Monitoring Secchi disk transparency of Warasgaon reservoir of Pune by using LISS III sensor

ABSTRACT Secchi disk transparency (SDT) is the simplest and the most efficient measure to monitor water quality of the reservoir. The nutrients from the agriculture fields, sewage from human settlements and industries drain into reservoirs and lakes. It contributes to the increasing level of suspended particles, algal growth and coloring of water and reduces transparency of water. These changes can be detectable by remote sensors. In this study LISS III sensor of Resourcesat-2 used to model SDT of Warasgaon reservoir, Pune. The green (r = 0.75), red (r = 0.79) and NIR (r = 0.75) bands of LISS III showed good correlation with observed SDT, while band ratios Red/NIR (r = 0.81) and NIR/SWIR (r = 0.81) showed significant correlations. Several linear and multiple linear regression models developed from the in-situ measurements of SDT and the radiance value of LISS III image. The multiple linear regression model based on green, red, NIR and red/SWIR found to be the best fit (r = 0.88) to the in-situ data. The results showed that the Warasgaon reservoir was oligotrophic in condition during the December 2012

Multi-temporal land use classification using hybrid approach

Land use and land cover (LULC) classification of a satellite image is one of the prerequisites and plays an indispensable role in many land use inventories and environmental modeling. Many studies viz., forest inventories, hydrology and biodiversity studies, etc., are in demand to account the dynamics of land use and phenology of vegetation. Multi-temporal land use classification accounts the phenology of vegetation and land use dynamics of the study area. In this study, a hybrid classification scheme was developed to prepare a multi-temporal land use classification data set of Sawantwadi taluka of Maharashtra state in India. Parametric classification methods like maximum likelihood and ISODATA clustering methods are combined with the non-parametric decision tree approach to generate the multi-temporal LULC dataset. The accuracy assessment results have shown very promising results with a 93% overall accuracy with a kappa of 0.92

What drives urban growth in Pune? A logistic regression and relative importance analysis perspective

Proactive planning and management of rapidly urbanizing cities using up-to-date spatially explicit datasets is an urgent need. This requires a good understanding of the driving factors responsible for urban growth. Using Pune metropolis as test site, this paper presents an approach to assess the relative importance of urban growth driving factors from inexpensive geospatial datasets with respect to (i) urbanization process, (ii) urban planning (iii) urban growth modelling by utilizing relative importance analysis (RIA) as a supplement to logistic regression. Furthermore, this research proposes a new approach to reduce the parameterization and data requirement of urban growth models. Our research shows, that proximity to essential infrastructure has the highest predictive power in explaining urban growth of Pune. The importance of policy factors increase with time. Our results reveal that RIA is a suitable method, which can assist planners in deeper understanding of the urbanization process and to devise sustainable urban development strategies, utilizing a limited amount of data, which can be easily updated from geospatial datasets. The proposed break point method based on RIA to reduce parameterization of urban models performed at par with the model results achieved with the traditional AIC approach using less than half of the total number of driving factors

Urban growth analysis and simulations using cellular automata and geo-informatics: comparison between Almaty and Astana in Kazakhstan

In this research, the SLEUTH urban growth model is calibrated and validated for the first time to post Soviet Union cities. The aim of the study is to monitor, assess, simulate and compare the spatiotemporal urban growth dynamics and spatial patterns of the two largest cities Almaty and Astana using free remote sensing data. The urban expansion metrics and SLEUTH model are used to assess the urban growth dynamics. Though the capital has been moved to Astana from Almaty in 1998, Almaty is still developing faster than Astana. The urban growth simulation results from SLEUTH show Astana will surpass the urban growth of Almaty to emerge as the largest city in Kazakhstan by 2030. Astana may experience more leapfrog and ribbon developments. In Almaty, the urban growth may likely to take place in north and north-west parts

Integration of remote sensing and gis with sleuth to characterize the urban growth of Matara, Sri Lanka

Urbanization causes population growth and physical expansion of built-up area in cities and its suburb. It puts immense pressure on natural resources, conversation of agricultural land and degradation of water, air qualities and have profound impact on human lifestyle and health. Since last two decades, Sri Lanka is experiencing speedy urbanization. The urban population of Sri Lanka is expected to reach 60% by 2030 from 14% in 2010. This rapid increase in urban population may cause serious socio-economic disparities. In-order to plan for a sustainable urban future in Sri Lanka, planners are in need of new tools that can be capable to monitor and predict the urban growth under various scenarios. In this paper, we attempted to characterize the urban growth characteristics of Matara city using Geoinformatics and SLEUTH model. SLEUTH is a well-known urban growth model based on cellular automata. Multi-temporal remote sensing datasets from 1980-2010 have been used quantify the urban growth of Matara. SLEUTH model is calibrated using the data sets prepared from aerial photographs, Landsat sensor data and topographical data from Survey department. The derived calibration coefficient are used to project the growth of Matara by 2030 to understand and analyze the areas that are likely to be urbanized by 2030. The model results showing that out of 66 Grama Niladari Divisions 29 (in Matara Divisional Secretariat Division) will be urbanized with a probability ranging from 80% to 90%

SUSM: a scenario-based urban growth simulation model using remote sensing data

The introduction of the Foreign Direct Investment (FDI) policy in 1991 made India one of the fastest growing economies in the world. This has had a profound effect on India’s urbanization. The rapid urbanization of Indian cities poses a threat to natural and social environments, as expansion of the cities often outpaces the urban planning process. Thus, smart and strategic planning processes that use current and easily available datasets in combination with future urbanization scenarios are needed. To this end, we developed the scenario-based urban growth simulation model (SUSM), which can be used for impact analysis of different planning measures in both spatial and temporal contexts. SUSM uses remote sensing derived inputs, such as land use maps, slope, roads and centres of urban areas along with urban development scenarios. It uses logistic regression for calibration and a constrained stochastic cellular automaton for simulation of urban growth. SUSM is tested in one of the fastest growing urban agglomerations of India: The Pune metropolis, which covers an area of 1642 km2. SUSM is calibrated using urban growth maps derived from LANDSAT satellite images from 1992 to 2001. Subsequently, SUSM was used to simulate urban growth of Pune for 2013. A comparison of the SUSM simulation result with the actually measured urban growth derived from a LANDSAT 8 scene from 2013 is used to validate SUSM and to assess the effect of urban plans upon the growth of Pune. Our results show that: (i) SUSM is capable of predicting the location of future urbanization with an accuracy of 79% and a fuzzy kappa index of agreement 0.81; (ii) inclusion of official urban development plans as input for SUSM did not provide a better agreement with the observed growth; (iii) SUSM, parameterized with remote sensing data, can be used effectively to understand urban growth and assess the effects of alternative urban development plans in terms of the spatial expansion of cities

Spatiotemporal urban expansion in Pune metropolis, India using remote sensing

Indian cities are expanding at an unprecedented rate. The speed of development poses a challenge for urban planners, as the expansion of cities frequently outpaces the planning process. This leads to further challenges for urban planners, namely i) the database for the planning is often outdated and ii) processes and patterns of unplanned urban growth are not accounted for appropriately. This paper presents an approach to address these challenges by utilizing generally available and inexpensive remote sensing data to study i) the land use and land cover change and ii) by analyzing the extent of urban areas to study the patterns and processes of urban growth. We assesses land-use/land-cover for three years (1992, 2001, 2013) using multi-temporal Landsat datasets. A detailed spatiotemporal analysis of urban expansion and typologies of urban growth at the scale of individual administrative units. The dynamics of urban growth was quantified using different metrics of urban expansion. Three types of urban expansion patterns were identified in the Pune metropolis, i) coalescence phase of urbanization in the main city areas, ii) diffusion phase in the suburbs and iii) marginal growth in the cantonments. The overall process of urban expansion in the Pune metropolis can thus be referred to as a diffusion coalescence pattern. Furthermore, our results show that the speed of the urban expansion in the Pune metropolis area has doubled from 2001 to 2013 as compared to 1992-2001. Urban land has increased at the cost of grasslands, barren and agricultural lands. The percentage of change is high in the suburbs under semi-urban and village council jurisdictions, whereas in terms of total growth, areas under the municipal corporation jurisdictions are among the highest contributors to urban expansion. Administrative units governed by cantonment boards have shown marginal growth as compared to the civil administrative units in the study area. (C) 2015 Elsevier Ltd. All rights reserved

Modelling the socio-spatial impact of regional planning and climate change prevention strategies on land consumption in Western Germany 1985-2030

Growing metropolitan areas have potential to affect the climate of local neighborhoods and thus become a hot topic in regional planning. The study is a contribution to the climate change related land cover simulation efforts in Germany. It investigates future land consumption rates and population growth rates keeping goal 11 of the United Nation’s sustainable development goals (SDG) in the view. Secondly, the results are embedded in the sociological field of environmental justice, also dealing with SDG 3 and 11 by analyzing the socio-economic statuses in certain areas of distinct conditions. It analyzes the spatial impact of planning policies in regard to land use planning and official climate change prevention strategies in Western Germany. Scenario-based urban growth simulation model (SUSM) is used to simulate the future land use and cover of 2030 based on land use and cover maps of the years 1985, 2005, 2010, and 2017 derived by classified Landsat data. Two scenarios namely planned and unplanned were implemented to assess future land consumption 2030; the impacts of future urban growth with the projection of land consumption rate (LCR), population growth rate (PGR), and LCRPGR index on municipality level; and the impact on regions vulnerable to climate change. The comparison of simulated urban growth to observed urban growth from 2005-2017 shows that the producer accuracy of SUSM for the historic scenario is 68% with an overall accuracy of 97%, a Matthews correlation coefficient of 0.66, a figure of merit of 0.51 and area under curve of 0.84, all of which indicating good model performance. The total quantity of new urban areas of our SUSM simulation 2030 was approximately 283 km². Our results show that LCRPGR is negative in most municipalities reflecting opposing trends of population and land consumption development. Using Landsat data, the average summer land surface temperatures (LST) were estimated and trends for 1985-2020 calculated. Citizens depicted areas within zones with thermal compensation function in the city of Bonn. All areas have experienced an increase of summer LST of 3-5°C. About 33% of new urban areas in our region of interest can be found in these zones in the planning scenario and about 26% in the scenario without planning information in SUSM model. In addition, the study reveals socio-demographic patterns in context with past LST developments and future urban densification and sprawl processes. The presentation will show how socio-economic clusters are detected across spatials scales based on qualitative indications. Those findings may help to categorize certain city districts in terms of their residential composition and plan accordingly