Converts an index from the first length to the second length. For example, assume that c(2, 2) indexes an element in a matrix with 2 rows and 5 columns. If the matrix is transformed to a vector, the same element can be accessed with the index IndexTwoOne(c(2, 2), c(2, 5)) (=4).
The package provides tools to fill-in missing values in satellite data. It can be used to gap-fill, e.g., MODIS NDVI data and is helpful when developing new gap-fill algorithms. The methods are tailored to data (images) observed at equally-spaced points in time. This is typically the case for MODIS land surface products and AVHRR NDVI data, among others. The predictions of the missing values are based on a subset-predict procedure, i.e., each missing value is predicted separately by (1) selecting subsets of the data that are in a neighborhood around the missing point and (2) predicting the missing value based on the subset. The main function of the package is Gapfill.
Extend
(Package: gapfill) :
Implement an Alternative Gap-fill Algorithm
By default, the Gapfill function uses the Subset and Predict functions to predict missing values. To implement alternative gap-fill procedures, these functions can be replaced by user defined ones and passed to the Gapfill function via the arguments fnSubset and fnPredict. The example section below gives two such extensions:
● Data Source:
CranContrib
● Keywords:
● Alias: Extend, alternative
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0 images
The dataset was created to test gap-fill algorithms. It mimics a subset of the MODIS NDVI data (product MOD13A1) in the region of Alaska. The data product features one image per 16-day time interval, i.e., 24 images per year. The indicated images (see Image(ndvi)) were downloaded and stored as a 4 dimensional array. Its dimensions correspond to longitude, latitude, day of the year, and year.
The function summarizes the validation scenario and returns the root mean squared error (RMSE) of the predictions. The typical validation procedure is: start with the trueData. Remove some validation points to obtain artificially generated dataObserved. Predicting the validation points based on dataObserved leads to dataFilled.
Score
(Package: gapfill) :
Score Columns of a Matrix Containing NAs by its Values
Helper function for Predict used to score the columns of a matrix according to their values. The scoring of a given column is done by pair-wise comparisons with all other columns. The comparison of columns is done by pair-wise comparisons of the non-missing values. This procedure is robust to missing values, if all columns of the matrix have a similar (potentially shifted) distribution of values.
The Subset and Predict function used in the default configuration of Gapfill. To predict a missing value, the two function are called sequentially as described the help page of Gapfill.
Given an array data with 4 dimensions, a subset around the element with coordinates mp ("missing position") is extracted. The size of the subset in all four directions from mp is specified by size. ArrayAroundRandom returns a subset around a random location in data.
Gapfill
(Package: gapfill) :
Main Function for Gap-Filling
The function fills (predicts) missing values in satellite data. We illustrate it with MODIS NDVI data, but it can also be applied to other data, that is recorded at equally spaced points in time. Moreover, the function provides infrastructure for the development of new gap-fill algorithms. The predictions of the missing values are based on a subset-predict procedure, i.e., each missing value is predicted separately by (1) selcting a subset of the data to a neighborhood around the missing value and (2) predicting the values based on that subset.
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