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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.

Species distribution models (SDMs) have proven valuable in filling gaps in our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations in species occurrence data. These limitations include, in particular, issues related to sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality of SDMs as well as the approaches used to mitigate the impact of the aforementioned data limitations depend on species ecology. While numerous studies have evaluated the effects of these data limitations on SDM performance, a synthesis of their results is lacking. However, without a comprehensive understanding of their individual and combined effects, our ability to predict the influence of these issues on the quality of modelled species–environment associations remains largely uncertain, limiting the value of model outputs. In this paper, we review studies that have evaluated the effects of sample size, positional uncertainty, sampling bias, and species ecology on SDMs outputs. We build upon their findings to provide recommendations for the critical assessment of species data intended for use in SDMs. [ABSTRACT FROM AUTHOR]

Satellite‐derived global digital elevation models (DEMs) are essential for providing the topographic information needed in a wide range of hydrological applications. However, their use is limited by spatial resolution and vertical bias due to sensor limitations in observing bare terrain. Significant efforts have been made to improve the resolution of global DEMs (e.g., TanDEM‐X) and create bare‐earth DEMs (e.g., FABDEM, MERIT, CEDTM). We evaluated the vertical accuracy of bare‐earth and global DEMs in Central European mountains and submontane regions, and assessed how DEM resolution, vegetation offset removal, land cover, and terrain slope affect stream network delineation. Using lidar‐derived DTM and national stream networks as references, we found that: (a) bare‐earth DEMs outperform global DEMs across all land cover types. RMSEs increased with increasing slope for all DEMs in non‐forest areas. In forests, however, the negative effect of the slope was outweighed by the vegetation offset even for bare‐earth DTMs; (b) the accuracy of derived stream networks was affected by terrain slope and land cover more than by the vertical accuracy of DEMs. Stream network delineation performed poorly in non‐forest areas and relatively well in forests. Increasing slope improved the streams delineation performance; (c) using DEMs with higher resolution (e.g., 12 m TanDEM‐X) improved stream network delineation, but increasing resolution also increased the need for effective vegetation bias removal. Our results indicate that vertical accuracy alone does not reflect how well DEMs perform in stream network delineation. This underscores the need to include stream network performance in DEM quality rankings. Plain Language Summary: We evaluated the accuracy of different types of digital elevation models (DEMs) and derived stream networks in Central European mountains. Our focus was on satellite‐derived global DEMs, including bare‐earth DEMs (i.e., DEMs with vegetation and building offsets removed). Using lidar DTM and national stream networks as references, we found that bare‐earth DEMs consistently outperformed other DEMs across all evaluated land cover types. We observed that slope and land cover have contrasting effects on the accuracy of DEMs and delineated stream networks. As slope increases, DEMs accuracy decreases, while for delineated stream networks, the opposite trend is observed. Where land cover is concerned, DEMs' vertical accuracies (i.e., how well they represent the bare‐earth terrain) are lowest in forests, whereas the accuracy of stream network is lowest in non‐forest areas. Furthermore, we demonstrated that slope and land cover type considerably affect the accuracy of stream network delineation, more so than the vertical accuracy of the DEMs. Additionally, we found that using DEMs with higher resolution can improve stream network delineation, but increasing resolution also increases the need for effective vegetation bias removal. Therefore, removing vegetation and buildings offset from Tandem‐X DEM at a 12 m resolution would represent a major next step forward. Key Points: Differences between digital elevation models (DEMs) (i.e., their absolute vertical accuracy) have a lower effect on stream delineation than terrain slope and landcoverThe use of higher‐resolution DEMs to derive stream networks increases the importance of removing vegetation and building biasVertical accuracy alone does not reflect DEMs' performance in stream delineation, emphasizing the need to include this in quality evaluation [ABSTRACT FROM AUTHOR]

Filtering approaches on Global Ecosystem Dynamics Investigation (GEDI) data differ considerably across existing studies and it is yet unclear which method is the most effective. We conducted an in‐depth analysis of GEDI's vertical accuracy in mapping terrain and canopy heights across three study sites in temperate forests and grasslands in Spain, California, and New Zealand. We started with unfiltered data (2,081,108 footprints) and describe a workflow for data filtering using Level 2A parameters and for geolocation error mitigation. We found that retaining observations with at least one detected mode eliminates noise more effectively than sensitivity. The accuracy of terrain and canopy height observations depended considerably on the number of modes, beam sensitivity, landcover, and terrain slope. In dense forests, a minimum sensitivity of 0.9 was required, while in areas with sparse vegetation, sensitivity of 0.5 sufficed. Sensitivity greater than 0.9 resulted in an overestimation of canopy height in grasslands, especially on steep slopes, where high sensitivity led to the detection of multiple modes. We suggest excluding observations with more than five modes in grasslands. We found that the most effective strategy for filtering low‐quality observations was to combine the quality flag and difference from TanDEM‐X, striking an optimal balance between eliminating poor‐quality data and preserving a maximum number of high‐quality observations. Positional shifts improved the accuracy of GEDI terrain estimates but not of vegetation height estimates. Our findings guide users to an easy way of processing of GEDI footprints, enabling the use of the most accurate data and leading to more reliable applications. Plain Language Summary: The Global Ecosystem Dynamics Investigation (GEDI) collected terrain and canopy observations using laser altimetry. The quality of terrain and canopy observations is influenced by acquisition conditions and land (cover) characteristics. Consequently, a considerable amount of GEDI observations is discarded as noise, and further filtering is necessary to retain only high‐quality observations. Our objective was to assess how environmental and acquisition characteristics influence the accuracy of terrain and canopy height of GEDI observations. Although the main objective of the GEDI mission was to map forests, we also focused on grasslands. GEDI serves not only as an essential source of information on canopy height but also provides accurate terrain observations. Furthermore, it is important to know that GEDI does not overestimate the height of low vegetation as this can result in an overestimation of carbon storage. We distinguished four steps in the GEDI data processing: (a) removal of noise observations, (b) removal of low‐quality data, (c) effect of additional acquisition characteristics, and (d) mitigation of geolocation error. We found that the accuracy of terrain and canopy height observations depended considerably on the number of detected modes, beam sensitivity, landcover, and terrain slope. Key Points: Terrain is crucial for estimates of canopy height, however only 20%–30% of footprints have an absolute error of terrain estimates <3 mThe quality of terrain and canopy height estimates depends on the interplay of number of modes, sensitivity, land cover, and terrain slopeNoise and low‐quality footprints can be successfully removed using number of modes, sensitivity, quality flag and difference from TanDEM‐X [ABSTRACT FROM AUTHOR]

Global mapping of forest height is an extremely important task for estimating habitat quality and modeling biodiversity. Recently, three global canopy height maps have been released, the global forest canopy height map (GFCH), the high‐resolution canopy height model of the Earth (HRCH), and the global map of tree canopy height (GMTCH). Here, we assessed their accuracy and usability for biodiversity modeling. We examined their accuracy by comparing them with the reference canopy height models derived from airborne laser scanning (ALS). Our results show considerable differences between the evaluated maps. The root mean square error ranged between 10 and 18 m for GFCH, 9–11 m for HRCH, and 10–17 m for GMTCH, respectively. GFCH and GMTCH consistently underestimated the height of all canopies regardless of their height, while HRCH tended to overestimate the height of low canopies and underestimate tall canopies. Biodiversity models using predicted global canopy height maps as input data are sufficient for estimating simple relationships between species occurrence and canopy height, but their use leads to a considerable decrease in the discrimination ability of the models and to mischaracterization of species niches where derived indices (e.g., canopy height heterogeneity) are concerned. We showed that canopy height heterogeneity is considerably underestimated in the evaluated global canopy height maps. We urge that for temperate areas rich in ALS data, activities should concentrate on harmonizing ALS canopy height maps rather than relying on modeled global products. [ABSTRACT FROM AUTHOR]