Cloud and cloud shadow screening across Queensland, Australia: An automated method for Landsat TM/ETM+ time series

Goodwin, Nicholas; Collett, L.; Denham, Robert; Flood, Neil; Tindall, D.

doi:10.1016/j.rse.2013.02.019

Cited by 107 publications

(72 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, clouds and cloud shadows obstructed the view of the land surface during dataset acquisition in the study area. Clouds and their shadows are one of the main problems when using Landsat data in the tropics [47,48]. The brightening effect of the clouds and the darkening effect of cloud shadows create significant sources of noise in the Landsat data and cause problems in cloud detection, which is an initial step in most analyses, including error estimation for vegetation indexes [49].…”

Section: Data Usementioning

confidence: 99%

Changes in Gross Primary Production (GPP) over the Past Two Decades Due to Land Use Conversion in a Tourism City

Nuarsa

As-syakur

Gunadi

et al. 2018

IJGI

View full text Add to dashboard Cite

Understanding the changes in gross primary production (GPP), which is the total carbon fixation by terrestrial ecosystems through vegetation photosynthesis, due to land use conversion in a tourism city is important for carbon cycle studies. Satellite data from Landsat 5, Landsat 7 and Landsat 8 and meteorological data are used to calculate annual GPP for 1995, 2003 and 2014, respectively, using the vegetation production model (VPM) in the tourism city Denpasar, Bali, Indonesia. Five land use types generated from topographic maps in three different years over the past two decades are used to quantify the impacts of land use changes on GPP estimation values. Analysis was performed for two periods to determine changes in land use and GPP value as well as their speed. The results demonstrated that urban land development, namely, the increase of settlement areas due to tourism activity, had overall negative effects on terrestrial GPP. The total GPP of the whole area decreased by 7793.96 tC year −1 (12.65%) during the study period. The decline is due to the conversion of agriculture and grassland area into settlements, which caused the city to lose half of its ability to uptake carbon through vegetation. However, although forest area is declining, forest maintenance and restoration by making them protection areas has been helpful in preventing a drastic decline in GPP value over the past two decades. This study provides information that is useful for carbon resource management, tourism, policy making and scholars concerned about carbon reduction in a tourism city.

show abstract

Section: Data Usementioning

confidence: 99%

Changes in Gross Primary Production (GPP) over the Past Two Decades Due to Land Use Conversion in a Tourism City

Nuarsa

As-syakur

Gunadi

et al. 2018

IJGI

View full text Add to dashboard Cite

show abstract

“…and (Flood, 2014) have applied atmospheric, topographic and Bidirectional Reflectance Distribution Function (BRDF) radiometric calibrations to the Landsat data to derive surface reflectance imagery. Cloud masking has also been applied using techniques developed by (Goodwin et al, 2013). Single date path-row images in 16 day intervals from 1987 to present will be used in a time series based analyses.…”

Section: Preprocessing and Standardisationmentioning

confidence: 99%

Assessing Landsat Fractional Ground-Cover Time Series Across Australia’s Arid Rangelands: Separating Grazing Impacts From Climate Variability

Barnetson

Phinn

Scarth

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Suitable measures of grazing impacts on ground cover, that enable separation of the effects of climatic variations, are needed to inform land managers and policy makers across the arid rangelands of Australia. This work developed and tested a time-series, changepoint detection method for application to time series of vegetation fractional cover derived from Landsat data to identify irregular and episodic ground-cover growth cycles. Utilising the High Performance Computing power of the Google Cloud Compute Engine these cycles were segmented to distinguish grazing impacts from that of climate variability. A measure of grazing impact was developed using a multivariate technique to quantify the rate and degree of ground cover change. The method was successful in detecting both long term and short term growth cycles. Growth cycle detection was assessed against rainfall surplus measures indicating a relationship with high rainfall periods. During periods of ground cover decline, grazing utilisation was observed across four major grasslands. Ground cover change associated with grazing impacts was also assessed against field measurements of ground cover indicating a relationship between both field and remotely sensed ground cover. Cause and effects between grazing practices and ground cover resilience can now be explored in isolation to climatic drivers. This is important to the long term balance between ground cover utilisation and overall landscape function and resilience.

show abstract

“…Automated cloud classification methods based on a single Landsat image [41][42][43][44][45][46][47][48] achieved high accuracies in detecting clouds and their shadows. Recent cloud classification efforts based on multi-temporal images [49][50][51][52][53][54][55][56] have been proposed to better detect clouds and cloud shadows. The method proposed in [57] only deals with clouds and ignored cloud shadows, while the method proposed by Zhu et al [48] is designed to detect clouds and associated shadows simultaneously in Landsat images.…”

Section: Etm+ Ndvi Composingmentioning

confidence: 99%

Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery

Zhang

Thau

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

Urban areas play a very important role in global climate change. There is increasing need to understand global urban areas with sufficient spatial details for global climate change mitigation. Remote sensing imagery, such as medium resolution Landsat daytime multispectral imagery and coarse resolution Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light imagery, has provided a powerful tool for characterizing and mapping cities, with advantages and disadvantages. Here we propose a framework to merge cloud and cloud shadow-free Landsat Normalized Difference Vegetation Index (NDVI) composite and DMSP/OLS Night Time Light (NTL) to characterize global urban areas at a 30 m resolution, through a Normalized Difference Urban Index (NDUI) to make full use of them while minimizing their limitations. We modify the maximum NDVI value multi-date image compositing method to generate the cloud and cloud shadow-free Landsat NDVI composite, which is critical for generating a global NDUI. Evaluation results show the NDUI can effectively increase the separability between urban areas and bare lands as well as farmland, capturing large scale urban extents and, at the same time, providing sufficient spatial details inside urban areas. With advanced

show abstract

Cloud and cloud shadow screening across Queensland, Australia: An automated method for Landsat TM/ETM+ time series

Cited by 107 publications

References 27 publications

Changes in Gross Primary Production (GPP) over the Past Two Decades Due to Land Use Conversion in a Tourism City

Changes in Gross Primary Production (GPP) over the Past Two Decades Due to Land Use Conversion in a Tourism City

Assessing Landsat Fractional Ground-Cover Time Series Across Australia’s Arid Rangelands: Separating Grazing Impacts From Climate Variability

Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery

Contact Info

Product

Resources

About