released through autotrophic respiration from the vegetation to the atmosphere. Diagnosing the variability of CUE with climate and other environmental factors is fundamental to understand its driving factors, and to further fill the current gaps in knowledge about the environmental controls on CUE. Thus, to study CUE variability and its driving factors, this study established a global database of site-year CUE based on observations from 188 field measurement sites for five ecosystem types – forest, grass, wetland, crop and tundra. The spatial pattern of CUE was predicted from global climate and soil variables using Random Forest, and compared with estimates from Dynamic Global Vegetation Models (DGVMs) from the TRENDY model ensemble. Globally, we found two prominent CUE gradients in ecosystem types and latitude, that is, CUE varied with ecosystem types, being the highest in wetlands and lowest in grassland, and CUE decreased with latitude with the lowest CUE in tropics, and the highest CUE in higher latitude regions. CUE varied greatly between data-derived CUE and TRENDY-CUE, but also among TRENDY models. Both data-derived and TRENDY-CUE challenged the constant value of 0.5 for CUE, independent of environmental controls. However, given the role of CUE in controlling the spatial and temporal variability of the terrestrial biosphere C cycle, these results emphasize the need to better understand the biotic and abiotic controls on CUE to reduce the uncertainties in prognostic land-process model simulations. Finally, this study proposed a new estimate of net primary production based on CUE and gross primary production, offering another benchmark for net primary production comparison for global carbon modelling.

Remote sensing quantitative retrieval of ecological water (eco-water) has been foundational in systemic and quantitative research for water resources. Eco-water resource levels indicate conservation ability for the eco-water layer and influence of this on precipitation transformation and runoff regulation. The remote sensing quantitative inversion retrieved the MEC (Modulus of eco-water Conservation) of the Upper Minjiang River Basin study area in 1994 and 2001, and combined with climate data between 1990 and 2005, the influence of conservation water on the eco-water layer on runoff was then analyzed. Results revealed significant efficacy for flood control and water supply during the drought from the hydrologic cycle of ecowater. Thus protection and restoration of the eco-water layer for flood and drought prevention are crucial. Influencia del agua ecológica en la escorrentía de la cuenca alta del río Minjiang medida a través de teledetección cuantitativa ResumenEl sondeo remoto del agua ecológica (del inglés Eco-water, agua conservada en la superficie terrestre) es indispensable en la investigación sistemática y cuantitativa de las fuentes de agua. Los niveles de suministros de agua ecológica indican la capacidad de conservación de la capa de agua ecológica y la influencia de esta en la transformación de precipitación y la regulación de escorrentía. La inversión cuantitativa por sondeo remoto estableció el Módulo de Conservación de Agua Ecológica (MEC, del inglés Modulus of Eco-Water Conservation) para el área de estudio en la cuenca alta del río Minjiang entre 1994 y 2001, y combinada con la información climática de entre 1990 y 2005, se analizó la influencia de conservacion de agua en la capa ecoacuática. Los resultados mostraron una gran eficacia en el control de inundaciones y en el suministro de agua durante la sequía a lo largo del ciclo hidrológico. Por esta razón, la protección y restauración de la capa de agua ecológica para la prevención de inundaciones y sequía es necesaria.

Vegetation water content is an important indicator of vegetal state, and a vital parameter of studying agriculture, ecological and hydrological. The diagnosis of vegetation water content has great significance for forest fire forecast and natural vegetation drought condition monitoring. The correlation analysis of the vegetation spectral reflectance and vegetation water content shows that the relativity between the spectral reflectance of different wavelengths and the vegetation water content varies considerably. The spectral reflectance of red band of visible light (620～700 nm) and the near-infrared band(800～1 350, 1 600～1 950, 2 200～2 400 nm) had a higher correlation with the vegetation water content. The slope angle indexes were used as parameters for estimating the vegetation water content based on analyzing the relation between the slope angle indexes and vegetation water content. An evaluation model of vegetation water content was set up by utilizing statistical linear regression model method. The band of 660, 850, 1 630, 2 200 nm were selected as RED, NIR, SWIR1 and SWIR2 band value of the slope angle index based on the analysis of the correlation between spectral reflectance and vegetation water content. A large amount of vegetation spectral information and vegetation water content were collected in the study area(the upstream of Minjiang River), and the linear regression model of the slope angle index (SANI, SASI, ANIR) and vegetation water content (FMC) was build. The linear regression model of ANIR and FMC has the highest of linear fitting and the linearity is up to 0.791. The near infrared angle index(ANIR)was improved on the basis of the analysis the linear regression results of angle slope vegetation index and water content. Near infrared angle normalized index (NANI) and near infrared angle slope index (NASI) were defined, and the linear regression model was established. Compared with the slope angle index (SANI, SASI, ANIR) which were proposed by Palacios-Orueta, NANI had more advantages in the vegetation water content inversion in the study area. The determination coefficient (R2) of the inversion model increased from 0.791 to 0.853, and root-mean-square error (RMSE) reduced from 0.047 to 0.039. Angle slope index had higher linear fitting and estimation accuracy by improving the angle of slope index. NANI and FMC linear regression model was established to estimate the vegetation water content in the study area. In this paper, the main innovation point is that the slope angle index NANI and NASI has been proposed on the basis of predecessors’ research results, and the improved angle slope index has higher linear fitting and estimation accuracy compared with SANI, SASI, ANIR.

The amount of eco-water resources reflects the land surface water conservation capability, and the underlying surface condition in the hydrologic cycle. In the upper Minjiang River Basin, the amounts of eco-water resources were retrieved from remotely sensed data during 1992 to 2005. Through regression analysis between the retrieved eco-water data and the climate hydrological data mainly including the temperature, the precipitation, and the runoff in the same period, the model of eco-water driving force affecting the evolvement of runoff was established. The accuracy analysis indicates that the model can well describe the relationship between dry season runoff and its driven factors, the measured data validation proves that the model has high precision and good practicability. The eco-water remote sensing inversion provides a valid method to quantify the land surface water conservation capability, and suggests an interesting approach for the driving function quantitative researches of underlying surface factor in the hydrologic cycle.

Site selection of post-earthquake reconstruction needs to process spatial data, and economic and human factors. There are both certainties and fuzzy uncertainties. Also quantitative data and qualitative data coexist due to different data types and precision. A reasonable location decision requires a comprehensive understanding and full use of these data. This paper introduced the Fuzzy Synthetic Evaluation (FSE) approach to the assessment of candidate areas acquired from the geological safety analysis of the study area. The proposed evaluation indices mainly consisted of natural resources, ecological protection, human protection, urban construction, transportation and development potential. The valid results show that the proposed method can be applied to site selection of post-earthquake reconstruction.

This paper introduces a new dictionary learning approach for hyperspectral images classification with structured sparse representation based on Compressed Sensing (CS), An important contribution of our paper is partition the pixels of a hyperspectral image into a number of spatial neighborhoods called pixel groups and the pixel group can be modeled of different size. The idea is to use of hyperspectral remote sensing image spatial correlation between pixels and the aim is to obtain a dictionary of each pixel. The dictionary is a linear combination of a few dictionary elements learned from the hyperspectral data and can accurately represent hyperspectral remote sensing images with less coefficients. The pixels are induced a common sparsity pattern and have a implicitly spectral correlation between pixels which are in a identical pixel group. The sparse coefficients are then used for classification hyperspectral images by a linear Support Vector Machine. The experiments show that the proposed method can get a better representation of hyperspectral images and has a higher overall accuracy and Kappa coefficients.

It is important to study the effects of pedestrian crossing behaviors on traffic flow for solving the urban traffic jam problem. Based on the Nagel–Schreckenberg (NaSch) traffic cellular automata (TCA) model, a new one-dimensional TCA model is proposed considering the uncertainty conflict behaviors between pedestrians and vehicles at unsignalized mid-block crosswalks and defining the parallel updating rules of motion states of pedestrians and vehicles. The traffic flow is simulated for different vehicle densities and behavior trigger probabilities. The fundamental diagrams show that no matter what the values of vehicle braking probability, pedestrian acceleration crossing probability, pedestrian backing probability and pedestrian generation probability, the system flow shows the "increasing–saturating–decreasing" trend with the increase of vehicle density; when the vehicle braking probability is lower, it is easy to cause an emergency brake of vehicle and result in great fluctuation of saturated flow; the saturated flow decreases slightly with the increase of the pedestrian acceleration crossing probability; when the pedestrian backing probability lies between 0.4 and 0.6, the saturated flow is unstable, which shows the hesitant behavior of pedestrians when making the decision of backing; the maximum flow is sensitive to the pedestrian generation probability and rapidly decreases with increasing the pedestrian generation probability, the maximum flow is approximately equal to zero when the probability is more than 0.5. The simulations prove that the influence of frequent crossing behavior upon vehicle flow is immense; the vehicle flow decreases and gets into serious congestion state rapidly with the increase of the pedestrian generation probability.