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.

Climate change-related acute or long-term drought stress can weaken forest ecosystems and result in widespread bark beetle infestations. Eurasian spruce bark beetle (Ips typographus L.) infestations have been occurring in Norway spruce [Picea abies (L.) Karst.]-dominated forests in central Europe including the Czechia. These infestations appear regularly, especially in homogeneous spruce stands, and the impact varies with the climate-induced water stress conditions. The removal of infected trees before the beetles leave the bark is an important step in forest pest management. Early identification of susceptible trees to infestations is also very important but quite challenging since stressed tree-tops show no sign of discolouration in the visible spectrum. We investigated if individual spectral bandwidths or developed spectral vegetation indices (SVIs), can be used to differentiate non-attacked trees, assumed to be healthy, from trees susceptible to attacks in the later stages of a growing season. And, how the temporal-scale patterns of individual bands and developed SVIs of susceptible trees to attacks, driven by changes in spectral characteristics of trees, behave differently than those patterns observed for healthy trees. The multispectral imagery from the PlanetScope satellite coupled with field data were used to statistically test the competency of the individual band and/or developed SVIs to differentiate two designated classes of healthy and susceptible trees. We found significant differences between SVIs of the susceptible and healthy spruce forests using the Enhanced Vegetation Index (EVI) and Visible Atmospherically Resistant Index (VARI). The accuracy for both indices ranged from 0.7 to 0.78; the highest among all examined indices. The results indicated that the spectral differences between the healthy and susceptible trees were present at the beginning of the growing season before the attacks. The existing spectral differences, likely caused by water-stress stimuli such as droughts, may be a key to detecting forests susceptible to early infestations. Our introduced methodology can also be applied in future research, using new generations of the PlanetScope imagery, to assess forests susceptibility to bark beetle infestations early in the growing season.

Biological invasion is a major contributor to local and global biodiversity loss, in particular in dune ecosystems. In this study we evaluated current and future cover expansion of the invasive plant species, Heterotheca subaxillaris, and Acacia saligna, in the Mediterranean coastal plain of Israel. This is the first effort to quantify current surface cover of the focal species in this area. We reconstructed plant cover for 1990–2020 using Landsat time series and modeled future potential expansion using cellular automata (CA) modeling. The overall accuracy of the results varied in the range 85–95% and the simulated plant growth using CA varied between 74% and 84%, for A. saligna and H. subaxillaris, respectively. The surface area covered by H. subaxillaris in 2020, 45 years since its introduction, was approximately 81 km². Acacia saligna covered an area of 74.6 km², while the vacant area available for potential spread of these two species was 630 km². Heterotheca subaxillaris showed a mean expansion rate of 107% per decade from 2000 to 2020, while the mean expansion rate of A. saligna was lower, ranging between 48% and 54% within the same time period. Furthermore, based on the plant expansion model simulation we estimated that A. saligna and H. subaxillaris will continue to spread by 60% per decade, on average, from 2020 to 2070, with a maximum growth rate of 80% per decade during 2040–2050. According to future expansion projections, the species will cover all open vacant areas by 2070 (95% of the total vacant area) and most areas will be shared by both species.