Updating Landsat-based forest cover maps with MODIS images using multiscale spectral-spatial-temporal superresolution mapping

Abstract With the high deforestation rates of global forest covers during the past decades, there is an ever-increasing need to monitor forest covers at both fine spatial and temporal resolutions. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat series images have been used commonly for satellite-derived forest cover mapping. However, the spatial resolution of MODIS images and the temporal resolution of Landsat images are too coarse to observe forest cover at both fine spatial and temporal resolutions. In this paper, a novel multiscale spectral-spatial-temporal superresolution mapping (MSSTSRM) approach is proposed to update Landsat-based forest maps by integrating current MODIS images with the previous forest maps generated from Landsat image. Both the 240 m MODIS bands and 480 m MODIS bands were used as inputs of the spectral energy function of the MSSTSRM model. The principle of maximal spatial dependence was used as the spatial energy function to make the updated forest map spatially smooth. The temporal energy function was based on a multiscale spatial-temporal dependence model, and considers the land cover changes between the previous and current time. The novel MSSTSRM model was able to update Landsat-based forest maps more accurately, in terms of both visual and quantitative evaluation, than traditional pixel-based classification and the latest sub-pixel based super-resolution mapping methods The results demonstrate the great efficiency and potential of MSSTSRM for updating fine temporal resolution Landsat-based forest maps using MODIS images

Spatial-temporal super-resolution land cover mapping with a local spatial-temporal dependence model

The mixed pixel problem is common in remote sensing. A soft classification can generate land cover class fraction images that illustrate the areal proportions of the various land cover classes within pixels. The spatial distribution of land cover classes within each mixed pixel is, however, not represented. Super-resolution land cover mapping (SRM) is a technique to predict the spatial distribution of land cover classes within the mixed pixel using fraction images as input. Spatial-temporal SRM (STSRM) extends the basic SRM to include a temporal dimension by using a finer-spatial resolution land cover map that pre-or postdates the image acquisition time as ancillary data. Traditional STSRM methods often use one land cover map as the constraint, but neglect the majority of available land cover maps acquired at different dates and of the same scene in reconstructing a full state trajectory of land cover changes when applying STSRM to time series data. In addition, the STSRM methods define the temporal dependence globally, and neglect the spatial variation of land cover temporal dependence intensity within images. A novel local STSRM (LSTSRM) is proposed in this paper. LSTSRM incorporates more than one available land cover map to constrain the solution, and develops a local temporal dependence model, in which the temporal dependence intensity may vary spatially. The results show that LSTSRM can eliminate speckle-like artifacts and reconstruct the spatial patterns of land cover patches in the resulting maps, and increase the overall accuracy compared with other STSRM methods

Spatial-Temporal Sub-Pixel Mapping Based on Swarm Intelligence Theory

In the past decades, sub-pixel mapping algorithms have been extensively developed due to the large number of different applications. However, most of the sub-pixel mapping algorithms are based on single-temporal images, and the results are usually compromised without auxiliary information due to the ill-posed problem of sub-pixel mapping. In this paper, a novel spatial-temporal sub-pixel mapping algorithm based on swarm intelligence theory is proposed for multitemporal remote sensing imagery. Swarm intelligence theory involves clonal selection sub-pixel mapping (CSSM), which evolves the solution by emulating the biological advantage of the human immune system, and differential evolution sub-pixel mapping (DESM), which optimizes the solution by intelligent operations and heuristic searching in the solution pool. In addition, considering the under-determined problem of sub-pixel mapping, the spatial-temporal sub-pixel mapping method is used to obtain the distribution information at a fine spatial resolution from the bitemporal image pair, which exactly regularizes the ill-posed problem. Furthermore, the short-interval temporal information and the fine spatial distribution information within the bitemporal image pair can be integrated for further use, such as timely and detailed land-cover change detection (LCCD). To verify the validation of the swarm intelligence theory based spatial-temporal sub-pixel mapping algorithm, the proposed algorithm was compared with several traditional sub-pixel mapping algorithms, in both synthetic and real image experiments. The experimental results confirm that the proposed algorithm outperforms the traditional approaches, achieving a better sub-pixel mapping result both qualitatively and quantitatively, as well as improving the subsequent LCCD performance