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TitleDevelopment and assessment of a 250 m spatial resolution MODIS annual land cover time series (2000-2011) for the forest region of Canada derived from change-based updating
AuthorPouliot, D; Latifovic, R; Zabcic, N; Guindon, L; Olthof, I
SourceRemote Sensing of Environment vol. 140, 2014 p. 731-743,
Alt SeriesEarth Sciences Sector, Contribution Series 20130261
PublisherElsevier BV
Mediapaper; on-line; digital
File formatpdf
ProvinceBritish Columbia; Alberta; Saskatchewan; Manitoba; Ontario; Quebec; New Brunswick; Nova Scotia; Prince Edward Island; Newfoundland and Labrador; Northwest Territories; Yukon; Nunavut
NTS1; 2; 3; 10; 11; 12; 13; 14; 15; 16; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 52; 53; 54; 55; 56; 57; 58; 59; 62; 63; 64; 65; 66; 67; 68; 69; 72; 73; 74; 75; 76; 77; 78; 79; 82; 83; 84; 85; 86; 87; 88; 89; 92; 93; 94; 95; 96; 97; 98; 99; 102; 103; 104; 105; 106; 107; 114O; 114P; 115; 116; 117; 120; 340; 560
Subjectsenvironmental geology; Nature and Environment; remote sensing; MODIS; land cover; time series; change detection; boreal forest; accuracy; Air quality
Illustrationslocation maps; tables; flow charts; satellite images; graphs; histograms
ProgramLand Surface Characterization, Remote Sensing Science
AbstractDetailed information on the spatial and temporal distribution of land cover is required to evaluate the effects of land cover change on environmental processes. The development of temporally consistent land cover time series (LCTS) from satellite-based earth observation has proven difficult because multi-year observations are acquired under different conditions resulting in high inter-annual reflectance variability. This leads to spurious differences in land cover when standard approaches for image classification are applied to generate multi-year land cover data. To reduce this effect, a common solution has been to first detect change and update a base map for only these change areas. As long as the change commission error is low, this approach will ensure high consistency between maps in the time series. Here we present an approach for change-based LCTS development following from previous research, but with significant advancements in change detection, training, classification, and evidence-based refinement. The method was applied to generate an annual LCTS covering Canada spanning 2000-2011 that is consistent between years and can be used to identify dominant change transitions. Assessment of the LCTSwas challenging becausemultiplemaps needed to be evaluated and can be prohibitive particularly for annual time series covering several years. Three approaches were undertaken involving visual examination, comparison with a reference sample derived from Landsat, and comparison with the MODIS Global LCTS V5.1. Visual assessment revealed high inter-map consistency and logical temporal change trajectories of land cover classes. Comparison with the reference sample showed an accuracy of 70% at the 19 class thematic resolution. Accounting for mixed pixels by considering the first or second reference land cover label as correct increasedthe accuracy to 80%. Comparison with the MODIS Global LCTS showed that the Canada LCTS achieved higher
inter-map consistency and accuracy as expected with national relative to global land cover products.
Summary(Plain Language Summary, not published)
Monitoring over large spatial extents, specifically multi-temporal land cover mapping, is challenging because of inconsistencies present in satellite measurements. To reduce these effects, sophisticated algorithms based on advances in the fields of pattern recognition and machine learning were used to develop a set of maps spanning 2000-2011, at an annual time step, that could be compared to determine change. These data will be used for numerous reporting, monitoring, and planning activities involving a wide array of applications such as wildlife habitat, climate change, water resource availability, fire risk, carbon storage and sequestration, numerical weather forecasting, and air quality, as well as general assessments. The accuracy and consistency of these data is a key aspect that allows researchers to understand the effect of land cover dynamics on environmental processes and facilitates improved capacity to predict how future landscape changes will impact ecosystem goods and services.