The Leaf Area Index (LAI) is one of the most frequently applied measures to characterize vegetation and its dynamics and functions with remote sensing. Satellite missions, such as NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) operationally produce global datasets of LAI. Due to their role as an input to large-scale modeling activities, evaluation and verification of such datasets...

Recent research studies have highlighted the decrease in the coverage of Mediterranean seagrasses due to mainly anthropogenic activities. The lack of data on the distribution of these significant aquatic plants complicates the quantification of their decreasing tendency. While Mediterranean seagrasses are declining, satellite remote sensing technology is growing at an unprecedented pace, result...

Remote sensing provides timely, economic, and objective data over a large area and has become the main data source for land cover/use area estimation. However, the classification results cannot be directly used to calculate the area of a given land cover/use type because of classification errors. The main purpose of this study is to explore the performance and stability of several model-assiste...

Native vegetation is vulnerable to fragmentation and loss of condition due to various environmental and anthropogenic drivers. Regrowth can vary depending upon species composition within that vegetation community. As well as maintaining biodiversity native vegetation provides a range of ecosystem services. Up-to date and reliable information on the distribution of native vegetation is essential...

1 The original use of remote sensing using infrared photography for yield variation was conducted by Colwell (1956). Since the launch of the Landsat 1 imaging satellite in 1972 (Mulla, 2013), satellite imagery has been widely used in agriculture for yield prediction and most lately for site-specific N management. Bhatti et al. (1991) used Landsat imagery and auxiliary data to estimate wheat yie...