skotikot/ASAL-Regenerative-Landscape-Indicators

R scripts and spatial workflows for evaluating landscape heterogeneity, forest fragmentation, and functional adjacency indicators across group ranch and privatized land tenure regimes in Narok County, Kenya, using Land Change Modeler (LCM) scenario outputs. This repository contains the replication code, spatial analysis workflows, and data visualization scripts associated with the manuscript: > **Spatial indicators of regenerative landscape capacity under alternative management and policies (submitted for publication)** --- ## Project Overview Multifunctional arid and semi-arid landscapes (ASALs) face intensifying pressures from climate variability, shifting land tenure systems, and land-use change. This project implements a spatially explicit scenario framework using a calibrated **Land Change Modeler (LCM)** execution to simulate future landscape patterns under two contrasting land tenure regimes in **Narok County, Kenya**: 1. **Group Ranch (GR) Governance** (Communal/Traditional arrangements) 2. **Privatization (PR)** (Subdivided, individually managed parcels) Across these tenure regimes, various **regenerative management strategies** are modeled at distinct baseline transition adjustments (Sensitivity levels: **Sens1 = 25%**, **Sens2 = 50%**, and **Sens3 = 75%**). These scripts evaluate landscape configuration indicators, specifically landscape heterogeneity, forest fragmentation, and functional landscape adjacency (cropland-forest and rangeland-forest interfaces), to act as diagnostic signals of regenerative landscape capacity. --- ## Repository Structure & Core Scripts The repository is organized into data processing workflows (`calc_*.R`), global data configurations (`Load_*.R`), and visualization scripts (`plot_*.R`). ### 1. Data Ingestion & Setup * **`Load_scenarioOutputs.R`** * *Purpose:* Standardizes environment setups and auto-loads raw raster projections (`.rst` format) from LCM simulations. * *Functions:* Converts zero/background pixels uniformly to `NA`, applies consistency flags for spatial computations, and divides outputs into respective sensitivity group lists (`lst_sens1`, `lst_sens2`, `lst_sens3`) matching the baseline and intervention runs. ### 2. Spatial Indicator & Metric Computations …