Xihong Cui

Change Vector Analysis in Posterior Probability Space: A New Method for Land Cover Change Detection

Postclassification comparison (PCC) and change vector analysis (CVA) have been widely used for land use/cover change detection using remotely sensed data. However, PCC suffers from error cumulation stemmed from an individual image classification error, while a strict requirement of radiometric consistency in remotely sensed data is a bottleneck of CVA. This letter proposes a new method named CVA in posterior probability space (CVAPS), which analyzes the posterior probability by using CVA. The CVAPS approach was applied and validated by a case study of land cover change detection in Shunyi District, Beijing, China, based on multitemporal Landsat Thematic Mapper data. Accuracies of “change/no-change” detection and “from-to” types of change were assessed. The results show that error cumulation in PCC was reduced in CVAPS. Furthermore, the main drawbacks in CVA were also alleviated effectively by using CVAPS. Therefore, CVAPS is potentially useful in land use/cover change detection.

Estimating Tree-Root Biomass in Different Depths Using Ground-Penetrating Radar: Evidence from a Controlled Experiment

Roots have important functions in the ecosystem. Therefore, establishing root-related parameters such as root size, biomass, and 3-D architecture is necessary. Traditional methods for measuring tree roots are labor intensive and destructive to nature, limiting quantitative and repeated assessments in long-term research. Ground-penetrating radar (GPR) provides a nondestructive method for measuring tree roots. This study investigates the feasibility of a GPR system with 500-MHz, 900-MHz, and 2-GHz measurement frequencies for detecting tree roots and estimating root biomass under controlled experimental conditions in a sandy area. After energy attenuation correction and velocity analysis, not only the individual root in subsurface is able to be located but also the parameters that correlate well with root biomass can be extracted from the processed GPR data. The major findings were as follows. First, both the amplitude and amplitude-area indices were confirmed to be more effective for estimating root biomass after attenuation-effect compensation. This result suggests that the calibration of GPR wave-attenuation effects and velocity changes with depth are helpful in estimating root biomass from GPR parameters. Second, the selection of GPR system frequency was mainly dependent on field conditions, particularly soil water content. Lower frequency was recommended for developing root biomass estimation model under varied soil conditions. Third, the new method based on the metal reflector experiment was effective and easy to perform in situ for attenuation-effect correction.

Forward simulation of root’s ground penetrating radar signal: simulator development and validation

Background and aimsIt remains unclear how the limiting factors (e.g., root size, root water content, spacing between roots, and soil water content) affect root investigation using ground penetrating radar (GPR). The objective of this study is to develop a theoretical forward simulation protocol of synthesizing root’s GPR signal and test the feasibility of our proposed simulation protocol in evaluating the impacts of limiting factors on GPR-based root detection and quantification.MethodsThe proposed forward simulation protocol was developed by integrating several existing numerical models, such as the Root Composition Model, the Root Dielectric Constant Model, the Root Electrical Conductivity Model, the Soil Dielectric Constant Model, the Soil Electrical Conductivity Model, and a newly-established model (Root Length-Biomass Model). Resolution and GPR index obtained from both field collected radargrams and corresponding simulations were compared to validate the accuracy of simulation.ResultsSimulated radargrams exhibit similar resolution with that of the in situ collected. The same trends of root radar signals against different levels of root size, root water content, interval between roots, root depth, and antenna frequency were observed on both in situ radargrams and simulated radargrams. Strong correlations (correlation coefficients ranging from 0.87 to 0.96) were found between GPR indices extracted from the simulated data and those from the field collected data.ConclusionsOur proposed forward simulation is effective for assessing the impacts of limiting factors on root detection and quantification using GPR. This forward simulation protocol can be used to provide guidance for in situ GPR root investigation and can predict the accuracy of GPR-based root quantification under site-specific conditions.

Comment on: “root orientation can affect detection accuracy of ground-penetrating radar”

IntroductionIn a recent paper, Tanikawa et al. Plant Soil 373:317–327, (2013) reported a considerable impact of root orientation on the accuracy of root detection and root diameter estimation by ground-penetrating radar (GPR).MethodsIn Tanikawa et al. Plant Soil 373:317–327, (2013), buried root samples in a sand box were scanned from multiple cross angles between root orientation and GPR transecting line under controlled conditions. Changes in radar waveform parameter of roots to different cross angles were investigated.ResultsTanikawa et al. Plant Soil 373:317–327, (2013) clarified that 1) the variation in amplitude area (a signal strength related waveform parameter) to different cross angles fitted a sinusoidal waveform; and 2) the impact of root orientation on root diameter estimation by GPR could be mathematically corrected by applying a grid transect survey. However, we found that the quantitative relationship established in Tanikawa et al. Plant Soil 373:317–327, (2013) between amplitude area and cross angle was incorrect, and the application of a grid transect survey still underestimated root diameter.ConclusionThe change in amplitude area to cross angle between transecting line and root orientation fits a sinusoidal waveform but different to that reported in Tanikawa et al. Plant Soil 373:317–327, (2013). The polarization of GPR wave may explain such sinusoidal variation in amplitude area to cross angle. The effect of root orientation on GPR-based root diameter estimation remains to be calibrated.

Evaluation of wildfire propagation susceptibility in grasslands using burned areas and multivariate logistic regression

This research simulated wildfire propagation susceptibility based on multivariate logistic regression. Moderate Resolution Imaging Spectrometer (MODIS)-derived fuel indicators and topographic factors were the independent variables, and burnt areas served as the dependent variable. MODIS data were collected daily during the wildfire seasons of April to May and September to October from 2001 to 2007 to acquire information about live and dead fuel in the Mongolia–China grasslands. The inputs for the independent parameters for wildfire propagation susceptibility modelling were the normalized difference vegetation index (NDVI), optimized soil-adjusted vegetation index (OSAVI), moisture stress index (MSI), global vegetation moisture index (GVMI), dead fuel INDEX (DFI), elevation, slope, and aspect. Multivariate logistic regression ranking indicates that DFI, MSI, DEM, and OSAVI are the top four factors, with an overall accuracy of 80%. ‘Leave one out’ cross-validation demonstrated that the overall accuracy of the propagation susceptibility modelling ranged from 65% to 87%. Finally, the model was used to produce 10 day average wildfire propagation susceptibility maps during the wildfire seasons of 2001–2007 and to predict the location of burned areas. This research will be useful for understanding the propagation susceptibility of wildfires in grassland areas and for creating policies for preventing wildfire spread.

Detection of Root Orientation Using Ground-Penetrating Radar

Due to its in situ and nondestructive nature, ground-penetrating radar (GPR) has recently been applied to the field investigation of plant roots. The discrepancy between the roots and surrounding soils creates a dielectric constant contrast, forming clear hyperbolic reflections on the GPR radargram. The intensity and shape of the reflecting signals from roots are substantially affected by the root orientation as well as the relative geometry between the root in the subsurface and the GPR survey direction on the ground surface. However, no previous study has utilized the information on the intensity and shape of a root’s GPR reflection to map its orientation, which is crucial in interpreting radargrams and rebuilding 3-D root system architecture. In this paper, a mathematical formulation of hyperbolic reflection formed by a single root was first deduced based on the principles of electromagnetic wave propagation. Then, using this formulation, curve fitting was conducted on both simulated and field collected data sets by GPR. Information on the horizontal orientation and vertical inclination of a single root was acquired according to the formulation coefficient retrievals. Conditions for this method of application and factors impacting the extraction of root orientation information were analyzed. The results indicated fairly precise root orientation estimations. The proposed method has extended the application of GPR in root investigation, thus advancing the frontier of noninvasive root system architecture mapping.

Measurement of soil water content using ground-penetrating radar: a review of current methods

ABSTRACT Soil water content (SWC) is a crucial parameter in ecology, agriculture, hydrology, and engineering studies. Research on non-invasive monitoring of SWC has been a long-lasting topic in these fields. Ground penetrating radar (GPR), a non-destructive geophysical technique, has the advantages of high resolution, deep detection depth, and high efficiency in SWC measurements at medium scale. It has been successfully applied in field investigations. This paper summarizes the recent progress in developing GPR-based SWC measurement methods, including reflected wave, ground wave, surface reflection, borehole GPR, full waveform inversion, average envelope amplitude, and frequency shift methods. The principles, advantages, limitations, and applications of these methods are described in detail. A comprehensive technical framework, which comprises the seven methods, is proposed to understand their principles and applicability. Two key procedures, namely, data acquisition and data processing, are emphasized as crucial to method applications. The suitable methods that will satisfy diverse application demands and field conditions are recommended. Future development, potential applications, and advances in hardware and data processing techniques are also highlighted.

Effect of training strategy on PUL-SVM classification for cropland mapping by Landsat imagery

Positive and unlabeled learning (PUL) algorithm, an one-class classifier which is trained by positive samples and unlabeled samples, has been used in remote sensing classification. However, the effect of training strategy of PUL has not been investigated. This study tested the performances of PUL-SVM on cropland mapping by Landsat TM data using the training samples with different sizes and different purity levels. It is found that the highest accuracy is achieved when the sizes of positive sample and unlabeled sample are comparable if using the random strategy. In contrast, if using the purer positive samples, it is more difficult to find the optimal unlabeled sample size. Therefore, it is recommended the random strategy for the positive samples, and the balanced sizes for positive and unlabeled samples when using PUL-SVM.

A Novel Cloud Removal Method Based on IHOT and the Cloud Trajectories for Landsat Imagery

Cloud removal is a prerequisite for the application of Landsat datasets, as such satellite images are invariably contaminated by clouds. Clouds affect the transmission of radiation signal to different degrees because of their different thicknesses, shapes, heights and distributions. Existing methods utilize pixel replacement to remove thick clouds and pixel correction techniques to rectify thin clouds in order to retain the land surface information in contaminated pixels. However, a major limitation of these methods refers to their deficiency in retrieving land surface reflectance when both thick clouds and thin clouds exist in the images, as the two types of clouds differ in the transmission of radiation signal. As most remotely sensed images show rather complex cloud contamination patterns, an efficient method to alleviate both thin and thick cloud effects is in need of development. To this end, the paper proposes a new method to rectify cloud contamination based on the cloud detection of iterative haze-optimized transformation (IHOT) and the cloud removal of cloud trajectory (IHOT-Trajectory). The cloud trajectory is able to take consideration of signal transmission for different levels of cloud contamination, which characterizes the spectral response of a certain type of land cover under increasing cloud thickness. Specifically, this method consists in four steps. First, the cloud thicknesses of contaminated pixels are estimated by the IHOT. Second, areas affected by cloud shadows are marked. Third, cloud trajectories are fitted with the aid of neighboring similar pixels under different cloud thickness. Last, contaminated areas are rectified according to the relationship between the land surface reflectance and the IHOT. The experimental results indicate that the proposed approach is able to effectively remove both the thin and thick clouds and erase the cloud shadows of Landsat images under different scenarios. In addition, the proposed method was compared with the dark object subtraction (DOS), the modified neighborhood similar pixel interpolator (MNSPI) and the multitemporal dictionary learning (MDL) methods. Quantitative assessments show that the IHOT-Trajectory method is superior to the other cloud removal methods overall. For specific spectral bands, the proposed method performs better than other methods in visible bands, whereas it does not necessarily perform better in infrared bands.

Mechanisms, monitoring and modeling of shrub encroachment into grassland: a review

ABSTRACT Shrub encroachment into arid and semi-arid grasslands has elicited extensive research attention worldwide under the background of climate change and increasing anthropogenic activities. Shrub encroachment may considerably impact local ecosystems and economies, including the conversion of the structure and function of ecosystems, the shift in ambient conditions, and the weakness of local stock farming capacity. This article reviews recent research progresses on the shrub encroachment process and mechanism, shrub identification and dynamic monitoring using remote sensing, and modeling and simulation of the shrub encroachment process and dynamics. These studies can help to evaluate the ecological effect of shrub encroachment, and thus, practically manage and recover the ecological environment of degraded areas. However, the lack of effective measures and data for monitoring shrub encroachment at a large spatial scale severely limits research on the mechanism, modeling, and simulation of shrub encroachment, and the shrub encroachment stages can hardly be quantitatively defined, resulting in insufficient analysis and simulation of shrub encroachment for different spatiotemporal scales and stages shift. Improvement in remote sensing-based shrub encroachment dynamic monitoring might be crucial for analyzing and understanding the process and mechanism of shrub encroachment, and multi-disciplinary and multi-partnerships are required in the shrub encroachment studies.