Assessing a Temporal Change Strategy for Sub-Pixel Land Cover Change Mapping from Multi-Scale Remote Sensing Imagery

Abstract

Abstract:Remotely sensed imagery is an attractive source of information for mapping and monitoring land cover. Fine spatial resolution imagery is typically acquired infrequently, but fine temporal resolution systems commonly provide coarse spatial resolution imagery. Sub-pixel land cover change mapping is a method that aims to use the advantages of these multiple spatial and temporal resolution sensing systems. This method produces fine spatial and temporal resolution land cover maps, by updating fine spatial resolution land cover maps using coarse spatial resolution remote sensing imagery. A critical issue for sub-pixel land cover change mapping is downscaling coarse spatial resolution fraction maps estimated by soft classification to a fine spatial resolution land cover map. The relationship between a historic fine spatial resolution map and a contemporary fine spatial resolution map to be estimated at a more recent date plays an important role in the downscaling procedure. A change strategy based on the assumption that the change for each land cover class in a coarse spatial resolution pixel is unidirectional was shown to be a promising means to describe this relationship. This paper aims to assess this change strategy by analyzing the factors that affect the accuracy of the change strategy, using six subsets of the National Land Cover Database (NLCD) of USA. The results show that the spatial resolution of coarse pixels, the time interval of the previous fine resolution land cover map and the current coarse spatial resolution images, and the thematic resolution of the used land cover class scheme have considerable influence on the accuracy of the change strategy. The accuracy of the change strategy decreases with the coarsening of spatial resolution, an increase of time interval, and an increase of thematic resolution. The results also indicate that, when the historic land cover map has a 30 m resolution, like the NLCD, the average accuracy of the change strategy is still as high as 92% when the coarse spatial resolution data used had a resolution of~1000 m, confirming the effectiveness of the change strategy used in sub-pixel land cover change mapping for use with popular remote sensing systems.

Spatial-temporal fraction map fusion with multi-scale remotely sensed images

“…However, their spatial resolutions are often too coarse to allow the detection of land cover changes occurring in small areas. Therefore, to deal with this dilemma, methods for spatial-temporal data fusion are highly desirable for application to both kinds of remotely sensed imagery to provide remote sensing data with fine spatial and temporal resolutions for studying land surface dynamics (Gao et al 2006;Gong et al 2013;Hansen and Loveland 2012;Li et al 2015;Ling et al 2016a;Ling et al 2011;Zhu and Woodcock 2014).…”

“…Recently, the spatial-temporal super-resolution mapping (STSRM) method proposed by Ling et al (2011) has become a promising spatial-temporal fusion method to extract fine spatial and temporal resolution land cover change information (Li et al 2016;Ling et al 2016a;Wang et al 2015;Wu et al 2017;Xu et al 2017). STSRM aims to predict a FR land cover map from CR fraction maps, assuming that another FR land cover map, acquired at previous time for the same area, is available.…”

Learning-Based Sub-Pixel Change Detection Using Coarse Resolution Satellite Imagery

“…CR data are effective for phenological change detection due to their high revisit frequencies, but their low spatial resolutions limit their applications for accurate monitoring of urban growth dynamics-especially for rapidly growing areas [7], where dynamic changes commonly occur in sub-pixel scales (like fields, water areas, roads). To enhance the capability of remote sensing for monitoring these dynamics at a sub-pixel scale, researchers have attempted to apply some unmixing approaches to recover high spatial resolution (HR) data directly from CR data [7,8,[14][15][16][17]. In particular, Le Hégarat-Mascle et al [8] proposed a statistically-based change detection model in which sub-pixel LCCs are estimated by utilizing previous land cover information as a reminder.…”

“…In particular, Le Hégarat-Mascle et al [8] proposed a statistically-based change detection model in which sub-pixel LCCs are estimated by utilizing previous land cover information as a reminder. Ling et al [15,16] presented an improved sub-pixel mapping algorithm for change detection using prior land cover percentages, with which temporal contextual information was used to conduct sub-pixel change mapping. However, high-quality land cover percentages are required as input for this approach, which limits its real value.…”

Enhancing Land Cover Mapping through Integration of Pixel-Based and Object-Based Classifications from Remotely Sensed Imagery

“…In the pixel-based classification, the super-resolution mapping (SRM) technique was developed as a post-processing step of PSC to deal with the land cover spatial distribution uncertainty of mixed pixels. SRM can determine where different classes spatially distribute within a mixed pixel [20,[28][29][30][31][32][33][34][35][36][37][38][39][40]. Unfortunately, no research such as SRM for mixed objects has been proposed for handling the land cover spatial distribution uncertainty of mixed objects by estimating the accurate spatial distribution of different classes within mixed objects at the pixel scale.…”