The landuse or land-cover map depicts the physical coverage of the Earth’s terrestrial surface according to its use. Landuse map generation from remotely sensed images is one of the challenging tasks of remote sensing technology. In this article, motivated from group forming behavior of real ants, we have proposed two novel ant based (one supervised and one unsupervised) algorithms to automatic...

Analysis of the multi–spectral remotely–sensed images of the areas destroyed by an earthquake is proved to be a helpful tool for destruction assessments. The performance of such methods is highly dependant on the preprocess that registers the two shots before and after an event. In this paper, we propose a new fast and reliable change detection method for remotely– sensed images and analyze its...

A novel dynamic threshold method for unsupervised change detection from remotely sensed images Pengfei He, Wenzhong Shi, Hua Zhang & Ming Hao To cite this article: Pengfei He, Wenzhong Shi, Hua Zhang & Ming Hao (2014) A novel dynamic threshold method for unsupervised change detection from remotely sensed images, Remote Sensing Letters, 5:4, 396-403, DOI: 10.1080/2150704X.2014.912766 To link to ...

Facilitation is a major force shaping the structure and diversity of plant communities in terrestrial ecosystems. Detecting positive plant-plant interactions relies on the combination of field experimentation and the demonstration of spatial association between neighboring plants. This has often restricted the study of facilitation to particular sites, limiting the development of systematic ass...

Hyperspectral remotely sensed data are useful for studying ecosystem processes and patterns. However, spatial characterization of such remotely sensed images is needed to optimize sampling procedures and address scaling issues. We have investigated spatial scaling in ground-based and airborne hyperspectral data for canopyto watershed-level ecosystem studies of southern California chaparral and ...

Virtually all remotely sensed data contain spatial autocorrelation, which impacts upon their statistical features of uncertainty through variance inflation, and the compounding of duplicate information. Estimating the nature and degree of this spatial autocorrelation, which is usually positive and very strong, has been hindered by computational intensity associated with the massive number of pi...