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Spatio-temporal quality distribution of MODIS vegetation collections 5 and 6: implications for forest-non-forest separability

ABSTRACT Moderate Resolution Imaging Spectroradiometer (MODIS) has been employed for continuous monitoring of land surface dynamics to facilitate the examination of spatial aspects of the environment. Periodical generation of MODIS products enables temporal analysis, and the interpretation of temporal patterns requires information about image quality. The MODIS Scientific Data Set (SDS) provides information on image properties. Some research has utilized the SDS to assist in analysis and interpretation, particularly in supporting time series forecasting and estimating ‘invalid’ data from near-dates observation. Our research compares the usability and reliability information provided in the MODIS SDS for collections 5 and 6 to describe the spatio-temporal distribution of image quality. This research compared the ability of the MODIS collections to identify the extent of water and to differentiate forest from non-forest. Four sites representing tropical and temperate regions were selected in Brazil, Congo, Colorado (United States of America), and the European Alps. The robustness of usability and reliability information for assessing MODIS vegetation collections 5 and 6 was compared over these sites by using 16-day composite products over a year of observations (2015). The spatio-temporal distribution of invalid pixels and gaps derived from usability and reliability information were assessed by using TiSeG (Time series Generator) and GeoDa. Moran’s I indicated a large number of invalid pixels and temporal gaps were clustered in a few areas. Collection 6 appears more consistent in the identification of waterbodies, either for inland water or ocean, but the error detection of ice fractions in two tropical sites tends to increase. Masking data by using Quality Assurance (QA)-SDS information improved the separability between forest and non-forest. This research demonstrated that evaluating the quality of image products using the SDS assisted the selection of period and location to better differentiate forest and non-forest. The seasonal fluctuation of separability metrics showed the importance of exploring temporal pattern for better understanding of the dynamics of forest cover.