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2021_nkwasa_et_al

Scripts used to incorporate global phenology datasets into regional-global SWAT+ models

adqsm

AdQSM is a new tree quantitative structure model (QSM) that can reconstruct the 3D branch geometry of individual tree from Terrestrial Laser Scanning (TLS) point clouds. Many attributes of the trunk or branch can also be quantitatively calculated (as shown in the table below). For example, tree volume (trunk and branch), DBH and tree height parameters can be extracted directly. It allows point clouds collected by different sensors to serve as input point clouds. In addition to TLS, UAV LiDAR, mobile LiDAR, SLAM and even photogrammetry are also included under the premise of appropriate point cloud density.

anl-polinsar

An Adaptive Nonlocal Method for PolInSAR Complex Coherence Estimation - ANL-SAR

arctic_greening

Analysis of tundra "greenness" across the Arctic tundra biome (Berner et al. 2020 Nature Communications)

attribution_workshop

Code and Colab notebooks for attribution workshop analysis code.

awesome-pytorch-list

A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

awesome-sar

A curated list of awesome Synthetic Aperture Radar (SAR) software, libraries, and resources.

bambang-polsarprosim-gpu

Automatically exported from code.google.com/p/bambang-polsarprosim-gpu

bh_tomo

A Matlab borehole radar/seismic tomography package

bootcamp

brooks_range_greening

Analysis of greening and browning in the Alaska Brooks Range

canopy-cover-model

python model that determines canopy cover in a selected area (GDAL and Liblas)

canopy_cover_extraction

Extraction of canopy cover data from photos

canopyconstructor

canopycover

Python code to compute canopy cover attributes

carbon-budget

Calculate gross GHG emissions, gross carbon removals (sequestration), and net flux from forests globally

ccdc

Algorithm developed for Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data.

code

collect-earth

Open Foris Collect Earth. Augmented Visual Interpretation through Google Earth

contiguous_sif

this contains the code for generating the CSIF dataset and the NN parameters

courses

Code and HTML page repository for courses.spatialthoughts.com

coursework

csdl

Corn Soy Data Layer

ctsm

Community Terrestrial Systems Model (includes the Community Land Model of CESM)

deep-learning-polsar-book

deepnetsforeo

Deep networks for Earth Observation

demonstration-dft-ps-psd

This is a demonstration to show how to calculate power spectra and power spectral densities in real time. We calculate power spectra directly using DFT (or FFT). There are many conventions for DFT. We use the convention is the paper “Analysis of Relationship between Continuous Time Fourier Transform (CTFT), Discrete Time Fourier Transform (DTFT), Fourier Series (FS), and Discrete Fourier Transform (DFT)”. We calculate power spectral and power spectral densities using the MATLAB function periodogram. We could use pwelch to replace periodogram. The only difference between periodogram and pwelch is that pwelch supports segmentation and averaging, whereas periodogram does not. For the sake of simplicity, we only use periodogram in this demonstration. One will see that the power spectrum is equal to the square of the absolute value of DFT. When manually calculating a power spectrum, the hard job is to calculate the argument vector, or the independent variable vector, which is a frequency vector in this case. The frequency vector depends on the representation of the power spectrum. In general, there are three ways to represent a power spectrum for a real valued signal. One way is called “two-sided”. This is the default way to represent a power spectrum with DFT. However, this representation is not intuitive. The frequency vector is calculated by f = (0:N-1)/T, where T is the time period (or duration) of the input signal. When using the MATLAB function, periodogram, one can specify this representation using “onesided”. A more natural way is to use a centered representation. In this case, the frequency 0 is centered in the spectrum. If the number of spectral lines (equal to the number of input points) is odd, then we have a unique centered representation. If the number of spectral lines is even, then we have a problem. Let us assume that we use a zero-based index for spectral lines. The spectral line 0 is the DC component, and it is put in the f = 0 location. However, the spectral line N/2 can be placed on the positive side or the negative side. Different conventions may have different placements. In order to obtain this representation, one has to shift the FFT result. One way is to use the MATLAB function fftshift. This MATLAB function always places the N/2 spectral line on the negative side. When using the MATLAB function, periodogram, one can specify this representation using “centered”. It should be noted that the MATLAB function, periodogram, usually puts the N/2 spectral line on the positive side. The last way to represent a power spectrum is the one-sided representation. For this representation, we need to combine negative frequency components and positive components together, and we only show the positive half as well as the DC component. The combination process depends the evenness or oddness of the number of spectral lines. If the number of spectral lines is odd, we can simply combine spectral lines 1 to (N-1)/2 with spectral lines (N+1)/2 to N-1. The spectral line 0 is left untouched. If the number of spectral lines is even, we need to combine spectral lines 1 to N/2-1 with lines N/2+1 to N-1. The spectral lines 0 and N/2 are left untouched. In order to obtain this representation, one has to manually carry out the combination process. The combination process is different depending on the evenness or oddness of the number of spectral lines. When using the MATLAB function, periodogram, one can specify this representation using “onesided”. In this demonstration, we only use the centered representation. Hence, there is no need to do combination. One can see that the sum of all power spectral lines in a power spectrum is equal to the power of the input signal. One can alternatively calculate the PSD with the periodogram function by specifying “psd” instead of “power”. In fact, the PSD obtained by periodogram is an equivalent noise power spectral density. One can see that ENPSD is related to PS by a factor of 1/T. It should be noted that a power spectrum is a discrete sequence, or a discrete continuous-argument function, whereas an ENPSD is a non-discrete continuous argument function. For emphasize this, I used stem for power spectra and plot for ENPSD. In this demonstration, we start with a sinusoidal signal with various parameters. We then proceed with an actual audio signal.

drone-pipeline

Generalizing the TERRA REF pipelines for processing data from UAV's

earthengine-api

Python and JavaScript bindings for calling the Earth Engine API.