DOI: 10.5281/zenodo.15763523
title: South Manchurian mixed forests (SMMFs): distribution maps (2001 & 2021) and habitat suitability models

description: This repository contains data and code accompanying the study "Satellite-based mapping of South Manchurian mixed forests highlights critical conservation needs", which assesses the status and threats to one of the most biodiverse temperate forests globally.

Background and Significance

The South Manchurian mixed forests (SMMF), spanning northeastern China, southern Primorsky Krai (Russia), and the Korean Peninsula, are experiencing significant degradation. They are being replaced by secondary growth, agricultural land, and anthropogenic vegetation. Precise quantification of the remaining SMMF patches is critical for their conservation.

Materials and Methods

Mapping: Integration of original geobotanical data (relevés, literature, regional maps) to train classifiers using Landsat time-series (GLAD ARD, 30m). Ensemble decision tree methods were employed.

Potential Distribution Modelling: Application of habitat suitability modelling (ecological niche modelling) using community-level presence/absence data, topographic (elevation, slope, aspect), and bioclimatic predictors (a proxy for precipitation, warmth and continentality indices).

Key Outputs:

- Actual Distribution Maps: Digital forest cover and South Manchurian mixed forest (SMMF) maps at 30m resolution for 2001 and 2021.

- Validation Data: Data for assessing the mapping accuracy.

- Stable Forest Paches: A composite 2021 "actual" SMMF map identifying stable forest patches that persisted for 20 years.

- Potential Distribution: Models of the potential (pre-anthropogenic) extent of SMMF, generated using habitat suitability modelling.

Data Structure:






File / Dataset
Format
Description




1. Source Training Data
 
 


forest-non-forest_2001_2021.zip
Shapefile
Polygons for creating binary "forest/non-forest" masks (target and background).


smmf-other_forest_2001.zip
Shapefile
Polygons for classifying SMMF within the 2001 forest mask.


smmf-other_forest_2021.zip
Shapefile
Polygons for classifying SMMF within the 2021 forest mask.


2. Resulting Raster Maps
 
 


forest2001mask.tif, forest2021mask.tif
GeoTIFF
Binary forest cover masks (1 = forest, 0 = non-forest).


smmf_2001_binary.tif, smmf_2021_binary.tif
GeoTIFF
Binary SMMF maps (1 = SMMF, 0 = other forest).


smmf_combined.tif
GeoTIFF
Actual SMMF distribution for 2021. Composite map integrating patches stable over 20 years.


3. Vector Data & Statistics
 
 


actual_SMMF_map_area.zip
Shapefile
Area distribution of actual SMMF (2021) per 10×10 km grid cell.


actual_SMMF_map_patches.zip
Shapefile
Spatial boundaries and attributes (area) of individual identified SMMF patches.


grid10x10_potential.zip
Shapefile
Zonal statistics for the actual and all potential distribution models in a 10×10 km grid.


4. Validation Data
 
 


Sampling.csv
CSV
Stratified sampling points for map accuracy assessment.


sampling_sum.tif
GeoTIFF
Raster aggregation layer for error quantification.


5. Potential Distribution Models
 
 


proj_Current_SMMF.tb[...].tif
GeoTIFF
Ensemble of potential distribution models (based on random sampling from target/background polygons).


proj_Current_SMMF.sample[...].tif
GeoTIFF
Ensemble of potential distribution models (based on stratified sampling presence/absence data).


Results_by_algo_SMMF_tb.csv
CSV
Model evaluation metrics (target-background sampling ensemble).


Results_by_algo_SMMF_sample.csv
CSV
Model evaluation metrics (stratified sampling ensemble).


6. Analysis Scripts (R)
 
 


01_biomod_potential_distribution.R
R
Script for modelling the potential distribution of SMMF.


02_potential_area_calc.R
R
Script for comparing actual and potential SMMF distribution.