Creating multi-sensor time series using data from Landsat-5 TM and Landsat-7 ETM+ to characterise vegetation dynamics

Lymburner, Leo; McIntyre, A.; Li, Fuqin; Ip, Alex; Thankappan, Medhavy; Sixsmith, Joshua

doi:10.1109/igarss.2013.6721321

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…The ULA Project has enabled this archive of data to be used in new ways that allow users to expose and exploit the immense value of the time series of Landsat observations throughout the entire archive at the individual pixel level rather than on an individual scene-by-scene (or image by image) basis [26]. It is now possible to perform statistical analyses of the entire Australian Landsat Archive from 1987 to 2012 for the entire continent (more than 21 Â 10^24 individual pixels) in approximately 6 h. Before the ULA Project this analysis would have taken more than 8½ years to conduct.…”

Section: Discussionmentioning

confidence: 99%

Unlocking the Australian Landsat Archive – From dark data to High Performance Data infrastructures

Purss

Lewis

Oliver

et al. 2015

GeoResJ

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Section: Discussionmentioning

confidence: 99%

Unlocking the Australian Landsat Archive – From dark data to High Performance Data infrastructures

Purss

Lewis

Oliver

et al. 2015

GeoResJ

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“…Among the data archives available today, the Landsat archive offers a unique homogeneous dataset available since several decades and usable at a decameter scale [27], [28]. Landsat Tasseled Cap inter-annual products have been used to classify the land cover of large floodplains and interannual changes [29], [30].…”

Section: B Remote Sensing Of Mountainous and Sub-mountainous Floodplmentioning

confidence: 99%

Characterizing Flood Impact on Swiss Floodplains Using Interannual Time Series of Satellite Imagery

Milani

Kneubühler

Tonolla

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Pressure on the biodiversity of ecosystems along many rivers is growing continuously due to the increasing number of hydropower facilities regulating downstream flow and sediment regimes. Despite a thorough understanding of the shortterm processes and interactions at this hydro-biosphere interface, long-term analyses of the impacts on floodplain dynamics are lacking. We used inter-annual Landsat 4, 5, 7 and 8 time series to analyze the effects of hydrological events on floodplain vegetation in four mountainous floodplains in the Swiss Alps. Using a spectral mixture analysis approach, we demonstrate that the floodplain vegetation dynamics of mountainous rivers can be recovered at a spatial resolution of 30 meters. Our results suggest that interactions between floods and floodplain vegetation are complex and not exclusively related to flood magnitude. Of the four reaches analyzed, only data gathered along the submountainous reach with a quasi-natural flow regime show a clear link between remotely sensed vegetation indices and floods. In addition, our 29-year time series shows a continuous upward trend in vegetation indices along the floodplains, strongest in the reaches affected by hydropower facilities. The approach presented in this study can be easily replicated in other mountain ranges by providing available flow data to verify the impact of hydropower on floodplain vegetation dynamics.

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“…Iceland (Figure 2). Landsat TM images are widely used in environmental studies due to the accessibility, repeatability of survey and coverage (Bryant et al 2002;Lymburner et al 2013). The aim is to highlight the distribution of various land cover types.…”

Section: Introductionmentioning

confidence: 99%

Evaluating land cover types from Landsat TM using SAGA GIS for vegetation mapping based on ISODATA and K-means clustering

Lemenkova¹

2021

Acta agriculturae Serbica

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The paper presents the cartographic processing of the Landsat TM image by the two unsupervised classification methods of SAGA GIS: ISODATA and K-means clustering. The approaches were tested and compared for land cover type mapping. Vegetation areas were detected and separated from other land cover types in the study area of southwestern Iceland. The number of clusters was set to ten classes. The processing of the satellite image by SAGA GIS was achieved using Imagery Classification tools in the Geoprocessing menu of SAGA GIS. Unsupervised classification performed effectively in the unlabeled pixels for the land cover types using machine learning in GIS. Following an iterative approach of clustering, the pixels were grouped in each step of the algorithm and the clusters were reassigned as centroids. The paper contributes to the technical development of the application of machine learning in cartography by demonstrating the effectiveness of SAGA GIS in remote sensing data processing applied for vegetation and environmental mapping.

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Creating multi-sensor time series using data from Landsat-5 TM and Landsat-7 ETM+ to characterise vegetation dynamics

Cited by 3 publications

References 2 publications

Unlocking the Australian Landsat Archive – From dark data to High Performance Data infrastructures

Unlocking the Australian Landsat Archive – From dark data to High Performance Data infrastructures

Characterizing Flood Impact on Swiss Floodplains Using Interannual Time Series of Satellite Imagery

Evaluating land cover types from Landsat TM using SAGA GIS for vegetation mapping based on ISODATA and K-means clustering

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