Cyclostratigraphic calibration of cretaceous magnetic polarity events (Cismon, Southern Alps, Italy)     

Helmut Mayer 《Mathematical Geology》1994,26(7):783-798

In an introductory section the problems of constructing a geologic time scale and the role of magnetic reversals and Milankovitch cycles in geochronology are outlined. Results of a detailed cyclostratigraphic and magnetostratigraphic study of the Valanginian/Hauterivian part of the pelagic limestone section at Cismon in the Southern Alps are presented and used in conjunction to estimate the duration of magnetic subchrons between CM10N and CM8. The new estimates are shorter than in most published time scales by a factor of two to three. More research along these lines may make a revision of the Early Cretaceous time scale necessary.  相似文献   

Monitoring aboveground forest biomass dynamics over three decades using Landsat time-series and single-date inventory data     

《International Journal of Applied Earth Observation and Geoinformation》2020

Understanding forest biomass dynamics is crucial for carbon and environmental monitoring, especially in the context of climate change. In this study, we propose a robust approach for monitoring aboveground forest biomass (AGB) dynamics by combining Landsat time-series with single-date inventory data. We developed a Random Forest (RF) based kNN model to produce annual maps of AGB from 1988 to 2017 over 7.2 million ha of forests in Victoria, Australia. The model was internally evaluated using a bootstrapping technique. Predictions of AGB and its change were then independently evaluated using multi-temporal Lidar data (2008 and 2016). To understand how natural and anthropogenic processes impact forest AGB, we analysed trends in relation to the history of disturbance and recovery. Specifically, change metrics (e.g., AGB loss and gain, Years to Recovery - Y2R) were calculated at the pixel level to characterise the patterns of AGB change resulting from forest dynamics. The imputation model achieved a RMSE value of 132.9 Mg ha⁻¹ (RMSE% = 46.3%) and R² value of 0.56. Independent assessments of prediction maps in 2008 and 2016 using Lidar-based AGB data achieved relatively high accuracies, with a RMSE of 108.6 Mg ha⁻¹ and 135.9 Mg ha⁻¹ for 2008 and 2016, respectively. Annual validations of AGB maps using un-changed, homogenous Lidar plots suggest that our model is transferable through time (RMSE ranging from 109.65 Mg ha⁻¹ to 112.27 Mg ha⁻¹ and RMSE% ranging from 25.38% to 25.99%). In addition, changes in AGB values associated with forest disturbance and recovery (decrease and increase, respectively) were captured by predicted maps. AGB change metrics indicate that AGB loss and Y2R varied across bioregions and were highly dependent on levels of disturbance severity (i.e., a greater loss and longer recovery time were associated with a higher severity disturbance). On average, high severity fire burnt from 200 Mg ha⁻¹ to 550 Mg ha⁻¹ of AGB and required up to 15 years to recover while clear-fell logging caused a reduction in 250 Mg ha⁻¹ to 600 Mg ha⁻¹ of AGB and required nearly 20 years to recover. In addition, AGB within un-disturbed forests showed statistically significant but monotonic trends, suggesting a mild gradual drop over time across most bioregions. Our methods are designed to support forest managers and researchers in developing forest monitoring systems, especially in developing regions, where only a single date forestry inventory exists.  相似文献   

中国红树林制图的遥感时空概率阈值方法     

黄可  孟祥珍  杨刚  孙伟伟 《遥感学报》2022,26(6):1083-1095

红树林作为热带和亚热带海岸带上特有的森林群落,具有独特的生态功能以及重大的社会、经济价值。中国红树林经历了反复的破坏与保护,遥感监测手段可以为实施大规模的红树林生态系统保护和恢复行动提供科学技术支撑。本研究依托Google Earth Engine平台提出一种时空概率阈值法对中国红树林范围进行提取。研究选取2015年516景Landsat 8数据,首先使用非监督分类法进行水陆分离,其次通过生成缓冲区确定红树林潜在生长区,然后协同多种指数与光谱信息构建多特征决策树提取红树林粗略的生长范围,最后基于长时序的红树林粗略范围数据计算红树林生长概率,并通过实验确定概率阈值对红树林进行精细提取。通过实验对比发现时空概率阈值法的红树林生产者精度达90.36%,且能较好地提取幼小、分散的红树林。研究得到了2015年中国红树林分布结果,全国红树林面积为21932 ha,广西和广东两省红树林面积占中国红树林总面积的73.22%,为中国红树林主要分布区域。  相似文献   

Argos: An optimized time-series photometer     

Anjum S. Mukadam  R. E. Nather 《Journal of Astrophysics and Astronomy》2005,26(2-3):321-330

We designed a prime focus CCD photometer, Argos, optimized for high speed time-series measurements of blue variables (Nather & Mukadam 2004) for the 2.1 m telescope at McDonald Observatory. Lack of any intervening optics between the primary mirror and the CCD makes the instrument highly efficient. We measure an improvement in sensitivity by a factor of nine over the 3-channel PMT photometers used on the same telescope and for the same exposure time. The CCD frame transfer operation triggered by GPS synchronized pulses serves as an electronic shutter for the photometer. This minimizes the dead time between exposures, but more importantly, allows a precise control of the start and duration of the exposure. We expect the uncertainty in our timing to be less than 100μs. Hubble Fellow  相似文献   

The application of artificial neural networks to magnetotelluric time-series analysis   总被引：4，自引：0，他引：4  

C. Manoj  Nandini Nagarajan 《Geophysical Journal International》2003,153(2):409-423

  相似文献   

秘鲁外海鲯鳅生物学特性及资源分布初步研究     

许骆良  ;王伟杰  ;钱卫国 《广东海洋大学学报》2014,(3):5-9

根据2012年5~7月东南太平洋秘鲁外海(探捕海域在83°W~87°W、10°S~14°S)鲯鳅资源的探捕调查资料,对其生物学特性作了初步分析。结果表明:秘鲁外海鲯鳅的叉长范围为31~121cm,优势叉长为30~50cm,占57.4%;在个体大小的空间分布上,探捕海域北部比南部个体略微偏大;雌性叉长(l)与体质量(m)关系式为m=5×10-4·l2.236 2(R2=0.940 5;n=104 9),雄性为m=5×10-4l2.209 6(R2=0.958 0,n=851);雌性鲯鳅比例稍高,占55.2%。探捕海域鲯鳅鱼资源密度较高,CPUE值范围为2~23.6 kg/(人·h),平均为11.6 kg/(人·h);不同区域资源密度有所差异,其中CPUE最高的区域在84°W~86°W、12°30′S~13°30′S附近;探捕期间6月份的最高平均CPUE为16.37 kg/(人·h)。  相似文献   

天津地区的水平形变特征     

张风霜  郭良迁 《测绘科学》2014,39(12):86-90,115

根据天津及周边地区GNSS连续观测站资料,在计算得到各站三维站坐标时间序列结果的基础上,文章分析了天津地区GNSS基线时间序列的变化情况和水平形变变化特征.结果显示,天津地区总体呈现压扭变化,不同地段存在差异.天津地区NE-SW向和NW-SE向GNSS基线的伸长和缩短变化呈北西向条带状相间分布.日本3·11地震使天津地壳出现近东西向拉张为主的活动,一定程度上释放消减了天津地区的挤压应变.  相似文献   

Accurate mapping of forest types using dense seasonal Landsat time-series     

《ISPRS Journal of Photogrammetry and Remote Sensing》2014

An accurate map of forest types is important for proper usage and management of forestry resources. Medium resolution satellite images (e.g., Landsat) have been widely used for forest type mapping because they are able to cover large areas more efficiently than the traditional forest inventory. However, the results of a detailed forest type classification based on these images are still not satisfactory. To improve forest mapping accuracy, this study proposed an operational method to get detailed forest types from dense Landsat time-series incorporating with or without topographic information provided by DEM. This method integrated a feature selection and a training-sample-adding procedure into a hierarchical classification framework. The proposed method has been tested in Vinton County of southeastern Ohio. The detailed forest types include pine forest, oak forest, and mixed-mesophytic forest. The proposed method was trained and validated using ground samples from field plots. The three forest types were classified with an overall accuracy of 90.52% using dense Landsat time-series, while topographic information can only slightly improve the accuracy to 92.63%. Moreover, the comparison between results of using Landsat time-series and a single image reveals that time-series data can largely improve the accuracy of forest type mapping, indicating the importance of phenological information contained in multi-seasonal images for discriminating different forest types. Thanks to zero cost of all input remotely sensed datasets and ease of implementation, this approach has the potential to be applied to map forest types at regional or global scales.  相似文献   

Biome-level relationships between vegetation indices and climate variables using time-series analysis of remotely-sensed data     

Margaret Phiri  Yegnanew A. Shiferaw 《地理信息系统科学与遥感》2020,57(4):464-482

ABSTRACT

Climatic factors such as rainfall and temperature play a vital role in the growth characteristics of vegetation. While the relationship between climate and vegetation growth can be accurately predicted in instances where vegetation is homogenous, this becomes complex to determine in heterogeneous vegetation environments. The aim of this paper was to study the relationship between remotely-sensed monthly vegetation indices (i.e. Normalized Difference Vegetation Index and Enhanced Vegetation Index) and climatic variables (temperature and precipitation) using time-series analysis at the biome-level. Specifically, the autoregressive distributed lag model (ARDL1 and ARDL2, corresponding respectively to one month and two month lags) and the Koyck-transformed distributed lag model were used to build regression models. All three models estimated NDVI and EVI fairly accurately in all biomes (Relative Root-Mean-Squared-Error (RMSE): 12.0–26.4%). Biomes characterized by relative homogeneity (Grassland, Savanna, Indian Ocean Coastal Belt and Forest Biomes) achieved the most accurate estimates due to the dominance of a few species. Comparisons of lag size (one month compared to two months) generally showed similarities (Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood) with quite high comparability in certain biomes – this indicates the utility of the ARDL1 and ARDL2 model, depending on the availability of appropriate data. These findings demonstrate the variation in estimation linked to the biome, and thus the validity of biome-level correlation of climatic data and vegetation indices.  相似文献   

Standardization of CPUE for Chilean Jack Mackerel （Trachurus murphyl） from Chinese Trawl Fleets in the High Seas of the Southeast Pacific Ocean     

LI Gang  ZOU Xiaorong  CHEN Xinjun  ZHOU Yinqi  ZHANG Min 《青岛海洋大学学报(英文版)》2013,(3):441-451

The generalized linear model （GLM） and generalized additive model （GAM） were applied to the standardization of catch per unit effort （CPUE） for Chilean jack mackerel from Chinese factory trawl fishing fleets in the Southeast Pacific Ocean from 2001 to 2010 by removing the operational, environmental, spatial and temporal impacts. A total of 9 factors were selected to build the GLM and GAM, i.e., Year, Month, Vessel, La Nifia and E1 Nifio events （ELE）, Latitude, Longitude, Sea surface temperature （SST）, SST anomaly （SSTA）, Nino3.4 index and an interaction term between Longitude and Latitude. The first 5 factors were significant components in the GLM, which in combination explained 27.34% of the total variance in nominal CPUE. In the stepwise GAM, all factors explained 30.78% of the total variance, with Month, Year and Vessel as the main factors influencing CPUE. The higher CPUE occurred during the period April to July at a SST range of 12-15℃ and a SSTA range of 0.2-1.0℃. The CPUE was significantly higher in normal years compared with that in La Nifia and E1 Nifio years. The abundance of Chilean jack mackerel declined during 2001 and 2010, with an increase in 2007. This work provided the relative abundance index of Chilean jack mackerel for stock as- sessment by standardizing catch and effort data of Chinese trawl fisheries and examined the influence of temporal, spatial, environ- mental and fisheries operational factors on Chilean jack mackerel CPUE.  相似文献