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ndvi-lst-tvdi-annual-trend

Pair NDVI and LST rasters to compute yearly TVDI means and linear dryness trends.

Updated Apr 2, 2026
Search Tags:#time-series#geospatial#trend-analysis#lst#raster#ndvi#tvdi
One-click install
npx skills add https://github.com/DECORATOR0/project_skills-3.18dhc-19.40 --skill ndvi-lst-tvdi-annual-trend
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ndvi-lst-tvdi-annual-trend
Source: https://github.com/DECORATOR0/project_skills-3.18dhc-19.40/tree/main/skill_library/ndvi-lst-tvdi-annual-trend
Command: npx skills add https://github.com/DECORATOR0/project_skills-3.18dhc-19.40 --skill ndvi-lst-tvdi-annual-trend

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill computes annual dryness trend from NDVI and LST raster time series by pairing filenames by date, generating TVDI rasters, averaging TVDI within each available year, extracting yearly means, and fitting a linear trend.

Core Features & Use Cases

  • Pair NDVI and LST time-series rasters by date tokens and generate TVDI rasters.
  • Group TVDI outputs by year; for each available year, call calculate_tif_average and then extract yearly means.
  • Compute a linear trend across years to indicate changes in dryness or wetness over time.

Quick Start

Supply the data_dir and file_list for NDVI/LST files and run the batched compute_tvdi workflow to produce yearly TVDI means and the final trend.

Frequently Asked Questions about ndvi-lst-tvdi-annual-trend

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compute annual dryness trends from NDVI and LST raster time series?▼

To compute annual dryness trends from NDVI and LST raster time series, pair the input rasters by date tokens to generate TVDI rasters, group them by year to calculate yearly means, and fit a linear trend across the available years.

What is the TVDI method for tracking drought conditions using NDVI and LST data?▼

The TVDI method tracks drought conditions by pairing NDVI and LST raster time-series data to generate Temperature Vegetation Dryness Index rasters, which are then averaged per year to derive a linear trend indicating changes in dryness or wetness.

How do I batch process multi-year NDVI and LST raster files to extract yearly TVDI means?▼

You can batch process multi-year NDVI and LST raster files by supplying a data directory and file list, pairing the rasters by date, generating TVDI outputs, and grouping them by year to extract yearly TVDI means.

Can I analyze geospatial dryness trends if my raster time-series data has missing dates?▼

Yes, you can analyze geospatial dryness trends with missing dates, as the batch processing workflow handles missing dates gracefully and reports when insufficient data prevents calculating a reliable linear trend.

What happens when there is insufficient raster time-series data to fit a reliable dryness trend?▼

When there is insufficient raster time-series data to fit a reliable dryness trend, the workflow reports the issue gracefully, ensuring that the linear trend fitting across yearly TVDI means only proceeds when adequate data is available.

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