Land Use Change Detection Using Remote Sensing Technology

Andualem, Tesfa Gebrie; Belay, Gizew; Guadie, Adebabay

doi:10.4172/2157-7617.1000496

Cited by 48 publications

(23 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous researches reported a 44%, 36.4%, and 65% rise in cultivated land by [ 91 , 92 ] respectively. When subsistence farming is not supported by modern technology, agricultural yield boosting can be attained by the addition of parcels of land under farming, and this is exactly seen in the current study area [ 90 , 93 , 94 , 95 ]. Similarly reported progressive agricultural land increase at different periods from different localities.…”

Section: Discussionmentioning

confidence: 72%

Land use and land cover change, and analysis of its drivers in Ojoje watershed, Southern Ethiopia

Mariye

Maryo

2022

Heliyon

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 72%

Land use and land cover change, and analysis of its drivers in Ojoje watershed, Southern Ethiopia

Mariye

Maryo

2022

Heliyon

View full text Add to dashboard Cite

“…To detect changes in forest cover over a long time span [37][38][39][40][41], Landsat 5 Thematic Mapper (19 June, 1991 and6 June, 2004) and Landsat 8 Operational Land Imager (25 May, 2017) images were downloaded from the United States Geological Survey website [37]. ArcGIS 10.2 software was used to preprocess (radiometric correction) the images.…”

Section: Methodsmentioning

confidence: 99%

An Analysis of the Causes of Deforestation in Malawi: A Case of Mwazisi

Ngwira

Watanabe

2019

Land

View full text Add to dashboard Cite

Deforestation is recognized as a major driver of the loss of biodiversity and ecosystem services. It also disturbs natural processes such as biogeochemical, hydrological, and ecological cycles. In Malawi, deforestation is estimated to be responsible for the loss of 33,000 hectares per year, and is mainly attributed to agriculture expansion, tobacco growing, and excessive use of biomass. However, little research has been conducted at either the local level or that of forests located on customary land. This research aimed to identify and analyze the underlying driving factors associated with the proximate factors of agriculture expansion, tobacco growing, and brick burning in Mwazisi. Landsat images for 1991, 2004, and 2017 were downloaded from the United States Geological Survey website and used to analyze changes in forest cover. Interviews with households (n = 399) and Natural Resource Committee members, a focus group discussion with key officers, and observations were conducted during field data collection in 2017. The results of the land cover analysis showed that forest covered 66% of the study area in 1991, and by 2017 it had decreased to 45.8%. Most households depend on wood from customary land forests for tobacco curing (69%) and brick burning (68%). Furthermore, 47.6% of the households have expanded their agriculture land by approximately 0.57 hectares during the past 15 years. The interview survey and the focus group discussion identified that the underlying driving factors towards these anthropogenic activities are: (a) population growth, (b) poverty, (c) expensive alternative building materials, (d) lack of awareness, (e) lack of resources, (f) lack of commitment from the tobacco companies, and (g) market system of the cash crops grown in the area. In conclusion, a set of economic, institutional, social, and demographic factors, which are associated with imbalanced relationship between rural and urban areas, underpin agriculture expansion, tobacco growing, and brick burning, and have thereby contributed to the decline of the forest cover in Mwazisi, Malawi.

show abstract

“…The collected ground truth ROI is used to resolve the accuracy of the classifier. The error matrix is the most pervasively used method to determine the classification accuracy (Andualem et al, 2018;Manandhar et al, 2009). Figure 2.…”

Section: Post-classification and Accuracy Assessmentmentioning

confidence: 99%

Performance Assessment of Classification Algorithms for Landuse / Landcover Change Using Sentinel 2 Data – A Case Study of Tiruppur

Prawin

Palanisamy

Rahaman

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. In the history of mankind, one of the vibrant geographical phenomena is urbanization. The urbanization process is characterized by the expansion of the city from the core to peripheral areas which includes economic development, social, political forces and population density. Very rapid urbanization in the highly populated country like India, which changes natural land cover into urban land use, which is unavoidable. However, the study region Tiruppur is known as the knitwear capital of India that induces urban development in the region which results in the modification of the natural land cover. For understating the interaction between the natural landscape and human activities, land use and land cover (LULC) is considered as the important indicator. Research on land-use and land cover changes using remote sensing technology has a long history to evident. The advancement in the Remote Sensing and GIS techniques provide the fine resolution of data sets to proceed. Sentinel-2B imagery was chosen for this study for two main reasons one is that compare to Landsat imagery it has a high spatial resolution of 10 m and its radiometry includes three vegetation red edge bands. These two characteristics make the Sentinel-2B data appealing for LULC mapping. Different types of classification algorithms have been used to perform land use and land cover mapping. The study aims to create land use and land cover classification by making a comparison between different algorithms in Tiruppur by using Sentinel-2B satellite imagery. The commonly known classification algorithms, K-means, IsoData, support vector machines (SVMs), and maximum likelihood (ML) classification are adopted for investigation. This is followed by the selection of training pixels from the remaining classes to perform and compare different supervised learning algorithms for the first- and second-level classification in terms of accuracy rates. Accuracy was assessed through metrics derived from an error matrix, but primarily overall accuracy and kappa coefficient was used in allocating algorithm hierarchy. Finally, after the comparison, the highly accurate algorithm was suggested for the mapping of urban areas. The highest overall accuracy and kappa coefficient was produced by support vector machine (SVM) is due to the algorithm’s relatively small number of complex decision boundaries. The results are helpful to understand the performance of the classification algorithm for the future studies.

show abstract

Land Use Change Detection Using Remote Sensing Technology

Cited by 48 publications

References 0 publications

Land use and land cover change, and analysis of its drivers in Ojoje watershed, Southern Ethiopia

Land use and land cover change, and analysis of its drivers in Ojoje watershed, Southern Ethiopia

An Analysis of the Causes of Deforestation in Malawi: A Case of Mwazisi

Performance Assessment of Classification Algorithms for Landuse / Landcover Change Using Sentinel 2 Data – A Case Study of Tiruppur

Contact Info

Product

Resources

About