1. Automated Tree-Crown Delineation Using Photogrammetric Analyses Austin Pinkerton * and Eben Broadbent ** Spatial Ecology and Conservation Lab ( http://speclab.ua.edu ) Department of Geography, The University of Alabama *Computer Based Honors Program, email: aapinkerton@crimson.ua.edu, ** email: eben@ua.edu Abstract Pre-processing Forest management requires monitoring and assessment of tree health. Current field-based methods of acquiring this data are expensive and timely. We provide an update on our work to develop an automated tree-crown delineation algorithm which analyzes multi-spectral imagery collected by Unmanned Aerial Vehicles (UAVs). This approach has the potential to provide an inexpensive solution to this problem capable of working over large areas and being easily repeatable through time. The project includes the design, programming, field validation and parameterization, and implementation of an automated pattern recognition algorithm to identify individual tree-crown dimensions. We then use statistical analyses to link tree crown dimensions with related forest structural and compositional attributes to derive a full suite of forest metrics related to structure, composition and productivity, such as might be obtained from extensive field data collection. Introduction Future Plans The University of Alabama, SPEC Lab, CBHP, ANU Forest at the National Arboretum, Florida International University Acknowledgements Figure 1.0 – A tree canopy in Bolivia which will be used as an example to describe the functionality of the algorithm Figure 1.2 – The detection of the valleys and canopies Figure 1.1 – The original photo after the application of a mean filter Figure 1.4 – The isolation of the canopies Figure 1.3 – The isolation of the valleys Humans are able to distinguish various tree crowns from an aerial viewpoint through the darker shading between canopies, indicating “valleys.” This algorithm uses this method to identify the canopies in the example image shown in Figure 1.0. The program is written in using the Interactive Data Language (IDL), with additional important software being ENVI, Pix4D and ESRI. IDL is a powerful, easy to use language specialized to manipulate and augment spatial imagery data in an efficient manner. Although currently we are testing our algorithm against high resolution satellite data (Quickbird) and airborne LiDAR data derived top of canopy maps (see Figure 2 for example) our goal is for this algorithm to be parameterized to data collected by Unmanned Aerial Vehicles (UAVs) in general, and specifically to the PrecisionHawk UAV with whom the SPEC Lab has partnered for research and development. PrecisionHawk specializes in data- gathering with custom UAVs fitted with high precision cameras. We intend for initial parameterization to be for pine plantations which represent the easiest scenario of forest structure, followed by more complex ecosystems. (1) The initial photo is run through a mean filter. The mean filter essentially is a “smoothing” process i.e. reducing the amount of intensity variation from one pixel to the next. Figure 1.1 shows the smoothing of the “noise” that came from the input image. (2) The saturation of the image is increased to amply contrast between the valleys and canopies. This allows for an easier separation of the two. Figure 1.2 shows the results of this process. This is aimed to create a super-saturated version of the shading that human eye and distinguish from an aerial viewpoint. (3) Once the image has the amplified contrast, the valleys become easily identifiable. The algorithm searches for those harsh, black shadows in Figure 1.2, and in Figure 1.3, you can see the various valleys identified and shown in white. (4) Essentially the inverse of the process in step (3), the canopies are shown by analyzing the contrasted image. In Figure 1.4, you can see the various canopies shown in white. Future development may involve other processes of identifying these canopies. In large, dense areas where the shading is almost nonexistent, the valley method falls somewhat short. (a) Gather our own high resolution, spectral data of a local pine plantation with our UAV and sensors developed by PrecisionHawk in collaboration with the USFS here in the Tuscaloosa region. (b) Implement a function to count the number of crowns and their individual dimensions. (c) Compare the results of the algorithm with the field data. (d)Research other methods of determining this data, comparing the accuracies between each method. (e) Expand the input parameters of the algorithm with other sources of data (e.g. LiDAR) to obtain more accurate results. (f) Output a Shapefile format from the algorithm that can spatially describe vector features: points, lines, and polygons, representing, for example, water wells, rivers, and lakes. (g) Expand the functionality of the algorithm to include more information of the input area. (h) The SPEC Lab has partnered with PrecisionHawk.com (PH) to license our algorithms for fee use by the general public through DataMapper.com, a subsidiary company of PH, to analyze UAV imagery of all types, including LiDAR, multi-spectral, and hyperspectral imagery. 1. Hernández-Clemente R, Navarro-Cerrillo RM, Romero Ramírez FJ, Hornero A, Zarco-Tejada PJ (2014) A Novel Methodology to Estimate Single-Tree Biophysical Parameters from 3D Digital Imagery Compared to Aerial Laser Scanner Data. Remote Sensing 6(11): 11627-11648. 2. Koukoulas C, Blackburn GA (2007) Mapping individual tree location, height and species in broadleaved deciduous forest using airborne LIDAR and multi ‐ spectral remotely sensed data. International Journal of Remote Sensing 26.3: 431-455. 3. Pouliot DA, King DJ, Bell FW, Pitt DG (2002) Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration. Remote Sensing of Environment 82.2: 322- 334. Implementation A large number of overlapping aerial visual or multi-spectral images acquired from the UAV are then processed through Pix4D software to derive digital top of canopy surface models (see figure to right) having a spatial resolution of up to 1 cm and an accuracy of < 5 cm horizontal and vertical. Figure 2.