Exploring geospatial techniques for spatiotemporal change detection in land cover dynamics along Soan River, Pakistan

Bashir, Hafsa; Ahmad, Sheikh Saeed

doi:10.1007/s10661-017-5935-1

Cited by 13 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Land use land cover (LULC) classification [33], changes detection [11,12,[34][35][36][37][38], and its impact on water reservoirs were studied by Musaoglu, Moniruzzaman to help authorities and decision makers in changing quality conditions of reservoirs [39,40].…”

Section: Hydrology and Water Resource Engineeringmentioning

confidence: 99%

“…e climatic, environmental, economic, and sociopolitical challenges within civil projects make it di cult in digitization within the construction industry. Since last decade, computer-based monitoring systems and simulations using image processing, AI, ML, and others have piqued the interest among civil engineering community as a cost-e ective and unobtrusive solution for image and video data collection and analysis in many construction organizations [1][2][3][4][5][6][7][8][9][10][11][12][13]. As a result, several applications were targeted by previous researchers in 3D imaging technologies for construction management, progress monitoring [9,10], safety [14], and quality control [15,16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Progress and Trends in Image Processing Applications in Civil Engineering: Opportunities and Challenges

Salunkhe

Gobinath²,

Vinay³

et al. 2022

Advances in Civil Engineering

View full text Add to dashboard Cite

Technological advancements in electronic storage have been trending for cloud computing. The revolution of this computer technology with machine learning and artificial intelligence has created prodigious platforms to the various disciplines of science and technology. Civil engineering is the oldest discipline, and due to the never-ending demand of this domain, it is rapidly adapting to newer computer techniques like image processing, deep learning, big data analysis, neural networks, building information modeling (BIM), unmanned aerial vehicle (UAV) system, digital image correlation (DIC), and many more. In the current paper, we portrayed the primary research and achievements of AI and image processing applications in the civil domain. The paper is divided in two parts. The first part provides analysis of existing methods along with examples relevant to the civil domain where it is incorporated. The second part elaborates scientometric study constituting 605 documents (Science Direct database) published in the last two decades. The bibliometrics are further used for producing analytical frameworks based on publications, citations, top journals, top institutions, and funding sources. This study serves as a guide for readers to identify research gaps and use the review for potential future study.

show abstract

Section: Hydrology and Water Resource Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Progress and Trends in Image Processing Applications in Civil Engineering: Opportunities and Challenges

Salunkhe

Gobinath²,

Vinay³

et al. 2022

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…In the early stages, methods for LCCD relied on traditional machine learning techniques such as Maximum Likelihood [70][71][72][73][74][75][76][77][78][79], Support Vector Machine (SVM) [80], and Random Forest (RF) [81,82] for classifying land features, followed by vector change comparison to generate change maps. Fabian and Tikuye utilized satellite remote sensing data to train an RF model to classify land features into different types and subsequently analyzed the land cover change [81,82].…”

Section: Supervised Learning Methodsmentioning

confidence: 99%

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Gu,

Zeng

2023

Sustainability

View full text Add to dashboard Cite

The integration of Artificial Intelligence (AI) and Satellite Remote Sensing in Land Cover Change Detection (LCCD) has gained increasing significance in scientific discovery and research. This collaboration accelerates research efforts, aiding in hypothesis generation, experiment design, and large dataset interpretation, providing insights beyond traditional scientific methods. Mapping land cover patterns at global, regional, and local scales is crucial for monitoring the dynamic world, given the significant impact of land cover distribution on climate and environment. Satellite remote sensing is an efficient tool for monitoring land cover across vast spatial extents. Detection of land cover change through satellite remote sensing images is critical in influencing ecological balance, climate change mitigation, and urban development guidance. This paper conducts a comprehensive review of LCCD using remote sensing images, encompassing exhaustive examination of satellite remote sensing data types and contemporary methods, with a specific focus on advanced AI technology applications. Furthermore, the study delves into the challenges and potential solutions in the field of LCCD, providing a comprehensive overview of the state of the art, offering insights for future research and practical applications in this domain.

show abstract

“…First, we calculate the area of each land cover type for both the images and perform the equation shown below to analyze the change over a period of time. Many researchers have used the same equation to perform the same (Bashir & Ahmad, 2017;Hassen & Assen, 2018).…”

Section: Lulc Change Detectionmentioning

confidence: 99%

Dam-triggered Land Use Land Cover change detection and comparison (transition matrix method) of Urmodi River Watershed of Maharashtra, India: a Remote Sensing and GIS approach

Bagwan

Gavali

2021

Geology, Ecology, and Landscapes

View full text Add to dashboard Cite

The present study evaluates the impact of reservoir on changing the landscape pattern in the Urmodi River watershed of Satara district, Maharashtra, India, by employing Remote Sensing (RS)-based change detection technique. To map the change in surface cover due to the reservoir, we have used Landsat TM and Landsat 8 imagery which represents before-dam and after-dam status of land cover in the year 1996 and 2016. The supervised classification used with the maximum likelihood algorithm was employed to generate the thematic map. Further, to detect the change, SAGA GIS software was used which has an inbuilt tool of the confusion matrix (grid-based) to represent changes in two grids. The tool has significant importance in showing the detection of the transformation of land cover in previous to next condition. The technique is found to be scientific and robust with more precision as compared to the traditional methods. The rate of change in land cover of agriculture land shows −2.50%, barren land 0.31%, settlement 6.64%, and forest 2.20% change per annum. The water body area has a sudden increment in the year 2016 with coverage of 11.86 km 2 as compared to a previous area of 0.64 km 2 in the year 1996, and is not considered under the rate of change as it was a one-time activity. The study has a potential role in analyzing the land change dynamics.

show abstract

Exploring geospatial techniques for spatiotemporal change detection in land cover dynamics along Soan River, Pakistan

Cited by 13 publications

References 25 publications

Progress and Trends in Image Processing Applications in Civil Engineering: Opportunities and Challenges

Progress and Trends in Image Processing Applications in Civil Engineering: Opportunities and Challenges

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Dam-triggered Land Use Land Cover change detection and comparison (transition matrix method) of Urmodi River Watershed of Maharashtra, India: a Remote Sensing and GIS approach

Contact Info

Product

Resources

About