With the increasing role that unmanned aerial systems (UAS) are playing in data collection for environmental studies, two key challenges relate to harmonizing and providing standardized guidance for data collection, and also establishing protocols that are applicable across a broad range of environments and conditions. In this context, a network of scientists are cooperating within the framework of the Harmonious Project to develop and promote harmonized mapping strategies and disseminate operational guidance to ensure best practice for data collection and interpretation. The culmination of these efforts is summarized in the present manuscript. Through this synthesis study, we identify the many interdependencies of each step in the collection and processing chain, and outline approaches to formalize and ensure a successful workflow and product development. Given the number of environmental conditions, constraints, and variables that could possibly be explored from UAS platforms, it is impractical to provide protocols that can be applied universally under all scenarios. However, it is possible to collate and systematically order the fragmented knowledge on UAS collection and analysis to identify the best practices that can best ensure the streamlined and rigorous development of scientific products.

Providing accurate maps of mangroves, where the spatial scales of the mapped features correspond to the ecological structures and processes, as opposed to pixel sizes and mapping approaches, is a major challenge for remote sensing. This study developed and evaluated an object-based approach to understand what types of mangrove information can be mapped using different image datasets (Landsat TM, ALOS AVNIR-2, WorldView-2, and LiDAR). We compared and contrasted the ability of these images to map five levels of mangrove features, including vegetation boundary, mangrove stands, mangrove zonations, individual tree crowns, and species communities. We used the Moreton Bay site in Australia as the primary site to develop the classification rule sets and Karimunjawa Island in Indonesia to test the applicability of the rule sets. The results demonstrated the effectiveness of a conceptual hierarchical model for mapping specific mangrove features at discrete spatial scales. However, the rule sets developed in this study require modification to map similar mangrove features at different locations or when using image data acquired by different sensors. Across the hierarchical levels, smaller object sizes (i.e., tree crowns) required more complex classification rule sets. Incorporation of contextual information (e.g., distance and elevation) increased the overall mapping accuracy at the mangrove stand level (from 85% to 94%) and mangrove zonation level (from 53% to 59%). We found that higher image spatial resolution, larger object size, and fewer land-cover classes result in higher mapping accuracies. This study highlights the potential of selected images and mapping techniques to map mangrove features, and provides guidance for how to do this effectively through multi-scale mangrove composition mapping.

The objective of this paper was to evaluate the accuracy of two advanced blending algorithms, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) indices to the spatial resolution of Landsat. We tested two approaches: (i) “Index-then-Blend” (IB); and (ii) “Blend-then-Index” (BI) when simulating nine indices, which are widely used for vegetation studies, environmental moisture assessment and standing water identification. Landsat-like indices, generated using both IB and BI, were simulated on 45 dates in total from three sites. The outputs were then compared with indices calculated from observed Landsat data and pixel-to-pixel accuracy of each simulation was assessed by calculating the: (i) bias; (ii) R²; and (iii) Root Mean Square Deviation (RMSD). The IB approach produced higher accuracies than the BI approach for both blending algorithms for all nine indices at all three sites. We also found that the relative performance of the STARFM and ESTARFM algorithms depended on the spatial and temporal variances of the Landsat-MODIS input indices. Our study suggests that the IB approach should be implemented for blending of environmental indices, as it was: (i) less computationally expensive due to blending single indices rather than multiple bands; (ii) more accurate due to less error propagation; and (iii) less sensitive to the choice of algorithm.