Interpreting remotely sensed data typically requires expensive specialized computing machinery capable of stor ing and manipulating large amounts of data quickly In this paper we present a method for accurately analyzing and categorizing remotely sensed data on much smaller less expensive platforms Data size is reduced in such a way as to retain the integrity of the original data where the form...

Interpreting remotely-sensed data typically requires expensive, specialized computing machinery capable of storing and manipulating large amounts of data quickly. In this paper, we present a method for accurately analyzing and categorizing remotely-sensed data on much smaller, less expensive platforms. Data size is reduced in such a way as to retain the integrity of the original data, where the...

This article investigates how remotely sensed lawn characteristics, such as parcel lawn area and parcel lawn greenness, combined with household characteristics, can be used to predict household lawn fertilization practices on private residential lands. This study involves two watersheds, Glyndon and Baisman's Run, in Baltimore County, Maryland, USA. Parcel lawn area and lawn greenness were deri...

Texture features play a predominant role in land cover classification of remotely sensed images. In this study, for extracting texture features from data intensive remotely sensed image, Gabor wavelet has been used. Gabor wavelet transform filters frequency components of an image through decomposition and produces useful features. For classification of fuzzy land cover patterns in the remotely ...

Remotely sensed multi-spectral and -spatial data facilitates the study of mosquito-borne disease vectors and their response to land use and cover composition in the urban environment. In this study we assess the feasibility of integrating remotely sensed multispectral reflectance data and LiDAR (Light Detection and Ranging)-derived height information to improve land use and land cover classific...

Assessment and mapping of burned areas using satellite data has been implemented so far at local, regional and global scale, by employing various image analysis techniques, according to the spectral and spatial characteristics of the remotely sensed data. The spatial resolution of satellite data together with landscape configuration guides of the decision for the classification approach to be a...

The orbiting cycle and frequent cloud contamination have limited the applications of the moderate-resolution remotely sensed data for detecting rapid land cover changes that are critical to the monitoring of wetlands. It is necessary to use multiple remotely sensed data sources that have different spatial resolution and temporal frequency, because both spatial and temporal details are important...

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...

Thermal infrared (TIR) cameras perfectly bridge the gap between (i) on-site measurements of land surface temperature (LST) providing high temporal resolution at the cost of low spatial coverage and (ii) remotely sensed data from satellites that provide high spatial coverage at relatively low spatio-temporal resolution. While LST data from satellite (LSTsat) and airborne platforms are routinely ...