Mapping Forest Thinning, Systemic and Selective Logging Operations Using Various Imaging Modes of X-Band SAR Images

2021 ◽  
Author(s):  
Oleg Antropov ◽  
Anne Lonnqvist ◽  
Yrjo Rauste ◽  
Kimmo Kortelainen ◽  
Tuomas Hame
Keyword(s):  
Selective Logging ◽  
Sar Images ◽  
Forest Thinning ◽  
X Band

scholarly journals A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3341
Author(s):  
Tahisa Neitzel Kuck ◽  
Edson Eyji Sano ◽  
Polyanna da Conceição Bispo ◽  
Elcio Hideiti Shiguemori ◽  
Paulo Fernando Ferreira Silva Filho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brazilian Amazon ◽  
Selective Logging ◽  
Machine Learning Techniques ◽  
Optical Data ◽  
Sar Images ◽  
Synthetic Aperture Radar Data ◽  
Tropical Regions ◽  
Learning Techniques ◽  
X Band

The near-real-time detection of selective logging in tropical forests is essential to support actions for reducing CO2 emissions and for monitoring timber extraction from forest concessions in tropical regions. Current operating systems rely on optical data that are constrained by persistent cloud-cover conditions in tropical regions. Synthetic aperture radar data represent an alternative to this technical constraint. This study aimed to evaluate the performance of three machine learning algorithms applied to multitemporal pairs of COSMO-SkyMed images to detect timber exploitation in a forest concession located in the Jamari National Forest, Rondônia State, Brazilian Amazon. The studied algorithms included random forest (RF), AdaBoost (AB), and multilayer perceptron artificial neural network (MLP-ANN). The geographical coordinates (latitude and longitude) of logged trees and the LiDAR point clouds before and after selective logging were used as ground truths. The best results were obtained when the MLP-ANN was applied with 50 neurons in the hidden layer, using the ReLu activation function and SGD weight optimizer, presenting 88% accuracy both for the pair of images used for training (images acquired in June and October) of the network and in the generalization test, applied on a second dataset (images acquired in January and June). This study showed that X-band SAR images processed by applying machine learning techniques can be accurately used for detecting selective logging activities in the Brazilian Amazon.

scholarly journals Remote Sensing X-Band SAR Data for Land Subsidence and Pavement Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4751 ◽  
Author(s):  
Sadra Karimzadeh ◽  
Masashi Matsuoka
Keyword(s):  
Land Subsidence ◽  
Sar Images ◽  
Nw Iran ◽  
Traffic Jams ◽  
X Band ◽  
Pavement Monitoring ◽  
Negative Impacts ◽  
Small Baseline ◽  
Sbas Insar ◽  
The City

In this study, we monitor pavement and land subsidence in Tabriz city in NW Iran using X-band synthetic aperture radar (SAR) sensor of Cosmo-SkyMed (CSK) satellites (2017–2018). Fifteen CSK images with a revisit interval of ~30 days have been used. Because of traffic jams, usually cars on streets do not allow pure backscattering measurements of pavements. Thus, the major paved areas (e.g., streets, etc.) of the city are extracted from a minimum-based stacking model of high resolution (HR) SAR images. The technique can be used profitably to reduce the negative impacts of the presence of traffic jams and estimate the possible quality of pavement in the HR SAR images in which the results can be compared by in-situ road roughness measurements. In addition, a time series small baseline subset (SBAS) interferometric SAR (InSAR) analysis is applied for the acquired HR CSK images. The SBAS InSAR results show land subsidence in some parts of the city. The mean rate of line-of-sight (LOS) subsidence is 20 mm/year in district two of the city, which was confirmed by field surveying and mean vertical velocity of Sentinel-1 dataset. The SBAS InSAR results also show that 1.4 km2 of buildings and 65 km of pavement are at an immediate risk of land subsidence.

scholarly journals Wake Component Detection in X-Band SAR Images for Ship Heading and Velocity Estimation

2016 ◽  
Vol 8 (6) ◽  
pp. 498 ◽  
Author(s):  
Maria Graziano ◽  
Marco D’Errico ◽  
Giancarlo Rufino
Keyword(s):  
Velocity Estimation ◽  
Sar Images ◽  
X Band

scholarly journals Brief Communication: Rapid mapping of landslide events: the 3 December 2013 Montescaglioso landslide, Italy

2014 ◽  
Vol 14 (7) ◽  
pp. 1835-1841 ◽  
Author(s):  
A. Manconi ◽  
F. Casu ◽  
F. Ardizzone ◽  
M. Bonano ◽  
M. Cardinali ◽  
...  
Keyword(s):  
High Resolution ◽  
Synthetic Aperture Radar ◽  
Southern Italy ◽  
Surface Deformation ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Main Road ◽  
Rapid Mapping ◽  
X Band ◽  
Sar Data

Abstract. We present an approach to measure 3-D surface deformations caused by large, rapid-moving landslides using the amplitude information of high-resolution, X-band synthetic aperture radar (SAR) images. We exploit SAR data captured by the COSMO-SkyMed satellites to measure the deformation produced by the 3 December 2013 Montescaglioso landslide, southern Italy. The deformation produced by the deep-seated landslide exceeded 10 m and caused the disruption of a main road, a few homes and commercial buildings. The results open up the possibility of obtaining 3-D surface deformation maps shortly after the occurrence of large, rapid-moving landslides using high-resolution SAR data.

Wet Snow Cover Mapping Algorithm Based on Multitemporal COSMO-SkyMed X-Band SAR Images

2012 ◽  
Vol 5 (3) ◽  
pp. 1045-1053 ◽  
Author(s):  
Thomas Schellenberger ◽  
Bartolomeo Ventura ◽  
Marc Zebisch ◽  
Claudia Notarnicola
Keyword(s):  
Snow Cover ◽  
Sar Images ◽  
Mapping Algorithm ◽  
X Band ◽  
Wet Snow

scholarly journals Autofocusing SAR images via local estimates of flight trajectory

2016 ◽  
Vol 8 (6) ◽  
pp. 881-889 ◽  
Author(s):  
Oleksandr O. Bezvesilniy ◽  
Ievgen M. Gorovyi ◽  
Dmytro M. Vavriv
Keyword(s):  
Radar Data ◽  
Navigation Systems ◽  
Sar Images ◽  
Time Intervals ◽  
Quadratic Map ◽  
X Band ◽  
Resolution Imaging ◽  
Airborne Sar ◽  
Short Time ◽  
Cross Track

High-resolution imaging with an airborne synthetic aperture radar (SAR) calls for precise trajectory measurements that can hardly be achieved with common navigation systems. In this paper, an efficient method called the local-quadratic map-drift autofocus is developed for the estimation of residual (uncompensated) motion errors directly from the received radar data. The map-drift autofocus is applied locally on short time intervals to estimate the cross-track components of the aircraft acceleration. The estimated acceleration is then integrated to evaluate the residual trajectory errors on the whole data frame interval. The method has been successfully tested with an X-band airborne SAR system.

scholarly journals X-Band/C-Band-Comparison of Ship Wake Detectability

2020 ◽  
Author(s):  
Björn Tings ◽  
Sven Jacobsen ◽  
Stefan Wiehle ◽  
Egbert Schwarz ◽  
Holger Daedelow
Keyword(s):  
Detection Performance ◽  
Automatic Identification ◽  
Identification System ◽  
Sar Images ◽  
Ship Wake ◽  
X Band ◽  
System Data ◽  
Wake Detection ◽  
Sar Imagery ◽  
The Individual

Recent studies investigated the detectability of ship wake signatures on SAR imagery using a large number of SAR images collocated with Automatic Identification System data for training machine learning models. These detectability models are in agreement with oceanographic expectations from preceding studies and can therefore be used for comparing the performance of different SAR sensors in terms of wake detectability. Previous model comparisons showed better wake detection performance of TerraSAR-X (TS-X) than of RADARSAT-2 (RS2) and Sentinel-1 (S1). A comparison between CosmoSkymed (CSK) and RS2 is performed here, to examine the hypothesis that X-Band is generally better for wake detection than C-Band. Finally, this hypothesis is not confirmed, as the detectability models for TS-X, CSK and RS2 reveal similar performances. A comparison of wake detection performance should take the individual wake components into account separately.

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