首页 | 本学科首页   官方微博 | 高级检索  
文章检索
  按 检索   检索词:      
出版年份:   被引次数:   他引次数: 提示:输入*表示无穷大
  收费全文   2374篇
  免费   321篇
  国内免费   414篇
测绘学   474篇
大气科学   453篇
地球物理   417篇
地质学   670篇
海洋学   241篇
天文学   8篇
综合类   191篇
自然地理   655篇
  2024年   5篇
  2023年   28篇
  2022年   134篇
  2021年   125篇
  2020年   170篇
  2019年   133篇
  2018年   129篇
  2017年   149篇
  2016年   133篇
  2015年   146篇
  2014年   133篇
  2013年   222篇
  2012年   171篇
  2011年   130篇
  2010年   87篇
  2009年   140篇
  2008年   140篇
  2007年   131篇
  2006年   105篇
  2005年   81篇
  2004年   71篇
  2003年   60篇
  2002年   66篇
  2001年   49篇
  2000年   27篇
  1999年   35篇
  1998年   39篇
  1997年   34篇
  1996年   24篇
  1995年   23篇
  1994年   28篇
  1993年   32篇
  1992年   28篇
  1991年   20篇
  1990年   15篇
  1989年   15篇
  1988年   8篇
  1987年   9篇
  1986年   5篇
  1985年   6篇
  1984年   2篇
  1983年   2篇
  1982年   2篇
  1977年   1篇
  1976年   1篇
  1974年   1篇
  1973年   5篇
  1972年   4篇
  1971年   1篇
  1954年   1篇
排序方式: 共有3109条查询结果,搜索用时 46 毫秒
11.
The Lower Mississippi Alluvial Valley (LMAV) was home to about ten million hectare bottomland hardwood (BLH) forests in the Southern U.S. It experienced over 80 % area loss of the BLH forests in the past centuries and large-scale afforestation in recent decades. Due to the lack of a high-resolution cropland dataset, impacts of land use change (LUC) on the LMAV ecosystem services have not been fully understood. In this study, we developed a novel framework by integrating the machine learning algorithm, county-level agricultural census, and satellite-based cropland products to reconstruct the LMAV cropland distribution during 1850–2018 at a 30-m resolution. Results showed that the LMAV cropland area increased from 0.78 × 104 km2 in 1850 to 6.64 × 104 km2 in 1980 and then decreased to 6.16 × 104 km2 in 2018. Cropland expansion rate was the largest in the 1960s (749 km2 yr−1) but decreased rapidly thereafter, whereas cropland abandonment rate increased substantially in recent decades with the largest rate of 514 km2 yr−1 in the 2010s. Our dataset has three notable features: (1) the depiction of fine spatial details, (2) the integration of the county-level census, and (3) the inclusion of a machine-learning algorithm trained by satellite-based land cover product. Most importantly, our dataset well captured the continuous increasing trend in cropland area from 1930–1960, which was misrepresented by other cropland datasets reconstructed from the state-level census. Our dataset would be important to accurately evaluate the impacts of historical deforestation and recent afforestation efforts on regional ecosystem services, attribute the observed hydrological changes to anthropogenic and natural driving factors, and investigate how the socioeconomic factors control regional LUC pattern. Our framework and dataset are crucial to developing managerial and policy strategies for conserving natural resources and enhancing ecosystem services in the LMAV.  相似文献   
12.
土壤粒径的光谱响应特性研究   总被引:1,自引:0,他引:1  
以实验室制备的5个不同粒径水平的土壤样本和室内高光谱数据为基础,通过对光谱数据进行重采样、数学变换等预处理并进行单因素方差分析、相关性分析和回归分析,探讨土壤粒径的高光谱特性,建立了光谱数据预测土壤粒径的校正模型。结果表明,土壤粒径对反射光谱有显著的影响,波长越长影响越大;在全波段范围内土壤粒径和光谱数据都呈负相关关系,对原始光谱数据进行微分变换能增加其与土壤粒径的相关性;以反射率一阶微分建立的回归模型为反演土壤粒径的最佳模型,其建模决定系数■、预测决定系数■、预测相对偏差RPD分别为0.666,0.653,2.043,预测均方根误差RMSE为0.175。  相似文献   
13.
近60a来新疆不同海拔气候变化的时空特征分析   总被引:1,自引:0,他引:1       下载免费PDF全文
全球变暖是当前全球气候变化研究的热点之一,新疆深居亚欧大陆内陆,地形气候复杂,探讨该区域气候变化与海拔的关系对全球气候变化研究具有重要的参考意义。基于1958—2017年新疆41个气象站的月和年平均气候数据,采用一元线性回归、Mann Kendall(M-K)趋势分析和突变检验等方法分析该地区气候变化的时空分布与海拔的关系。结果表明:1958—2017年新疆年均气温、年均降水量均呈上升趋势,但增加幅度具有时间和空间差异。在时间上,北疆四季平均气温增温幅度均大于南疆(冬季除外),四季降水量增幅北疆大于南疆(夏季除外);在空间上,北疆气温和降水的增幅均大于南疆。研究区各个站点气温呈现出南部高而北部低的空间格局,年均降水量北部多,南部低。各个站点气温倾向率总体随海拔增加而减少,年均降水量变化率随海拔升高而增加,在不同海拔带内部存在差异。综上所述,受全球气候变暖的影响,近60 a来新疆年均气温和年均降水量均呈上升趋势,尤其是北疆对全球气候变暖的响应较为敏感。  相似文献   
14.
For many basins, identifying changes to water quality over time and understanding current hydrologic processes are hindered by fragmented and discontinuous water‐quality and hydrology data. In the coal mined region of the New River basin and Indian Fork sub‐basin, muted and pronounced changes, respectively, to concentration–discharge (C–Q) relationships were identified using linear regression on log‐transformed historical (1970s–1980s) and recent (2000s) water‐quality and streamflow data. Changes to C–Q relationships were related to coal mining histories and shifts in land use. Hysteresis plots of individual storms from 2007 (New River) and the fall of 2009 (Indian Fork) were used to understand current hydrologic processes in the basins. In the New River, storm magnitude was found to be closely related to the reversal of loop rotation in hysteresis plots; a peak‐flow threshold of 25 cubic meters per second (m3/s) segregates hysteresis patterns into clockwise and counterclockwise rotational groups. Small storms with peak flow less than 25 m3/s often resulted in dilution of constituent concentrations in headwater tributaries like Indian Fork and concentration of constituents downstream in the mainstem of the New River. Conceptual two or three component mixing models for the basins were used to infer the influence of water derived from spoil material on water quality. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   
15.
Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km2 sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R2. Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R2) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R2 (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R2 scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R2 skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R2. The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   
16.
“一带一路”倡议是新时期中国为加强对外开放提出的全球化合作倡议,资源环境的优化配置对全球化发展意义重大。气温作为重要的基础数据和输入要素,对其进行空间化处理是实现大尺度区域资源环境优化配置的前提。本文基于地理信息技术(GIS),运用距离平方反比法(IDS)、协同克里格法(CK)、回归距离平方反比法(RIDS)和回归协同克里格法(RCK),对“一带一路”地区1980—2017年的2679个气象站点的月平均气温和年平均气温数据进行插值,获得了“一带一路”地区10 km分辨率的气温空间分布数据。交叉验证结果表明:① IDS、CK、RIDS和RCK插值法在整体上均较好地展示了“一带一路”地区气温的地理空间分布规律,4种插值方法的月均气温的均方根误差分别在1.93~2.43、1.78~2.14、1.31~2.23和1.23~1.92 ℃之间;年均气温的均方根误差分别为1.94、1.83、1.37和1.27 ℃;② 在“一带一路”地区,加入协变量分析的CK插值精度整体优于IDS,并且削弱了IDS的极值现象;③ RIDS和RCK对年均气温的插值精度分别较IDS和CK提高了29.4%和30.6%,表明加入地理要素并进行残差修正的插值精度得到了进一步提高。总体来看,RCK插值法对气温数据的插值精度最高,可以考虑将此方法作为“一带一路”地区温度等气象要素的插值方法。  相似文献   
17.
Polynomial chaos expansions (PCEs) have been widely employed to estimate failure probabilities in geotechnical engineering. However, PCEs suffer from two deficiencies: (a) PCE coefficients are solved by the least-square minimization method which easily causes overfitting issues; (b) building a high order PCE is often computationally expensive. In order to overcome the aforementioned drawbacks, the Bayesian regression technique is employed to evaluate PCE coefficients, which not only provides a sparse solution but also avoids overfitting. With the aid of the predictive means and variances given by Bayesian analysis, a learning function is proposed to sequentially select the most informative samples that are critical to build a PCE. This sequential learning scheme can highly enhance the computational efficiency of PCEs. Besides, importance sampling (IS) is incorporated into the sequential learning (SL)-PCEs to deal with geotechnical problems with small failure probabilities. The proposed method of SL-PCE-IS is applied to three illustrative examples, which shows that the improved PCE method is more effective and efficient than the common PCEs method, leading to accurate estimations of small failure probabilities using fewer training samples.  相似文献   
18.
稀疏多项式逻辑回归在分类中仅利用图像光谱信息,导致分类效果不太理想。本文提出了一种顾及局部与结构特征的稀疏多项式逻辑回归高光谱图像分类方法。首先利用加权均值滤波与拓展形态学多属性剖面对原始高光谱图像进行局部与结构特征提取;然后对二者进行加权平均特征级融合以获取更具唯一性的像元特征;最后由稀疏多项式逻辑回归分类器对融合结果进行分类。结果表明,本文方法能有效地提高分类精度,而且具有较强的稳健性。  相似文献   
19.
机载LiDAR采集的点云数据中会存在一些局部区域地面点稀疏的情况,利用这些稀疏地面点构建DEM时会出现“三角面片化”的问题,严重影响DEM的质量。为此,本文提出了一种局部稀疏地面点云与已有DEM的融合方法:将稀疏点云作为高精度控制点,在尽量保持原始DEM的地形形态特征的前提下,通过高斯核函数加权迭代插值算法对DEM进行高程局部改正,实现稀疏点云与DEM的一致性融合。试验分析表明,融合后的点云数据得到了较好的补充,由此构建的DEM地形形态自然,在精度上相对于融合前的稀疏地面点云有一定改善,在弱精度区域的可靠性有显著提升。  相似文献   
20.
The proto‐Paratethys Sea covered a vast area extending from the Mediterranean Tethys to the Tarim Basin in western China during Cretaceous and early Paleogene. Climate modelling and proxy studies suggest that Asian aridification has been governed by westerly moisture modulated by fluctuations of the proto‐Paratethys Sea. Transgressive and regressive episodes of the proto‐Paratethys Sea have been previously recognized but their timing, extent and depositional environments remain poorly constrained. This hampers understanding of their driving mechanisms (tectonic and/or eustatic) and their contribution to Asian aridification. Here, we present a new chronostratigraphic framework based on biostratigraphy and magnetostratigraphy as well as a detailed palaeoenvironmental analysis for the Paleogene proto‐Paratethys Sea incursions in the Tajik and Tarim basins. This enables us to identify the major drivers of marine fluctuations and their potential consequences on Asian aridification. A major regional restriction event, marked by the exceptionally thick (≤ 400 m) shelf evaporites is assigned a Danian‐Selandian age (ca. 63–59 Ma) in the Aertashi Formation. This is followed by the largest recorded proto‐Paratethys Sea incursion with a transgression estimated as early Thanetian (ca. 59–57 Ma) and a regression within the Ypresian (ca. 53–52 Ma), both within the Qimugen Formation. The transgression of the next incursion in the Kalatar and Wulagen formations is now constrained as early Lutetian (ca. 47–46 Ma), whereas its regression in the Bashibulake Formation is constrained as late Lutetian (ca. 41 Ma) and is associated with a drastic increase in both tectonic subsidence and basin infilling. The age of the final and least pronounced sea incursion restricted to the westernmost margin of the Tarim Basin is assigned as Bartonian–Priabonian (ca. 39.7–36.7 Ma). We interpret the long‐term westward retreat of the proto‐Paratethys Sea starting at ca. 41 Ma to be associated with far‐field tectonic effects of the Indo‐Asia collision and Pamir/Tibetan plateau uplift. Short‐term eustatic sea level transgressions are superimposed on this long‐term regression and seem coeval with the transgression events in the other northern Peri‐Tethyan sedimentary provinces for the 1st and 2nd sea incursions. However, the 3rd sea incursion is interpreted as related to tectonism. The transgressive and regressive intervals of the proto‐Paratethys Sea correlate well with the reported humid and arid phases, respectively in the Qaidam and Xining basins, thus demonstrating the role of the proto‐Paratethys Sea as an important moisture source for the Asian interior and its regression as a contributor to Asian aridification.  相似文献   
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号