Land use land cover (LU/LC) change analysis using earth observation data sets over Jharsuguda districts of Odisha

Sahu, Smruti Ranjan; Rawat, Kishan Singh; Singh, Sudhir Kumar; Bahuguna, Anoop

doi:10.1063/5.0117977

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…Due to increased mining area with an annual change of +0.03%, the vegetation cover of Jharsuguda district continuously decreased with an annual change of -0.04% from 2006 to 2018 (Ranjan et al 2021). Sahu et al (2022) also got same result in decrease of vegetation from 1995 to 2020. The present study also revealed the same trend of increased built-up area by 243.48 Sq.…”

Section: Discussionmentioning

confidence: 90%

“…This urbanization process often leads to changes in land cover, such as converting natural and agricultural lands into built up areas which, in turn, can profoundly impact the local climate and environment. Ranjan et al (2021) and Sahu et al (2022) studied how vegetation cover changes in different years from 1995 to 2020 using only LU/LC practices in Jharsuguda District. We used both LU/LC and LST to assess the consequence of urbanization.…”

Section: Discussionmentioning

confidence: 99%

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Monitoring land use land cover transformations and its effects on land surface temperature using geospatial approach in Jharsuguda District, Odisha, India

Mansingh,

Kujur,

Sahoo

et al. 2024

EEC

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The aim of the work was to analyse Land use land cover (LU/LC) changes and their correlation with the increased Land surface temperature (LST) in Jharsuguda district, Odisha using geospatial techniques and transformation analysis in ArcGIS 10.4 software. Remotely sensed data from Landsat 8 operational land imager (OLI) for March 2013 and Landsat 9 OLI for March 2023 were utilized to investigate LU/LC and LST changes. The satellite data was classified using the maximum likelihood supervised classification algorithm (MLSC) to derive LULC maps. The overall accuracy of these classified LULC maps was determined to be more than 85% in both years. In order to obtain LST information from the satellite images, the spectral radiance model was utilized. The findings of the study revealed a clear correlation between the loss of vegetation cover (VC) and the expansion of built-up areas, which consequently contributed to an increase in the urban heat islands (UHI). The LU/LC estimation indicates substantial changes in the landscape over the past ten years. Specifically, there was a notable net increase in urban area (UA) by 55.12%, while very dense forest (VDF) experienced a reduction of 49.28%, moderately dense forest (MDF) decreased by 18.60%, and open forest (OF) by 42.58% as well as non-forest (NF) by 1.76% between 2013 and 2023. Furthermore, the study observed that the maximum temperature of the city rises from 46.8°C in 2013 to 48.3°C in 2023. So, the municipal authority can take new decision policies and management to reduce the effects of LST for sustainable development in the further future.

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Section: Discussionmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Monitoring land use land cover transformations and its effects on land surface temperature using geospatial approach in Jharsuguda District, Odisha, India

Mansingh,

Kujur,

Sahoo

et al. 2024

EEC

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“…By applying LU/LC change detection techniques, researchers and land managers can gain valuable insights into the drivers, impacts, and trajectories of land cover change, informing sustainable land management practices, conservation strategies, and policy decisions [14].…”

Section: Lu/lc Change Detectionmentioning

confidence: 99%

Unveiling spatial insights: navigating the parameters of dynamic Geographic Information Systems (GIS) analysis

Mohammad Aman Ullah Sunny

2024

Int. J. Sci. Res. Arch.

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In today's rapidly evolving landscape of Geographic Information Systems (GIS), understanding the intricate parameters that govern GIS analysis is paramount. This title encapsulates the essence of delving into the multifaceted dimensions of GIS, where a myriad of factors converges to shape the process and outcomes of spatial analysis. From the selection of input data and spatial layers to the fine-tuning of analytical techniques and model parameters, every step in GIS analysis is governed by a set of parameters that influence the accuracy, reliability, and relevance of the results. The title beckons researchers, practitioners, and enthusiasts alike to embark on a journey of exploration, unraveling the complexities of GIS analysis and its underlying parameters. It speaks to the dynamic nature of GIS, where the interplay of spatial data, computational algorithms, and user-defined parameters creates a rich tapestry of insights and discoveries. Whether mapping urban growth patterns, assessing natural resource availability, or modeling environmental change, understanding the parameters of GIS analysis is essential for unlocking the full potential of spatial data and informing decision-making processes. Moreover, the title hints at the transformative power of GIS as a tool for understanding and addressing complex spatial phenomena. By scrutinizing the parameters of GIS analysis, researchers can gain deeper insights into the underlying processes driving spatial patterns and trends. They can identify optimal parameter settings, refine analytical workflows, and enhance the accuracy and precision of spatial models and predictions. In essence, "Exploring the Parameters: Understanding the Dynamics of GIS Analysis" encapsulates the essence of GIS as a dynamic and evolving field, where the exploration of parameters serves as a gateway to unlocking the full potential of spatial analysis and harnessing the power of geography to address real-world challenges and opportunities.

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“…proposed a hierarchical knowledge-based classification (HKBC) approach using ASTER imagery and the Global DEM for the segmentation of glacier landscape classes and mapping of glacier limits (GDEM). Sahu et al (2022) use NDVI classification to analyze the LU/LC change. According to , ASTER DEMs provide more elevation-specific information, while SRTM DEMs are more precise.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of integrated classification data for glacier terrain mapping using the Maximum likelihood classifier (MLC) approach: A case study of Kolahoi Glacier in Jammu & Kashmir

Rawat,

Meer,

Ali

et al. 2024

Preprint

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Efficiently addressing challenges in mapping spectrally similar classes and overcoming limitations imposed by the restricted spectral sensitivity of remote sensing data is crucial for accurate image processing. In this context, the significance of the Maximum Likelihood Classifier (MLC) becomes apparent in optimizing classification accuracy. This study focuses on classifying glacierized terrain surrounding the Kolahoi glacier in the Kashmir Valley, integrating a diverse set of data sources, including Digital Elevation Model (DEM)-derived topographic attributes and transformed spectral data. The integration of ancillary sources, such as slope, aspect, plane, and profile curvatures derived from DEM, along with spectral information extracted from the LISS-III image using Red/SWIR ratio, NIR/SWIR ratio, and Normalized Difference Snow Index (NDSI), is explored to classify nine distinct glacier terrain classes. These classes encompass a range of features, including snow, ice, mixed-ice, debris, supraglacial debris, periglacial debris, water, vegetation, bare land, and shadow.To optimize the classification process, various scenarios are examined by combining each ancillary data source with the four spectral bands. The MLC is employed as the classification algorithm, and the resulting glacier terrain maps are validated against an ASTER image of Area of Interest (AOI). Notably, the study reveals that the glacier terrain map achieving the highest overall accuracy, an impressive 90.75%, emerges when incorporating the Red/SWIR ratio with the spectral data. This outcome underscores the effectiveness of the proposed classification approach in accurately mapping diverse glacier terrain classes.The successful application of the Maximum Likelihood Classifier in this study holds promising implications for applications such as glacier monitoring and environmental assessment in glacierized regions. The optimized classification approach presented here demonstrates its potential to contribute significantly to remote sensing applications, offering a valuable tool for understanding and monitoring complex terrain features in glacierized environments.

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Land use land cover (LU/LC) change analysis using earth observation data sets over Jharsuguda districts of Odisha

Cited by 8 publications

References 7 publications

Monitoring land use land cover transformations and its effects on land surface temperature using geospatial approach in Jharsuguda District, Odisha, India

Monitoring land use land cover transformations and its effects on land surface temperature using geospatial approach in Jharsuguda District, Odisha, India

Unveiling spatial insights: navigating the parameters of dynamic Geographic Information Systems (GIS) analysis

Evaluation of integrated classification data for glacier terrain mapping using the Maximum likelihood classifier (MLC) approach: A case study of Kolahoi Glacier in Jammu & Kashmir

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Land use land cover (LU/LC) change analysis using earth observation data sets over Jharsuguda districts of Odisha

Cited by 8 publications

References 7 publications

Monitoring land use land cover transformations and its effects on land surface temperature using geospatial approach in Jharsuguda District, Odisha, India

Monitoring land use land cover transformations and its effects on land surface temperature using geospatial approach in Jharsuguda District, Odisha, India

Unveiling spatial insights: navigating the parameters of dynamic Geographic Information Systems (GIS) analysis

Evaluation of integrated classification data for glacier terrain mapping using the Maximum likelihood classifier (MLC) approach: A case study of Kolahoi Glacier in Jammu &amp; Kashmir

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Evaluation of integrated classification data for glacier terrain mapping using the Maximum likelihood classifier (MLC) approach: A case study of Kolahoi Glacier in Jammu & Kashmir