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Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the ‘spectral species’ concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species‐specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.

The role of Earth observation (EO) data in addressing societal problems from environmental through to humanitarian should not be understated. Recent innovation in EO means provision of analysis ready data and data cubes, which allows for rapid use of EO data. This in combination with processing technologies, such as Google Earth Engine and open source algorithms/software for EO data integration and analyses, has afforded an explosion of information to answer research questions and/or inform policy making. However, there is still a need for both training and validation data within EO projects–often this can be challenging to obtain. It has been suggested that citizen science can help here to provide these data, yet there is some perceived hesitancy in using citizen science within EO projects. This paper reports on the Citizen Science 4 EO (Citizens4EO) project that aimed to obtain an in-depth understanding of researchers’ and practitioners’ experiences with citizen science data in EO within the UK. Through a mixed methods approach (online and in-depth surveys and a spotlight case study) it was found that although the benefits of using citizen science data in EO projects were many (and highlighted in the spotlighted “Slavery from Space” case study), there were a number of common concerns around using citizen science. These were around the mechanics of deploying citizen science and the unreliability of a potentially misinformed or undertrained citizen base. As such, comparing the results of this study with those of a similar survey undertaken in 2016, it is apparent that progress towards optimizing citizen science use in EO has been incremental but positive with evidence of the realization of the benefits of citizen science for EO (Citizens4EO). As such, we conclude by offering priority action areas to support further use of citizen science by the EO community within the UK, which ultimately should be adopted further afield.

Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, diversity patterns and change, metapopulation dynamics, population connectivity or gene flow. In this paper, we present a new R package—rasterdiv—to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open-source algorithms.

Chandler, C. J., van der Heijden, G. M. F., Boyd, D. S., Cutler, M. E. J., Costa, H., Nilus, R., & Foody, G. M. (2021). Remote sensing liana infestation in an aseasonal tropical forest: addressing mismatch in spatial units of analyses. Remote Sensing in Ecology and Conservation, 7(3), 397-410. https://doi.org/10.1002/rse2.197 ---------------------------------------------------------------- The authors also thank the Natural Environment Research Council [NE/P004806/1 to MEJC, DSB, GMF, GMFvdH; NE/I528477/1 (ARSF MA14/11) to MEJC, DSB, GMF and NE/L002604/1 to CJC, GMF, GMFvdH]. The FCT (Fundação para a Ciência e a Tecnologia) under the project UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC) to HC. The authors also thank the University of Nottingham for an Anne McLaren Research Fellowship to GMFvdH which funded the collection of the ground data. The ability to accurately assess liana (woody vine) infestation at the landscape level is essential to quantify their impact on carbon dynamics and help inform targeted forest management and conservation action. Remote sensing techniques provide potential solutions for assessing liana infestation at broader spatial scales. However, their use so far has been limited to seasonal forests, where there is a high spectral contrast between lianas and trees. Additionally, the ability to align the spatial units of remotely sensed data with canopy observations of liana infestation requires further attention. We combined airborne hyperspectral and LiDAR data with a neural network machine learning classification to assess the distribution of liana infestation at the landscape-level across an aseasonal primary forest in Sabah, Malaysia. We tested whether an object-based classification was more effective at predicting liana infestation when compared to a pixel-based classification. We found a stronger relationship between predicted and observed liana infestation when using a pixel-based approach (RMSD = 27.0% ± 0.80) in comparison to an object-based approach (RMSD = 32.6% ± 4.84). However, there was no significant difference in accuracy for object- versus pixel-based classifications when liana infestation was grouped into three classes; Low [0–30%], Medium [31–69%] and High [70–100%] (McNemar’s χ2 = 0.211, P = 0.65). We demonstrate, for the first time, that remote sensing approaches are effective in accurately assessing liana infestation at a landscape scale in an aseasonal tropical forest. Our results indicate potential limitations in object-based approaches which require refinement in order to accurately segment imagery across contiguous closed-canopy forests. We conclude that the decision on whether to use a pixel- or object-based approach may depend on the structure of the forest and the ultimate application of the resulting output. Both approaches will provide a valuable tool to inform effective conservation and forest management. publishersversion published

Mapping vegetation as hard classes based on remote sensing data is a frequently applied approach, even though this crisp, categorical representation is not in line with nature's fuzziness. Gradual transitions in plant species composition in ecotones and faint compositional differences across different patches are thus poorly described in the resulting maps. Several concepts promise to provide better vegetation maps. These include (1) fuzzy classification (a.k.a. soft classification) that takes the probability of an image pixel's class membership into account and (2) gradient mapping based on ordination, which describes plant species composition as a floristic continuum and avoids a categorical description of vegetation patterns. A systematic and comprehensive comparison of these approaches is missing to date. This paper hence gives an overview of the state of the art in fuzzy classification and gradient mapping and compares the approaches in a case study. The advantages and disadvantages of the approaches are discussed and their performance is compared to hard classification (a.k.a. crisp or boolean classification). Gradient mapping best conserves the information in the original data and does not require an a priori categorization. Fuzzy classification comes close in terms of information loss and likewise preserves the continuous nature of vegetation, however, still relying on a priori classification. The need for a priori classification may be a disadvantage or, in other cases, an advantage because it allows using categorical input data instead of the detailed vegetation records required for ordination. Both approaches support spatially explicit accuracy analyses, which further improves the usefulness of the output maps. Gradient mapping and fuzzy classification offer various advantages over hard classification, can always be transformed into a crisp map and are generally applicable to various data structures. We thus recommend the use of these approaches over hard classification for applications in ecological research.