This article presents estimation and validation of tree heights extracted from synthetic aperture radar interferometry (InSAR) digital elevation model (DEM). InSAR technique was used to generate DEM in terms of top of the canopy digital surface model (TCDSM). Multitemporal Radarsat-1 InSAR data pair of February 12, 2004 and March 7, 2004 and Sentinel-1 data pair of December 9, 2017 and December 21, 2017 were used. The TCDSM values at ground truth locations were subtracted from the DEM obtained at forest gap areas (i.e., forest floor elevation). The satellite-derived TCDSM-based tree height was compared with the ground observed data. For this purpose, an experiment in Tundi Reserved Forest, Dhanbad, Jharkhand, India was conducted using global positioning system and altimeter. The study area is a hilly forest tract dominated by Shorea robusta (Sal) with average height of 12 m. Total 275 numbers of trees were selected at 11 locations in different elevation strata. Ground truth locations were also selected on the basis of variability, tree height categories, and accessibility in the area. It was noticed that mean absolute error (MAE) and root-mean-square error (RMSE) between in situ and estimated heights for Radarsat-1 were 1.48 and 1.53 m, respectively, whereas for Sentinel-1 in 2017, MAE and RMSE were 1.3 and 1.34 m, respectively, with strong correlation of >80%.

Ultra-wide Band (UWB) technology has emerged as a pivotal tool for human motion detection, finding applications in diverse areas ranging from smart homes to automotive safety. This paper presents a comprehensive survey of methodologies employed in UWB-based motion detection, elucidating their strengths, challenges, and performance metrics. While several methods, including Convolutional Neural Network (CNN) approaches, have been explored, challenges such as motion state overlaps, the necessity for enhanced spatial resolution, and background noise interference persist. Among the various methods analyzed, the SGWO-based RMDL technique emerges as a frontrunner, offering superior accuracy, reduced mean squared error, and impressive true negative and positive rates. Moreover, its computational efficiency sets a precedent in human motion detection. This paper provides insights into the state-of-the-art Through the wall imaging and human vital signs observation for future research and realtime applications.

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.