SAR – OPTICAL REMOTE SENSING BASED FOREST COVER AND GREENNESS ESTI-MATION OVER INDIA

Why Is Remote Sensing of Amazon Forest Greenness So Challenging?

The prevalence of clouds and aerosols and their impact on satellite-measured greenness levels of forests in southern and central Amazonia are explored in this article using 10 years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) greenness data: normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). During the wet season (October–March), cloud contamina...

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite) software package and the Global Forest Change dataset (GFCD) being two of the most recently developed optical remote sensing-based tools for analysing forest cover and change. Due to the relatively nascent state of these technologies, their abilities to classif...

Compressive Sensing Based Parameter Esti- Mation for Monostatic Mimo Noise Radar

The novelty of this letter is that it capitalizes on noise waveform to construct measurement operator at the transmitter and presents a new method of how the analogue to digital converter (ADC) sampling rate in the monostatic multiple-input multiple-output (MIMO) noise radar can be reduced — without reduction in waveform bandwidth — through the use of compressive sensing (CS). The proposed meth...

An Improved Scheme for Parameter Esti- Mation of G0 Distribution Model in High- Resolution Sar Images

Statistical modeling of Synthetic Aperture Radar (SAR) images is of great importance for speckle noise filtering, target detection and classification, etc. Moreover, it can provide a comprehensive understanding of terrain electromagnetics scattering mechanism. Over the past three decades, many sophisticated models have been developed for SAR images, such as Rayleigh, Gamma, K and G etc. The G0 ...

Vegetation Outlook (VegOut): Predicting Remote Sensing–Based Seasonal Greenness