DOI: 10.5281/zenodo.15395349
title: NSW 25-ha Drone Survey Grid

description: NSW 25-ha Drone Survey Grid


This repository provides a 25-hectare (500m x 500m) resolution spatial grid for New South Wales.

This grid layer was used to align systematic drone surveys and spatially structure binomial N-mixture models for estimating the abundance of koalas at the landscape-scale. It supports presence/absence and abundance frameworks and is suitable for use in large-scale ecological monitoring programs.

The grid was used in the following study:



Ryan, S.A., Southwell, D.M., Beranek, C.T., Clulow, J., Jordan, N.R., Witt, R.R., 2025. Estimating the landscape-scale abundance of an arboreal folivore using thermal imaging drones and binomial N-mixture modellingBiological Conservation. Manuscript ID: 111207. https://doi.org/10.1016/j.biocon.2025.111207


📘 Abstract

Estimating the abundance of wildlife populations at a landscape-scale is vital for conservation, but is often hampered by survey costs, data processing and imperfect detection. In this study, we developed a framework that combines a protocol for validating nocturnal thermal drone detections in real-time with N-mixture modelling to estimate the landscape-scale abundance of arboreal folivores. As a case study, we estimated the abundance of koalas (Phascolarctos cinereus) across seven reserves (673 km²) in New South Wales, Australia. We conducted thermal drone surveys of 208, 25-ha sites stratified across vegetation type and fire history, on average, three times over consecutive nights (range 1–12 repeats), between 18:00–04:00 h (May to September). All koala detections were validated by field personnel or in real-time with drones equipped with a thermal camera and searchlight. Koalas were detected on 245 occasions. We fitted N-mixture models to validated repeat count data to quantify the effect of site and observation variables on abundance and detectability. Using our top set of competing models, we estimated that 4357 koalas (95 % CI = 2319–8307) occupy the seven reserves, with a mean detection probability of 0.22 (95 % CI = 0.15–0.31) over all survey occasions. We found detection probability decreased with increases in relative humidity and temperature. Koala abundance was negatively associated with fire severity, elevation, tree height and soil clay content, and positively associated with available water content, forest cover and soil organic carbon. Our framework, which combines real-time field validated drone data while accounting for imperfect detection, improves the accuracy of abundance estimates for arboreal folivores across large-scales.



📂 Contents




Grid_Albers_00500m_NSW_Polys.shp and associated filesA shapefile representing 25-ha (500 m × 500 m) grid cells across New South Wales.




🗺️ Spatial Details




CRS: GDA94 / Australian Albers (EPSG:3577)

Geometry Type: Polygon

Cell Size: 500 m × 500 m (25 hectares)

Total Features: 3,222,693

Attribute Fields: Id (unique cell identifier)

Bounding Box (minx, miny, maxx, maxy):(826250.0, –4212250.0, 2082750.0, –3181250.0)




✅ Intended Applications




Thermal drone survey planning

Spatial alignment of repeatable wildlife monitoring

Koala and arboreal mammal detection

Binomial or Poisson N-mixture model design

Landscape-scale ecological stratification




⚠️ Data Use and Licensing


This grid layer was provided by Allen Mcilwee (NSW Government) and is published with permission as open-access supplementary material to support the following paper:



Ryan, S.A., Southwell, D.M., Beranek, C.T., Clulow, J., Jordan, N.R., Witt, R.R. (2025)Estimating the landscape-scale abundance of an arboreal folivore using thermal imaging drones and binomial N-mixture modellingBiological Conservation. Manuscript ID: 111207. https://doi.org/10.1016/j.biocon.2025.111207


The dataset is made available to support open ecological research and systematic drone survey planning in New South Wales. 

Users applying this grid for survey or monitoring purposes in NSW are encouraged to submit resulting species detection records to NSW BioNet to contribute to state-wide biodiversity data and conservation efforts.