Spatiotemporal estimation of snow depth using point data from snow stakes,digital terrain models,and satellite data          下载免费PDF全文

Antonio‐Juan Collados‐Lara  Eulogio Pardo‐Igúzquiza  David Pulido‐Velazquez 《水文研究》2017,31(10):1966-1982

Snow availability in Alpine catchments plays an important role in water resources management. In this paper, we propose a method for an optimal estimation of snow depth (areal extension and thickness) in Alpine systems from point data and satellite observations by using significant explanatory variables deduced from a digital terrain model. It is intended to be a parsimonious approach that may complement physical‐based methodologies. Different techniques (multiple regression, multicriteria analysis, and kriging) are integrated to address the following issues: We identify the explanatory variables that could be helpful on the basis of a critical review of the scientific literature. We study the relationship between ground observations and explanatory variables using a systematic procedure for a complete multiple regression analysis. Multiple regression models are calibrated combining all suggested model structures and explanatory variables. We also propose an evaluation of the models (using indices to analyze the goodness of fit) and select the best approaches (models and variables) on the basis of multicriteria analysis. Estimation of the snow depth is performed with the selected regression models. The residual estimation is improved by applying kriging in cases with spatial correlation. The final estimate is obtained by combining regression and kriging results, and constraining the snow domain in accordance with satellite data. The method is illustrated using the case study of the Sierra Nevada mountain range (Southern Spain). A cross‐validation experiment has confirmed the efficiency of the proposed procedure. Finally, although it is not the scope of this work, the snow depth is used to asses a first estimation of snow water equivalent resources.  相似文献   

近20年京津唐主体城区地表热场空间特征变化分析   总被引：3，自引：1，他引：2  

于琛  胡德勇  张旸  曹诗颂  段欣  张亚妮 《地理科学》2019,39(6):1016-1024

基于Landsat遥感影像获取京津唐主体城区1995~2015年地表温度(Land Surface Temperature, LST)和不透水地表盖度(Impervious Surface Percentage, ISP)数据。采用热点聚集和阈值分割法,依据地表的温度和不透水盖度属性将京津唐主体城区划分成9种地表热场类型,分析并探讨地表热场的发展规律、年际变化状况和区域贡献作用。研究发现,京津唐主体城区地表温度与不透水地表盖度间存在显著的正向相关关系,两者分别呈现“阶梯降”和“两端高、中间低”的变化特征。京津唐主体城区地表热场的发展主轴保持在西北-东南方向,且随时间推移沿主轴呈聚集态势。 京津唐主体城区地表热场的影响范围在空间上持续扩张,对于不同的主体城区,其在整体区域的热场贡献中有差异化表现。  相似文献   

2000-2015年柴达木盆地植被覆盖度时空变化及其与环境因子的关系          下载免费PDF全文

张斯琦  陈辉  宋明华  付阳  牛慧慧  杨祎  张博雄 《干旱区地理》2019,42(5):1124-1132

基于MODIS-NDVI、DEM和气象数据，分析柴达木盆地2000—2015年植被覆盖度（FVC）时空变化特征，并与降水、温度、日照时数、相对湿度、蒸散量和海拔进行相关、偏相关或叠加分析，探讨FVC与各环境因子的关系。结果表明：FVC整体自东南向西北内陆呈半环状递减，FVC集中在20%以下，人类活动及径流等打破植被地带性规律；2000—2015年FVC明显改善，广泛分布于盆地中西部地区，2001—2002年年际变化最显著；FVC与降水、相对湿度以正相关为主，与温度关系不显著，与日照时数和蒸散量主要为负相关，降水对FVC贡献最大，温度通过影响蒸散量等间接影响FVC，而土壤蒸发对蒸散量的影响大于植物蒸腾；FVC与等高线空间分布较吻合，FVC在2 800~2 900 m和4 600~4 700 m出现两个峰值，4 700 m以上FVC迅速降低。  相似文献   

2001-2015年中国植被覆盖人为影响的时空格局   总被引：3，自引：0，他引：3  

王建邦  赵军  李传华  朱钰  康重阳  高超 《地理学报》2019,74(3):504-519

基于MODIS-NDVI和气温、降水数据,使用基于变异系数的人为影响模型定量计算了2001-2015年中国植被覆盖人为影响,辅以趋势分析、Hurst指数等方法探讨了中国植被覆盖人为影响的时空变化特征及未来演变趋势。研究发现：① 2001-2015年,中国植被覆盖人为影响由南向北空间分异愈发明显,年均值为-0.0102,植被覆盖在人类活动影响下轻微减少,负影响面积占51.59%,略大于正影响面积。② 中国植被覆盖人为影响年际变化特征明显,整体呈负影响波动减少趋势,降速为0.5%/10a,其中正影响、负影响均呈增大趋势,正影响增速（0.3%/10a）远大于负影响（0.02%/10a）。③ 2001-2015年间,中国植被覆盖人为正影响重心向东北方向移动,负影响重心向西南方向移动,东北部植被覆盖在人为影响下不断改善,西南部人类活动对植被破坏程度不断增大。④ 中国植被覆盖人为影响主要呈负影响减少和正影响增大趋势,面积占比分别为28.14%和25.21%,生态环境趋于改善。⑤ Hurst指数分析表明,中国植被覆盖人为影响未来演变趋势的反向特征强于正向特征,主要呈人为负影响先减少后增大趋势,面积占比15.59%。  相似文献   

Landscape controls on long‐term runoff in subhumid heterogeneous Boreal Plains catchments          下载免费PDF全文

Kevin J. Devito  Kelly J. Hokanson  Paul Adrian Moore  Nicholas Kettridge  Axel E. Anderson  Laura Chasmer  Chris Hopkinson  Maxwell C. Lukenbach  Carl A. Mendoza  Julienne Morissette  Daniel L. Peters  Richard Michael Petrone  Uldis Silins  Brian Smerdon  James Michael Waddington 《水文研究》2017,31(15):2737-2751

We compared median runoff (R) and precipitation (P) relationships over 25 years from 20 mesoscale (50 to 5,000 km²) catchments on the Boreal Plains, Alberta, Canada, to understand controls on water sink and source dynamics in water‐limited, low‐relief northern environments. Long‐term catchment R and runoff efficiency (RP^?1) were low and varied spatially by over an order of magnitude (3 to 119 mm/year, 1 to 27%). Intercatchment differences were not associated with small variations in climate. The partitioning of P into evapotranspiration (ET) and R instead reflected the interplay between underlying glacial deposit texture, overlying soil‐vegetation land cover, and regional slope. Correlation and principal component analyses results show that peatland‐swamp wetlands were the major source areas of water. The lowest estimates of median annual catchment ET (321 to 395 mm) and greatest R (60 to 119 mm, 13 to 27% of P) were observed in low‐relief, peatland‐swamp dominated catchments, within both fine‐textured clay‐plain and coarse‐textured glacial deposits. In contrast, open‐water wetlands and deciduous‐mixedwood forest land covers acted as water sinks, and less catchment R was observed with increases in proportional coverage of these land covers. In catchments dominated by hummocky moraines, long‐term runoff was restricted to 10 mm/year, or 2% of P. This reflects the poor surface‐drainage networks and slightly greater regional slope of the fine‐textured glacial deposit, coupled with the large soil‐water and depression storage and higher actual ET of associated shallow open‐water marsh wetland and deciduous‐forest land covers. This intercatchment study enhances current conceptual frameworks for predicting water yield in the Boreal Plains based on the sink and source functions of glacial landforms and soil‐vegetation land covers. It offers the capability within this hydro‐geoclimatic region to design reclaimed catchments with desired hydrological functionality and associated tolerances to climate or land‐use changes and inform land management decisions based on effective catchment‐scale conceptual understanding.  相似文献   

Diagnosing snow accumulation errors in a rain‐snow transitional environment with snow board observations          下载免费PDF全文

Nicholas E. Wayand  Martyn P. Clark  Jessica D. Lundquist 《水文研究》2017,31(2):349-363

Diagnosing the source of errors in snow models requires intensive observations, a flexible model framework to test competing hypotheses, and a methodology to systematically test the dominant snow processes. We present a novel process‐based approach to diagnose model errors through an example that focuses on snow accumulation processes (precipitation partitioning, new snow density, and snow compaction). Twelve years of meteorological and snow board measurements were used to identify the main source of model error on each snow accumulation day. Results show that modeled values of new snow density were outside observational uncertainties in 52% of days available for evaluation, while precipitation partitioning and compaction were in error 45% and 16% of the time, respectively. Precipitation partitioning errors mattered more for total winter accumulation during the anomalously warm winter of 2014–2015, when a higher fraction of precipitation fell within the temperature range where partition methods had the largest error. These results demonstrate how isolating individual model processes can identify the primary source(s) of model error, which helps prioritize future research.  相似文献   

Snow model sensitivity analysis to understand spatial and temporal snow dynamics in a high‐elevation catchment          下载免费PDF全文

Michael Engel  Claudia Notarnicola  Stefano Endrizzi  Giacomo Bertoldi 《水文研究》2017,31(23):4151-4168

In this paper, we addressed a sensitivity analysis of the snow module of the GEOtop2.0 model at point and catchment scale in a small high‐elevation catchment in the Eastern Italian Alps (catchment size: 61 km²). Simulated snow depth and snow water equivalent at the point scale were compared with measured data at four locations from 2009 to 2013. At the catchment scale, simulated snow‐covered area (SCA) was compared with binary snow cover maps derived from moderate‐resolution imaging spectroradiometer (MODIS) and Landsat satellite imagery. Sensitivity analyses were used to assess the effect of different model parameterizations on model performance at both scales and the effect of different thresholds of simulated snow depth on the agreement with MODIS data. Our results at point scale indicated that modifying only the “snow correction factor” resulted in substantial improvements of the snow model and effectively compensated inaccurate winter precipitation by enhancing snow accumulation. SCA inaccuracies at catchment scale during accumulation and melt period were affected little by different snow depth thresholds when using calibrated winter precipitation from point scale. However, inaccuracies were strongly controlled by topographic characteristics and model parameterizations driving snow albedo (“snow ageing coefficient” and “extinction of snow albedo”) during accumulation and melt period. Although highest accuracies (overall accuracy = 1 in 86% of the catchment area) were observed during winter, lower accuracies (overall accuracy < 0.7) occurred during the early accumulation and melt period (in 29% and 23%, respectively), mostly present in areas with grassland and forest, slopes of 20–40°, areas exposed NW or areas with a topographic roughness index of ?0.25 to 0 m. These findings may give recommendations for defining more effective model parameterization strategies and guide future work, in which simulated and MODIS SCA may be combined to generate improved products for SCA monitoring in Alpine catchments.  相似文献   

Exploration of remotely sensed forest structure and ultrasonic range sensor metrics to improve empirical snow models          下载免费PDF全文

Andrés Varhola  Nicholas C. Coops  Younes Alila  Markus Weiler 《水文研究》2014,28(15):4433-4448

Current methods to estimate snow accumulation and ablation at the plot and watershed levels can be improved as new technologies offer alternative approaches to more accurately monitor snow dynamics and their drivers. Here we conduct a meta‐analysis of snow and vegetation data collected in British Columbia to explore the relationships between a wide range of forest structure variables – obtained from Light Detection and Ranging (LiDAR), hemispherical photography (HP) and Landsat Thematic Mapper – and several indicators of snow accumulation and ablation estimated from manual snow surveys and ultrasonic range sensors. By merging and standardizing all the ground plot information available in the study area, we demonstrate how LiDAR‐derived forest cover above 0.5 m was the variable explaining the highest percentage of absolute peak snow water equivalent (SWE) (33%), while HP‐derived leaf area index and gap fraction (45° angle of view) were the best potential predictors of snow ablation rate (explaining 57% of variance). This study reveals how continuous SWE data from ultrasonic sensors are fundamental to obtain statistically significant relationships between snow indicators and structural metrics by increasing mean r² by 20% when compared to manual surveys. The relationships between vegetation and spectral indices from Landsat and snow indicators, not explored before, were almost as high as those shown by LiDAR or HP and thus point towards a new line of research with important practical implications. While the use of different data sources from two snow seasons prevented us from developing models with predictive capacity, a large sample size helped to identify outliers that weakened the relationships and suggest improvements for future research. A concise overview of the limitations of this and previous studies is provided along with propositions to consistently improve experimental designs to take advantage of remote sensing technologies, and better represent spatial and temporal variations of snow. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

新疆地区土地覆被遥感数据的一致性研究     

徐泽源  罗庆辉  许仲林 《地球信息科学学报》2019,21(3):427-436

鉴于新疆地区对中国乃至中亚有着特殊的战略意义,本文针对不同数据源及分类系统在土地覆被数据的空间分布上缺乏互通性问题,结合2010年目视解译土地利用现状遥感监测数据、GlobeLand30和GlobCover2009共3种土地覆被数据,采用类型相似分析、类型混淆分析、混淆矩阵分析、空间一致性分析4种方法开展精度评价及一致性分析,以期对土地覆被数据在中国西北干旱区的适用性及适用范围提供有效建议。结果表明,3种土地覆被数据对新疆地区土地覆被类型构成基本一致,且对裸地类型的辨识度最高;新疆地区中高度一致区域占新疆总面积的95%;3种数据两两对比时,总体精度在64.11%~72.57%之间,其中目视解译数据/GlobeLand 30组合表现出最高水平,且仍有提高空间,反映出目前相同卫星传感器是提升精度评价结果的重要因素之一,且不同分类系统、分类方法、空间分辨率及卫星过境时间等因素对精度评价结果也会产生巨大影响。为解决此类问题,利用多源土地覆被遥感数据的融合技术提高数据精度,或是利用深度学习对遥感影像资料进行精确地解译和判读,将是今后全球土地覆被制图及应用领域的主要发展趋势。  相似文献   

Update and spatial extension of strategic forest inventories using time series remote sensing and modeling     

《International Journal of Applied Earth Observation and Geoinformation》2020

Up-to-date forest inventory information relating the characteristics of managed and natural forests is fundamental to sustainable forest management and required to inform conservation of biodiversity and assess climate change impacts and mitigation opportunities. Strategic forest inventories are difficult to compile over large areas and are often quickly outdated or spatially incomplete as a function of their long production cycle. As a consequence, automated approaches supported by remotely sensed data are increasingly sought to provide exhaustive spatial coverage for a set of core attributes in a timely fashion. The objective of this study was to demonstrate the integration of current remotely-sensed data products and pre-existing jurisdictional inventory data to map four forest attributes of interest (stand age, dominant species, site index, and stem density) for a 55 Mha study region in British Columbia, Canada. First, via image segmentation, spectrally homogenous objects were derived from Landsat surface-reflectance pixel composites. Second, a suite of Landsat-based predictors (e.g., spectral indices, disturbance history, and forest structure) and ancillary variables (e.g., geographic, topographic, and climatic) were derived for these units and used to develop predictive models of target attributes. For the often difficult classification of dominant species, two modelling approaches were compared: (a) a global Random Forests model calibrated with training samples collected over the entire study area, and (b) an ensemble of local models, each calibrated with spatially constrained local samples. Accuracy assessment based upon independent validation samples revealed that the ensemble of local models was more accurate and efficient for species classification, achieving an overall accuracy of 72% for the species which dominate 80% of the forested areas in the province. Results indicated that site index had the highest agreement between predicted and reference (R² = 0.74, %RMSE = 23.1%), followed by stand age (R² = 0.62, %RMSE = 35.6%), and stem density (R² = 0.33, %RMSE = 65.2%). Inventory attributes mapped at the image-derived unit level captured much finer details than traditional polygon-based inventory, yet can be readily reassembled into these larger units for strategic forest planning purposes. Based upon this work, we conclude that in a multi-source forest monitoring program, spatially localized and detailed characterizations enabled by time series of Landsat observations in conjunction with ancillary data can be used to support strategic inventory activities over large areas.  相似文献