118,538 research outputs found
Estimating snow accumulation and ablation with L-band InSAR: R and Python code for analysis and figure creation
No full textThis is the R and Python code used to conduct the analysis and create the figures for the forthcoming article in The Cryosphere, "Estimating snow accumulation and ablation with L-band InSAR". Further information on the article will be updated upon publication.
By Jack Tarricone. Last updated: October 14, 2022
Estimating snow accumulation and ablation with L-band InSAR: Code and data for analysis and figure creation
No full textThis is the code (R and Python) and data used to conduct the analysis and create the figures for the forthcoming article in The Cryosphere, "Estimating snow accumulation and ablation with L-band InSAR" by Tarricone et al. Further information on the article will be updated upon publication. Specific information about products is located in the Supplementary Material.
Author: Jack Tarricone
Contact: [email protected]
Users must first unzip the downloaded file, install the libraries needed on top of each script, and set the working directory to the unzipped folder.
# set path to '/jemez_lband_swe_code_data' that was downloaded and unzipped from zenodo
# all other file paths are relative
setwd("path/to/jemez_lband_swe_code_data")
list.files() #pwd
Information on scripts and data
rasters/
amplitude/ UAVSAR amplitude data from 12, 19, and 26 Feb.
atm_corrected_unw/ Atmospherically corrected UAVSAR UNW
dem/ Lidar DEM
dswe/ InSAR-derived dSWE data
fsca/ Landsat fSCA from 18 February and 5 March
gpr/ GPR dSWE data
incidence_angle/ Inc. Angle and other products
lvk/ LVK raster and component vectors
no_snow_uncert/ No fSCA mask dSWE data
slc/ Single Look Complex data
uavsar_p1_feb12-19/. 12-19 February InSAR
uavsar_p2_feb19-26/ 19-26 February InSAR
uavsar_p3_feb12-26/ 12-26 February InSAR
scripts/
create_figures/
create_fig05: In situ time series plot
create_fig07: Plot showing atm delay
create_fig10_table04: dSWE histograms and metrics table
create_fig11: InSAR vs. (a) depth sensors, pits, (b) gpr dSWE
processing/
atmospheric_correction_feb12-19.R: Atmospherically correct 12-19 Feb. UNW
atmospheric_correction_feb12-26.R: Atmospherically correct 12-26 Feb. UNW
categorize_aspect_raster.R: Bin DEM into north and south facing slopes
convert_amp_to_db.R: Convert linear amplitude to decibel
create_lkv_raster.R: Make geocoded LVK rasters
create_new_inc.R: Make new lidar-derived incidence angle raster
fsca_stats.R: Generate fSCA stats
geolocate_lvk_jemez.py: Geolocate SLC data using uavsar_pytools
insar_swe_functions.R: SWE inversion function
landsat_fsca_processing.R: Formatting fSCA data
no_snow_swe_uncert_analysis.R Calculate no snow SWE change for uncertainty analysis
swe_inversion_feb12-feb19.R: SWE inversion for 12-19 February
swe_inversion_feb12-feb26.R: SWE inversion for 19-26 February
swe_inversion_feb19-feb26.R: SWE inversion for 12-26 February
uavsar_data_download.py: Download UAVSAR data using uavsar_pytool
Estimating snow accumulation and ablation with L-band InSAR: Code and data for analysis and figure creation
No full textThis is the code (R and Python) and data used to conduct the analysis and create the figures for the forthcoming article in The Cryosphere, "Estimating snow accumulation and ablation with L-band InSAR" by Tarricone et al. Further information on the article will be updated upon publication. Specific information about products is located in the Supplementary Material.
Author: Jack Tarricone
Contact: [email protected]
Users must first unzip the downloaded file, install the libraries needed on top of each script, and set the working directory to the unzipped folder.
# set path to '/jemez_lband_swe_code_data' that was downloaded and unzipped from zenodo
# all other file paths are relative
setwd("path/to/jemez_lband_swe_code_data")
list.files() #pwd
Information on scripts and data
rasters/
amplitude/ UAVSAR amplitude data from 12, 19, and 26 Feb.
atm_corrected_unw/ Atmospherically corrected UAVSAR UNW
dem/ Lidar DEM
dswe/ InSAR-derived dSWE data
fsca/ Landsat fSCA from 18 February and 5 March
gpr/ GPR dSWE data
incidence_angle/ Inc. Angle and other products
lvk/ LVK raster and component vectors
no_snow_uncert/ No fSCA mask dSWE data
slc/ Single Look Complex data
uavsar_p1_feb12-19/ 12-19 February InSAR
uavsar_p2_feb19-26/ 19-26 February InSAR
uavsar_p3_feb12-26/ 12-26 February InSAR
scripts/
create_figures/
create_fig05: In situ time series plot
create_fig07: Plot showing atm delay
create_fig10_table04: dSWE histograms and metrics table
create_fig11: InSAR vs. (a) depth sensors, pits, (b) gpr dSWE
processing/
atmospheric_correction_feb12-19.R: Atmospherically correct 12-19 Feb. UNW
atmospheric_correction_feb12-26.R: Atmospherically correct 12-26 Feb. UNW
categorize_aspect_raster.R: Bin DEM into north and south facing slopes
convert_amp_to_db.R: Convert linear amplitude to decibel
create_lkv_raster.R: Make geocoded LVK rasters
create_new_inc.R: Make new lidar-derived incidence angle raster
fsca_stats.R: Generate fSCA stats
geolocate_lvk_jemez.py: Geolocate SLC data using uavsar_pytools
insar_swe_functions.R: SWE inversion function
landsat_fsca_processing.R: Formatting fSCA data
no_snow_swe_uncert_analysis.R Calculate no snow SWE change for uncertainty analysis
swe_inversion_feb12-feb19.R: SWE inversion for 12-19 February
swe_inversion_feb12-feb26.R: SWE inversion for 19-26 February
swe_inversion_feb19-feb26.R: SWE inversion for 12-26 February
uavsar_data_download.py: Download UAVSAR data using uavsar_pytool
Fisiologia e qualità della produzione nel vitigno Sangiovese su suolo lavorato o protetto da manto erboso, in ambiente caldo-arido.
No full textL’inerbimento è diffusamente ritenuta essere il più ecologico sistema di gestione del suolo. Ciononostante, la difficoltà nel gestire la competizione esercitata dal manto erboso scoraggia gli imprenditori agricoli dall’impiego di tale pratica negli ambienti caldo-aridi. In un ambiente dell’Italia Meridionale è stata condatto una prova su Sangiovese allevato con interfilare lavorato (L), protetto da graminacee (G) o da trifoglio sotterraneo (T), nella pianura di Capitanata. A fioritura del vitigno, la biomassa erbosa secca in G e T è risultata 2 e 5 volte quella della infestanti presenti in L. In G e T, la competizione erbosa ha ridotto del 40% e del 60% l’area fogliare dei ceppi, del 20% e 30 % il parametro indice dello stato idrico, del 50% gli scambi gassosi fogliari. Minore l’area fogliare, maggiore il regime radiativo a livello dei grappoli. Il trifoglio è disseccato dopo l’allegagione della vite; le graminacee sono parzialmente disseccate ad inizio invaiatura. In quest’epoca, rispetto ad L, lo stato idrico del ceppo è migliorato del 30% e l’intensità fotosintetica del 70% in T. Le variazioni produttive sono risultate simili a quelle dell’area fogliare; all’opposto sono variati il rapporto superficie:volume della bacca ed il potenziale polifenolico ed antocianico delle bucce. Nel vino, questi composti sono risultati più concentrati in L che in G e T, indicando ridotta estraibilità fenolica in queste ultime tesi. La concentrazione antocianica in T è risultata correlata al regime radiativo nella fascia dei grappoli
Estimating snow accumulation and ablation with L-band InSAR: Code and data for analysis and figure creation
No full textThis is the code (R and Python) and data used to conduct the analysis and create the figures for the forthcoming article in The Cryosphere, "Estimating snow accumulation and ablation with L-band InSAR" by Tarricone et al. Further information on the article will be updated upon publication. Specific information about products is located in the Supplementary Material.
Author: Jack Tarricone
Contact: [email protected]
Users must first unzip the downloaded file, install the libraries needed on top of each script, and set the working directory to the unzipped folder.
# set path to '/jemez_lband_swe_code_data' that was downloaded and unzipped from zenodo
# all other file paths are relative
setwd("path/to/jemez_lband_swe_code_data")
list.files() #pwd
Information on scripts and data
rasters/
amplitude/ UAVSAR amplitude data from 12, 19, and 26 Feb.
atm_corrected_unw/ Atmospherically corrected UAVSAR UNW
dem/ Lidar DEM
dswe/ InSAR-derived dSWE data
fsca/ Landsat fSCA from 18 February and 5 March
gpr/ GPR dSWE data
incidence_angle/ Inc. Angle and other products
lvk/ LVK raster and component vectors
no_snow_uncert/ No fSCA mask dSWE data
slc/ Single Look Complex data
uavsar_p1_feb12-19/. 12-19 February InSAR
uavsar_p2_feb19-26/ 19-26 February InSAR
uavsar_p3_feb12-26/ 12-26 February InSAR
scripts/
create_figures/
create_fig05: In situ time series plot
create_fig07: Plot showing atm delay
create_fig10_table04: dSWE histograms and metrics table
create_fig11: InSAR vs. (a) depth sensors, pits, (b) gpr dSWE
processing/
atmospheric_correction_feb12-19.R: Atmospherically correct 12-19 Feb. UNW
atmospheric_correction_feb12-26.R: Atmospherically correct 12-26 Feb. UNW
categorize_aspect_raster.R: Bin DEM into north and south facing slopes
convert_amp_to_db.R: Convert linear amplitude to decibel
create_lkv_raster.R: Make geocoded LVK rasters
create_new_inc.R: Make new lidar-derived incidence angle raster
fsca_stats.R: Generate fSCA stats
geolocate_lvk_jemez.py: Geolocate SLC data using uavsar_pytools
insar_swe_functions.R: SWE inversion function
landsat_fsca_processing.R: Formatting fSCA data
no_snow_swe_uncert_analysis.R Calculate no snow SWE change for uncertainty analysis
swe_inversion_feb12-feb19.R: SWE inversion for 12-19 February
swe_inversion_feb12-feb26.R: SWE inversion for 19-26 February
swe_inversion_feb19-feb26.R: SWE inversion for 12-26 February
uavsar_data_download.py: Download UAVSAR data using uavsar_pytool
Gaussian pulse expansion of modulated signals in double-negative slab
No full textA novel approach, based on a theoretical formulation, is proposed for the analysis of finite-bandwidth signals in a lossy double-negative (DNG) slab. It adopts a signal expansion into Gaussian pulse functions, and provides an immediate approximate form of a modulated signal after the propagation in the dispersive medium. The accuracy and flexibility of the approach is proven studying the propagation of amplitude- and phase-modulated signals, and considering two different realizations of the DNG medium. © 2006 IEEE
Modifications of the parameters of a stochastic model for the acetylcholine channel induced by microwaves
No full textMovement Recognition through Inductive Wireless Links: Investigation of Different Fabrication Techniques
No full textIn this paper, an inductive wireless link for motion recognition is investigated. In order to validate the feasibility of a wearable implementation, the use of three different materials is analyzed: a thin copper wire, a conductive yarn, and a conductive non-woven fabric. Results from the application of the developed devices on an arm are reported and discussed. It is demonstrated that the proposed textile inductive resonant wireless links are well suited for developing a compact wearable system for joint flexion recognition
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