Analysis of snow cover and climatic variability in Bhaga basin located in western Himalaya

Bhaga Basin has complex mountainous terrain; little study has been done on the spatial and temporal characteristics of snow cover in the region. The Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day snow cover products between 2001 and 2012 for winter period (November–April) have been used to study the variation in snow cover area (SCA). The statistical analysis based on non-parametric Mann Kendall and Sen’s slope methods have been used for detecting and estimating trends for climatic variables (temperature and snowfall) and SCA for winter period. Results of statistical analysis indicate rise in minimum temperature (0.02 °C year^?1) and fall in maximum temperature (0.17 °C year^?1). It also shows decrease in mean seasonal snowfall (0.07 cm year^?1). The seasonal SCA was found to decrease at the rate of 0.002% year^?1. This study indicates that the climate change is probably one of the major causes for depleting SCA.     

MAPPING THE AVALANCHE HAZARD FOR MOUNTAINOUS AREAS OF AFGHANISTAN

The authors describe a procedure for the compilation of maps of the avalanche hazard in high-mountain regions of Afghanistan. The basic sources of information include space imagery (photographs and scanner imagery), weather station data, and other geographic information on relief, elevation, location of moisture sources, etc. The methodology features the compilation of series of increasingly more specific and informative maps and graphs regarding the avalanche hazard: terrain types, snow cover depth and seasonal extent, duration of snow cover and its elevational zonation, snow as a factor in avalanche formation, and summary map of avalanche hazard. Translated by Elliott B. Urdang, Providence, RI 02906 from: Materialy glyatsiologicheskikh issledovaniy, 1991, No. 71, pp. 86–93.     

Land degradation mapping in the upper catchment of river Tons

A study on land degradation in the upper catchment of river Tons, a tributary of Yamuna river, in Uttarkashi district of the Uttarakhand state, was carried out using on-screen visual interpretation of IRS LISS-III + PAN merged data. The study area, which is largely mountainous, includes Govind Wildlife Sanctuary and National Park. Vegetation cover, slope and erosion status were used as criteria for the delineation of four major land degradation categories viz., undegraded, moderately degraded, degraded and severely degraded. More than 50 per cent of the study area is reported to be covered with snow and grassland. The moderate to severely degraded area worked out to be 42.4 per cent of the total area. The 32.8 per cent of area was found to be moderately degraded, followed by degraded (6.63%) and severely degraded (2.88%) areas. The depletion of vegetation cover on mountainous terrain and subsequent cultivation without proper protection measures is the reason for severe soil erosion and land degradation. In view of the existing land degradation situation, the catchment requires immediate treatment on priority for the sustenance of agriculture and wild life. It is expected that these measures will reduce the silt load in the river Tons and eventually, in river Yamuna.     

D-InSAR技术的积雪深度反演

积雪深度是大量气候、水文、农业及生态模型的重要输入变量。选用欧空局Sentinel-1主动微波数据,利用合成孔径雷达SAR(Synthetic Aperture Radar)差分干涉测量技术的二轨法,根据积雪相位与雪深之间的转换关系,反演新疆天山中段的巴音布鲁克盆地典型区的积雪雪深分布,提出了基于InSAR二轨差分的雪深估计方法,反演得到2016年12月18日的空间分辨率为13.89 m的雪深分布。研究表明:(1)对Sentinel-1数据进行正确的预处理以后,可以应用SAR差分干涉测量技术的二轨法反演区域雪深分布。但由于像对相干性和积雪状态的差异,积雪深度超过10 cm,可以获取较准确的雪深反演结果,R=0.65,反演误差的均方根误差RMSE=4.52 cm,平均相对误差为22.42%,反演雪深结果均比实测结果略偏低;而当雪深小于10 cm时,雪深反演值较实测值存在较大的误差,相对误差均高于34.52%,且反演雪深值均比实测值偏高。(2)雪深反演精度受高程及实际雪深的差异影响显著,另外雪深反演精度也受限于干涉像对相干性。结果表明,对于获取区域积雪雪深,InSAR技术较光学及被动微波遥感具有非常广阔的应用前景。     

Development and Application of 2D and 3D GRASS Modules for Simulation of Thermally Driven Slope Winds

The ability to manage and process fully three‐dimensional information has only recently been made available for a few Geographical Information Systems (GIS). An example of integrated and complementary use of 2D and 3D GRASS modules for the evaluation and representation of thermally induced slope winds over complex terrain is presented. The analytic solution provided by Prandtl (1942) to evaluate wind velocity and (potential) temperature anomaly induced by either diurnal heating or nocturnal cooling on a constant angle slope is adopted to evaluate wind and temperature profiles at any point over both idealised and real complex terrain. As these quantities depend on the slope angle of the ground and on the distance from the slope surface suitable procedures are introduced to determine the coordinate n of a point in the 3D volume measured along the direction locally normal to the terrain surface. A new GRASS module has been developed to evaluate this quantity and to generate a 3D raster file where each cell is assigned the value of the cell on the surface belonging to the normal vector. The application of the algorithm implemented in GRASS to an ideal valley and to a real valley close to the city of Trento in the Alps provides results in accordance with data reported in the literature. An extension of Prandtl's (1942) model to take into account humidity and evaporation processes on the soil is also proposed and implemented.     

基于改进的坡度滤波算法的LiDAR数据滤波

以规则格网来组织数据对坡度滤波方法进行改进,通过比较格网最低点与邻域最低点的坡度得到初始地形,再通过比较格网点与邻域格网最低点的判断获得数据点的地形信息,试验结果表明,对坡度的滤波方法有很大改进,也改善了滤波效果。     

Development of an inversion algorithm for dry snow density estimation and its application with ENVISAT-ASAR dual co-polarization data

Radar remote sensing has great potential to determine the extent and properties of snow cover. Availability of space-borne sensor dual-polarization C-band data of environmental satellite- (ENVISAT-) advanced synthetic aperture radar (ASAR) can enhance the accuracy in measurement of snow physical parameters as compared with single polarization data measurement. This study shows the capability of C-band synthetic aperture radar (SAR) data for estimating dry snow density over snow covered rugged terrain in Himalayan region. The snow density is an important parameter for the snow hydrology and avalanche forecasting related studies. An algorithm has been developed for estimating snow density, based on snow volume scattering and snow-ground scattering components. The radar backscattering coefficients of both horizontal–horizontal (hh) and vertical–vertical (vv) polarization and incidence angle are used as inputs in the algorithm to provide the snow dielectric constant which can be used to derive snow density using Looyenga's semi-empirical formula. Comparison was made between snow density estimated from algorithm using ENVISAT-ASAR hh and vv polarization data and the measured field value. The mean absolute error between estimated and measured snow density was found to be 0.024 g/cm³.     

How much variation in land surface phenology can climate oscillation modes explain at the scale of mountain pastures in Kyrgyzstan?

Climate oscillation modes can shape weather across the globe due to atmospheric teleconnections. We built on the findings of a recent study to assess whether the impacts of teleconnections are detectable and significant in the early season dynamics of highland pastures across five rayons in Kyrgyzstan. Specifically, since land surface phenology (LSP) has already shown to be influenced by snow cover seasonality and terrain, we investigated here how much more explanatory and predictive power information about climatic oscillation modes might add to explain variation in LSP. We focused on seasonal values of five climate oscillation indices that influence vegetation dynamics in Central Asia. We characterized the phenology in highland pastures with metrics derived from LSP modeling using Landsat NDVI time series together with MODIS land surface temperature (LST) data: Peak Height (PH), the maximum modeled NDVI and Thermal Time to Peak (TTP), the quantity of accumulated growing degree-days based on LST required to reach PH. Next, we calculated two metrics of snow cover seasonality from MODIS snow cover composites: last date of snow (LDoS), and the number of snow covered dates (SCD). For terrain features, we derived elevation, slope, and TRASP index as linearization of aspect. First, we used Spearman’s rank correlation to assess the geographical differentiation of land surface phenology metrics responses to environmental variables. PH showed weak correlations with TTP (positive in western but negative in eastern rayons), and moderate relationships with LDoS and SCD only in one northeastern rayon. Slope was weakly related to PH, while TRASP showed a consistent moderate negative correlation with PH. A significant but weak negative correlation was found between PH and SCAND JJA, and a significant weak positive correlation with MEI MAM. TTP showed consistently strong negative relationships with LDoS, SCD, and elevation. Very weak positive correlations with TTP were found for EAWR DJF, AMO DJF, and MEI DJF in western rayons only. Second, we used Partial Least Squares regression to investigate the role of oscillation modes altogether. PLS modelling of TTP showed that thermal time accumulation could be explained mostly by elevation and snow cover metrics, leading to reduced models explaining 55 to 70% of observed variation in TTP. Variable selection indicated that NAO JJA, AMO JJA and SCAND MAM had significant relationships with TTP, but their input of predictive power was neglible. PLS models were able to explain up to 29% of variability in PH. SCAND JJA and MEI MAM were shown to be significant predictors, but adding them into models did not influence modeling performance. We concluded the impacts of climate oscillation anomalies were not detectable or significant in mountain pastures using LSP metrics at fine spatial resolution. Rather, at a 30 m resolution, the indirect effects of seasonal climatic oscillations are overridden by terrain influences (mostly elevation) and snow cover timing. Whether climate oscillation mode indices can provide some new and useful information about growing season conditions remains a provocative question, particularly in light of the multiple environmental challenges facing the agropastoralism livelihood in montane Central Asia.     

Integrating terrain and vegetation indices for identifying potential soil erosion risk area

The present paper offers an innovative method to monitor the change in soil erosion potential by integrating terrain and vegetation indices derived from remote sensing data. Three terrain indices namely, topographic wetness index (TWI), stream power index (SPI) and slope length factor (LS), were derived from the digital elevation model. Normalized vegetation index (NDVI) was derived for the year 1988 and 2004 using remote sensing images. K-mean clustering was performed on staked indices to categorize the study area into four soil erosion potential classes. The validation of derived erosion potential map using USLE model showed a good agreement. Results indicated that there was a significant change in the erosion potential of the watershed and a gradual shifting of lower erosion potential class to next higher erosion potential class over the study period.     

栅格DEM坡体因子融合方法北大核心CSCD

坡体是自然地形实体的基本组成单元,利用综合性坡体因子分析和挖掘地形隐含信息,有助于区域地形地貌分析及规划利用。本文针对当前坡体综合表达因子(LS)未顾及坡宽因子的缺陷,采用归一化几何运算法将坡长(L)、坡度(S)、坡宽(W)进行融合,获取了一种新的坡体融合因子LSW。以DEM为数据源、黄土高原某小流域局部范围为典型研究区,在提取L、S、W及LS因子的基础上,分别以L×S×W、L+S+W、LS×W及LS+W共4种方式进行融合试验。结果表明,L+S+W融合方式获取的坡体LSW因子标准差矩阵均值最小,为0.234 4,有效平衡了各单地形因子对融合坡体因子LSW的贡献度,信息熵值计算分析也实证了L+S+W融合方式具有最优性。     

基于ICESat-GLAS完整波形的坡地森林冠层高度反演研究——以吉林长白山林区为例

为了评价大光斑激光雷达在坡地反演森林冠层高度的能力,以长白山汪清林区为例,利用冰、云和陆地高程卫星(ICESat)上搭载的地学激光测高系统(GLAS)提供的完整波形数据,在不同地形坡度范围内,以激光波形长度和地形指数为变量建立森林冠层高度反演模型。研究结果显示,在0~5°坡度范围所建立的模型能够解释90%的冠层高度,随着坡度范围的扩大,相对应的模型反演精度显著下降。总体来讲,在地形坡度不大于15°的地区,GLAS波形能够比较合理地反演森林冠层高度,具有很大的应用潜力。     

影响黄土高原集水面积阈值的地形因子主成分分析

以ASTER GDEM为信息源、22个典型小流域为样区,分析黄土高原集水面积阈值与沟谷密度的关系,利用均值变点法确定最佳阈值,探讨了影响阈值的数字高程模型（digital elevation model,DEM）地形因子主成分。结果表明,集水面积阈值由北向南、自东向西逐步增大,宏观上受黄土高原地貌类型制约,地形因子对其的影响成分归纳为坡面、起伏及高程变异因子。坡面因子的最大值与阈值正相关,坡度>粗糙度>地形曲率>平面曲率>剖面曲率。起伏因子的均值与阈值正相关,起伏度>切割深度。高程变异因子与阈值负相关。三者的主成分贡献率依次为58.754%、18.915%、11.388%,权重为0.527、0.229、-0.569。研究表明,坡面特征是影响黄土沟谷发育的重要因子。     

Effect of slope steepness parameter computations on soil loss estimation: review of methods using GIS

In the analysis of soil loss equation, the researchers have suggested two methods of deriving the slope steepness parameter. One method is having percentage slope term, while the other method is having sinθ as its term. In this paper, both the methods were analysed and compared in soil loss computation using Revised Universal Soil Loss Equation, over a Gangapur catchment area in India, having steep slopes. The soil loss rates derived were 0.98 million tonnes per year in case of steepness parameter derived by sinθ and 1.226 million tonnes per year in case of steepness parameter derived by percentage slope term. The observed rate of soil loss is 1.23 million tonnes per year. This methodology of soil loss estimation was also validated with similar catchment of Punegaon dam. It is concluded that for medium to steep terrain, percentage slope method estimates more accurate soil loss than other empirical methods for slope steepness estimation.     

顾及地形因素的S-RVOG模型和PD相干最优算法联合反演植被高度

针对极化干涉SAR植被高度反演中RVOG模型未考虑地形影响,且三阶段算法受到地面相位估计误差和纯体相干性估计误差影响,提出了一种植被高度反演思路,采用考虑地形因素的S-RVOG模型作为反演模型校正地形影响,同时引入PD相干最优算法用于改善三阶段算法中直线拟合地表相位估计和纯体相干性估计精度。为验证算法的有效性,首先采用欧空局提供的PolSARpro软件模拟了不同地形坡度水平的PolInSAR数据进行仿真试验,然后采用德国宇航局提供的E-SAR机载全极化SAR数据进行真实植被场景测试,并进行了定性和定量分析。结果表明,本文方法对于不同坡度水平数据,均能有效改善传统RVOG反演模型中地形影响和三阶段算法自身误差影响,反演精度更高。     

激光雷达回波模型辅助的坡地森林冠层高度反演

大光斑激光雷达数据已广泛应用于森林冠层高度提取,但通常仅限于地形坡度小于20°的平缓地区。在地形坡度大于20°的陡峭山区,地形引起的波形展宽使得地面回波和植被回波信息混合在一起,给森林冠层高度提取带来巨大挑战。本文利用激光雷达回波模型和地形信息,提出了一种模型辅助的坡地森林冠层高度反演算法。该方法以激光雷达回波信号截止点为参考,定义了波形高度指数H50和H75,使用激光雷达回波模型与已知地形信息模拟裸地的激光雷达回波,将裸地回波信号截止点与森林激光雷达回波信号截止点对齐,利用裸地回波计算常用的波形相对高度指数RH50和RH75,对森林冠层高度进行反演。并与高斯波形分解法和波形参数法的反演结果进行了比较。研究结果表明:(1)利用所提取的波形指数RH50和RH75对胸高断面积加权平均高(Lorey’s height)进行了估算,在坡度小于20°时,高斯波形分解法、波形参数法和模型辅助法的估算结果与实测值线性拟合的相关系数(R2)分别为0.70,0.78和0.98,对应的均方根误差(RMSE)分别为2.90 m,2.48 m和0.60 m,模型辅助法略优于其他两种方法;(2)在坡度大于20°时,高斯波形分解法、波形参数法和模型辅助法的R2分别为0.14,0.28和0.97,相应的RMSE分别为4.93 m,4.53 m和0.81 m,模型辅助法明显优于其他两种方法;(3)在0°—40°时,模型辅助法对Lorey’s height估算结果与实测值的R2为0.97,RMSE为0.80 m。本研究提出的模型辅助法具有更好的地形适应性,在0°—40°的坡度范围内具备对坡地森林冠层高度反演的潜力。     

机载LiDAR点云密度对DEM精度的影响

机载激光雷达技术(LiDAR)作为一项先进的遥感技术,是植被覆盖区DEM获取的重要手段之一,而不同地形坡度条件及点云密度对DEM产品质量有重要影响。本文以辽宁省某市的机载LiDAR数据为基础,选取5种不同地形坡度的点云数据,通过随机、等间距及基于曲率3种不同的点云抽稀方法,按照点云保留率为80%、60%、40%、20%和10%共5个不同梯度的抽稀倍数对原始点云进行抽稀简化处理,生成与之对应的DEM并对其进行精度评价,以此研究地形坡度、点云抽稀方法、抽稀倍数对DEM精度的影响。结果表明,DEM精度与地形坡度呈负相关关系,即RMSE随地形坡度升高不断增加;基于曲率的抽稀方法在地形坡度＞30°时,相较于其他两种方法RMSE较小,具有明显优势;40%的点云保留率是平衡DEM精度与数据存储效率的一个节点,当点云保留率<40%时,DEM的高程RMSE会迅速增大。该研究对于利用机载LiDAR进行大范围DEM生产具有一定的指导和借鉴意义。     

一种顾及坡度因子与极值的地表面积计算法

针对局部地形的非线性变化与图形的极值信息对地表面积计算结果的影响,提出了一种顾及坡度因子与图形极值的地表面积计算法,分析了地表面积计算理论与误差。最后以中国西部具有典型地貌特征的两县数据为例进行试验分析。试验结果表明,该方法在一定程度上对局部地形逼近具有一定的改进,宏观上与规格格网交叉对角划线法具有一定的相似度。文中推导的三角网面积传递误差可初步表明考虑局部地形坡度因子与极值的地表面积计算结果可靠性更高。     

The effect of some upscaling procedures on the slope derived from digital elevation models

Digital elevation models (DEMs) and their derived parameters play an important role in geosciences. When upscaling, the influence of the upscaling procedure on the accuracy of DEMs' derived parameters has not been systematically studied. In this study, slope is taken as an example to explore the effect of upscaling procedure on the accuracy of DEMs' derived parameters. The meaning of upscaling is converting the data from finer resolution to coarser resolution. The two upscaling procedures of EES (elevation to elevation first, then elevation to slope) and ESS (elevation to slope first, then slope to slope) combined with four aggregation rules of maximum, mean, median, and minimum were used to obtain the slope result at the desired resolution in plain, hill, and mountain areas. From the experimental results, it can be concluded that the upscaling procedure significantly affected slope accuracy at the desired resolution. To obtain the slope result with higher accuracy during upscaling, it was recommended that the upscaling procedure of ESS combined with the mean or median aggregation rule was adopted whatever the terrain complexity. This study can be applied for the selection of upscaling procedure and aggregation rule in the upscaling of slope and it warns us to pay more attention to the upscaling procedure when obtaining the DEM‐derived parameters at multi‐resolutions.     

Assessment of terrain elevation derived from satellite laser altimetry over mountainous forest areas using airborne lidar data

Gaussian decomposition has been used to extract terrain elevation from waveforms of the satellite lidar GLAS (Geoscience Laser Altimeter System), on board ICESat (Ice, Cloud, and land Elevation Satellite). The common assumption is that one of the extracted Gaussian peaks, especially the lowest one, corresponds to the ground. However, Gaussian decomposition is usually complicated due to the broadened signals from both terrain and objects above over sloped areas. It is a critical and pressing research issue to quantify and understand the correspondence between Gaussian peaks and ground elevation. This study uses ～2000 km² airborne lidar data to assess the lowest two GLAS Gaussian peaks for terrain elevation estimation over mountainous forest areas in North Carolina. Airborne lidar data were used to extract not only ground elevation, but also terrain and canopy features such as slope and canopy height. Based on the analysis of a total of ～500 GLAS shots, it was found that (1) the lowest peak tends to underestimate ground elevation; terrain steepness (slope) and canopy height have the highest correlation with the underestimation, (2) the second to the lowest peak is, on average, closer to the ground elevation over mountainous forest areas, and (3) the stronger peak among the lowest two is closest to the ground for both open terrain and mountainous forest areas. It is expected that this assessment will shed light on future algorithm improvements and/or better use of the GLAS products for terrain elevation estimation.     

Improving MODIS Spatial Resolution for Snow Mapping Using Wavelet Fusion and ARSIS Concept

We propose to fuse the high spatial content of two 250-m spectral bands of the moderate resolution imaging spectroradiometer (MODIS) into its five 500-m bands using wavelet-based multiresolution analysis. Our objective was to test the effectiveness of this technique to increase the accuracy of snow mapping in mountainous environments. To assess the performance of this approach, we took advantage of the simultaneity between the advanced spaceborne thermal emission and reflection radiometer (ASTER) and MODIS sensors. With a 15-m spatial resolution, the ASTER sensor provided reference snow maps, which were then compared to MODIS-derived snow maps. The benefit of the method was assessed through the investigation of various metrics, which showed an improvement from 3% to 20%. Therefore, the enhanced snow map is of great benefit for environmental and hydrological applications in steep terrain.