Derived annual pixel tables, landscape-position proxy outputs, boundary-risk outputs, and analysis scripts for feasible-frontier analysis of forest restoration outcomes in Tibet (2002–2024)

This dataset supports the manuscript “Landscape-position controls and hydroclimatic constraints on the feasible frontier of forest restoration in Tibet (2002–2024)”. The study evaluates restoration-related forest outcomes across Tibet within a unified 1-km LAEA analytical framework for 2002–2024. All annual climate, topographic, and productivity/evapotranspiration layers were standardized to a common projection, spatial resolution, and spatial index and were organized into harmonized annual pixel tables for frontier identification, threshold analysis, environmental heterogeneity analysis, landscape-position augmented modeling, and boundary-risk mapping.
The archived dataset includes: (1) derived annual harmonized pixel tables used in the analysis; (2) annual variables related to carbon gain, water cost, water yield, and late-spring exposure, including NPP, ET, WY, WYfrac, precipitation, and heat, dry, and snow-transition exposure metrics; (3) corridor- and bottleneck-based positional proxy outputs used to construct aligned landscape-position variables; (4) boundary-risk outputs used to identify frontier proximity, hydroclimatic tightening, and risk classes; and (5) analysis scripts used for preprocessing, model fitting, robustness analysis, and figure generation.

The structural dimension in this study is represented by positional proxy variables rather than by direct continuous annual network-process observations. Accordingly, the corridor and bottleneck outputs in this dataset are provided as aligned proxy inputs supporting the construction of landscape-position variables used in the manuscript.
Input datasets from original public providers, including MODIS productivity and evapotranspiration products, the CHIRPS precipitation dataset, and topographic source data, are not redistributed here. Monthly preprocessing and harmonization were conducted in Google Earth Engine.