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Abstract

Human activities on land have grown significantly changing the entire landscape, while most of the changes have occurred in the tropics. The change has become a serious environmental concern at the local, regional and global scales. The intensity, speed, and degree of land use / land cover (LULC) changes are nowadays quicker compared to the past because of the development of society. Moreover, the rapid increase in population resulted in disturbing a large number of landscapes on the Earth. The main objective of this study was to detect historical (1990– 2020) and predicted (2020–2050) LULC changes in the Welmel River Watershed, which is located in the Genale-Dawa Basin, South Eastern Ethiopia. The dataset of 1990, 2005, and 2020 was generated from Landsat 5, Landsat 7 and Landsat 8 respectively to determine the historical LULC map. The result of this study revealed that agriculture/ settlement increased by 6.85 km 2∙y –1, while forestland declined by 9.16 km 2∙y –1 over the last 31 years between 1990 and 2020. In the coming 31 years (by 2050), if the existing trend of the LULC change continues, agriculture/settlement land is expected to increase from 290.64 km 2 in 2020 to 492.51 km 2 in 2050 at the rate of 6.73 km 2∙y –1, while forestland is expected to shrink from 690.48 km2 in 2020 to 427.01 km 2 in 2050 by a rate of 8.78 km 2∙y –1.
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Authors and Affiliations

Solomon E. Ayalew
1
Tewodros A. Nigussie
2

  1. Ministry of Labor and Skills, Addis Ababa, Ethiopia
  2. Hawassa University, Institute of Technology, Hawassa, Ethiopia
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Abstract

This paper presents unsupervised change detection method to produce more accurate change map from imbalanced SAR images for the same land cover. This method is based on PSO algorithm for image segmentation to layers which classify by Gabor Wavelet filter and then K-means clustering to generate new change map. Tests are confirming the effectiveness and efficiency by comparison obtained results with the results of the other methods. Integration of PSO with Gabor filter and k-means will providing more and more accuracy to detect a least changing in objects and terrain of SAR image, as well as reduce the processing time.
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Bibliography


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[5] Xinzheng Zhang, Hang Su, Ce Zhang, Peter M. Atkinson, Xiaoheng Tan, Xiaoping Zeng and Xin Jian." A Robust Imbalanced SAR Image Change Detection Approach Based on Deep Difference Image and PCANet", arXiv.org > cs > arXiv:2003.01768, 2020
[6] Feng Gao, Xiao Wang, Junyu Dong, Shengke Wang, " SAR Image Change Detection Based on Frequency Domain Analysis and Random Multi-Graphs", Journal of Applied Remote Sensing, 2017
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Authors and Affiliations

Jinan N. Shehab
1
Hussein A. Abdulkadhim
1

  1. University of Diyala, College of Engineering, Dept. of Communication Engineering, Iraq
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Abstract

Ethiopia has lost sizable forest resources due to rapid population growth and subsequent increase in the demand for agricultural land and fuel woods. In this study, GIS and remote sensing techniques were used to detect forest cover changes in relation to climate variability in the Kafa zone, southwest Ethiopia. Landsat Thematic Mapper (TM) images of 1986 and 1990, Enhanced Thematic Mapper plus (ETM+) image of 2010 and Landsat-8 Operational Land Imager (OLI-8) image of 2018 were acquired at a resolution of 30 m to investigate spatial-temporal forest cover and land use changes. A supervised image classification was made using a maximum likelihood method in ERDAS imagine V9.2 to identify the various land use and land cover classes. Both spectral (normalised difference vegetation index – NDVI) and post classification change detection methods were used to determine the forest cover changes. To examine the extent and rate of forest cover changes, post classification comparisons were made using ArcGIS V 10.4.1. A net forest cover change of 1168.65 ha (12%) was detected during the study period from 1986 to 2018. The drop in the NDVI from 0.06–0.64 in 1986 to (–0.08)–0.12 in 2018 indicated a marked forest cover change in the study area. The correlation of NDVI values with climate data indicated the forest was not in a stable condition. The declining of the forest cover was most likely caused by climate variability in the study area.
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Authors and Affiliations

Dejene Beyene Lemma
1
Kinde Teshome Gebretsadik
1
Seifu Kebede Debela
1

  1. Jimma Institute of Technology, Faculty of Civil and Environmental Engineering, Jimma University, Jimma, P.O.Box: 378, Ethiopia

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