Geographic Information Systems (GIS) and Remote Sensing are presently recognized generally as an improve instrument for overseeing, breaking down, and showing gigantic volumes of fluctuated information suitable to numerous neighborhood and provincial arranging exercises. Because of the composite idea of the travel industry arranging issues, the planned of GIS in settling these issues is progressively perceived. This paper will think a portion of the conceivable outcomes of GIS applications in the travel industry arranging. For the most part, GIS applications in the travel industry have been tight to recreational office stock, the travel industry situated land the board, and diversion untamed life strife; and have been thin by absence of financing, and awkward techniques. Utilizing the case of site wellness investigation for the travel industry improvement and mapping, this paper features a few uses of GIS in the travel industry arranging in vaishali square, Bihar. According to our present investigation; the most reasonable the travel industry site recognized by the examination is inside significant towns. The urban focus with plausibility to develop into the travel industry focuses. The rest of the land shows a low appropriateness scale because of absence of significant appreciation for make a solid force factor. Availability is an essential for the travel industry advancement. Great street organize availability with closeness to railroads station or air terminal demonstrated solid vacationer potential site, this combined with proximity to grand magnificence delineates high appropriateness. Significant vacation destinations, for example, legacy locales, gardens and water bodies or lake demonstrated high appropriateness. This can be corresponded to the way that legacy destinations and other high appropriate highlights are converted into reasonable the travel industry site.
Rapid development in remote sensing technologies provides more and more reliable methods for environmental assessment. For most wetlands, it is difficult to walk-in without disturbing the endangered species living there; therefore, application of opportunities provided by remote sensing has a great importance in population-mapping. One effective tool of vegetation pattern estimation is hyperspectral remote sensing, which can be used for association and species level mapping as well, due to high ground resolution. The Rakamaz-Tiszanagyfalui Nagy-morotva is an oxbow lake, located in the north-eastern part of Hungary. For this study, a wetland area of 1.17 km2 containing the original water bad and shoreline was selected. For the image analysis, images taken by an AISA DUAL system hyperspectral sensor were used. At the same time, 7 main vegetation classes were separated, which are typical for the sample plot designated on the test site. Classification was performed by the master areas signed by the most common associations of the Rakamaz-Tiszanagyfalui Nagy-morotva with determined spectrums. During the image analysis, SAM classification method was used, where radian values were optimized by the results of classification performed at the control area.
The red mud disaster occurred on 4th October 2010 in Hungary has raised the necessity of rapid intervention and drew attention to the long-term monitoring of such threat. Both the condition assessment and the change monitoring indispensably required the prompt and detailed spatial survey of the impact area. It was conducted by several research groups - independently - with different recent surveying methods. The high spatial resolution multispectral aerial photogrammetry is the spatially detailed (high resolution) and accurate type of remote sensing. The hyperspectral remote sensing provides more information about material quality of pollutants, with less spatial details and lower spatial accuracy, while LIDAR ensures the three-dimensional shape and terrain models. The article focuses on the high spatial resolution, multispectral electrooptical method and the evaluation methodology of the deriving high spatial resolution ortho image map, presenting the derived environmental information database
One of the largest industrial spills in Europe occurred in the village of Kolontár (Hungary) on October 4, 2010. The primary objective of the hyperspectral remote sensing mission was to monitor that is necessary in order to estimate the environmental damage, the precise size of the polluted area, the rating of substance concentration in the mud, and the overall condition of the flooded district as soon as possible. The secondary objective was to provide geodetic data necessary for the high-resolution visual information from the data of an additional Lidar survey, and for the coherent modeling of the event. For quick assessment and remediation purposes, it was deemed important to estimate the thickness of the red mud, particularly the areas where it was deposited in a thick layer. The results showed that some of the existing tools can be easily modified and implemented to get the most out of the available advanced remote sensing data.
The study aimed to analyze the process of Landuse/Landcover change of two rural communes (Saé
Saboua and Chadakori) of Maradi region (Republic of Niger) over the past 28 years (1986 – 2014),
through landscape structure analysis by diachronic cartographic approach and landscape indices. Mixed
classification of temporal series of Landsat images led to identifying six Landuse/Landcover (LULC)
classes, namely ”cultivated land under shrubs and trees”, ”cultivated land under trees”, “continuous
cropland”, ”fallow/pasture land”, ”forest reserve”, and ”settlement”. The composition and structure of
the studied landscapes have greatly changed from 1986 to 2014. The class ”cultivated land under trees”
was the landscape matrix in 1986 with 38.65% of landscape total area but in 2001 and 2014 the class
”continuous cropland” became the landscape matrix. The changes also affected the ”forest reserve”
which was transformed to smallholder agricultural land from 1986 to 2014. The area occupied by
classes ”cultivated land under trees” changed from 38.65% in 1986 to 8.78% in 2014; and from 1986
to 2014, the area occupied by ”fallow/pasture land” has decreased of about 16%. The decrease in these
classes was in favor of ¨continuous crop land¨, ¨settlement¨ and “cultivated land under shrubs and trees”
which respectively gained 38%, 0.3% and 8.15% of their areas in 1986. The results of this study reflect
the problem of access to land and even land saturation in semi-arid region, a consequence of strong
population growth. They also contribute to a better rethinking of agricultural practices in order to initiate
adaptation and resilience strategies for the population facing food insecurity and poverty.
Remote sensing resources are usually used in research to better understand urban built-up density, spatial structure and the processes of change. Based on results of image segmentation, landscape metrics indexes, texture and pattern may be analyzed beside the spatial changes in urban reflectance. Social processes within the settlement can be analyzed efficiently, although the census data may also be connected to the urban land cover data through geoinformation systems. On the research project different parameters of urban segments, i.e. patch number, mean patch area, total patch area, total patch perimeter, patch density and edge density, formations that make up the urban pattern were analyzed. Urban functional districts of different built-up density were separated using appropriate indexes, and extending the database with spectral content made it possible to review district boundaries and to mark new boundaries due to these changes.
Satellite images and aerial photos support settlement surveys and provide valuable information of their physical environment. Aerial photos are excellent tools to overview large areas and simultaneously provide high-resolution images making them efficient tools to monitor built-up areas and their surroundings. Aerial photos can also be used to collect complex spatial data as well as to detect various temporal changes on the land surface, such as construction of illegal edifices and waste dumps. The 10 to 30-meter resolution SPOT and Landsat images are usually insufficient for site specific data collection and analysis. However, the recently available 0.5-meter resolution satellite images have broadened the scope of monitoring and data collection projects. Beyond environmental and urban monitoring, the new available high-resolution satellite images simplify the everyday work of local authorities and will facilitate the development of governmental databases that include spatial information for public utilities and other communal facilities.
The retention of surface runoff and the preservation of soil moisture are among the most important water-related ecosystem services. In addition to field monitoring, advanced remote sensing techniques have been devised to reveal soil moisture dynamics on agricultural land. In our study we compare two soil moisture indices, TWI and SAVI, in three agricultural areas with different land use types. The SAVI has been found suitable to point out spatial variation on the moisture conditions of the vadose zone.
The change in an area’s natural surroundings is called landscape change. This change may be gradual or accelerated depending on the factors that influence the change. Natural elements such as native animals and birds seldom bring about any modification to the environment. However, human-induced change is devastating and severely transforms the environment. Such environmental transformation can be evaluated with the land use/ land cover assessment through satellite imagery and calculation of landscape indices. This paper attempts to ascertain the direction and the nature of the human-induced change in the city of Aizawl. To this end, the city has been divided into four zones to enable inter-zone comparisons. A northeast and southwest direction of human landscape transformation has been ascertained with the help of GIS and remote sensing techniques and landscape indices in Aizawl city.
In the past few decades there has been an increasing pressure of population all over the world,
especially in India, resulting in the utilization of every available patch of available land from
woodlands to badlands. The study area represents a basin which is economically growing fast by
converting the fallow lands, badlands and woodlands to agricultural land for the past few decades.
IRS (Indian Remote sensing Satellites) 1 C – LISS III and IRS 1 C PAN and IRS P6 – LISS III and
IRS 1 D PAN Images were merged to generate imageries with resolution matching to the landscape
processes operating in the area. The images of the year 1997, 2000, 2004 and 2007 were analyzed to
detect the changes in the landuse and landcover in the past ten years. The analysis reveals that there
has been 20% increase in the agricultural area over the past ten years. Built up area also has increased
from 1.35% to 6.36% of the area and dense vegetation also has marginally increased. The remarkable
increase in the agricultural area occurs owing to the reclaim of the natural ravines and fallow lands.
Presently the area looks promising, but it is necessary to understand the sedimentological and
geomorphological characteristics of the area before massive invasion on any such landscapes because
the benefit may be short lived.
Satellite images are important information sources of land cover analysis or land cover change monitoring. We used the sensors of four different spacecraft: TM, ETM+, OLI and ALI. We classified the study area using the Maximum Likelihood algorithm and used segmentation techniques for training area selection. We validated the results of all sensors to reveal which one produced the most accurate data. According to our study Landsat 8’s OLI performed the best (96.9%) followed by TM on Landsat 5 (96.2%) and ALI on EO-1 (94.8%) while Landsat 7’s ETM+ had the worst accuracy (86.3%).
Soil erosion is one of the problems threatening the Algerian environment. In agriculture, soil erosion leads to the thinning of the topsoil under the effect of the natural erosive forces of water, or under the effect of agricultural activities. The present study aims to estimate average soil loss rate and to identify vulnerable zones. Through the integration of RUSLE model at the Saf Saf watershed, various parameters are utilized such as the rainfall erosivity factor (R), soil erodibility factor (K), slope length - slope factor (LS), crop management factor (C) and practice management factor (P). All these parameters are prepared and processed through a geographic information system (GIS) and remote sensing using various database sources. The results reveal that the river basin has an average annual soil loss of 3.9 t ha−1 yr−1, and annual soil loss of 4.53 million tonnes for the period 1975-2017. Meanwhile, eighty five percent of the study area is experiencing acceptable rate of soil erosion loss, which is ranging between 0 to 5 t ha−1 yr−1. The present study of risk assessment can contribute to understand the spatial pattern of soil erosion in order to use appropriate conservation practices for sustainable soil management.
The remote sensing techniques provide a great possibility to analyze the environmental processes in
local or global scale. Landsat images with their 30 m resolution are suitable among others for land
cover mapping and change monitoring. In this study three spectral indices (NDVI, NDWI, MNDWI) were
investigated from the aspect of land cover types: water body (W); plough land (PL); forest (F); vineyard
(V); grassland (GL) and built-up areas (BU) using Landsat-7 ETM+ data. The range, the dissimilarities
and the correlation of spectral indices were examined. In BU – GL – F categories similar NDVI values
were calculated, but the other land cover types differed significantly. The water related indices (NDWI,
MNDWI) were more effective (especially the MNDWI) to enhance water features, but the values of other
categories ranged from narrower interval. Weak correlation were found among the indices due to the
differences caused by the water land cover class. Statistically, most land cover types differed from each
other, but in several cases similarities can be found when delineating vegetation with various water
content. MNDWI was found as the most effective in highlighting water bodies.