NDVI and NDWI based Change Detection Analysis of Bordoibam Beelmukh Wetlandscape, Assam using IRS LISS III data

This paper analyses per pixel change detection of Bordoibam Beelmukh wetlandscape located in Dhemaji district of Assam, India, which covers around 23 sq. km of geographical area. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) are the two remote sensing indices applied for change detection analysis in the study area. IRS LISS III satellite imageries with 23.5 meter spatial resolution have been used to conduct the analysis. These satellite imageries have been collected from NRSC Bhuvan portal with 5 years temporal interval (2008 to 2013). Image differencing technique has been applied to detect per pixel change using NDVI and NDWI difference image results of the wetlandscape. The study has observed per pixel change detection in five distinct categories for both NDVI and NDWI results of the study area. These are increased more than 5 percent, decreased more than 5 percent, some increase, some decrease and unchanged. In this regard, the study reveals that there is maximum change (79% to total change) in increased more than 5 percent category for NDVI, whereas for NDWI maximum change (96% to total change) is observed under decreased more than 5 percent category of the study area. It has been also observed that there are significant changes of both NDVI and NDWI values from 2008 to 2013 in the study area which in turn indicate changes of vegetation and water cover areas of the same

Borders of water bodies and their water protection zones in wetlands (based on the example of the Iksa river, Tomsk region, Russian Federation)

Relevance. Currently, there are a number of contradictions in defining the boundaries of water bodies, as well as special zones for their protection and use, because there is an urgent need for the correct establishment of such zones. In addition, the calculated probabilities for assessing the position of coastlines are not legally established. In this regard, the article examines the importance of reliably defining the boundaries of coastlines and river water protection zones in wetlands where there is the most contradictions. Aim. Comparative analysis of various methods for assessing the position of the coastline and the boundaries of the water protection zone of a river with a heavily swamped catchment. Methods. Statistical and cartographic methods, interpretation of space and aerial photographs. Results and conclusions. The authors have carried out the analysis of long-term data from routine hydrometeorological observations (1933–2007), materials from field surveys and interpretation of remote sensing data of the river Iksa section near the village Plotnikovo. It is shown that for this river the most rational way to assess the boundaries of a water body is to determine the average long-term maximum water level (the boundaries of the lower floodplain), and the boundaries of the water protection zone – by the maximum water level with a supply of 1% (the boundaries of the upper floodplain). These boundaries are quite noticeable during reconnaissance surveys during engineering studies and when using Earth remote sensing materials. All this increases the efficiency of environmental protection measures due to a more reasonable identification of water protection zones and makes it possible to optimize the methodology for determining their boundaries due to the use of these data instead of formal interpolation between observation points

Vectorization of Large Amounts of Raster Satellite Images in a Distributed Architecture Using HIPI

Vectorization process focus on grouping pixels of a raster image into raw line segments, and forming lines, polylines or poligons. To vectorize massive raster images regarding resource and performane problems, weuse a distributed HIPI image processing interface based on MapReduce approach. Apache Hadoop is placed at the core of the framework. To realize such a system, we first define mapper function, and then its input and output formats. In this paper, mappers convert raster mosaics into vector counterparts. Reduc functions are not needed for vectorization. Vector representations of raster images is expected to give better performance in distributed computations by reducing the negative effects of bandwidth problem and horizontal scalability analysis is done.Comment: In Turkish, Proceedings of International Artificial Intelligence and Data Processing Symposium (IDAP) 201

IQPC 2015 Track: Water Detection And Classification On Multi-Source Remote Sensing And Terrain Data

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Spatiotemporal Impact of Precipitation Trend on LULC Using Satellite Remote Sensing Technique in Khirthar National Park, Sindh Pakistan

: Water footprint techniques are extensively used for essential life chores. It also maintains the naturalecosystem. The variations in climatic spell are not only important to investigate the past and current scenarios, but it isalso useful to develop the water resource projects. The current study explored the spatial-temporal climatic variation ofdry and wet periods (between 1998 and 2010) using the digital image processing technique of ENVI (Environment forVisualizing Images) classics, satellite remote sensing g (SRS), and GIS. The results are organized for the reported periodi.e. between 1998 and 2010, showing the change detection of the hydrological effect in the dry and wet years. It shows asignificant change in the land use land cover (LULC) of vegetation, water, settlement, and ephemeral rivers followed by91%, 97.45%, 94.40%, and 62.94 % respectively through the wet year of 2010, in association with the dry period of1998. For more authentications, the Normalized Difference Vegetative Index (NDVI) image difference of the wet anddry period has also been evaluated, which has shown vegetation in large areas with more water potential in the wet year2010. The water potential can be used by diverting it to the natural depressions, ditches, and ponds for storage purposesand to increase recharge of groundwater by increasing its quality and quantity. The stored water could be utilized in thedrought-prone days for sustainable agriculture activities, to reduce the migration rate of the community, and to improvethe socio-economic conditions in the study area of Khirthar National Park

Detection of Changes in Surface Water Bodies Urban Area with NDWI and MNDWI Methods

Land surface water bodies, an essential part of the Earth’s water cycle such as rivers, lakes, swamp, and reservoirs, influence the global ecosystem and climate global significantly. Makassar, one of the most populated cities in Indonesia, recently experiences massive development that affects the existence of vegetation area and urban aquatic ecosystem. This study attempts to detect the urban surface water bodies and to monitor the change by using Landsat OLI TIRS. In order to extract the high accuracy of data, the image data utilized in this study was acquired by Landsat 8 OLI TIRS sensor on 14 December 2000, 27 December 2009 and 06 January 2019 from the United States Geological Survey (USGS) portal analyzed by Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI). These methods are scientifically used to classify the data into two categories consisted of water and non-water objects. The result shows that in the last nine years, urban surface water bodies increased around 129.8 ha distributed mostly in Manggala area. Due to rapid urban development such as housing, makes the urban runoff concentrating in low land and creates giant swamp as well as an urban wetland. In coastal areas however decline of the water body due to dominantly by massive reclamation, housing and factory settlement. The increase of urban surface water bodies can lower the urban heat while massive development in the built-up area can worsen the urban heat

Assessment of wetland change dynamics of Chennai coast, Tamil Nadu, India, using satellite remote sensing

1258-1266The coastal wetlands of Chennai are increasingly being affected by anthropogenic factors, such as urbanization, residential, and industrial development. This study helps to monitor and map the dynamics of the coastal wetlands of Chennai using Landsat satellite images of 1988, 1996, 2006, and 2016 by following a supervised classification method. Post-classification wetland change detection was done in three temporal phases, that is, 1988 1996, 1996 2006, and 2006 2016. Change detection matrix analysis was performed to identify the from to changes. Ground truthing was carried out to validate the wetland classes. The overall accuracy of the classified image was 79.29% and the kappa coefficient was 0.7600. These results were imported into a GIS environment for further analysis. It was found that the wetlands have decreased to an alarming extent in the past 28 years from 23.14% in 1988 to 15.79% in 2016 of the total study area, owing to conversion of wetlands into industrial development, urban expansion, and other developmental activities