Pixel-Based Image Compositing for Large-Area Dense Time Series Applications and Science

Figures & data

TABLE 1 Review of studies applying Landsat pixel-based image compositing approaches

Study	Sensor	Physical Unit	Processing	# of unique path/row s	# of images	Compositing period	Cloud-masking	Rule-base
Hansen et al. 2008	TM, ETM+	TOA reflectance	MODIS forest/non-forest mask used as reference for normalization via dark object subtraction (DOS) and a regression-based surface anisotropic correction.	20	∼4–7 per path/row (98 total)	Two epochs: pre-1996 and post-1996	Custom (supervised classification using decision trees)	Selected image date with the lowest cloud and shadow likelihood; if two were equal used the pixel value closest to the forest reference value (TOA = 100).
Roy et al. 2010	ETM+	TOA reflectance	Standard practice for TOA estimation (Chander et al. 2009).	459	6521	Monthly, seasonal, annual (Dec 2007 to Nov 2008)	Custom (ACCA and supervised classification using decision trees)	Valid surface observations with minimal cloud, snow, and atmospheric contamination. Heritage maximum NDVI for vegetated pixels; maximum brightness temperature for unvegetated pixels; cloud masks.
Potapov et al. 2011	ETM+	TOA reflectance	MODIS forest/non-forest mask used as reference for normalization via DOS and regression-based surface anisotropic correction	406	7227	Two epochs: 2000 and 2005	Custom (supervised classification using CART decision trees)	Per-pixel QA assessment based on criteria for cloud/shadow and image date (year and season), then for probability of water and “no data”. QA assessment provided a data pool for selecting “best” observation. All data pool observations for each pixel were then ranked based on NIR values (band 4). The observation with a band 4 value closest to the pixel's band 4 median was selected for compositing.
Potapov et al. 2012	ETM+	TOA reflectance	Standard practice for TOA estimation (Chander et al. 2009). Normalized to MODIS 10-year surface reflectance using band-wise mean bias. Regression-based surface anisotropic correction.	120	8881	Two epochs: 2000–2005 and 2005–2010	Custom (supervised classification using CART decision trees)	As per Potapov et al. (2011) and described above.
Flood 2013	TM, ETM+	Surface reflectance	Atmospheric BRDF Topographic	1	Not reported. March 2000 to November 2012, minimum of 3 images per season	Seasonal composites (spring, summer, autumn, winter)	Fmask (Zhu and Woodcock 2012)	Medoid NDVI as a seasonally representative value
Griffiths et al. 2013	TM, ETM+	Surface reflectance	LEDAPS	42	Y2000 = 890; Y2005 = 1478; Y2010 = 1590	Three epochs: 2000, 2005, 2010.	Fmask (Zhu and Woodcock 2012)	Weighted pixel-based scoring system based on acquisition year, acquisition day of year, and distance of a given pixel to cloud.

TABLE 2 Key attributes for Canada's NFI and Carbon Accounting programs

Attributes
Land cover
Crown closure
Age
Species
Height
Volume
Biomass
Pre-disturbance land cover
Post-disturbance land cover
Disturbance agent
Disturbance year
Disturbance extent (area)
Disturbance intensity

TABLE 3 A lexicon of pixel-based image composites

Composite type	Typical compositing period	Typical rule-base
Annual BAP	Target DOY ± 30 days (for a single year)	1. DOY (relative to a target DOY, i.e., Aug 1)
		2. Distance to cloud and cloud shadow
		3. Sensor
		4. Atmospheric opacity
Multi-year BAP	For a given target year and target DOY ± 30 days (± 1 or 2 years)	1. Year (relative to a target year)
		2. DOY (relative to a target DOY, i.e., Aug 1)
		3. Distance to cloud and cloud shadow
		4. Sensor
		5. Atmospheric opacity
Proxy value composite	Same as per annual BAP	Annual BAP composite used as source (generated using contextual rule-base as described above. Areas of no data or anomolous values are assigned a proxy value by examining a temporal trajectory of pixel values at the same or neightbouring pixel locations.

FIG. 1. An overview of pixel-based image compositing methods used to generate prototype products for Saskatchewan and Newfoundland.

FIG. 2. Study area: Canada, Saskatchewan, and Newfoundland.

FIG. 3. 2010 annual best available pixel (BAP) composite (A) using August 1 ± 30 days. Approximately 17% of pixels have no observations within that date range. The distance to target day of year (DOY; B) indicates the DOY distribution; less than 1% of pixels have no observations (no data).

FIG. 4. Observation yield for 2010 annual BAP composite for Canada.

FIG. 5. Multi-year BAP composite for Canada incorporating pixels from years 2009, 2010, 2011.

FIG. 6. The observation yield of BAP pixels that provide the basis for composite development for Saskatchewan (A) and Newfoundland (B).

FIG. 7. The number and spatial distribution for consecutive years of missing data for Saskatchewan (A) and Newfoundland (B).

FIG. 8. Quality assessment of annual BAP composite for a sample path/row in Saskatchewan (p38r22) and Newfoundland (p3r26). For a randomly selected set of 500 pixels, surface reflectance values from a reference image are plotted against BAP composite surface reflectance values (reflectance values are scaled by 10000).

TABLE 4 Examples of different information requirements and associated compositing criteria

Application area	Date range	Rule-base
Land cover	Aug 1 ± 30 days	Sensor
		Day of year
		Distance to cloud/cloud shadow
		Opacity
Forest structural attribution (height, volume, biomass)	Aug 1 ± 30 days	Sensor
		Day of year
		Distance to cloud/cloud shadow
		Opacity
Deforestation monitoring	Sep 1 +30, −60 days	Sensor
		Day of year
		Distance to cloud/cloud shadow
		Opacity
		Pixel temporal grouping
Wildfires	Nominally	Sensor
	Aug 20 to Oct 10	Day of year
	Pre- and post-burn image dates are influenced by early or late season burns	Distance to cloud/cloud shadow
		Opacity
		Date of end fire date
Insects	Variable depending on insect	Sensor
		Day of year
		Distance to cloud/cloud shadow
		Opacity
		Type of damage: eg., defoliator vs bark beetle based on agency aerial survey

FIG. 9. Areas of cloud in a single-date image (LT50440202010210EDC00) (A) are replaced with cloud-free observations in the annual BAP composite for 2010 (B). The composite uses observations from several unique images (C), with the greatest difference visible for those images acquired outside the August 1 ± 30 day window (D).

Hansen, M.C., Roy, D.P., Lindquist, E., Adusei, B., Justice, C.O., Alstatt, A. 2008. A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin. Remote Sensing of Environment, Vol. 112(No. 5): pp. 2495–2513.

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Roy, D.P., Ju, J., Kline, K., Scaramuzza, P.L., Kovalskyy, V., Hansen, M., Loveland, T.R., Vermote, E., and Zhang, C. 2010. Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States. Remote Sensing of Environment, Vol. 114: pp. 35–49.

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Chander, G., Markham, B.L., and Helder, D.L. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+ and EO-1 ALI sensors. Remote Sensing of Environment, Vol. 113: pp. 893–903.

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Potapov, P., Turubanova, S., and Hansen, M.C. 2011. Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia. Remote Sensing of Environment, Vol. 115: pp. 548–561.

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Potapov, P., Turubanova, S., Hansen, M.C., Adusei, B., Broich, M., Alstatt, A., Mane, L., and Justice, C.O. 2012. Quantifying forest cover loss in Democratic Republic of the Congo, 2000-2010, with Landsat ETM+ data. Remote Sensing of Environment, Vol. 122: pp. 106–116.

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Flood, N. 2013. Seasonal composite Landsat TM/ETM +images using the Medoid (a multi-dimensional median). Remote Sensing, Vol. 5(No. 12): pp. 6481–6500.

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Zhu, Z., and Woodcock, C.E. 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, Vol. 118: pp. 83–94.

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Griffiths, P., van der Linden, S., Kuemmerle, T., and Hostert, P. 2013. A pixel-based Landsat compositing algorithm for large area land cover mapping. Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 6(No. 5): pp. 2088–2101.

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