Characterization of the Landsat-7 ETM+ Automated Cloud-Cover Assessment (ACCA) Algorithm

Irish, Richard R.; Barker, J. L.; Goward, Samuel N.; Arvidson, Terry

doi:10.14358/pers.72.10.1179

Cited by 369 publications

(224 citation statements)

References 11 publications

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“…Most clouds and cloud shadows cover the agriculture and grass, settlement, and forest land use types. The area of clouds and cloud shadows for the three main Landsat datasets was determined using the automated cloud-cover assessment (ACCA) algorithm [50]. The application of the ACCA algorithm requires green, red, near-infrared (NIR), shortwave infrared (SWIR), and thermal infrared (TIR) spectral bands in surface reflectance form.…”

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

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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.

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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

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“…We build upon concepts developed in the prior automatic cloud cover assessment (ACCA) algorithm [57] to improve the discrimination of cloud and shadow on a per-pixel basis [58]. Instead of relying on information from a single date of imagery, clouds were identified as deviations from the mean Tasseled Cap brightness and thermal band values through time for each Landsat pixel.…”

Section: Landsat Cloud Maskingmentioning

confidence: 99%

An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

et al. 2014

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Forests contain a majority of the aboveground carbon (C) found in ecosystems, and understanding biomass lost from disturbance is essential to improve our C-cycle knowledge. Our study region in the Wisconsin and Minnesota Laurentian Forest had a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 2007, observed with the National Ocean and Atmospheric Administration's (NOAA) series of Advanced Very High Resolution Radiometer (AVHRR). To understand the potential role of disturbances in the terrestrial C-cycle, we developed an algorithm to map forest disturbances from either harvest or insect outbreak for Landsat time-series stacks. We merged two image analysis approaches into one algorithm to monitor forest change that included: (1) multiple disturbance index thresholds to capture clear-cut harvest; and (2) a OPEN ACCESS Remote Sens. 2014, 6 2783 spectral trajectory-based image analysis with multiple confidence interval thresholds to map insect outbreak. We produced 20 maps and evaluated classification accuracy with air-photos and insect air-survey data to understand the performance of our algorithm. We achieved overall accuracies ranging from 65% to 75%, with an average accuracy of 72%. The producer's and user's accuracy ranged from a maximum of 32% to 70% for insect disturbance, 60% to 76% for insect mortality and 82% to 88% for harvested forest, which was the dominant disturbance agent. Forest disturbances accounted for 22% of total forested area (7349 km 2 ). Our algorithm provides a basic approach to map disturbance history where large impacts to forest stands have occurred and highlights the limited spectral sensitivity of Landsat time-series to outbreaks of defoliating insects. We found that only harvest and insect mortality events can be mapped with adequate accuracy with a non-annual Landsat time-series. This limited our land cover understanding of NDVI decline drivers. We demonstrate that to capture more subtle disturbances with spectral trajectories, future observations must be temporally dense to distinguish between type and frequency in heterogeneous landscapes.

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“…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.…”

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

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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

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Characterization of the Landsat-7 ETM+ Automated Cloud-Cover Assessment (ACCA) Algorithm

Cited by 369 publications

References 11 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

An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

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

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