Programming code for: Quantification of the financial viability of invasive alien tree clearing for ecological restoration

Programming code for: Quantification of the financial viability of invasive alien tree clearing for ecological restoration This repository hosts the code for the processing the raw spatial layers to derive final benefit and cost values for a net-present value and benefit-cost ratio analysis SummaryThis study presents a spatially explicit framework that combines remotely sensed, locally validated evapotranspiration and biomass estimates with operational clearing cost estimation to evaluate the long-term financial feasibility of invasive alien tree clearing in South Africa as part of ecological restoration programmes. The workflow combines satellite-derived invasive tree maps with Earth Engine-based evapotranspiration and biomass models to estimate two potential benefits of clearing: additional water supply and biomass feedstock value. These are then compared against clearing costs, including chemical, transport, and personnel/equipment costs, which are adjusted for invasion density, slope, and accessibility. The code calculates annual and discounted net present value (NPV) and benefit-cost ratio (BCR) over a 50-year horizon, and includes sensitivity analyses for biomass market uncertainty and hydrological uncertainty. Note: 'Invasive alien trees' refer to taxa such as Pinus, Eucalyptus, Neltuma, and Acacia spp that are non-native to South Africa that can displace native vegetation and cause environmental and economic harm. Repository ComponentsThe components of this repository include two primary phases each including a set of method workflow notes and code scriptsThe two phases include the following: (1) Compute difficulty levels for clearing invasions Slope Distance from road Density (2) Apply spatial layers in a Google Earth Engine app (3) Compute NPV and BCR within sensitivity scenarios Google Earth Engine app The app links for each case study in the spatial framework application section can be found here: Case study 3: https://ee-lscogill4.projects.earthengine.app/view/iatmanagementcasestudy3 Case study 4: https://ee-lscogill4.projects.earthengine.app/view/iatmanagementcasestudy4 Case study 7: https://ee-lscogill4.projects.earthengine.app/view/iatmanagementcasestudy7