Sub-Pixel Classification of MODIS EVI for Annual Mappings of Impervious Surface Areas
AbstractRegular monitoring of expanding impervious surfaces areas (ISAs) in urban areas is highly desirable. MODIS data can meet this demand in terms of frequent observations but are lacking in spatial detail, leading to the mixed land cover problem when per-pixel classifications are applied. To overcome this issue, this research develops and applies a spatio-temporal sub-pixel model to estimate ISAs on an annual basis during 2001–2013 in the Jakarta Metropolitan Area, Indonesia. A Random Forest (RF) regression inferred the ISA proportion from annual 23 values of MODIS MOD13Q1 EVI and reference data in which such proportion was visually allocated from very high-resolution images in Google Earth over time at randomly selected locations. Annual maps of ISA proportion were generated and showed an average increase of 30.65 km2/year over 13 years. For comparison, a series of RF per-pixel classifications were also developed from the same reference data using a Boolean class constructed from different thresholds of ISA proportion. Results from per-pixel models varied when such thresholds change, suggesting difficulty of estimation of actual ISAs. This research demonstrated the advantages of spatio-temporal sub-pixel analysis for annual ISAs mapping and addresses the problem associated with definitions of thresholds in per-pixel approaches. View Full-Text
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Tsutsumida, N.; Comber, A.; Barrett, K.; Saizen, I.; Rustiadi, E. Sub-Pixel Classification of MODIS EVI for Annual Mappings of Impervious Surface Areas. Remote Sens. 2016, 8, 143.
Tsutsumida N, Comber A, Barrett K, Saizen I, Rustiadi E. Sub-Pixel Classification of MODIS EVI for Annual Mappings of Impervious Surface Areas. Remote Sensing. 2016; 8(2):143.Chicago/Turabian Style
Tsutsumida, Narumasa; Comber, Alexis; Barrett, Kirsten; Saizen, Izuru; Rustiadi, Ernan. 2016. "Sub-Pixel Classification of MODIS EVI for Annual Mappings of Impervious Surface Areas." Remote Sens. 8, no. 2: 143.