The paper investigated the application of MODIS data for mapping regional land cover at moderate resolutions (250 and 500 m), for regional conservation purposes. Land cover maps were generated for two major conservation areas (Greater Yellowstone Ecosystem—GYE, USA and the Pará State, Brazil) using MODIS data and decision tree classifications. The MODIS land cover products were evaluated using ...

Accurate production of regional burned area maps are necessary to reduce uncertainty in emission estimates from African savannah fires. Numerous methods have been developed that map burned and unburned surfaces. These methods are typically applied to coarse spatial resolution (1 km) data to produce regional estimates of the area burned, while higher spatial resolution (,30 m) data are used to a...

In recent years, enteromorpha prolifera detection has received increasing attention. Supervised learning with remote sensing images can achieve satisfactory performances for green tide monitoring. However, data distributions between images obviously differ, and it would be too costly to label a massive amount of images for enteromorpha prolifera detection. Thus, this paper focuses on detecting ...

This article empirically demonstrates the use of fine resolution satellite-based aerosol optical depth (AOD) to develop time and space resolved estimates of ambient particulate matter (PM) ≤2.5 µm and ≤10 µm in aerodynamic diameters (PM(2.5) and PM(10), respectively). AOD was computed at three different spatial resolutions, i.e., 2 km (means 2 km × 2 km area at nadir), 5 km, and 10 km, by using...

We have examined the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5.1 Level 2 Aerosol Optical Depth (AOD) from Deep Blue algorithm and Dark Target approach over Ahmedabad, India for 2002 to 2005. Deep Blue algorithm is observed to be able to retrieve AOD over the Rann of Kuchchh, a region of high surface reflectance which poses difficulty for the Dark Target approach. Microt...

Currently available land-cover data sets for large geographic regions are produced on an intermittent basis and are often dated. Ideally, annually updated data would be available to support environmental status and trends assessments and ecosystem process modeling. This research examined the potential for vegetation phenology based land-cover classification over the 52,000 km Albemarle-Pamlico ...

Developing applications for analyzing and processing different remotely sensed data is very important for environmental predictions and management strategies. Applications focusing on environmental and natural resource monitoring need large data sets to be processed and fast response to actions. These requirements mostly imply high computing power that can be achieved through the parallel and d...

The limitations on spatial resolution and on the availability and measurement accuracy of remote sensing images are the primary problems in the estimation of the large-scale planting area for maize. The integration of midand low-resolution images is the one of primary methods used for the estimation of large-scale crop planting areas using remote sensing. The use of a single-temporal thematic m...

This work shows the potential use of neural networks in the characterization of eruptive events monitored by satellite, through fast and automatic classification of multispectral images. The algorithm has been developed for the MODIS instrument and can easily be extended to other similar sensors. Six classes have been defined paying particular attention to image regions that represent the diffe...

Remote sensing can be used to rapidly generate land use maps for assisting emergency response personnel with resource deployment decisions and impact assessments. In this study we focus on constructing accurate land cover maps to map the impacted area in the case of a nuclear material release. The proposed methodology involves integration of results from two different approaches to increase cla...