Risk management support through India Remote Sensing Satellites

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Modelling species distributions with remote sensing data: bridging disciplinary perspectives

The classification of optical satellite-derived remote sensing images is an important satellite remote sensing application. Due to the wide variety of artificial features and complex ground situations in urban areas, the classification of complex urban features has always been a focus of and challenge in the field of remote sensing image classification. Given the limited information that can be obtained from traditional optical satellite-derived remote sensing data of a classification area, it is difficult to classify artificial features in detail at the pixel level. With the development of technologies, such as satellite platforms and sensors, the data types acquired by remote sensing satellites have evolved from static images to dynamic videos. Compared with traditional satellite-derived images, satellite-derived videos contain increased ground object reflection information, especially information obtained from different observation angles, and can thus provide more information for classifying complex urban features and improving the corresponding classification accuracies. In this paper, first, we analyze urban-area, ground feature characteristics and satellite-derived video remote sensing data. Second, according to these characteristics, we design a pixel-level classification method based on the application of machine learning techniques to video remote sensing data that represents complex, urban-area ground features. Last, we conduct experiments on real data. The test results show that applying the method designed in this paper to classify dynamic, satellite-derived video remote sensing data can improve the classification accuracy of complex features in urban areas compared with the classification results obtained using static, satellite-derived remote sensing image data at the same resolution.

Remote Sensing, Geospatial Technologies and Coastal Ecosystems.- Remote Sensing, Geospatial Technologies and Coastal Ecosystems.- Conceptual and Technical Issues.- Sensors and Techniques for Observing Coastal Ecosystems.- Geographic Information Systems and Spatial Analysis for Coastal Ecosystem Research and Management.- Fuzzy Approach for Integrated Coastal Zone Management.- Spatial Data Infrastructures for Coastal Environments.- Remote Sensing of Coastal Waters.- Airborne Remote Sensing of Chlorophyll in Chesapeake Bay, USA.- Bio-Optical Characteristics and Remote Sensing in the Mid Chesapeake Bay Through Integration of Observations and Radiative Transfer Closure.- Mapping Submerged Aquatic Vegetation and Benthic Habitats.- High-Resolution Ocean Color Remote Sensing of Coral Reefs and Associated Benthic Habitats.- An Integrated Approach to Benthic Habitat Mapping Using Remote Sensing and GIS: An Example from the Hawaiian Islands.- Assessment of the Abundance of Submersed Aquatic Vegetation (SAV) Communities in the Chesapeake Bay and its Use in SAV Management.- Distribution and Spatial Change of Hudson River Estuary Submerged Aquatic Vegetation: Implications for Coastal Management and Natural Resource Protection.- Mapping Marine Macrophytes along the Atlantic Coast of Tierra Del Fuego (Argentina) by Remote Sensing.- Shoreline Change, Coastal Wetland and Watershed Characterization.- Shoreline Mapping and Coastal Change Studies Using Remote Sensing Imagery and LIDAR Data.- Remote Sensing of Coastal Mangrove Forest.- Remote Sensing Support for Tidal Wetland Vegetation Research and Management.- Assessment of Coastal-Vegetation Habitats Using Airborne Laser Remote Sensing.- Measuring Habitat Changes in Barrier Island Marshes: An Example from Southeastern North Carolina, USA.- Mapping Fire Scars and Marsh Recovery with Remote Sensing Data.- Response of Reed Mudflats in the Caspian Coastal Zone to Sea Level Fluctuations.- Integrating Satellite Imagery and Geospatial Technologies for Coastal Landscape Pattern Characterization.- Integrated Coastal Ecosystem Assessment.- Remote Sensing and Spatial Analysis of Watershed and Estuarine Processes for Conservation Planning in Elkhorn Slough, Monterey County, California.- Runoff Water Quality, Landuse and Environmental Impacts on the Bellairs Fringing Reef, Barbados.

Structurally disturbed zones of Himalaya are among the worst landslide affected regions in the world. Although landslides are induced/triggered either by torrential rain during monsoon or by seismic activity in the region, the inherent terrain conditions characterize the prevailing basic conditions susceptible to landslides. Using remotely sensed data and Geographic Information System (GIS), geological and terrain factors can be integrated for preparation of factor maps and demarcation of areas susceptible to landslides. Moderate to high resolution data products available from Indian Remote Sensing satellites have been utilized for deriving geological and terrain factor maps, which were integrated using knowledge driven heuristic approach in Integrated Land and Water Information System (ILWIS) GIS. The resultant map shows division of the area into landslide susceptibility classes ranked in terms of hazard potential in one of the structurally disturbed zones in western Himalaya around Rishikesh.

<strong class="journal-contentHeaderColor">Abstract.</strong> The Carnegie–Ames–Stanford Approach (CASA) model is widely used to estimate vegetation net primary productivity (NPP) at regional scales. However, the CASA is still driven by multisource data, e.g. satellite remote sensing (RS) data, and ground observations that are time-consuming to obtain. RS data can conveniently provide real-time regional information and may replace ground observation data to drive the CASA model. We attempted to improve the CASA model in this study using the Moderate Resolution Imaging Spectroradiometer (MODIS) RS products, the GlobeLand30 RS product, and the digital elevation model data derived from radar RS. We applied it to simulate the NPP of alpine grasslands in the Qinghai Lake basin, which is located in the northeastern Qinghai–Tibetan Plateau, China. The accuracy of the RS-data-driven CASA, with a mean absolute percent error (MAPE) of 22.14 % and root mean square error (RMSE) of 26.36 g C m<span class="inline-formula">^−2</span> per month, was higher than that of the multisource-data-driven CASA, with a MAPE of 44.80 % and RMSE of 57.43 g C m<span class="inline-formula">^−2</span> per month. The NPP simulated by the RS-data-driven CASA in July 2020 shows an average value of 108.01 <span class="inline-formula">±</span> 26.31 g C m<span class="inline-formula">^−2</span> per month, which is similar to published results and comparable with the measured NPP. The results of this work indicate that simulating alpine grassland NPP with satellite RS data rather than ground observations is feasible. We may provide a workable reference for rapid simulation of grassland NPP to satisfy the requirements of accounting carbon stocks and other applications.

Data conversion From raster to vector (R2V) is a key function in Geographic Information Systems (GIS) and remote sensing (RS) image processing for integrating GIS and RS data. The R2V module is available in commercial RS software packages, but there is still room to improve the computation efficiency. This paper presents an efficient R2V algorithm that processes large images and automatically builds GIS topology while scanning image lines one by one. The new algorithm, termed Two-Arm Chains Edge Tracing (TACET), has several significant advantages. First, it converts all types of area objects of RS classification in only one processing cycle. Secondly, it constructs complete area topological relationship by recording the shared edge between two polygons only once. Finally, it is scalable when processing large images. The program based on the algorithm is faster in processing large RS images with comparison to commercial software such as ENVI.

Land consumption is increasing rapidly with the exponential growth of population. The built-up environment configuration influences the management processes for development and other municipality works. Population growth also affects the availability of land for different purposes in its spatial distribution. The present study was conducted using satellite remote sensing data Landsat MSS (Multi-spectral Scanner), ETM+ (Enhanced Thematic Mapper), IRS P-6 (Indian Remote Sensing Satellite), LISS IV (Linear Imaging Self-Scanner), and IRS P-5 Cartosat-1 for the assessment of urban area change dynamics between years 1976 and 2008 in Bhagalpur city in the state of Bihar in India. The ground truth and coordinate points were collected using a Global Positioning System (GPS) for the location of the built-up themes prepared in the Geographic Information System (GIS). Land Consumption Rate (LCR) and Land Absorption Coefficient (LAC) were introduced to aid in the quantitative assessment changes. The results show a rapid growth in land consumption rate till 1976, reduction in 1999 and again increase in 2008.

This paper reports the development of a new shaped beam antenna for remote sensing satellite data transmission applications at S-band. For medium altitude, near earth satellites such as those used in remote sensing applications in sun-synchronous mode, data transmission link can be optimised for the whole pass of the satellite by making the antenna pattern profile vary so as to compensate for the path loss profile - i.e., the pattern profile becomes an inversion of the path loss profile. Such an antenna pattern, having all other parameters fixed, provides a uniform data level throughout the pass. The present development is for a similar application for an Indian Remote Sensing Satellite and this development provides for 6.5 dBi gain with higher polarisation purity using a shaped reflector and helical feeding system.

Soil and water are important natural resources needed for the sustained quality of human life and the foundation of agricultural development. Efficient management of these natural resources is the major challenge for the agricultural scientists, planners, administrators and farmers to ensure food, water and also environmental security for the present and future generations. Soil is the essence of life. An intimate knowledge on their characteristics, classification, location, extent and distribution, potentials and problems is a prerequisite for developing rational land use planning. Soil resource inventory provides an insight into the potentialities and limitation of soil for its effective exploitation. Modern tools such as satellite Remote Sensing, Global Positioning System (GPS) and Geographical Information System (GIS) have been providing newer dimensions to monitor and manage soil resources for their effective utilization. Especially remote sensing techniques have reduced our field work to a considerable extent and soil boundaries are more precisely delineated than in conventional methods. Hence it is a highly proven technology that is effective for mapping and characterizing land resources.

Remote sensing technologies have enabled rapid collection of data on environmental disasters where contemporaneous field observations are unavailable or incomplete. We focus on satellite-remote-sensing-based approaches to monitor environmental disasters caused by surplus or deficient water resources, and outline methods to detect events and evaluate the damage caused by them. These disasters include flood and drought. Flood can be detected easily from the remote sensing image based on the salient characteristics of spectral reflectance or the backscattering coefficient. The occurrence of a drought can be predicted from a combination of some indices based on the satellite remote sensing data and meteorological data, However, many problems remain as to how to effectively predict a disaster and avert its damage before the disaster actually occurs, using a combination of remote sensing techniques, geographical information system (GIS) and administrative frames.

Geometrical and thematic data about terrain objects stored in a geographical information system (GIS) can be kept up-to-date by using remote sensing (RS) data. Geometrical and thematic data can be extracted from the RS data by segmentation and classification techniques respectively. The possibilities and reliability of the information extraction from RS data can be improved by the use of ancillary data and knowledge about the terrain objects. Object classification and aggregation hierarchies can be used to describe relationships between terrain objects; the categorization of the different types of terrain object dynamics that is presented will be based partly on these hierarchical relationships. Object models will be applied in a case study in which both the field geometry (field boundaries) and the crop type of agricultural fields are updated from a Landsat TM image. For that purpose, a three-stage strategy has been developed. In the first stage, the results of an edge detection procedure are integrated with fixed geometrical data already contained in the GIS by using knowledge about the aggregation structure and shape of the fields. In the next stage, the crop type of the fields is determined by means of object-based classification. Finally, conditional merging is performed to solve the problem of oversegmentation. The resulting field geometry was found to agree for 87% with the field geometry as determined by a photo-interpreter. >

With the rapid development of satellite technology, remote sensing data has entered the era of big data, and the intelligent processing of remote sensing image has been paid more and more attention. Through the semantic research of remote sensing data, the processing ability of remote sensing data is greatly improved. This paper aims to introduce and analyze the research and application progress of remote sensing image satellite data processing from the perspective of semantic. Firstly, it introduces the characteristics and semantic knowledge of remote sensing big data; Secondly, the semantic concept, semantic construction and application fields are introduced in detail; then, for remote sensing big data, the technical progress in the study field of semantic construction is analyzed from four aspects: semantic description and understanding, semantic segmentation, semantic classification and semantic search, focusing on deep learning technology; Finally, the problems and challenges in the four aspects are discussed in detail, in order to find more directions to explore.

Interfaces between protected areas and their peripheries in southern Africa are subject to interactions between wildlife and livestock that vary in frequency and intensity. In these areas, the juxtaposition between production and conservation land uses in a context of increasing anthropisation can create issues associated with human-wildlife coexistence and raises concerns for biodiversity conservation, local development and livelihoods. This literature review aimed at addressing the need to consolidate and gather in one article current knowledge on potential uses of satellite remote sensing (SRS) products by movement ecologists to investigate the sympatry of wildlife/domestic ungulates in savanna interface environments. A keyword querying process of peer reviewed scientific paper, thesis and books has been implemented to identify references that (1) characterize the main environmental drivers impacting buffalo (Syncerus caffer caffer) and cattle (Bos taurus &amp; Bos indicus) movements in southern Africa environments, (2) describe the SRS contribution to discriminate and characterize these drivers. In total, 327 references have been selected and analyzed. Surface water, precipitation, landcover and fire emerged as key drivers impacting the buffalo and cattle movements. These environmental drivers can be efficiently characterized by SRS, mainly through open-access SRS products and standard image processing methods. Applying SRS to better understand buffalo and cattle movements in semi-arid environments provides an operational framework that could be replicated in other type of interface where different wild and domestic species interact. There is, however, a need for animal movement ecologists to reinforce their knowledge of remote sensing and/or to increase pluridisciplinary collaborations.

Identification of homogeneous regions in a satellite image is essentially the clustering of pixels in intensity space. Importantly remote sensing image like satellite images contain varieties of land cover types. Some of the covers are significantly large areas whereas some are relatively smaller regions. Therefore, automatically detecting such wide varying areas is a challenging task. Hence, this fact motivated us to propose an improved clustering technique viz. Ensemble based Differential Evolution for Fuzzy Clustering (EDEFC). For this purpose, very recently developed three variants of differential evolution (DE) are used in order to perform the clustering with different set of solutions. As a result, better clustering solution yields from the ensemble of DEs by exhaustive exploration of search space. The proposed EDEFC technique is applied on two numeric remote sensing datasets and Indian Remote Sensing (IRS) satellite image of Kolkata. The results of the EDEFC are shown quantitatively and visually by comparing with eight other clustering techniques. Moreover, the statistical significance test has also been performed in order to judge the superiority of EDEFC.