Mapping and classification of hydrological parameters from digital terrain data in the Musandam Peninsula,UAE and Oman

Groundwater exploration and modelling requires hydrological parameters and a large volume of hydrologic database. This study integrates remote sensing and geographic information system (GIS) to map and classify hydrological parameters indicates areas of groundwater recharge and discharge. Bivariate quadratic surfaces with moving window size of 5 × 5 were fitted to the digital elevation model and drainage basins, drainage network, topographic wetness index (TWI) and hydroforms were derived. The eight-direction algorithm (D8) that determines in which neighbouring pixel any water in a central pixel will flow naturally was used to delineate drainage basin and drainage network in the study area. The TWI was used to quantify the effect of local topography on hydrological processes and for modelling soil moisture. The results indicate the presence of intensive of stream network (1336 km²), wettest zones and accumulation zones (63.99 km²) within Wadi Bih, the UAE and Wadi Khasb, Oman, suggesting regional recharge.     

Cartosat-1 height product and ICESat/GLAS data for digital elevation surface generation

Space born systems like Geoscience Laser Altimeter System (GLAS) onboard collect data for ice, cloud and Land. Elevation satellite (ICESat) collects an unparalleled data set as waveform over terrestrial targets, helps in evaluating the global elevation data. In this study we compared the Digital Elevation Surface (DES) generated by Cartosat-1 point data and DES generated by merging the Cartosat-1 data with ICESat data. Outputs in the form of interpolated surfaces were evaluated with the help of differential global positioning system (DGPS) points collected from study area. The study showed the results that the DES generated from Cartosat — 1 data had less elevation accuracy when compared with the DGPS data. While merging Cartosat-1 point height data with ICESat/GLAS data resulted in better accuracy. On the practical side for processing the interpolation, based on the research the ICESat /GLAS with Cartosat-1 height data can produce better DES compared to the Cartosat-1 stereo data. The DES was generated using geostatistical interpolation methods in which the global polynomial method proved to be the better for generating the surface compare to other interpolation techniques studied in this work. For co-kriging method, the accuracy decreases compare to the kriging interpolation, due to the complexity of parameters that were used for interpolation. On the theory side, based on this research the statement of which interpolation technique is better than the other cannot be mentioned easily, because these are based on the data type, parameters and also on method of interpolation. So research experiment should be more intensely and with more focused.     

Change detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis

Automatic change detection of land cover features using high-resolution satellite images, is a challenging problem in the field of intelligent remote sensing data interpretation, and is becoming more and more effective for its applications viz. urban planning and monitoring, disaster assessment etc. In the present study, a change in detection approach based on the image morphology that analyses change in the local image grids is proposed. In this approach, edges from both the images are extracted and grid wise comparison is made by probabilistic thresholding and power spectral density analysis for identifying change area. One of the advantages of the proposed methodology is that the temporal images used in the change analysis need not be radiometrically corrected as analysis is based on edge extractions. The grid-based analysis further reduces the error, which might have been introduced by image mis-registration. The proposed methodology is validated by finding the temporal changes in the linear land cover features in parts of Kolkata city, India using three different image data-sets from LISS IV, Cartosat-1 and Google earth having varied spatial resolutions of 5.8 m, 2.5 m and about 1 m, respectively. The overall accuracy in identifying changes is found to be 64.82, 73.86 and 80.93% for LISS IV, Cartosat-1 and Google earth data-set, respectively.     

Validation of Indian National DEM from Cartosat-1 Data

CartoDEM is an Indian National DEM generated from Cartosat-1 stereo data. Cartosat-1, launched in May, 2005, is an along track (aft ?5°, Fore +26°) stereo with 2.5 m GSD, give base-height ratio of 0.63 with 27 km swath. The operational procedure of DEM generation comprises stereo strip triangulation of 500?×?27 km segment with 10 m posting along with 2.5 m resolution ortho image and free—access posting of 30 m has been made available (bhuvan.nrsc.gov.in). A multi approach evaluation of CartoDEM comprising (a) absolute accuracy with respect to ground control points for two sites namely Jagatsinghpur -flat and Dharamshala- hilly; second site i.e. Alwar-plain and hilly with high resolution aerial DEM, (b) relative difference between SRTM and ASTERDEM (c) absolute accuracy with ICESat GLAS for two sites namely Jagatsinghpur-plain and Netravathi river, Western Ghats-hilly (d) relative comparison of drainage delineation with respect to ASTERDEM is reported here. The absolute height accuracy in flat terrain was 4.7 m with horizontal accuracy of 7.3 m, while in hilly terrain it was 7 m height with a horizontal accuracy of 14 m. While comparison with ICESat GLAS data absolute height difference of plain and hilly was 5.2 m and 7.9 m respectively. When compared to SRTM over Indian landmass, 90 % of pixels reported were within ±8 m difference. The drainage delineation shows better accuracy and clear demarcation of catchment ridgeline and more reliable flow-path prediction in comparison with ASTER. The results qualify Indian DEM for using it operationally which is equivalent and better than the other publicly available DEMs like SRTM and ASTERDEM.     

Influence of geological structures on groundwater accumulation and groundwater salinity in Musandam Peninsula,UAE and Oman

The hydrological setting and groundwater quality of Musandam Peninsula was studied to investigate the influence of geological structures on the groundwater accumulation and groundwater salinity. Five sets of modified morphometric maps were used to reduce errors and carry out the geological structures. The modification was modified by applying mean filter to the Digital Elevation Model (DEM) then applying Soble filter with 10% threshold and equalization enhancement. D8 and algorithms were used to reveal the drainage basins and drainage networks of the entire area. The algorithm determines into which neighbouring pixel any water in a central pixel will flow naturally. Flow direction in a DEM is calculated for every central pixel of input blocks of a 3 × 3 window, all the time comparing the value of the central pixel in the window with the value of its eight neighbours. The spatial association between geological structures and drainage networks was studied using 2D graph and rose diagrams. Flood basin model was applied to simulate the Arabian Gulf water intrusion into the coastal aquifer. The concentrations of solutes in groundwater samples collected from Wadi Al Bih well field and well locations were correlated with the geological structure trends and intersections. The results of the study reveal that the drainage basins, drainage network and groundwater quality are structurally controlled by subsurface geological structure displacements.     

利用排水区间计算城市暴雨积水

提出了给定淹没水量条件下基于流域排水区间分布的城市暴雨积水过程计算模型。该模型针对城市暴雨积水过程的形成机理和特点提出了两个基本假设,以各积水淹没源的空间分布特征规律为依据将城市流域划分为若干个排水区间,并将排水区间作为积水淹没计算的基本单元。为了保证计算的效率,根据流域地形起伏分布状况,该模型将各个排水区间内的空间区域范围简化为一个规则几何体,并结合体积法和多种漫流计算思想模拟暴雨积水在各排水区间内部和淹没源点之间的漫延过程。实验结果表明,该模型高效、可行,对流域资料要求较低,在信息不完备条件下的城市暴雨积水模拟中具有一定的应用价值。     

Reclamative grouping of ravines using Cartosat-1 PAN stereo data

Information on the depth and bed width of ravines (network of gullies) at large scales is critical for their reclamation and management. Hitherto such information has been generated from aerial photographs and space borne stereo images with medium to coarse ‘z’ – axis resolution. The present study, aims at demonstrating the potential of Cartosat ?1 (an Indian Earth observations satellite) stereo images with 2.5 m spatial resolution in deriving morphometric information on ravines for their reclamative grouping. The study area is a part of Jhansi and Hamirpur districts of Uttar Pradesh, northern India. The approach involves acquiring precise ground control points using Differential GPS (DGPS), triangulation, DEM extraction and generation of ortho image as well as anaglyphs for stereo viewing. The depth and bed width of ravines were measured in the field for validation. A comparison with field observations reveal that the bed width of ravines and depth can be measured successfully with Carto-1 stereo data. The anaglyph data was used to delineate various categories of ravines based on their depth and bed width. Results indicate that the Cartosat-1 stereo images are quite suitable for delineation of three categories of ravines, namely shallow (<3 m deep and <18 m bed width), medium deep (3–9 m deep and >18 m bed width) and deep (>9 m deep) which are important for their reclamation.     

Studying Land Use Travel Demand Interaction Using 3S Technology for Tiruchirappalli City

This paper gives an overview of a major application of modern geospatial tools such as remote sensing, GIS and GPS, i.e., 3S technology in estimating travel demand along Indian roads by considering the study area, Tiruchirappalli urban city in Tamil Nadu. In the study, an attempt was made to estimate travel demand based on the current land-use classification as the pattern of travel depends on the type of land use activity in a zone. IRS high resolution image Cartosat-1 of year 2009 was used to extract the land-use information required. The travel demand model developed was validated with the field obtained OD-matrix to understand the accuracy of the method considered.     

Cartosat-1 derived DEM (CartoDEM) towards Parameter Estimation of Microwatersheds and Comparison with ALTM DEM

Hydrologic analysis of microwatersheds is essential for water resources planning at large scale. Space based input for decentralized planning at panchayat level use high resolution DEM. Drainage and slope play important role in planning and Digital Elevations Models (DEM) are widely being used for estimation of hydrologic parameters which are useful as input for hydrologic models. The estimates vary as per resolution and type of DEM. This paper evaluates the suitability of DEM derived through Cartosat-1 satellite stereo data(CartoDEM) for hydrologic parameter estimation of microwatersheds and compares the results with Airborne Laser Terrain Mapper (ALTM) based DEM data. Comparison is based on the hydrologic parameters delineated in Geographical Information System. Microwatersheds are delineated and drainage length extracted using two different cell sizes for both DEMs. Correctness Index, Figure of Merit, visual comparison, Percent within buffer and Junction comparison method, compared extracted river network. Average watershed slope is calculated using three different methods. CartoDEM derived drainage is comparable with ALTM derived drainage. There is high correlation between Carto5 and Caro10 DEMs in terms of drainage delineation and slope calculation. Average watershed slope vary as per calculation methods but average channel slope value (S3) although less, is comparable across DEMs.     

Demarcation of potential avalanche sites using remote sensing and ground observations: a case study of Gangotri glacier

This study demonstrates the effectiveness of remote sensing and analytical hierarchy process for avalanche hazard mapping. The layers incorporated in this study were of slope, aspect, profile curvature, ground cover and elevation. The accuracy of output was determined using the registered avalanche sites based on ground observations and field-based modelling techniques. 93.35% of avalanche-affected areas came under maximum and moderate hazard zones, thus proving the effectiveness of this technique for Gangotri glacier basin. A parallel study was done to observe the change in the results, if any, by using high-resolution DEM and Cartosat-1 imagery. Similar methodology was adopted and the outcome was having significant improvement over the previous result as 98.8% of the preregistered avalanche area falling within maximum and moderate hazard zones.     

Monitoring Urban Landscape Dynamics Over Delhi (India) Using Remote Sensing (1998–2011) Inputs

Urbanization is increasingly becoming a widespread phenomenon at all scales of development around the globe. Be it developing or developed nations, all are witnessing urbanization at very high pace. In order to study its impacts, various methodologies and techniques are being implemented to measure growth of urban extents over spatial and temporal domains. But urbanization being a very dynamic phenomenon has been facing ambiguities regarding methods to study its dynamism. This paper aims at quantifying urban expansion in Delhi, the capital city of India. The process has been studied using urban land cover pattern derived from Landsat TM/ETM satellite data for two decades (1998–2011). These maps show that built-up increased by 417 ha in first time period (1998–2003) and 6,633 ha during next period (2003–2011) of study. For quantification of metrics for urban expansion, the Urban Landscape Analysis Tool (ULAT) was employed. Land cover mapping was done with accuracy of 92.67 %, 93.3 % and 96 % respectively for years 1998, 2003 and 2011. Three major land covers classes mapped are; (i) built-up, (ii) water and (iii) other or non-built-up. The maps were then utilized to extract degree of urbanization based on spatial density of built-up area consisting of seven classes, (i) Urban built-up, (ii) Suburban built-up,(iii) Rural built-up, (iv) Urbanized open land, (v) Captured open land, (vi) Rural open land and (vii) Water. These classes were demarcated based on the urbanness of cells. Similarly urban footprint maps were generated. The two time maps were compared to qualitatively and quantitatively capture the dynamics of urban expansion in the city. Along with urbanized area and urban footprint maps, the new development areas during the study time periods were also identified. The new development areas consisted of three major categories of developments, (i) infill, (ii) extension and (iii) leapfrog.     

Identification of drainage patterns using a graph convolutional neural network

Various geological factors shape drainage patterns. Identifying drainage patterns is a classic problem in topographical knowledge mining and map generalization. Existing rule-based methods rely heavily on the parameter settings of cartographers for drainage-pattern recognition. These methods effectively identify drainage patterns in specific areas but require manual parameter tuning to identify drainage patterns in other areas. Owing to the complexity of topological and geometric characteristics, drainage pattern recognition involves nonlinear problems, and it is difficult to build mapping relationships between characteristics and patterns using rule-based methods. Therefore, we proposed a data-driven method based on a graph convolutional neural network to avoid heavy reliance on human experience and automatically mine implicit relationships between characteristics and drainage patterns. First, six typical drainage patterns (dendritic, rectangular, parallel, trellis, reticulate, and fanned) were listed based on map specifications, and the unique characteristics of each drainage pattern were illustrated. Subsequently, the drainage graphs were constructed. The characteristics of the whole, local, and individual units in the drainage networks were quantified based on drainage vector data. Finally, an identification model was developed using graph convolution, self-attention pooling, and multiple fully connected layers for drainage pattern recognition. After training and testing, the accuracy of our model (0.801 ± 0.014) was better than that of the rule-based method (0.572 ± 0.000) and the traditional machine learning methods (less than 0.733 ± 0.016). The results demonstrate that the ability of our model to identify drainage patterns surpasses that of other methods.     

Accuracy Assessment of Digital Elevation Model Generated by SAR Stereoscopic Technique Using COSMO-Skymed Data

Radargrammetry technique using the stereoscopic synthetic aperture radar (SAR) images is used for the generation of a digital elevation model (DEM) of a region requires only the amplitude images. SAR stereoscopic technique is analogous to the stereo-photogrammetric technique where the optical stereoscopic images are used for DEM generation. While the advantages of the SAR images are their indifference to atmospheric transparency and solar illumination conditions, the side-looking geometry of the SAR increases the complexity in the SAR stereo analysis. The availability of high spatial and temporal resolution SAR data in recent years has facilitated generation of high-resolution DEM with greater vertical accuracy using radargrammetric technique. In the present study, attempt has been made to generate the DEM of Dehra Dun region, India, from the COSMO-Skymed X-band SAR data-pair acquired at 8 days interval through the radargrammetry technique. Here, radargrammetric orientation approach has been adopted to generate the DEM and various issues and processing steps with the radargrammetry technique have been discussed. The DEM was validated with ground measured elevation values using a differential global positioning system and the root-mean-square error of the DEM was found as 7.3 m. The DEM was compared with the reference DEM of the study area generated from the Cartosat-1 stereo data with a model accuracy of 4 m.     

Extraction and validation of cartosat-1 DEM

CARTOSAT-1 satellite, launched in May, 2005 is a dedicated along track stereo mission providing high quality data for topographic and cartographic applications. The present paper describes the evaluation of the Cartosat-1 stereo data, mainly through the generation and validation of DEM for moderately undulating and hilly areas. Photogrammetric techniques have been used for generation of DEM and Orthoimage for two cases i.e. 1) using RPCs (Rational Polynomial Coefficients) and 2) using RPCs along with ground control points. Root Mean Square Error (RMSE) in elevation values for the moderately undulating (Dehradun) and hilly area (Shimla), are found to be 4.38 and 3.69m respectively.     

Water logging and drainage Assessment in Ravi-Tawi irrigation command (J&K) using remote sensing approach

Water logging is one of the major land degradation processes that restricts the economic and efficient utilisation of soil and land resources in command areas. Since independence, various irrigation schemes, for providing water for agriculture and drinking have been taken up by Central and State agencies in India. In most of these schemes very little efforts have been made for proper drainage. Obstruction of natural drainage by way of construction of roads, railways, aerodrome, various structures, etc., causes the ponding of monsoon runoff on the upstream of the structures. Periodic monitoring of command areas helps in analysing the extent of water logging, and should help in taking suitable remedial measures. Remote sensing and GIS are powerful tools, which could be effectively used to study the dynamic behaviour of waterlogged areas. In this study, waterlogged and salt-affected areas have been estimated in the command area of Ravi-Tawi Irrigation Complex in Jammu region. About 14% of the total command area is water logged/ salt-affected. Being a new project, this area is likely to grow in future when the project runs with its installed capacity, and as the distributaries expand in the command area. Plausible causes of water logging have been discussed, and remedial measures suggested for reclaiming operations.     

CO-Kriging Approach for Cartosat-1 Height Product with ICESat/GLAS Data for Digital Elevation Surface Generation

Nowadays, Geostatistics and its various interpolation techniques have become a major threshold area in the field of research in GIS. In this research work poorly sampled (less accurate height data relative to ICESat/GLAS height data) Cartosat-1 height data has been used with well sampled (more accurate height data relative to Cartosat-1 height data) ICESat/GLAS LiDAR (Light Detection and Ranging) height point data using Cokriging Interpolation technique, to study the effect of ICESat/GLAS on Cartosat-1 height data. Space borne LiDAR data has led researchers to explore its utilities in many applications. Space borne LiDAR data can be acquired through space borne LiDAR sensors also, like; GLAS (Geoscience Laser Altimeter System) system onboard ICESat (Ice, Cloud and land Elevation Satellite) satellite. In this study, it has been tried to apply Cokriging interpolation on two different sources of data sets, with a common variable (elevation) to generate DES and assessment of this surface has been conducted by DGPS data. After optimizing Cokriging parameters, results of digital elevation surface (DES) generated using Cokriging showed that RMSE has been second least than global polynomial in comparison to Kriging interpolation RMSE after being evaluated by GPS values. So, global polynomial as well as cokriging interpolation technique out performs while comparing with kriging technique for DES generation.     

基于网络Voronoi图启发式和群智能的最大覆盖空间优化

提出一种基于网络Voronoi面域图的最大覆盖选址模型及相应的粒子群优化方法,并应用于城市响应时间敏感型公共服务设施的空间优化。本文考虑设施功能沿交通网络辐射以及需求非均匀分布情形,对设施在网络连续空间上进行布局优化,选址模型采用网络Voronoi面域图划分布局设施的功能辐射域,以启发空间优化最小化重叠覆盖。模型同时顾及了设施利用率的最大化,规定设施对给定距离以内的需求实行的完全服务覆盖和对给定距离以外的需求实行随距离衰减的部分服务覆盖。本研究提出基于遗传机制和广义Voronoi图改进的粒子群算法以提高其空间优化性能,通过对南京市消防站最大覆盖空间优化实验表明,该研究取得了较为理想的结果,可应用于城市化区域应急设施最大覆盖空间优化。     

Soil Resource Database at Village Level for Developmental Planning

A study was carried out in village Mohammadabad, Nalgonda district, Andhra Pradesh to demonstrate the potential of high-resolution remote sensing data in generating soil resource database at village level that would facilitate natural resource developmental planning. For this study, detail soil resource database was generated by interpreting IRS Resourcesat-1 LISS-IV + Cartosat-1 merged data and by undertaking soil survey and soil analysis. This database was integrated with cadastral map and was analysed for preparation of an action plan for the village that included recommendation on suitable crops, soil and water conservation measures and distribution of soil health card to farmers. The study indicates that the LISS-IV + Cartosat-1 merged satellite data helps in mapping soils at phase level and in delineation of more number of pure soil mapping units i.e. consociations at 1:10.000 scale. The plot-wise resource potential and constraints reveals that significant inter and intra-field soil variability exists. Soil fertility assessment indicates that the soils of the village are low to medium in fertility (available NPK Content) and micronutrients like copper, manganese and iron content is above the critical level for optimum growth of crops while zinc content is less than the critical level.     

Delineation and characterization of waterlogging and salt affected areas in a canal irrigated semiarid region of north west India

Indian Remote Sensing (IRS) Linear Imaging Self Scanning (LISS II) data are interpreted, followed by ground verification facilitated identification of waterlogged areas (ponded water), salt affected soils (salt efflorescence) and high water table zones (potential waterlogging zones) in the Indira Gandhi Nahar Pariyojona (IGNP) command area (India). The false colour composites (bands 4, 3, 2) for February 1996, November 1996 and June 1998 on 1:50 000 scale revealed occurrence and seasonal dynamics of permanent waterlogging in low-lying flats and depressions. The extent of waterlogging was higher in February 1996 due to less evaporation and more agricultural operation during the period. Salt accumulation was higher in November 1996 due to freshly precipitated seasonal salts. Seepage and accumulation of excess irrigation water through coarse sandy mass was primarily responsible for the development of waterlogging in the irrigated zone. The capillary rise of soluble salts with a rising water table and high evaporative demand caused secondary soil salinization. A ground truth study found areas with a high water table (<1.5 m) with patchy crop stands and a potentially sensitive zone with a fluctuating (1.5–6.0 m) water table with poor vegetative growth. The soil characteristics showed moderate to high soil salinity in the control section of soil profiles. These were characterized by medium to coarse texture, weak to moderately strong structure, weak consistency, low organic matter content and the presence of abundant CaCO₃ nodules. The composition of saturated soil paste showed a preponderance of chlorides and sulphates of sodium, calcium and magnesium. The presence of fine texture and calcium carbonate layers at a depth below the surface caused the development of a perched water table indicating unsuitability for traditional irrigated agriculture. The quality of pond water was extremely poor and unfit for reuse. The ground water was saline in some areas but normally lies within the prescribed limit. The quality of drainage water was poor in saline depressions and unsuitable for reuse but moderate in other areas suggesting its safe reuse when mixed with good quality water. Suitable soil and water management practices were necessary for sustainable crop production in the command area.     

Spatio-temporal Assessment of Urban Heat Island Effects in Kuala Lumpur Metropolitan City Using Landsat Images

Alteration in climatic pattern has resulted to a steady decline in quality of life and the environment, especially in and around urbanized areas. These areas are faced with increasing surface temperature arising mostly from human activities and other natural sources; hence land surface temperature has become an important variable in global climate change studies. In this paper, Landsat TM/ETM imagery acquired between 1997 and 2013 were used to extract ground brightness temperature and land use/land cover change in Kuala Lumpur metropolis. The main objective of this paper is to examine the effectiveness of quantifying UHI effects, in space and time, using remote sensing data and, also, to find the relationship between UHI and land use change. Four land use types (forest, farmland, built-up area and water) were classified from the Landsat images using maximum likelihood classification technique. The result reveals that Greater KL experienced an increase in average temperature from 312.641°K to 321.112°K which was quite eminent with an average gain in surface temperature of 8.4717°K. During the period of investigation (1997–2013), generally high temperature is been experienced mostly in concentrated built-up areas, the less concentrated have a moderate to intermediate temperature. Again, the study also shows that low and intermediate temperature classes loss more spatial extent from 2,246.89 Km² to 1,164.53 Km² and 6,102.42 Km² to 3,013.63 Km² and a gain of 4,165.963 Km² and 307.098 Km² in moderate and high temperature respectively from 1997 to 2013. The results of this study may assist planners, scientists, engineers, demographers and other social scientists concerned about urban heat island to make decisions that will enhance sustainable environmental practices.