Spatio-temporal analysis is a process for city development with growing population and economy for better implementation of planning policies with advance technology. In this research work, three dates (1995, 2005 & 2016) satellite images were used to mapping and monitoring of Moscow region, Russia. This study focuses on the further classification of the study area into different categories on the basis of use and association by implementing a rule-based classification system on remotely sensed data. This research provides useful and up-to-date information to local land use planners, managers and policy-makers to step up towards sustainable development in Moscow region, Russia.
Moscow region is the one of the most densely populated regions in the country and is the second most populated federal region. The Oblast has no official administrative center, it is public authorities are located in Moscow and across other locations in the oblast. As of the 2010 Census, its population was 7,095,120 and 7,231,068 recorded in the 2015 Census. The latitude of the city is 55° 45’ 7” N and longitude is 37° 36’ 56" E. The region is highly industrialized, such as metallurgy, oil refining, mechanical engineering, food, energy and chemical industries [7].
The climate of Moscow region is humid continental, short but warm summers and long cold winters. The average temperature is 3.5 °C (38.3 °F) to 5.5 °C (41.9 °F).The coldest months are January and February average temperature of −9 °C (16 °F) in the west and −12 °C (10 °F) in the east. The minimum temperature is −54 °C (−65 °F). Here are more than three hundred rivers in Moscow region. The first largest river is Volga, most river belong to the basin of the Volga. Which itself only crosses a small part in the north of Moscow Oblast. They are mostly fed by melting snow and the flood falls on April-May. The water level is low in summer and increases only with heavy rain. The river freezes over from late November until April.
The Landsat program is a series of Earth-observing satellite mission jointly managed by NASA and the U.S. geological survey
[8]. The first Landsat satellite was launched in 1972 and the most recent one Landsat 8 was launched on February 11, 2013.
Data from Landsat 8 has eight spectral bands with spatial resolutions ranging from 15 to 60 m. The Landsat satellite data of
1995, 2005
Image Processing, Geoinformation Technology and Information Security / Komal Choudhary, M.S. Boori, A. Kupriyanov ratios and correlation. The data used in this paper were divided into two categories first satellite data and second ancillary data. Satellite data for the other hand consisted of multi- spectral data acquired by Landsat satellite provided by USGS gloves [11]. Ancillary data include ground truth data for the land use/cover classes and topographic maps. Spectral charts were prepared to distinguish and find out the difference in pixel values of different land use/cover classes in different bands. Primary land use classes were defined, such as agriculture, barren land, forest, settlements, scrubland, water body and wetland. The land use classes are defined in Table 1. 3.1 Database preparation
Any study of land use changes will involve the analysis of both conventional and remotely sensed data. Conventional data is
more accurate and site specific, but its collection is time consuming, manpower hungry and difficult to extrapolate over a larger
area. Remotely sensed data, on the other hand, has several advantages due to its repetitive and synoptic coverage of large and
inaccessible areas in a quick and economical fashion. In the present study both conventional and remotely sensed data were
used. The specific satellite images used were Landsat ETM+ (Enhanced Thematic Mapper plus) for 1995 and 2005, Landsat
OLI (Oper
Land cover classes are typically mapped from digital remotely sensed data using some sort of supervised, digital image classification. The overall objective of the image classification procedure is to automatically categorize all pixels in an image into land-cover classes or themes and the maximum likelihood classifier quantitatively evaluates both the variance and covariance of the category’s spectral response patterns whenever it classifies an unknown pixel. This is why it is considered to be one of the most accurate classifiers - it is based on statistical parameters. Supervised classification was performed here using ground checkpoints and digital topographic maps 3.3 Land use/cover change detection and analysis
Land use maps shows in figures 2, were prepared using Landsat data. The accuracy of these classified maps was checked using the GIS tools. The accuracy for these periods is 90% respectively. There is a big change in land use during this time period. To order increase the accuracy of the land use mapping of the two images, ancillary data, and the result of visual
Image Processing, Geoinformation Technology and Information Security / Komal Choudhary, M.S. Boori, A. Kupriyanov
interpretation was integrated with the classification results using Arc GIS [12, 13]. The classification of imagery from each
individual year, a multi-date, post-classification comparison, change-detection algorithm was used to determine changes during
two intervals from 1995-
Agriculture Barren land Forest Scrubland Settlements Water body
Wetland 0 20 40 80 120 160
Kilometers
There is a big change in land use during this time period, as show in the graphical representation of the data in figure 2.
Classification maps were generated for all of the sixteen years shown in figure and the individual class area and change statistics
are summarizes in table 1. In 1995 the urban area covered 3898.31 km2 (8.34 %), but by 2005 it had increased to approximately
4361.75 km2 (9.33 %)
Image Processing, Geoinformation Technology and Information Security / Komal Choudhary, M.S. Boori,
All the urban categories increased continuously, with the urban area increasing by 1953.69 km2 (4.17%) since 1995. Results show that forest area has been most dominant class in the study area for all three dates. The land use transition during the 19952016 periods is shows in table 2.
Table 2 shows both positive and negative land use/cover changes in the study area from 1995 to 2005, the major change was in agriculture and forest area. Forest was increase 3,536.13 km2 (7.56) and agriculture was decrease 7169.51 km2 (15.33%) of the total study area due to hares climatic conditions. From 2005 to 2016 total agriculture area was increase from 6,899.62 km2. In the same time period other classes such as barren land, scrubland, settlements, water body and wetland increase respectively. From 2005 to 2016 total agricultural area was increase from 6,899.62 km2and other classes settlements and waterbody were increased.
Class Agriculture Barren land Forest Scrubland Settlements Water body Wetland Total
Total 13549.18 2976.32 19791.85 3484.42 5965.35 451.06 549.92 46768.12
The results show that from1995 to 2005, 3820.91 km2 agriculture areas was stable but 990.98 km2 areas converted from forest
to agriculture (table 3). In the same time period 15982.20 km2 forest areas was stable but 1662.39 km2 wetland area was
encroached by forest. Maximum stable class was water body, where 293.26 km2 areas were stable from 1995 to
As show by our study, land –cover change is mainly driven by the expansion of socio-economic activities. The increase of agricultural areas, if poorly managed has impacts above those previously mentioned changes in the soil water cycle, nutrient
Image Processing, Geoinformation Technology and Information Security / Komal Choudhary, M.S. Boori, A. Kupriyanov depletion and an increased risk of soil erosion and land degradation even though the expansion of croplands leads to a growth in agricultural outputs like food and fibers to positively impact on the country’s economy and human well- being. As well as the huge increased in agricultural area there has also been a considerable increase in urban settlements. Such changes require rapid adjustments to land management in order to avoid crises in food. From a socio-economic point of view this means not only a loss of ecosystem services, but also a decline of earn money and cultural values, not to mention a subsequent reduction of income from tourism. A consequence of this is to make protected areas some of the few remaining zones where fuel wood, rich pastures and game resources are left and so they attract more and more legal activities.
In this new period of globalization cities should have quality infrastructure, energy and environment condition to sustain growth and attract foreign investment. The planning authorities should adopt new technologies such as remote sensing and GIS to address these issues. Remote Sensing and GIS are adequate of providing the necessary information and intelligence for planning proposal. In this research remote sensing and GIS have been unified to exhibit the changes in urban development and its future growth trends. This study focus on discover the expansion of the urban area of Moscow region. A large percentage of barren land was transformed into urban area during the study period. The urban growth shows maximum detail on the outskirts of the region. This expansion also indicates of industrial growths. Only remote sensing data can provide complete spatial information for the efficient assignment of urban growth in developing countries over the time period.