Impacts of spatial heterogeneity on crop area mapping in Canada using MODIS data

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 119; pp. 451 - 461
• Main Authors: Chen, Yaoliang, Song, Xiaodong, Wang, Shusen, Huang, Jingfeng, Mansaray, Lamin R.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2016
• Subjects: Agricultural land; Area estimation error; Classification accuracy; Landscape heterogeneity; MODIS NDVI; Sample impurity; MODIS NDVI; Sample impurity; Landscape heterogeneity; Agricultural land; Classification accuracy; Area estimation error
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2016.07.007

Accurately mapping crop area using coarse spatial resolution remote sensing imageries is challenging due to the existence of various spatial heterogeneities. The objective of this study is to analyze the accuracy of crop classification and area estimation affected by spatial heterogeneities, especially for sample impurity and landscape heterogeneity. The Normalized Difference Vegetation Index (NDVI) time series calculated from Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09Q1 8-day composites and the derived phenology metrics were used to classify crop areas over Manitoba, Canada. The Classification and Regression Trees (CART) approach was applied in the classification. The Agriculture and Agri-Food Canada (AAFC) Land Cover Dataset with 30m spatial resolution was used as the base map to determine the study regions and training and validation samples. The results allowed to conclude that: (1) the classification accuracy of MODIS imagery is sensitive to both sample impurity and landscape heterogeneity. Purity limitations in samples can have a large impact on the classification accuracy. Regions with more homogenous pixels are more likely to be accurately classified and vice versa; (2) the crop area estimation error is less sensitive to sample impurity. It is not only determined by the purity of training samples but also by the actual purity condition of the crop type. The purest training sample group does not correspond well with the lowest error; (3) the impact of configurational heterogeneity on the area estimation is more significant than that of the compositional heterogeneity. Overall, both the sample impurity and landscape heterogeneities can largely affect the classification accuracy while only configurational heterogeneity has significant influence on crop area estimation.