The use of WorldView-2 satellite imagery to model urban drainage system with low impact development (LID) Techniques

Within a wide range of best management practices for stormwater management in urban areas, there has been an increasing interest in source control measures. Source controls such as low-impact development (LID) techniques are potentially attractive as retrofit options for older developed areas that lack available land to implement conventional measures such as stormwater management ponds. Hence, distributed urban drainage models requiring detailed representation of developed drainage areas should be developed to accurately estimate the benefits that LIDs may provide. This study (1) presents a two-stage classification process on a high-resolution WorldView-2 image, and (2) demonstrates how to use the extracted land cover information in the subsequent hydrologic modelling and assessment of different LIDs’ performance. The proposed two-stage classification method achieved an overall accuracy of 80.6%, whereas a traditional pixel-based achieved 68.4% in classifying the same urban area into six land cover classes. From the classification results, the hydrologic properties of micro-subcatchments were imported in the United States Environmental Protection Agency Storm Water Management Model to assess the performance of LIDs. A reduction of run-off volume 18.2% and 37.1% was found with the implementation of porous pavement and bioretention, respectively, in a typical low-rise residential area located in the city of San Clemente, California, US. The study demonstrates the use of high-resolution remote sensing image to aid in evaluating LID retrofit options, and thus benefits in situations where detailed drainage area information is not available.     

Use of terain factors in evaluation and classifications of Darjeeling district,West bengal

The use of LANDSAT data has proved to be a major aid to semi-macroscale geomorphic mapping of terrain. The geomorphological contribution to the classifications of the Darjeeling District, using quantitative measures of selected terrain factors apart from conventional landsystem mapping on MSS-imagery, has been found to be more useful means of terrain evaluation as well as its proper development in resource management. Qualitative estimates of terrain factors, viz. altitude, relief, slope and dissection nature, drainage texture and pattern, valley forms and crest types coupled with landcover types etc. are considered in preparation of landsystem map of the area. Because of its subjective biasness, an alternative approach, the parametric method of terrain classification is carried out to analyse the spatial distributions of quantitative parameters on terrain factors viz. maximum altitude, average altitude, relative relief, average slope, dissection index, drainage density and ruggedness index etc. and to combine the resulting patterns into compositive generalizations. The composite map thus prepared reveals six terrain units of distinct morphological classes and are sufficiently equatable to certain physical conditions of terrain.     

Landslide Mapping and Assessment by Integrating Landsat-8, PALSAR-2 and GIS Techniques: A Case Study from Kelantan State,Peninsular Malaysia

Integration of satellite remote sensing data and GIS techniques is an applicable approach for landslide mapping and assessment in highly vegetated regions with a tropical climate. In recent years, there have been many severe flooding and landslide events with significant damage to livestock, agricultural crop, homes, and businesses in the Kelantan river basin, Peninsular Malaysia. In this investigation, Landsat-8 and phased array type L-band synthetic aperture radar-2 (PALSAR-2) datasets and analytical hierarchy process (AHP) approach were used to map landslide in Kelantan river basin, Peninsular Malaysia. Landslides were determined by tracking changes in vegetation pixel data using Landsat-8 images that acquired before and after flooding. The PALSAR-2 data were used for comprehensive analysis of major geological structures and detailed characterizations of lineaments in the state of Kelantan. AHP approach was used for landslide susceptibility mapping. Several factors such as slope, aspect, soil, lithology, normalized difference vegetation index, land cover, distance to drainage, precipitation, distance to fault, and distance to the road were extracted from remotely sensed data and fieldwork to apply AHP approach. The excessive rainfall during the flood episode is a paramount factor for numerous landslide occurrences at various magnitudes, therefore, rainfall analysis was carried out based on daily precipitation before and during flood episode in the Kelantan state. The main triggering factors for landslides are mainly due to the extreme precipitation rate during the flooding period, apart from the favorable environmental factors such as removal of vegetation within slope areas, and also landscape development near slopes. Two main outputs of this study were landslide inventory occurrences map during 2014 flooding episode and landslide susceptibility map for entire Kelantan state. Modeled/predicted landslides with a susceptible map generated prior and post-flood episode, confirmed that intense rainfall throughout Kelantan has contributed to produce numerous landslides with various sizes. It is concluded that precipitation is the most influential factor for landslide event. According to the landslide susceptibility map, 65% of the river basin of Kelantan is found to be under the category of low landslide susceptibility zone, while 35% class in a high-altitude segment of the south and south-western part of the Kelantan state located within high susceptibility zone. Further actions and caution need to be remarked by the local related authority of the Kelantan state in very high susceptibility zone to avoid further wealth and people loss in the future. Geo-hazard mitigation programs must be conducted in the landslide recurrence regions for reducing natural catastrophes leading to loss of financial investments and death in the Kelantan river basin. This investigation indicates that integration of Landsat-8 and PALSAR-2 remotely sensed data and GIS techniques is an applicable tool for Landslide mapping and assessment in tropical environments.     

浅谈珠海市土地利用现状图的利用和统计方法

土地利用现状图,即反映土地利用的现状的图形。它以土地现状调查图为依据,用界址拐点,权属界线相应的地物图式符号及注记,表示土地权属的合法性,并利用权属界线量算出土地的权属单位的准确面积,从而满足土地登记和管理的需要。它来源于现代测绘科学技术结合地理信息系统。本文详细介绍了珠海市土地利用现状图的利用和统计方法。     

Remote Sensing and GIS Based Landslide Susceptibility Assessment using Binary Logistic Regression Model: A Case Study in the Ganeshganga Watershed, Himalayas

A comprehensive Landslide Susceptibility Zonation (LSZ) map is sought for adopting any landslide preventive and mitigation measures. In the present study, LSZ map of landslide prone Ganeshganga watershed (known for Patalganga Landslide) has been generated using a binary logistic regression (BLR) model. Relevant thematic layers pertaining to the causative factors for landslide occurrences, such as slope, aspect, relative relief, lithology, tectonic structures, lineaments, land use and land cover, distance to drainage, drainage density and anthropogenic factors like distance to road, have been generated using remote sensing images, field survey, ancillary data and GIS techniques. The coefficients of the causative factors retained by the BLR model along with the constant have been used to construct the landslide susceptibility map of the study area, which has further been categorized into four landslide susceptibility zones from high to very low. The resultant landslide susceptibility map was validated using receiver operator characteristic (ROC) curve analysis showing an accuracy of 95.2 % for an independent set of test samples. The result also showed a strong agreement between distribution of existing landslides and predicted landslide susceptibility zones.     

Estimation of Sediment Yield and Selection of Suitable Sites for Soil Conservation Measures in Ahar River Basin of Udaipur,Rajasthan using RS and GIS Techniques

The present study aimed at quantification of sediment yield for Ahar River basin of Udaipur district in Rajasthan, India by a regional empirical model using RS and GIS techniques. The land use/land cover (LULC) map of the study area was prepared by supervised classification using satellite imagery of IRS-P6 LISS III. Overall accuracy of the prepared LULC map was 90.78%. The major portion of the study area (49%) is covered with rangeland. Slope map for the study area was developed using digital elevation model. The slope in most of the study area (40% of the total area) ranges from 1% to 4%. In addition, drainage density map of the study area was generated on micro-watershed basis. The study area is covered by a dendritic pattern of drainage which shows that rocks in the area are homogeneous and uniformly resistant to water flow. The drainage density in the study area is 1.11 km km⁻². Annual sediment yield of the study area was quantified by Garde model. The mean annual runoff and sediment yield for the area was 37.58 million m³ and 8,760 m³/year, respectively. Finally, appropriate sites for construction of soil conservation measures are suggested using Boolean logic analysis method on combined slope and drainage maps.     

Remote Sensing and Geographical Information System for canopy cover mapping

Availability of remote sensing data from earth observation satellites has made it convenient to map and monitor land use/land cover at regional to local scales. A land cover map is very critical for a various planning activities including watershed planning. The spectral and spatial resolutions are major constraints for mapping the crop resources at microlevel. The cropping pattern zones have been mapped using the false color composite, physiography, irrigation and toposheets. The IRS LISS-III data is classified into various categories depending on spectral reflectance from crop canopy and are overlaid on cropping zones map. The re-classified resultant map provides land use/land cover information including dominant cropping systems. The canopy cover is estimated monthly considering the crop calendar for the area.     

Quantifying uncertainty in remote sensing-based urban land-use mapping

Land-use/land-cover information constitutes an important component in the calibration of many urban growth models. Typically, the model building involves a process of historic calibration based on time series of land-use maps. Medium-resolution satellite imagery is an interesting source for obtaining data on land-use change, yet inferring information on the use of urbanised spaces from these images is a challenging task that is subject to different types of uncertainty. Quantifying and reducing the uncertainties in land-use mapping and land-use change model parameter assessment are therefore crucial to improve the reliability of urban growth models relying on these data. In this paper, a remote sensing-based land-use mapping approach is adopted, consisting of two stages: (i) estimating impervious surface cover at sub-pixel level through linear regression unmixing and (ii) inferring urban land use from urban form using metrics describing the spatial structure of the built-up area, together with address data. The focus lies on quantifying the uncertainty involved in this approach. Both stages of the land-use mapping process are subjected to Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The robustness to uncertainty of the land-use mapping strategy is addressed by comparing the most likely land-use maps obtained from the simulation with the original land-use map, obtained without taking uncertainty into account. The approach was applied on the Brussels-Capital Region and the central part of the Flanders region (Belgium), covering the city of Antwerp, using a time series of SPOT data for 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original land-use map – indicating absence of bias in the mapping process – it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, uncertainties observed in the derived land-use maps should be taken into account when using these maps as an input for modelling of urban growth.     

Photogeomorphological mapping of southern parts of Jadukata-Umngi river valleys,Meghalaya

Photogeomorphological mapping was carried out in parts of the lower reaches of the Jadukata-Umngi river valleys in the southern fringes of Meghalaya. The area has been divided into five geomorphic units. Topography in the area is controlled both by structure and lithology and the geomorphic features greatly facilitate in structural and lithological interpretation. This becomes quite evident from the photogeological map of the corresponding area.     

Geologic and geomorphic controls in land use patterns — examples using remote sensing techniques

Land use on earth’s landscape primarily indicates the degree of human activity which in turn reflects the man’s life style. Though land use is controlled by several factors like soil, hydrologic, climatic, socio-economic and political yet geology and gecmorphology play an important role. The basic lanscape formation depends mainly on the inherent lithology and sturcture. Certain landforms in the present landscape are such that they are suitable to a particular land use, and the human being from the very beginning without knowing this natural relationship between the landform and landuse, have been using that part of land for that particular use only. Remote sensing techniques are used effectively to map the landforms, to find out the land use pattern and to delineate the lithology and structure. Some examples from east coast deltaic plains and sedimentary terrain of Tripura state have been given here to show the influence of geology and geomorphology on broad land use pattern.     

Morphology and hydrodynamics of few fluvial forms in the sand bar of Yamuna at Wazirabad,Delhi

Delineation and mapping of several fluvial forms in the Wazirabad Sand bar have been made in the field after a base map of Delhi area has been prepared on 1:30,000 scale from air photos. On the basis of the morphology, internal structures and lithology it has been possible to interpret the genesis and history of these fluvial forms. Current vector analysis of both the sand body strucutres and channel has been carried out to show an interrelationship.     

基于低空航摄技术对古遗址地面拾取精度的探讨

随着国民经济的飞速发展与城市现代化建设速度的日益加快,城市空间范围内部原有古迹遗址的保护与现代化城市空间格局建设之间的矛盾日益显现。如何使这两者之间达到平衡需要政府部门进行考量。传统田野测绘方法已不能满足现代大面积遗址测绘的要求,因此本文以苏州发掘的某处古迹为例,运用低空航空摄影测量的技术原理制作了数字正射影像图(DOM),并对生产数字线划图(DLG)及如何获取大比例尺地形图做出了详细介绍。同时基于此次实验案例分析的过程,总结了低空航空摄影测量的关键技术理论和方法,运用Pix4D mapper软件和CASS软件进行航片DOM及大比例尺地形图的生成,通过分析其处理结果的精度,总结出影响精度结果的因素。     

Delineation of ground water potential zones in a hard rock terrain of Mysore district, Karnataka using IRS data and GIS techniques

In the present study, the ground water prospect of Mysore district has been delineated using remotely sensing data, base map of GSI, ground truth data, and geographic information system. Based on these integrated studies, it has been noticed that the lithology of the area mainly represents by amphibolite schists, hornblende-biotite gneiss, chamockite, quartzite and granite belonging to Archean to Lower Proterozoic. Resent alluvial deposits mainly occur as valley fills. Major lineaments are mainly confined to valley regions. Structurally the erosional and depositional landforms are occurring as hills, ridges, weathered pediments, weathered pediplains and valley fills. Geonaorphology, lineaments, drainage density, type of lithology and surface water bodies are directly influencing the ground water conditions and it is very good iu valley regions and in remaining area, it is moderate to poor.     

Assessment of radargrammetric DSMs from TerraSAR-X Stripmap images in a mountainous relief area of the Amazon region

The Brazilian Amazon is a vast territory with an enormous need for mapping and monitoring of renewable and non-renewable resources. Due to the adverse environmental condition (rain, cloud, dense vegetation) and difficult access, topographic information is still poor, and when available needs to be updated or re-mapped. In this paper, the feasibility of using Digital Surface Models (DSMs) extracted from TerraSAR-X Stripmap stereo-pair images for detailed topographic mapping was investigated for a mountainous area in the Carajás Mineral Province, located on the easternmost border of the Brazilian Amazon. The quality of the radargrammetric DSMs was evaluated regarding field altimetric measurements. Precise topographic field information acquired from a Global Positioning System (GPS) was used as Ground Control Points (GCPs) for the modeling of the stereoscopic DSMs and as Independent Check Points (ICPs) for the calculation of elevation accuracies. The analysis was performed following two ways: (1) the use of Root Mean Square Error (RMSE) and (2) calculations of systematic error (bias) and precision. The test for significant systematic error was based on the Student’s-t distribution and the test of precision was based on the Chi-squared distribution. The investigation has shown that the accuracy of the TerraSAR-X Stripmap DSMs met the requirements for 1:50,000 map (Class A) as requested by the Brazilian Standard for Cartographic Accuracy. Thus, the use of TerraSAR-X Stripmap images can be considered a promising alternative for detailed topographic mapping in similar environments of the Amazon region, where available topographic information is rare or presents low quality.     

METRIC CAMERA DATA: ASSESSMENT AND POTENTIAL USE AT L'INSTITUT GÉOGRAPHIQUE NATIONAL

A short review is given of the involvement of the Institut Géographique National (IGN) in the European Space Agency Spacelab Metric Camera (MC) experiment. IGN has participated in this project from the early stages of its preparation in 1974. The assessment of MC data has been carried out for both metric and thematic purposes. Using a Matra Traster analytical plotter, a contoured map of Carpentras. included in the European standard test site of Marseille, has been prepared at a scale of 1:100 000 with 100 m and 50 m contour intervals. Preliminary comparison of this map with the topographic data base shows a height accuracy of about 30 m. Orthophotographs have been produced of the same area, at 1:100000 scale and at 1:250000 scale. showing good agreement with the basic line map. An infrared colour pseudo-orthophotograph has been produced for Khartoum (assuming that the ground was flat) and a space map of a desert area of Algeria has been printed at 1:200000 scale, allowing a good comparison with the drainage shown on the existing line map. Interpretation of the MC photography has also been assessed and compared with conventional IGN aerial photography taken over the European standard test site. Due to the low sun elevation and the apparent image motion which affected the MC photography, the result is not as good as was expected. Some linear features of 6m width are visible (for example, roads and hedges) but for positive identification a width of 18m is necessary. It is difficult to determine the extent of cities and land parcels less than 40 × 40m are not visible. However, the MC data can be used for checking and revising some features of small scale maps (smaller than 1:100000). Two experiments have been performed in thematic interpretation, one concerned with land use and the other with forest mapping. Some improvements of the MC are suggested in order to satisfy the major requirements of users while the possibilities of topographic mapping from these data are summarised.     

Groundwater potential modelling using remote sensing and GIS: a case study of the Al Dhaid area,United Arab Emirates

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM and LANDSAT images of spatial resolution 30?m were used to construct groundwater potential zones (GPZ) map by integrating geological fractures, drainage network, slope and relief, and convergence index maps of the study area. Weight and score of each map were developed according to their level of contribution toward groundwater accumulation and spatial distribution of groundwater wells. The area that has very high potential for groundwater is located at the foot of Oman Mountains and Al Dhaid Depression covering an area of about 59.33?km², which is 4.40% of the study area. Further hydrological map and data on hydraulic properties of shallow aquifer, as recorded from observation wells in the regions, have been used to validate the produced GPZ map. The validation result showed sufficient agreement between the produced GPZ map.     

Groundwater resources development in hard rock terrain - an approach using remote sensing and GIS techniques

To demonstrate the capabilities of remote sensing and Geographic Information System (GIS) techniques for groundwater resources development in hard rock terrains, specifically for the demarcation of suitable sites for artificial recharge of groundwater aquifers, a study was carried out in the Kallar Basin, which is located in parts of the Salem and Tiruchirapalli districts, Tamil Nadu, India. Thematic maps defining lithology, lineaments, landforms, landuse, drainage density, thickness of weathered zone, thickness of fractured zone, hydrological soils, and well yield were prepared from data collected by the Indian Remote Sensing Satellite (IRS) -1C and by conventional methods. All the thematic layers were integrated using a GIS-based model developed specifically for this purpose, enabling a map showing artificial recharge zones to be generated. The exact type of artificial recharge structure, eg, check dam, nallabund, gully plugging and percolation pond, suitable for replenishing groundwater was identified by superposing a drainage network map over an artificial recharge zones map. The GIS-based demarcation of artificial zones developed in the study was based on logical conditions and reasoning, so that the same techniques (with appropriate modifications) could be adopted elsewhere, especially in hard rock terrain, where the occurrence of groundwater is restricted and subject to greater complexity.     

Automated lithological mapping by integrating spectral enhancement techniques and machine learning algorithms using AVIRIS-NG hyperspectral data in Gold-bearing granite-greenstone rocks in Hutti,India

In this study, we proposed an automated lithological mapping approach by using spectral enhancement techniques and Machine Learning Algorithms (MLAs) using Airborne Visible Infrared Imaging Spectroradiometer-Next Generation (AVIRIS-NG) hyperspectral data in the greenstone belt of the Hutti area, India. We integrated spectral enhancement techniques such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformation and different MLAs for an accurate mapping of rock types. A conjugate utilization of conventional geological map and spectral enhancement products derived from ASTER data were used for the preparation of a high-resolution reference lithology map. Feature selection and extraction methods were applied on the AVIRIS-NG data to derive different input dataset such as (a) all spectral bands, (b) shortwave infrared bands, (c) Joint Mutual Information Maximization (JMIM) based optimum bands, and (d) optimum bands using PCA, to choose optimum input dataset for automated lithological mapping. The comparative analysis of different MLAs shows that the Support Vector Machine (SVM) outperforms other Machine Learning (ML) models. The SVM achieved an Overall Accuracy (OA) and Kappa Coefficient (k) of 85.48% and 0.83, respectively, using JMIM based optimum bands. The JMIM based optimum bands were more suitable than other input datasets to classify most of the lithological units (i.e. metabasalt, amphibolite, granite, acidic intrusive and migmatite) within the study area . The sensitivity analysis performed in this study illustrates that the SVM is less sensitive to the number of samples and mislabeling in the model training than other MLAs. The obtained high-resolution classified map with accurate litho-contacts of amphibolite, metabasalt, and granite can be coupled with an alteration map of the area for targeting the potential zone of gold mineralization.     

Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang Watershed,Iran

The objective of this study is to produce groundwater potential map (GPM) and its performance assessment using a data-driven evidential belief function (EBF) model. This study was carried out in the Koohrang Watershed, Chaharmahal-e-Bakhtiari Province, Iran. It’s conducted in three main stages such as data preparation, groundwater potential mapping using EBF and validation of constructed model using receiver operating characteristic (ROC) curve. At first, 864 groundwater data were collected from spring locations; out of that, 605 (70%) locations were selected for training/model building and the remaining 259 (30%) cases were used for the model validation. In the next step, 12 effective factors such as altitude, slope aspect, slope degree, slopelength (LS), topographic wetness index (TWI), plan curvature, land use, lithology, distance from rivers, drainage density, distance from faults and fault density were extracted from the spatial database. Subsequently, GPM was prepared using EBF model in ArcGIS environment. Finally, the ROC curve and area under the curves (AUC) were drawn for verification purposes. The validation of results showed that the AUC for EBF model is 81.72%. In general, this result can be helpful for planners and engineers in water resource management and land-use planning.     

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.