Geomorphological signatures of the evolution of active normal faults along the Langshan Mountains,North China

Segmentation, propagation, and linkage of normal faults often occur in regions of active extension, and observations of the distribution and structural properties of segment boundaries can provide important insights for seismic hazard assessment. In this study, we carry out quantitative geomorphological analysis to evaluate the relative tectonic activity along the Langshan Piedmont Fault (LPF), which bounds the NW margin of the Hetao Graben, North China. On the basis of obtained morphometric indices (HI, BS, Smf, VF, SLK, and χ), tectonic knickpoint heights, footwall topography, and small unmanned aerial vehicles (sUAV)-based field observations, we demonstrate that: (i) The Langshan landscape is in a state of disequilibrium in response to active rock uplift and channel incision; (ii) The LPF consists of two major fault segments with lengths of 65 and 95 km, respectively, which likely have been linked with each other; (iii) Rupturing of the whole of one segment can generate an earthquake of M_w ~7.3–7.5, and earthquake magnitude may reach M_w ~7.8 if the entire fault trace of ~160 km is ruptured, posing a significant seismic risk in the western Hetao Graben. These findings would further our understanding of normal fault evolution through space and time in actively extending regions.     

A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America

Abstract

It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations. To better manage the natural resources, and to better understand the closely linked Earth systems, the concept of Digital Earth has been strongly promoted since US Vice President Al Gore's speech in 1998. One core element of Digital Earth is the use and integration of remote sensing data. Only satellite imagery can cover the entire globe repeatedly at a sufficient high-spatial resolution to map changes in land cover and land use, but also to detect more subtle changes related for instance to climate change. To uncover global change effects on vegetation activity and phenology, it is important to establish high quality time series characterizing the past situation against which the current state can be compared. With the present study we describe a time series of vegetation activity at 10-daily time steps between 1998 and 2008 covering large parts of South America at 1 km spatial resolution. Particular emphasis was put on noise removal. Only carefully filtered time series of vegetation indices can be used as a benchmark and for studying vegetation dynamics at a continental scale. Without temporal smoothing, subtle spatio-temporal patterns in vegetation composition, density and phenology would be hidden by atmospheric noise and undetected clouds. Such noise is immanent in data that have undergone solely a maximum value compositing. Within the present study, the Whittaker smoother (WS) was applied to a SPOT VGT time series. The WS balances the fidelity to the observations with the roughness of the smoothed curve. The algorithm is extremely fast, gives continuous control over smoothness with only one parameter, and interpolates automatically. The filtering efficiently removed the negatively biased noise present in the original data, while preserving the overall shape of the curves showing vegetation growth and development. Geostatistical variogram analysis revealed a significantly increased signal-to-noise ratio compared to the raw data. Analysis of the data also revealed spatially consistent key phenological markers. Extracted seasonality parameters followed a clear meridional trend. Compared to the unfiltered data, the filtered time series increased the separability of various land cover classes. It is thus expected that the data set holds great potential for environmental and vegetation related studies within the frame of Digital Earth.     

Monitoring nitrogen concentration of oilseed rape from hyperspectral data using radial basis function

Remote sensing technology is the important tool of digital earth, it can facilitate nutrient management in sustainable cropping systems. In the study, two types of radial basis function (RBF) neural network approaches, the standard radial basis function (SRBF) neural networks and the modified type of RBF, generalized regression neural networks (GRNN), were investigated in estimating the nitrogen concentrations of oilseed rape canopy using vegetation indices (VIs) and hyperspectral reflectance. Comparison analyses were performed to the spectral variables and the approaches. The Root Mean Square Error (RMSE) and determination coefficients (R²) were used to assess their predictability of nitrogen concentrations. For all spectral variables (VIs and hyperspectral reflectance), the GRNN method produced more accurate estimates of nitrogen concentrations than did the SRBF method at all ranges of nitrogen concentrations, and the better agreements between the measured and the predicted nitrogen concentration were obtained with the GRNN method. This indicated that the GRNN method is prior to the SRBF method in estimation of nitrogen concentrations. Among the VIs, the Modified Chlorophyll Absorption in Reflectance Index (MCARI), MCARI₁₅₁₀, and Transformed Chlorophyll Absorption in Reflectance Index are better than the others in estimating oilseed rape canopy nitrogen concentrations. Compared to the results from VIs, the hyperspectral reflectance data also gave an acceptable estimation. The study showed that nitrogen concentrations of oilseed rape canopy could be monitored using remotely sensed data and the RBF method, especially the GRNN method, is a useful explorative tool for oilseed rape nitrogen concentration monitoring when applied on hyperspectral data.     

Scleractinian coral communities of Hormuz Island in the Persian Gulf

The abundance and health of scleractinian coral communities of Hormuz Island were investigated. For this purpose, we employed 20 m line intercept transects—12 in the intertidal zone and 15 subtidally to evaluate coral cover and community composition. The estimated dead coral coverage was 6.21%±0.81%, while live coral coverage was 16.93%±1.81%, considered as very poor. Totally, 12 genera were recorded, of which Porites with 11.9%±1.4% live cover was the dominant, while Goniopora had the least cover (0.07%±0.08%). Based on Mann-Whitney U-test, live coral coverage, dead coral coverage, algal coverage, cover of other benthic organisms and abiotic components showed significant univariate differences between zones (p<0.05). The Spearman correlation test between the abundance of biotic and abiotic components indicated significant negative correlation of live coral and sand with zoantharian and significant positive correlation of algae and other benthic organisms with rubble. The reef health indices used for the corals indicated that, in general, the environmental conditions were not suitable, which could be attributed to both natural and anthropogenic factors, the most important of which was zoantharian’ overgrowth on the scleractinian corals in this region.     

Simulation of frozen ground distribution in northeast China based on a surface frost number model

Journal of Geographical Sciences - Against the background of global warming, environmental and ecological problems caused by frozen ground degradation have become a focus of attention for the...     

广东干湿气候的特征

提出一种月、季干湿气候指数，并用于广东各地干湿气候的分析，结果表明，广东以４－９月为湿季，１０月到翌年１月为干季，２－３月为干转湿的过渡期，干季短而湿季长，干湿季节的互换呈纬向自北向南推南，３－４月呈北湿南干，９月呈北干南湿，干湿季的转换时间与自然季节的转换基本相符。     

WorldView-2纹理的森林地上生物量反演

使用高空间分辨率卫星WorldView-2的多光谱遥感影像,构建植被指数和纹理因子等遥感因子与森林地上生物量的关系方程,并计算模型估测精度和均方根误差,探索高分辨率数据的光谱与纹理信息在温带森林地上生物量估测应用中的潜力。以黑龙江省凉水自然保护区温带天然林及天然次生林为研究对象,通过灰度共生矩阵(GLCM)、灰度差分向量(GLDV)及和差直方图(SADH)对高分辨率遥感影像进行纹理信息提取,并利用外业调查的74个样地地上生物量与遥感因子建立参数估计模型。提取的遥感因子包括6种植被指数(比值植被指数RVI、差值植被指数DVI、规一化植被指数NDVI、增强植被指数EVI、土壤调节植被指数SAVI和修正的土壤调节植被指数MSAVI)以及3类纹理因子(GLCM、GLDV和SADH)。为避免特征变量个数较多对估测模型造成过拟合,利用随机森林算法对提取的遥感因子进行特征选择,将最优的特征变量输入模型参与建模估测。采用支持向量回归(SVR)进行生物量建模及验证,结果显示选入模型的和差直方图均值(sadh_mean)、灰度共生矩阵方差(glcm_var)和差值植被指数(DVI)等遥感因子对森林地上生物量有较好的解释效果;植被指数+纹理因子组合的模型获得较精确的AGB估算结果(R2=0.85,RMSE=42.30 t/ha),单独使用植被指数的模型精度则较低(R~2=0.69,RMSE=61.13 t/ha)。     

Predicting Thaumastocoris peregrinus damage using narrow band normalized indices and hyperspectral indices using field spectra resampled to the Hyperion sensor

Thaumastocoris peregrinus (T. peregrinus) is a sap sucking insect that feeds on Eucalyptus leaves. It poses a threat to the forest industry by reducing the photosynthetic ability of the tree, resulting in stunted growth and even death of severely infested trees. Remote sensing techniques offer the potential to detect and map T. peregrinus infestations in plantation forests using current operational hyperspectral scanners. This study resampled field spectral data measured from a field spectrometer to the band settings of the Hyperion sensor in order to assess its potential in predicting T. peregrinus damage. Normalized indices based on NDVI ratios were calculated using the resampled visible and near-infrared bands of the Hyperion sensor to assess its utility in predicting T. peregrinus damage using Partial Least Squares (PLS) regression. The top 20 normalized indices were based on specific biochemical absorption features that predicted T. peregrinus damage with a mean bootstrapped R² value of 0.63 on an independent test dataset. The top 20 indices were located in the near-infrared region between 803.3 nm and 894.9 nm. Twenty three previously published hyperspectral indices which have been used to assess stress in vegetation were also used to predict T. peregrinus damage and resulted in a mean bootstrapped R² value of 0.59 on an independent test dataset. The datasets were combined to assess its collective strength in predicting T. peregrinus damage and significant indices were chosen based on variable importance scores (VIP) and were then entered into a PLS model. The indices chosen by VIP predicted T. peregrinus damage with a mean bootstrapped R² value of 0.71 on an independent test dataset. A greedy backward variable selection model was further tested on the VIP selected indices in order to find the best subset of indices with the best predictive accuracy. The greedy backward variable selection model identified 3 indices and performed the best by predicting damage with an R² value of 0.74 with the lowest RMSE of 1.30% on an independent test dataset. The best three indices identified include the anthocyanin reflectance index, carotenoid reflectance index and the normalized index calculated at 864.4 and 884.7 nm. Individual relationships between these indices and T. peregrinus damage indicate that high correlations are obtained with the inclusion of a few severely infested trees in the sample size. When the severely infested trees were removed from the study, the normalized index (864.4 and 884.7 nm) and the anthocyanin reflectance index still yielded significant correlations at the 99% confidence interval. This study indicates the significance of normalized indices and spectral indices calculated from the visible and near-infrared bands in hyperspectral data for the prediction of T. peregrinus damage.     

Modelling the spectral response of the desert tree Prosopis tamarugo to water stress

In this paper, we carried out a laboratory experiment to study changes in canopy reflectance of Tamarugo plants under controlled water stress. Tamarugo (Prosopis tamarugo Phil.) is an endemic and endangered tree species adapted to the hyper-arid conditions of the Atacama Desert, Northern Chile. Observed variation in reflectance during the day (due to leaf movements) as well as changes over the experimental period (due to water stress) were successfully modelled by using the Soil-Leaf-Canopy (SLC) radiative transfer model. Empirical canopy reflectance changes were mostly explained by the parameters leaf area index (LAI), leaf inclination distribution function (LIDF) and equivalent water thickness (EWT) as shown by the SLC simulations. Diurnal leaf movements observed in Tamarugo plants (as adaptation to decrease direct solar irradiation at the hottest time of the day) had an important effect on canopy reflectance and were explained by the LIDF parameter. The results suggest that remote sensing based assessment of this desert tree should consider LAI and canopy water content (CWC) as water stress indicators. Consequently, we tested fifteen different vegetation indices and spectral absorption features proposed in literature for detecting changes of LAI and CWC, considering the effect of LIDF variations. A sensitivity analysis was carried out using SLC simulations with a broad range of LAI, LIDF and EWT values. The Water Index was the most sensitive remote sensing feature for estimating CWC for values less than 0.036 g/cm², while the area under the curve for the spectral range 910–1070 nm was most sensitive for values higher than 0.036 g/cm². The red-edge chlorophyll index (CI_red-edge) performed the best for estimating LAI. Diurnal leaf movements had an effect on all remote sensing features tested, particularly on those for detecting changes in CWC.     

Inconsistent linear trends in Senegalese rainfall indices from 1950 to 2007

Abstract

Daily precipitation data from 31 Senegalese stations spanning the period from 1950 to 2007 were used to examine the inter-annual variations of seven rainfall indices: the annual mean precipitation (MEAN); the annual standard deviation of daily precipitation (STD); the frequency of wet days (Prcp1); the maximum number of consecutive dry days (CDD); the maximum 3-day rainfall total (R3D); the wet day precipitation intensity (SDII); and the 90th percentile of rain-day precipitation (Prec90p). The indices were spatially averaged over three agro-climatic regions in Senegal. Trends in the time series of the averaged indices were assessed using both visual examination and a modified version of the Mann-Kendall (MM-K) test. Initially negative significant trends in all seven indices suggest gradually drier conditions over the three agro-climatic regions between 1950 and 1980. In contrast, no significant trends, or even positive significant trends, were observed from the mid-1980s to 2007. The MM-K test was applied to all available data (1950–2007) and the period from 1971 to 2000. While several indices were found to have significant trends towards drier conditions for the 1950–2007 period, only PRCP1 showed a positive significant trend for the 1971–2000 period. The MM-K test did not detect a significant trend for the other indices. It was found that the rainfall deficit and therefore drought is no longer intensifying, and that the region may even become wetter. However, the period covered by the observations is still too short to resolve the question of whether there is now a trend towards wetter conditions.

Editor Z.W. Kundzewicz; Associate editor K. Hamed