In this paper, a scheme to remove clouds and their shadows from remotely sensed images of Landsat TM over land has been proposed. The scheme uses the image fusion technique to automatically recognize and remove contamination of clouds and their shadows, and integrate complementary information into the composite image from multitemporal images. The cloud regions can be detected on the basis of t...

Existing image segmentation algorithms have recently been ported to the widely used ERDAS Imagine graphical user interface. Within the USDA Forest Service Region 5 Remote Sensing Lab these algorithms have traditionally been applied to Landsat TM data for the purpose of landscape delineation. A less confining image processing environment, combined with the wide availability of finer resolution d...

Operational monitoring of vegetation and land surface change over large areas can make good use of satellite sensors that measure radiance reflected from the Earth’s surface. Monitoring programs use multiple images for complete spatial coverage over time. Accurate retrievals of vegetation cover and vegetation change estimates can be hampered by variation, in both space and time, in the measured...

Leaf area index (LAI) is a key variable for many ecological models, but it is typically not available from basic forest inventories. In this study, we (1) construct a high-resolution LAI map using k nearest-neighbor (k-NN) imputation based on National Forest Inventory data and Landsat 5 TM images (Landsat-NFI LAI), and (2) examine a moderate-resolution LAI map produced based on reduced simple r...

The utility of satellite imagery for water quality studies in New York Harbor is investigated. Ground data from a routine sampling program (New York Harbor Water Quality Survey) are compared to imagery from the Landsat Thematic Mapper (TM) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. New York Harbor is a challenging environment for remote sensing because of the compl...