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NRT-CCDC | NearReal Time Forest Monitoring with MODIS data

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kandi X-RAY | NRT-CCDC Summary

NRT-CCDC is a Python library. NRT-CCDC has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
#Near-Real Time CCDC (NRT-CCDC) This repository contains code to perform forest monitoring in near-real time. Monitoring depends on existing time series models to perform predictions. Right now, the only supported model results are from the CCDC algorithm contained in the Yet Another Time Series Model (YATSM) package from Chris Holden (https://github.com/ceholden/yatsm). #Installation It is recommended that NRT-CCDC be installed in a virtual Python environment using virtualenv.

kandi-support Support

  • NRT-CCDC has a low active ecosystem.
  • It has 3 star(s) with 4 fork(s). There are 2 watchers for this library.
  • It had no major release in the last 12 months.
  • NRT-CCDC has no issues reported. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of NRT-CCDC is current.

quality kandi Quality

  • NRT-CCDC has no bugs reported.

securitySecurity

  • NRT-CCDC has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

license License

  • NRT-CCDC is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.

buildReuse

  • NRT-CCDC releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
Top functions reviewed by kandi - BETA

kandi has reviewed NRT-CCDC and discovered the below as its top functions. This is intended to give you an instant insight into NRT-CCDC implemented functionality, and help decide if they suit your requirements.

  • Perform composite image composition .
  • Perform a cloud monitor .
  • Create a stack from a pair of pairwise output .
  • Runs monitor on the given csv file .
  • Find result attributes .
  • Function to get the NRT class
  • Helper function to find the X - result attributes .
  • Process MODIS images .
  • Find MODIS image pairs .
  • Write raster to file

NRT-CCDC Key Features

Near-Real Time Forest Monitoring with MODIS data

NRT-CCDC Examples and Code Snippets

  • default

default

virtualenv venv

Community Discussions

No Community Discussions are available at this moment for NRT-CCDC.Refer to stack overflow page for discussions.

No Community Discussions are available at this moment for NRT-CCDC.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install NRT-CCDC

You can download it from GitHub.
You can use NRT-CCDC like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .