Estimating the starting time and identifying the type of urbanization based on dense time series of landsat-derived Vegetation-Impervious-Soil (V-I-S) maps – A case study of North Taiwan from 1990 to 2015     

《International Journal of Applied Earth Observation and Geoinformation》2020

Land cover and land use change (LCLUC) is a global phenomenon, and LCLUC in urbanizing regions has substantial impacts on humans and their environments. In this paper, a semi-automatic approach to identifying the type and starting time of urbanization was developed and tested based on dense time series of Vegetation-Impervious-Soil (V-I-S) maps derived from Landsat surface reflectance imagery. The accuracy of modeled V-I-S fractions and the estimated time of initial change in impervious cover were assessed. North Taiwan, one of the regions of the island of Taiwan that experienced the greatest urban LCLUC, was chosen as a test area, and the study period is 1990 to 2015, a period of substantial urbanization. In total, 295 dates of Landsat imagery were used to create 295 V-I-S fraction maps that were used to construct fractional cover time series for each pixel. Root Mean Square Error (RMSE)s for the modeled Vegetation, Impervious, and Soil were 25 %, 22 %, 24 % respectively. The time of Urban Expansion is estimated by logistic regression applied to Impervious cover time series, while the time of change for Urban Renewal is determined by the period of brief Soil exposure. The identified location and estimated time for newly urbanized lands were generally accurate, with 80% of Urban Expansion estimated within ±2.4 years. However, the accuracy of identified Urban Renewal was relatively low. Our approach to identifying Urban Expansion with dense time series of Landsat imagery is shown to be reliable, while Urban Renewal identification is not.  相似文献   

Integration of remote sensing,county-level census,and machine learning for century-long regional cropland distribution data reconstruction     

《International Journal of Applied Earth Observation and Geoinformation》2020

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   

整点气温缺测的插补方法研究及其初步应用     

闫丽莉  温少妍  高文晶  刘传军  杨甜 《震灾防御技术》2019,14(2):446-455

长期连续完整的历史气温资料是震前气温异常判别研究的重要数据基础。本文考虑了参考站与缺测站之间的距离，建立改进的线性回归模型。利用该模型插补缺测和错误的气温整点值数据，在一定程度上解决了长期连续观测数据缺测的情况。通过对收集的唐山观测站气温整点值数据进行插补，并应用插补完整的数据分析研究了2012年5月28日唐山4.8级地震前兆异常。结果表明:①插补值与其前后观测值衔接吻合，插补后完整连续数据符合夏高冬低的年变规律；②插补误差在±0.5℃范围内的比例为60.2%，在±0.8℃范围内的比例为80.3%，其误差绝对值大于1.0℃的比例为9.6%，平均绝对误差为0.84℃，插补值与观测值的相关系数大部分在0.9以上；③从3月27日起出现增温异常，特别是震前2天增温幅度约8℃。  相似文献   

土壤粒径的光谱响应特性研究   总被引：1，自引：0，他引：1  

曾远文  段松江 《山东国土资源》2019,35(11)

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   

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

张音  古丽贤·吐尔逊拜  苏里坦  刘迁迁 《干旱区地理》2019,42(4):822-829

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   

Concentration–discharge relationships in the coal mined region of the New River basin and Indian Fork sub‐basin,Tennessee, USA          下载免费PDF全文

J. C. Murphy  G. M. Hornberger  R. G. Liddle 《水文研究》2014,28(3):718-728

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   

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures          下载免费PDF全文

J. L. McCreight  A. G. Slater  H. P. Marshall  B. Rajagopalan 《水文研究》2014,28(3):933-957

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   

基于GIS的“一带一路”地区气温插值方法比较研究     

杨艳昭  郎婷婷  张超  贾琨 《地球信息科学学报》2020,22(4):867-876

“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   

A sequential sparse polynomial chaos expansion using Bayesian regression for geotechnical reliability estimations     

Qiujing Pan  Xingru Qu  Leilei Liu  Daniel Dias 《国际地质力学数值与分析法杂志》2020,44(6):874-889

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   

西藏努林晚白垩世花岗闪长岩地球化学特征及成因     

石洪召  段志明  李光明  张林奎 《沉积与特提斯地质》2020,40(4):71-82

南冈底斯晚白垩世岩浆岩的成因及地球动力学机制一直存在争议。本文对冈底斯南缘努林花岗闪长岩开展地球化学、锆石U-Pb年代学及同位素示踪研究。结果显示，该岩体具有富SiO₂（66.62%～67.81%）、高Al₂O₃（15.11%～15.66%）、高Sr（>481×10^-6），低Y（≤8.13×10^-6）和低Yb（≤0.73×10^-6）特征，Sr/Y比值达59～111，显示埃达克岩的特征；岩石轻稀土富集，重稀土亏损，具有显著的Eu正异常；富集大离子亲石元素，亏损高场强元素。（⁸⁷Sr/⁸⁶Sr）_i=0.704011～0.704244，ε_Nd（t）=＋3.61～＋5.75，总体反映地幔源区的Sr、Nd同位素特征。锆石U-Pb LA-ICP-MS测年显示存在83Ma和89Ma两组年龄。结合地质及地球化学分析，认为努林花岗闪长岩是新特提斯洋洋脊俯冲引起的镁铁质新生下地壳部分熔融的产物。  相似文献