Soil resource inventory of Punjab using remote sensing technique

Landsat MSS data in the form of BW imagery were used to generate Soil Map of Punjab convering an area of about 5 million ha. MSS bands 2 and 4 (L4) were interpreted singly and combined to form a compostie interpretetion map with which field check, was translated in terms of soils. The abstraction level attained was Great Groups of Soil Taxonomy. The distribution of soils of Punjab, with Aridisols in the SW through Inceptisols in the Central zone, to Alfisols in the NE sectors suggested a strong geographic bias in their evolution. The major soils of the aridic zone (SW sectors of the state) are: Camborthids, Calciorthids, Torripsamments and Torrifluvents and of the Ustic zone (Central Punjab) are Ustochrepts and Haplustalfs (the most productive soils of the State), Ustipsamments and Ustifluvents. The salt affected soils are found interspersed with these soils. In the udic zone (NE fringe), Hapludalfs, Eutrochrepts, Udifluvents, Udorthents and Hapludolls are the major soil formations. The soil map reveals that about one-third of the total area of the state suffers from various soil problems, such as soil salinity and sodicity, water logging, and soil erosion. For increasing agricultural production, these soils need to be brought under the plough. The study leads to conclude that for quick and precise macro level land use planning, the use of Landsat imagery is imperative.     

An assessment of biodiversity hotspots using remote sensing and GIS

This study focuses on the assessment of the status of world’s remaining closed forests (WRCF), population distribution, and protected areas in global biodiversity hotspots using remote sensing and Geographic Information System (GIS). Conservation International (CI) has identified 25 eco-regions, called biodiversity hotspots that are especially rich in endemic species and are particularly threatened by human activities. This study uses globally consistent and comprehensive geo-spatial data sets generated using rerriote sensing and other sources, and the application of GIS layering methods. The consistent data set has made it possible to identify and quantify relationships between the WRCF, human population, and protected areas in biodiversity hotspots. It is expected that such information will provide a scientific basis for biodiversity hotspots management and assist in policy formulations at the national and international levels.     

Landslide susceptibility mapping in a hilly terrain using remote sensing and GIS

Remote sensing and Geographic Information System (GIS) are well suited to landslide studies. The aim of this study is to prepare a landslide susceptibility map of a part of Ooty region, Tamil Nadu, India, where landslides are common. The area of the coverage is approximately 10 × 14 km in a hilly region where planting tea, vegetables and cash crops are in practice. Hence, deforestation, formation of new settlements and changing land use practices are always in progress. Land use and land cover maps are prepared from Indian Remote Sensing Satellite (IRS 1C - LISS III) imagery. Digital Elevation Model (DEM) was developed using 20 m interval contours, available in the topographic map. Field studies such as local enquiry, land use verification, landslide location identification were carried out. Analysis was carried out with GIS software by assigning rank and weights for each input data. The output shows the possible landslide areas, which are grouped for preparation of landslide susceptibility maps.     

Sustainability assessment of sodic land reclamation using remote sensing and GIS

Sodicland reclamation in the Indo-Gangetic plains is being done on a large scale in the states of Uttar Pradesh, Punjab and Haryana in India. However, in certain areas, the reclamation has been reported to be unsustainable and the soils are reverting back to sodicity condition. A study was conducted in one of the reclamation sites of Etawah district for sustainability assessment of sodic land reclamation using remote sensing, Geographic Information system (GIS) and ancillary ground information. Multitemporal satellite data were used for delineation of reclaimed sodiclands and reverted sodic land. Field survey was conducted to find out the various causative factors. Groundwater level information and detailed field survey data were analysed in GIS environment. Results showed that in the reclamation site covering 3,905 ha. in 57 villages of the district, about 27 per cent of reclaimed lands were reverted to sodicity. High water table condition, improper drainage, nearness to canal (within 500 m), and hard pan in the sub-soil were found to be the reasons for unsustainability of reclamation.     

Soil erosion modeling using MMF model -A remote sensing and GIS perspective

Hardly any part of the world has remained unchanged since the arrival of the speciesHomo sapiens including the mountain ecosystems. Himalayan physiographic unit of India in due course has become populated and is tolerating all kinds of human interventions. Soil erosion in this region has been identified as a major problem due to both natural and anthropogenic factors. Remote sensing and Geographical Information system (GIS) techniques hold great promises in the assessment and conservation of natural resources including the surface soil. The major objective of the present study was to apply a process based model to quantify soil erosion and to prioritize the sub-watershed on this basis. The sub-watershed located at Jakhan rao area of Western Dun in lower Himalayan belt was taken as the test site for the study at 1: 50,000 scale. Deforestation, unscientific agricultural practices, terrace farming, cattle grazing and land degradation in the sub-watershed are some of the anthropogenic factors causing soil erosion in the area. Here, MMF model was used for estimation of soil erosion by incorporating layers derived from both remote sensing and ancillary data. IRS 1C LISS III satellite data was used for the preparation of land use map that was used to derive RD map, BD map and K map. Digital Elevation Model (DEM) provided slope map, an intermediate layer used in equation 6 to generate G map, and soil map provided MS map, BD map and K map. The above intermediate layers generated were then integrated in GIS domain to estimate the amount of soil erosion in the sub-watershed area. Results show high values 4572.333 kg/m² for G map, which depicted transport capacity of overland flow. Comparatively lower values 13.15, and 7.98 kg/m²were observed for F map, which depicted soil detachment by raindrop impact. The subtracted image of the aforesaid layers produced the real picture, where in the highest value 3.770 kg/m² was found in the midland region of the site. The crossed erosion map was then classified into different erosion classes for sub-watershed area. This study illustrates the applications of remote sensing and GIS techniques for soil erosion modeling.     

Soil organic carbon mapping using remote sensing techniques and multivariate regression model

Soil organic carbon (SOC) is an important aspect of soil quality and plays an imperative role in soil productivity in the agriculture ecosystems. The present study was applied to estimate the SOC stock using space-borne satellite data (Landsat 4–5 Thematic Mapper [TM]) and ground verification in the Medinipur Block, Paschim Medinipur District and West Bengal in India. In total, 50 soil samples were collected randomly from the region according to field surveys using a hand-held Global Positioning System (GPS) unit to estimate the surface SOC concentrations in the laboratory. Bare soil index (BSI) and normalized difference vegetation ndex (NDVI) were explored from TM data. The satellite data-derived indices were used to estimate spatial distribution of SOC using multivariate regression model. The regression analysis was performed to determine the relationship between SOC and spectral indices (NDVI and BSI) and compared the observed SOC (field measure) to predict SOC (estimated from satellite images). Goodness fit test was performed to determine the significance of the relationship between observed and predicted SOC at p ≤ 0.05 level. The results of regression analysis between observed SOC and NDVI values showed significant relationship (R² = 0.54; p < 0.0075). A significant statistical relationship (r = ?0.72) was also observed between SOC and BSI. Finally, our model showed nearly 71% of the variance of SOC distribution could be explained by SOC and NDVI values. The information from this study has advanced our understanding of the ongoing ecological development that affects SOC dissemination and might be valuable for effective soil management.     

Soil erosion assessment and mapping of the algar river watershed (uttar pradesh) using remote sensing technique

This study was undertaken to prepare an inventory on soil erosion of a hilly river watershed — the Aglar watershed, part of Tehri Garhwal and Dehradun districts (U.P.), using terrain physiography and soil survey data obtained from interpretation and analysis of Landsat TM FCC (1:62,500 scale) and limited ground investigations. The watershed is divided into four broad physiographic units viz. higher Himalayas (> 2000m elevation); lower Himalayas (< 2000m elevation); river terraces and flood plains. Each physiographic unit has been further divided into subunits on the basis of aspects and landuse. Three major orders of soils viz. Inceptisols, Mollisols and Entisols were found in different physiographic units. Soil, and land properties of soilscape units viz. soil depth, texture, structure, slope, landuse and soil temperature regime were evaluated for soil-erosion hazard. The results indicate that in the whole watershed 19.13%, 45.68%, 26.51% and 7.92% areas have been found to be under none to slight, moderate, severe and very severe soil erosion hazard categories, respectively.     

Disaggregating and mapping crop statistics using hypertemporal remote sensing

Governments compile their agricultural statistics in tabular form by administrative area, which gives no clue to the exact locations where specific crops are actually grown. Such data are poorly suited for early warning and assessment of crop production. 10-Daily satellite image time series of Andalucia, Spain, acquired since 1998 by the SPOT Vegetation Instrument in combination with reported crop area statistics were used to produce the required crop maps. Firstly, the 10-daily (1998–2006) 1-km resolution SPOT-Vegetation NDVI-images were used to stratify the study area in 45 map units through an iterative unsupervised classification process. Each unit represents an NDVI-profile showing changes in vegetation greenness over time which is assumed to relate to the types of land cover and land use present. Secondly, the areas of NDVI-units and the reported cropped areas by municipality were used to disaggregate the crop statistics. Adjusted R-squares were 98.8% for rainfed wheat, 97.5% for rainfed sunflower, and 76.5% for barley. Relating statistical data on areas cropped by municipality with the NDVI-based unit map showed that the selected crops were significantly related to specific NDVI-based map units. Other NDVI-profiles did not relate to the studied crops and represented other types of land use or land cover. The results were validated by using primary field data. These data were collected by the Spanish government from 2001 to 2005 through grid sampling within agricultural areas; each grid (block) contains three 700 m × 700 m segments. The validation showed 68%, 31% and 23% variability explained (adjusted R-squares) between the three produced maps and the thousands of segment data. Mainly variability within the delineated NDVI-units caused relatively low values; the units are internally heterogeneous. Variability between units is properly captured. The maps must accordingly be considered “small scale maps”. These maps can be used to monitor crop performance of specific cropped areas because of using hypertemporal images. Early warning thus becomes more location and crop specific because of using hypertemporal remote sensing.     

月球遥感制图回顾与展望

对月球探测任务、月球遥感制图技术与产品进行综述。从1958年开始,全世界已开展126次(其中70次成功)月球探测工程任务,其中月球遥感制图是其必需的基础性工作。由于月球环境的特殊性,其遥感制图技术与对地观测制图相比具有很大的挑战和更大的难度。目前,中国嫦娥二号轨道器获取的7 m分辨率立体影像是覆盖全月球分辨率最高的立体影像数据,美国月球侦察轨道器LRO任务的激光雷达高度计LOLA数据是精度和密度最高的激光测高数据,LRO NAC影像的分辨率最高(0.5—2 m)但未覆盖全球。在各个探测任务中,基于月球遥感数据和摄影测量技术,已经制作了大量的全球及区域的影像拼图、正射影像图和数字高程模型等制图产品。对月球遥感制图技术发展进行展望,探讨了利用国际多探测任务数据建立新一代控制网和进行精细制图的必要性及技术思路。     

Multistage approach in soil mapping using remote sensing techniques

The use of Landsat images at 1∶1 Million and 1∶250,000 scales and aerial photographs at 1∶60,000 scale for preparation of soil maps has been discussed. It was possible to prepare soil maps at Suborder and its association from the Landsat images as the base and Subgroup and its association map using the aerial photographs as base. The compatability of classification of landscape units has been discussed keeping the API map as the standard.     

Combined use of socio economic analysis,remote sensing and GIS data for landslide hazard mapping using ANN

The term landslide includes a wide range of ground movements, such as slides, falls, flows etc. mainly based on gravity with the aid of several conditioning and triggering factors. Particularly in the last two decades, there has been an increasing international interest in the landslide susceptibility, hazard or risk assessments. In this paper we present a combined use of socioeconomic, remote sensing and GIS data for developing a technique for landslide susceptibility mapping using artificial neural networks and then to apply the technique to the selected study areas at Nilgiris district in Tamil Nadu and to analyze the socio economic impact in the landslide locations.     

Mapping and characterization of inland wetlands using remote sensing and GIS

Sustainable management of wetland ecosystem is necessary as it serves the important functions such as food storage, water quality maintenance and providing habitat for different species of wildlife. Hence, an inventory of wetlands in any given area is a pre-requisite for their conservation and management. A study has been carried out to delineate the wetlands of east Champaran district of Bihar, India, using IRS ID LISS III data. The data for the pre and post monsoon seasons have been analysed and the wetlands have been qualitatively characterized based on the turbidity and aquatic vegetation status. The extent of water logging problem in the study area was inferred from the seasonal variation of waterspread during both the seasons. The three categories of wetlands (ponds/lakes, water logged areas and oxbow lakes) have been identified. From the analysis, it has been observed that the inland wetlands constitute 2.7% of the study area, of which 1.8% is subjected to water logging. Thus, this study highlights the usefulness of remotely sensed data for wetland mapping, seasonal monitoring and characterization.     

Mapping of active tectonics intensity zones using remote sensing and GIS

The Bundi-Indergarh sector in southeast Rajasthan is characterized by folded and faulted Vindhyan rocks that are exposed as NE-SW trending long parallel ridges. The sector is separated from older rocks by the Great Boundary Thrust and is traversed by younger cross faults at several localities. The thematic maps of geomorphology, slope, vegetation index and morphotectonic parameters of Bundi-Indergarh sector have been prepared using IRS ID L1SS III and WiFS and, Landsat ETM digital data. These theme are integrated in GIS environment to assess the neotectonic potential in the area. The neotectonic potential map of the sector has been generated that indicates relative potential values as high (55–85), medium (35–55) and low (5–35) on 100-point scale. The observed four high potential zones in the area are located at the intersection of NE-SW and NW-SE lineaments. The study brings out methodology for assessing active tectonic potential of the area.     

基于遥感与GIS的中国湖泊形态分析

基于2010年Landsat TM/ETM数据,结合Google Earth影像和其他资料,采用面向对象的分类方法,以30 m的分割尺度对图像进行分割,并通过比值指数、归一化差值水体指数及谱间关系等指数来提取水体信息,将水体分为河渠、湖泊及水库坑塘3类,并对分类结果进行精度验证,最终得到中国湖泊空间分布信息;以此为基础,计算出反映湖泊形态的景观指数(如形状指数、近圆形指数及分维数等),并结合湖泊的面积和湖泊岸线长度来说明湖泊的基本状况及其形状的复杂程度。结果表明:利用面向对象分类方法可以得到较高的分类精度,3种类型水体分类的总精度为93%,湖泊分类精度达到90%以上;在中国内陆,面积大于1.0 km2的自然湖泊2 477个,总面积约77 934.72 km2;在5个湖区中,青藏高原湖区的湖泊面积最大,占湖泊总面积的54.34%,其形状指数和分维数均值为最小,湖泊形态比较简单;东部平原湖区降雨充沛,水系发达,其形状指数和分维数均值最大,湖泊岸线更加曲折,形态复杂多变;通过分析得出,形状指数与分维数有明显的相关性(R2=0.95),湖泊岸线分维数值越高,其形状指数越大,岸线越复杂。     

Urban cadastral mapping using very high resolution remote sensing data

With growing urban expanses, one of the pre-requisites for effective governance is Urban Information Systems (UIS) with content down to individual properties (and individuals). The basic input i.e., a map, in UIS should show individual property boundaries showing the plan outline of all structures existing within, at a scale of 1:1000 and larger with sub-metre to centimeters planimetric and geometric accuracy. With very high resolution remote sensing data of the order of 1m available in hand, it is possible to prepare maps with high resolution spatial content. The present exercise demonstrates a method of preparing a geometrically and planimetrically accurate urban cadastral map on very large scale for a small area of about 5 sq km. IKONOS merged data with 1m resolution is used for the purpose. Mapping was done in conjunction with on-site measurements and sketches. Guides are used to maintain shape symmetry and accuracy of buildings and other features. Working out cost of mapping per unit area is another objective in the present exercise. For want of fully or semi-automatic methods of information extraction from very high resolution remote sensing data, it is imperative that mapping should be carried out in conjunction with some on-site measurements wherever necessary.     

Database management and analysis for CAPE project using remote sensing and GIS

The present study attempts to conceptualise an approach to integrate the remotely sensed and spatial?n-spatial data generated over the years under Crop Acreage and Production Estimation (CAPE) project through GIS for their easy retrieval and comparison; and to develop a program in dBASE to calculate crop acreage using non-spatial attributes imported from GIS. The “Crop Information System” thus developed would help the planners in analysis/comparison of the database related to crops over the years.     

Monitoring and modelling of urban sprawl using remote sensing and GIS techniques

The concentration of people in densely populated urban areas, especially in developing countries, calls for the use of monitoring systems like remote sensing. Such systems along with spatial analysis techniques like digital image processing and geographical information system (GIS) can be used for the monitoring and planning purposes as these enable the reporting of overall sprawl at a detailed level.