- Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data
Mascolo, Lucio | Martínez Marín, Tomás | Lopez-Sanchez, Juan M.
(2021-10-28) Mascolo L, Martinez-Marin T, Lopez-Sanchez JM. Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data. Remote Sensing. 2021; 13(21):4332. https://doi.org/10.3390/rs13214332 ISSN: 2072-4292Phenology | Grid-based filter | SAR | Sentinel-1
[Abstract] [Sponsors] [DOI] [URL] [BibTex] Abstract.
In the last decade, suboptimal Bayesian filtering (BF) techniques, such as Extended Kalman Filtering (EKF) and Particle Filtering (PF), have led to great interest for crop phenology monitoring with Synthetic Aperture Radar (SAR) data. In this study, a novel approach, based on the Grid-Based Filter (GBF), is proposed to estimate crop phenology. Here, phenological scales, which consist of a finite number of discrete stages, represent the one-dimensional state space, and hence GBF provides the optimal phenology estimates. Accordingly, contrarily to literature studies based on EKF and PF, no constraints are imposed on the models and the statistical distributions involved. The prediction model is defined by the transition matrix, while Kernel Density Estimation (KDE) is employed to define the observation model. The approach is applied on dense time series of dual-polarization Sentinel-1 (S1) SAR images, collected in four different years, to estimate the BBCH stages of rice crops. Results show that 0.94 ≤ R2 ≤ 0.98, 5.37 ≤ RMSE ≤ 7.9 and 20 ≤ MAE ≤ 33.
Sponsors.
This research was funded in part by the Spanish Ministry of Science and Innovation (State Agency of Research, AEI) and the European Funds for Regional Development (EFRD) under Projects TEC2017-85244-C2-1-P and PID2020-117303GB-C22, and in part by the University of Alicante (ref. VIGROB-114).
- Three-dimensional and long-term landslide displacement estimation by fusing C- and L-band SAR observations: A case study in Gongjue County, Tibet, China
Liu, Xiaojie | Zhao, Chaoying | Zhang, Qin | Yin, Yueping | Lu, Zhong | Samsonov, Sergey | Yang, Chengsheng | Wang, Meng | Tomás, Roberto
(2021-10-21) Remote Sensing of Environment. 2021, 267: 112745. https://doi.org/10.1016/j.rse.2021.112745 ISSN: 0034-4257 (Print) | 1879-0704 (Online)Landslide | Jinsha River | Tibet | InSAR | 3D displacements | Long-term displacement time series
[Abstract] [Sponsors] [DOI] [URL] [BibTex] Abstract.
Recently, a large number of synthetic aperture radar (SAR) images has been introduced into landslide investigations with the growing launch of new SAR satellites, such as ALOS/PALSAR-2 and Sentinel-1. Therefore, it is appropriate to develop new approaches to retrieve three-dimensional (3D) displacements and long-term (> 10 years) displacement time series to investigate the spatio-temporal evolution and creep behavior of landslides. In this study, a new approach for the estimation of 3D and long-term displacement time series of landslides, based on the fusion of C- and L-band SAR observations, is presented. This method is applied to map 3D and long-term displacements (nearly 12 years) of the landslides in Gongjue County, Tibet in China; four sets of SAR images from different platforms (i.e., L-band ascending ALOS/PALSAR-1, C-band descending ENVISAT, and C-band ascending and descending Sentinel-1 SAR datasets) covering the period of January 2007 to November 2018 were collected and exploited. First, the assumption that the landslide moves parallel to its ground surface is used to produce 3D displacement rates and time series by fusing ascending and descending Sentinel-1 SAR images, from which the optimal sliding direction for each pixel of the slope is well estimated. Then, the long-term displacement time-series of the landslide between January 2007 and October 2018 in the estimated sliding direction is recovered by fusing L-band ALOS/PALSAR-1 and C-band Sentinel-1 SAR images. In order to fill the time gap of nearly four years between ALOS/PALSAR-1 and Sentinel-1 SAR images, the Tikhonov regularization (TR) method is developed to establish the observational equation. Moreover, to solve the problem arising from ALOS/PALSAR-1 and Sentinel-1 images with different wavelengths, incidence angles and flight directions, the measurements from ALOS/PALSAR-1 and Sentinel-1 images are both projected to the estimated optimal sliding direction to achieve a unified displacement datum. Our results from ascending and descending Sentinel-1 images suggest that the maximum displacement rates of the study area in the vertical and east-west directions from December 2016 to October 2018 were greater than 70 and 80 mm/year, respectively, and 2D displacement results reveal that the displacement patterns and movement characteristics of all the detected landslides are not identical in the study area. Specifically, the 3D displacement results successfully revealed the spatiotemporal displacement patterns and movement direction of each block of the Shadong landslide, and long-term displacement time series showed for the first time that the maximum cumulative displacement exceeds 1.3 m from January 2007 to October 2018. Moreover, the kinematic evolution and possible driving factors of landslides were investigated using 2D and 3D and long-term displacement results, coupled with hydrological factors and unidimensional constitutive models of the rocks.
Sponsors.
This research was financially funded by the Natural Science Foundation of China (Grant Nos. 41874005, 41929001, 41731066), the Fundamental Research Funds for the Central University (Grant Nos. 300102269712 and 300102269303), and China Geological Survey Project (DD20190637 and DD20190647). This research was also supported by a Chinese Scholarship Council studentship awarded to Xiaojie Liu (Ref. 202006560031). Roberto Tomás was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO), the State Agency of Research (AEI) and European Funds for Regional Development (FEDER) under project TEMUSA (TEC2017-85244-C2-1-P).
- Fusion of Multi-Temporal PAZ and Sentinel-1 Data for Crop Classification
Busquier, Mario | Valcarce-Diñeiro, Rubí©n | Lopez-Sanchez, Juan M. | Plaza, Javier | Sánchez, Nilda | Arias-Pí©rez, Benjamín
(2021-09-30) Busquier M, Valcarce-Diñeiro R, Lopez-Sanchez JM, Plaza J, Sánchez N, Arias-Pí©rez B. Fusion of Multi-Temporal PAZ and Sentinel-1 Data for Crop Classification. Remote Sensing. 2021; 13(19):3915. https://doi.org/10.3390/rs13193915 ISSN: 2072-4292Crop classification | Synthetic aperture radar | Fusion | Time series
[Abstract] [Sponsors] [DOI] [URL] [BibTex] Abstract.
The accurate identification of crops is essential to help environmental sustainability and support agricultural policies. This study presents the use of a Spanish radar mission, PAZ, to classify agricultural areas with a very high spatial resolution. PAZ was recently launched, and it operates at X band, joining the synthetic aperture radar (SAR) constellation along with TerraSAR-X and TanDEM-X satellites. Owing to its novelty and its ability to classify crop areas (both taking individually its time series and blending with the Sentinel-1 series), it has been tested in an agricultural area of the central-western part of Spain during 2020. The random forest algorithm was selected to classify the time series under five alternatives of standalone/fused data. The map accuracy resulting from the PAZ series standalone was acceptable, but it highlighted the need for a denser time-series of data. The overall accuracy provided by eight PAZ images or by eight Sentinel-1 images was below 60%. The fusion of both sets of eight images improved the overall accuracy by more than 10%. In addition, the exploitation of the whole Sentinel-1 series, with many more observations (up to 40 in the same temporal window) improved the results, reaching an overall accuracy around 76%. This overall performance was similar to that obtained by the joint use of all the available images of the two frequency bands (C and X).
Sponsors.
This work was funded by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P.
- Observation of Surface Displacement Associated with Rapid Urbanization and Land Creation in Lanzhou, Loess Plateau of China with Sentinel-1 SAR Imagery
Wei, Yuming | Liu, Xiaojie | Zhao, Chaoying | Tomás, Roberto | Jiang, Zhuo
(2021-09-01) Wei Y, Liu X, Zhao C, Tomás R, Jiang Z. Observation of Surface Displacement Associated with Rapid Urbanization and Land Creation in Lanzhou, Loess Plateau of China with Sentinel-1 SAR Imagery. Remote Sensing. 2021; 13(17):3472. https://doi.org/10.3390/rs13173472 ISSN: 2072-4292Loess Plateau | Lanzhou | Mountain excavation and city construction | Surface displacement | InSAR | Sentinel-1 | DEM errors | Remote sensing images
[Abstract] [Sponsors] [DOI] [URL] [BibTex] Abstract.
Lanzhou is one of the cities with the higher number of civil engineering projects for mountain excavation and city construction (MECC) on the China’s Loess Plateau. As a result, the city is suffering from severe surface displacement, which is posing an increasing threat to the safety of the buildings. However, up to date, there is no comprehensive and high-precision displacement map to characterize the spatiotemporal surface displacement patterns in the city of Lanzhou. In this study, satellite-based observations, including optical remote sensing and synthetic aperture radar (SAR) sensing, were jointly used to characterize the landscape and topography changes in Lanzhou between 1997 and 2020 and investigate the spatiotemporal patterns of the surface displacement associated with the large-scale MECC projects from 2015 December to March 2021. First, we retrieved the landscape changes in Lanzhou during the last 23 years using multi-temporal optical remote sensing images. Results illustrate that the landscape in local areas of Lanzhou has been dramatically changed as a result of the large-scale MECC projects and rapid urbanization. Then, we optimized the ordinary time series InSAR processing procedure by a “dynamic estimation of digital elevation model (DEM) errors†step added before displacement inversion to avoid the false displacement signals caused by DEM errors. The DEM errors and the high-precision surface displacement maps between December 2015 and March 2021 were calculated with 124 ascending and 122 descending Sentinel-1 SAR images. By combining estimated DEM errors and optical images, we detected and mapped historical MECC areas in the study area since 2000, retrieved the excavated and filling areas of the MECC projects, and evaluated their areas and volumes as well as the thickness of the filling loess. Results demonstrated that the area and volume of the excavated regions were basically equal to that of the filling regions, and the maximum thickness of the filling loess was greater than 90 m. Significant non-uniform surface displacements were observed in the filling regions of the MECC projects, with the maximum cumulative displacement lower than 40 cm. 2D displacement results revealed that surface displacement associated with the MECC project was dominated by settlements. From the correlation analysis between the displacement and the filling thickness, we found that the displacement magnitude was positively correlated with the thickness of the filling loess. This finding indicated that the compaction and consolidation process of the filling loess largely dominated the surface displacement. Our findings are of paramount importance for the urban planning and construction on the Loess Plateau region in which large-scale MECC projects are being developed.
Sponsors.
This research is funded by the Natural Science Foundation of China (Grant No. 41874005), the Natural Science Foundation in Gansu Province of China (Grant Nos. 1508RJZA094 and 20JR10RA180). This research was also supported by a Chinese Scholarship Council studentship awarded to Xiaojie Liu (Ref. 202006560031).
- Synergistic Use of TanDEM-X and Landsat-8 Data for Crop-Type Classification and Monitoring
Dey, Subhadip | Chaudhuri, Ushasi | Bhogapurapu, Narayanarao | Lopez-Sanchez, Juan M. | Banerjee, Biplab | Bhattacharya, Avik | Mandal, Dipankar | Rao, Yalamanchili S.
(2021-08-10) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021, 14: 8744-8760. https://doi.org/10.1109/JSTARS.2021.3103911 ISSN: 1939-1404 (Print) | 2151-1535 (Online)Agriculture | Classification | Crop-type mapping | Landsat-8 | Phenology | TanDEM-X
[Abstract] [Sponsors] [DOI] [URL] [BibTex] Abstract.
Classification of crop types using Earth Observation (EO) data is a challenging task. The challenge increases many folds when we have diverse crops within a resolution cell. In this regard, optical and Synthetic Aperture Radar (SAR) data provide complementary information to characterize a target. Therefore, we propose to leverage the synergy between multispectral and Synthetic Aperture Radar (SAR) data for crop classification. We aim to use the newly developed model-free three-component scattering power components to quantify changes in scattering mechanisms at different phenological stages. By incorporating interferometric coherence information, we consider the morphological characteristics of the crops that are not available with only polarimetric information. We also utilize the reflectance values from Landsat-8 spectral bands as complementary biochemical information of crops. The classification accuracy is enhanced by using these two pieces of information combined using a neural network-based architecture with an attention mechanism. We utilize the time series dual co-polarimetric (i.e., HH–VV) TanDEM-X SAR data and the multispectral Landsat-8 data acquired over an agricultural area in Seville, Spain. The use of the proposed attention mechanism for fusing SAR and optical data shows a significant improvement in classification accuracy by 6.0% to 9.0% as compared to the sole use of either the optical or SAR data. Besides, we also demonstrate that the utilization of single-pass interferometric coherence maps in the fusion framework enhances the overall classification accuracy by ≈ 3.0%. Therefore, the proposed synergistic approach will facilitate accurate and robust crop mapping with high-resolution EO data at larger scales.
Sponsors.
This work was supported in part by the German Aerospace Center (DLR) which provided all the TanDEM-X data under project POLI6736, in part by the State Research Agency (AEI), in part by the Spanish Ministry of Science and Innovation, and in part by the EU EFDR funds under Project TEC2017-85244-C2-1-P. The work of N. Bhogapurapu and S. Dey was supported by the Ministry of Education (formerly Ministry of Human Resource and Development-MHRD), Government of India.
- A Review of Crop Height Retrieval Using InSAR Strategies: Techniques and Challenges
Romero-Puig, Noelia | Lopez-Sanchez, Juan M.
(2021-07-30) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021, 14: 7911-7930. https://doi.org/10.1109/JSTARS.2021.3100874 ISSN: 1939-1404 (Print) | 2151-1535 (Online)Bistatic radar | Height | Interferometric SAR (InSAR) | Polarimetric SAR interferometry (PolInSAR) | Rice | TanDEM-X
[Abstract] [Sponsors] [DOI] [URL] [BibTex] Abstract.
This article compares the performance of four different interferometric synthetic aperture radar (SAR) techniques for the estimation of rice crop height by means of bistatic TanDEM-X data. Methods based on the interferometric phase alone, on the coherence amplitude alone, on the complex coherence value, and on polarimetric SAR interferometry (PolInSAR) are analyzed. Validation is conducted with reference data acquired over rice fields in Spain during the Science Phase of the TanDEM-X mission. Single- and dual-polarized data are exploited to also provide further insights into the polarization influence on these approaches. Vegetation height estimates from methodologies based on the interferometric phase show a general underestimation for the HH channel (with a bias that reaches around 25 cm in mid-July for some fields), whereas the VV channel is strongly influenced by noisy phases, especially at large incidences [root-mean-square error (RMSE) = 31 cm]. Results show that these approaches perform better at shallower incidences than the methodologies based on coherence amplitude and on PolInSAR, which obtain the most suitable results at steep incidences, with RMSE values of 17 and 23 cm. On the contrary, at shallower incidences, they are highly affected by very low input coherence levels. Hence, they tend to overestimate vegetation height.
Sponsors.
This work was supported by the Spanish Ministry of Science and Innovation, in part by the State Agency of Research, and in part by the European Funds for Regional Development under Project TEC2017-85244-C2-1-P. The work of Noelia Romero-Puig was supported in part by the Generalitat Valenciana and in part by the European Social Fund under Grant ACIF/2018/204.
- Rice phenology mapping using novel target characterization parameters from polarimetric SAR data
Dey, Subhadip | Bhogapurapu, Narayanarao | Bhattacharya, Avik | Mandal, Dipankar | Lopez-Sanchez, Juan M. | McNairn, Heather | Frery, Alejandro C.
(2021-05-16) International Journal of Remote Sensing. 2021, 42(14): 5519-5543. https://doi.org/10.1080/01431161.2021.1921876 ISSN: 0143-1161 (Print) | 1366-5901 (Online)Supervised classification | Dual Co-pol | Radarsat-2 | Phenology mapping | Full polarimetry | Model-free decomposition | Information content
[Abstract] [Sponsors] [DOI] [URL] [BibTex] Abstract.
We require spatio-temporal information about rice for executing and planning diverse management practices. In this regard, data obtained from Synthetic Aperture Radar (SAR) sensors are well suited for tracking morphological developments of rice across its phenology stages. This study proposes different target characterization parameters from polarimetric SAR data for rice phenology mapping. Six C-band Radarsat-2 images acquired over Vijayawada, India, are used for complete analysis. It is known that polarimetric information provides excellent sensitivity for identifying crop phenology stages. Hence, in this study, we assessed phenology classification results using a scattering-type parameter and scattering powers for full-polarimetric (FP) and extracted dual-polarimetric (DP) SAR data. Here, we utilized the real 4í—4 Kennaugh matrix elements to derive these parameters equivalently for the two polarimetric modes (i.e. FP and DP). We obtained better overall classification accuracy for each phenology stages using the proposed parameters than the existing ones from FP and DP SAR data. We noted that the overall classification accuracy using the DP SAR data was only marginally lower than the FP SAR data. This marginal difference in the accuracies could be due to the absence of the cross-polarized component in the DP SAR data. We also demonstrate the usefulness of the scattering powers from DP SAR data for rice phenology monitoring.
Sponsors.
This work was supported in part by the Spanish Ministry of Science, Innovation and Universities, the State Agency of Research (AEI), and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P.
- We lose ground: Global assessment of land subsidence impact extent
Dinar, Ariel | Esteban, Encarna | Calvo, Elena | Herrera García, Gerardo | Teatini, Pietro | Tomás, Roberto | Li, Yang | Ezquerro Martín, Pablo | Albiac, Jose
(2021-04-29) Science of The Total Environment. 2021, 786: 147415. https://doi.org/10.1016/j.scitotenv.2021.147415 ISSN: 0048-9697 (Print) | 1879-1026 (Online)Aquifer overdraft | Water scarcity | Groundwater pumping regulations | Impacts | Policy effectiveness | Land subsidence extent index | Delphi technique
[Abstract] [Sponsors] [DOI] [URL] [BibTex] Abstract.
Depletion of groundwater aquifers along with all of the associated quality and quantity problems which affect profitability of direct agricultural and urban users and linked groundwater-ecosystems have been recognized globally. During recent years, attention has been devoted to land subsidence—the loss of land elevation that occurs in areas with certain geological characteristics associated with aquifer exploitation. Despite the large socioeconomic impacts of land subsidence most of these effects are still not well analyzed and not properly recognized and quantified globally. In this paper we developed a land subsidence impact extent (LSIE) index that is based on 10 land subsidence attributes, and applied it to 113 sites located around the world with reported land subsidence effects. We used statistical means to map physical, human, and policy variables to the regions affected by land subsidence and quantified their impact on the index. Our main findings suggest that LSIE increases between 0.1 and 6.5% by changes in natural processes, regulatory policy interventions, and groundwater usage, while holding all other variables unchanged. Effectiveness of regulatory policy interventions vary depending on the lithology of the aquifer system, in particular its stiffness. Our findings suggest also that developing countries are more prone to land subsidence due to lower performance of their existing water governance and institutions.
Sponsors.
Partial funding was provided by the Giannini Foundation of Agricultural Economics Minigrant Program. Dinar would like to acknowledge support from the W4190 Multistate NIFA-USDA-funded Project, “Management and Policy Challenges in a Water-Scarce World.†Esteban, Calvo, and Albiac would like to acknowledge support from the project INIA RTA2017-00082-00-00 by the Spanish Ministry of Science and Innovation, and support by funding to the research group ECONATURA from the Government of Aragon. Tomás would like to acknowledge support from the Spanish Ministry of Economy and Competitiveness, the State Agency of Research and the European Funds for Regional Development under project TEC2017-85244-C2-1-P. Tomás, Herrera, Ezquerro, and Teatini acknowledge the European Union support from the RESERVOIR project (GA nº 1924) developed in the framework of the PRIMA program.
- Crop Monitoring and Classification Using Polarimetric RADARSAT-2 Time-Series Data Across Growing Season: A Case Study in Southwestern Ontario, Canada
Xie, Qinghua | Lai, Kunyu | Wang, Jinfei | Lopez-Sanchez, Juan M. | Shang, Jiali | Liao, Chunhua | Zhu, Jianjun | Fu, Haiqiang | Peng, Xing
(2021-04-05) Xie Q, Lai K, Wang J, Lopez-Sanchez JM, Shang J, Liao C, Zhu J, Fu H, Peng X. Crop Monitoring and Classification Using Polarimetric RADARSAT-2 Time-Series Data Across Growing Season: A Case Study in Southwestern Ontario, Canada. Remote Sensing. 2021; 13(7):1394. https://doi.org/10.3390/rs13071394 ISSN: 2072-4292Synthetic aperture radar (SAR) | Polarimetric SAR (PolSAR) | Crop classification | Crop monitoring | Time-series | RADARSAT-2 | Agriculture
[Abstract] [Sponsors] [DOI] [URL] [BibTex] Abstract.
Multitemporal polarimetric synthetic aperture radar (PolSAR) has proven as a very effective technique in agricultural monitoring and crop classification. This study presents a comprehensive evaluation of crop monitoring and classification over an agricultural area in southwestern Ontario, Canada. The time-series RADARSAT-2 C-Band PolSAR images throughout the entire growing season were exploited. A set of 27 representative polarimetric observables categorized into ten groups was selected and analyzed in this research. First, responses and temporal evolutions of each of the polarimetric observables over different crop types were quantitatively analyzed. The results reveal that the backscattering coefficients in cross-pol and Pauli second channel, the backscattering ratio between HV and VV channels (HV/VV), the polarimetric decomposition outputs, the correlation coefficient between HH and VV channel ÏHHVV, and the radar vegetation index (RVI) show the highest sensitivity to crop growth. Then, the capability of PolSAR time-series data of the same beam mode was also explored for crop classification using the Random Forest (RF) algorithm. The results using single groups of polarimetric observables show that polarimetric decompositions, backscattering coefficients in Pauli and linear polarimetric channels, and correlation coefficients produced the best classification accuracies, with overall accuracies (OAs) higher than 87%. A forward selection procedure to pursue optimal classification accuracy was expanded to different perspectives, enabling an optimal combination of polarimetric observables and/or multitemporal SAR images. The results of optimal classifications show that a few polarimetric observables or a few images on certain critical dates may produce better accuracies than the whole dataset. The best result was achieved using an optimal combination of eight groups of polarimetric observables and six SAR images, with an OA of 94.04%. This suggests that an optimal combination considering both perspectives may be valuable for crop classification, which could serve as a guideline and is transferable for future research.
Sponsors.
This research was funded in part by the National Natural Science Foundation of China (Grant No. 41,804,004, 41,820,104,005, 41,531,068, 41,904,004), the Canadian Space Agency SOAR-E Program (Grant No. SOAR-E-5489), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. CUG190633), and the Spanish Ministry of Science, Innovation and Universities, State Research Agency (AEI) and the European Regional Development Fund under project TEC2017-85244-C2-1-P.
- Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) Post-Disaster Landslides Based on High-Resolution UAV Imagery
Liang, Rubing | Dai, Keren | Shi, Xianlin | Guo, Bin | Dong, Xiujun | Liang, Feng | Tomás, Roberto | Wen, Ningling | Fan, Xuanmei
(2021-03-31) Liang R, Dai K, Shi X, Guo B, Dong X, Liang F, Tomás R, Wen N, Fan X. Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) Post-Disaster Landslides Based on High-Resolution UAV Imagery. Remote Sensing. 2021; 13(7):1330. https://doi.org/10.3390/rs13071330 ISSN: 2072-4292Jiuzhaigou earthquake | Landslide mapping | Unmanned aerial vehicle imagery | Support vector machine | Landslide-distribution analysis
[Abstract] [Sponsors] [DOI] [URL] [BibTex] Abstract.
The Ms 7.0 Jiuzhaigou earthquake that occurred on 8 August 2017 triggered hundreds of landslides in the Jiuzhaigou valley scenic and historic-interest area in Sichuan, China, causing heavy casualties and serious property losses. Quick and accurate mapping of post-disaster landslide distribution is of paramount importance for earthquake emergency rescue and the analysis of post-seismic landslides distribution characteristics. The automatic identification of landslides is mostly based on medium- and low-resolution satellite-borne optical remote-sensing imageries, and the high-accuracy interpretation of earthquake-triggered landslides still relies on time-consuming manual interpretation. This paper describes a methodology based on the use of 1 m high-resolution unmanned aerial vehicle (UAV) imagery acquired after the earthquake, and proposes a support vector machine (SVM) classification method combining the roads and villages mask from pre-seismic remote sensing imagery to accurately and automatically map the landslide inventory. Compared with the results of manual visual interpretation, the automatic recognition accuracy could reach 99.89%, and the Kappa coefficient was higher than 0.9, suggesting that the proposed method and 1 m high-resolution UAV imagery greatly improved the mapping accuracy of the landslide area. We also analyzed the spatial-distribution characteristics of earthquake-triggered landslides with the influenced factors of altitude, slope gradient, slope aspect, and the nearest faults, which provided important support for the further study of post-disaster landslide distribution characteristics, susceptibility prediction, and risk assessment.
Sponsors.
This work was funded by the National Key Research and Development Program of China (Project No. 2018YFC1505202), the National Natural Science Foundation of China (41941019), the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (SKLGP2020Z012), the project on identification and monitoring of potential geological hazards with remote sensing in Sichuan Province (510201202076888) and the Everest Scientific Project at Chengdu University of Technology (2020ZF114103).
- Impact of SAR Image Resolution on Polarimetric Persistent Scatterer Interferometry With Amplitude Dispersion Optimization
Zhao, Feng | Mallorquí Franquet, Jordi J. | Lopez-Sanchez, Juan M.
(2021-02-26) IEEE Transactions on Geoscience and Remote Sensing. 2021. https://doi.org/10.1109/TGRS.2021.3059247 ISSN: 0196-2892 (Print) | 1558-0644 (Online)Ground deformation | Interferometric phase optimization | Persistent scatterer interferometry (PSI) | Polarimetry | Synthetic aperture radar (SAR) | Image resolution
[Abstract] [Sponsors] [DOI] [URL] [BibTex] Abstract.
Polarimetric persistent scatterer interferometry (PolPSI) takes advantage of polarimetric optimization algorithms that enhance interferograms' phase quality by adequately combining the available polarization channels (e.g., HH, VV, HV, and VH) into an improved one. Amplitude dispersion (DA) is one of the commonly used phase quality metrics for this optimization. The resolution of the images is supposed to have an impact on the performance of DA-based PolPSI in terms of both pixel density and quality. In this research, this impact is investigated. Specifically, 30 quad-pol RADARSAT-2 images over Barcelona with a resolution around 5 m in both range and azimuth are employed to generate additional data sets with degraded resolutions, ranging from 7.5 to 20 m. The results confirm that, in all cases, the ability of DA to select high-quality pixels, i.e., persistent scatterers, decreases when the spatial resolution worsens because the loss of resolution increases the number of scatterers present in a resolution cell. In addition, it would be expected that the performance of the polarimetric optimization of DA would tend to decrease when the spatial resolution worsens. However, for all employed resolutions, the polarimetric optimization improves the density and quality of PSs with respect to that of any single polarimetric channel. Moreover, this improvement is more noticeable, in relative terms, as the image resolution degrades.
Sponsors.
This work was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO), the State Research Agency (AEI) and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P and Project TEC2017-85244-C2-2-P, in part by the National Natural Science Foundation of China under Grant 42004011 and Grant 41874044, in part by the China Postdoctoral Science Foundation under Grant 2020M671646, in part by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions (Science and Technology of Surveying and Mapping), and in part by the CommSensLab, which is Unidad de Excelencia Maria de Maeztu MDM-2016-0600 financed by the AEI, Spain.
- Canopy Height Estimation in Mediterranean Forests of Spain with TanDEM-X Data
Gómez, Cristina | Lopez-Sanchez, Juan M. | Romero-Puig, Noelia | Zhu, Jianjun | Fu, Haiqiang | He, Wenjie | Xie, Yanzhou | Xie, Qinghua
(2021-02-19) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021, 14: 2956-2970. https://doi.org/10.1109/JSTARS.2021.3060691 ISSN: 1939-1404 (Print) | 2151-1535 (Online)TanDEM-X | Forest canopy height | Mediterranean forest | SAR interferometry | Spain
[Abstract] [Sponsors] [DOI] [URL] [BibTex] Abstract.
Canopy height is an essential feature in forest inventory, and for assessment of biomass and carbon budgets. Spatially explicit maps of forest height over large areas can be derived from satellite Synthetic Aperture Radar (SAR) data. We aimed to evaluate the capacity of TanDEM-X (TDX) data to assess canopy height in Mediterranean forests of Spain, which are of relatively short height (typically < 20 m), diverse in species and structure, and adapted to summer drought. Interferogram coherence was retrieved from single-pol image pairs. Forest height estimation was carried out by previously fitting a sinc-type function, with two empirical parameters, to the data measured. Six types of forest were defined to assess the convenience of stratification for model implementation. The influence of terrain slope, forest type, and interferometric baseline on model performance was evaluated, and a strategy for large area mapping was proposed and tested. TDX-derived heights were compared to a contemporaneous LiDAR-derived canopy height model for assessment of quality. Results limited to slopes below 10 degrees provided the best results, reaching R2 = 0.91 and RMSE = 1.24 m in one of the study sites. However, in some areas the results were much worse, especially in regions characterized by rugged terrain with broadleaved species. This work demonstrates the feasibility of deriving a forest height map over the entire area of Spain from TDX data. Stratification per slope interval, as well as selection of long interferometric baselines are recommended.
Sponsors.
The German Aerospace Center (DLR) provided all the TanDEM-X data under project OTHER7349. This work is supported by the Spanish Ministry of Science, Innovation and University and EU EFDR funds under Project TEC2017-85244-C2-1-P. CG work was supported by project FORESTCHANGE (AGL2016-76769-C2-1-R) funded by the Spanish Ministry of Science, Innovation and University. Additional support was obtained from the National Natural Science Foundation of China (Grant number: 41820104005, 41804004, 41904004).
- Crop Height Estimation of Corn from Multi-Year RADARSAT-2 Polarimetric Observables Using Machine Learning
Xie, Qinghua | Wang, Jinfei | Lopez-Sanchez, Juan M. | Peng, Xing | Liao, Chunhua | Shang, Jiali | Zhu, Jianjun | Fu, Haqiang | Ballester-Berman, J. David
(2021-01-23) Xie Q, Wang J, Lopez-Sanchez JM, Peng X, Liao C, Shang J, Zhu J, Fu H, Ballester-Berman JD. Crop Height Estimation of Corn from Multi-Year RADARSAT-2 Polarimetric Observables Using Machine Learning. Remote Sensing. 2021; 13(3):392. https://doi.org/10.3390/rs13030392 ISSN: 2072-4292Crop height | RADARSAT-2 | Corn | Synthetic Aperture Radar (SAR) | PolSAR | Machine learning | RFR | SVR | Agriculture
[Abstract] [Sponsors] [DOI] [URL] [BibTex] Abstract.
This study presents a demonstration of the applicability of machine learning techniques for the retrieval of crop height in corn fields using space-borne PolSAR (Polarimetric Synthetic Aperture Radar) data. Multi-year RADARSAT-2 C-band data acquired over agricultural areas in Canada, covering the whole corn growing period, are exploited. Two popular machine learning regression methods, i.e., Random Forest Regression (RFR) and Support Vector Regression (SVR) are adopted and evaluated. A set of 27 representative polarimetric parameters are extracted from the PolSAR data and used as input features in the regression models for height estimation. Furthermore, based on the unique capability of the RFR method to determine variable importance contributing to the regression, a smaller number of polarimetric features (6 out of 27 in our study) are selected in the final regression models. Results of our study demonstrate that PolSAR observables can produce corn height estimates with root mean square error (RMSE) around 40–50 cm throughout the growth cycle. The RFR approach shows better overall accuracy in corn height estimation than the SVR method in all tests. The six selected polarimetric features by variable importance ranking can generate better results. This study provides a new perspective on the use of PolSAR data in retrieving agricultural crop height from space.
Sponsors.
This research was funded in part by the National Natural Science Foundation of China (Grant No. 41804004, 41820104005, 41531068, 41904004), the Canadian Space Agency SOAR-E program (Grant No. SOAR-E-5489), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. CUG190633), and the Spanish Ministry of Science, Innovation and Universities, State Research Agency (AEI) and the European Regional Development Fund under project TEC2017-85244-C2-1-P.
- Thermal Noise Removal From Polarimetric Sentinel-1 Data
Mascolo, Lucio | Lopez-Sanchez, Juan M. | Cloude, Shane R.
(2021-01-21) IEEE Geoscience and Remote Sensing Letters. 2021. https://doi.org/10.1109/LGRS.2021.3050921 ISSN: 1545-598X (Print) | 1558-0571 (Online)Polarimetry | Sentinel-1 | Thermal noise
[Abstract] [DOI] [URL] [BibTex] Abstract.
This study proposes, for the first time, an approach to remove thermal noise from the wave coherency matrix, C₂, estimated from single-look complex dual-polarization Interferometric Wide Swath mode Sentinel-1 synthetic aperture radar data. The approach is straightforward; it exploits the ThermalNoiseRemoval module, provided by the European Space Agency (ESA) in its Sentinel Application Platform (SNAP) software, to remove thermal noise from the channel intensities. Then, noise correction on the complex data is applied, in order to estimate the noise-free C₂ matrix. As a further novelty, the proposed approach can be implemented in SNAP, through the use of a processing graph that is here provided. The method is applied on a dense time series of Sentinel-1 data, collected on an agricultural area located near Seville, Spain. The impact of thermal noise on the estimation of the eigendecomposition parameters of C₂, i.e., entropy (H₂), average alpha angle (α₂), and anisotropy (A₂), is assessed for different land-cover types, namely river, rice, forest, and urban areas. Monte Carlo simulations are implemented to assess the performance of the proposed approach in estimating H₂, α₂, and A₂. Results show that the proposed noise removal method improves the estimation of these parameters for the considered land-cover classes.
Sponsors.
- Mapping the global threat of land subsidence
Herrera García, Gerardo | Ezquerro Martín, Pablo | Tomás, Roberto | Bí©jar Pizarro, Marta | López-Vinielles, Juan | Rossi, Mauro | Mateos, Rosa María | Carreón-Freyre, Dora | Lambert, John | Teatini, Pietro | Cabral-Cano, Enrique | Erkens, Gilles | Galloway, Devin | Hung, Wei-Chia | Kakar, Najeebullah | Sneed, Michelle | Tosi, Luigi | Wang, Hanmei | Ye, Shujun
(2021-01-01) Science. 2021, 371(6524): 34-36. https://doi.org/10.1126/science.abb8549 ISSN: 0036-8075 (Print) | 1095-9203 (Online)Land subsidence | Groundwater depletion
[Abstract] [Sponsors] [DOI] [URL] [BibTex] Abstract.
Subsidence, the lowering of Earth's land surface, is a potentially destructive hazard that can be caused by a wide range of natural or anthropogenic triggers but mainly results from solid or fluid mobilization underground. Subsidence due to groundwater depletion (1) is a slow and gradual process that develops on large time scales (months to years), producing progressive loss of land elevation (centimeters to decimeters per year) typically over very large areas (tens to thousands of square kilometers) and variably affects urban and agricultural areas worldwide. Subsidence permanently reduces aquifer-system storage capacity, causes earth fissures, damages buildings and civil infrastructure, and increases flood susceptibility and risk. During the next decades, global population and economic growth will continue to increase groundwater demand and accompanying groundwater depletion (2) and, when exacerbated by droughts (3), will probably increase land subsidence occurrence and related damages or impacts. To raise awareness and inform decision-making, we evaluate potential global subsidence due to groundwater depletion, a key first step toward formulating effective land-subsidence policies that are lacking in most countries worldwide.
Sponsors.
Funding for this study was provided partly by the Spanish Research Agency (AQUARISK, PRX19/00065, TEC2017-85244-C2-1-P projects) and PRIMA RESERVOIR project, and by all the institutions represented in the Land Subsidence International Initiative from UNESCO.
