Management and development of river basins in north bengal using remote sensing techniques

River basin management and development are most important for utilising and conserving the water resources in any region where the average annual rainfall is highly fluctuating, the North Bengal is one of such region. Though there are a large number of rivers having a large catchment area in the hills and carry a huge amount of water during rainy season but they are completely dry in winter and summer. The upper part of majority of river basins arc highly sensitive to water erosion and land slides due to deforestation, intensive cultivation, increased settlements in sloping and erosion prone areas. So, floods in the rainy season and droughts in summer months are frequent features in North Bengal. In view of this proper management and effective conservation of land and water resources is necessary to develop the region. An integrated river basin management consisting soil conservation, afforestation, flood control, and adoption of suitable cropping pattern is essential for economic development of North Bengal.     

Crop acreage estimation using ERS-1 SAR data

Synthetic Aperture Radar (SAR) data from the European Remote Sensing Satellite (ERS-1) acquired in July, October and November, 1992 covering the kharif season of the region were used separately and in combination to identify the major crops and for estimation of their acreage before harvest Separability indices were calculated for major cover types and it was found that single-date SAR data cannot be used for accurate identification of various crops. Multi-temporal colour composite facilitated better identification of crop types. Comparison of area estimates made with ERS-1 SAR and IRS-1B LISS II data showed that the commonly used digital data analysis techniques (per pixel classifiers) are not adequate for accurate estimation of crop acreage using SAR data.     

Forest cover mapping of East district,Sikkim using IRS-1A LISS II satellite data

Remote sensing techniques have been applied to classify tour density classes within each of the forest type along with other major landuse/landcover classes in the East district, Sikkim using IRS-1A LISS II satellite data pertaining to the period of November, 1988. The shadow problem in rugged terrain and difficulty in acquiring cloud free data for different seasons pose problems to achieve considerable mapping accuracy. In the present study, the forests of the district were delineated through supervised classification techniques using maximum likelihood algorithm into five forest types as sal forests, subtropical broad-leaved forests, Himalayan wet temperate forests, Rhododendron forests and alpine forests. The alpine forests were further stratified into two categories as moist alpine scrub and dry alpine scrub. The statistical data obtained from the present study shows that 55.47 percent of the total geographical area of the East district was under forest cover. An overall accuracy of more than 85 percent in correctly delineating forest classes was achieved.     

Electricity consumption patterns within cities: application of a data-driven settlement characterization method

ABSTRACT

Urban areas presently consume around 75% of global primary energy supply, which is expected to significantly increase in the future due to urban growth. Having sustainable, universal energy access is a pressing challenge for most parts of the globe. Understanding urban energy consumption patterns may help to address the challenges to urban sustainability and energy security. However, urban energy analyses are severely limited by the lack of urban energy data. Such datasets are virtually non-existent for the developing countries. As per current projections, most of the new urban growth is bound to occur in these data-starved regions. Hence, there is an urgent need of research methods for monitoring and quantifying urban energy utilization patterns. Here, we apply a data-driven approach to characterize urban settlements based on their formality, which is then used to assess intra-urban urban energy consumption in Johannesburg, South Africa; Sana’a, Yemen; and Ndola, Zambia. Electricity is the fastest growing energy fuel. By analyzing the relationship between the settlement types and the corresponding nighttime light emission, a proxy of electricity consumption, we assess the differential electricity consumption patterns. Our study presents a simple and scalable solution to fill the present data void to understand intra-city electricity consumption patterns.     

利用NOAA NDVI数据集监测冬小麦生育期的研究

探索了利用NDVI研究作物生育期的方法，对黄淮海冬麦区的返青期、抽穗期、成熟期进行了估测，并利用地面实际观测资料进行了验证。结果表明，NDVI数据对大范围农作物生育期监测是可行的。冬小麦遥感反青期由南到北依次推迟，符合春季绿波由南到北推移规律。对冬小麦遥感生育期年际变化分析表明，黄淮海平原返青期变化相对较大，而抽穗期和成熟期变化较小。根据历年月平均温度与返青期分析，冬小麦返青日期与2月份平均温度密切相关。对于局部地区，利用5d合成1km分辨率数据，且按农业生态分区分别制定生育期判别标准，估测效果将更好。     

Laser-based validation of GLONASS orbits by short-arc technique

 The International GLONASS Experiment (IGEX-98) was carried out between 19 October 1998 and 19 April 1999. Among several objectives was the precise orbit determination of GPS and GLONASS satellites and its validation by laser ranging observations. Local laser-based orbit corrections (radial, tangential and normal components in a rotating orbital local reference frame) are computed using a geometrical short-arc technique. The order of magnitude of these corrections is at the level of few decimeters, depending on the considered components. The orbit corrections are analyzed as a function of several parameters (date, orbital plane, geographical area). The mean corrections are at the level of several centimeters. However, when averaging over the entire campaign and for all the satellites, no mean radial, tangential and normal orbit corrections are found. The origin of the observed corrections is considered (errors due to the geocentric gravitational constant, the non-gravitational forces, the thermal equilibrium of on-board equipment, the reference systems, the location and the signature of the retroreflector array, and the precision of the satellite laser ranges). Some features are also due to errors in the radio-tracking GLONASS orbits. Further investigations will be needed to better understand the origin of various biases. Received: 17 February 2000 / Accepted: 31 January 2001     

AnnGIS: Integration of GIS and a Continuous Simulation Model for Non-Point Source Pollution Assessment

Non-Point Source (NPS) models and monitoring data are often used to evaluate management practices and develop NPS pollution control plans. Application of a dynamic NPS model requires efficient input data acquisition, storage, organization, reduction, and analysis accompanied by manipulation, interpretation, reporting, and display of model outputs. A Geographic Information System (GIS) helps extract, store, and organize input data as well as manipulate and display model outputs. This paper illustrates the development of an integrated GIS system for a continuous simulation, pollutant-loading model, AnnAGNPS ( Ann ualized AG ricultural N on- P oint S ource Pollution). The integrated system, called AnnGIS, was developed using the ArcView GIS and related program extensions. Using AnnGIS, modeling studies and management plans can be efficiently and easily developed. AnnGIS helps store, organize, and manipulate spatial and tabular data, extract spatial input parameters, develop analysis scenarios, and visualize input and output data in spatial, tabular, and graphical forms. AnnGIS is generic in nature (not limited to a particular geographic location) and can be successfully used in regions for which AnnAGNPS is designed. AnnGIS's powerful graphical user interface and reference data sets facilitate efficient and informed decision-making concerning agricultural non-point pollution control and management.     

Thematic mapping from remotely sensed data with neural networks: MLP, RBF and PNN based approaches

 Neural networks are attractive tools for the derivation of thematic maps from remotely sensed data. Most attention has focused on the multilayer perceptron (MLP) network but other network types are available and have different properties that may sometimes be more appropriate for some applications. Here a MLP, radial basis function (RBF) and probabilistic neural network (PNN) were used to classify remotely sensed data of an agricultural site. The accuracy of these classifications ranged from 86.25–91.25%. The accuracy of the PNN classification could be increased through the incorporation of prior probabilities of class membership but the accuracy of each classification could also be degraded by the presence of an untrained class. Post-classification analyses, however, could be used to identify potentially misclassified cases, including those belonging to an untrained class, to increase accuracy. The effect of the post-classification analysis on the accuracy of the classification derived from each of the three network types investigated differed and it is suggested that network type be selected carefully to meet the requirements of the application in-hand. Received: 23 March 2000 / Accepted: 9 July 2000     

Lineaments and seismicity of Kerala — a remote sensing based analysis

Seismically active lineaments of Kerala State were identified by correlating the lineaments mapped using IRS LISS-I data with the earthquake occurrences. There are 31 earthquake incidences in Kerala since 1821, out of which 22 are falling on/close to 9 major lineaments/faults (length more than 20 km) indicating the possible correlation between lineaments/faults and earthquakes. It was observed that the earthquake occurrences are mostly associated with the NNW-SSE to NW-SE trending lineaments (6 out of 9 lineaments), which are considered to be formed sympathetic to the West Coast Fault. Hence, there is a need to monitor these seismically active lineaments using advanced techniques such as GPS, SAR Interferometry etc. for better understanding of the influence of these lineaments on the seismic activities of Kerala.     

Corridor analysis in Rajaji-Corbett elephant reserve — A Remote sensing and GIS approach

This paper deals with corridor analysis in Rajaji-Corbett Elephant Reserve in the Shivaliks of Uttaranchal state. Efforts were made to detect changes in the state of forest covervis- à -vis the status of corridors during the three periods i.e. 1967. 1986 and 1998 using remote sensing and GIS. TheERDAS Imagine digital image processing andArcView GIS software packages were used for this purpose. Temporal satellite imagery and ground observations in the Rajaji-Corbett Elephant Reserve revealed forest loss, degradation and disturbances in the corridor areas, hindering elephant movement and restricting them to forests islands. Motichur-Chilla corridor, despite being a highly favoured habitat for the elephants was found to be highly threatened followed by Kotdwar and Ramnagar corridors. Construction of wide bridges across Kunao-Chilla Canal, recreation of corridors through reforestation, reduction of all kinds of pressures in the corridor areas and providing higher protection to corridors are recommended.