Slum Categorization for Efficient Development Plan—A Case Study of Udhampur City, Jammu and Kashmir Using Remote Sensing and GIS

Farooq, Majid; Meraj, Gowhar; Rishabh,; Kanga, Shruti; Nathawat, Ritu; Singh, Suraj Kumar; Ranga, Vikram

doi:10.1007/978-981-16-7373-3_14

Cited by 28 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The 2011 Global Hunger Index (GHI) report ranks India among the three countries. The GHI increased from 22.9 in 1996 to 23.7 in 2011, with 78 of 81 developing countries being studied, such as Pakistan, Bangladesh, Nepal, Kenya, Nigeria, and Uganda [50]. Since its first five-year plan, the Indian government has initiated several programs such as support for food, other needs and enhanced access to loans, improved farming technology, price subsidies, and education [51].…”

Section: Land Surface Temperature Assessmentmentioning

confidence: 99%

Understanding the Linkage between Urban Growth and Land Surface Temperature—A Case Study of Bangalore City, India

et al. 2022

Self Cite

View full text Add to dashboard Cite

Planning for a sustainable future involves understanding the past and present problems associated with urban centers. Rapid urbanization has caused significant adverse impacts on the environment and natural resources. In cities, one such impact is the unsettling urban growth, resulting in the urban heat island (UHI) effect, which causes considerable positive feedback in the climate system. It can be assessed by investigating the relationships between urban Land Use/Land Cover (LULC) changes and changes in land surface temperature. This study links the urban transformations in Bangalore, India, between 2001 and 2021, with the city’s changing average land surface temperatures. LULC classification was performed on Landsat satellite images for the years 2001, 2011, and 2021, using the support vector machine (SVM) classification algorithm. LULC change analysis revealed an increase in the built-up area coinciding with a decreasing trend of water bodies, vegetation, and the area under the others (wasteland/open land/barren land) category. The results show that built-up increased from 462.49 km2 to 867.73 km2, vegetation decreased from 799.4 km2 to 485.72 km2, and waterbody declined from 34.28 km2 to 24.69 km2 in 20 years. The impact of urbanization was evident in Bangalore’s land temperature changes between 2001 and 2021, showing the average temperature increased by 0.34 °C per year between the highest UHI events, contrary to 0.14 °C per year in non-urbanized areas. It is hoped that the results of this study can help the urban planners of Bangalore city identify critical areas where improvement in urban dwelling could be planned sustainably according to the global smart cities concept, an offshoot concept of the Sustainable Development Goal (SDG)-11.

show abstract

Section: Land Surface Temperature Assessmentmentioning

confidence: 99%

Understanding the Linkage between Urban Growth and Land Surface Temperature—A Case Study of Bangalore City, India

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…For validating the acreage generated from SVM and RF, we conducted extensive ground-truthing and performed the accuracy assessment using the user's accuracy, producer's accuracy, and the kappa coefficient [36,37], and took about 120 ground-truthing points (randomly distributed, covering almost whole of the district) for this purpose. We used Garmin GPS explorer to record the GPS locations [38] (Figure 2). Daily average temperature, sunshine data, daily rainfall, and average solar energy data used here were downloaded from the NASA Power Data Access Viewer (https://power.larc.nasa.gov/data-access-viewer/ (accessed on 25 April 2021)).…”

Section: Datasetsmentioning

confidence: 99%

Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling

et al. 2022

Self Cite

View full text Add to dashboard Cite

Globally, estimating crop acreage and yield is one of the most critical issues that policy and decision makers need for assessing annual crop productivity and food supply. Nowadays, satellite remote sensing and geographic information system (GIS) can enable the estimation of these crop production parameters over large geographic areas. The present work aims to estimate the wheat (Triticum aestivum) acreage and yield of Maharajganj, Uttar Pradesh, India, using satellite-based data products and the Carnegie-Ames-Stanford Approach (CASA) model. Uttar Pradesh is the largest wheat-producing state in India, and this district is well known for its quality organic wheat. India is the leader in wheat grain export, and, hence, its monitoring of growth and yield is one of the top economic priorities of the country. For the calculation of wheat acreage, we performed supervised classification using the Random Forest (RF) and Support Vector Machine classifiers and compared their classification accuracy based on ground-truthing. We found that RF performed a significantly accurate acreage assessment (kappa coefficient 0.84) compared to SVM (0.68). The CASA model was then used to calculate the winter crop (Rabi, winter-sown, and summer harvested) wheat net primary productivity (NPP) in the study area for the 2020–2021 growth season using the RF-based acreage product. The model used for wheat NPP-yield conversion (CASA) showed 3100.27 to 5000.44 kg/ha over 148,866 ha of the total wheat area. The results showed that in the 2020–2021 growing season, all the districts of Uttar Pradesh had similar wheat growth trends. A total of 30 observational data points were used to verify the CASA model-based estimates of wheat yield. Field-based verification shows that the estimated yield correlates well with the observed yield (R2 = 0.554, RMSE = 3.36 Q/ha, MAE −0.56 t ha−1, and MRE = −4.61%). Such an accuracy for assessing regional wheat yield can prove to be one of the promising methods for calculating the whole region’s agricultural yield. The study concludes that RF classifier-based yield estimation has shown more accurate results and can meet the requirements of a regional-scale wheat grain yield estimation and, thus, can prove highly beneficial in policy and decision making.

show abstract

Assessment of Changes in River Morphology Due to Illegal Sand Mining by Geospatial Techniques

Singh,

Prajapat,

Pandey

et al. 2024

Disaster Management and Environmental Sustainability

View full text Add to dashboard Cite

Slum Categorization for Efficient Development Plan—A Case Study of Udhampur City, Jammu and Kashmir Using Remote Sensing and GIS

Cited by 28 publications

References 8 publications

Understanding the Linkage between Urban Growth and Land Surface Temperature—A Case Study of Bangalore City, India

Understanding the Linkage between Urban Growth and Land Surface Temperature—A Case Study of Bangalore City, India

Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling

Assessment of Changes in River Morphology Due to Illegal Sand Mining by Geospatial Techniques

Contact Info

Product

Resources

About