Reconstructing semi-arid wetland surface water dynamics through spectral mixture analysis of a time series of Landsat satellite images (1984–2011)

Halabisky, Meghan; Moskal, L. Monika; Gillespie, Alan R.; Hannam, Michael

doi:10.1016/j.rse.2016.02.040

Cited by 118 publications

(84 citation statements)

References 49 publications

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“…We applied a SMA on the Landsat imageries to track the changes in wetland coverage and identify the emergence of waterbodies [50,22]. SMA delivers pixel estimates for water extent delineated from other landcover pixels based on available radiometric data in imageries [51] (equations 1 & 2). , +…”

Section: Wetland Coverage and Surface Floodingmentioning

confidence: 99%

“…Where, DNi is the measured value of a mixed pixel in band i; DNj is the measured value of each endmember (wetland pixel); Fj is the fraction of each endmember; r is the root mean square (rms) residual that accounts for the difference between the observed and modeled values [51]. Thus, waterbodies and their extent were delineated from other landcover classes for each year during 2013- …”

Section: Wetland Coverage and Surface Floodingmentioning

confidence: 99%

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A Remote Sensing Approach to Subsidence and Vegetation Degradation in a Reclaimed Mine Area 

Padmanaban¹,

Ak²,

Cabral³

2017

Preprint

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Abstract:Mining for resources extraction may lead to several geological and associated environmental changes due to ground movements, collision with mining cavities and deformation of aquifers. Geological changes may continue in a reclaimed mine area, and the deformed aquifers may entail a breakdown of substrates and an increase in ground water tables, which may cause surface area inundation. Consequently, a reclaimed mine area may experience surface area collapse, i.e. subsidence, and degradation of vegetation health. Thus, monitoring short-term landscape dynamics in a reclaimed mine area may provide important information on the long-term geological and environmental impacts of mining activities. We studied landscape dynamics in Kirchheller Heide, Germany, which experienced extensive soil movement due to longwall mining without stowing, using Landsat imageries between 2013 and 2016. A Random Forest image classification technique was applied to analyse land-use and land-cover dynamics and the growth of wetland areas was assessed using a Spectral Mixture Analysis (SMA). We also analyzed the changes in vegetation health using a Normalized Difference Vegetation Index (NDVI). We observed a 19.9% growth of wetland area within the four years with 87.2% of growth in the coverage of two major waterbodies in the reclaimed mine area. NDVI values indicate that 66.5% of the vegetation of the study area was degraded due to changes in ground water tables and surface flooding. Our results inform environmental management and mining reclamation authorities about the subsidence spots and priority mitigation areas from land surface and vegetation degradation in Kirchheller Heide.

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Section: Wetland Coverage and Surface Floodingmentioning

confidence: 99%

Section: Wetland Coverage and Surface Floodingmentioning

confidence: 99%

A Remote Sensing Approach to Subsidence and Vegetation Degradation in a Reclaimed Mine Area 

Padmanaban¹,

Ak²,

Cabral³

2017

Preprint

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“…Remote-sensing data have been widely used for mapping the lake-water extent over time and space [10][11][12][13][14][15][16][17], with Landsat image data being one of the most common data types for monitoring and analyzing long-term lake-water extent changes, due to their high spatial resolution (30-60 m) and long data record [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Change of Lake Water Extent in Wuhan Urban Agglomeration Based on Landsat Images from 1987 to 2015

et al. 2017

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Abstract:Urban lakes play an important role in urban development and environmental protection for the Wuhan urban agglomeration. Under the impacts of urbanization and climate change, understanding urban lake-water extent dynamics is significant. However, few studies on the lake-water extent changes for the Wuhan urban agglomeration exist. This research employed 1375 seasonally continuous Landsat TM/ETM+/OLI data scenes to evaluate the lake-water extent changes from 1987 to 2015. The random forest model was used to extract water bodies based on eleven feature variables, including six remote-sensing spectral bands and five spectral indices. An accuracy assessment yielded a mean classification accuracy of 93.11%, with a standard deviation of 2.26%. The calculated results revealed the following: (1) The average maximum lake-water area of the Wuhan urban agglomeration was 2262.17 km 2 from 1987 to 2002, and it decreased to 2020.78 km 2 from 2005 to 2015, with a loss of 241.39 km 2 (10.67%). (2) The lake-water areas of loss of Wuhan, Huanggang, Xianning, and Xiaogan cities, were 114.83 km 2 , 44.40 km 2 , 45.39 km 2 , and 31.18 km 2 , respectively, with percentages of loss of 14.30%, 11.83%, 13.16%, and 23.05%, respectively. (3) The lake-water areas in the Wuhan urban agglomeration were 226.29 km 2 , 322.71 km 2 , 460.35 km 2 , 400.79 km 2 , 535.51 km 2 , and 635.42 km 2 under water inundation frequencies of 5%-10%, 10%-20%, 20%-40%, 40%-60%, 60%-80%, and 80%-100%, respectively. The Wuhan urban agglomeration was approved as the pilot area for national comprehensive reform, for promoting resource-saving and environmentally friendly developments. This study could be used as guidance for lake protection and water resource management.

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“…Compared to classifying each pixel as the water surface or non-water surface, it would be more appropriate and important to label the proportion of the wetland inundation for each pixel at a subpixel scale. To estimate the subpixel land cover proportion from remotely sensed data, different methods have been developed based on the pixel signal from remote sensing observation and the spectral differences between different land surface components [5,[10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…To assess the performance of the three model types, Halabisky et al [11] used LSU methods to reconstruct semi-arid wetland surface water dynamics based on a time series of Landsat satellite images from 1984 to 2011, and LSU method worked well for even small wetlands (<1800 m 2 ). Weng and Hu [23] applied both ANN and LSU methods to estimate impervious surfaces with medium spatial resolution satellite images from the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat-7 Enhanced Thematic Mapper plus (ETM+).…”

Section: Introductionmentioning

confidence: 99%

Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China

Xia

Zhao

et al. 2017

Remote Sensing

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Abstract:Wetland inundation is crucial to the survival and prosperity of fauna and flora communities in wetland ecosystems. Even small changes in surface inundation may result in a substantial impact on the wetland ecosystem characteristics and function. This study presented a novel method for wetland inundation mapping at a subpixel scale in a typical wetland region on the Zoige Plateau, northeast Tibetan Plateau, China, by combining use of an unmanned aerial vehicle (UAV) and Landsat-8 Operational Land Imager (OLI) data. A reference subpixel inundation percentage (SIP) map at a Landsat-8 OLI 30 m pixel scale was first generated using high resolution UAV data (0.16 m). The reference SIP map and Landsat-8 OLI imagery were then used to develop SIP estimation models using three different retrieval methods (Linear spectral unmixing (LSU), Artificial neural networks (ANN), and Regression tree (RT)). Based on observations from 2014, the estimation results indicated that the estimation model developed with RT method could provide the best fitting results for the mapping wetland SIP (R 2 = 0.933, RMSE = 8.73%) compared to the other two methods. The proposed model with RT method was validated with observations from 2013, and the estimated SIP was highly correlated with the reference SIP, with an R 2 of 0.986 and an RMSE of 9.84%. This study highlighted the value of high resolution UAV data and globally and freely available Landsat data in combination with the developed approach for monitoring finely gradual inundation change patterns in wetland ecosystems.

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Reconstructing semi-arid wetland surface water dynamics through spectral mixture analysis of a time series of Landsat satellite images (1984–2011)

Cited by 118 publications

References 49 publications

A Remote Sensing Approach to Subsidence and Vegetation Degradation in a Reclaimed Mine Area

A Remote Sensing Approach to Subsidence and Vegetation Degradation in a Reclaimed Mine Area

Spatio-Temporal Change of Lake Water Extent in Wuhan Urban Agglomeration Based on Landsat Images from 1987 to 2015

Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China

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Reconstructing semi-arid wetland surface water dynamics through spectral mixture analysis of a time series of Landsat satellite images (1984–2011)

Cited by 118 publications

References 49 publications

A Remote Sensing Approach to Subsidence and Vegetation Degradation in a Reclaimed Mine Area&nbsp;

A Remote Sensing Approach to Subsidence and Vegetation Degradation in a Reclaimed Mine Area&nbsp;

Spatio-Temporal Change of Lake Water Extent in Wuhan Urban Agglomeration Based on Landsat Images from 1987 to 2015

Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China

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A Remote Sensing Approach to Subsidence and Vegetation Degradation in a Reclaimed Mine Area

A Remote Sensing Approach to Subsidence and Vegetation Degradation in a Reclaimed Mine Area