scholarly journals A Novel Four-Stage Method for Vegetation Height Estimation with Repeat-Pass PolInSAR Data via Temporal Decorrelation Adaptive Estimation and Distance Transformation

2021 ◽  
Vol 13 (2) ◽  
pp. 213
Author(s):  
Cheng Xing ◽  
Tao Zhang ◽  
Hongmiao Wang ◽  
Liang Zeng ◽  
Junjun Yin ◽  
...  
Keyword(s):  
Adaptive Estimation ◽  
Estimation Accuracy ◽  
Height Estimation ◽  
Distance Transformation ◽  
Forest Mapping ◽  
Vegetation Height ◽  
Multiple Observations ◽  
Temporal Decorrelation ◽  
Novel Method ◽  
German Aerospace

Vegetation height estimation plays a pivotal role in forest mapping, which significantly promotes the study of environment and climate. This paper develops a general forest structure model for vegetation height estimation using polarimetric interferometric synthetic aperture radar (PolInSAR) data. In simple terms, the temporal decorrelation factor of the random volume over ground model with volumetric temporal decorrelation (RVoG-vtd) is first modeled by random motions of forest scatterers to solve the problem of ambiguity. Then, a novel four-stage algorithm is proposed to improve accuracy in forest height estimation. In particular, to compensate for the temporal decorrelation mainly caused by changes between multiple observations, one procedure of temporal decorrelation adaptive estimation via Expectation-Maximum (EM) algorithm is added into the novel method. On the other hand, to extract the features of amplitude and phase more effectively, in the proposed method, we also convert Euclidean distance to a generalized distance for the first time. Assessments of different algorithms are given based on the repeat-pass PolInSAR data of Gabon Lope Park acquired in AfriSAR campaign of German Aerospace Center (DLR). The experimental results show that the proposed method presents a significant improvement of vegetation height estimation accuracy with a root mean square error (RMSE) of 6.23 m and a bias of 1.28 m against LiDAR heights, compared to the results of the three-stage method (RMSE: 8.69 m, bias: 4.81 m) and the previous four-stage method (RMSE: 7.72 m, bias: −2.87 m).

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

scholarly journals Sub-Frame Evaluation of Frame Synchronization for Camera Network Using Linearly Oscillating Light Spot

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6148
Author(s):  
Hyuno Kim ◽  
Masatoshi Ishikawa
Keyword(s):  
Visual Information ◽  
Evaluation Method ◽  
Measurement Instrument ◽  
Light Spot ◽  
Estimation Accuracy ◽  
Accurate Data ◽  
Frame Synchronization ◽  
Camera Network ◽  
Novel Method ◽  
Synchronization Accuracy

Precisely evaluating the frame synchronization of the camera network is often required for accurate data fusion from multiple visual information. This paper presents a novel method to estimate the synchronization accuracy by using inherent visual information of linearly oscillating light spot captured in the camera images instead of using luminescence information or depending on external measurement instrument. The suggested method is compared to the conventional evaluation method to prove the feasibility. Our experiment result implies that the estimation accuracy of the frame synchronization can be achieved in sub-millisecond order.

scholarly journals Vegetation Height Estimation using Satellite Remote Sensing in Peat Land of Central Kalimantan

2021 ◽  
Vol 6 (1) ◽  
pp. 024-034
Author(s):  
Atriyon Julzarika ◽  
Harintaka Harintaka ◽  
Tatik Kartika
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Geoid Undulation ◽  
Height Estimation ◽  
Central Kalimantan ◽  
Vegetation Height ◽  
Accuracy Test ◽  
Sensing Technology ◽  
The Difference ◽  
Difference Test

Vegetation height is an important parameter in monitoring peatlands. Vegetation height can be estimated using remote sensing. Vegetation height can be estimated by utilizing DSM and DTM. The data that can be used are LiDAR, X-SAR, and SRTM C. In this study, LiDAR data is used for DSM2018 and DTM2018 extraction. The purpose of this research is to detect the vegetation height in Central Kalimantan peatlands using remote sensing technology. The research location is in Bakengbongkei, Kalampangan, Central Kalimantan. The integration of X-SAR and SRTM C is used for DSM2000 and DTM2000 extraction. DSM2000, DTM2000, DSM2018, and DTM2018 performed height error correction with tolerance of 1.96? (95%). Then do the geoid undulation correction to EGM2008. The results obtained are DSM and DTM with a similar height reference field. If it meets these conditions it can be calculated the vegetation height estimation. Vegetation height can be obtained using the Differential DEM method. The Changing in vegetation height from 2000 to 2018 can be estimated from the difference in vegetation height from 2000 to vegetation height in 2018. Results of spatial information on vegetation height and its changes need to be tested for the accuracy. This accuracy-test includes a cross section test, height difference test, and comparison with measurements of vegetation height in the field. The results of this research can be used to monitor the changing the vegetation height in peatlands.

scholarly journals Four-Stage Inversion Algorithm for Forest Height Estimation Using Repeat Pass Polarimetric SAR Interferometry Data

2018 ◽  
Vol 10 (8) ◽  
pp. 1174 ◽  
Author(s):  
Tayebe Managhebi ◽  
Yasser Maghsoudi ◽  
Mohammad Valadan Zoej
Keyword(s):  
Extinction Coefficient ◽  
European Space Agency ◽  
Sar Interferometry ◽  
Experimental Result ◽  
Single Frequency ◽  
Height Estimation ◽  
Suggested Algorithm ◽  
Forest Height ◽  
Temporal Decorrelation ◽  
The Mean

This paper proposes a new method for forest height estimation using single-baseline single frequency polarimetric synthetic aperture radar interferometry (PolInSAR) data. The new algorithm estimates the forest height based on the random volume over the ground with a volume temporal decorrelation (RVoG+VTD) model. We approach the problem using a four-stage geometrical method without the need for any prior information. In order to decrease the number of unknown parameters in the RVoG+VTD model, the mean extinction coefficient is estimated in an independent procedure. In this respect, the suggested algorithm estimates the mean extinction coefficient as a function of a geometrical index based on the signal penetration in the volume layer. As a result, the proposed four-stage algorithm can be used for forest height estimation using the repeat pass PolInSAR data, affected by temporal decorrelation, without the need for any auxiliary data. The suggested algorithm was applied to the PolInSAR data of the European Space Agency (ESA), BioSAR 2007 campaign. For the performance analysis of the proposed approach, repeat pass experimental SAR (ESAR) L-band data, acquired over the Remningstorp test site in Southern Sweden, is employed. The experimental result shows that the four-stage method estimates the volume height with an average root mean square error (RMSE) of 2.47 m against LiDAR heights. It presents a significant improvement of forest height accuracy, i.e., 5.42 m, compared to the three-stage method result, which ignores the temporal decorrelation effect.

scholarly journals Potential of Multi-Temporal ALOS-2 PALSAR-2 ScanSAR Data for Vegetation Height Estimation in Tropical Forests of Mexico

2018 ◽  
Vol 10 (8) ◽  
pp. 1277 ◽  
Author(s):  
Mikhail Urbazaev ◽  
Felix Cremer ◽  
Mirco Migliavacca ◽  
Markus Reichstein ◽  
Christiane Schmullius ◽  
...  
Keyword(s):  
Spatial Autocorrelation ◽  
Test Data ◽  
Predictive Performance ◽  
Airborne Lidar ◽  
Height Estimation ◽  
Vegetation Height ◽  
Training Samples ◽  
Multi Temporal ◽  
L Band ◽  
And Training

Information on the spatial distribution of forest structure parameters (e.g., aboveground biomass, vegetation height) are crucial for assessing terrestrial carbon stocks and emissions. In this study, we sought to assess the potential and merit of multi-temporal dual-polarised L-band observations for vegetation height estimation in tropical deciduous and evergreen forests of Mexico. We estimated vegetation height using dual-polarised L-band observations and a machine learning approach. We used airborne LiDAR-based vegetation height for model training and for result validation. We split LiDAR-based vegetation height into training and test data using two different approaches, i.e., considering and ignoring spatial autocorrelation between training and test data. Our results indicate that ignoring spatial autocorrelation leads to an overoptimistic model’s predictive performance. Accordingly, a spatial splitting of the reference data should be preferred in order to provide realistic retrieval accuracies. Moreover, the model’s predictive performance increases with an increasing number of spatial predictors and training samples, but saturates at a specific level (i.e., at 12 dual-polarised L-band backscatter measurements and at around 20% of all training samples). In consideration of spatial autocorrelation between training and test data, we determined an optimal number of L-band observations and training samples as a trade-off between retrieval accuracy and data collection effort. In summary, our study demonstrates the merit of multi-temporal ScanSAR L-band observations for estimation of vegetation height at a larger scale and provides a workflow for robust predictions of this parameter.

scholarly journals Analysis of Plant Height Changes of Lodged Maize Using UAV-LiDAR Data

Agriculture ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 146 ◽  
Author(s):  
Longfei Zhou ◽  
Xiaohe Gu ◽  
Shu Cheng ◽  
Guijun Yang ◽  
Meiyan Shu ◽  
...  
Keyword(s):  
Plant Height ◽  
Maize Yield ◽  
Canopy Height ◽  
Estimation Accuracy ◽  
Yield Reduction ◽  
Lidar Data ◽  
Height Estimation ◽  
Aerial Vehicle ◽  
Yield Quality ◽  
Maize Plants

Lodging stress seriously affects the yield, quality, and mechanical harvesting of maize, and is a major natural disaster causing maize yield reduction. The aim of this study was to obtain light detection and ranging (LiDAR) data of lodged maize using an unmanned aerial vehicle (UAV) equipped with a RIEGL VUX-1UAV sensor to analyze changes in the vertical structure of maize plants with different degrees of lodging, and thus to use plant height to quantitatively study maize lodging. Based on the UAV-LiDAR data, the height of the maize canopy was retrieved using a canopy height model to determine the height of the lodged maize canopy at different times. The profiles were analyzed to assess changes in maize plant height with different degrees of lodging. The differences in plant height growth of maize with different degrees of lodging were evaluated to determine the plant height recovery ability of maize with different degrees of lodging. Furthermore, the correlation between plant heights measured on the ground and LiDAR-estimated plant heights was used to verify the accuracy of plant height estimation. The results show that UAV-LiDAR data can be used to achieve maize canopy height estimation, with plant height estimation accuracy parameters of R2 = 0.964, RMSE = 0.127, and nRMSE = 7.449%. Thus, it can reflect changes of plant height of lodging maize and the recovery ability of plant height of different lodging types. Plant height can be used to quantitatively evaluate the lodging degree of maize. Studies have shown that the use of UAV-LiDAR data can effectively estimate plant heights and confirm the feasibility of LiDAR data in crop lodging monitoring.

scholarly journals Underwater Bearing-Only Multitarget Tracking in Dense Clutter Environment Based on PMHT

2020 ◽  
Vol 38 (2) ◽  
pp. 359-365
Author(s):  
Xiaohua Li ◽  
Ya'an Li ◽  
Xiaofeng Lu ◽  
Chenxu Zhao ◽  
Jing Yu
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Data Association ◽  
Estimation Accuracy ◽  
Multitarget Tracking ◽  
Single Observation ◽  
Multiple Observations ◽  
Multiple Hypothesis Tracking ◽  
Kalman Filter Algorithm ◽  
Present Algorithm

Underwater bearing-only multitarget tracking in clutter environment is challenging because of the measurement nonlinearity, range unobservability, and data association uncertainty. In terms of the principle of expectation maximization, combining the extended Kalman filter (EKF) and unscented Kalman filter algorithm(UKF), a new bearing-only multi-sensor multitarget tracking via probabilistic multiple hypothesis tracking(PMHT) algorithm is proposed. The PMHT algorithm introduces an association variable to deal with the data association uncertainty problem between the measurements and the targets. Furthermore, the EKF-based PMHT for multi-sensor multitarget system is simplified, which obviate the need to "stack" the synthetic measurements and can reduce the computation cost. The estimation accuracy of the EKF based on PMHT approach and UKF based on PMHT approach in simulation experiments for underwater bearing-only cross-moving targets and closely spaced targets for the case of stationary multiple observations and maneuvering single observation under dense clutter environment is analyzed. The experimental results demonstrate that the present algorithm is very well in a highly clutter environment and its computational load is low, which confirms the effectiveness of the algorithm to the bearing-only multitarget tracking in dense clutter.

scholarly journals A HYBRID METHOD IN VEGETATION HEIGHT ESTIMATION USING POLINSAR IMAGES OF CAMPAIGN BIOSAR

2015 ◽  
Vol XL-1-W5 ◽  
pp. 141-146
Author(s):  
S. Dehnavi ◽  
Y. Maghsoudi
Keyword(s):  
Hybrid Method ◽  
Cross Correlation ◽  
Test Site ◽  
Sar Images ◽  
Height Estimation ◽  
Applied Model ◽  
Vegetation Height ◽  
Signal Parameters ◽  
Interferometric Phase ◽  
Two Parameters

Recently, there have been plenty of researches on the retrieval of forest height by PolInSAR data. This paper aims at the evaluation of a hybrid method in vegetation height estimation based on L-band multi-polarized air-borne SAR images. The SAR data used in this paper were collected by the airborne E-SAR system. The objective of this research is firstly to describe each interferometry cross correlation as a sum of contributions corresponding to single bounce, double bounce and volume scattering processes. Then, an ESPIRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) algorithm is implemented, to determine the interferometric phase of each local scatterer (ground and canopy). Secondly, the canopy height is estimated by phase differencing method, according to the RVOG (Random Volume Over Ground) concept. The applied model-based decomposition method is unrivaled, as it is not limited to specific type of vegetation, unlike the previous decomposition techniques. In fact, the usage of generalized probability density function based on the nth power of a cosine-squared function, which is characterized by two parameters, makes this method useful for different vegetation types. Experimental results show the efficiency of the approach for vegetation height estimation in the test site.

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