Exploring DEM error with geographically weighted regressionGeoCommunity
Michal Gallay, Christopher D. Lloyd, Jennifer McKinley: Exploring DEM error with geographically weighted regression (poster), 9th International Symposium GIS Ostrava, VŠB – Technical Univerzity of Ostrava, from 23rd to 25th January 2012
A knowledge-based model for identifying and mapping tropical wetlands and pea...ExternalEvents
This presentation was presented during the 2 Parallel session on Theme 3.1, Managing SOC in: Soils with high SOC – peatlands, permafrost, and black soils, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Thomas Gumbricht, from Center for International Forestry Research – Indonesia, in FAO Hq, Rome
Exploring DEM error with geographically weighted regressionGeoCommunity
Michal Gallay, Christopher D. Lloyd, Jennifer McKinley: Exploring DEM error with geographically weighted regression (poster), 9th International Symposium GIS Ostrava, VŠB – Technical Univerzity of Ostrava, from 23rd to 25th January 2012
A knowledge-based model for identifying and mapping tropical wetlands and pea...ExternalEvents
This presentation was presented during the 2 Parallel session on Theme 3.1, Managing SOC in: Soils with high SOC – peatlands, permafrost, and black soils, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Thomas Gumbricht, from Center for International Forestry Research – Indonesia, in FAO Hq, Rome
Measurement of Carbon content in plots under SFM and SLM in the Gran Chaco Am...ExternalEvents
This presentation was presented during the 2 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Matías Bosio, from PASCHACO - Argentina, in FAO Hq, Rome
Using Infrared Spectroscopy for Detection of Changes in Soil Properties in Se...ExternalEvents
This presentation was presented during the 3 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Ms. Caroline Ouko, from CETRAD - Kenya, in FAO Hq, Rome
New Measurement and Mapping of SOC in Australia supports national carbon acco...ExternalEvents
This presentation was presented during the 3 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Raphael Viscarra-Rossel from CSIRO - Australia, in FAO Hq, Rome
Measurement of Carbon content in plots under SFM and SLM in the Gran Chaco Am...ExternalEvents
This presentation was presented during the 2 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Matías Bosio, from PASCHACO - Argentina, in FAO Hq, Rome
Using Infrared Spectroscopy for Detection of Changes in Soil Properties in Se...ExternalEvents
This presentation was presented during the 3 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Ms. Caroline Ouko, from CETRAD - Kenya, in FAO Hq, Rome
New Measurement and Mapping of SOC in Australia supports national carbon acco...ExternalEvents
This presentation was presented during the 3 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Raphael Viscarra-Rossel from CSIRO - Australia, in FAO Hq, Rome
2013 ASPRS Track, Ozone Modeling for the Contiguous United States by Michael ...GIS in the Rockies
Ozone (O3) is a powerful oxidizer (e.g. reacting with oxygen). Ozone in the upper atmosphere is considered beneficial due to the ability of the compound to filter harmful UV rays generated from the sun. However, ground level concentrations of ozone influence animal and plant health. In animals, one symptom of ground level ozone is lung tissue damage resulting in respiratory complications. Excess ozone in plants can cause excessive water loss; thus, emulate drought conditions. Ozone simulates the stomata cell in plant leaves so that these cells do not function properly. That is the stomata cells do not close completely, resulting in excess water loss (Smith et al. 2008). Anthropogenic ozone can be created via internal combustion engines and coal fired power plants.
Collecting data from the Environmental Protection Agency (EPA) CASTnet site for the time periods 1990 to 2010 I use spatial interpolation techniques to create an ozone surface concentration for the contiguous United States.
Applied geophysics - 3D survey of the Lesser Antilles subduction zone present...Riccardo Pagotto
Presentazione in lingua inglese di un tema assegnato: "Structure of the Lesser Antilles subduction forearc and backstop from 3D seismic refraction tomography"
Geostatistical approach to the estimation of the uncertainty and spatial vari...IOSR Journals
Abstract. This article presents a case of application of geostatistical methods in geotechnical engineering:
There is a railway platform, going to be built on compressible soils which presents important settlement.
Geotechnical data were analyzed by a geostatistical approach using GIS software to characterize the spatial
variability of the thickness of the compressible soils and their deformation Module.
Then these data were crossed with settlement calculations by oedometer method to estimate the distribution of
soil compaction on the entire site.
Key words: Morocco, Kenitra, geotechnical studies, settlement, geostatistics, kriging.
TU4.L09 - RETRIEVAL OF SOIL MOISTURE UNDER VEGETATION USING POLARIMETRIC SCATTERING CUBES
1. Retrieval of Soil Moisture under Vegetation using Polarimetric Scattering Cubes Motofumi Arii (motofumi@gmail.com) Mitsubishi Space Software Co., Ltd., 792 Kami-machiya, Kamakura, Kanagawa 247-0065 Jakob J. van Zyl and Yunjin Kim Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
2. x,y : Polarization state for transmitting/receiving Many ways to invert soil moisture from radar backscattering from bare surfaces have been proposed as summarized in [1]. [1] J. J. van Zyl and Y. Kim, "A quantitative comparison of soil moisture inversion algorithms," Proc. of IGARSS01 , Sydney, Australia, Jul. 2001. Soil Moisture Retrieval from Bare Surface
3. p vi : Parameters to characterize vegetation More than 76% [2-3] of the earth surface is covered by vegetation. Surface Volume (Canopy) Double bounce (Ground-trunk, ground-canopy) Volume (Trunk) Soil Moisture Retrieval from Vegetated Terrain [2] R. T. Watson, I. R. Noble, B. Bolin, N. H. Ravindranath, D. J. Verado, and D. J. Dokken, Land use, land-use change and forestry , Cambridge, UK: Cambridge University Press, 2000. [3] E. Ezcurra, Global Desserts Outlook , Nairobi, Kenya: DEWA, UNEP, 2006.
5. - Inversion algorithm : A simple but robust technique proposed by Dubois et al .[4] for bare surfaces assuming L-band (24cm) Preparation for the Demonstration [4] P. C. Dubois, J. van Zyl, and T. Engman, “Measuring Soil Moisture with Imagin Radars,” IEEE Trans. Geosci. Remote Sensing , vol 33, no. 4, Jul. 1995. [5] J. J. van Zyl, M. Arii, and Y. Kim, “Model Based Decomposition of Polarimetric SAR Covariance Matrices Constrained for Non-Negative Eigenvalues,” IEEE Trans. on Geosci. Remote Sensing , 2010 (in press) - Polarimetric Decomposition : Non-Negative Eigenvalue Decomposition proposed by van Zyl et al.[5] assuming cosine squared distribution which is suitable for agricultural area After the decomposition, only HH and VV of the surface scattering component, C s , are utilized to estimate soil moisture by (1). (1) (2)
6. Iowa, at L-band Walnut creek watershed, Iowa observed by AIRSAR in July 2002. The area is widely covered by soy beans and corns. Residential area Densely vegetated area along the river Snapshot
7. Inversion Results -200 0 Maps of Negative The more vegetation, the smaller . Of course, negative is not physically acceptable! Dubois et al. algorithm with decomposition Dubois et al. algorithm only
8. Discrete Scatterer Model (DSM) [6-7] [6] S. L. Durden, J. J. van Zyl, H. A. Zebker, “Modeling and Observation of the radar polarization signature of forested areas,” IEEE Trans. Geosci. Remote Sensing , vol. 27, no. 3, May 1989. [7] M. Arii, “Soil moisture retrieval under vegetation using polarimetric radar,” Ph.D. dissertation California Institute of Technology, Pasadena, CA, pp. 68-101, 2009. [8] M.Arii, J. J. van Zyl, and Y. Kim, "Adaptive decomposition of polarimetric SAR covariance matrices," Proc. of IGARSS09 , Cape Town, South Africa, Jul. 2009. [9] M. Arii, J. J. van Zyl and Y. Kim, “Adaptive model-based decomposition of polarimetric SAR covariance matrices,” IEEE Trans. on Geosci. Remote Sensing , 2010 (in press) = 0.00 : Delta func. (methodical) = 0.57 : Cosine squared (medium) = 0.91 : Uniform (most complicated) Standard deviation of [8-9]
10. Inversion without Decomposition Backscatters from the grassland having specific soil moisture (10 to 60%) are generated as a test data in terms of vegetation water content. Then Dubois et al. ’s algorithm estimates dielectric constant using co-polarizations of the simulated data. m v (%)
11. What’s going on? Backscattering Cross Section [dB] m v = 40% Double-Bounce Volume
12. with Decomposition The ideal decomposition technique, which perfectly extracts the surface scattering component, makes the inversion even worse! Note that the DSM allows us to directly use the surface scattering. m v (%)
13. x = h , v Bare Surface Vegetated Terrain Attenuation coefficients are calculated by Optical Theorem as Then the scattering from bare surface are attenuated as Why?
14. Taking into account the attenuation, the inversion model is rewritten as For simplicity, let us take logarithm natural and assume same attenuation coefficients between co-polarizations. The inferred dielectric constant decreases in terms of the amount of vegetation. A new inversion algorithm which explicitly includes the attenuation effect is required!
15. From DSM, we can form the following cubes for backscattering cross sections. j = hhhv, hhvv, hvvv i = hh, hv, vv Polarimetric Scattering Cubes (PSC)
16. The same transformation is applied to measured data. Then the distance between the measured data and each point of the cubes are defined as where, w ’s are weighting functions. i = hh, hv, vv j = hhhv, hhvv, hvvv Minimum Distance Finally, we can determine the variables which minimize the distance.
17. RMSE( kh ) RMSE( m v )[%] RMSE( W c )[kg/m 2 ] The reference cubes (280x280x280 samples) for the grassland is applied to the area that has the same baseline parameters. The 300 test data sets for each plot are generated with randomly chosen m v , kh and W c . Note that the W c has a range between 0 and a number specified in the x axis. The error comes from the number of samples of the cubes. The HHVV plays an important role to remove the vegetation effect. Soil Moisture Vegetation water content Roughness Inversion by PSC
18. RMSE( m v )[%] Soil Moisture Vegetation water content Roughness RMSE( kh ) RMSE( W c )[kg/m 2 ] Sensitivity to Cylinder Radius
19. RMSE( kh ) RMSE( m v )[%] RMSE( W c )[kg/m 2 ] Soil Moisture Vegetation water content Roughness Sensitivity to Distribution, = 0.00 : Delta func. (methodical) = 0.57 : Cosine squared (medium) = 0.91 : Uniform (most complicated)
20. Now noise equivalent o = - 30 dB, is randomly added to the test data. The lower accuracy is achieved for smaller vegetation water content since smaller HV can be easily affected by the noise. RMSE( kh ) RMSE( m v )[%] RMSE( W c )[kg/m 2 ] Soil Moisture Vegetation water content Roughness Sensitivity to Noise
21. Cube Technique Cube Library Vegetation Type 1 Vegetation Type 2 Vegetation Type n Decomposition Technique Classification Inversion Vegetation Type Weighting Functions Cubes Comprehensive Inversion Strategy