Assessment of land use and land cover change in South Central Ethiopia during four decades based on integrated analysis of multi-temporal images and geospatial vector data

Kibret, Kefyalew Sahle; Marohn, Carsten; Cadisch, Georg

doi:10.1016/j.rsase.2015.11.005

Cited by 45 publications

(33 citation statements)

References 24 publications

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“…Significant LULC changes have been occurring at different levels of intensity in different parts of Ethiopia (Dessie & Kleman, ; Jansen et al., ; Muluneh & Arnalds, ; Dinka, ; Kindu et al., ; Kibret et al., ; Yesuf et al., ; Minta et al., ). Studies conducted in the Central Rift Valley (CRV) of Ethiopia have also illustrated the existence of LULC changes (Jansen et al., ; Garedew et al., ; Meshesha et al., ; Getnet et al., ).…”

Section: Introductionmentioning

confidence: 99%

Tempo‐spatial land use/cover change in Zeway, Ketar and Bulbula sub‐basins, Central Rift Valley of Ethiopia

2018

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Understanding tempo-spatial dynamics of land-use/cover (LULC) and its drivers is instrumental in synthesizing knowledge for informed natural resource management planning and associated decisions. The present study investigates tempo-spatial LULC changes, their drivers and the associated impacts in three sub-basins (Zeway, Ketar and Bulbula) in the Central Rift Valley (CRV) of Ethiopia. Satellite imageries of different periods in ArcGIS, field observations, focus group discussions (FGDs) and secondary data were used to analyse the LULC dynamics, their drivers and associated impacts from 1973 to 2014. The overall accuracy of 1973, 2003 and 2014 classification maps was 88.7%, 88.9% and 91.6%, respectively. The analysis results revealed a continuous increase of farmland and town built-up areas at the expense of grasslands, shrub-bush land and woodlands. It further indicated area of open irrigated agriculture, increasing from none to 2.61% of the total area. The FGDs demonstrated agricultural land expansion, resettlement and wood extraction were proximate causes of the observed LULC changes. Population increases, changes in land tenure system and decreased farmland productivity were determined to be the underlying causes of the changes. The FGDs further indicated these changes have negatively affected the natural resources. The present study findings indicate the need to reconsider land-use decision tradeoffs between economic, social and environmental demands, and their implications for other similar areas in Ethiopia and beyond. Quantitative analysis and periodic evaluation of the drivers of such change and the impacts of existing and emerging land-uses in the face of changing climate is recommended to facilitate sustainable use of the fragile ecosystems in the Ethiopian CRV. K E Y W O R D S GIS, Lake Zeway, land-cover, land-use, satellite image | 77 ABERA Et Al.

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Section: Introductionmentioning

confidence: 99%

Tempo‐spatial land use/cover change in Zeway, Ketar and Bulbula sub‐basins, Central Rift Valley of Ethiopia

2018

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“…Also, one study estimated the rate of deforestation to be about 0.3% and 0.8% per year for the temperate and tropical areas. This study showed that 84,000 km2 of forest cover were destroyed between 1976 and 2000 [8], [11].…”

Section: Introductionmentioning

confidence: 84%

“…Despite its importance, most countries, including Iran, do not have detailed statistics on the extent of deforestation. In Mexico, authors have reported deforestation rates of 0.3-0.5% per year [6], [7], [8], [9], [10], [11]. Also, one study estimated the rate of deforestation to be about 0.3% and 0.8% per year for the temperate and tropical areas.…”

Section: Introductionmentioning

confidence: 99%

Using GIS and Artificial Neural Network for Deforestation Prediction

Ahmadi¹

2018

Preprint

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Geospatial Information Systems (GIS) can provide a great environment for using machine learning algorithm for spatial data such as satellite images. Integrating this functionality with artificial intelligence algorithms for analyzing spatial data enables us to predict challenging disasters such as deforestation. Deforestation as an environmental problems has been recorded the most serious threat to environmental diversity and one of the main components of land-use change. In this paper, we investigate spatial distribution of deforestation using artificial neural networks and satellite imagery. We modeled deforestation process using various factors in determining the relationship between deforestation and environmental and socioeconomic factors. Hence, for this purpose, the proximity to roads and habitats, fragmentation of the forest, height from sea level, slope, and soil type are considered in the model. In this research, we modeled land cover changes (forests) to predict deforestation using an artificial neural network due to its significant potential for the development of nonlinear complex models. The procedure involves image registration and error correction, image classification, preparing deforestation maps, determining layers, and designing a multi-layer neural network to predict deforestation. The satellite images for this study are of a region in Hong Kong which are captured from 2012 to 2016. The results of the study demonstrate that neural networks approach for predicting deforestation can be utilized and its outcomes show the areas that destroyed during the research period.

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“…In the past decades, many change detection techniques have been developed and applied in LCCD [10][11][12][13]. These approaches usually involve two main steps: calculating the change magnitude image and determining a binary threshold.…”

Section: Introductionmentioning

confidence: 99%

Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images

Lv¹,

Liu²,

Wan

et al. 2018

Remote Sensing

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Abstract:In recent decades, land cover change detection (LCCD) using very high-spatial resolution (VHR) remote sensing images has been a major research topic. However, VHR remote sensing images usually lead to a large amount of noises in spectra, thereby reducing the reliability of the detected results. To solve this problem, this study proposes an object-based expectation maximization (OBEM) post-processing approach for enhancing raw LCCD results. OBEM defines a refinement of the labeling in a detected map to enhance its raw detection accuracies. Current mainstream change detection (preprocessing) techniques concentrate on proposing a change magnitude measurement or considering image spatial features to obtain a change detection map. The proposed OBEM approach is a new solution to enhance change detection accuracy by refining the raw result. Post-processing approaches can achieve competitive accuracies to the preprocessing methods, but in a direct and succinct manner. The proposed OBEM post-processing method synthetically considers multi-scale segmentation and expectation maximum algorithms to refine the raw change detection result. Then, the influence of the scale of segmentation on the LCCD accuracy of the proposed OBEM is investigated. Four pairs of remote sensing images, one of two pairs (aerial image with 0.5 m/pixel resolution) which depict two landslide sites on Landtau Island, Hong Kong, China, are used in the experiments to evaluate the effectiveness of the proposed approach. In addition, the proposed approach is applied, and validated by two case studies, LCCD in Tianjin City China (SPOT-5 satellite image with 2.5 m/pixel resolution) and Mexico forest fire case (Landsat TM images with 30 m/pixel resolution), respectively. Quantitative evaluations show that the proposed OBEM post-processing approach can achieve better performance and higher accuracies than several commonly used preprocessing methods. To the best of the authors' knowledge, this type of post-processing framework is first proposed here for the field of LCCD using VHR remote sensing images.

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Assessment of land use and land cover change in South Central Ethiopia during four decades based on integrated analysis of multi-temporal images and geospatial vector data

Cited by 45 publications

References 24 publications

Tempo‐spatial land use/cover change in Zeway, Ketar and Bulbula sub‐basins, Central Rift Valley of Ethiopia

Tempo‐spatial land use/cover change in Zeway, Ketar and Bulbula sub‐basins, Central Rift Valley of Ethiopia

Using GIS and Artificial Neural Network for Deforestation Prediction

Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images

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