Spatio-temporal dynamics along the terrain gradient of diverse landscape

Land use (LU) land cover (LC) information at a temporal scale illustrates the physical coverage of the Earth’s terrestrial surface according to its use and provides the intricate information for effective planning and management activities.  LULC changes are stated as local and location specifc, collectively they act as drivers of global environmental changes. Understanding and predicting the impact of LULC change processes requires long term historical restorations and projecting into the future of land cover changes at regional to global scales. The present study aims at quantifying spatio temporal landscape dynamics along the gradient of varying terrains presented in the landscape by multi-data approach (MDA). MDA incorporates multi temporal satellite imagery with demographic data and other additional relevant data sets. The gradient covers three different types of topographic features, planes; hilly terrain and coastal region to account the signifcant role of elevation in land cover change. The seasonality is another aspect to be considered in the vegetation dominated landscapes; variations are accounted using multi seasonal data. Spatial patterns of the various patches are identifed and analysed using landscape metrics to understand the forest fragmentation. The prediction of likely changes in 2020 through scenario analysis has been done to account for the changes, considering the present growth rates and due to the proposed developmental projects. This work summarizes recent estimates on changes in cropland, agricultural intensifcation, deforestation, pasture expansion, and urbanization as the causal factors for LULC change

Characterization and Visualization of Spatial Patterns of Urbanisation and Sprawl through Metrics and Modeling

Characterisation of spatial patterns of urban dynamics of Coimbatore, India is done using temporal remote sensing data of 1989 to 2013 with spatial metrics. Urban morphology at local levels is assessed through density gradients and zonal approach show of higher spatial heterogeneity during late1980’s and early 90’s. Urban expansion picked up at city outskirts and buffer region dominated with large number of urban fragments indicating the sprawl. Urban space has increased from 1.87% (1989) to 21.26 % (2013) with the decline of other land uses particularly vegetation. Higher heterogeneous land use classes during 90’s, give way for a homogeneous landscape (with simple shapes and less edges) indicating the domination of urban category in 2013. Complex landscape with high number of patches and edges in the buffer region indicate of fragmentation due to urban sprawl in the region. Visualisation of urban growth through Fuzzy-AHP-CA model shows that built up area would increase to 32.64% by 2025. The trend points to lack of appropriate regional planning leading to intensification of spatial discontinuity with the unsustainable urban growth

Impact assessment of Corridor Oriented development A case of urban agglomerations of India

Rapid urbanisation has been a factor affecting cities negatively and irreversibly in developing countries like India, adversely leading to depleting natural resources and promoting unbalanced and uneven urbanism. To handle the influx of population into core urban regions and to promote holistic, sustainable development, government and planning agencies are now looking upon regional development. Developing countries like India has laid plans for future urban corridor-oriented development. This study aims to understand the urban growth of two major developing cities influenced by transport corridor through a methodological approach using multi-temporal satellite data and its position in India\u27s network of cities. Land use analysis was validated with the aid of measures such as overall accuracy and kappa statistics, with good values of more than 85% and 0.75 respectively were achieved. The hierarchical network analysis indicated five different clusters based on the urban growth rate. Among these clusters, Bangalore, Ahmedabad and Pune cluster was further shortlisted for analysis based on the urban transport corridor affecting the growth of these cities. Cellular automata-based SLEUTH model was adopted in this work to carefully observe sub-division level details of the region under the influence of the corridor. Exhaustive calibration, with three phases of coarse, fine and final, validation procedure along with statistical fit measures reveal urban expansion for Ahmedabad region has witnessed an increase from 497.50 km2 (2017) to 826.24 km2 (2025) while Pune region has experienced tremendous urban area transformation of 901.11 km2 in the year 2025 against 497.27 km2 in 2017. Results of this analysis would help policymakers and planners to inculcate decisions concerning future urban trends accommodating safer, healthier, sustainable and liveable urban ecosystem

Urban land surface temperature forecasting: a data-driven approach using regression and neural network models

AbstractThe insinuations of the ailments associated with the unrestrained and disorganized proliferation of artificial impervious materials over natural surfaces are prevalent among city dwellers. These impacts can be comprehended by estimating land surface temperature (LST), as it is vital for evaluating urban climate, particularly to explain the intensity of urban heat islands and to define the health and welfare of the planet as well as the living beings. Urbanization-driven landscape changes severely disrupt comfortable living in almost every city, necessitating monitoring and modelling historical, current, and likely future LSTs. This research article proposes two forecasting techniques: Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models. These models have been widely accepted for the efficient prediction of climatic parameters, including LST, over an urban area. The landscape, elevation, and LST trend served as input to the models for an accurate prediction of LST. The analysis was performed over the Kolkata Metropolitan Area (KMA) with an additional 10 km buffer to understand urban growth and its effect on the LST of the entire region. The two developed models (MLR and ANN) effectively anticipated the LST over the KMA region. A continual increment in the surface temperatures ranging from 1 °C to 4 °C, over existing and likely-predicted urban areas was comprehended. It was anticipated that the regions near the urban areas will also experience severe discomfort and heat waves without proper mitigation measures. This scientific literature provides essential insights for decision-makers, stakeholders, and government officials to articulate new policies and modify the existing ones to create a sustainable and livable urban environment for the inhabitants

Valuation of Ecosystem Services, Karnataka State, India

Humans depend on the environment for their basic needs, such as food, fuel, minerals, water, air, etc. Burgeoning unplanned development activities to cater to the demands of the increasing population have put tremendous pressure on natural resources with the diversion of natural ecosystems to other uses. Over the years, the unsustainable practices involved in extracting and overexploiting natural resources have led to their degradation and depletion.India has been trying to accelerate economic growth and relax environmental laws. Hence, there is a pressing need to undertake the natural capital accounting and valuation of the ecosystem services, especially intangible benefits, provided by ecosystems in India. The value of all ecosystem services, including the degradation costs, needs to be understood for developing appropriate policies toward the conservation and sustainable use and management of ecosystems. Ecosystem services were quantified following the ecosystem services valuation protocol of the System of Environmental Economic Accounting (SEEA). This communication focuses on ecosystem services in forest and agricultural ecosystems in Karnataka state, India, for 2005 and 2019. A comparison of values of services in 2019 with 2005 (values adjusted through consumer price index) highlights that there has been a considerable decline in ecosystem services in Karnataka– a 28.5% reduction in provisioning services (51.6% reduction in forest ecosystems), a 21% reduction in regulatory services (mainly in forest ecosystems - 27.1% reduction), and a 1.9% reduction in cultural services.Ecosystem services were aggregated to compute the Total Ecosystem Supply Value (TESV). The TESV of forest and agricultural ecosystems in Karnataka was 3620 billion INR in 2005 (forest ecosystems: 2841 billion INR and agricultural ecosystems: 779 billion INR). However, overall, TESV declined in 2019 to 2912 billion rupees, with forest ecosystems driving this decline with a 35% decline in TESV. The TESV was also compared to the GDP of Karnataka, which is about 10128 billion rupees. The TESV of the forest ecosystem is equivalent to 18.1% of the GDP, and the TESV from agriculture ecosystems is equivalent to about 10.6% of the GDP in Karnataka. The decline in the TESV highlights the degradation of forest ecosystem assets from 2005 to 2019 due to the reduction in ecosystem extent and ecosystem condition. The decrease in value is also demonstrated by a fall in the net present value (NPV) of expected future returns of the ecosystem services supplied by forest ecosystem assets. The NPV of the assessed ecosystems based on 2005 ecosystem flows is about 93130 billion INR (forest ecosystem: 73099 billion INR, agriculture ecosystem: 20031 billion INR). However, the NPV of ecosystems in Karnataka, based on 2019 flows, indicates 74938 billion INR (forest ecosystem: 47214 billion INR, agriculture ecosystem: 27724 billion INR). The analysis highlights that there has been a decline of 35.4% in asset value of forest ecosystems with an increase in NPV of agriculture ecosystems by 38% due to transitions of forest ecosystems to croplands or horticulture (agriculture ecosystems).Ecosystem accounts make the value of ecosystem services visible, allowing them to be internalized into decision-making, enabling an assessment of trade-offs between economic development and environmental conservation and restoration, resulting in better-informed decisions. It also strengthens the economic case for conserving forests in states in India and developing countries where there is tremendous pressure to relax forest laws and divert forests to non-forest uses without proper consideration of the sustainability of such actions

Pap_01_Semantic-Segmentation-of-High-Resolution-Satellite-images.pdf

High-dimensional geospatial data visualization has gained much importance in recent decades. But to analyze it, traditional technologies used in machine learning are not convincing enough, and thus to switch to a subdomain of machine learning called deep learning that has gained popularity because of its accuracy and high dimensional data analysis power. Its convergence with geospatial data analytics shall prove to be a boon to the researchers working in the domain of geospatial data. Though Geospatial information is mostly used in the global mapping process of satellite images. The heterogeneity of the data makes it infeasible for global scale mapping. Therefore, to handle this problem is to partition the entire world into several regions. Semantic segmentation is one such technique and is widely used for information extraction from satellite images. The technique essentially refers to segmenting the input image pixel into multiple semantic regions, that is, to assign a semantic pixel category to each pixel in the image. In this context, we propose a semantic segmentation method that utilizes the spatial information of the high-resolution remote sensing data. The aim is to leverage the openly available data to automatically generate a larger training dataset with more variability and can be used to build more accurate deep learning models. The proposed automatic extraction can capture context information and its symmetric expanding path enables precise localization. The most characteristic property is the upsampling part that has feature channels that allow propagation of context information to higher resolution layers and makes the expansive path roughly symmetric to the contracting path yielding a U-shaped architecture. Mean IOU (mIOU) is used as the performance matrix and results yield 0.79. Since the model is trained on a small training dataset, that makes the deep learning model prone to overfitting. Training on such a small set of images makes this a challenging task. Validation dataset metrics obtained after training will signify the model’s general adaptability on other datasets of other segmentation tasks</p