Reactivating QuES-B

[dindiarto]

QuES-B (Quantification of Environmental Services for Biodiversity) is a LUMENS module designed to analyse focal area and habitat conditions and their changes within landscapes.

Objective

To reactivate and improve the QuES-B module into a fully functional yet simple Shiny application.

Key Input Parameters

Parameter	Type	Example	Variable Name
Land use/cover map (t1)	Raster file	lc2000.tif	lc_t1
Land use/cover map (t2)	Raster file	lc2005.tif	lc_t2
Land use/cover classification and Focal area lookup	Data frame	ID: 1, Legend: "Forest", BIODIV: 1	lulc_lut
Edge contrast table	Matrix	From: "Forest", To: "Agriculture", Value: 0.8	contab
Time period 1	Numeric	2000	t1
Time period 2	Numeric	2005	t2
Window shape	Numeric (1 or 2)	1 (Square)	window.shape
Window size (meters)	Numeric	1000	windowsize
Grid resolution (meters)	Numeric	10000	gridres
Raster nodata value	Numeric	0	raster.nodata

Proposed Workflow

1. Data Preparation and Validation

• Load and validate raster files and lookup tables
• Ensure consistent raster properties (extent, resolution, projection)
• Handle nodata values

2. Fragstats Input Preparation

• Generate focal area masks
• Create sampling grid
• Prepare Fragstats input files
  • prepare a class descriptors file (.fcd) from land use/cover classification and Focal area lookup
  • prepare an edge contrast table (.fsq)
• Set up working directories

3. Core Analysis

• Calculate TECI using Fragstats
• Analyze focal area changes
• Compute DIFA values
• Identify habitat changes
• Calculate landscape metrics

4. Results Processing

• Create data frames for TECI, focal area changes, and habitat changes
• Calculate DIFA curve and AUC
• Summarize results by land use change type
• Organize results for report generation

5. Reporting

• Generate a report with maps, charts, and tables

Tasks

• [x] Review and update input parameter definitions
• [x] Implement data preparation and validation steps
• [x] Develop Fragstats integration
• [x] Code core analysis functions
• [x] Create results processing and summary functions
• [x] Design and implement report generation

[dindiarto]

What are the required inputs for executing the TECI analysis in fragstats via R?

Required Inputs:

• Raster file of land cover, with the no data value identified
• Edge contrast weights matrix (contab)
• Fragstats model file (.fca file)
• Window size (windowsize)
• Window shape (window.shape)

Prepare Fragstats input files:

• Convert raster to Fragstats-compatible format (usually .tif)
• Create a Fragstats model file (.fca) specifying TECI calculation
• Prepare edge contrast weights file

Set up Fragstats execution

• Locate Fragstats executable
• Prepare command-line arguments

Execute Fragstats

• Use system() or system2() in R to run Fragstats

Handle Fragstats output:

• Read the output file generated by Fragstats
• Process the output into a usable format in R

---

Positive Background Values:

• Considered to be 'inside' the landscape of interest.
• Included in the total landscape area.
• Affects metrics that use total landscape area in their calculations.
• Not treated as a patch per se.
• Excluded from certain class and landscape metrics that involve summarizing patch or class metrics.
• Can exist as 'holes' in the landscape or partially/completely surround the landscape of interest.

We should assign nodata areas as positive background values. The result:

We might need to increase the dimensions of the raster by two times the moving-window radius on each side of the rectangle to handle the boundary effect.

[dindiarto]

The first attempt to calculate DIFA in a tidy style

head(teci_table, 5)
# A tibble: 954 × 2
    TECI FocalArea
   <dbl>     <dbl>
 1     0    0.0758
 2     0    0.165 
 3     0    0.266 
 4     0    0.362 
 5     0    0.462 

tail(teci_table, 5)
# A tibble: 5 × 2
   TECI FocalArea
  <dbl>     <dbl>
1  62.4      25.1
2  64.5      25.1
3  76.0      25.1
4  78.4      25.1
5 100        25.1

plot(teci_table)