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

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

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