Mapping and visualizing the Great Salt Lake landscape dynamics using multi‐temporal satellite images, 1972–1996

Hung, Ming-Chih; Wu, Yang

doi:10.1080/0143116042000298324

Cited by 38 publications

(17 citation statements)

References 11 publications

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“…Among these, the post-classification comparison method is particularly attractive due to its nature of clearly identified change (Hung and Wu 2005;Muttitanon and Tripathi 2005;Yuan et al 2005). This study employs the post-classification method to detect changes.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal trends of urban land use/land cover and green infrastructure change in two Ethiopian cities: Bahir Dar and Hawassa

Gashu

Gebre-Egziabher

2018

Environ Syst Res

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Background:The spatiotemporal analysis of urban land use/land cover change (LULCC) helps to understand the dynamics of the changing environment of green infrastructure (GI) on the basis of sustainable city development. There are important links between spatiotemporal land use/land cover and GI change in urban areas. Therefore, the main objective of this study was to examine the spatiotemporal trends of urban land use/land cover and GI changes in Bahir Dar and Hawassa cities for the last four decades . Three different sets of Landsat satellite data were procured from EMA for Bahir Dar and Hawassa from 1973, 2000 and 2015 using Landsat 4 MSS, 7 TM and 8 OLI respectively. Based on this, using ERDAS Imagine (ver. 9.2) and Arc GIS (Ver.10.3) five LULCC classes were identified for analysis purpose. Result:The results show that vegetation decreased by 30 and 14% in Bahir Dar and Hawassa respectively for the period 1973-2015, while built-up areas expanded by 10 and 24% respectively in the two cities. These land use changes have significant impacts on spatiotemporal trends of GI in urban areas. GI has increased in Bahir Dar and Hawassa in association with built-up area expansion and deliberate activity of city administrations with effective implementation of spatial plans of corresponding cities. Conclusions:There is a growing concern about GI in cities. Policy makers and stakeholders should also decide on how to use the land at present and in the future. LULCC policymaking processes should aim to balance GI and other types of land use/land cover for sustainable urban development. Urban LULCC has important effects on the urban GI system.

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal trends of urban land use/land cover and green infrastructure change in two Ethiopian cities: Bahir Dar and Hawassa

Gashu

Gebre-Egziabher

2018

Environ Syst Res

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“…The soft classification is generally a mixed pixel decomposition method [24,25]. This can be achieved by using techniques such as thematic classification [26,27], single band threshold [28,29], the spectral relationship method [30,31] and the water index method, which is the most commonly used method because of its specific application [27,32,33,34]. These methods make use of the reflectivity index of each band and provide water body information based on signature differences between water and other land surface features.…”

Section: Introductionmentioning

confidence: 99%

Recent Surface Water Extent of Lake Chad from Multispectral Sensors and GRACE

Buma

Seo

2018

Sensors

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Consistent observations of lakes and reservoirs that comprise the majority of surface freshwater globally are limited, especially in Africa where water bodies are exposed to unfavorable climatic conditions and human interactions. Publicly available satellite imagery has increased the ability to monitor water bodies of various sizes without much financial hassle. Landsat 7 and 8 images were used in this study to estimate area changes around Lake Chad. The Automated Water Extraction Index (AWEI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Vegetation Index (NDVI) were compared for the remote sensing retrieval process of surface water. Otsu threshold method was used to separate water from non-water features. With an overall accuracy of ~96% and an inter-rater agreement (kappa coefficient) of 0.91, the MNDWI was a better indicator for mapping recent area changes in Lake Chad and was used to estimate the lake’s area changes from 2003–2016. Extracted monthly areas showed an increasing trend and ranged between ~1242 km2 and 2231 km2 indicating high variability within the 13-year period, 2003–2016. In addition, we combined Landsat measurements with Total Water Storage Anomaly (TWSA) data from the Gravity Recovery and Climate Experiment (GRACE) satellites. This combination is well matched with our estimated surface area trends. This work not only demonstrates the importance of remote sensing in sparsely gauged developing countries, it also suggests the use of freely available high-quality imagery data to address existing lake crisis.

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“…The traditional error matrix and kappa coefficient method have been commonly used in accuracy assessment of multi-temporal land-cover change detection (e.g. Mertens and Lambin 2000, Petit et al 2001, Hung and Wu 2005. However, as this method was developed for single-date thematic mapping (Biging et al 1999a), it only provides accuracy based on individual classification.…”

Section: Introductionmentioning

confidence: 98%

“…In addition, multi-temporal remote sensing change detection is often based on data from different sensors with variable spatial and spectral resolutions (e.g. Griffith et al 2003, Yagoub 2004, Hung and Wu 2005. As the ability to delineate land-cover classes varies greatly with different image sources, boundary errors of detected change classes present more uncertainty than those based on data from the same sensor (Serra et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Accuracy assessment on multi‐temporal land‐cover change detection using a trajectory error matrix

Zhou

2009

International Journal of Remote Sensing

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This study develops a practical methodology to assess the accuracy of multitemporal change detection using a trajectory error matrix (TEM). In this error matrix one axis represents the land-cover change trajectory categories derived from single-date classified images, and the other represents the land-cover change trajectories identified from reference data. The overall accuracies of change trajectories and states of change/no-change are used as indices for accuracy assessment. As the number of possible land-cover change trajectories can be enormous, a practical processing flow for computing accuracy assessment indices has also been developed to avoid listing all possible change trajectories in the error matrix. A case study using this method was conducted to assess the accuracy of land-cover change over a period with five observations in a study area in China's arid zone. This method simplifies the process of estimating overall accuracy in the change trajectory analysis, and provides a more realistic and detailed assessment of the results of multi-temporal change detection using postclassification comparison methods.

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Mapping and visualizing the Great Salt Lake landscape dynamics using multi‐temporal satellite images, 1972–1996

Cited by 38 publications

References 11 publications

Spatiotemporal trends of urban land use/land cover and green infrastructure change in two Ethiopian cities: Bahir Dar and Hawassa

Spatiotemporal trends of urban land use/land cover and green infrastructure change in two Ethiopian cities: Bahir Dar and Hawassa

Recent Surface Water Extent of Lake Chad from Multispectral Sensors and GRACE

Accuracy assessment on multi‐temporal land‐cover change detection using a trajectory error matrix

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