User guide for MODIS derived vegetation fractional cover metrics

Transcription

1 User guide for MODIS derived vegetation fractional cover metrics Introduction The MODIS derived vegetation fractional cover metrics is a collection of image files which statistically summarise the time series vegetation fractional cover data set of Guerschman et al. (2009) for Australia from March 2000 to current. These metrics were constructed by the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) for the Australian Government Department of Agriculture, Fisheries and Forestry. The vegetation fractional cover data were developed by the Environmental Earth Observation Group, CSIRO Land and Water. These data were derived from the MODIS Nadir BRDF Adjusted Reflectance (MODIS NBAR) product, version 5 (MCD43A4.005). Resolution is 500 metres. MODIS NBAR data are 16 day composites produced every 8 days. The fractional cover data comprise a time series of image sets with four image themes; photosynthetic vegetation (PV theme), non photosynthetic vegetation (NPV theme), bare soil (BS theme) and data quality flag (FLAG theme). The fractional cover data were derived using a linear unmixing methodology (Guerschman et al. 2009). This method uses the NDVI and the ratio of MODIS bands 7 and 6 (2100 and 1600 nm respectively). The methodology was originally developed for the Australian tropical savannas. ABARES surveyed users of the fractional cover data to identify which metrics and which analysis periods were most commonly used to summarise the data. The most popular metrics were (in decreasing popularity): mean minimum and maximum standard deviation coefficient of variation, rate of rise, rate of drop and time of maximum time of minimum and frequency mode and range. The most popular periods were (in decreasing popularity): seasonal annual (for data produced every 16 days) archive monthly (for data produced every 8 days). Considering user needs, the MODIS derived vegetation fractional cover metrics data set comprises the following rasters: mean, standard deviation, minimum value, maximum value, time of minimum and time of maximum, for the PV, NPV and BS themes the percentage of each of the data quality flag classes (0, 1, 2 and 255) for the FLAG theme 1

2 and summarises the following periods: the entire fractional cover data archive from ordinal date to current the calendar years 2001 to current all available seasons (with spring beginning on 1 September, summer on 1 December, autumn on 1 March and winter on 1 June) from ordinal date (March 2000) to current each month from March 2000 to current. Prospective users of the data should refer to the README (ABARES, 2011) for page 0 metadata. The vegetation fractional cover data The MODIS derived vegetation fractional cover metrics data set was constructed on Version 2.1 of the fractional cover data available at: The fractional cover data were derived from the MODIS Nadir BRDF Adjusted Reflectance (MODIS NBAR) product, collection 5 (MCD43A4.005) as described in: ance/16_day_l3_global_500m/v5/combined The combined Terra Aqua MODIS NBAR data comprise 500 metre reflectance data adjusted using a bidirectional reflectance distribution function (BRDF) to model the values as if they were taken from nadir view. MODIS NBAR data are 16 day composites produced every 8 days. For example, the production period tagged as day 001 includes acquisition between days 001 and 016 while the production period tagged as day 009 includes acquisition between days 009 and 024. In each calendar year, the start date for the first composite period is day 001 and the last composite start date is day 361 whether it is a leap year or an ordinary year. The fractional cover data comprise a time series of image sets. Each image set has four image themes. Three of the themes are basic land covers: the PV theme for photosynthetic vegetation, the NPV theme for non photosynthetic vegetation and the BS theme for bare soil. Each pixel in these image themes has a percentage for the particular land covers. For a given time and pixel, the sum of the fractional cover percentages for all three cover types is 100 per cent. The fractional cover percentages represent the proportions of the exposed vegetation. In forested canopies the photosynthetic or non photosynthetic portions of trees may obscure those of the grass layer and any bare soil. The fourth theme is the FLAG theme and indicates for each pixel the data quality for the three land cover themes. The fractional cover data were derived using a linear unmixing methodology (Guerschman et al. 2009). This method uses the NDVI and the ratio of MODIS bands 7 and 6 (2100 and 1600 nanometres). An assumption of the method is that areas with high fractions of bare soil have a flat spectral feature in the shortwave infrared (SWIR) and thus a relative high (close to 1) ratio of MODIS bands 7 and 6. Areas with a high proportion of non photosynthetic vegetation have a lower reflectance in the 2100 nm region compared to the 1600 nm region 2

3 and therefore a lower (around 0.6) ratio of MODIS bands 7 to 6. The methodology was originally developed for the Australian tropical savannas and evaluated using field measurements of grass curing in 10 sites of which six are included in the tropical savannas and four are located in grasslands in the west and south east of Australia. The linear unmixing methodology requires a threshold value to be set, between 0 and 1, but close to 0. For Version 2.1 the threshold was fixed to 0.2. The processing of each pixel is given in the FLAG theme as: FLAG class 0 all three fractions are between 0 and 1, the pixel has been processed normally and the unmixing is unconstrained. FLAG class 1 one of the three fractions is between 0.2 and 0 or between 1 and 1.2. That fraction is capped at 0 (or 1) and the other two fractions are recalculated so the sum of all three adds to 1. In this case, the pixel was not processed normally and alternative values calculated with the unmixing partially constrained. This typically occurs for pixels representing water bodies and salt lakes. FLAG class 2 one or more of the three fractions is less than 0.2 or exceeds 1.2. The fractions are not calculated and each is set to 254. In this case, the pixel was not processed normally and alternative values are not calculated with unmixing constrained. FLAG class 255 the pixel could not be processed because MODIS NBAR data were not available. No fractions are calculated and each is set to 255. This is likely for pixels where cloud covered most of the 16 days of the composite. The fractional cover data are a growing archive of images in ERDAS IMAGINE format from ordinal date to current. Coordinates are unprojected geographic decimal degrees, referred to WGS84 with a resolution of 500 metres. Coordinates and resolution are the same as for the original MODIS data (Paget and King, 2008). Bounding box coordinates at the outer edges of cells are , , , The number of columns is 9580 and number of rows is The MODIS derived vegetation fractional cover metrics data set Overview The MODIS derived vegetation fractional cover metrics data set comprises rasters in compressed single band ERDAS IMAGINE image format, summarising the MODIS derived vegetation fractional cover data. Compression uses the method of the GNU Project s program gzip. The metric types are as listed in Table 1. 3

4 Table 1: Metrics in the MODIS derived vegetation fractional cover metrics data. Theme Metric String used in output file name Mean Mean Standard deviation Std Minimum value Min Maximum value Max Time of minimum TimeMax Time of maximum TimeMin FLAG Percentage of 0 flags Percent0 FLAG Percentage of 1 flags Percent1 FLAG Percentage of 2 flags Percent2 FLAG Percentage of 255 flags Percent255 The periods summarised by the metrics are listed in Table 2. For meteorological seasons and calendar months, the start and end days were chosen so that where a fractional cover image has its 16 day composite overlapping two consecutive periods in a given category, the image is assigned to the period that contains the greater part of the 16 day composite. Metric file naming The names of the metric rasters follow the naming of the input ERDAS IMAGINE images but with.gz extension (indicating gzip compression) added to the.img extension (indicating ERDAS IMAGINE image format). Strings are used in the file name to indicate the: version of the underlying fractional cover data (FractCover.V2_1) four digit year of the end date for the specified period period (Table 2) metric (Table 1) theme ( or FLAG). The end year and the period type together uniquely determine the start and end dates for the specified period (Table 2). For example, a file name containing the string 2010.Ann is a yearly mode metric covering the period to Examples of MODIS derived vegetation fractional cover metrics file names are: The name of the BS theme metric giving mean and summarising the whole archive of fractional cover images from ordinal date to ordinal date would be: FractCover.V2_ Arch.Mean.BS.img.gz The name of the NPV theme metric giving standard deviation and summarising the fractional cover images for the summer of from ordinal date to ordinal date would be: FractCover.V2_ Sum.Std.NPV.img.gz The name of the FLAG theme metric giving percentage of 255 flags and summarising the fractional cover images for May 2008 from ordinal date to ordinal date (note that 2008 is a leap year) would be: FractCover.V2_ May.Percent255.FLAG.img.gz 4

5 Table 2: Periods summarized by the MODIS derived vegetation fractional cover metrics data. Period category Period type Start date for specified year a End date for specified year a Whole archive Whole archive Day 361 Arch Calendar year Calendar year Day 001 Day 361 Ann String used in output file name Meteorological season Spring Day 241 Day 321 Spr Meteorological season Summer Day 329 Day 049 Sum Meteorological season Autumn ( Day f 057 b f ) Day 137 b Aut Meteorological season Winter Day 145 c Day 233 Win Calendar month January Day 361 Day 017 Jan Calendar month February ( Day f 025 b f ) Day 049 Feb Calendar month March Day 057 Day 081 Mar Calendar month April Day 089 Day 113 Apr Calendar month May Day 121 Day 137 b May Calendar month June Day 145 c Day 169 Jun Calendar month July Day 177 Day 201 Jul Calendar month August Day 209 Day 233 Aug Calendar month September Day 241 Day 265 Sep Calendar month October Day 273 Day 297 Oct Calendar month November Day 305 Day 321 Nov Calendar month December Day 329 Day 353 Dec a Start and end dates are the dates of the first and last fractional cover image included in the period. The start and end dates are given in ordinal date format as a four digit calendar year followed by a hyphen followed by three digit ordinal number of the day in that year. Where the year cannot be specified explicitly just the three digit ordinal number of the day is given. b Day 145 in a leap year. c Day 153 in a leap year. 5

6 Calculation of metrics The metrics are calculated using two Python modules metrics.py and floatgrd.py. These modules work with the ESRI ArcGIS Python libraries and have been used with ArcGIS and ArcGIS 10. The metrics.py module controls the processing. It does tasks such as validating the arguments, checking all the input fractional cover images are available, checking whether a complete set of outputs already exists (in which case no processing is needed) and preparing and working through a list of periods for processing. The floatgrd.py module is imported by metrics.py and does the core processing. It provides functions that calculate the metrics from the input images using binary floating point intermediates and utilities from the Python NumPy module for creating and processing numerical arrays. To commence processing the metrics.py is run at the DOS prompt with arguments. Running metrics.py without any arguments returns the usage. The package must be run on a machine that has ArcGIS installed. These modules have been imported into ArcGIS as a script tool. The tool is called Make Metrics and is stored in a toolset called Metrics, which is stored in a toolbox called Fractional Cover Data. The toolbox with the metrics.py and floatgrd.py scripts can be stored in any local or network directories and accessed from ArcGIS. The tool can be run from ArcGIS in three ways: from its dialogue window (opened by double clicking the tool s icon in the ArcToolbox navigation window); from the ArcGIS command line window; and from another Python script. The tool has a help document, which is accessed from ArcGIS in the same way as the help documents for standard ArcGIS tools. This document describes how to use the tool, and includes a discussion of the parameters that must be supplied by the user the arguments required by the underlying Python package and provides the syntax for running the tool in the ArcGIS command line window and for calling it from another Python script. The tool treats as no data values of 255 (no input data) or 254 (unmixing constrained) and as useable data values from 0 to 100 in the input fractional cover images for the PV, NPV and BS themes. All classes (0, 1, 2 and 255) in the FLAG theme images are treated as useable data. The user must provide the no data percentage threshold when running the tool. If the percentage a pixel has no data exceeds the value set by the user for the processing period then a no data flag (actually 9999 but called NoData) is written to all the output metrics for that pixel. Otherwise the metrics are calculated for that pixel using the useable data values. The maximum allowable percentage of no data has been set to 50 per cent for the MODISderived vegetation fractional cover metrics. The metrics are calculated as a two step process. The first processing step produces a set of cumulative rasters. The cumulative rasters fall into two groups, those for the PV, NPV and BS themes and those for the FLAG theme (Table 3). 6

7 Table 3: The cumulative rasters. Theme FLAG FLAG FLAG FLAG Cumulative raster Number of useable data values Number of no data values Sum of values a Sum of squares of values a Maximum value a Minimum value a Time of maximum a Time of minimum a Number of 0 flags Number of 1 flags Number of 2 flags Number of 255 flags a Calculated for useable data values. The second processing step calculates the metric rasters from the cumulative rasters. For the PV, NPV and BS themes, the metrics are calculated as follows: The number of data values and the number of no data values cumulative rasters are used to determine whether the no data threshold has been exceeded. If it has, NoData is written to all the metric rasters for the PV, NPV and BS themes for the pixel concerned. The mean metric is calculated from the sum of values and number of data values cumulative rasters. For each pixel, the formula used is: μ = X / N where μ represents the mean, X represents the usable data values, and N represents the number of usable data values. The standard deviation metric is calculated from the sum of values, sum of squares of values and number of data values cumulative rasters. For each pixel, the formula used is: σ = { X 2 / N ( X / N) 2 } where σ represents the standard deviation, X represents the usable data values, and N represents the number of usable data values. The minimum value metric is calculated from the minimum value cumulative raster. For each pixel, this metric gives the minimum value among the usable data values. The maximum value metric is calculated from the maximum value cumulative raster. For each pixel, this metric gives the maximum value among the usable data values. The time of minimum metric is calculated from the time of minimum cumulative raster. For each pixel, this metric gives an integer representing the ordinal date of the first day of the 16 day composite for the earliest image with the minimum value of the usable data values for the pixel concerned. 7

8 The time of maximum metric is calculated from the time of maximum cumulative raster. For each pixel, this metric gives an integer representing the ordinal date of the first day of the 16 day composite for the earliest image with the maximum value of the usable data values for the pixel concerned. For the FLAG theme, the metrics are calculated for each flag class as a percentage of that flag class in all the images for the period summarized. Thus the percent of 0 flags metric is calculated from the number of 0 flags cumulative raster and the total number of time points. The other flag classes of 1, 2 and 255 are treated similarly. The tool always checks, for the theme to be processed, whether a complete set of cumulative rasters is present that covers a part of the period to be processed and shares the same start date. If more than one cumulative rasters sets are found, the tool will choose the set that spans the longest time period and use this data rather than opening all the images for the time points spanned by the cumulative rasters. This enables processing for a long period to be split into sections, so processing times are manageable and problems do not arise from software limitations for processing data. For a given theme in a calendar year there are 46 fractional cover images. So instead of processing the whole period from ordinal date to in a single step, the processing can be done in two steps. First process the period from to ensuring that the cumulative rasters are written to disk. Then process the whole period from to using the cumulative rasters written in the first round with the images for the period to not re opened. To write cumulative rasters to disk the user sets the tool parameters of periodicity to ARCHIVE and output metric format to GRID. The cumulative rasters are written to disk as ESRI grid format. Data dictionary The values of all metrics, except the time of minimum and time of maximum, are either NoData or integers between 0 and 100 representing metrics derived from percentages and measured in percentage points. The values of the time of minimum and time of maximum metrics are either NoData or integers representing ordinal dates, constructed by multiplying the two digit year by 1000 and adding the ordinal date for the day. For example, the ordinal date is represented by the integer 73 and the ordinal date is represented by the integer Caveats relating to use of MODIS derived vegetation fractional cover metrics data 1. The values in the PV, NPV and BS theme images that result from partially constrained unmixing (flagged with class 1 in the FLAG theme images) are treated by the metrics tool as usable data. 8

9 2. In the construction of the metrics the no data percentage threshold was set to 50 per cent. 3. Though the metrics tool uses a no data percentage threshold it does not test the uniformity of the distribution of no data through time. Where the distribution of no data through time is markedly non uniform the metrics could be biased. This is likely to be more significant for the metrics representing longer time periods the whole archive and calendar years. 4. The metrics tool reports the time of minimum (or maximum) metric value as an integer representing the ordinal date of the first day of the 16 day compositing period for the earliest image with the minimum (or maximum) value of the usable data values for each pixel. The midpoint of the compositing period may be more appropriate particularly for metrics representing the shorter time periods the calendar months and meteorological seasons. The date reported is for the earliest image with the minimum (or maximum) value rather than for an image whose date is more representative of the image collection introduces bias that may be significant for metrics representing longer time periods the whole archive and calendar years. 5. The minimum value, maximum value, time of minimum and time of maximum metrics for the whole archive are not measures of central tendency for the collections of the corresponding metrics for the calendar years spanned by the archive. References ABARES (2011) Readme file for MODIS derived vegetation fractional cover metrics, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, Australia, Guerschman, J.P. (2009) Readme file for MODIS derived vegetation fractional cover, CSIRO Land and Water, Canberra, Australia, Guerschman, J.P., Hill, M.J., Renzullo, L., Barrett, D.J., Marks, A.S. and Botha, E. (2009) Estimating fractional cover of photosynthetic vegetation, non photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO 1 Hyperion and MODIS sensors. Remote Sensing of Environment, 113: , Paget, M.J., and King, E.A. (2008) MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research Internal Report No. 004, 96pp, CSIRO, Canberra, Australia, Australian Bureau of Agricultural and Resource Economics and Sciences June