Multi-Class Fine-Scale Vegetation Mapping: An Automated Object-Based Workflow for New Mexico Drylands

Fine-scale vegetation mapping supports habitat management, landscape change monitoring, and local planning. However, fine-scale maps that distinguish a large number of vegetation classes across broad extents remain scarce. Previous classifications using imagery with spatial resolutions of 1 m or finer have generally distinguished only six to ten classes at broad physiognomic levels and relied on training data generated exclusively through image interpretation. This study develops an object-based classification workflow to map vegetation at the Group Level of the United States National Vegetation Classification across two pilot study areas in New Mexico totaling 705,154 hectares, including the montane Jemez Mountains and the semi-arid Sevilleta region. Segmentation was performed using Geographic Object-Based Image Analysis (GEOBIA) at two hierarchical levels with 0.6 m National Agriculture Imagery Program (NAIP) imagery and a LiDAR-derived digital height model. Classification was conducted using a random forest model with 65 predictor variables derived from NAIP imagery, PlanetScope imagery, Sentinel-2 imagery, LiDAR-derived terrain surfaces, and Soil Moisture Active Passive (SMAP) soil moisture. Training and validation data were obtained from a legacy ecological plot database originally collected for purposes other than classification. Of the 34 classes initially targeted for mapping, 27 were ultimately mapped after spectrally inseparable classes were collapsed into coarser hierarchical levels. Mean overall accuracy across 1000 training and validation partitions was 54.8%, with a minimum of 51.2% to a maximum of 65.6%. Phenological variables derived from PlanetScope imagery improved overall accuracy by an average of 6.5 percentage points. Overall classification accuracy varied by 12.7 percentage points across partitions, indicating that overall accuracy estimates based on a single training and validation split may provide a poor representation of expected model performance.