Characterising soil moisture in transport corridor environments using airborne LIDAR and CASI data

Hardy, Andrew; Barr, SL; Mills, J. P.; Miller, Pauline E.

doi:10.1002/hyp.8217

Cited by 10 publications

(4 citation statements)

References 47 publications

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“…a wet , a dry , b wet , and b dry are the linear regression parameters (slope and intercept) of dry/wet edges, respectively. Based on TVDI, RSM can be related to LST min and LST max with the following equation [49,50]: (6) From Equation (6) RSM can be found as:…”

Section: Temperature Vegetation Dryness Index (Tvdi)mentioning

confidence: 99%

“…Microwave sensors have been used for SM retrieval due to the direct relationship between microwave radiation and soil dielectric, though providing a coarse spatial resolution [5]. As for indirect approaches, SM estimation from visible and infrared data is based on land surface reflectance at much higher spatial resolutions [6]. To evaluate SM estimation, there are many studies on the comparison of SM products and modeled SM on different scales [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Improving Soil Moisture Estimation by Identification of NDVI Thresholds Optimization: An Application to the Chinese Loess Plateau

Yuan

Zhang

et al. 2021

Remote Sensing

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Accuracy soil moisture estimation at a relevant spatiotemporal scale is scarce but beneficial for understanding ecohydrological processes and improving weather forecasting and climate models, particularly in arid and semi-arid regions like the Chinese Loess Plateau (CLP). This study proposed Criterion 2, a new method to improve relative soil moisture (RSM) estimation by identification of normalized difference vegetation index (NDVI) thresholds optimization based on our previously proposed iteration procedure of Criterion 1. Apparent thermal inertia (ATI) and temperature vegetation dryness index (TVDI) were applied to subregional RSM retrieval for the CLP throughout 2017. Three optimal NDVI thresholds (NDVI0 was used for computing TVDI, and both NDVIATI and NDVITVDI for dividing the entire CLP) were firstly identified with the best validation results (R¯) of subregions for 8-day periods. Then, we compared the selected optimal NDVI thresholds and estimated RSM with each criterion. Results show that NDVI thresholds were optimized to robust RSM estimation with Criterion 2, which characterized RSM variability better. The estimated RSM with Criterion 2 showed increased accuracy (maximum R¯ of 0.82 ± 0.007 for Criterion 2 and of 0.75 ± 0.008 for Criterion 1) and spatiotemporal coverage (45 and 38 periods (8-day) of RSM maps and the total RSM area of 939.52 × 104 km2 and 667.44 × 104 km2 with Criterion 2 and Criterion 1, respectively) than with Criterion 1. Moreover, the additional NDVI thresholds we applied was another strategy to acquire wider coverage of RSM estimation. The improved RSM estimation with Criterion 2 could provide a basis for forecasting drought and precision irrigation management.

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Section: Temperature Vegetation Dryness Index (Tvdi)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Soil Moisture Estimation by Identification of NDVI Thresholds Optimization: An Application to the Chinese Loess Plateau

Yuan

Zhang

et al. 2021

Remote Sensing

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“…The experimental results showed that this method performed well with limited training samples. In this work, we combined features extracted from airborne LiDAR data and hyperspectral compact airborne spectrographic imager (CASI) data [ 13 ]. For the hyperspectral CASI images, features of the normalized difference vegetation index (NDVI) and the gray-level co-occurrence matrix (GLCM) were calculated [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Fusion of Hyperspectral CASI and Airborne LiDAR Data for Ground Object Classification through Residual Network

Chang

Zhang

et al. 2020

Sensors

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Modern satellite and aerial imagery outcomes exhibit increasingly complex types of ground objects with continuous developments and changes in land resources. Single remote-sensing modality is not sufficient for the accurate and satisfactory extraction and classification of ground objects. Hyperspectral imaging has been widely used in the classification of ground objects because of its high resolution, multiple bands, and abundant spatial and spectral information. Moreover, the airborne light detection and ranging (LiDAR) point-cloud data contains unique high-precision three-dimensional (3D) spatial information, which can enrich ground object classifiers with height features that hyperspectral images do not have. Therefore, the fusion of hyperspectral image data with airborne LiDAR point-cloud data is an effective approach for ground object classification. In this paper, the effectiveness of such a fusion scheme is investigated and confirmed on an observation area in the middle parts of the Heihe River in China. By combining the characteristics of hyperspectral compact airborne spectrographic imager (CASI) data and airborne LiDAR data, we extracted a variety of features for data fusion and ground object classification. Firstly, we used the minimum noise fraction transform to reduce the dimensionality of hyperspectral CASI images. Then, spatio-spectral and textural features of these images were extracted based on the normalized vegetation index and the gray-level co-occurrence matrices. Further, canopy height features were extracted from airborne LiDAR data. Finally, a hierarchical fusion scheme was applied to the hyperspectral CASI and airborne LiDAR features, and the fused features were used to train a residual network for high-accuracy ground object classification. The experimental results showed that the overall classification accuracy was based on the proposed hierarchical-fusion multiscale dilated residual network (M-DRN), which reached an accuracy of 97.89%. This result was found to be 10.13% and 5.68% higher than those of the convolutional neural network (CNN) and the dilated residual network (DRN), respectively. Spatio-spectral and textural features of hyperspectral CASI images can complement the canopy height features of airborne LiDAR data. These complementary features can provide richer and more accurate information than individual features for ground object classification and can thus outperform features based on a single remote-sensing modality.

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“…LiDAR (light detection and ranging) technology was recently investigated as a remote sensing tool for soil moisture detection [20,[45][46][47][48][49]. This technique also enables highresolution terrain mapping and surface characterization [50][51][52][53][54].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of Near-Infrared Permanent Laser Scanning Intensity for Retrieving Soil Moisture on a Coastal Beach: Calibration Procedure Using In Situ Data

2021

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Anthropogenic activities and climate change in coastal areas require continuous monitoring for a better understanding of environmental evolution and for the implementation of protection strategies. Surface moisture is one of the important drivers of coastal variability because it highly affects shoreward sand transport via aeolian processes. Several methods have been explored for measuring surface moisture at different spatiotemporal resolutions, and in recent years, light detection and ranging (LiDAR) technology has been investigated as a remote sensing tool for high-spatiotemporal-resolution moisture detection. The aim of the present study is the assessment of the performance of a permanent terrestrial laser scanner (TLS) with an original setting located on a high position and hourly scanning of a wide beach area stretching from a swash zone to the base of a dune in order to evaluate the soil moisture at a high spatiotemporal resolution. The reflectance of a Riegl-VZ2000 located in Noordwijk on the Dutch coast was used to assess a new calibration curve that allows the estimation of soil moisture. Three days of surveys were conducted to collect ground-truth soil moisture measurements with a time-domain reflectometry (TDR) sensor at 4 cm depth. Each in situ measurement was matched with the closest reflectance measurement provided by the TLS; the data were interpolated using a non-linear least squares method. A calibration curve that allowed the estimation of the soil moisture in the range of 0–30% was assessed; it presented a root-mean-square error (RMSE) of 4.3% and a coefficient of determination (R-square) of 0.86. As an innovative aspect, the calibration curve was tested under different circumstances, including weather conditions and tidal levels. Moreover, the TDR data collected during an independent survey were used to validate the assessed curve. The results show that the permanent TLS is a highly suitable technique for accurately evaluating the surface moisture variations on a wide sandy beach area with a high spatiotemporal resolution.

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Characterising soil moisture in transport corridor environments using airborne LIDAR and CASI data

Cited by 10 publications

References 47 publications

Improving Soil Moisture Estimation by Identification of NDVI Thresholds Optimization: An Application to the Chinese Loess Plateau

Improving Soil Moisture Estimation by Identification of NDVI Thresholds Optimization: An Application to the Chinese Loess Plateau

Fusion of Hyperspectral CASI and Airborne LiDAR Data for Ground Object Classification through Residual Network

Sensitivity of Near-Infrared Permanent Laser Scanning Intensity for Retrieving Soil Moisture on a Coastal Beach: Calibration Procedure Using In Situ Data

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